It’s called “window dressing.”

This is when professional money managers start making their portfolios look a little prettier.

Fund managers know their clients are about to see what they own. And naturally, they would rather show off stocks with strong performance and great fundamentals than explain why they’re still hanging on to laggards.

Well, folks, we’re heading into the final days of September. And I think we’re already starting to see some bargain hunting in the kinds of fundamentally superior stocks that institutions may want to own as the third quarter draws to a close.

In fact, two artificial intelligence infrastructure stocks I follow closely have started to wake up.

One recently delivered a 22.1% earnings surprise, with sales forecast to rise 30.1% and earnings expected to surge 128.4%.

The other is expected to grow sales 45.3% and earnings 68.9%, while also benefiting from strong institutional buying pressure.

Neither one of these stocks is a household name. But both sit right in the middle of an interesting debate about the future of AI infrastructure.

So, in this week’s Navellier Market Buzz, I explain why these two stocks are starting to wake up, what quarter-end window dressing could mean for them – and why I think both could be winners in the AI boom.

I also explain how I use Stock Grader when a holding starts to weaken and address what the Fed’s latest rate hike means for some of my favorite AI infrastructure plays.

Click here or the image below to watch this week’s Navellier Market Buzz.

The two stocks I highlighted in the video help move massive amounts of data through data centers. Some investors have started treating those technologies like competitors, especially with all the talk about eventually putting data centers in space.

I think that misses the bigger picture.

Both technologies move data at the speed of light. And as AI creates exponentially more data that needs to move between chips, servers, data centers and, eventually, satellites, I believe both companies can be winners.

The fundamentals back that up. They’re expected to report sales increases of 30.1% and 45.3%, respectively. Earnings are expected to surge 128.4% and 68.9%, respectively.

That’s exactly what I want to see. But these two stocks also point to something much bigger.

AI Without the “Kill Us All” Problem?

Last week, we spent some time talking about former Anthropic researcher Jacob Coxon.

Coxon quit the company and warned that AI developers were “racing straight to self-improving superintelligence and gambling with our lives.” His post went viral, major AI leaders weighed in and Wall Street briefly punished many of the stocks powering the AI boom.

I mentioned that there are legitimate AI-safety issues worth taking seriously. But I also said investors should not confuse those risks with the end of the AI boom.

In fact, my research team and I have been studying a massive new AI initiative taking shape across America’s national laboratories.

The goal is to build a massive AI computing network designed specifically for scientific discovery.

That’s important because this is not the kind of artificial superintelligence the naysayers are worried about. They’re worried about a kind of general-purpose system capable of improving itself across virtually every field of human knowledge.

But Golden Dawn is being designed for something much more targeted.

It would use enormous amounts of computing power and specialized AI agents to attack specific scientific problems in fields such as energy, medicine, advanced materials and quantum computing.

The idea is to capture the extraordinary problem-solving power AI could eventually deliver while keeping it focused on defined scientific missions.

And the scale behind this project is staggering.

The resulting network could become what I call the world’s first AI Mega Computer, connecting enormous amounts of computing power across the country.

That means work that once took 10 years could potentially happen in about 10 days.

Some of the biggest names in tech are connected to this project, including Sam Altman, Jensen Huang and Jeff Bezos. My team has spent months tracing the contracts, infrastructure and companies involved.

And that trail led us to one off-the-radar AI stock I believe could be positioned to benefit directly.

Go here to get the full story now.

Sincerely,

抖阴最新版

Editor, Market 360

The post Why Quarter-End Window Dressing Could Lift These 2 AI Stocks appeared first on InvestorPlace.

There is a common pattern in new technology cycles, and it goes like this: 

The innovation itself appears. Then, a bottleneck emerges. Next, capital floods in to solve the problem. Finally, the regime changes.  

We saw this “regime change,” or complete reorganization of stock market winners and losers, in the dot-com bust phase. 

Capital rotated out of the high-profile names and into a variety of other sectors, including base metals, precious metals, energy insurance, and utilities. Those sectors delivered solid double-digit or triple-digit returns over the early part of the 2000s, even while the Amazons, Intels, and Ciscos of the world fell 80% or more. 

Another regime change is happening now.  

Since the early AI revolution, the Magnificent Seven companies have been sitting securely on the throne. The group includes Alphabet Inc. (GOOGL), Amazon.com Inc. (AMZN), Apple Inc. (AAPL), Meta Platforms Inc. (META),Microsoft Corp. (MSFT), Nvidia Corp. (NVDA), and Tesla Inc. (TSLA).  

But their seat is soon to be usurped. We are starting to see a rotation out of some of the highest profile, high beta tech stocks and into more real-world, asset-backed sectors. 

I’ll share the name of one such company below. But first, let’s take a look at what we covered here at Smart Money last week.

Smart Money Roundup

September 16, 2026

If the Race for Smarter AI Slows, This May Be the Next Wave of Profits

In my colleague Luke Lango’s view, even if frontier AI development slows – following an Anthropic researcher’s resignation and calls from Amodei, Altman, and Musk to pace the industry – the bigger opportunity lies in applying today’s AI. He also highlights a young, private food-service robotics startup and explains how investors can back such private companies before an acquisition or IPO.

September 17, 2026

The AI Race Is Getting Harder to Predict, and That’s the Opportunity

Though the industry warns AI may be uncontrollable, investors are floating a $2 trillion valuation for Anthropic’s IPO. In other words, nobody knows which future is coming. That’s why I favor “AI Survivors” – companies that thrive whether AI accelerates, stalls, turns dangerous, or proves a bubble. Thursday’s piece shows how to invest in AI without predicting the outcome.

September 19, 2026

Not All AI Stocks Will Survive — Here’s How to Tell Which Are Likely to Fail

Two Spokane gas stations that slashed prices to 59 cents a gallon, losing thousands, offer a vivid example of what Tom Yeung calls a “bad business model,” in which selling identical products destroys profits. He argues the same commoditization is squeezing AI, pointing to GPU-rental “neoclouds” alongside interchangeable model makers and power producers. Read more about the winners with real pricing power who could survive the squeeze.

September 20, 2026

How an AI Slowdown Could Spark a Robotics Boom

Fresh from the All-In Summit – where he heard from Nadella, Huang, Musk, and President Trump – Luke Lango came away more bullish on AI. After witnessing no signs of slowing infrastructure, he argues robotics could be a major beneficiary, using Agility Robotics’ safety-focused Digit 5 to illustrate what turns an impressive machine into a repeat customer. The private company he’s backing could open a far bigger opportunity – see why before the deadline at midnight.

The AI-Powered Copper Play

Freeport-McMoRan Inc. (FCX), a metals mining company with a focus on copper, is maximizing this regime-change opportunity by boosting copper production in every way possible. For example, the company has developed cost-effective methods for extracting commercial quantities of copper from waste rock through advanced leaching techniques. 

Freeport uses AI to optimize ore sequencing, mill throughput, equipment uptime, and geological modeling across massive copper operations. Algorithms improve recovery rates, reduce energy consumption, and minimize downtime. 

Because mining is capital intensive and operationally complex, small efficiency gains can scale into enormous dollar impact. AI helps Freeport decide which rock to move, how fast to process it, and when to service machinery. 

The company also applies machine learning to geological data, improving reserve estimates and guiding long-term mine planning. 

This is applied intelligence in its purest form: more output from the same ore body, with fewer people and lower costs. 

I believe “asset-heavy” companies, like Freeport, will rise in rank as Big Tech household names continue to fall.

Regards,

抖阴最新版

The post Don’t Miss the Great AI Rotation appeared first on InvestorPlace.

This Week’s Ratings Changes:

Upgraded: Strong to Very Strong

Downgraded: Very Strong to Strong

Upgraded: Neutral to Strong

Downgraded: Strong to Neutral

Upgraded: Weak to Neutral

Downgraded: Neutral to Weak

Upgraded: Very Weak to Weak

Downgraded: Weak to Very Weak

To stay on top of my latest stock ratings, plug your holdings into Stock Grader, my proprietary stock screening tool. But, you must be a subscriber to one of my premium services.

To learn more about my premium service, Growth Investor, and get my latest picks, go here. Or, if you are a member of one of my premium services, you can go here.

Sincerely,

抖阴最新版

Editor, Market 360

The post eBay Upgraded, Airbnb Downgraded: Updated Rankings on Top Blue-Chip Stocks appeared first on InvestorPlace.

This summer, Hugging Face became famous for two very different reasons.

First, agents from an OpenAI experiment broke through the isolation meant to contain them and targeted Hugging Face’s infrastructure. The incident quickly became a central example in the industry’s debate over AI safety.

Then Nvidia (NVDA) reportedly agreed to acquire Hugging Face for nearly $13 billion.

The platform now hosts more than 3 million AI models, 500,000 datasets, and 1 million applications used by over 18 million developers. More than 200,000 companies use it to discover, test, customize, and deploy AI.

Nvidia could have spent years trying to recreate that ecosystem.

It bought the company instead.

Days later, the AI industry began moving toward stronger guardrails. Anthropic proposed embedding independent evaluators inside frontier labs, giving them broad access to systems and model behavior. OpenAI agreed to follow, though plenty of questions remain about how independent those watchdogs will be.

The safety shift did not cause Nvidia’s deal; that transaction was already underway.

But the timing shows where the market may be heading.

AI’s largest companies still want the smartest models. Now they also need everything around those models – the safety layers, the trusted data, the distribution, the path into the physical world.

Some of those capabilities can be developed internally.

Others have already taken private startups years to build.

And when time is the scarce resource, Silicon Valley reaches for its checkbook.

Silicon Valley Has Always Paid to Skip the Queue

In 2012, Meta (META) (then Facebook) paid $1 billion for Instagram.

At the time, Instagram had 13 employees, little revenue, and a product known mostly for putting vintage filters on photos.

But Facebook was buying far more than a photo app.

Instagram gave it a mobile social network, a rapidly growing community, and cultural momentum that would have taken years to reproduce.

The same pattern has repeated across every major technology cycle.

Google acquired Android before smartphones became the center of computing. It bought YouTube before online video dominated media. Microsoft (MSFT) acquired GitHub as software development moved toward cloud-based collaboration.

Those companies had the money and talent to build competing products. What they couldn’t build was the head start.

Nvidia’s Hugging Face purchase follows the same logic.

Nvidia Bought an Ecosystem It Would Have Taken Years to Recreate

Hugging Face already sits between model builders, developers, datasets, cloud services, and the open-source AI community. Its value comes from the network that has formed around it.

The hack also made the platform’s strategic role much easier to see.

A repository holding millions of models, applications, and datasets is more than a developer website. It is part of AI’s distribution and safety infrastructure.

Nvidia’s deal brings that entire network under one roof.

As safety requirements grow, other private companies may find themselves in a similar position: too important to ignore and too difficult to recreate quickly.

Four Types of AI Startups Big Tech May Buy Next

While the obvious targets may be companies building more models, the more interesting candidates sit around them.

1. AI Safety and Security Startups

AI agents are gaining access to code, corporate systems, financial information, and outside tools.

Every new connection creates another place for something to go wrong.

Startups that evaluate models, catch threats, manage identity and permissions, or keep constant watch over deployed agents could become prime targets as companies move AI into real workflows.

2. Proprietary AI Data

Public internet data helped train the first generation of AI models.

Robotics requires something different.

A robot has to learn how objects move, how materials respond, how people behave nearby, and how to recover when a task goes wrong. Much of that information must be collected from the physical world.

A company that owns a unique robotics-data loop may hold something a larger buyer cannot simply download or reproduce.

3. AI Distribution and Developer Workflows

The smartest model still needs users.

Coding platforms, business applications, cloud marketplaces, and consumer interfaces give AI companies a direct path into work people already perform every day.

Buying an established workflow can place a model in front of millions of users much faster than launching another standalone chatbot.

4. Physical AI and Robotics Startups

Robotics may produce the most urgent shopping list of all.

A commercially useful robot needs to see, practice, learn, move precisely, and fail safely – and each of those capabilities is its own technology stack that takes years to develop and validate. 

An automaker, chip company, cloud platform, or industrial giant that wants a robotics business may decide that acquiring one of those pieces is faster than beginning from zero.

The strongest targets will own something scarce: difficult technology, trusted data, a specialized team, an established customer base, or a product that dramatically shortens the buyer’s roadmap.

Why the Best Targets May Vanish Before Their IPOs

The original version of this article began with Spark Capital.

In May 2023, Spark made its largest investment ever, writing an initial $75 million check to help fund Anthropic when the company was still a relatively unknown OpenAI challenger.

Three years later, Spark’s stake was estimated to be worth roughly $7 billion on paper.

Spark did not need dozens of investments like that. It just needed one.

That is the part of the AI boom most public-market investors rarely see.

A promising safety startup may never reach the stock market. Nvidia, Microsoft, Google, OpenAI, Anthropic, or some major cybersecurity firm may decide its technology is too strategically important to remain independent.

A robotics startup could grow into a major standalone company.

It could also attract an offer from a manufacturer or technology giant looking to move into Physical AI several years faster.

Either route can create substantial value for early private investors.

By the time a company reaches an IPO, much of the technical uncertainty is gone. 

So is much of the upside.

The Bottom Line: A Slower AI Frontier Could Speed Up Acquisitions

AI’s leading companies may release frontier models more carefully. 

The competition around those models is doing the opposite. 

Labs now need stronger safety tools, better monitoring, proprietary data, trusted distribution, and systems capable of moving intelligence into the physical world.

Building every layer internally would take years.

Silicon Valley has spent decades buying years.

That is why I believe the safety push could accelerate acquisitions across AI infrastructure and robotics.

One private company has captured my attention in particular.

The “Nvidia of Robotics” is building technology I believe could become increasingly valuable as companies demand safer, more reliable machines for factories, warehouses, and other real-world environments.

Everyday investors can claim a stake with as little as $500 – though not for much longer. The current investment window is scheduled to close to new investors tonight, Sept. 21, at midnight.

The AI giants may take more time before releasing their most powerful models.

They have less time to secure the safety, data, distribution, and robotics capabilities those models will need.

Get the company name and the complete investment details before the opportunity closes at midnight.

The post Nvidia’s $12.9 Billion Hugging Face Deal Is a Sign of What Comes Next appeared first on InvestorPlace.

And he came away more bullish about where the AI Boom goes from here.

In today’s Smart Money, he explains why – and how robotics could be one of the biggest beneficiaries of AI’s next phase.

Luke recently recommended a young private robotics company that he believes is particularly well-positioned. It’s accepting investments starting at $500 through tomorrow, September 21. You can get the company’s name and Luke’s full investment case in his free 2026 AI Megadeal Event here.

First, here’s Luke with what he saw and heard at All-In.

I just spent two days at the All-In Summit listening to some of the most powerful people in technology talk about artificial intelligence at a pretty extraordinary moment.

On Monday morning alone, I heard from Microsoft Corp. (MSFT) CEO Satya Nadella and Nvidia Corp. (NVDA) CEO Jensen Huang. I was sitting there when President Donald Trump called Jensen onstage, and the conversation went on speakers for everyone in the room to hear. Elon Musk appeared later that day.

So, yes, it was quite a time to be in that room.

Especially after what had happened just days earlier. Some of the biggest names building frontier AI had begun calling for a deliberate slowdown in the development of increasingly powerful models. AI stocks got hammered as investors tried to figure out what that could mean for the hundreds of billions of dollars pouring into chips, data centers, power plants, and everything else supporting the AI buildout.

But at All-In, I didn’t hear much talk about slowing down.

I paid particular attention to Nadella. Microsoft is one of the companies writing the biggest checks in the AI Boom.

And the check writer didn’t say anything about writing fewer checks. I didn’t hear anything about cutting AI spending, reducing infrastructure commitments, or backing away from new data centers.

That matters because the slowdown everyone is talking about is primarily about the “frontier” – training the next generation of increasingly powerful AI models. That’s only one part of the AI economy.

Running AI for customers, testing AI, and training robots all will still require a lot more data centers full of compute. So I don’t look at this debate and conclude that the infrastructure spending cycle is over. There is still a pathway to years of growth.

In fact, as a long-term investor, I came away from All-In more bullish about how long this AI Boom could last.

My concern has never been this quarter’s earnings or next quarter’s earnings. I care about what happens two or three years from now. What happens if companies build too much capacity too quickly? What happens if a serious AI safety problem scares the public? What happens if regulators come down with a hammer?

Slowing down at the frontier could reduce some of those risks. We may give up some speed in the short term, but I think the industry has an opportunity to make this boom more durable over the long run.

And for investors, that could shift some of the biggest opportunities toward companies finding valuable new ways to put AI to work.

I recently recommended one young private robotics company pursuing exactly that opportunity. I’ll tell you more about it in a moment. But first, another robotics company gives us a good look at what it takes to turn an impressive machine into something customers will actually pay for.

What Robotics Can Teach Us 抖阴最新版 Dependable AI

An AI-powered robot working on a warehouse floor doesn’t have to contemplate the fate of humanity, but it does have to recognize the guy who accidentally steps into its path and stop before it runs him over.

That may sound straightforward, but making it happen reliably around people who aren’t following a carefully rehearsed demonstration is a major engineering challenge.

A machine might handle a container perfectly when the aisle is empty. A real warehouse has people moving around, awkwardly placed pallets, changing conditions, and shifts that need to stay on schedule.

And that turns AI safety into a business problem. It may be something that keeps you up at night when the conversation turns to superintelligence. But for a warehouse owner, AI safety is a line item.

A warehouse operator can love your technology and still have very good reasons to hold off on buying it.

That’s what caught my attention about Agility Robotics’ new Digit 5.

The company says its humanoid robot can lift 50 pounds, reach to heights of 7.2 feet, and operate for more than 20 hours a day, with rapid recharging between stretches of work. It also has an independent safety controller monitoring what’s happening around the robot. If someone gets too close, Digit can avoid them, stop, or sit down.

That doesn’t resolve the broader debate over AI’s risks, but it does illustrate how addressing a safety problem can help move the technology forward.

Think about that from an investment perspective. Teaching a robot when to stop – making it safer and more dependable – could help a company sell more robots and get them deployed more quickly.

For a robotics company, the distance between an impressive demonstration and a repeat customer can be enormous. I want to see whether that robot can do useful work for an entire shift, how often an employee has to intervene, what it costs to keep running, and whether the customer comes back for more.

Agility says the previous generation of Digit logged more than 65,000 hours with customers. That’s experience with actual operating conditions, actual customer requirements, and actual problems to fix.

Digit 5 still has to deliver, though. Early access is expected in the first half of 2027. And the reported $300 million in orders comes from one unnamed customer and depends on hitting milestones. Those orders aren’t guaranteed revenue, so we still need to see execution.

I want to see conditional demand turn into deliveries, productive use, and repeat orders. That will tell us much more about the business than a video of a robot completing one difficult task

Teaching Robots to Learn

Safety is only one part of making robots useful in the real world. They also need to learn new jobs without an engineering team spending weeks programming every movement.

Across the industry, vision-language-action models, or VLAs, are helping developers address these challenges. Put simply, these systems connect what a robot sees with an instruction and the actions needed to carry it out.

Developers can also train robots in simulated environments, letting them practice over and over under different conditions before testing what they’ve learned on a physical machine. But there are still gaps between simulation and reality. A successful virtual run doesn’t prove the physical machine will perform reliably in the messy real world.

For investors, I think the important question is whether that training produces a machine customers can deploy with less setup and less supervision.

If every new installation requires an engineering team to spend weeks adapting the product, expansion could become expensive. A company that can reduce that burden may have a better chance of growing profitably.

And that brings me to a young private company I recently recommended.

It started in food-service robotics. Its robot servers are already working in real commercial locations, and I’ve visited one of those locations myself to see the technology in action.

But what really caught my attention was what the company has been building behind that business.

Think of it as a training academy for robots. The company has developed technology that uses human demonstrations to teach robots new physical skills. The idea is pretty intuitive: You show the robot how to perform a task, it learns from the demonstration, and it gets better with practice.

The company says it can teach a robot some new hands-on tasks in as little as 30 minutes, without an engineer programming every movement.

Food service gives the company a place to train its technology every day, in front of real customers, with all the little complications that come with the physical world. But I think the opportunity will stretch much further. The same approach could be used to teach robots to handle products in a warehouse, work with equipment in a factory, or perform other complicated physical tasks.

Now we’re talking about a much bigger potential market.

What I Want to See Before a Company Scales

This is how I’m thinking about young robotics companies: Can they turn one successful installation into many without letting service costs swallow the gains?

A customer expanding from one location to several would be an encouraging sign. So would a machine completing more work with fewer interruptions.

I also want to know whether the company can support those additional customers without hiring people faster than it grows revenue. Selling more robots and building a profitable robotics business are separate accomplishments.

Those are some of the questions I brought to the private robotics company I recently recommended. It’s still young. There are real risks here, and plenty the company still has to prove. But I believe its combination of an operating food-service business and technology for teaching robots new skills makes it worth a much closer look.

Whenever I evaluate a young private company like this, I put extra weight on three things: the People building it, the Product they’ve created, and the Timing of the opportunity. I call it my PPT framework.

During my free 2026 AI Megadeal Event, I’ll show you the team behind this company (the People), how its robot-training technology works (the Product), the financials and risks, and why I think the Timing is especially interesting as AI moves off our screens and into the physical world.

For a limited time, it is accepting new investors with a minimum investment of $500. The offering is scheduled to close to new investors at midnight on Monday, September 21.

If you’ve spent your investing life buying stocks through a brokerage account, investing in a private company may be unfamiliar territory. So, during that event, I’ll explain how it works, what you’re actually buying, and what I think you should understand before deciding whether an opportunity like this belongs in your portfolio.

You’ve seen what Agility is doing to make robots safer and more useful. Now I want to show you the private company I’ve recommended – and why I think its approach to teaching robots could open up a much larger opportunity.

You can watch my 2026 AI Megadeal Event here.

Sincerely,

Luke Lango

Senior Investment Analyst, InvestorPlace

P.S. Luke picked one heck of a week to attend the All-In Summit. He heard directly from Satya Nadella, Jensen Huang, Donald Trump, and Elon Musk as Wall Street wrestled with the AI slowdown. Luke came away more bullish about the AI Boom – and particularly interested in where the next wave of money could flow. His free event shows you one young private robotics company he believes could benefit. Check it out here.

The post How an AI Slowdown Could Spark a Robotics Boom appeared first on InvestorPlace.

In 2012, a University of Toronto grad student named Alex Krizhevsky entered a contest to see whose software could best label photographs of cats… clothing… mushrooms… and a strangely large collection of dog breeds.

He didn’t have much of a budget. But what he had was a hunch that everyone else in the field was doing it wrong. The crowd was chasing precision by telling their programs what cats and dogs looked like.

Meanwhile, Krizhevsky wanted scale. He believed that more data and more computing power could get better results. And so, he crammed two Nvidia Corp. (NVDA) graphics cards into a home computer and used over a million images to train his AI model.

It worked. His program, AlexNet, beat the runner-up by such an embarrassing margin that the entire AI industry adopted his methods and those graphics cards.

Ten years later, ChatGPT was born using the same “more is better” training techniques.

Quantum computing went through its own “more is better moment” in 2024. In December that year, the Willow chip from Alphabet Inc. (GOOGL) finally cracked a scaling problem that had plagued companies trying to apply quantum computing tech for 30 years. Those bigger chips reduced error rates, allowing it to finish a calculation in five minutes that would have taken the world’s best supercomputer 10 septillion years.

So, when people ask me what the “next AI-type” technology will be, “quantum computing” is my answer. It has the scaling echoes of AI and recently offered the hyped-up stocks to match. One company, Quantum Computing Inc (QUBT) surged 1,900% in 2024 and 2025 despite generating almost no revenues and having a relatively weak technology.

That mania has now subsided, with many quantum stocks down 50% or more since May. So, I’d like to take the opportunity to recommend three early-stage quantum stocks for long-term investors to buy.

Meanwhile, InvestorPlace Senior Analyst Luke Lango has also identified several early-stage startups in a separate industry that is seeing its own AlexNet moment.AI-powered robotics is finally reaching a tipping point, he says, and will soon reach commercialization.

And in his free 2026 AI Megadeal Event, Luke recommends one upcoming private robotics company that is accepting investments until later this week.

Click here to watch that presentation.

Meanwhile, let’s go over the three companies leading the quantum computing charge. These are long-term picks that could take years (or even a decade) to play out. After all, it took 10 years between AlexNet and ChatGPT.

But these quantum stocks now offer the kind of potential that early AI companies once did.

A Primer on Quantum Computing

Before we get started, a quick note on quantum computing tech.

Almost every computer today is digital. They use only “1s” and “0s” in their calculations and understand nothing in the middle. (That’s why everything is ultimately coded in binary.)

Quantum computing is different. It uses the strange properties of quantum mechanics to allow for the shades between the black-and-white world of common computer code.

In fact, quantum “bits” can even be both “1” and “0” at once. (Don’t worry, it took a lot of very smart people in the early 1900s to figure this out.) And the science is proven. We use quantum mechanics in items like laser pointers, atomic clocks, and MRIs – machines that would have seemed like witchcraft 200 years ago.

And if quantum mechanics can allow computers to think in shades of gray… that would make them far more powerful than ones that can only compute in black and white.

The problem, however, is that quantum “bits” are very hard to contain. These are atoms that slip in and out of physical barriers, and scientists have long struggled to get them to behave. Error rates are high simply because quantum bits often disappear into nothingness.

Alphabet’s Willow chip finally changed that in 2024. With some clever techniques, researchers were able to increase the size of the chip and decrease error rates at the same time. It was the first time quantum computing reached the “bigger is better” stage, creating a roadmap for building massive and accurate quantum chips.

These efforts are now coalescing around four main technologies:

The picks below pull from the first three techniques because the fourth is not yet mature enough. And it’s worth considering all three, since we don’t yet know which approach will ultimately work best. (In fact, there could be multiple winners as well.)

The 800-Pound Startup

IonQ Inc. (IONQ) is by far the largest and most advanced pure-play quantum computing company on the market. The firm is pursuing a quantum technology known as trapped ions (the precision approach) and claims to have the most reliable machines in the industry. In lab tests, its qubits perform correctly 99.99% of the time.

The Maryland-based firm has pursued one of the most audacious strategies in the quantum industry: raising huge sums of cash at high valuations and then using the money to buy its best rivals.

To illustrate: In July 2025, IonQ raised $1 billion at around $55 a share. Three months later, management raised another $2 billion at $93 per share… at a 20% premium to market prices.

IonQ has used this cash (plus more from its 2021 IPO) to buy a vertically integrated quantum computing empire. Here’s some of the companies it’s snapped up in recent quarters:

• ID Quantique: Maker of “unhackable” encryption hardware, bought in April 2025 for $116 million
• Oxford Ionics: Chip-scale ion traps, purchased in June 2025 for $1.1 billion
• Capella Space: Radar-imaging satellites designed to put quantum encryption in orbit, bought for $425 million in July 2025
• SkyWater Technology: The largest U.S.-only semiconductor foundry, bought in July 2026 for $1.8 billion

That’s allowed IonQ to post stunning figures. Revenue in the most recent quarter jumped 287% to $80 million, and analysts expect that figure to almost triple by 2028.

The strategy has also let IonQ promise the most aggressive roadmap in the industry. By 2030, it says it will ship 2 million physical qubits and 80,000 error-correct ones. To give you a sense of scale, IBM Corp.’s (IBM) flagship 2029 machine is targeting just 200 qubits.

The trouble here is valuation. Even after falling 50% since early June, IonQ is still one of the more expensive quantum names on the market. The company is worth $16 billion, or 35X forward sales.

Yet, early chipmakers have a major advantage. Developers typically build computing standards around the first-available chip, cementing a first-mover lead.

I should also note that IonQ’s high share price allows it to continue raising money at elevated valuations, keeping the acquisition machine humming along. As another AI company – Tesla Inc. (TSLA) – has proved, high share prices can become a self-fulfilling prophecy.

A Different “Cold” War

Infleqtion Inc. (INFQ) is the first neutral-atom quantum company (the scaling approach) to go public.

Its approach is sometimes called “cold” quantum, because its machines use lasers to chill individual atoms to near-absolute zero temperatures and then hold them in place with beams of light. The advantage here is that you can hold thousands of these atoms in a dense grid, which is why I would call it the “scaling” approach as an investor.

Infleqtion also offers a margin of safety because its executives have taken a far more conservative financing approach than IonQ’s. The company burns only $14 million a quarter, and so its near-$600 million of cash should last into the 2030s.

Furthermore, the Colorado-based firm is already shipping basic quantum computers to the U.S. government. This includes quantum clocks, atom-based receivers, and navigational tools that use quantum computing instead of GPS. Infleqtion has contracts with several government agencies (including the Pentagon and NASA), and it received a letter of intent from the Commerce Department last March that could bring in $100 million of government funding.

So, even though Infleqtion might not have IonQ’s size, it remains a compelling long-term stock to buy for the quantum age.

The Moonshot Bet

IQM Quantum Computers Oyj (IQMX) is the final of this week’s three picks. It is the riskiest of the trio, but it also offers the greatest upside due to its smaller size. (IonQ is 10 times larger by market cap.)

IQM is Europe’s leading quantum computing company. It was spun out of a Finnish university in 2018 and has since become a leading supplier for quantum research laboratories. It has sold 26 quantum computers across the world and builds these machines in-house.

The company is pursuing a third type of quantum technology, called superconduction (the established approach), that Alphabet and IBM are also chasing.

With superconductivity, there are no atoms involved. Instead, IQM etches tiny circuits onto a silicon chip and cools these to near-zero Kelvin temperatures. These chips then start behaving like artificial atoms.

Superconducting chips are extremely fast because silicon gates can open and shut in nanoseconds. That means IQM’s chips are up to a thousand times faster than those made by IonQ and Infleqtion. It’s also worth noting that Alphabet’s 2024 quantum chip that started the whole “AlexNet moment” bonanza was achieved using this approach.

The drawback of superconduction, however, is that the chips are forgetful. They lose their memory in under a millisecond.

IQM is also risky because it doesn’t have the liquidity that IonQ and Infleqtion enjoy. The company has just $337 million in cash, which means it will have to raise more money by mid-2028, if not earlier.

Nevertheless, IQM is an excellent bet on this more established quantum approach. Plus, the company’s smaller $1.8 billion valuation makes it a prime takeover target. After all, IonQ paid $1.1 billion last year for Oxfor Ionics, another quantum startup with no revenue.

The Next Tech Revolution

There’s one unfortunate thing that ties this week’s picks together:

None of them yet have fully working products.

Quantum computers are still stuck in the lab, and we’ll have to wait until at least 2030 for a useful machine. My guess is probably closer to 2034. That means these three stocks could take almost a decade to fully play out.

That’s a very long time to wait.

Robotics has the reverse problem. Boston Dynamics had a humanoid doing backflips in 2017. Industrial robots have been welding and assembling cars since the early 1960s.

So why aren’t humanoid robots everywhere yet?

The limiting factor of robotics has never been arms or legs. It’s been the brains that control what robots can do.

Artificial intelligence is finally catching up. Robot makers are using the same approach that powered AlexNet and ChatGPT to make humanoid robots a reality. In fact, we already saw some highly capable ones at this year’s World Humanoid Robot Games in Beijing.

And so, I highly encourage you to watch Luke’s 2026 AI Megadeal Event, where he will go into the details of one early-stage startup that’s already nearing that technological finish line.

Quantum computing will involve a 10-year time horizon. Luke’s robotics pick – and the other companies he’ll tell you more about during that event – is for the here and now. You can find that free broadcast here.

Until next week,

Thomas Yeung, CFA

Market Analyst, InvestorPlace

Thomas Yeung is a market analyst and portfolio manager of the Omnia Portfolio, the highest-tier subscription at InvestorPlace. He is the former editor of Tom Yeung’s Profit & Protection, a free e-letter about investing to profit in good times and protecting gains during the bad.

The post 3 Quantum Stocks to Buy for the Next Technological Revolution appeared first on InvestorPlace.

First, you give it access to company files. It does a good enough job, so then you give it access to customer records. Eventually, when you trust it enough, you give it permission to take actions on your behalf.

Now imagine discovering that it has been opening files outside its assignment and communicating with other assistants at different companies.

Suddenly, the productivity discussion becomes a security discussion.

Who gave it access? What else can it reach? And how quickly can you shut it down?

We could pace the frontier, as Dario suggested, but a slower release schedule doesn’t revoke the access already granted to increasingly capable AI systems.

Companies still need to verify identities, restrict permissions, monitor activity, and stop threats. In my view, those requirements create a durable spending opportunity for cybersecurity businesses, whether frontier development accelerates or becomes more deliberate.

For investors, that offers a way to approach the AI trade without having to predict the date of the next breakthrough.

The industry is already preparing for this. Palo Alto Networks (PANW) completed its CyberArk acquisition in February, adding identity security as a core platform pillar explicitly covering human, machine, and AI-agent identities. Its announcement makes clear how central controlling access has become.

But recognizing a growing need is only half the investment decision. The other half is deciding what to pay.

In this week’s episode of Being Exponential with Luke Lango, I examine five cybersecurity stocks I like. Each offers different ways to invest in securing AI, from protecting corporate systems to managing automated internet traffic. Their growth prospects and valuations differ, too.

Some already command substantial premiums. One offers a comparatively lower valuation, alongside slower expected growth. And another has the combination of growth potential, valuation, and an emerging stock-price recovery that makes it my favorite entry among the five.

That said, higher interest rates could still pressure expensive stocks. So a strong business needs an investment case that accounts for its price.

Watch the episode below for my breakdown of all five, and which cybersecurity stock I think offers the greatest upside potential over the next 12 months.

Palo Alto Networks (PANW): A Broader Security Platform for the AI Economy

Every AI agent a company deploys creates another identity to manage, another set of permissions to control, and another potential opening for attackers.

Palo Alto Networks is building a business around securing that expanding environment.

Its strategy brings network security, cloud security, and security operations into a comprehensive platform. That gives customers a way to consolidate vendors and gives Palo Alto opportunities to sell more services within existing relationships.

The February acquisition of CyberArk added identity security as a fourth core pillar. That becomes especially valuable when software agents need access to sensitive information and permission to act on a company’s behalf.

Why buy now: I believe heightened concern about AI safety can translate into larger security budgets, strengthening an already attractive growth outlook. Palo Alto has the breadth to capture spending across several categories instead of depending on demand for a single product.

In the episode, I explain why I think revenue growth could exceed current expectations, and how that could support faster earnings growth.

Investors already pay a substantial premium for this business. I think its profitability, comprehensive platform, and durable demand justify paying up, provided you’re investing beyond the next quarter.

CrowdStrike (CRWD): Protecting Companies as AI Agents Get More Freedom

CrowdStrike (CRWD) is my clearest investment in the growing need to make AI deployment safer.

The company’s core strategy starts with Falcon endpoint protection, which secures devices connected to a company’s network. From there, CrowdStrike sells additional services covering identity, cloud security, and other vulnerabilities.

That approach gives it an established customer base through which to introduce new protections for autonomous AI agents.

CEO George Kurtz has also made an argument I agree with: One laboratory choosing restraint will not necessarily slow the entire industry. Cybersecurity companies need to help customers operate safely as the technology advances.

Why buy now: CrowdStrike can build on its existing customer relationships as businesses reassess the risks of giving AI systems greater access and autonomy. Its agent-security products address a need that remains relevant regardless of how quickly the next frontier model arrives.

I see an opportunity for additional security spending to lift growth expectations. Combined with potential margin expansion, that could support strong earnings growth over several years.

The shares carry a demanding valuation, so execution matters. But rising earnings estimates and improving stock-price momentum reinforce my conviction. For investors with a multiquarter outlook, I think the business warrants that premium.

Cloudflare (NET): Securing the Internet That AI Agents Increasingly Use

AI agents become more useful when they can venture beyond a company’s internal systems to research, interact with websites, and complete transactions.

That also creates more traffic for website operators to identify and control.

Cloudflare (NET) already helps make websites faster and safer. As automated activity expands, its experience managing bots and protecting internet traffic becomes increasingly relevant.

Tools such as AI Crawl Control allow website owners to manage how AI crawlers access their content. Its work in securing agent-driven commerce extends the opportunity into transactions.

Why buy now: I believe the shift toward an internet with more autonomous activity can expand demand for Cloudflare’s services. Customers need to distinguish useful automation from abusive traffic, and enforce their decisions at scale.

Among these five companies, Cloudflare offers one of the strongest growth profiles in my analysis. I also see room for margins to improve as the business expands, potentially allowing earnings to grow faster than revenue.

The valuation is high. This is an investment in sustained growth, and disappointing results could pressure the shares.

Still, the combination of rising earnings estimates, an improving chart, and a growing role in managing AI traffic makes Cloudflare particularly attractive to me.

Fortinet (FTNT): An Established Security Business at a Lower Relative Valuation

Investors looking for cybersecurity exposure without paying the group’s highest valuations should take a closer look at Fortinet (FTNT).

The company already has an extensive base of customers using its firewalls and network-security products. That gives it a practical route for introducing AI capabilities through relationships it has already established.

Its FortiAI tools help security teams assess alerts and respond to incidents. Fortinet is also expanding protection for AI systems themselves, including defenses against prompt injection, data leakage, and other threats.

The acquisition of Virtue AI adds capabilities for testing AI systems and applying safeguards.

Why buy now: Fortinet can sell additional AI-security capabilities into its installed base while trading at a substantially lower forward earnings multiple than several faster-growing peers discussed in the episode.

That lower valuation comes with a trade-off: I expect slower revenue growth than at CrowdStrike or Cloudflare. But Fortinet’s profitability and potential for margin expansion still support an attractive earnings outlook.

I also like the improving technical setup, including the breakout discussed in the episode.

Fortinet is my choice here for investors who prefer steadier expected growth and less exposure to an exceptionally high valuation. A lower multiple does not eliminate risk, but it changes what the business must deliver to justify its price.

Zscaler (ZS): My Favorite Entry Among the Five

Zscaler’s (ZS) central principle fits the emerging AI economy: Access should be verified rather than granted automatically.

Its Zero Trust Exchange applies that approach to users, devices, and workloads. As companies deploy autonomous agents, the same question becomes more urgent: What should this system be allowed to access, and under what conditions?

Zscaler is extending its platform to address those needs. Its acquisition of Symmetry Systems adds technology for understanding how people, applications, AI agents, and data interact.

Why buy now: Zscaler offers what I consider the most attractive combination of growth potential and valuation in this group.

In the analysis presented in the episode, its forward earnings multiple sits below its recent historical average, while several peers trade at premiums. I believe new AI-security products could help revenue growth exceed the expectations embedded in that valuation.

The stock’s recovery also appears earlier than those of several peers. Improving price action and rising earnings estimates give me reasons to take that recovery seriously.

The opportunity depends on execution: New products must win business, and faster growth must materialize.

But if those developments unfold as I expect, Zscaler has room to benefit from both earnings growth and a stronger investor assessment of the business. It is my favorite entry of the five, and the one I believe offers the most upside potential over the next 12 months.

The Next AI Opportunity Is Already Getting to Work

The five cybersecurity companies we’ve covered address a problem businesses have to solve before giving AI more responsibility: making sure those systems can operate securely.

That’s one reason I remain bullish. Even if frontier development becomes more deliberate, companies still have plenty of work to do turning today’s AI into products customers can trust, and will pay to use.

Some of the most interesting businesses pursuing that opportunity are still private. However, I recently recommended a young robotics company doing exactly that.

It started in food service, where its robots are already working in commercial locations. I visited one myself and watched a robot take an order, prepare it, and deliver the finished product.

But what interested me most was the technology behind those movements.

The company is developing a system that teaches robots physical skills through human demonstrations. If that approach succeeds at scale, its opportunity could extend well beyond food service into warehouses, factories, and other workplaces.

That’s why I’ve called it the “Nvidia of Robotics.” It’s my No. 1 private robotics opportunity at this turning point in the AI boom. In my free 2026 AI Megadeal Event, I explain the business, the risks, and why I decided to recommend it.

However, the offering closes to new investors at midnight Monday, Sept. 21.

Watch the free presentation now to get the company’s name, understand what you’re buying, and review the offering before that window closes.

Watch the Free 2026 AI Megadeal Event Before Monday’s Deadline.

The post The AI Security Playbook: 5 Cybersecurity Stocks to Buy Now appeared first on InvestorPlace.

Two gas stations – Hamilton Market and Exxon Mobil – sit on opposite corners of the same intersection in Spokane, Washington. Same fuel, same pumps, same dull branding that was decided on by committee. The only thing that ever changes is the number on the big sign out front.

Last December, that number turned into a weapon.

Hamilton Market dropped its price by a penny as a holiday gift to the neighborhood – and Mobil answered right back by matching it.

The dueling gas stations cut prices all day, all the way down to 59 cents per gallon. The high demand led to so many cars filling up their tanks that fuel trucks had to be called in to refuel the pumps. 抖阴最新版 12 hours later, Hamilton Market station lost a few thousand dollars, while the Mobil station lost around $25,000.

When you sell the exact same thing as the guy across the street, you have no power to charge more – and profits get squeezed until there’s nothing left.

This is what I call a “bad business model.”

And today, a similar pattern is happening in artificial intelligence.

In this Smart Money, I’ll show you where AI profits are already getting squeezed, and which companies to stay away from. 

Then, I’ll share how to spot companies that have something more valuable than a commodity.

Let’s jump in…

Where AI’s Commoditization Is Already Showing

Today, one of the clearest instances of a “bad business model” in action is GPU rental companies – also known as “neoclouds.”

These are like two gas stations on opposite corners.

For instance, CoreWeave Inc. (CRWV) and Nebius Group N.V. (NBIS) are both neocloud rental companies. And both companies buy the same fuel – Nvidia Corp. (NVDA) GPUs, electricity, and power systems – and produce the same AI computing power.

That makes it hard to stand out, and the financial results show the pressure. CoreWeave’s adjusted operating margin was just 5% in the most recent quarter, down from 16% in 2025, and Nebius’ was negative. The reason is simple: Switching is easy. Most customers don’t care if their AI workloads run on a CoreWeave server or a Nebius one. The decision usually comes down to price.

Now compare that with the Big Tech giants like Microsoft Corp. (MSFT) and Alphabet Inc. (GOOGL). These hyperscalers can charge far more for their services, of which they offer more than just computing power. There’s specialized software (Azure), built-in AI models (Gemini), and custom-designed chips to run certain models faster (Maia, TPU 8). So, they can charge far more for their services.

“Bad business models” in AI also exist in other (temporarily) red-hot areas:

1. General purpose AI models.

Many Chinese AI labs producing open-weight models like Z.ai and MiniMax (both traded in Hong Kong) are surprisingly interchangeable.

2. Routine services.

Companies like Veritone Inc. (VERI) and SoundHound AI Inc. (SOUN) produce AI audio software products that are replaceable by those from larger players.

3. Independent power producers.

Electricity has long been a commodity, and so high-cost electrical utilities like Clearway Energy Inc. (CWEN) and Capital Power Corp. (CPXWF) struggle to earn high profits even in good times.

When the air goes out of the AI trade, you will see these companies buckle first. We want to be on the other side of the equation…

The AI Winners That Survive the Squeeze

Companies with “good business models” don’t compete on price alone. They sell differentiated products that customers actively seek out, giving them the power to raise prices without destroying demand.

Upscale luxury hotels are great examples. The Oriental Hotel in Milan offers private tours of Leonardo da Vinci’s “The Last Supper,” where guests can view the artwork without any crowds. The Four Seasons of London does the same with the British Crown Jewels.

Eric’s latest addition to his Fry’s Investment Report portfolio offers a similarly powerful example.

It is a major global pharmaceutical company that uses AI to advance its lifesaving drug development program – an industry where a single dose often costs more than a night at a five-star hotel. Drugs can cost multiple billions of dollars to develop, and this firm is funding this expensive research with robust cash flows from its existing drug business.

In his September monthly issue, released last Friday, Eric notes that the company has overseen one of the fastest-growing product launches in its history, taking only 12 weeks to reach the first million prescriptions and just four weeks to add the most recent million.

To access all of Eric’s latest research on this company, learn how to join Fry’s Investment Report here.

Of course, there are even more great AI businesses in the Fry’s Investment Report portfolio – companies with pricing power that will survive the eventual squeezing of the AI industry.

Click here to discover more about these compelling business models today.

Until next time,

Thomas Yeung, CFA

Market Analyst, InvestorPlace

The post Not All AI Stocks Will Survive — Here’s How to Tell Which Are Likely to Fail appeared first on InvestorPlace.

You probably didn’t notice a recent farewell note from a once-popular internet brand.

I almost missed it myself.

Go to Ask.com today and instead of a search box, you’ll find a simple message:

“Every great search must come to an end.”

After nearly three decades, Ask.com officially shut down on May 1.

But depending on your age, you may remember it by a different name: Ask Jeeves.

Back in the early days of the internet, Jeeves was the friendly digital butler who promised to help you find whatever you were looking for online.

But he wasn’t the only helpful name. Maybe you used these search engines…?

• AltaVista.
• Lycos.
• Excite.
• WebCrawler.
• Yahoo.

If you were online in the late 1990s, you likely used at least a few of them.

Today, most are gone or barely recognizable, but at the time, these weren’t obscure internet startups. They were how we navigated the World Wide Web.

By the late 1990s, you might reasonably have concluded that the search-engine business was already getting crowded, but then two Stanford graduate students came along with yet another one.

Google.

You know what happened next.

Google was faster, simpler and extraordinarily good at finding what people were actually looking for.

Gradually, those familiar names began disappearing.

AltaVista shut down. Lycos faded from prominence. Ask Jeeves became Ask.com and eventually abandoned its own search technology.

Google became so dominant that its name became a verb.

We don’t say, “I’ll search for that.” We say, “I’ll Google it.”

But it’s easy to overlook something in that history. Google didn’t invent the search engine. It wasn’t even particularly early to the game.

Google provided a better way of doing something millions of people were already doing.

And I’ve been thinking about that a lot lately, because right now, we may be watching a remarkably similar story unfold with AI. ChatGPT has become so closely associated with AI that the two terms can almost seem interchangeable.

But what if consumers are making the same mistake people made with those early search engines?

What if the technology that first popularizes a revolution isn’t necessarily the technology that ultimately dominates it?

AI’s Google Moment

I am not saying ChatGPT is about to disappear or that it will meet the same fate as AltaVista.

OpenAI deserves enormous credit for introducing millions of people to the possibilities of generative AI. But remember, Yahoo didn’t disappear the day Google arrived, either.

Better technology simply begins to take more market share.

First, someone develops a better approach and only a relatively small number of people notice. Then usage begins to grow, and that’s where the money flows. And seemingly overnight, the technology everyone assumed would dominate suddenly has a serious challenger.

That’s why I’m paying close attention to what legendary growth investor 抖阴最新版 is seeing in AI right now.

Louis has spent more than four decades studying the forces that can turn relatively unknown companies into some of the market’s biggest winners.

For example, it’s easy to forget now that when Louis first recommended Nvidia (NVDA) to his Growth Investor subscribers, the stock was selling for $4.19. Today it sells closer to $220 per share.

Quanta Services (PWR) is another example.

Louis recommended the infrastructure company in May 2021 because he saw how it could benefit from the rollout of 5G. But that same electrical infrastructure has since become critical to another technological revolution: AI.

Quanta helps upgrade the electrical grid and build the infrastructure needed to power today’s enormous data centers.

Since Louis recommended the stock, it has climbed 400%.

That’s the pattern Louis is looking for again today.

And lately, he’s been investigating a new AI technology that he believes could represent the next major leap beyond the AI systems millions of us use today.

In fact, he believes its advantages are potentially so significant that he’s given it a provocative nickname:

The “ChatGPT Killer.”

He believes we could be reaching another one of those moments when the technology that introduced millions of people to a new idea gives way to an even more powerful second act.

Think back to those search engines.

By the time Google arrived, the opportunity wasn’t “search engines.” Those already existed.

The opportunity was a better search engine.

And if Louis is right, we could be approaching a similar inflection point in AI.

The opportunity is simply “AI.” The question is what comes next.

That’s why Louis recently put together a special presentation explaining what he believes is happening behind the scenes.

He reveals the breakthrough he believes could challenge today’s dominant AI models… why it could spread much faster than most investors realize… and, most importantly, the little-known company he believes could become one of its biggest beneficiaries.

Because as exciting as it is to speculate about what comes after ChatGPT, that isn’t why Louis has spent so much time investigating this shift.

He wants to know who could make money from it.

And that’s the question that should matter to us, too.

He’s revealing the full story – including the name and ticker of that company – in his special presentation.

Click here to see Louis’ “ChatGPT Killer” presentation now.

I can’t promise we’re witnessing another Google.

Nobody could have known that with certainty in 1998, either.

But that’s precisely why opportunities like this can become so valuable.

By the time everyone recognizes the winner, the biggest part of the opportunity may already be behind you.

Click here to see what Louis believes could come after ChatGPT — and the company he’s recommending to profit from it.

Enjoy your weekend

Luis Hernandez

Editor in Chief, InvestorPlace

The post AI’s “Google Moment” May Still Be Ahead appeared first on InvestorPlace.

I spent 28 years on the trading floors at the CBOE and CME before building Masters in Trading, and one lesson from the pits never changes: when everyone crowds the same trade, you look for the adjacent one nobody’s fighting over. Right now, that trade is cybersecurity stocks.

Here’s the logic, and it isn’t complicated. AI is a double-edged sword. The same technology racing through corporate America is also arming attackers, enabling faster, cheaper, autonomous attacks at a scale we’ve never seen. That means security stops being a “nice-to-have” line item and becomes a bill companies cannot stop paying. Cyber vendors get paid whether AI succeeds spectacularly or blows up in someone’s face. Both outcomes create threats. Both require defense.

We got a live preview of this rotation on Monday, September 14, the day the chip names sold off on fresh AI-risk warnings — with Nvidia down nearly 3% and the VanEck Semiconductor ETF (SMH) off 4.4% — the pure-play cybersecurity names had their best session in years. CrowdStrike hit a record high. Zscaler and SentinelOne jumped double digits. The market wasn’t fleeing AI. It was repricing who benefits from it.

Below are the 15 best cybersecurity stocks to buy now, each positioned to ride that shift and grouped by the role it plays in a portfolio.

How Big Is the Cybersecurity Market in 2026?

Before we look at the names, consider these numbers — because the size of this market is the whole thesis.

Gartner projects global information security spending will hit roughly $249 billion in 2026, up nearly 13% year over year, on track for about $373 billion by 2030. That’s up from just $193 billion in 2024 — a ~29% jump in two years, at a time when most enterprise software budgets are getting squeezed.

Global information security spending, 2024–2030. Source: Gartner.

Add in security services, and Cybersecurity Ventures pegs the broader market above $520 billion. A Morgan Stanley survey of CIOs found cybersecurity budgets are expected to grow roughly 50% faster than overall software spending.

The fastest-growing slice of the entire market is a category that barely existed 18 months ago: “securing AI” — projected to overtake endpoint protection as the single largest security category by 2029.

Translation: this isn’t a headline-of-the-week trade. It’s a multi-year, structurally funded spending supercycle. Now onto the stocks.

Want to see how I’m trading this theme in real time? I break down setups like these — with live entries, exits, and risk levels — every weekday at 11 a.m. ET on MiT Live.

The 15 Best Cybersecurity Stocks at a Glance

Figures are approximate, based on the most recent quarterly reports as of September 2026; *Varonis total revenue grew ~9% but its SaaS ARR is growing far faster (~60%+) through its subscription transition. Valuation reflects EV/revenue relative to the ~25x sector median.

The Rose Take: Where I Would (and Wouldn’t) Put New Money

After 28 years reading order flow, I’ve learned the best company and the best stock aren’t always the same thing. Here’s how I’d rank them:

• Best business, hands down — CrowdStrike (CRWD). Nothing else in the group has its brand, retention, or platform breadth. The only knock is the price: it’s trading near Wall Street’s most bullish targets, so you’re buying perfection.
• My No. 1 pick for new money — Palo Alto (PANW). It has the scale of CrowdStrike, faster-improving platform economics, a brand-new identity engine from the CyberArk deal, and it still trades at a discount to CRWD. It’s the best risk-adjusted way to own the theme.
• Boldest high-upside bet — SentinelOne (S). The cheapest of the leaders, growing more than 20%, with real takeover optionality. If it keeps closing the gap with CrowdStrike, it has the most torque on the list.
• Handle with care — Cloudflare (NET) and CrowdStrike (CRWD). Two of the best stories in tech, at two of the richest multiples. I’d rather wait for a pullback than chase them here.
• The one I wouldn’t chase — Rapid7 (RPD). Revenue is essentially flat. It’s a turnaround-and-takeover bet, not a growth story — treat it that way.

The Blue-Chip Generals: Best Cybersecurity Stocks for Core Exposure

Large-cap, profitable platforms. They offer core exposure to the theme , and they’re the names institutions buy first.

1. CrowdStrike (CRWD)

The category gold standard. Its Falcon platform is the purest expression of the “AI threat means more security spend” trade, and management now openly calls the company “AI security infrastructure.” Net new annual recurring revenue grew more than 50% year over year in its most recent quarter, its new AI-detection product is scaling fast, and gross retention sits near 97% — a sign customers simply don’t leave. The catch: it’s the most expensive name in the group, and after a 4-for-1 split this summer, it’s already trading near Wall Street’s most bullish price targets. You’re paying up for the best.

2. Palo Alto Networks (PANW)

The consolidation play. While rivals sell point products, Palo Alto is pitching enterprises on running their entire security stack through one vendor — and it’s working, with next-generation security ARR above $9 billion and growing north of 30%. Its roughly $25 billion acquisition of CyberArk, which closed earlier this year, bolts on a dedicated identity pillar right as AI agents make machine identity a top priority. The catch: growth is decelerating off a huge base, and the stock has already had a monster year.

3. Fortinet (FTNT)

The value general. Fortinet owns better than half the global firewall market, differentiates with its own custom silicon, and — critically — is consistently profitable. It typically screens as the cheapest of the mega-cap pure plays, which makes it the “sleep at night” name in the group. The catch: it carries more hardware exposure than the pure cloud names, so its growth is steadier but slower.

4. Zscaler (ZS)

The zero-trust leader. Zscaler replaced the old perimeter-firewall model with cloud-native traffic inspection, and it’s leaning hard into securing agentic AI as its next growth leg, with ARR growing around 25% to nearly $4 billion. It often trades at a meaningful discount to CrowdStrike on forward earnings. The catch: the market punishes any hint of a growth slowdown here — recent guidance wobbles have triggered sharp drops.

5. Microsoft (MSFT)

The elephant nobody counts as a “cyber stock.” Microsoft has quietly built a security business worth roughly $37 billion — larger than CrowdStrike, Palo Alto, and Zscaler combined — and bundles it into enterprise agreements customers already sign. You won’t get pure-play torque, but you get the most defensible distribution in the entire industry with a fraction of the single-name risk. The catch: security is a rounding error in Microsoft’s overall story, so it won’t move the stock on its own.

The High-Growth Disruptors: AI Cybersecurity Stocks With the Most Upside

More torque, more risk. These are the challengers and turnarounds with the most upside if they execute.

6. SentinelOne (S)

The direct CrowdStrike challenger. Its AI-native Singularity platform and Purple AI tools are built exactly for the autonomous-threat era, ARR has crossed $1 billion, and it’s the cheapest of the leaders on a price-to-sales basis — trading around 3.5x forward revenue versus far richer peers. It’s also a perennial takeover candidate. That combination gives it the most torque on the list. The catch: it’s still proving durable profitability and faces the risk of being out-muscled by the platform giants.

7. Okta (OKTA)

The identity turnaround. Identity is the front door to every network — and with AI agents multiplying, every one of them needs an identity to manage. Okta is the leader in that lane and has been climbing steadily back after years in the wilderness. The catch: the platform giants are bundling identity too, so Okta has to keep proving it’s the better standalone tool.

8. Rubrik (RBRK)

The “after the breach” play. While most names focus on preventing attacks, Rubrik focuses on cyber resilience — clean, fast recovery after ransomware hits. That’s a fundamentally different exposure, and a critical one, since no defense is perfect. It’s been one of the stronger performers among the newer public names. The catch: it’s a younger, higher-volatility stock still scaling toward consistent profitability.

9. Cloudflare (NET)

The AI-infrastructure hybrid. Cloudflare isn’t a pure cybersecurity play — it sits at the intersection of edge computing, AI infrastructure, and security, growing revenue nearly 30%. For investors who want the AI build-out and the security angle in one ticker, it’s a rare two-for-one. The catch: that breadth means it competes on multiple fronts, and it trades at a premium valuation.

The Under-the-Radar Value & Niche Cybersecurity Stocks

Cheaper, specialized, less-crowded corners of the space — where the asymmetric setups often hide.

10. Check Point Software (CHKP)

The steady compounder. Check Point rarely grabs headlines, but it delivers consistent margins and cash flow while quietly building out its cloud and zero-trust offerings. It’s the low-drama way to own the theme.

11. Qualys (QLYS)

The profitable niche leader in cloud-based vulnerability scanning and compliance — the unglamorous plumbing that every regulated enterprise has to buy.

12. Varonis (VRNS)

A direct “securing AI” play hiding in plain sight. Varonis discovers, classifies, and monitors sensitive data and catches insider threats — exactly the problem that explodes when companies point AI models at their internal data. Its shift to a SaaS model is gaining traction.

13. Tenable (TENB)

The exposure-management specialist. Tenable helps organizations find what’s actually exploitable before attackers do — a discipline that only grows in importance as AI accelerates vulnerability discovery on the offensive side.

14. Rapid7 (RPD)

The small-cap wildcard. Same vulnerability-management and security-operations lane as the bigger names, but at a fraction of the market cap — which means more torque on any sector re-rating, and more risk if execution slips.

15. Gen Digital (GEN)

The consumer angle. The owner of Norton and Avast is the steadier, income-oriented way to play cybersecurity — protecting individuals rather than enterprises, with a profile closer to a cash-flow compounder than a hyper-growth bet.

Bonus: 3 Cybersecurity ETFs for One-Click Exposure

Not sure which horse to back? Own the whole field. These three ETFs give you diversified exposure without single-name blowup risk:

• First Trust NASDAQ Cybersecurity ETF (CIBR) — the largest and most liquid, weighted toward Palo Alto, CrowdStrike, and Fortinet. It’s pulled in roughly $1.5 billion of net inflows over the past year, so it’s where sector money shows up first.
• Amplify Cybersecurity ETF (HACK) — the original cyber ETF, with a similar large-cap tilt.
• Global X Cybersecurity ETF (BUG) — a cleaner pure-play tilt with less legacy-tech overlap.

The Bottom Line on the Cybersecurity Play

Cybersecurity is one of the only enterprise budgets still expanding through an uncertain economy — and AI is pouring fuel on it from both sides. That’s the definition of a durable trade.

A few rules to play it well. Treat the tiers as roles, not rankings — the generals for core exposure, the disruptors for torque, the niche names for less-crowded setups, and the ETFs when you’d rather own the theme than pick a winner. Watch net new ARR at every earnings report; it’s the one number that separates a real spending shift from a headline pop. And remember that after a big move like Monday’s, headlines fade but bookings don’t — the names that hold their gains are the ones with the recurring revenue to back it up.

The AI era needs defending. These are the companies getting paid to do it.

Editor’s Note: What ever happened to the AI stock boom? Even AI darlings like Nvidia have essentially gone nowhere since summer 2025. Our friend and colleague at InvestorPlace, 抖阴最新版, may have the answer. According to Louis, the AI industry is quietly “staging” ahead of the next great AI breakthrough… a new class of AI he calls “Superintelligence… but better.” How will it trigger a $100 trillion reset of the AI markets. How will the launch of this tech send some stocks to zero, and others soaring? And why does Louis say: Don’t buy or sell an AI stock in 2026 until you see what’s coming next? Go here for the full story (and Louis’ #1 pick).  

The post The 15 Best Cybersecurity Stocks to Buy Whether AI Booms or Busts appeared first on InvestorPlace.

Over the years, I’ve discussed some of the psychological tendencies that can cause investors to stumble, including Recency Bias and Crowd-Seeking Bias.

The bottom line is that the more you know about the workings of your own mind, the “bugs” inside it and how they may work against our investment performance, the more you can develop strategies to mitigate their negative effects.

In today’s Market 360, we’ll talk about another major psychological challenge investors face: Overconfidence. I’ll detail overconfidence bias, how it works and how you can neutralize its negative effects.

Let’s get started.

Confidence vs. Overconfidence

First, let me clear the air a bit. I believe confidence is a good thing.

Without it, you wouldn’t do many of the things that make life great. Whether it’s applying for a job, asking someone for a promotion or even investing money in the market, confidence is part of what gets you to a great result.

However, overconfidence refers to the phenomenon that people’s confidence in their judgments and knowledge is higher than the accuracy of these judgments.

Put more simply, overconfidence blinds you to the reality of your ability and the circumstances around you.

It’s why 65% of Americans think they’re smarter than others; it’s why more than 50% of business owners view their businesses as more than 90% ethical than their competitors; it’s why 93% of American drivers think they’re above average.

Overconfidence is even partly to blame for the Titanic, which was considered to be an “unsinkable” ship.

It’s called the “mother of all cognitive biases” for a reason!

If overconfidence can sink a ship, it can certainly affect your investing life any number of ways. And sometimes it can take some time to experience the consequences.

For instance, the latest Retirement Confidence Survey by the Employee Benefit Research Institute found that 61% of workers are confident they’ll have enough money to live comfortably throughout retirement.

Of course, confidence alone doesn’t guarantee that you’re prepared. When it comes to something as important as retirement, you want the numbers to back it up.

That’s a lesson all of us should heed.

And it’s the same lesson I’ve applied to investing for decades.

People have used my quantitative system to invest in blue chip stocks, or to find small caps that can grow 10X.  Now, I’ve taken that same data-driven approach and refined it to look for another important signal – where the biggest institutional investors may be moving before the rest of the market catches on.

That’s what I call Precursor Intelligence.

How Do You Combat Overconfidence?

I’m a numbers guy. Always have been. Since I was a kid, I’ve loved math and I knew that math was the right way to understand the world.

Said another way, I depend on evidence for my decisions.

And by sticking with the facts, I’ve found stocks that have made huge moves over short periods of time. We’re talking about moves of 100%, 200% and even 500% in months instead of years.

Take Sezzle Inc. (SEZL), for example.

Back in September 2024, my system identified a shift in Sezzle’s ownership structure. It showed me that the big institutional investors were moving in.

So, I did some final vetting and recommended the stock to my subscribers. In less than a year, they had the chance to capture a 555% gain.

That’s the power of following the numbers instead of relying on a hunch.

And the same principle works when it’s time to sell.

When you’re sitting on a great profit, it can be tough to let go. It’s all too easy to become emotionally invested – or too confident that a stock will keep climbing.

But numbers don’t lie… and you’re usually better off heeding them.

That’s especially important today, when so many investors are being pushed toward the same obvious stocks and the same popular ideas.

Precursor Intelligence is designed to help me look beneath the surface and identify shifts in fundamentals and institutional buying pressure before the broader crowd catches on. The system focuses in part on whether institutional investors are moving into or out of a stock.

The reality is that as wonderful as the human brain is, it is a terrible tool for investing. It’s like trying to eat soup with a fork.

That’s why I prefer to let the data guide me. And right now, Precursor Intelligence is helping me identify where the big money may be moving before those changes become obvious to everyone else.

I recently recorded a special presentation explaining how it works and the opportunities it’s uncovering today. Click here to learn all the details.

Sincerely,

抖阴最新版

Editor, Market 360

The Editor hereby discloses that as of the date of this email, the Editor, directly or indirectly, owns the following securities that are the subject of the commentary, analysis, opinions, advice, or recommendations in, or which are otherwise mentioned in, the essay set forth below:

Sezzle Inc. (SEZL)

The post How to Keep Overconfidence From Getting in the Way of Profits appeared first on InvestorPlace.

On one of AI’s toughest interactive tests, Claude solved fewer than one in three challenges.

Then Nvidia (NVDA) surrounded the same model family with better memory, outside tools, a structured work loop, and a supervisor that stepped in whenever Claude got stuck.

The system solved every challenge – and its score jumped from 30.2% to 100%.

Nvidia changed other parts of the test setup, too, so this was not a perfect apples-to-apples comparison. Still, the result points toward a major shift in the AI market.

We first covered this result last month. It matters even more now. 

With the industry’s safety push dominating headlines – and some labs signaling a more deliberate pace on their most capable releases – the central question in AI has changed: how much value can be unlocked around the models that already exist?

According to Nvidia, a lot. 

Today’s best models may already contain more useful intelligence than their surrounding systems allow them to express. And the companies that turn that capability into dependable, cost-effective – and now, provably safe – workflows could become some of the next major AI winners.

Powerful AI Agents Still Need Guardrails

Today’s AI agents are pretty good at sprints. Marathons are where they lose the plot. 

Give Claude or GPT a short assignment. It can browse the web, write some code, pull information from another app, and come back with an answer.

But long projects are a different story. Imagine asking an agent to migrate a company’s financial software, redesign a supply chain, or optimize thousands of lines of GPU code. The work may require hundreds of decisions made over hours or days.

Frontier models still struggle with that kind of sustained work. They lose context. Repeat old mistakes. Chase dead ends. Occasionally become so confused that they damage the project they were supposed to complete – and we’re left to try to clean up the mess.

Nvidia’s Agentic Variation Operators system, or AVO, was designed to keep that from happening.

AVO gives the model persistent memory and its own repeated cycle of planning, acting, testing, and revising. Nvidia also added a supervising agent that watches the work and nudges the main agent when it gets stuck or starts exploring an unproductive path.

Think of it as a talented employee with a good project-management system and an experienced boss nearby.

The intelligence was already there.

Nvidia helped it stay organized long enough to finish the job.

AI Safety Is Becoming Its Own Infrastructure Layer

For the first few years of this boom, the model leaderboard commanded almost all the attention.

Which lab had the best reasoning score? Which model had the largest context window or wrote the cleanest code?

Those are still important questions; better models still retain a huge competitive advantage.

But the new safety push is expanding the market around the model.

Independent evaluators can’t do their jobs from the outside. They need access – model checkpoints to probe, environments to test in, and the logs that show how a system actually behaved. Companies deploying agents need something different: control. An audit trail behind every action, a clear point where a human takes over, and software that can stop or redirect an AI the moment something goes wrong. 

Those safeguards have to remain active after the model launches, too.

A company will want to know:

• What information the agent accessed
• Which tools it used
• Why it took a particular action
• When a human should intervene
• If the system can recover safely after a mistake

That requires real infrastructure: software that watches the agent, systems that secure it, memory that records what it did – plus simulation platforms and an enormous amount of testing. 

It also requires more compute.

Each independent evaluation runs the model again. Each monitor adds another layer of processing. Simulations, safety checks, recovery loops… all of it consumes more infrastructure.

The labs may take more time before releasing their most capable systems.

But the work surrounding those systems – proving them safe before launch, watching them after – keeps expanding. 

AI Safety Gets More Serious When Machines Start Moving

AI safety becomes a much bigger issue when intelligence leaves the screen.

A coding assistant can generate a bad line of software. A human can review it, reject it, and run the task again.

A robot operating on a factory floor deals with physical consequences.

It may be carrying a heavy part through a crowded warehouse, inches from expensive equipment – and people. A bad decision can cause serious damage.

That raises the bar dramatically.

A useful robot needs far more than a capable model.

It needs cameras and sensors to understand its surroundings. Control software has to translate a decision into precise movement. Simulation tools must expose the system to unusual situations before it encounters them in the real world.

The robot also needs a plan for the moments when things go sideways. If it loses track of an object, it has to know how to reacquire it. If a person steps into its path, it has to stop or reroute instantly. It has to recognize when it’s out of its depth and call for help. And afterward, it has to be able to prove it acted safely at every step. 

Those requirements are becoming central to commercial robotics.

Factories and warehouses are not waiting for a robot that can do everything a person can do. They need machines that can perform a handful of useful tasks reliably, repeatedly, and safely.

Stronger AI guardrails can help unlock those deployments. That makes safety more than a regulatory cost.

Better AI Infrastructure Changes Both Safety and Economics

Performance is only half the equation, though. Companies also care what it costs to finish the job.

As Databricks CEO Ali Ghodsi explained to TechCrunch, two agent systems built around the same model can produce dramatically different bills. Choose the wrong setup, and the same task may cost roughly twice as much to complete.

A clumsy workflow sends a routine job to an expensive frontier model when a smaller one would do. Poor memory leads the system to reread huge amounts of old information again and again.

A better setup preserves what matters, sends each task to the right model, and eliminates unnecessary loops.

Safety systems add work of their own. But they can also unlock far more valuable tasks.

A company may happily accept a little more processing overhead if the result is an agent – or robot – it can trust with meaningful work.

Better systems can lower the cost of each completed task and reduce expensive failures, making more jobs worth automating.

In another Nvidia experiment, AVO tested more than 500 approaches to improving a piece of GPU software and saved 40 separate versions before beating a leading implementation by as much as 10.5%.

The agent kept experimenting, checking, and revising until it found a better answer.

Every loop consumed compute. Every successful result made that compute more valuable.

Frontier releases may slow at the margin. The market around them won’t. 

Physical AI Startups Are Building the Layer Between Models and Machines

The public market remains focused on the largest model labs and the companies supplying their chips.

But much of the work required to bring AI safely into the physical world is happening inside smaller, private companies.

They are collecting robot-training data.

Building simulation software.

Developing machine vision, control systems, and safety layers.

Creating the tools that let robots learn new tasks and operate around people.

This is the “second layer” of the AI boom: companies taking raw intelligence and applying it to specific industries, factory floors, warehouses, and machines.

The safety turn could make that layer even more valuable.

A more deliberate frontier race gives robotics companies time to improve reliability, integrate monitoring, and turn today’s models into systems that businesses can actually deploy.

That brings me to one private company I have called the “Nvidia of Robotics.”

For a limited time, everyday investors can claim a stake with as little as $500. But the opportunity is set to close to new investors on Monday, Sept. 21.

Get the name, the full investment details, and everything you need to claim your stake before the window closes on Monday.

No paywall. No credit card required.

The post Slower AI Models Could Mean Faster Growth Somewhere Else appeared first on InvestorPlace.

But in today’s Friday Digest takeover, our technology expert, Luke Lango, makes an important distinction. Building tomorrow’s AI is only one side of the boom. The other is figuring out profitable ways to use the extraordinarily powerful AI we already have – and that process is still in its infancy.

Luke believes that could create an entirely new crop of winners as entrepreneurs apply existing AI to a variety of industries. He highlights a fascinating example – a young private robotics company that’s already putting AI to work in the real world.

Luke recently recommended the company to members of his Venture Capital Investor service, and he walked through the opportunity – and its risks – in his free 2026 AI Megadeal Event. You can watch the replay right here.

Bottom line: Even if the race for smarter AI is hitting a speed bump, the race to make money with today’s AI is just getting started.

I’ll let Luke take it from here.

Have a good evening,

Jeff Remsburg

Hello, Reader.

The AI industry just had one hell of a weekend.

It started last week when 27-year-old Anthropic researcher Jacob Coxon quit and accused Anthropic and OpenAI of racing toward superintelligence while “gambling with our lives.” Then on Saturday, Anthropic CEO Dario Amodei published an essay called “We Must Pace the Frontier,” arguing that the industry needs to slow the development of increasingly powerful AI models.

OpenAI CEO Sam Altman agreed. So did Elon Musk.

Wall Street responded pretty much as you’d expect. AI stocks sold off as investors started asking what a deliberate slowdown could mean for the hundreds of billions of dollars pouring into chips, data centers, power plants, and everything else supporting the AI buildout.

I’ve spent a lot of time thinking about that question. And while I don’t think this changes the direction of the AI Boom, it could change the speed.

I’ve said for months that politics, regulation, and public concerns about AI safety could put some speed bumps in front of the industry. We may be seeing the beginning of that now. Some of the more aggressive forecasts for how quickly AI develops may have to come down.

But there’s another part of this story I think investors should understand.

Amodei is primarily talking about slowing the development of frontier AI – increasingly powerful models capable of reasoning, coding, operating autonomously, and even helping researchers build better AI. That could mean longer development cycles, more safety testing, new rules, or limits on some types of training.

Meanwhile, companies all over the world are still figuring out what to do with the AI we’ve already built.

And there’s a lot left to figure out. For investors, I think there’s a lot of money left to be made there, too.

Because the next big AI winner doesn’t necessarily have to build a smarter model than OpenAI or Anthropic. It could take the extraordinary AI we already have and find valuable new ways to use it.

Today, I want to show you why I think that opportunity could keep growing even if frontier AI slows down. Then I’ll tell you about one young private company I recently recommended – a food-service robotics startup that’s already putting AI to work in the real world.

We’ve Barely Started Putting AI to Work

Think about the AI models available today. Businesses are already using them to write software, review documents, answer customer questions, analyze medical images, design products, and automate parts of their operations. Most companies are still early in that process.

AI requires computing power for two main jobs. Training is how developers build and improve a model. Inference is what happens every time somebody puts that model to work. Deloitte’s 2026 outlook projected that inference could account for roughly two-thirds of AI computing this year, up from about half in 2025.

So even if tomorrow’s AI takes longer to arrive, more people using today’s AI can keep demand growing for servers, memory chips, networking equipment, cooling, and electricity.

That’s one reason I remain bullish on AI infrastructure stocks.

But I’m also interested in the companies doing the actual using.

There are millions of businesses out there applying AI to real problems.

One of the companies I’ve been studying recently is doing that with robots.

And food.

Teaching Robots to Learn

The company I mentioned earlier started in food-service robotics. Its robots are already working in real commercial locations, serving actual customers.

I’ve visited one of those locations myself. I watched a robot server take an order, prepare it, and deliver the finished product. And I came away impressed.

But the robot server itself is only part of what interested me. Behind that business, the company has spent years developing what amounts to a training academy for robots.

Humans learn physical skills largely by watching other humans.Someone shows you how to do something, you try it yourself, they correct you, and you get better with practice. Robots have traditionally required specialized engineers to program their movements, which makes teaching them new physical tasks expensive and painfully slow.

This company is working on a different approach. Its AI system uses human demonstrations to teach robots new physical skills. The company says it can teach a robot some new hands-on tasks in as little as 30 minutes, without an engineer programming every movement.

That’s when this became a much more interesting company to me.

Food service gives these robots a place to learn and improve every day. But if this training technology works at scale, the same approach could eventually teach robots to handle products in warehouses, work with equipment in factories, perform tasks in healthcare, and take on other complicated physical jobs.

Now we’re talking about a much bigger potential market.

The Opportunity Beyond Smarter Models

This is the part of the AI Boom I think could get overlooked amid all the headlines about superintelligence, slowing down frontier development, and even the possibility that advanced AI could threaten humanity.

We already have extraordinarily capable AI, and businesses are putting it to work fast. Government data tends to produce more conservative adoption estimates, while business surveys have found anywhere from roughly 70% to nearly 90% of companies using AI in some fashion. The exact percentage depends heavily on what you count as “using AI.”

The larger point is that adoption has a long way to run. Entrepreneurs will spend years finding new applications for today’s technology, and successful ones will create demand for more computing power, infrastructure, robotics, and technologies we haven’t even thought of yet.

That’s why I’m paying close attention to young companies like the food-service robotics company I just described.

And increasingly, I’m looking for some of these companies while they’re still private. New technologies often start with small companies solving one narrow problem extremely well. If that technology proves valuable, a larger company may eventually decide it’s faster to acquire the business than spend years trying to re-create it. For the early investors who backed that young company, an acquisition can provide the payday long before an IPO ever arrives.

That’s one reason I’ve started looking beyond the stock market for AI opportunities. It gives me a chance to study promising young companies while they’re still building – and, in certain cases, invest alongside them.

Of course, investing that early comes with plenty of risk. The company I’ve been telling you about is young, it’s losing money, and its robot-training technology is still early. Its current valuation also puts a hefty price on growth that still has to materialize.

That’s where my PPT framework comes in.

Whenever I evaluate a young, privately held company like this, I don’t have years of SEC filings or a stock market history to look at. Instead, I start with three things: the People building it, the Product they’ve created, and the Timing of the opportunity. I call that my PPT framework.

This company checks some important boxes. Its CEO previously built a computer-vision startup that was acquired by Amazon.com Inc. (AMZN). Its robots are already operating in the real world. And its robot-training technology is arriving as major technology companies pour money into robotics and physical AI.

That’s why I recently recommended the company to members of my new Venture Capital Investor service.

During my free 2026 AI Megadeal Event, I walk you through the company from top to bottom. I go over its founders, food-service robotics business, robot-training technology, financials, and risks… and the reasons I decided to recommend it.

I also explain how individual investors can invest in private companies like it. If you’ve spent your investing life buying stocks through a brokerage account, this will probably be unfamiliar territory. I’ll show you how it works, what you’re actually buying, and what you should understand before putting your own money into one of these opportunities.

Watch the free replay of my 2026 AI Megadeal Event here.

Sincerely,

Luke Lango

Senior Investment Analyst, InvestorPlace

P.S. I’ve worked with Luke for a long time, and when he gets interested in a company, he likes to kick the tires himself. In this case, that meant visiting one of the company’s locations and watching its robots work firsthand. It’s a cool story, a fascinating young company, and a side of AI investing most of us rarely get to see. Check out Luke’s free event here.

The post AI Stocks Just Hit a Speed Bump, so Here’s Where Luke Is Looking Next appeared first on InvestorPlace.

As I explained in Tuesday’s Market 360, the latest concerns center on whether the race to develop more powerful AI may start to slow.

But my colleague Luke Lango thinks there’s another part of this story investors shouldn’t overlook.

Even if the industry takes a little more time developing the next generation of AI, companies are still just beginning to figure out how to put the AI we already have to work. And that could create a whole new wave of opportunities for investors.

In today’s guest piece, Luke explains why he remains bullish on the AI Boom and introduces you to one young company already using AI in a very interesting way.

It’s also the company he recently highlighted during his free 2026 AI Megadeal Event. You can watch the replay here.

But first, I’ll let Luke explain why the latest headlines haven’t changed his outlook.

Take it away, Luke…

*

Hello, Reader.

The AI industry just had one hell of a weekend.

It started last week when 27-year-old Anthropic researcher Jacob Coxon quit and accused Anthropic and OpenAI of racing toward superintelligence while “gambling with our lives.” Then on Saturday, Anthropic CEO Dario Amodei published an essay called “We Must Pace the Frontier,” arguing that the industry needs to slow the development of increasingly powerful AI models.

OpenAI CEO Sam Altman agreed. So did Elon Musk.

Wall Street responded pretty much as you’d expect. AI stocks sold off as investors started asking what a deliberate slowdown could mean for the hundreds of billions of dollars pouring into chips, data centers, power plants, and everything else supporting the AI buildout.

I’ve spent a lot of time thinking about that question. And while I don’t think this changes the direction of the AI Boom, it could change the speed.

I’ve said for months that politics, regulation, and public concerns about AI safety could put some speed bumps in front of the industry. We may be seeing the beginning of that now. Some of the more aggressive forecasts for how quickly AI develops may have to come down.

But there’s another part of this story I think investors should understand.

Amodei is primarily talking about slowing the development of frontier AI – increasingly powerful models capable of reasoning, coding, operating autonomously, and even helping researchers build better AI. That could mean longer development cycles, more safety testing, new rules, or limits on some types of training.

Meanwhile, companies all over the world are still figuring out what to do with the AI we’ve already built.

And there’s a lot left to figure out. For investors, I think there’s a lot of money left to be made there, too.

Because the next big AI winner doesn’t necessarily have to build a smarter model than OpenAI or Anthropic. It could take the extraordinary AI we already have and find valuable new ways to use it.

Today, I want to show you why I think that opportunity could keep growing even if frontier AI slows down. Then I’ll tell you about one young private company I recently recommended – a food-service robotics startup that’s already putting AI to work in the real world.

We’ve Barely Started Putting AI to Work

Think about the AI models available today. Businesses are already using them to write software, review documents, answer customer questions, analyze medical images, design products, and automate parts of their operations. Most companies are still early in that process.

AI requires computing power for two main jobs. Training is how developers build and improve a model. Inference is what happens every time somebody puts that model to work. Deloitte’s 2026 outlook projected that inference could account for roughly two-thirds of AI computing this year, up from about half in 2025.

So even if tomorrow’s AI takes longer to arrive, more people using today’s AI can keep demand growing for servers, memory chips, networking equipment, cooling, and electricity.

That’s one reason I remain bullish on AI infrastructure stocks.

But I’m also interested in the companies doing the actual using.

There are millions of businesses out there applying AI to real problems.

One of the companies I’ve been studying recently is doing that with robots.

And food.

Teaching Robots to Learn

The company I mentioned earlier started in food-service robotics. Its robots are already working in real commercial locations, serving actual customers.

I’ve visited one of those locations myself. I watched a robot server take an order, prepare it, and deliver the finished product. And I came away impressed.

But the robot server itself is only part of what interested me. Behind that business, the company has spent years developing what amounts to a training academy for robots.

Humans learn physical skills largely by watching other humans. Someone shows you how to do something, you try it yourself, they correct you, and you get better with practice. Robots have traditionally required specialized engineers to program their movements, which makes teaching them new physical tasks expensive and painfully slow.

This company is working on a different approach. Its AI system uses human demonstrations to teach robots new physical skills. The company says it can teach a robot some new hands-on tasks in as little as 30 minutes, without an engineer programming every movement.

That’s when this became a much more interesting company to me.

Food service gives these robots a place to learn and improve every day. But if this training technology works at scale, the same approach could eventually teach robots to handle products in warehouses, work with equipment in factories, perform tasks in healthcare, and take on other complicated physical jobs.

Now we’re talking about a much bigger potential market.

The Opportunity Beyond Smarter Models

This is the part of the AI Boom I think could get overlooked amid all the headlines about superintelligence, slowing down frontier development, and even the possibility that advanced AI could threaten humanity.

We already have extraordinarily capable AI, and businesses are putting it to work fast. Government data tends to produce more conservative adoption estimates, while business surveys have found anywhere from roughly 70% to nearly 90% of companies using AI in some fashion. The exact percentage depends heavily on what you count as “using AI.”

The larger point is that adoption has a long way to run. Entrepreneurs will spend years finding new applications for today’s technology, and successful ones will create demand for more computing power, infrastructure, robotics, and technologies we haven’t even thought of yet.

That’s why I’m paying close attention to young companies like the food-service robotics company I just described.

And increasingly, I’m looking for some of these companies while they’re still private. New technologies often start with small companies solving one narrow problem extremely well. If that technology proves valuable, a larger company may eventually decide it’s faster to acquire the business than spend years trying to re-create it. For the early investors who backed that young company, an acquisition can provide the payday long before an IPO ever arrives.

That’s one reason I’ve started looking beyond the stock market for AI opportunities. It gives me a chance to study promising young companies while they’re still building – and, in certain cases, invest alongside them.

Of course, investing that early comes with plenty of risk. The company I’ve been telling you about is young, it’s losing money, and its robot-training technology is still early. Its current valuation also puts a hefty price on growth that still has to materialize.

That’s where my PPT framework comes in.

Whenever I evaluate a young, privately held company like this, I don’t have years of SEC filings or a stock market history to look at. Instead, I start with three things: the People building it, the Product they’ve created, and the Timing of the opportunity. I call that my PPT framework.

This company checks some important boxes. Its CEO previously built a computer-vision startup that was acquired by Amazon.com Inc. (AMZN). Its robots are already operating in the real world. And its robot-training technology is arriving as major technology companies pour money into robotics and physical AI.

That’s why I recently recommended the company to members of my new Venture Capital Investor service.

During my free 2026 AI Megadeal Event, I walk you through the company from top to bottom. I go over its founders, food-service robotics business, robot-training technology, financials, and risks… and the reasons I decided to recommend it.

I also explain how individual investors can invest in private companies like it. If you’ve spent your investing life buying stocks through a brokerage account, this will probably be unfamiliar territory. I’ll show you how it works, what you’re actually buying, and what you should understand before putting your own money into one of these opportunities.

Watch the free replay of my 2026 AI Megadeal Event here.

Sincerely,

Luke Lango

Senior Investment Analyst, InvestorPlace

P.S. I’ve worked with Luke for a long time, and when he gets interested in a company, he likes to kick the tires himself. In this case, that meant visiting one of the company’s locations and watching its robots work firsthand. It’s a cool story, a fascinating young company, and a side of AI investing most of us rarely get to see. Check out Luke’s free event here.

The post The Next AI Winners May Already Have All the AI They Need appeared first on InvestorPlace.

You might see a robot coming toward you with a heavy container while workers move pallets off trucks for the bots to pick up.

You suddenly step in the path of the robot, which doesn’t recognize you, and just like that, you’ve got a workers’ comp claim.

The next day, the “days since last accident” sign has to be reset to zero. Yet, if factory robots are to work as intended, they must be able to recognize people and respond appropriately.

That may sound straightforward, but making it happen reliably, around people who aren’t following a carefully rehearsed demonstration, is a major engineering challenge.

Now, think about the broader AI doomsday fears going around right now. Behind those warnings is a concern about increasingly powerful systems behaving in ways their builders cannot predict or control.

A warehouse accident and a runaway AI system are very different risks. But both bring us back to the importance of safeguards that work when something unexpected happens.

Listen, you don’t have to believe every doomsday prediction to take that problem seriously.

It’s also a business problem. Because a warehouse manager can love your technology and still have very good reasons to hold off on buying it.

That’s what caught my attention about Agility Robotics’ new Digit 5.

Digit 5 Is Designed to Work Safely Around People

The company says its humanoid can lift 50 pounds, reach 7.2 feet high, and operate for more than 20 hours a day, with rapid recharging between stretches of work. But it also has an independent safety controller overseeing its response when people get too close. Depending on the situation, the robot can avoid them, stop, or sit down.

That doesn’t resolve the broader debate over AI’s risks. It does illustrate how addressing a safety problem can help move the technology forward.

Now, think about that from an investment perspective.

Teaching a robot when to stop could help a company sell more robots. Making the technology more dependable could help customers deploy it faster.

That’s the investment question I want to focus on today: What turns a promising robot into a product customers keep ordering?

For a robotics company, the distance between an impressive demonstration and a repeat customer can be enormous. Closing that gap is where I think some of the most valuable businesses will be built.

And after what I just heard at the All-In Summit, I think investors need to pay much closer attention.

Why AI Safety Does Not Necessarily End the AI Boom

I just spent two days at the All-In Summit listening to some of the most powerful people in technology talk about artificial intelligence at a pretty extraordinary moment.

On Monday morning alone, I heard from Microsoft (MSFT) CEO Satya Nadella and Nvidia (NVDA) CEO Jensen Huang. I was sitting there when President Donald Trump called Jensen onstage, and the conversation went on speakerphone.

So, yes, it was quite a time to be in that room.

Especially after what had happened just days earlier. Some of the biggest names building frontier AI had begun calling for a deliberate slowdown in the development of increasingly powerful models. AI stocks got hammered as investors tried to figure out what that could mean for the hundreds of billions of dollars pouring into chips, data centers, power plants, and everything else supporting the AI buildout.

But at All-In, I didn’t hear much talk about slowing down…

I paid particular attention to Nadella. Microsoft is one of the companies writing the biggest checks in the AI Boom. Critically, I didn’t hear Satya announce that Microsoft was cutting its AI spending or abandoning its infrastructure commitments.

Running AI for customers takes compute. Testing AI takes compute. Training robots takes compute.

So I don’t look at this debate and conclude that the infrastructure spending cycle is over. I think there is still a pathway to years of growth.

In fact, as a long-term investor, I came away from All-In more bullish about how long this AI Boom could last.

And I think companies have an opportunity here to trade short-term speed for long-term durability.

A Slower Frontier Could Produce a More Durable Boom

My concern has never been this quarter’s earnings or next quarter’s earnings. I care about what happens two or three years from now. What happens if companies build too much capacity too quickly? What happens if a serious AI safety problem scares the public? What happens if regulators come down with a hammer?

Slowing down at the frontier could reduce some of those risks. We may give up some speed in the short term, but I think the industry has an opportunity to make this boom more durable over the long run.

Think about a workout. 

If you push too hard, you’ll pull a muscle and you’re done. Manage the pace, and you give yourself a better chance of finishing.

Robotics gives us a practical example of what making AI more dependable can accomplish.

And for investors, that could shift some of the biggest opportunities toward companies finding valuable new ways to put AI to work. 

I recently recommended one young private robotics company pursuing exactly that opportunity. I’ll tell you more about it in a moment. But first, another robotics company gives us a good look at what it takes to turn an impressive machine into something customers will actually fork over their hard-earned cash for.

What Separates a Great Robot Demo From a Great Business

An AI-powered robot working on a warehouse floor doesn’t have to contemplate the fate of humanity, but it does have to recognize a person who accidentally steps into its path, so it can stop before it runs him over. 

Imagine you’re the manager deciding whether to expand a robot trial across your operation.

A machine might handle a container perfectly when the aisle is empty. But your warehouse has people moving around, awkwardly placed pallets, and shifts that need to stay on schedule.

Learning a task is only the beginning. You need to know how much useful work it completes in a shift, how often an employee has to intervene, and what it costs to keep running. 

Agility says the previous generation of Digit logged more than 65,000 hours with customers. That’s experience with actual operating conditions, actual customer requirements, and actual problems to fix.

Digit 5 still has to deliver, though. Early access is expected in the first half of 2027. And the reported $300 million in orders comes from one unnamed customer and depends on hitting milestones. Those orders aren’t guaranteed revenue, so we still need to see execution. I want to see conditional demand turn into deliveries, productive use, and customers coming back for more.

That progression would tell us much more about the business than a video of a robot completing one difficult task.

Vision-Language-Action Models Help Robots Understand the Job

Across the industry, vision-language-action models, or VLAs, are helping developers address these challenges. Put simply, these systems connect what a robot sees with an instruction and the actions needed to carry it out. Nvidia is developing tools that help robots make those connections.

But understanding an instruction is only part of the job. The machine also has to carry it out reliably under changing conditions. Then you have simulation-to-real training, or Sim2Real.

Instead of doing every practice run with a physical robot, developers can train in virtual environments. Nvidia’s Isaac Lab supports running simulated environments in parallel, giving developers a way to generate training experience at scale.

There are still gaps between simulation and reality, and a successful virtual run doesn’t prove the physical machine will perform reliably. This is why real-world testing remains essential.

For investors, I think the important question is whether that training produces a machine customers can deploy with less setup and less supervision.

If every new installation requires an engineering team to spend weeks adapting the product, expansion could become expensive. A company that can reduce that burden may have a better chance of growing profitably.

What Investors Should Look for Before a Robotics Company Scales

This is how I’m thinking about young robotics companies: Can they turn one successful installation into many without letting service costs swallow the gains?

A customer expanding from one location to several would be an encouraging sign. So would a machine completing more work with fewer interruptions.

I also want to know whether the company can support those additional customers without hiring people faster than it grows revenue. Selling more robots and building a profitable robotics business are separate accomplishments.

And that brings me to a young private company I recently recommended. It started in food-service robotics. Its robot servers are already working in real commercial locations, and I’ve visited one of those locations myself to see the technology in action.

But what really caught my attention was what the company has been building behind that business.

Think of it as a training academy for robots. The company has developed technology that uses human demonstrations to teach robots new physical skills. The idea is pretty intuitive: You show the robot how to perform a task, it learns from the demonstration, and it gets better with practice.

The company says it can teach a robot some new hands-on tasks in as little as 30 minutes, without an engineer programming every movement. 

Food service gives the company a place to train its technology every day, in front of real customers, with all the little complications that come with the physical world. But I think the opportunity will stretch much further. The same approach could be used to teach robots to handle products in a warehouse, work with equipment in a factory, or perform other complicated physical tasks.

Now we’re talking about a much bigger potential market.

The Bottom Line: Dependable Robots Could Unlock the Physical AI Market

During my free 2026 AI Megadeal Event, I explain the business and the reasons I decided to recommend it.

It’s a company I’ve dubbed the “Nvidia of Robotics,” and it’s my No. 1 private robotics opportunity at this turning point in the AI boom.

For a limited time, it is accepting new investors with a minimum investment of $500.

I think this company could become a major player in robotics. But you only have until midnight on Monday, Sept. 21, before this opportunity closes to new investors.

If you’ve spent your investing life buying stocks through a brokerage account, investing in a private company may be unfamiliar territory. So, during that event, I’ll explain how it works, what you’re actually buying, and what I think you should understand before deciding whether an opportunity like this belongs in your portfolio.

You’ve seen what Agility is doing to make its machines more useful. Now I want to show you the private company I’ve recommended, and why I think its approach deserves a closer look.

Watch my presentation for the company’s name and all the details you need to decide whether to claim your stake before Monday’s deadline.

The post AI’s Next Big Opportunity Is Right in Front of You appeared first on InvestorPlace.

As I write on Thursday morning, the stock market is roaring higher – and it’s the tech and AI names, the very ones that took a beating all week, leading the charge.

The Nasdaq is up nearly 1.5%, small caps are joining in, and Treasury yields are easing back from the near-5% levels that rattled everyone this week.

It’s quite the reversal. Just yesterday, the Dow Jones Industrial Average plunged more than 600 points after the Federal Reserve hiked and signaled it isn’t done. Today, the mood has flipped: Investors have decided that a Fed willing to get tough on inflation is actually reassuring, and risk appetite has come rushing back.

There’s no single new headline driving it – no blockbuster earnings, no policy surprise. Just sentiment, swinging hard in the opposite direction less than 24 hours later.

It’s the perfect setup for today’s Digest. It’s a reminder that in the short term, investor sentiment can and will swing the market up and down. But the daily mood isn’t what will decide the next three years for the AI trade. One thing is. And this week’s Digests have been building toward it…

On Monday, we covered the warning about AI from an Anthropic researcher. It resulted in widespread fear, a bipartisan political scramble for regulation, and AI’s own founders calling to slow the pace of frontier development. The market read it as a reason to sell.

In Tuesday’s Digest, we highlighted how that fear collided with a trio of macro headaches – oil back above $100, the 10-year Treasury at 5%, and a Fed poised to hike. But we shared the historical stats on why the first rate hike in years is actually bullish if we look six months or more out.

Yesterday, we put those reassuring stats under a microscope and found the one condition where they fail: a supply-driven hiking cycle. That led us to the bedrock point…

The AI trade – the engine under this entire bull market – rests on one thing: the torrent of spending from a handful of hyperscalers.

Sentiment can cause it to wobble, but it’s earnings from AI infrastructure companies (spend from the hyperscale data center builders) that will ultimately make or break it.

So, today, let’s tackle the obvious related question…

Will the hyperscaler spending keep flowing as AI fears explode and calls for regulation grow louder?

The bull case: capability pacing, capacity racing

On the “yes” side, we’ll go to our technology expert, Luke Lango, editor of Innovation Investor. He’s in an especially good position to make this case because he’s spent this week in Los Angeles at the All-In Summit, a gathering of some of the biggest names in tech and finance with a hand-selected attendance list. Luke heard from Jensen Huang, Elon Musk, and even President Trump, who called in.

For Luke’s first point, he flags the distinction that Wall Street is blowing right past. From Monday’s Innovation Investor Daily Notes:

The immediate debate centers on when a frontier model should be released and who gets to inspect it first.

None of it touches the amount of computing power being purchased, built, or consumed. That distinction is the single most important thing to us.

To understand why, recognize that an AI model burns computing power on two very different jobs. “Training” is the massive, one-time effort that builds a new frontier model. “Inference” is everything after – every answer and task the AI bot performs once it’s live.

Luke notes that pre-training – the stage the entire safety conversation is trying to moderate – has collapsed from more than 60% of frontier computing power in early 2024 to under 10% today. Meanwhile, the workloads that took its place, post-training and inference, are barely touched by any safety procedures being discussed.

So, even a real slowdown in the one thing the safety hawks are targeting would barely dent total AI spending. Inference (use by customers) already makes up roughly two-thirds of AI compute, according to Deloitte, and McKinsey expects it to grow about 35% a year through 2030.

Luke notes that Bloomberg Intelligence landed in the same place this week: With the industry capacity-constrained for years, there’s little reason for anyone to walk away from their commitments now.

Here’s Luke’s overall bottom line:

Capability pacing. Capacity racing.

Model releases may slow. The infrastructure required to train, test, monitor, and run AI keeps growing.

Then there’s his second point – the one I’d underline for anyone rattled by this week’s doom-and-gloom headlines:

Follow what they do, not what they say.

And those actions are hard to argue with.

Back to Luke:

At All-In, I didn’t hear much talk about slowing down.

I paid particular attention to [Microsoft CEO Satya] Nadella. Microsoft is one of the companies writing the biggest checks in the AI Boom.

And the check writer didn’t say anything about writing fewer checks. I didn’t hear anything about cutting AI spending, reducing infrastructure commitments, or backing away from new data centers.

A fresh Bank of America industry report, in Luke’s telling, put it just as plainly – analysts there wrote that they “see no signs of slowing in customer orders, capacity commitments, or semis pricing,” while projecting global semiconductor sales to nearly double to $3.2 trillion by 2030.

This theme was the throughline of the All-In Summit

According to Luke, the conference amounted to a coordinated counterpunch to the recent wave of AI doom.

Beyond Nadella saying nothing about slowing capex, Luke reported that Nvidia Corp.’s (NVDA) Jensen Huang called the human-extinction fears “irresponsible and wrong” and sounded as bullish as Luke’s ever heard him.

Then, President Trump phoned in to vow no regulation and no slowdown, dismissing the whole episode as a Chinese psyop. Finally, Elon Musk pitched a private peer-review system in place of heavy-handed government oversight.

Here’s Luke, summing it all up:

The people who control the capital, the chips, and the policy just told you they aren’t slowing down — and selloffs built on the assumption that they will tend to reverse quickly…

It’s worth waiting through this volatility because we deeply believe that on the other side of this, we will see a massive and sustained rally in AI stocks.

So, is it time to sound the all-clear?

Perhaps, but keep these overhangs in mind

First, Luke is clear that despite his overall bullishness, things could get very bumpy on the road directly ahead:

The short-term setup has gotten tougher… markets trade on fear as well as fundamentals… That could keep pressure on AI stocks over the next few weeks.

But the immediate aside, I still wonder about risks out on the horizon.

First, Luke told us to “follow what they do, not what they say” – that’s reassuring on spending. But if we apply it to “safety,” it creates some issues.

If the labs’ actions tell us they aren’t really slowing down – if the capex keeps racing while the safety talk remains just talk – then the thing that actually frightened people this week isn’t being addressed. And unaddressed risk is exactly what feeds the public and political backlashes.

Regular readers will recognize the shape of this. In our April 6 Digest, I laid out the Prisoner’s Dilemma running through every layer of AI – the competing priorities that leave everyone worse off when each player does what’s individually rational. And the conversation this week looks like another Prisoner’s Dilemma, maybe the biggest of all…

AI leadership versus AI safety.

A frontier lab can’t fully maximize both at once. Lean all the way into leadership – keep spending, keep racing – and safety gets shortchanged, leading to more AI agent hacks and handing ammunition to every politician looking for a cause.

In this case, even if the hyperscalers want to spend, the government can find ways to interrupt those dollars.

But lean all the way into safety – actually slow down – and you validate the capex fear that hammered these stocks this week (not to mention the geopolitical fear of China “winning” the AI race).

This leaves us in what I’ve called “The Messy Middle” – pacing in their rhetoric, but racing in their capital budgets. That middle is bullish for spending right now. But the longer it holds up – potentially resulting in more AI security breaches – the louder the case grows for someone in Washington to force the issue.

On that note, a headline from CNBC this morning reads “OpenAI reports 6 new instances of ‘concerning model behavior’ since March.”

If the AI industry can’t control itself, the politicians will – in a far more heavy-handed way. Luke himself has said that what ends this trade won’t be a tech failure or a recession – it’ll be politics.

Plus, the bull case rests on one thing

The broader bull case sits on a single assumption – demand for all this AI compute will keep compounding.

I think there’s a strong case for this, but if that demand ever wobbles – if enterprises decide the returns on their AI spending just aren’t there yet – then “supply-constrained” can flip to “overbuilt” faster than anyone expects. And we have history to tell us what could happen then…

In 2000, telecom companies laid enough fiber to wire the world for a decade, all of it justified by demand curves that pointed straight up. Then the spending paused, and the suppliers who’d bet on it got crushed – even though the internet ultimately proved every bit as transformational as promised.

The technology can be real, and yet the stocks can get hurt. That’s the lesson of 2000.

So, where does that leave us?

Luke’s “capability is pacing, capacity is racing” analysis is reassuring, and the money is still moving in one direction. So, this isn’t a moment to run from the AI trade. But it is a moment to own it the way we’ve been describing all week.

On Wednesday, I made the case for stocks built to survive higher-for-longer rates – companies with real earnings and real cash today, not rich multiples riding on profits promised a decade out. That same lens is your protection here.

When multiples compress – and in this environment, they most certainly can – the names supported by actual cash flow should hold up better. The ones priced purely on story will have a tougher time.

Finally, on Monday, we handed out a homework assignment: Sort every AI position you own into a bucket. Bucket 1: the businesses you believe in deeply enough to hold through any drawdown. Bucket 2: the momentum trades you’ll exit the moment they turn – knowing exactly why, when, and how.

If you haven’t done it yet, the recent fireworks are a good illustration of why it’s important.

A quick heads-up

Luke is putting together a full recap of everything he saw and heard at the All-In Summit – the conversations on stage and off – and we’ll bring it to you here in the Digest in the days ahead. In short – he remains very bullish.

But for now, one corner of the AI boom that he’s especially bullish on is robotics. In fact, he recently recommended one young private robotics company that he believes is particularly well-positioned.

Our Digest is running long, so I won’t dive into those details today. But to hear more about it from Luke directly, you can check out his free 2026 AI Megadeal Event right here.

For now, watch the spending, not the Fed. It’s still flowing. Just make sure you’re holding the names built to survive whatever the coming months throw at them.

Have a good evening,

Jeff Remsburg

The post The One Question That Decides the AI Trade appeared first on InvestorPlace.

And yesterday, one of the market’s biggest uncertainties disappeared.

The Federal Reserve voted to raise its key interest rate by 25 basis points, bringing the target range to 3.75% to 4%. It was the Fed’s first rate hike since July 2023.

But what really caught my attention was the vote.

It was unanimous. All 12 voting members agreed.

Think about that for a moment. Back in July, Fed Chair Kevin Warsh joked that policymakers might have a good old-fashioned “family fight” over interest rates.

You can barely get 12 family members sitting around a Thanksgiving table to agree on what time to eat dinner. Somebody is going to argue.

Instead, there wasn’t a fight at all. That tells you something.

The Fed clearly believed it needed to act. And Wall Street took the news surprisingly well. Why? Because the rate hike itself was hardly a surprise.

As Fed Chair Kevin Warsh noted yesterday in his press conference following the hike decision: “The plain fact is that inflation is too high and has been for too long.”

There’s no denying the recent culprit has been energy.

And as I explained in a Special Market Podcast to my subscribers yesterday, I expected the Fed to raise rates. In my view, standing pat would have damaged the Fed’s credibility.

So, the question now isn’t whether the Fed has turned more hawkish. It has.

The more important question for investors is why, and what we should do about it.

So in today’s Market 360, I’m going to show you why there was a consensus, and what drove the Fed’s decision. Then we’ll look at a less obvious point Warsh raised about the spending behind the AI buildout and where you should be focused in this market.

What’s Driving the Energy Squeeze

We all know the conflict in the Middle East has been roiling global energy markets since late February.

As Warsh put it yesterday, “There’s no hiding from hot spots around the world.”

The Iran War has pushed oil prices above $100 per barrel in recent days. Prices for refined products like jet fuel have soared. Diesel prices recently reached record highs of $6.29 per gallon.

Iran-backed Houthi attacks have threatened Saudi Arabian infrastructure and Red Sea shipping routes.

A separate drone attack forced Saudi Arabia to shut down its East-West oil pipeline, a route that bypasses the Strait of Hormuz. The pipeline has a maximum capacity of 7 million barrels per day (bpd).

The big question for investors is how these disruptions will hit the cost of doing business. Because higher energy prices eventually work their way through the economy.

The latest inflation reports show the pressure.

The Producer Price Index (PPI), which looks at prices producers receive, rose 0.4% in August. Core PPI, which excludes food and energy, increased 0.2%. That was better-than-expected.

But look at what happened with energy.

Energy prices surged 4.2%, even before the latest jump in diesel prices. Food prices, by comparison, increased just 0.1%.

Here’s the real problem, folks. PPI is running at 5.4% over the past 12 months. And those diesel price increases could easily make their way into more inflation in the next report.

The Consumer Price Index (CPI) also sent a mixed message. Overall consumer prices rose 0.4% in August and 3.4% over the past 12 months. Core CPI increased 0.3% for the month, slightly above economists’ expectations, and 2.4% year-over-year.

Shelter costs climbed to 0.3% in August. So, the cost of housing remains part of the CPI problem, too. But if energy prices stay high for much longer, you can bet the cost of just about everything else will rise, too.

What’s interesting about all this is that the consumer remains surprisingly resilient through all of this mess.

Retail sales jumped 1.2% in August, rising across the board. Core retail sales climbed 1.4%, which was the biggest gain since September 2024.

And employers added 162,000 jobs in August, easily topping economists’ expectations.

What the Fed Sees Next

In his press conference, Warsh identified three reasons for the hike: a stronger economy, too little progress on inflation and a changed geopolitical outlook.

And if the rate hike itself was expected, what came next was more revealing.

The Fed’s latest “dot plot” showed that 16 of 18 officials expect at least one more rate hike before the end of the year.

In other words, policymakers aren’t treating Wednesday’s move as necessarily one-and-done.

Chair Warsh didn’t submit a dot, nor was he expected to. “I’m not in the forward guidance business,” he said in his press conference.

But Warsh also raised another issue that caught my attention.

Artificial intelligence.

“We care very much about what’s happening in artificial intelligence,” he said, pointing to AI’s effects on both demand and the economy’s productive capacity.

The Fed even has a task force studying AI’s economic impact, with findings expected by year-end.

And one particular point Warsh made gets directly to where I think investors should be looking now…

The Other Side of Higher Rates

Warsh was also asked about another issue that has been rattling investors lately: the rise in long-term Treasury yields.

The 10-year Treasury yield recently climbed above 5% for the first time since 2007. That matters because the 10-year serves as an important benchmark for borrowing costs across the economy.

And Warsh pointed to AI spending as one reason yields have moved higher.

“The so-called hyperscalers are out in the market raising funding,” he said. “And so the competition for capital is real. And I think it partly explains the increase in yields.”

Folks, we’re talking about an extraordinary amount of money.

Bank of America says the five biggest hyperscalers sold $121 billion worth of U.S. corporate bonds last year. For some perspective, they had averaged just $28 billion from 2020 through 2024.

And the borrowing has only accelerated in 2026. Morgan Stanley estimates AI-related debt worldwide had already reached nearly $236 billion by the end of May. At that pace, the firm expects the total to approach $570 billion by year-end.

Why all the borrowing?

Because the AI buildout has become so large that even some of the richest companies on Earth can no longer fund it entirely out of their cash flow.

So, they are increasingly turning to the bond market to help finance new data centers, chips, power systems and other AI infrastructure.

That helps explain Warsh’s point about “competition for capital.”

These companies are competing with the U.S. government, other corporations and other borrowers for the same pool of money. When demand for capital rises, borrowing costs can rise with it.

But here’s the part I want you to focus on as an investor.

One company’s investment bill can become another company’s sales.

Every dollar being spent on data centers, computing systems, power infrastructure and other AI capacity has to go somewhere.

Of course, that does not make every supplier a winner.

But this is where I want to be looking for the next big winners, folks. Not just at who is spending the money, but at who can turn that spending into growing sales and earnings.

My team and I have been studying the next phase of AI computing taking shape at America’s national laboratories.

The goal goes beyond better chatbots. These systems are being designed for scientific work in fields like energy, medicine and advanced manufacturing.

I don’t need to predict which breakthrough arrives first to study the businesses helping build that computing capacity. But the numbers still have to hold up.

I want fundamentally superior stocks with outstanding sales and earnings growth, not just a good AI story.

And in my AI Reset presentation, I explain the opportunity and reveal the name and ticker of a company I believe is positioned to benefit, no matter what the Fed does next.

Click here to watch my AI Reset presentation now.

Sincerely,

抖阴最新版

Editor, Market 360

The post The Fed Just Revealed Something Big 抖阴最新版 the AI Boom appeared first on InvestorPlace.

Slow and steady wins the race. That old saying has guided everyone from tortoises to marathon runners. But it needs an update in the age of artificial intelligence:

Slow and steady AI development might save the human race.

Some of the people building the world’s most powerful AI systems are warning that the technology is advancing faster than our ability to control it.

Anthropic CEO Dario Amodei published a 3,800-word essay this past weekend, calling for the AI industry to slow the pace of development to allow safety measures to catch up. This warning came just days after 27-year-old Anthropic researcher Jacob Coxon quit and accused Anthropic and OpenAI of racing toward superintelligence while “gambling with our lives.”

Other prominent AI leaders have since backed Amodei’s concerns, and AI-related stocks sold off on Monday. (At the same time, cybersecurity stocks benefited from the growing focus on AI safety and security.)

That puts a rather large question mark at the end of the AI race. “Slow and steady” may keep the track from blowing up. But the people building AI are openly questioning whether they can slow it down safely. So why do they keep pushing ahead at full speed?

In today’s Smart Money, let’s examine that contradiction – and why the uncertainty surrounding AI could point toward a different kind of opportunity.

The $2 Trillion Contradiction

While Anthropic’s CEO is calling for slower AI development because of catastrophic risks, the company is still preparing to go public this year.

Anthropic confidentially filed IPO paperwork with the SEC in June, although the final valuation, offering price, and timing have not been set. Investors have reportedly discussed a potential valuation as high as $2 trillion, but that figure comes from market expectations, not Anthropic itself.

And that creates a two-trillion-dollar contradiction.

The AI industry is warning that its technology may be moving too quickly for humanity to safely control. But investors are simultaneously putting extraordinary valuations on the companies developing that out-of-control AI.

If AI really does have a chance of destroying humanity, then a $2 trillion valuation for an AI company becomes a pretty strange investment thesis. And if the machines really do inherit the Earth – and economy – who exactly is left holding the shares?

Existential questions aside, that contradiction extends beyond any Anthropic IPO – showing up in the conflicting views of AI executives and the reaction across AI-related stocks.

Since AI fears came roaring back late last week and over the weekend, Sam Altman has said that “right now would be an ill-advised moment” for OpenAI to go public. And Nvidia Corp. (NVDA) CEO Jensen Huang pushed back on Tuesday against halting development, calling any new laws or regulations “completely unnecessary.”

After selling off on Monday, semiconductor and other AI infrastructure stocks have recovered some of their losses. But many remain below where they started the week as questions linger over what a slower pace of AI development could mean for the massive spending boom.

That’s the problem we now face: Nobody knows exactly which version of the future is coming. The bag may be mixed, but it still sits elephant-sized in the middle of the trading room. It can’t be ignored, but we also don’t exactly know what to do with it.  

And that, I believe, is the opportunity.

Because the safest way to invest in an unpredictable AI future may be to own companies that don’t require you to predict it.

Investing for Any AI Future

Maybe AI becomes the most transformative technology in human history. Maybe regulators slow its development. Maybe the AI boom eventually goes bust.

Or maybe the technology becomes so powerful that controlling it becomes the biggest challenge of all.

There is one strategy that doesn’t require knowing these answers: investing in AI Survivors.

AI Survivors aren’t just businesses that can survive AI disruption. They’re ones that can survive the uncertainty surrounding AI itself. Let’s consider a few different potential outcomes:

1. AI keeps accelerating.

In this scenario, AI continues spreading into more industries, threatening companies whose business models depend on human labor. However, the AI Survivors that sell physical experiences, products, or services that are difficult to replace with software are less exposed to direct AI displacement.

2. AI development slows because of safety concerns.

If governments or regulators pump the brakes on frontier AI, the massive spending behind the AI boom could slow with it. Companies tied to data centers, chips, and other AI infrastructure could feel the impact. AI Survivors, however, don’t need that spending spree to keep growing.

3. AI becomes genuinely dangerous or uncontrollable.

This is the most extreme scenario raised by Amodei and others. In that world, companies that depend heavily on AI could face serious disruption. AI Survivors built around essential goods, physical experiences, and human needs can remain valuable even if society puts tighter limits on AI.

4. The AI boom becomes a bubble.

If the $2 trillion Anthropic valuation shows that AI stocks have gotten ahead of themselves, investors could pull money out of AI stocks. But AI Survivors aren’t dependent on AI enthusiasm or sky-high valuations, so their investment case doesn’t rely on the AI boom continuing indefinitely.

AI’s biggest opportunity may not be in predicting what comes next, but in owning the companies that can survive whatever comes next.

In other words, the smartest way to play the AI race may be to own companies that don’t need to run it at all.

You can click here to learn how to access all of my AI Survivor recommendations.

Regards,

抖阴最新版

The post The AI Race Is Getting Harder to Predict, and That’s the Opportunity appeared first on InvestorPlace.

“This has to be fake,” he thought, as a person moved in front of a camera and a digital skeleton followed along on screen, mirroring the person’s movements in real time.

But Eden Shochat was getting a real demonstration from PrimeSense.

PrimeSense’s technology went into Microsoft’s (MSFT) Kinect gaming system. In 2013, Apple (AAPL) bought the company for a reported $350 million.

One of its founders, Aviad Maizels, eventually started another business, Q.ai. This time, his team worked on technology involving audio, machine learning, and subtle facial movements.

Shochat got another look at a prototype that seemed almost too ambitious to believe.

This time, he invested.

In January, Apple acquired Q.ai for a reported price approaching $2 billion. By the time most investors heard about the company, Apple had already bought it.

I keep thinking about that distinction as the AI industry debates whether to slow the development of its most powerful models.

On Wall Street, investors are asking what a longer wait for the next breakthrough could mean for AI companies’ growth, and what their stocks are worth today.

But many smaller companies are working on a different problem: How do we turn the AI we already have into something customers will pay to use?

That could mean helping a factory spot defective parts or teaching a robot to perform a useful task.

Those businesses don’t necessarily need a more powerful AI model to keep growing. They need to make existing technology reliable, affordable, and useful enough to win customers.

And that’s the opportunity private markets can offer during a public-market slowdown: a chance to own companies whose next stage of growth comes from solving those practical problems, even while enthusiasm for publicly traded AI stocks cools.

A private startup can keep winning customers, increasing revenue, and becoming more attractive to a potential buyer without having its shares repriced every time sentiment shifts on Wall Street.

That doesn’t make it immune to a downturn, but it creates another way to participate in AI’s growth… through business progress that can continue even when the stock-market rally doesn’t.

Pacing the Frontier Leaves Plenty of Work to Do

On Sept. 12, Anthropic CEO Dario Amodei called for a more deliberate pace of frontier AI development.

I recently explored whether this represents his “Oppenheimer moment.” But for investors, the useful question extends beyond the historical comparison.

What, exactly, would be slowing down?

There is an enormous amount of work between demonstrating an impressive capability and making it useful every day. Someone has to connect the software to a customer’s systems, make it reliable, reduce its cost, and teach employees to use it. Most importantly, someone has to prove that it saves more money than it consumes.

That work creates businesses. And it can continue even while investors become less enthusiastic about AI.

Consider a startup that helps a factory spot defective parts. Its next year of growth might come from installing cameras on more production lines, improving accuracy, and winning a second customer. None of those achievements requires the entire AI industry to break a new intelligence record.

Now, imagine that company reaches those milestones during a selloff in AI stocks.

A publicly traded business could announce similar progress and still see its shares fall. Investors might be reacting to higher interest rates, disappointing earnings elsewhere, or a headline that changes their expectations for the entire sector.

A private company generally doesn’t face that minute-by-minute public accounting. Its next financing, a share transaction, or a buyout can provide a new reference point for its value. Between those events, its founders can keep building, signing customers, and improving the product.

That’s what interests me about private markets during a public-market slowdown: the opportunity to own a business that is making measurable progress while the broader AI story is being repriced.

Now, I’m not suggesting you buy private companies simply to stop seeing red numbers on a screen.

I want to find businesses whose next milestone depends on serving a customer, with progress I can evaluate through installations, repeat orders, and improving economics.

If those businesses can keep building value while public markets struggle, investing before they reach the stock market could offer an opportunity worth considering.

That’s where I’m looking.

The Buyers Still Have Problems to Solve

A longer wait for the next frontier model doesn’t eliminate the need for better interfaces, more reliable automation, or cheaper ways to deploy the systems already built. In some cases, it could make those improvements more valuable.

The giants can develop those improvements themselves. They can partner with specialists. Or they can buy a company that has already done the difficult work.

Alphabet (GOOGL) bought Android and YouTube. Meta (META) bought Instagram. Apple bought PrimeSense and Q.ai.

Different technologies across different eras, but a familiar business decision: acquiring an existing capability can be faster than recreating it.

That’s why I watch the companies receiving the buyout checks as closely as those writing them. A young company’s first product may give us only a partial view of what its technology (and the team behind it) could eventually become.

The challenge is recognizing that potential early, when the business is still taking shape.

Even experienced investors miss it. Bessemer Venture Partners maintains an “anti-portfolio” of businesses it passed on, including Google, eBay (EBAY), PayPal (PYPL), and FedEx (FDX).

Those missed opportunities are a reminder of how difficult it is to evaluate a company before its success becomes obvious. You have to assess what the founders have built, what remains unproved, and whether they have a credible path forward.

That’s where my People, Product, and Timing (or PPT) framework comes in.

People comes first because early companies rarely develop exactly as planned. I want founders who can adapt, recruit talented colleagues, and use their capital well.

Q.ai’s Maizels had already built a company Apple wanted to own. That gave investors concrete experience to investigate: what he had built, how he had executed, and whether those strengths could carry into another business. It didn’t guarantee another sale.

Product means asking what problem the company solves and whether customers care enough to pay. An impressive demonstration is a starting point. I want to understand whether the technology works repeatedly, whether the economics make sense, and how useful it could become beyond its initial application.

That last question matters when considering a potential acquisition. A technology serving one narrow market today could address a problem inside a much larger company.

Timing means understanding why the opportunity exists now. Has the technology become affordable? Are customers ready to use it? Does the company have enough cash to reach its next meaningful milestone?

Those questions help me evaluate whether a startup can build something valuable. Then I examine the valuation and investment terms to determine whether that potential could translate into a worthwhile return.

Finding opportunities like that means evaluating the people, the product, and the timing while the business is still private, and being disciplined about what you pay.

One Robotics Company Brought This Into Focus

That framework led me to the private robotics opportunity I discuss at The 2026 AI Megadeal Event.

Its early work involved something wonderfully ordinary: making coffee.

Think about what that requires from a machine. It has to recognize objects, move precisely, handle equipment, and repeat a sequence reliably in a real environment.

The larger opportunity is in the system that teaches the robot how to do those things. That’s what interested me about the company’s effort to turn its operating experience into a broader robot-training platform.

If that technology can help other businesses train useful machines more efficiently, its potential extends well beyond the coffee counter.

That’s why I’ve described it as a potential “Nvidia of Robotics.” The company still has to prove it can build a successful platform business. But helping businesses train robots remains a valuable problem to solve, even if frontier AI development becomes more deliberate.

In the presentation, I explain the founders’ backgrounds, the technology, and why I recommended the company. I also address the challenges ahead: scaling hardware, competing with well-funded rivals, and turning its training platform into a successful licensing business.

This is the kind of research I built Venture Capital Investor to provide. Members receive detailed Opportunity Memos, guidance on getting started, and ongoing research as we build a portfolio of private opportunities over time.

The goal is to help readers evaluate promising companies while they’re still private — before an IPO or acquisition changes the opportunity.

But you don’t have to purchase a membership to hear this recommendation.

We’ve reopened the invitation for a limited period, and it closes Monday, Sept. 21, at midnight.

If you missed the original event, give yourself time to watch, understand the business, and review the offering materials. Then decide whether the investment belongs in the speculative portion of your portfolio.

You can get the company’s name and learn how to review the offering free at The 2026 AI Megadeal Event.

The post The AI Slowdown Could Change Where the Biggest Checks Go appeared first on InvestorPlace.

As I write on Wednesday afternoon, the Federal Reserve has raised its benchmark rate by a quarter point, to a target range of 3.75% to 4% – its first rate hike since July 2023.

The move itself was no shock. Heading in, futures traders had priced the odds above 92%, and even Wall Street’s hike skeptics had come around. What mattered more was everything around the decision.

Starting with the vote itself, it was unanimous – and that’s noteworthy.

Just weeks ago, this committee looked badly split – at the July meeting, three members who wanted a hike were outvoted. Today, even July’s doves fell in line. When a divided Fed suddenly speaks with one voice, that unity is itself a signal. And today, that signal leans hawkish.

Turning to the dot plot, the Fed’s projections now point to at least one more hike this year, possibly two. That’s up from the lone hike its June forecast implied.

Only two officials think the Fed is already done; four see two more hikes coming. The committee raised its inflation forecast, too, and doesn’t expect prices back to its 2% target until sometime after 2028. Clearly, this is not a Fed that thinks it’s finished.

Finally, there was Fed Chair Kevin Warsh’s press conference – or what passes for a presser in the Warsh era.

Longtime Digest readers know that I would routinely feature quotes from Fed Chair Powell that were substantive, or market moving. We can forget that.

The new chair has turned the presser into a masterclass in saying nothing despite lots of words: no forward guidance, no hints about the next move, no meaningful answers to reporter questions – all by design. He called inflation “sticky” and the economy “solid,” then spent the better part of an hour gracefully declining the follow-ups. He didn’t even place his own dot on the dot plot.

That’s less a complaint than a heads-up. In Warsh’s Fed, what actually matters now is the hard economic data between meetings and the official statement itself.

The one concrete thing he conceded was a reporter’s point that no rate hike can reopen the shipping lanes keeping oil above $100.

Turning to the market’s reaction, stocks initially popped when the dots showed only one more hike likely, but then gave it back as the reality of a still-hiking Fed set in.

The Dow closed down about 630 points while the S&P and Nasdaq were off 0.45% and flat, respectively. The 10-year Treasury sits right at 5%, its highest spot since 2007.

Now, with this hike behind us, we can turn our attention to the next question that Wall Street will labor over – is this a one-and-done or the first step of a longer climb?

Let’s dig in.

One hike, or the first of many?

In one camp are the hawks, who argue a single hike does almost nothing against inflation this sticky. Former Cleveland Fed President Loretta Mester made the case in an interview on Monday:

A single hike won’t suffice. I would imagine you’d want to front-load that, starting this year into early next year, and then pause to see how the economy reacts.

You have to be forward-looking.

In the other camp are the doves, led by Fed Governor Christopher Waller, who has signaled he’d rather hold rates where they are and give the economy room to breathe.

So, which camp will win out?

We lean toward “likely more” – and the reason is sitting in the oil market.

As I write on Wednesday, Brent crude trades at $105 and West Texas Intermediate crude sits at nearly $102 thanks to shipping through the Strait of Hormuz that’s largely choked off. And here’s the bigger issue for the Fed: This surge above $100 is too recent to have appeared in the latest inflation data. The most recent CPI covers August, when oil wasn’t far from its summer low back in early July. September’s spike won’t show up until the October report.

Translation: the inflation numbers the Fed sees next are likely to run hotter, not cooler. And hotter data is exactly what keeps a hiking campaign alive.

The Fed’s own dots try to split the difference: one more hike this year, then a pause through 2027 and a single cut in 2028. Not an endless climb – more like one more step up, then a long plateau.

But I’d hold that map loosely. Those dots reflect the Fed’s forecast of how September’s oil spike feeds through – a spike that hasn’t yet shown up in a single inflation report.

The next one, in October, is the first read on it. If it runs hotter than the Fed is banking on, “one more then done” won’t hold up under pressure from the economic data. Four officials already see more hiking than the median. And the dot plot has a long history of missing badly.

Why this hike isn’t the one from the history books

In yesterday’s Digest, we showed you what history says happens after the Fed’s first hike: a short-term stumble, followed by a strong medium- and long-term recovery. The averages were reassuring. Stocks are higher a year later, again and again.

But averages hide an important reality: Not all rate hikes are the same.

There are two very different reasons that the Fed raises rates. The first is a strong economy. Think 2004: The Fed hiked into strength, stocks wobbled, then climbed. In this case, a rate hike is really a vote of confidence – the economy is strong enough to take it. This is the world behind most of yesterday’s cheerful statistics. The Fed hiked into strength, stocks wobbled, then climbed.

The second reason is very different. Prices spike not because the economy is booming, but because something got scarce – a supply shock. And right now, that something is oil, the lifeblood of our economy.

When the Fed hikes into a supply shock, it’s not tapping the brakes on a roaring economy. It’s raising rates into an economy that expensive energy could slow from here – one where today’s solid job market may not stay that way. Remember, while unemployment is low, this is – as former Fed Chair Powell often said – a “low hire, low fire” jobs market. Perhaps less sturdy than the headline unemployment number suggests. Hiking in this environment is a far more dangerous setup.

And it brings us back to a detail we flagged yesterday…

Remember the one ugly outcome in all that bullish data? It was 2022. We noted it came during an inflation-driven scramble, with the Fed slamming on the brakes to catch up.

That wasn’t a random outlier. It broke the pattern for precisely the reason this cycle might: Inflation, not strength, was driving the Fed’s hand.

To be clear, I’m not reversing yesterday’s optimism – I’m just filling in some details. The question isn’t whether the first hike breaks the bull. History says it won’t. The question is what happens if this isn’t a “one and done,” but rather, the start of a supply-driven rate-hike campaign – the rare kind the reassuring averages don’t cover.

The risk we’re watching

Raising rates into a supply shock – with the risk it eventually cracks a still-solid labor market – is the textbook recipe for stagflation: the toxic mix of stubborn inflation and stalling growth. It’s the ghost of the 1970s, when soaring oil prices and a cornered Fed combined to punish stocks for years.

I’m not predicting that outcome. The economy today is more resilient, and the Fed is more experienced at fighting inflation than it was 50 years ago. But let’s not pretend the risk isn’t real. When energy is the driver, the Fed has fewer good options – because no interest rate can drill a new oil well or reopen a shipping lane.

So, what do you do with all this?

You don’t try to predict which way it breaks. You position for both.

The move that works either way

The good news is that you don’t need to know whether the months ahead will bring zero hikes or four. You just need to own the kind of companies that come out fine either way.

We see two qualities to look for. Think of them as the two ends of a barbell.

On one end: businesses built to handle higher rates.

These are companies whose success doesn’t depend on cheap money. They don’t need to borrow constantly to grow. They aren’t valued purely on profits promised a decade from now – the kind of stock that gets hit hardest when rates climb.

Instead, they generate real cash today, carry strong balance sheets, and in some cases actually benefit from higher rates. Banks, for one, tend to earn more as rates rise. So do companies tied to real assets and energy – the very corner of the market that thrive when supply is tight and prices are firm.

This is what legendary investor 抖阴最新版 looks for over at Growth Investor. He tunes out the noise and anchors to earnings power and fundamental strength. As we highlighted from Louis just yesterday in the Digest, you get rich by buying great companies and holding them as long as they dominate.

On the other end: businesses built to handle a squeezed consumer.

If inflation keeps grinding and the job market softens, households will feel it. Wallets tighten. And when that happens, you want to own the companies that can raise their prices without losing their customers – pricing power.

These are the dominant brands, the everyday staples, and the low-cost necessities people buy no matter what the economy is doing. When money gets tight, shoppers cut the extras, not the basics.

Circling back to Louis again, that staying power – the ability to raise prices and keep customers – is exactly the kind of dominance he looks for. For more on the specific dominators in Louis’ Growth Investor portfolio today, click here to learn about joining him.

Put those two ends together and you have a portfolio that doesn’t live or die by the next Fed meeting. If inflation eases and the hikes end quickly, your fundamentally strong names ride the bull that yesterday promised. If inflation proves stubborn and the Fed keeps hiking, your pricing-power names hold the line while weaker companies buckle.

But what about my AI stocks?

A string of hikes won’t crush the earnings of the picks-and-shovels names powering the buildout – think chipmakers, optical and connectivity suppliers, and electrical-equipment firms. Their profits don’t come from cheap money. They come from hyperscaler spending. And the tech giants funding that buildout are running a strategic arms race financed out of mountains of cash, not debt.

But that doesn’t mean they’d come out of a rate-hiking cycle unscathed.

The further out a company’s profits stretch, the more its stock behaves like a long-dated bond – and nothing is more sensitive to rising rates.

Higher rates can shrink the multiple investors will pay, even when the earnings are strong (price is a function of earnings and the multiple investors are willing to pay for those earnings).

Remember, what sank many stocks in 2022 wasn’t rates alone. It was rates landing on companies whose earnings were vanishing or purely hypothetical to begin with.

So, this flips the question. For your AI stocks, interest rates are the second-order risk. The real risk is whether the hyperscaler spending faucet stays on. If it does, a hiking cycle will make them more volatile – they’ll swing on rate headlines – but it’s unlikely to break them, as long as the spending keeps flowing.

Wrapping up

This isn’t a call to run for cover – the bull likely has plenty of life left (though expect some heavy volatility along the way). It’s a call to make sure the stocks you own are built for the road ahead – a road that’s now looking likelier to run through higher rates and tighter household budgets.

We’ll keep tracking it here in the Digest.

Have a good evening,

Jeff Remsburg

The post One Hike Down. How Many to Go? appeared first on InvestorPlace.

My colleague Luke Lango has been following all of this closely.

He thinks investors may be overlooking an important part of the story: Even if the industry takes longer to develop tomorrow’s AI, businesses have barely begun figuring out what to do with the AI we already have.

I’ve invited Luke onto today’s Smart Money to explain why that distinction matters – and introduce you to one young company already putting AI to work in a surprising place. It’s also the company he recently showed folks during his free 2026 AI Megadeal Event. You can watch the replay here.

Now, take it away, Luke…

Hello, Reader.

The AI industry just had one hell of a weekend.

It started last week when 27-year-old Anthropic researcher Jacob Coxon quit and accused Anthropic and OpenAI of racing toward superintelligence while “gambling with our lives.” Then on Saturday, Anthropic CEO Dario Amodei published an essay called “We Must Pace the Frontier,” arguing that the industry needs to slow the development of increasingly powerful AI models.

OpenAI CEO Sam Altman agreed. So did Elon Musk.

Wall Street responded pretty much as you’d expect. AI stocks sold off as investors started asking what a deliberate slowdown could mean for the hundreds of billions of dollars pouring into chips, data centers, power plants, and everything else supporting the AI buildout.

I’ve spent a lot of time thinking about that question. And while I don’t think this changes the direction of the AI Boom, it could change the speed.

I’ve said for months that politics, regulation, and public concerns about AI safety could put some speed bumps in front of the industry. We may be seeing the beginning of that now. Some of the more aggressive forecasts for how quickly AI develops may have to come down.

But there’s another part of this story I think investors should understand.

Amodei is primarily talking about slowing the development of frontier AI – increasingly powerful models capable of reasoning, coding, operating autonomously, and even helping researchers build better AI. That could mean longer development cycles, more safety testing, new rules, or limits on some types of training.

Meanwhile, companies all over the world are still figuring out what to do with the AI we’ve already built.

And there’s a lot left to figure out. For investors, I think there’s a lot of money left to be made there, too.

Because the next big AI winner doesn’t necessarily have to build a smarter model than OpenAI or Anthropic. It could take the extraordinary AI we already have and find valuable new ways to use it.

Today, I want to show you why I think that opportunity could keep growing even if frontier AI slows down. Then I’ll tell you about one young private company I recently recommended – a food-service robotics startup that’s already putting AI to work in the real world.

We’ve Barely Started Putting AI to Work

Think about the AI models available today. Businesses are already using them to write software, review documents, answer customer questions, analyze medical images, design products, and automate parts of their operations. Most companies are still early in that process.

AI requires computing power for two main jobs. Training is how developers build and improve a model. Inference is what happens every time somebody puts that model to work. Deloitte’s 2026 outlook projected that inference could account for roughly two-thirds of AI computing this year, up from about half in 2025.

So even if tomorrow’s AI takes longer to arrive, more people using today’s AI can keep demand growing for servers, memory chips, networking equipment, cooling, and electricity.

That’s one reason I remain bullish on AI infrastructure stocks.

But I’m also interested in the companies doing the actual using.

There are millions of businesses out there applying AI to real problems.

One of the companies I’ve been studying recently is doing that with robots.

And food.

Teaching Robots to Learn

The company I mentioned earlier started in food-service robotics. Its robots are already working in real commercial locations, serving actual customers.

I’ve visited one of those locations myself. I watched a robot server take an order, prepare it, and deliver the finished product. And I came away impressed.

But the robot server itself is only part of what interested me. Behind that business, the company has spent years developing what amounts to a training academy for robots.

Humans learn physical skills largely by watching other humans.Someone shows you how to do something, you try it yourself, they correct you, and you get better with practice. Robots have traditionally required specialized engineers to program their movements, which makes teaching them new physical tasks expensive and painfully slow.

This company is working on a different approach. Its AI system uses human demonstrations to teach robots new physical skills. The company says it can teach a robot some new hands-on tasks in as little as 30 minutes, without an engineer programming every movement.

That’s when this became a much more interesting company to me.

Food service gives these robots a place to learn and improve every day. But if this training technology works at scale, the same approach could eventually teach robots to handle products in warehouses, work with equipment in factories, perform tasks in healthcare, and take on other complicated physical jobs.

Now we’re talking about a much bigger potential market.

The Opportunity Beyond Smarter Models

This is the part of the AI Boom I think could get overlooked amid all the headlines about superintelligence, slowing down frontier development, and even the possibility that advanced AI could threaten humanity.

We already have extraordinarily capable AI, and businesses are putting it to work fast. Government data tends to produce more conservative adoption estimates, while business surveys have found anywhere from roughly 70% to nearly 90% of companies using AI in some fashion. The exact percentage depends heavily on what you count as “using AI.”

The larger point is that adoption has a long way to run. Entrepreneurs will spend years finding new applications for today’s technology, and successful ones will create demand for more computing power, infrastructure, robotics, and technologies we haven’t even thought of yet.

That’s why I’m paying close attention to young companies like the food-service robotics company I just described.

And increasingly, I’m looking for some of these companies while they’re still private. New technologies often start with small companies solving one narrow problem extremely well. If that technology proves valuable, a larger company may eventually decide it’s faster to acquire the business than spend years trying to re-create it. For the early investors who backed that young company, an acquisition can provide the payday long before an IPO ever arrives.

That’s one reason I’ve started looking beyond the stock market for AI opportunities. It gives me a chance to study promising young companies while they’re still building – and, in certain cases, invest alongside them.

Of course, investing that early comes with plenty of risk. The company I’ve been telling you about is young, it’s losing money, and its robot-training technology is still early. Its current valuation also puts a hefty price on growth that still has to materialize.

That’s where my PPT framework comes in.

Whenever I evaluate a young, privately held company like this, I don’t have years of SEC filings or a stock market history to look at. Instead, I start with three things: the People building it, the Product they’ve created, and the Timing of the opportunity. I call that my PPT framework.

This company checks some important boxes. Its CEO previously built a computer-vision startup that was acquired by Amazon.com Inc. (AMZN). Its robots are already operating in the real world. And its robot-training technology is arriving as major technology companies pour money into robotics and physical AI.

That’s why I recently recommended the company to members of my new Venture Capital Investor service.

During my free 2026 AI Megadeal Event, I walk you through the company from top to bottom. I go over its founders, food-service robotics business, robot-training technology, financials, and risks… and the reasons I decided to recommend it.

I also explain how individual investors can invest in private companies like it. If you’ve spent your investing life buying stocks through a brokerage account, this will probably be unfamiliar territory. I’ll show you how it works, what you’re actually buying, and what you should understand before putting your own money into one of these opportunities.

Watch the free replay of my 2026 AI Megadeal Event here.

Sincerely,

Luke Lango

Senior Investment Analyst, InvestorPlace

P.S. I’ve worked with Luke for a long time, and when he gets interested in a company, he likes to kick the tires himself. In this case, that meant visiting one of the company’s locations and watching its robots work firsthand. It’s a cool story, a fascinating young company, and a side of AI investing most of us rarely get to see. Check out Luke’s free event here.

The post If the Race for Smarter AI Slows, This May Be the Next Wave of Profits appeared first on InvestorPlace.

Now the United States has acknowledged that it is prepared to fight there, too.

On Monday, Air Force Secretary Troy Meink publicly confirmed for the first time that the U.S. has deployed what he called “on-orbit space-control weapons.”

He did not say what they are. He did not say how many exist. And he would not explain whether they jam hostile signals, disable enemy satellites electronically, or use some other method entirely.

The details may be classified, but the message is not…

Space is becoming the next battlefield.

That would be a major story on its own.

It is also arriving as a French-UAE consortium commits $1 billion to an AI-enabled satellite constellation, Planet Labs (PL) reports record revenue, BlackSky (BKSY) grows sales 50%, and another rocket startup – Stoke Space – raises $1 billion to expand launch capacity.

For years, investors valued the space economy largely on what it might become. Global internet from orbit. Persistent surveillance. Lunar infrastructure. Factories in microgravity. Data centers floating above Earth.

Now the vision has customers. 

Government budgets are becoming contracts. Satellite fleets are becoming recurring revenue. AI is moving onboard spacecraft. Launch providers are raising billions to meet an expanding manifest.

The space economy is beginning to show up where Wall Street can measure it.

Defense Spending Is Becoming a Growth Engine for Space Stocks

We’ll start with the national security angle.

Governments are waking up to the uncomfortable fact that space is the new strategic battleground.

Satellites provide battlefield intelligence, secure communications, missile warnings, navigation, targeting, and surveillance. 

Modern militaries would struggle to operate without them. Yet, that dependence also makes satellites targets.

China and Russia have spent years developing systems that could jam, disable, deceive, or destroy spacecraft. The U.S. government has repeatedly warned that losing access to orbital systems could cripple military operations on Earth.

Now Washington has publicly acknowledged its response.

Meink said the Space Force has weapons in orbit capable of defending American forces from hostile action. The exact systems remain classified, so investors should resist the temptation to guess which contractors built them.

Which Space Stocks Are Exposed to Defense Spending? 

Instead, remember that this is a long-term arms race – one that favors nimble, responsive space companies with launch capacity, satellite imaging capabilities, and hardware manufacturing.

The usual suspects benefit here:

• BlackSky and Planet Labs provide Earth intelligence.
• Rocket Lab (RKLB) offers launch, spacecraft, and space-system capabilities.
• Palantir (PLTR) helps military customers turn enormous streams of information into decisions.
• And traditional defense companies such as L3Harris (LHX) supply communications, sensors, electronic warfare, and command systems.

We estimate national defense TAM in space is about $30- to $40 billion today. But as intelligence demand and geopolitical tensions escalate, it could easily double over the next decade.

And it’s just one vertical of the multi-faceted Space Economy…

Satellite Internet Is Expanding the Space Economy

Another big vertical here is space-based communications because the entire communications industry is being rewritten from orbit.

The world is moving toward a space-powered internet: a global, always-accessible broadband network delivered from thousands of small satellites in low Earth orbit (LEO).

This is more than a theoretical future. It’s already in action:

• Starlink now has over 10,000 satellites in orbit and serves 12 million users worldwide.
• Amazon Leo (formerly dubbed Project Kuiper) continues building out its constellation to support AWS and global internet.
• AST SpaceMobile (ASTS) is taking another route, building satellites that connect directly to ordinary smartphones without requiring a separate dish or terminal.

That’s a major step up, especially considering that 2.2 billion people worldwide still lack reliable internet access, and billions more suffer from poor mobile coverage.

Not to mention, we still don’t have cell coverage on airplanes. And natural disasters like earthquakes, fires, and tsunamis often knock out cell coverage when we need it most.

The opportunity here extends beyond just the companies operating the constellations.

Every network needs antennas, radio-frequency chips, optical links, ground equipment, spectrum, cybersecurity, and a steady flow of replacement satellites.

A successful constellation is not a one-time hardware sale.

That’s why we think the total addressable market here is huge. We see it climbing toward $40 billion by 2035 – possibly much more if these constellations become the backbone for rural broadband, global telecom, and even cloud connectivity.

Orbital Data Centers Could Become the Space Economy’s Biggest New Market

Here’s the vertical that didn’t exist when we first started writing about the space economy – and it may end up being the biggest of them all.

AI’s growth is running into hard physical limits on Earth. Data centers need enormous amounts of land, power, and water – and communities increasingly don’t want them nearby. Lawmakers in at least 14 states have introduced legislation to restrict new data center construction.

That has pushed companies toward a much bigger idea: Move some of the computing into orbit.

The most ambitious version involves large orbital data centers powered by solar energy and linked through laser communications.

Of course, that will take time. Launch remains expensive. Radiation damages electronics. Hardware is difficult to repair. And while space is exceptionally cold, engineers haven’t yet cracked efficient GPU cooling in orbit because there’s no air for convection.

The long-term race is already drawing some of the biggest names in technology and space. 

SpaceX has filed with the FCC to launch up to one million orbital data centers. Google has entered talks with SpaceX to expand its own space-based compute efforts. Anthropic has expressed interest in partnering on orbital AI capacity. And Jeff Bezos’ Blue Origin just asked the government for permission to launch more than 50,000 orbital data centers of its own.

Most recently, on Sept. 9, a consortium involving companies in France and the UAE committed $1 billion to a new 50-satellite constellation carrying radar, optical cameras, and other sensors.

BlackSky will serve as the exclusive provider of its very-high-resolution optical satellites. Mistral AI is involved on the model side to process information in orbit and deliver useful alerts within seconds.

Giant data centers can come later. The first commercial win may simply be making today’s satellites much smarter.

And that leads directly into one of the space economy’s most established markets: Earth observation.

Four More Space Economy Markets Investors Should Watch

Defense, communications, orbital AI, and launch are the most visible parts of the space economy buildout.

They are far from the only ones.

Earth Observation: Turning Satellite Images Into Intelligence

There’s Earth observation.

We’re entering the age of persistent planetary surveillance. Think:

• Monitoring crop yields (for commodity traders)
• Tracking cargo ships (for logistics and supply chains)
• Detecting oil spills, deforestation, wildfires, and droughts
• Verifying carbon emissions and ESG compliance

Governments, hedge funds, insurers, farmers, and climate groups all want this data.

PL and BKSY are two of the biggest players in this niche. They control massive constellations of satellites and sell high-frequency data with AI analytics on top.

Planet just reported record quarterly revenue of $116.1 million, up 58% year over year. Adjusted EBITDA reached $13.9 million, while backlog ended the quarter near $815 million. The company also raised its full-year revenue outlook.

BKSY’s revenue rose 50% to $33.3 million. Adjusted EBITDA turned positive at $4.7 million. And its space-based intelligence and AI-services unit produced record revenue before the company was even selected for the new $1 billion constellation.

This market is becoming a real-time intelligence business.

Lunar Infrastructure: Building a Commercial Economy Around the Moon

Then there is the moon.

NASA’s Artemis program is building toward a sustained human and scientific presence beyond Earth. Private companies are developing landers, communications relays, navigation systems, and cargo services so the moon can eventually support:

• Water-ice extraction
• Rocket-fuel production
• Scientific equipment
• Communications infrastructure
• Telescopes
• Deeper-space logistics

Rocket Lab’s Photon spacecraft has already supported a mission to lunar orbit. And companies like Intuitive Machines (LUNR) are developing landers and the communications, navigation, and cargo systems needed to support repeat missions around the moon.

This remains a very early market.

But every commercial economy begins with infrastructure.

The moon will be no different.

In-Space Manufacturing: What Microgravity Makes Possible

There’s also in-space manufacturing because… let’s face it… why make stuff on Earth when microgravity offers the perfect conditions for making certain things? Like:

• ZBLAN fiber optics: cleaner fiber that carries data farther with less signal loss 
• Protein crystal growth: larger, more orderly crystals that make it easier to study proteins and design better drugs
• Semiconductors: more uniform materials for advanced chips and electronics

Startups like Varda Space are building in-space factories. Redwire is printing tools on the ISS. In fact, Rocket Lab is already launching some of these missions.

Tiny TAM today – but potential for $10- to $20 billion by 2040.

Satellite Servicing: The Maintenance Layer of the Space Economy

And then you have the whole satellite servicing market.

Satellites are expensive. They age, fail… and then crash, rendering them nothing more than junk. The solution therein?

• Servicing and refueling in orbit
• ‘Tugboats’ for moving satellites
• Bots to clean up space debris

This is like the equivalent of AAA for space. And we think it could be a $10-billion-plus market by the 2030s.

The Bottom Line: Space Stocks Are Starting to Look Like Real Businesses

Put all this together – defense, communications, orbital compute, EO, infrastructure, manufacturing, servicing – and the total space economy TAM is already near $100 billion.

Depending on regulation and global policy, that number could stretch to unfathomable heights over the coming decades.

Now here’s the real kicker: outside of SpaceX, not many own this trade yet.

Planet Labs has a market cap of approximately $6 billion. AST SpaceMobile is right around $23 billion. And BlackSky is valued at about $870 million.

In terms of their addressable market, these are penny stocks with planetary potential.

Now, to be sure, not every company will win. Some will fizzle or get acquired. Some might crash and burn, literally.

But the winners will provide the foundational infrastructure for the next trillion-dollar economy. And as we saw during the early internet era, a single winner could 20X, 50X, even 100X in a decade.

So, the smartest approach here might be a simple one: buy a basket of them now. Don’t try to pick the single winner. Just be exposed.

Because if this space economy thesis plays out – and the signs are saying it’s already well underway – the upside will vastly outweigh any individual misfires.

There’s just one wrinkle to the basket approach.

For the first time, all the puzzle pieces I just described are being assembled under a single roof, by a single man.

Elon Musk took SpaceX public in the largest IPO in history. He merged it with xAI. And now, virtually every Silicon Valley insider, from his own biographer to the president of SpaceX herself, expects him to complete the consolidation with the biggest merger of all time.

The estimates around what it could be worth are staggering; bigger than AI, robotics, clean energy, and driverless cars combined.

And just like the space stocks in this piece, the biggest gains won’t come from owning the giant at the center. They’ll come from the small, little-known suppliers riding its coattails – including one that trades for just $15 a share.

I’ve laid out the full story – and the three steps to get positioned – right here.

P.S. Some of the biggest opportunities in a new industry can emerge long before most public-market investors gain access. Take SpaceX, for example. It started in a warehouse. Today, it launches more mass into orbit than every government space program on Earth combined.

I’m currently at the All-In Summit, where I’m sitting down with Jensen Huang and SpaceX’s Gwynne Shotwell behind closed doors to learn more. I don’t yet know which ideas I’ll come home with, but these conversations tend to be game-changing

As Jensen put it, a “second layer” is now forming beneath the AI Boom. It’s made up of smaller, private companies building the AI infrastructure this very boom relies on. One of them is a private company I see as the “Nvidia of Robotics.” And for a limited time, everyday investors can claim a stake in it with as little as $500.

But the window closes at midnight on Monday, Sept. 21. Get the company name and full details right here – for free – before it’s too late.

The post Space Is Becoming a Battlefield – and a Business appeared first on InvestorPlace.

You can choose to be manipulated. Or you can choose to follow the earnings and the fundamentals.

That’s legendary investor 抖阴最新版, from yesterday’s Growth Investor Flash Alert podcast.

As I write on Tuesday, we’re still cleaning up after yesterday’s AI panic – the selloff touched off by last week’s viral warning from an Anthropic researcher who believes AI could pose existential risks to humanity.

We need to handle this topic carefully. And on that note, Louis chimed in with an angle that hasn’t received much press – some of the fear now washing over the AI trade may not be organic at all.

Let’s go back to Louis:

We’re getting all this false information from over 200 Chinese bots spreading AI fears – fears of data centers destroying your community, polluting the air, taking all the good water.

China is in an AI race with us. We’re winning. They’re losing. So, they’re trying to derail us by spreading false propaganda.

His bottom line: much of the AI backlash is propaganda, so don’t let it manipulate you – follow the earnings and the fundamentals instead.

If you’re skeptical, there’s evidence supporting Louis’ take

In late August, Elon Musk’s X reported that its safety team had uncovered a suspected Chinese bot farm of roughly 200,000 fake accounts. From the X Safety Team:

Within this farm, we found 200 accounts posting in a manner that could manipulate a legitimate debate about American AI and energy policy.

These posts contained claims that AI data centers are driving up household electricity prices and straining the grid.

Others included AI-generated cartoons that depicted data-center operators enriching themselves at the public’s expense.

It wasn’t just X. Back in June, OpenAI reported banning a likely Chinese network it nicknamed the “Data Center Bandwagon” campaign. The operators used ChatGPT to produce posts and comics claiming that AI data centers were driving up electricity prices for ordinary families, posing as Americans and logging in through VPNs from inside China.

Two different companies, two different data sets, one conclusion: a coordinated foreign effort to turn Americans against the very technology we’re leading the world in building.

To be clear, the bots didn’t invent this backlash – they’re piling onto a real one. As we’ve covered in the Digest, plenty of Americans have genuine questions about data centers and their power bills. The foreign hand here is amplification, not creation.

But Louis’ larger point holds up: when fear starts driving your decisions, it’s worth asking who benefits from that fear – and then getting back to earnings and fundamentals:

We want to ride through this. We want to profit from it. As long as the sales and earnings are there, we should stay.

You get rich by buying great companies and holding them as long as possible, as long as they dominate.

Easy to say, harder to do – especially when the next fear is already on the calendar. And this one isn’t coming from a Chinese bot farm. It’s coming from the bond market and the Federal Reserve.

Will an interest rate hike tomorrow break the bull?

As I write on Tuesday morning, the 10-year Treasury yield trades just a hair above 5%, a 19-year high. It’s the bond market sending a very clear message to the Fed – you’d better raise rates tomorrow.

The CME Group’s FedWatch Tool currently puts the odds of a quarter-point rate hike at tomorrow’s FOMC meeting at 92.7%.

Even Louis, who for much of this year has argued that the Fed won’t raise rates, is calling for a hike now. Back to his podcast:

The Fed really does have to raise rates on Wednesday because if they don’t, they’ll weaken the dollar…

Market rates went up. The Fed doesn’t fight market rates. So, the Fed will have to raise rates on Wednesday.

Given that a hike seems to be a lock, will it be the straw that breaks the bull’s back?

Before you answer, factor in that higher rates aren’t the only potential headwind…

We’re in the middle of September, historically the single worst month of the year for stocks, so Wall Street is on edge. Plus, we’re in a midterm election year, which happens to be the weakest year of the entire four-year Presidential Cycle.

Three bearish forces, converging at once. So, is it time to batten down the hatches and go full bear?

Let’s see what the data tell us.

What history says happens after the first hike

Lucas Downey, editor of the TradeSmith Investment Report over at our corporate partner, TradeSmith, just put together a chart that tracks how stocks have performed after the first rate hike of each Fed cycle going back to 1987.

As you’ll see, in the first month after a first hike, the S&P 500 has fallen 2.9% on average, and the Nasdaq 100 has dropped 2.3%. And three months out, both are still underwater. That’s the near-term turbulence everyone fears.

But keep your eye moving to the right…

By the six-month mark, the picture flips. The S&P is up 4.2% on average, and the Nasdaq has climbed 12.4%. At 12 months, they’re up 5.7% and 15.2%.

And two years out, the gains are substantial: 21.1% for the S&P and a whopping 37.1% for the Nasdaq.

Source: Lucas Downey / TradeSmith / Money Flows / FactSet

The broader research tells the same story. Market strategist Ryan Detrick notes that following a quarter-point first hike, the S&P 500 has been higher one year later 100% of the time, with an average gain of 12.5%. The initial sting is real. But so is the recovery.

The one ugly outcome – 2022 – came during an inflation-driven scramble in which the Fed was slamming on the brakes with jumbo half-point hikes to catch up after falling behind the curve. No one expects that tomorrow, and history is clear that the size of the first hike matters. A modest one has never left stocks lower a year later.

The calendar tells the same story – twice

When we layer the seasonal and presidential-cycle data, the medium-term story only gets stronger.

As far as “September” goes, yes, it’s the worst month of the year for stocks. Since 1950, the S&P 500 has averaged a decline of roughly 0.7% in September, the only month of the year with a negative average return. And it’s finished higher just 44% of the time, the worst odds on the calendar.

So, a drawdown this month shouldn’t catch anyone by surprise. But it’s critical to remember what comes after September.

From October through December, the S&P 500 has averaged a gain of 4.2% and finished higher 80% of the time since 1950. So, the very weakness that scares investors out in September has, time and again, set the stage for the year-end rally that follows.

Finally, don’t forget where we are in the four-year Presidential Cycle.

Midterm years like this one tend to follow a well-worn script: choppy weakness through the summer and early fall, a bottom that usually arrives in the September-October window, and then a powerful rally.

How powerful?

Since 1950, the S&P 500 has been higher 12 months after every single midterm election. That’s 19 for 19. And the pre-election year following a midterm is historically the strongest of the entire cycle, averaging more than 17%.

So, we have two independent forces – one seasonal, one political – pointing to the same place as Lucas’s hike research: short-term bumpy, medium/long-term bullish.

Circling back to our question: Is a rate hike tomorrow the straw that breaks the bull’s back? Unlikely. Expect a stumble – but history says there’s plenty of life remaining.

Last call for Luke’s workshop

One last thing before we wrap up today.

If these forecasts have you thinking about how to position for the next two to three years of the AI trade, our technology expert Luke Lango, editor of Early Stage Investor, laid out his roadmap in a first-ever InvestorPlace workshop last week – mapping which of Elon Musk’s suppliers stand to benefit most as he builds out his “Vertical AI” empire.

Here’s Luke:

While the market debates whether AI demand can hold up, Musk has spent 20 years assembling the pieces of a very different bet – one that we believe converges on September 24.

If he’s right, the fallout won’t stay contained to Tesla or SpaceX. It could ripple through the same infrastructure names we’re watching for AI demand signals, and open up an entirely new market that could dwarf today’s AI trade.

Luke laid out the full case – company names, tickers, and the four bottlenecks Musk still needs to solve last week. We’re taking the free replay down tonight at midnight, so this is officially “last call.” You can watch it right here.

We’ll keep you updated on these stories here in the Digest.

Have a good evening,

Jeff Remsburg

The post Are Chinese Bots Driving the AI Panic? appeared first on InvestorPlace.

Then, he published his first post on X (formerly Twitter). In it, he warned that artificial intelligence could eventually kill humanity.

The post exploded across social media.

Within days, it racked up more than 150 million views. Major news organizations seized on the story. Politicians demanded action. And some of the biggest names in AI started debating whether development should slow down.

Then, on Monday, Wall Street reacted. Investors started dumping many of the very companies powering the AI boom.

But something doesn’t add up, folks. It all happened remarkably fast. So before we let one viral narrative dictate what we do with our money, I think investors need to ask a very old question:

Cui bono?

Who benefits?

Cicero used that Latin phrase more than 2,000 years ago. And I think it is worth asking again today.

So, in today’s Market 360, we’ll take a closer look at what was behind the latest AI panic and who stands to benefit from it.

I’ll also show you why I think investors would be making a mistake by treating this latest scare as the end of the AI boom.

The Post Heard Round the World

Let’s start with Coxon himself.

He spent about three years researching how advanced AI models are trained, first at OpenAI and then at Anthropic. But he only joined Anthropic in May. So, after just a few months at the company, he quit.

In his post, Coxon accused OpenAI and Anthropic of “racing straight to self-improving superintelligence and gambling with our lives.”

Then came the line that really grabbed people’s attention:

“The people building AI earnestly believe that it could kill us all by the end of the decade.”

Washington reacted almost immediately.

Illinois Gov. JB Pritzker called for the federal government to “sound the alarm” on AI. Sen. Bernie Sanders declared that “Mr. Coxon is right” and renewed his push to ban artificial superintelligence and temporarily pause advanced AI development.

Then there’s the rollout.

We know Coxon spoke with a reporter from The Wall Street Journal before publishing his post. We also know he was closely advised by friends and associates in the AI safety sphere of nonprofits and organizations who helped spread the message.

Then, on Saturday, Anthropic CEO Dario Amodei published an essay arguing that AI capabilities were advancing faster than safety measures could keep up. He called for the industry to slow frontier development.

That means slowing work on the most advanced AI models at the cutting edge – the systems designed to push beyond what today’s leading models can already do.

OpenAI CEO Sam Altman quickly responded:

“I agree with Dario that we need to pace the frontier.”

Then Elon Musk weighed in:

“Dario is right.”

So, within days of Coxon’s post, three of the most powerful figures in frontier AI were publicly endorsing some form of slowdown.

That leads us back to our old question: Cui bono?

Who Benefits?

First, let me acknowledge something important.

There are legitimate AI-safety concerns. We’ve already seen AI agents break out of controlled cybersecurity tests and gain unauthorized access to real systems. Those incidents deserve serious attention, and the industry needs better safeguards.

But that is very different from concluding that AI could kill us all, or that America should deliberately slow the entire race.

That’s when investors should start thinking about incentives.

The reality is there is an entire network of researchers, nonprofits and donors that has spent years warning about existential AI risks. They have devoted substantial resources to lobbying for AI safety.

Coxon’s viral post gave that movement an enormous opening. That opening, especially with the midterm elections looming, could lead to the creation of regulatory bodies  over the future of AI. (Which they should be in charge of, naturally.)

Another thing to consider is that building a frontier AI model already costs billions of dollars. Add expensive audits, licensing requirements, outside reviews and other compliance costs, and ask yourself who can afford them.

OpenAI can. Anthropic can. xAI can. Big Tech can.

A small startup trying to challenge them may not.

That’s why critics accuse the frontier labs of trying to “pull up the ladder” behind them. Regulation can address legitimate risks while also creating a formidable moat around the companies already at the top.

That doesn’t prove bad motives. But it does mean we should consider who benefits, folks.

And that’s also why I’m paying particularly close attention to someone whose incentives point in almost the opposite direction…

In Jensen We Trust

That someone is NVIDIA Corporation (NVDA) CEO Jensen Huang.

I’ve said it before: When it comes to AI, my motto is “In Jensen We Trust.”

And at the All-In Summit this week, Jensen gave investors a much-needed dose of perspective.

He acknowledged that AI safety matters and that companies should take real security problems seriously. But he flatly rejected the leap from “AI creates risks” to “AI could wipe out humanity.”

Jensen called those apocalyptic predictions “complete nonsense” and argued that the industry will make AI safer by continuing to build, test and deploy it – not by freezing progress based on hypothetical doomsday scenarios.

That matters. But it also helps to understand Jensen’s incentives.

Anthropic, OpenAI and xAI are all building frontier AI models. If new regulations make those models vastly more expensive to develop, the biggest players can probably absorb the added costs.

NVIDIA sits in a different position. Jensen wants AI everywhere.

Earlier this month, NVIDIA agreed to acquire Hugging Face for nearly $13 billion. Hugging Face is one of the largest hubs for open AI development, with more than 18 million developers and more than 3 million models on its platform. And NVIDIA has explicitly promised to keep it open, including support for competing chips and computing platforms.

Why? Because the more AI models developers build, customize and deploy, the more computing power the world needs.

So, yes, Jensen has skin in the game, too.

But his economic interests point toward proliferation rather than restriction.

And when the man supplying the picks and shovels for the AI boom says the answer is to keep building, I think that’s what investors need to focus on.

Don’t Be Manipulated

You can choose to be manipulated. Or you can choose to follow the earnings and the fundamentals.

Sales. Earnings. Orders. Backlogs. That’s what matters most for investors.

If those start rolling over, I’ll pay attention.

But they’re not. And until they do, I’m not going to let one viral post, one political panic or one round of scary headlines talk me out of the biggest technology boom of our lifetime.

The reality is that the AI boom is just getting started.

My research team and I have spent months studying a massive new effort taking shape across America’s national laboratories. President Trump has compared it to a new Manhattan Project for AI.

At the center of it is a network of supercomputers and AI infrastructure that I call Golden Dawn.

Its goal is to accelerate breakthroughs in AI, energy, medicine, quantum computing and other strategically critical technologies – and help ensure that America, not China, leads what comes next.

That creates a very different question for investors.

Instead of asking whether the AI boom is over, we should be asking which companies are positioned to benefit as this competition enters its next stage.

That’s exactly what I reveal in my special presentation, The AI Reset of 2026.

I’ll show you what Golden Dawn is, why I believe it could reshape the AI landscape and the companies I expect to benefit as America races to maintain its technological lead.

Click here to watch it now.

Sincerely,

抖阴最新版

Editor, Market 360

The Editor hereby discloses that as of the date of this email, the Editor, directly or indirectly, owns the following securities that are the subject of the commentary, analysis, opinions, advice, or recommendations in, or which are otherwise mentioned in, the essay set forth below:

NVIDIA Corporation (NVDA)

The post Why You Shouldn’t Worry 抖阴最新版 the Latest AI Panic appeared first on InvestorPlace.

The Trinity test was a success.

J. Robert Oppenheimer and the scientists at Los Alamos helped turn theory into the world’s first nuclear explosion.

But proving that something can be built begs another question: What happens once people start using it?

That’s the cloud hanging over the AI industry today.

Over the weekend, Anthropic CEO Dario Amodei called for a slower pace of frontier AI development. His argument is that capabilities are advancing far faster than the industry’s ability to manage their risks. Both Sam Altman and Elon Musk agreed with the need for additional safeguards.

It invites an uncomfortable comparison…

Is Amodei having his “Oppenheimer moment”? In other words, confronting the consequences of a technology he helped create, while he believes there is still time to influence its course?

But here’s the relevant question for you:

Would slowing the development of the most powerful AI models also slow the demand for the chips, electricity, and data centers that run them?

My view is that we need to examine those questions separately. The historical analogy can frame the debate. It cannot, by itself, tell us what happens to AI infrastructure earnings.

The Oppenheimer Comparison

In June 1945, before the first atomic bomb test or the bombing of Hiroshima, Oppenheimer joined a panel of scientists that supported using the bomb against Japan. But the panel acknowledged that scientists disagreed. Knowing how to build the bomb, they wrote, did not make them uniquely qualified to decide how it should be used.

After the bombings, Oppenheimer and his colleagues warned that having the most advanced weapons would not necessarily keep America safe. In 1946, he helped develop a proposal to put atomic energy under international oversight.

That’s where the comparison with Amodei becomes useful: How much say should the people building a powerful technology have over its future? And who else deserves a seat at the table?

In We Must Pace the Frontier, Amodei calls for independent reviewers to work inside AI companies, for leading labs to coordinate their efforts, and for governments to get more involved. Under Anthropic’s proposal, outside reviewers would get a close look at the company’s work and could publish their findings, with limits to protect confidential information and security.

There is an important difference, though. Oppenheimer helped build a weapon through a government-run program. Amodei runs a private company and is asking for more outside oversight.

Calling that “handing off responsibility” assumes more than we know. Amodei is proposing changes to who can examine and influence AI development. That alone doesn’t tell us he is trying to escape responsibility for what his company builds.

The Scientists Speaking From Inside

Another parallel involves the people doing the work and their concerns about where it might lead.

In July 1945, Leo Szilard and fellow scientists petitioned President Harry Truman, arguing that the United States should not use atomic bombs before giving Japan clear surrender terms and a chance to accept them. They also warned that America’s decision would set an example for how other countries might use these weapons.

Last week, Anthropic researcher Jacob Coxon publicly announced his resignation and accused Anthropic and OpenAI of “gambling with our lives.” In an interview with WIRED, he described colleagues’ concerns about how quickly AI was advancing and the pressure to keep up with competitors.

In both cases, the warnings came from people who knew the work firsthand. That gives us a reason to listen. It doesn’t mean every danger they foresee will come to pass.

And just because Coxon’s departure and Amodei’s essay appeared close together doesn’t mean one caused the other.

Amodei had already discussed serious risks in his January essay, The Adolescence of Technology. His latest proposal builds on concerns he has raised publicly before.

The biggest difference between Oppenheimer and Amodei is what had already happened when each man spoke out.

Oppenheimer’s postwar push for oversight came after atomic bombs had devastated Hiroshima and Nagasaki. Amodei is calling for action to prevent future harm from increasingly powerful AI, while also responding to problems already reported.

The comparison helps us think about the responsibilities of people who build powerful technologies. It does not mean the consequences are the same.

Conscience and Commercial Interests

The Oppenheimer comparison also raises a question about how history will remember the people building AI.

When a technology leader publicly warns about the dangers of his own work, that warning becomes part of his legacy. Years from now, people can look back and say: He saw the risks and spoke up.

But we can’t know how much of that warning comes from personal concern, a desire to protect his reputation, or business strategy. We can only guess.

What we can examine is how his proposals might affect the industry.

One concern is that expensive safety reviews and complicated rules could help the biggest AI companies hold on to their lead. Those companies have the money and staff to meet new requirements. Smaller rivals may struggle to keep up.

The OECD identifies complicated regulations as a potential obstacle for new competitors. It also notes that safety and certification rules can determine which companies are allowed to serve certain markets.

But oversight can also help competition. Making AI systems easier to inspect and easier to use together could give customers more confidence and more choices.

The details will matter: Who has to follow the rules? How much will that cost? And will those rules make it easier or harder for new companies to compete?

A proposal can address a real safety concern and benefit the company promoting it. Both can be true.

For investors, the business effects deserve attention. Guessing what’s on a CEO’s conscience won’t tell us much about future earnings.

Slower Development Doesn’t Mean Demand Stops

Here’s where this debate becomes especially useful for investors in AI.

AI needs computing power for two main jobs.

Training is how developers build and improve a model. Inference is what happens when someone puts that model to work – asking a question, writing code, reviewing a document, or completing another task.

Finishing the training doesn’t end the need for computing power. Every time someone uses the model, computers have to do more work.

Deloitte’s 2026 outlook projected that running AI models would account for roughly two-thirds of AI computing, up from about half in 2025. That’s a forecast, but it shows how much demand could come from using the technology already built.

A company can put an existing AI model to work in more departments while the next version goes through safety testing. Developers can create new products using capabilities already available.

All of that still needs servers, memory chips, networking equipment, cooling, and electricity.

That’s the basis of my investment case for AI infrastructure: More people using today’s AI can keep demand growing, even if tomorrow’s AI takes longer to arrive.

But that doesn’t mean a slowdown would leave the industry untouched.

Amodei says the industry should consider limits on the computing power used to train models, the training process itself, and the use of AI to improve AI. His proposal goes beyond making companies wait longer to release a finished product.

Limits on training could affect equipment orders. Delayed releases could also hold back applications that need abilities today’s models don’t have.

Extra safety testing and monitoring would require some computing power, too. But we shouldn’t assume that work would make up for everything delayed or canceled.

The investment question is whether growing everyday use outweighs any slowdown in development.

What Would Change My View

I’m watching what businesses actually do: how much they plan to spend, whether they keep ordering equipment, how much of their computing capacity they use, and whether more customers are paying for AI.

If customers cut spending plans, that matters. If businesses slow their adoption of AI, that matters. If chip orders weaken, we need to understand why.

But if companies keep finding useful ways to put existing AI systems to work, demand for the equipment supporting those systems can hold up even as development slows.

That still doesn’t make every AI stock a good buy at any price.

A business can grow and its stock can fall. If investors paid a price that assumed much faster growth, even solid results can disappoint. Shares can also drop well before a slowdown shows up in reported sales.

I still see a strong long-term opportunity in AI infrastructure, with those conditions in mind. The case rests on more customers finding useful, valuable things to do with AI. It doesn’t depend on every lab releasing its next model as quickly as possible.

Perhaps this is Amodei’s Oppenheimer moment. History will judge that through his decisions and their consequences.

For investors today, the more immediate question is, are customers continuing to find valuable work for the machines already running?

There’s one more piece of this puzzle I haven’t touched on yet… and it comes from Elon Musk directly.

While the market debates whether AI demand can hold up, Musk has spent 20 years assembling the pieces of a very different bet – one that we believe converges on September 24. If he’s right, the fallout won’t stay contained to Tesla or SpaceX. It could ripple through the same infrastructure names we’re watching for AI demand signals, and open up an entirely new market that could dwarf today’s AI trade.

We laid out the full case – company names, tickers, and the four bottlenecks Elon still needs to solve – in our Vertical AI Event.

Take a look before it comes offline soon!

P.S. The last time Jensen Huang stood in front of a room full of shareholders and journalists, he used a word most executives never get to say and mean it: parabolic. Demand had gone parabolic. Compute capacity was converting straight into revenue and profit. And the numbers backed him up — $82 billion in a single quarter, up 85% from a year earlier. The fourteenth straight quarter of growth stacked on top of growth. But that’s not the number that stopped me. What stopped me was what Jensen called the “second layer.” A part of the AI economy he says is poorly understood. I agree with him.

Underneath the hyperscalers and the frontier labs sits a tier of hundreds (soon hundreds of thousands) of smaller companies building AI infrastructure for their own industries, their own countries, and their own factory floors. They are doing an enormous share of the actual heavy lifting in this buildout. And almost none of them are public yet. Gwynne Shotwell is living proof of what happens when that kind of buildout is allowed to run its course. Her company started in a warehouse. Today it puts more mass into orbit than every government space program on Earth combined.

I’m sitting down with both of them behind closed doors at the All-In Summit this week. You won’t see this on CNBC or Bloomberg, and all attendees are hand-picked. Which means what gets said in that room is nothing you’ll find in an SEC filing or a press release. What I come back with, I don’t know yet. But the last time I walked into a room like that one, it changed how I think about AI wealth entirely. Stay tuned for an update when I return Wednesday.

The post What Dario Amodei’s ‘Oppenheimer Moment’ Means for AI Stocks appeared first on InvestorPlace.

As I write on Monday morning, the AI trade is in the red.

Over the weekend, the people building artificial intelligence spent their time warning the world about it – a panic that started with a single viral post last week.

A 27-year-old Anthropic researcher named Jacob Coxon quit the AI industry with a seven-part post on X. It has since been viewed more than 150 million times.

Coxon had spent three years in pretraining research – first at OpenAI, then at Anthropic, the lab many consider the most safety-conscious of them all. Here’s the heart of what he wrote:

I spent the last three years doing pretraining research at both OpenAI and Anthropic. Neither company is acting responsibly.

They are racing straight to self-improving superintelligence and gambling with our lives.

He went further, claiming the people building AI, “earnestly believe that it could kill us all by the end of the decade” – and that this was “not a marketing stunt.”

You can debate whether Coxon is right about the existential stuff. Plenty of smart people agree with him. Plenty think the doom scenarios are wildly overblown.

But for our purposes here in the Digest, whether he’s right almost doesn’t matter. What matters is what happened next. Because within 48 hours, the story had escaped the tech world entirely.

From viral post to political ammunition

The commentary from the political class was nearly immediate.

Illinois Gov. JB Pritzker – a Democrat and a 2028 presidential contender – fired back at Coxon’s post the very next day:

It’s time to sound the alarm – louder – on reining in Artificial Intelligence.

It’s becoming more clear the threat AI poses to humanity, so I’m calling for immediate action from the industry and Washington.

When AI researchers are whistleblowing, we need to listen.

Let’s be candid – this is a 2028 presidential hopeful test-driving AI fear as a campaign plank. That’s a sign of where we are.

Of course, he wasn’t alone…

Sen. Bernie Sanders, who just days earlier had teamed with Rep. Greg Casar to introduce the Ban Artificial Superintelligence Act – a bill to permanently ban superintelligence and pause advanced AI development until a federal regulator writes the rules – posted:

Mr. Coxon is right. The very people building this technology admit that it could threaten the future of humanity. That is why I will soon be introducing legislation to ban superintelligence and pause AI development.

All told, more than 20 lawmakers joined the chorus within days. Whatever you make of the policy, the signal is unmistakable – the AI rollout now faces a serious political headwind.

Then the CEOs agreed

Rather than defend themselves, over the weekend, the people running these companies didn’t push back on Coxon. They agreed.

On Saturday, Anthropic CEO Dario Amodei published a lengthy open letter titled “We Must Pace the Frontier.” His central argument:

We must slow the pace at which we improve the capabilities of AI models. Progress will still seem fast – and we must make wise use of the time we gain.

Amodei pointed to two things that had changed his thinking: AI systems increasingly capable of building more advanced AI systems, and the July “Hugging Face” incident we covered here, in which autonomous AI agents slipped their controls and compromised another company’s systems.

Then OpenAI’s Sam Altman backed him up:

I agree with Dario that we need to pace the frontier.

Even Elon Musk chimed in, saying, “Dario is right.”

Not everyone is on board. President Trump rejected the idea outright on Sunday, framing it as a matter of national security. “Whoever wins AI wins,” he told reporters – making clear he has no interest in slowing America down while China races ahead.

The market is circling the wagons – but is that the right response?

Put it all together – a viral extinction warning, a bipartisan political scramble, and the industry’s own founders calling for a slowdown – and you get this morning’s selloff.

The logic is straightforward…

If the companies driving the AI buildout deliberately slow down, the torrent of spending that has lifted everything from chips to data centers to power companies could ease. Slower buildout, slower revenue, softer stock prices. Sell first, ask questions later.

But is this the right take?

Picture two cars racing down a twisting mountain road…

The driver who never touches the brakes probably doesn’t get down the mountain faster. He goes off the first sharp curve. It’s the driver who brakes into the turns who makes the quickest descent – because control is what lets him carry speed the whole way down.

For three years, the AI trade has been the car with no brakes.

The entire story has been “faster, bigger, more” – more compute, more capital, more capability, with little regard for the curves ahead. And the sharpest curve of all was never going to be technological. It’s political.

Which is exactly why a self-imposed slowdown might not kill this bull market. It might prolong it.

The single biggest threat to the AI trade was never a soft earnings quarter. It was the risk of a public backlash so fierce that Washington slams on the brakes for the industry – bans, restrictions, and heavy-handed rules that land all at once and choke off the whole thing.

If the industry taps its own brakes first – pacing the frontier, adding guardrails, taking the safety concerns seriously – it takes the ammunition away from the Pritzkers and Sanders of the world. It defuses the very backlash that could otherwise end this cycle years early.

A controlled deceleration, in other words, might be exactly what keeps this AI bull alive longer than a reckless sprint ever could.

That is not the conclusion Wall Street reached this morning. But it may be the more useful one.

Which is what Luke Lango has been flagging all along

Regular Digest readers know this framing didn’t come out of nowhere. Our technology expert Luke Lango, editor of Early Stage Investor, has been building this exact case for months.

Luke has been wildly bullish on AI. But for months, he’s been just as clear about what will eventually end the trade – and it isn’t a tech failure or a recession:

The force that will derail the AI Boom is not a technological failure, demand collapse, or even a recession.

It is politics – specifically, a populist backlash against AI that is already building momentum.

Luke has said that backlash is likely a multi-year story, mostly tied to the 2028 election cycle – “not this earnings season or even this year.” The Coxon firestorm and the scramble of 2028 hopefuls that followed within 48 hours is that forecast assembling itself in real time.

That’s why, despite headwinds for the AI trade in recent months, his advice hasn’t been to run from AI but to recognize that the window is finite – and to make the most of it while it stays open:

Make your money now. The window for transformational wealth creation in this AI cycle is the next two to three years. This trade will not last forever.

Now, that quote came before this weekend’s turn – we’ll update you on Luke’s latest thinking when he sounds off. But the framework he’s laid out points to the same question either way: not whether to be in the AI trade, but how to be positioned before the politics fully catch up.

This ties into what Luke has spent the last several months mapping out in the investment markets. As we’ve tracked here in the Digest, he’s been identifying which of Elon Musk’s suppliers stand to benefit most as Musk builds out his “Vertical AI” empire – the smaller companies quietly supplying the physical capabilities his empire still leans on.

In a first-ever InvestorPlace workshop last week, Luke put Musk’s empire up on screen, zeroed in on the key bottlenecks, and highlighted the companies positioned to fill them as Musk’s spending accelerates. We’ll be taking the free replay down soon, but you can still catch it here for now.

If you’d rather trade the turbulence

Wall Street read this weekend as a reason to sell. We’d read it differently – and not only for the long-term reasons above.

In the near term, it’s a reason to know exactly what you own, and to be honest about how much of an AI name’s price is riding on a rich multiple rather than the business underneath it.

That’s the lens our trading expert Jonathan Rose, editor of Masters in Trading, is bringing to the moment. Here’s what he wrote this morning:

The AI story isn’t going anywhere… What worries me isn’t the story. It’s the multiple.

Rates, cooling global conflicts, the midterms – none of those stories in isolation can kill AI. But they can change what investors are willing to pay for it – and that can change fast…

When multiples compress and volatility picks up, those same names can pull back hard without anything actually being wrong with the long-term thesis. That’s not a story shift. That’s a repricing.

It’s exactly why Jonathan has been telling his readers to treat volatility as something to position for rather than fear – getting long VIX options as insurance, and hunting setups in supply-side names like Freeport-McMoRan (FCX) and The Metals Company (TMC) built to surge on volatility instead of getting crushed by it.

Today, he broke it down in today’s free episode of Masters in Trading Live, flagging where he sees the biggest friction points for AI, and exactly where he’s been making money in metals, infrastructure, and the names built to handle whatever comes next.

By the way, Jonathan publishes these free episodes every day that the market is open at 11 a.m. ET. He profiles market trends, explains entries and exits, discusses the opportunities he’s watching in real time, and offers plenty of tickers along the way. You can sign up right here to receive daily reminders and links to the upcoming episodes.

Coming full circle

In last Wednesday’s Digest, I gave readers a homework assignment:

Sit down with every AI position you own, one at a time, and sort each into a bucket.

Bucket one: “I believe in this so deeply I’ll hold through any pullback, any panic, any ugly headline — no matter how far it drops.”

Bucket two: “This is a momentum trade. If it turns against me, I’m out — and I know exactly why, when and how I’ll sell.”

There’s no wrong answer. The wrong move is not knowing which bucket a stock belongs in until you’re staring at a 30% drawdown, deciding in the heat of the moment.

Days like today are what those assignments are for. Are you prepared?

From here, whether you’re trading the volatile names pushed and pulled by emotion, adding to your long-term portfolio as fear drags great stocks lower, or protecting what you own by being clear-eyed about which names carry the richest multiples – and the most exposure to the political attacks now taking shape – the posture is the same…

Don’t fear the volatility – follow your plan. That way, you’re prepared no matter what happens with the AI trade.

Have a good evening,

Jeff Remsburg

The post The AI Trade Slams on the Brakes appeared first on InvestorPlace.

Treasury yields have been climbing as investors react to higher energy prices and the inflationary pressure that comes with them. And that is putting the Federal Reserve in a difficult spot.

In my view, the Fed should not raise interest rates simply because oil, diesel and jet fuel prices are moving higher. There is very little monetary policy can do to solve an energy-price shock.

But Fed Chair Kevin Warsh has made it clear that he does not want to fight market rates.

And with market yields still elevated, I believe next week’s Federal Open Market Committee meeting could become much more contentious than investors expect.

That does not mean I’m sitting on the sidelines.

In fact, I’m still buying.

In this week’s Navellier Market Buzz, I explain why the bond vigilantes are back, what higher energy prices could mean for the Fed and the economy and which stocks I believe are positioned to benefit from the current environment.

That includes two energy names benefiting from stronger refining economics, a technology giant with a major new product catalyst and a shipping stock benefiting from longer global trade routes.

Click the image below to watch now.

Looking Past the Noise

The four stocks I discussed in this week’s Navellier Market Buzz are very different businesses.

But the reason I’m buying them is the same: Their fundamentals are strong, and they are benefiting from clear trends in the economy.

That is exactly what my Stock Grader system (subscription required) is designed to identify.

We recently completed our latest quarterly backtest, and I have to tell you, the results were better than I expected.

The market went through plenty of turbulence this summer. We had July’s mean reversion, the forced liquidation of the Situational Awareness hedge fund and some wild swings in AI-related stocks.

Yet the stocks with the strongest fundamentals still separated themselves from the pack. Specifically, the top 20% of Stock Grader – our A-rated stocks – are performing especially well.

Heading into the fall, I expect that trend to continue. But I’m also watching closely where the next group of market leaders may come from.

In fact, my research team and I have spent months studying a massive new AI initiative taking shape across America’s national laboratories.

I call it the AI Reset of 2026.

And I believe the companies helping build and power this new infrastructure could represent the next major group of AI winners.

I recently put together a special presentation explaining what is coming, which companies I believe are best positioned and what investors should be watching as this next phase unfolds.

Click here to watch my AI Reset presentation now.

Sincerely,

抖阴最新版

Editor, Market 360

The post 4 Stocks to Buy Now as the Fed Faces a Big Decision appeared first on InvestorPlace.

This Week’s Ratings Changes:

Upgraded: Strong to Very Strong

Downgraded: Very Strong to Strong

Upgraded: Neutral to Strong

Downgraded: Strong to Neutral

Upgraded: Weak to Neutral

Downgraded: Neutral to Weak

Upgraded: Very Weak to Weak

Downgraded: Weak to Very Weak

To stay on top of my latest stock ratings, plug your holdings into Stock Grader, my proprietary stock screening tool. But, you must be a subscriber to one of my premium services.

To learn more about my premium service, Growth Investor, and get my latest picks, go here. Or, if you are a member of one of my premium services, you can go here.

Sincerely,

抖阴最新版

Editor, Market 360

The post Intel Upgraded, CVS Health Downgraded: Updated Rankings on Top Blue-Chip Stocks appeared first on InvestorPlace.

Luke further explores these opportunities with 抖阴最新版 and me during our Vertical AI Event, available now for a limited time. You’ll even get the name and ticker of one stock completely free. Watch the event now before access closes.

Take it away, Luke…

When John Frank Stevens came to Panama to dig a canal in 1905, he found the area ravaged by yellow fever.

The previous chief engineer had just resigned, and the workers were in desperate need of housing, food, and the strength to finish the job. Those who had enough gathered along the waterfront, waiting for passage home. Despite this, the full weight of the U.S. government and all its money stood behind the Panama Canal project.

But not even President Theodore Roosevelt could persuade the scores of sick, injured, and tired to continue digging. So, Stevens did something unexpected. He suspended the bulk of the excavation.

Instead of digging harder, Stevens started fixing everything that made digging nearly impossible.

He improved housing and food supplies. He backed the sanitation campaign led by U.S. Army physician William Gorgas to bring yellow fever and malaria under control. And he rebuilt the railroad needed to bring supplies in and haul millions of tons of excavated dirt out.

In other words, before the Panama Canal was dug, Stevens had to make sure it was a place where both people and machinery could work.

I thought about that when I heard Elon Musk had just raised $3 billion for The Boring Company – his effort to build networks of underground tunnels that can move cars beneath congested cities.

The new funding is meant to help Boring expand its engineering, production, and operations teams and push ahead with projects from Las Vegas and Nashville to Dubai and the broader United Arab Emirates.

But Musk apparently wanted something else from some of the people writing those checks.

According to The Wall Street Journal, the company told certain investors they would also need to help recruit employees or assist with business development. That could mean introducing The Boring Company to government officials in places where it wants to dig new tunnels.

The money will help expand the company’s engineering, production, and operations teams, support projects in Las Vegas, Nashville, and Dubai, and fund more than 150 kilometers of planned underground infrastructure across the United Arab Emirates.

That’s what caught my attention.

This comparison between Roosevelt’s canal and Musk’s tunnels has its limits, of course. But financing a project and assembling everything that’s required to build it are two different achievements. That’s something Stevens would have been familiar with.

At first glance, this looks like another story about investors lining up to hand Musk billions.

Look closer, and it’s a story about what money alone can’t buy him.

Across Musk’s empire, the gaps are filled by engineers, specialized factories, component suppliers, and infrastructure that could take years of development. For investors, it’s exactly those dependencies that I want you to pay attention to.

Because every time Musk runs into something he can’t build fast enough, cheaply enough, or on his own, somebody else gets an opportunity to sell it to him.

And some of those companies could be tiny compared with Musk’s empire. A big order from Tesla Inc. (TSLA), Space Exploration Technologies Inc. (SPCX), or The Boring Company might barely register on Musk’s spending – while transforming the supplier getting the check.

That’s the opportunity I want to show you today: Follow what Musk still needs, find the companies that can provide it, and you may find some of the biggest winners of his next expansion before Wall Street does.

Why The Boring Company Asked Investors for More Than Money

Now, look at who participated in this Boring Company funding round.

Among others, Sequoia Capital, Andreessen Horowitz, Temasek, Baron Capital, and UAE-backed investors.

These are some of the biggest, best-connected investors in the world. They have plenty of money. But they also know people. They know engineers, business leaders, and government officials. And I think that’s a big part of what Musk is trying to bring into the company here.

You can build a really good tunneling machine but still run into red tape getting permission to put it underneath a city. You need engineers, local partners, and government approvals. And those can be harder to come by than another billion dollars.

So when I look at this round, I see Musk both raising money and recruiting a network that could help him put that money to work.

The UAE-linked investors are a good example. The Boring Company plans more than 150 kilometers of underground infrastructure there, including its Dubai Loop – a planned network of underground tunnels and stations designed to move passengers around the city in Tesla vehicles.

Bringing in well-connected local investors could help Boring recruit people, find partners, and navigate the dozens of approvals required before the first tunnel gets dug.

To me, that explains why Musk wants more from these investors than a check. He needs help turning a funded project into a working tunnel.

Dubai’s 48 Permits Show Why Capital Is Not Enough

Look at what needs to happen in Dubai.

The first phase of the Loop is expected to cover about 6.4 kilometers and include four passenger stations connected by underground tunnels, with Tesla vehicles carrying riders between them. Before digging can begin, The Boring Company says it needs to seek roughly 48 permits and no-objection certificates from around 10 different entities.

That’s just the first phase. And it’s in an extremely business-friendly jurisdiction. Imagine what Musk would need to dig in Chicago or Paris.

Now, Musk can build a faster tunneling machine. But how fast that machine digs doesn’t matter much if you’re still waiting for permission to put it in the ground.

Utility lines have to be mapped. Roads and buildings above the route have to be accounted for. Safety requirements have to be met. And all of it requires people who understand how to get a complicated infrastructure project approved and built in that particular market.

You can’t create that kind of expertise overnight.

And that’s the larger point. Across Musk’s empire, he keeps running into things that money alone can’t produce quickly – capabilities that other people and companies have spent years building.

For investors, that’s where this story gets much bigger than The Boring Company.

Across Musk’s Empire, Capital Is Only the Starting Point

Take SpaceX. Musk can build a more powerful rocket, but to launch it more often, he needs manufacturing capacity, specialty materials, advanced electronics, and trained workers. He also needs permission to launch.

You see the same problem at xAI. Musk can spend billions building enormous data centers packed with AI chips, but those chips need electricity, cooling systems, and high-speed networking to work. If you’re waiting on a grid connection, buying another thousand chips doesn’t solve the problem.

Then look at Tesla Inc. (TSLA). Musk wants to mass-produce robotaxis and humanoid robots. That means taking technology that works in development and turning it into something you can manufacture, deploy, and service at enormous scale.

For robotaxis, you need regulatory approvals, charging infrastructure, and people who can keep the fleet operating. For humanoids, you need sensors, semiconductors, precision components, and manufacturing partners that can deliver them reliably.

And remember, the suppliers have to scale right alongside you. If you want to build a million robots, you need a supply chain capable of supporting a million robots.

That’s where this gets really interesting for investors.

Musk can raise billions almost overnight. But he can’t build a new power plant, qualify a new factory, train thousands of workers, or create years of manufacturing expertise overnight.

Other companies already have some of those capabilities. And as Musk’s ambitions get bigger, I want to know which ones can deliver what he needs, when he needs it.

Because Musk’s next expansion could become their next major order.

Follow Musk’s Spending to the Companies Getting Paid

When I study Musk’s empire, I keep coming back to one question: What does he need that somebody else is better positioned to deliver?

It could be a utility with power available where he wants to build. A manufacturer with capacity ready to go. A supplier whose components have already passed years of testing.

The next question is: How much could that business grow if Musk starts buying more?

Because an order that represents a small part of his spending could make a meaningful difference to a smaller supplier’s revenue. That’s the opportunity I’ve spent months researching across his AI, robotics, and space businesses.

And I reveal a lot of that research during my Vertical AI Event.

In that workshop, I map Musk’s empire on screen and trace his spending into the outside companies he still depends on. My colleagues 抖阴最新版 and 抖阴最新版 join me to examine the opportunity, and you’ll get the name and ticker of one stock poised to benefit completely free.

You can watch it now, but access is available for a limited time.

You’ll see where we believe Elon Musk’s most important supply constraints are developing, which companies could help resolve them, and why I’ve circled September 24 as a potential catalyst for Musk’s next move.

More than a century ago, the Panama Canal couldn’t be built with money and ambition alone. John Frank Stevens needed the people, equipment, transportation, and infrastructure that could turn President Roosevelt’s enormous project into something that actually worked.

Musk faces his own version of that problem today. He has the money. He has the ambition. What he doesn’t have is everything required to build it all himself.

That’s where I see the opportunity. I want to show you the companies that could get paid to supply what Musk still needs.

Watch the Vertical AI Event now and get your free stock pick before access closes.

Sincerely,

Luke Lango

Senior Investment Analyst, InvestorPlace

P.S. If you take one thing from Luke’s essay, make it this: Elon Musk can raise billions, but he still can’t build everything himself. That gap is where Luke believes some of the most interesting opportunities may be hiding. In his Vertical AI Event, Luke, Louis, and I follow Musk’s spending into the companies that could get paid to fill those gaps. Watch it here – and get one stock name and ticker completely free.

The post Follow Elon Musk’s Billions to the Companies Getting Paid appeared first on InvestorPlace.

It was called cinchona.

And for a long time, it was one of the most strategically important plants on Earth.

Its bark contained quinine, which was then the world’s best defense against malaria. If European powers wanted to build railroads, man military outposts, or expand deeper into the tropics, they needed quinine.

And the Dutch controlled almost all of it.

By the 1920s, plantations in the Dutch East Indies supplied more than 90% of the world’s cinchona. Prices were effectively controlled by a cartel formed by growers and manufacturers that set production quotas and prices, referred to as Amsterdam’s Kina Bureau.

And for years, everyone paid the toll.

Then the supply disappeared.

Germany occupied the Netherlands in 1940 while Japan captured Java in 1942, and, almost overnight, the Allies lost access to both the cinchona plantations and its processing infrastructure.

The consequences were brutal.

During the Pacific campaign, malaria hospitalized more soldiers than enemy fire. By December 1942, more than 8,500 American troops were hospitalized with the disease. In some wards, in eight out of every 10 beds laid a soldier suffering from fever rather than wounds from combat.

A massive military machine had been built on top of one tiny bottleneck. And almost nobody appreciated how important it was until that bottleneck broke.

I’ve been thinking about that story this week because Nvidia Corp. (NVDA) recently reported earnings, which spoke to where the next big profits in the AI Boom could show up.

It wasn’t Nvidia’s revenue or its earnings. Rather, the big story is in Nvidia’s margins.

Let me explain.

The Number That Still Matters

Weeks after Nvidia’s earnings report, one number still deserves investors’ attention: 70%.

That’s the revenue growth Nvidia expects in fiscal 2028, which roughly corresponds to calendar 2027. Before the report, analysts were expecting growth closer to 45%. That gap helped reset expectations for how much runway the AI boom has left.

Using the revenue baseline behind those earlier estimates, that’s the difference between roughly $580 billion and $680 billion in annual sales.

抖阴最新版 $100 billion in additional revenue.

That comparison captures the size of the surprise Nvidia delivered weeks ago. The question now is what it would take to deliver on it.

You can understand why analysts had expected a sharper slowdown. Nvidia has become enormous. Every percentage point of growth requires more sales, more manufacturing capacity, and more infrastructure to support the chips it ships.

Yet management’s outlook suggests growth could remain exceptionally strong even as the business gets larger.

To be clear, 70% is an outlook for one fiscal year, not a permanent cruising speed. But achieving it would still require an extraordinary expansion at Nvidia’s scale.

And that brings us to the part of the story we think deserves more attention today.

Revenue expectations can rise with a few changes to a spreadsheet. The supply chain has to expand in the physical world — where factories take time to build, specialized components remain difficult to produce, and suppliers can’t always increase output on command.

For investors, that creates a second question alongside “How fast can Nvidia grow?”

Who supplies the things Nvidia needs to reach those numbers, and how much pricing power do they have?

That’s where the conversation gets especially interesting…

The Number That Matters More

Nvidia’s growth outlook is still drawing attention weeks after earnings. But its margin outlook may tell investors more about where the next AI opportunities are taking shape.

The company expects rising memory costs to pressure gross margins through the second half of the year. In other words, Nvidia anticipates keeping less gross profit from each dollar of sales.

Normally, that would give investors pause.

Here, though, the reason matters: The components needed to support AI’s expansion are becoming more expensive.

That shifts the question from how much Nvidia can sell to how much it must pay the companies that make those sales possible.

And it gives investors a reason to look one layer down the supply chain.

Nvidia’s higher costs can become a supplier’s higher revenue. When that supplier can raise prices faster than its own costs rise, more of the spending can reach its bottom line.

That isn’t an automatic, dollar-for-dollar transfer of profit. But it is the kind of shift in bargaining power we want to watch.

Micron Technology Inc. (MU) belongs in that conversation because of its role in memory. SanDisk Corp. (SNDK) offers exposure to storage, another piece of the AI infrastructure buildout. Optical networking suppliers warrant attention for the connections that allow these systems to move data.

The opportunity in each depends on what it supplies, how scarce that product becomes, and how much pricing power it can sustain.

Which companies control the components that could hold up the next stage of AI growth?

That’s the bottleneck trade. And Nvidia’s margin outlook gives investors a concrete reason to keep following it.

Follow the Bottleneck

The AI Boom has never really been one trade but a rolling series of bottlenecks.

And each bottleneck has created a new group of winners.

First came compute.

AI companies couldn’t train frontier models without GPUs, so Nvidia became the critical supplier. Revenue exploded from about $27 billion to well over $100 billion, and the stock followed.

Then came servers.

Someone had to package all those GPUs into usable systems. For a stretch, Super Micro Computer Inc. (SMCI) became one of the fastest-growing companies in the S&P 500.

Then came cooling.

Stuff that much computing power into one building and traditional air cooling stops working. Suddenly, liquid cooling became mission-critical, and Vertiv Holdings Co. (VRT) transformed from a relatively obscure infrastructure company into a major AI trade.

Then came energy.

Data centers started consuming more electricity than utilities could easily deliver. Nuclear power went from yesterday’s technology to one of Wall Street’s hottest AI infrastructure themes, helping stocks like Constellation Energy Corp. (CEG) soar.

Then came memory.

AI inference requires enormous memory bandwidth. High-bandwidth memory became scarce, creating another wave of winners.

That’s five bottlenecks in roughly three years, each following an identifiable pattern.

First, hardly anyone cares.

Then supply tightens.

Then pricing power improves.

Then earnings explode.

Then Wall Street notices.

And eventually, everyone piles into the trade.

That is why the most useful question in AI investing is to ask where the next bottleneck is forming.

Because wherever the hyperscalers are about to spend their next $100 billion, there is probably a shortage forming somewhere nearby.

Right now, two of the biggest constraints appear to be memory and networking. And I think networking is especially interesting.

Most investors still think the main constraint inside an AI data center is the chip. But once you pack hundreds of thousands of processors into one facility, those processors need to operate together like one giant computer.

If they cannot communicate fast enough, say goodbye to performance.

Suddenly, the cable that connects racks can become almost as strategically important as the chips powering them.

That’s why Nvidia’s moves in optical networking are critical.

Earlier this year, the company committed billions of dollars to secure supply from Lumentum Holdings Inc. (LITE) and Coherent Corp. (COHR). Companies only make commitments like that when they are worried about supply.

In other words, Nvidia is showing us where one of its own chokepoints lies. And historically, that is exactly where investors should be looking.

Which Brings Me to Elon

Every company in the AI Boom pays a bottleneck tax.

The hyperscalers pay it to Nvidia.

Nvidia pays it to memory and optical suppliers.

And everyone pays it to utilities and power infrastructure providers.

But one person has spent years trying to eliminate as many of those tollbooths as possible.

Elon Musk.

Ignore whatever you think about Musk personally and just look at how his companies are structured.

He owns a massive source of real-time training data.

He builds enormous AI compute clusters.

He owns rockets.

He owns a satellite communications network.

And he builds physical machines that could eventually use the intelligence produced by those systems.

Look at those businesses individually, and they can seem chaotic. Look at them together, and a pattern emerges.

Musk is systematically trying to control more of the infrastructure required to produce and distribute intelligence. That means owning bottlenecks instead of paying someone else to control them.

It’s an old industrial playbook.

John D. Rockefeller built his own barrels because suppliers charged too much.

Henry Ford bought mines, railroads, forests, and shipping assets because he wanted more control over his supply chain.

Musk has repeatedly done the same thing.

If a component is too expensive, too slow, or too difficult to procure, his instinct is often to bring it in-house. But that creates an interesting investing filter because Musk still buys plenty of things from outside suppliers.

And after two decades of aggressive vertical integration, the companies that remain inside his supply chain are probably there for a reason.

Whatever they make, Musk has likely asked some version of the same question:

Can we build this ourselves?

And if the answer was no, that tells you something no Wall Street analysis could. It suggests that supplier may possess technology, manufacturing expertise, scale, or intellectual property that is unusually difficult to replicate.

In other words, a moat.

And I think Wall Street dramatically underestimates how valuable that information can be.

The Bottom Line

Nvidia’s latest quarter told investors two important things: The AI boom remains powerful, and the bottleneck is moving.

Even at its enormous scale, Nvidia sees a path to growth near 70%. Yet suppliers are gaining leverage, putting pressure on its margins — and pointing investors toward the next pockets of pricing power.

We’ve watched this happen repeatedly over the past three years: compute, servers, cooling, energy, memory, and now networking.

The names change. The pattern stays the same.

When something becomes scarce in a massive investment boom, pricing power flows toward whoever controls it. That was true when global empires depended on cinchona bark growing on a Javanese plateau. And it is true today when AI giants depend on specialized components buried deep inside their data centers.

So don’t just watch what the giants are building. Watch what they cannot build themselves.

That question has guided my research into Elon Musk’s empire — and it’s at the heart of The Vertical AI Event.

On the surface, his businesses can look like separate bets: social media, artificial intelligence, rockets, and robots. But I believe the connections between them reveal something much bigger: a push to bring AI out of the chatbot window and into the physical world.

Those ambitions also create a revealing tension. Musk wants to control more of the technology behind his businesses. Yet even his empire depends on outside suppliers to deliver critical pieces.

Which suppliers control something he can’t easily replace?

In the workshop, I connect those pieces, examine four bottlenecks standing between Musk and his ambitions, and share the names and tickers of companies positioned to help solve them.

If Nvidia’s results have you wondering where the AI opportunity moves next, this is the next step in that conversation.

Watch The Vertical AI Event before the replay comes down at midnight Tuesday, Sept. 15.

Follow the bottleneck. The company trying to change the world may depend on a much-smaller company that makes the change possible

The post What Nvidia Told Us 抖阴最新版 the Next Big AI Bottleneck appeared first on InvestorPlace.

In March 2025, I recommended three blue-chip companies to buy for the AI Revolution:

• Monolithic Power Systems Inc. (MPWR), a leader in power management chips for AI devices.
• Workday Inc. (WDAY), an AI-enhanced cloud-based HR platform.
• Xometry Inc. (XMTR), a marketplace for AI-powered manufacturing.

The trio has since performed exceptionally well, which isn’t surprising given that we’re in the middle of the AI Revolution. Despite a pullback in Workday from a broader “SaaSpocalypse” panic, these three stocks have returned 105% on average, more than doubling the returns of the tech-heavy Nasdaq Composite index.

However, this presents a new problem. Much like mining a gold seam dry, these stunning gains mean there’s not much left for latecomers. You either got in early and enjoyed high returns, or came in late when only dry rocks remained.

Fortunately, InvestorPlace Senior Analyst Luke Lango believes there’s still one last rich AI seam that’s been overlooked:

Physical AI: taking artificial intelligence out of computers and putting it into cars, robots, factories, and other machines operating in the real world.

Luke argues that shift is arriving faster than most investors expect, and that it will take four layers to make it work. And in a first-of-its-kind InvestorPlace workshop, he maps them onto Elon Musk’s businesses:

• Data. X, a real-time feed of human behavior.
• Compute. Colossus, the supercomputer xAI built in Memphis to train Grok.
• Connectivity. Starlink, SpaceX’s satellite network.
• Robots. Optimus, Tesla’s humanoid robot.

No conglomerate builds all four by itself. Not even Elon Musk’s empire.

That’s Luke’s point: Every layer depends on a network of outside suppliers, the firms he calls “Chosen One” companies during his special broadcast. These make the parts that Musk’s businesses can’t produce themselves. In his presentation, Luke reveals one free pick that feeds these layers, then points to where you can find seven more.

Today, I’ve been given special permission to share Credo Technology Group Holding Ltd. (CRDO) – one of these seven picks to give you a glimpse of how essential (and overlooked) Luke’s picks are to the Physical AI Revolution.

Wiring Up the Physical AI Revolution

Credo Technology runs a straightforward business:

It creates high-tech cables known as active electrical cable (AEC) that run inside AI datacenters.

You see, most datacenters up to this point have been wired up using an old technology known as direct-attach copper (DAC). That’s just a fancy way of saying “copper wire,” the same tech that elementary school children use when building their first electrical circuit. DACs are cheap and easy to produce.

However, copper is not perfect. Some energy is always lost as heat, and electrical signals get distorted and “attenuated” as they pass through the wire. It’s why you sometimes hear power lines buzz, and why professional DJs insist on buying hundred-dollar Monster Cables instead of using the $1 spools from the local hardware store.

The issue is even more problematic for AI datacenters, which require far higher precision.

Credo’s AEC cables fix this problem by adding tiny digital signal processing (DSP) chips along a copper wire. These little devices do the following:

These active cables work fantastically well. They can handle far more data than traditional copper wires, send information along further distances, and prevent the dreaded “link flap” where a network connection drops because the signal has become so garbled.

Why Credo? Why Now?

AI datacenters today require much more data than ever before. For example, a single Blackwell AI chip from Nvidia Corp. (NVDA) can move around 8 terabytes of data per second… or roughly 680 DVDs in the time it takes someone to blink an eye. These Blackwell chips are then typically run together in clusters of 72, and medium-sized AI datacenters can run thousands of these clusters.

To keep these chips, servers, and datacenters supplied with fresh data, researchers have turned to using higher frequencies to transmit information. Think of it like talking very fast: It’s much easier to say things quickly in a squeaky high-pitched voice than in a rich, gravy-like baritone. (Try it yourself!)

And because we humans tend to take technologies to their extremes, today’s AI datacenters use frequencies that are over a million times higher than what humans can hear.

This presents an issue for traditional copper wire. Distortion becomes worse at high frequencies, and data becomes more garbled the longer it travels down a wire.  An ultra-fast AI chip might transmit the best information in the world… and no one will understand what it’s saying if it’s connected by the wrong cable.

Now, this frequency issue was not a hurdle for older datacenters. Most only required several chips to be closer together and data frequencies were lower back then. In fact, Microsoft Corp. (MSFT) in 2023 allegedly decided to cut back on AECs because they were so expensive. Shares of Credo fell 46% in a day following that announcement.

But new AI datacenters need AECs not only because frequencies are now higher… but because AI servers are physically so large (since there are so many chips) that a single cluster of them might run 3 to 7 meters from end to end. That’s too far for traditional copper to reach, and wastefully close for fiber optics. AECs serve this “sweet spot” in between.

That’s why growth at Credo has suddenly accelerated. Revenue growth in fiscal 2026 hit 206% (up from 127% a year earlier), and GAAP net profits rose ninefold. Credo’s products sit at the perfect 3 to 7 meter wire lengths, and the company is now enjoying being in the right place at exactly the right time.

Credo: An Essential AI Player

Luke and I expect demand for AECs to continue rocketing higher. The entire Physical AI Revolution will require more computing power than ever before, and that means more datacenters… more chips… and more specialty wires that connect everything together.

In addition, Nvidia’s Vera Rubin next-generation AI chips will use a new communication standard that requires communication frequencies of 53 GHz. That’s double what the current Blackwell generation uses, and will cut the effective range of DAC copper wires to barely 1 meter (3 feet). Imagine an IT manager being given only 3-foot cables to wire up a datacenter the size of a football field!

That’s why Credo is so essential to the Physical AI Revolution. It has over a hundred active patents on its AEC technology (plus another 80 pending), decades of experience in building the technology, and numerous legal wins where it successfully defended its intellectual property. The company also co-designs its AECs with customers, creating a “lock-in” effect.

Now, I must mention there is an alternative known as active copper cable (ACC) that can boost signals to roughly 2-3 meters. This technology has spooked some investors, since it is cheaper than the AECs that Credo produces. But ACC technology faces the same problem as copper wire because it lacks the multiple signal boosters that AECs have. Once we get to the next high-frequency standard after Vera Rubin, ACCs will face the same wall that pure copper does today.

The Value Behind the Tech

Shares of Credo are extremely attractive at current prices. They are down almost 50% since peaking in June, even though fiscal 2027 guidance has been revised up. The customer list has also broadened, with AI datacenter companies like Meta Platforms Inc. (META) and “neocloud” firms taking up more production. Even Microsoft has reversed course and returned as one of Credo’s largest customers.

CRDO shares now trade for just 26X forward earnings – the lowest 1% of Credo’s post-IPO range

Given this, the share price could easily double – and that’s without the additional demand from Physical AI. The world is going to need a lot more high-tech cabling to overcome copper’s physical limitations, and most regular investors haven’t figured out that a wave of demand is heading our way.

As I mentioned, this is just one of Luke’s “Chosen One” companies. To find out how to access all seven – plus his free pick – click here to watch his Vertical AI broadcast.

Until next week,

Thomas Yeung, CFA

Market Analyst, InvestorPlace

Thomas Yeung is a market analyst and portfolio manager of the Omnia Portfolio, the highest-tier subscription at InvestorPlace. He is the former editor of Tom Yeung’s Profit & Protection, a free e-letter about investing to profit in good times and protecting gains during the bad.

The post Missed Out on the AI Revolution? Here’s 1 More Stock to Buy appeared first on InvestorPlace.

But what are you supposed to do with a number like that? It’s a destination, not a signal – and destinations are hard to trade until you know the route. 

JPMorgan (JPM) just gave investors a much smaller one that may matter far more right now: $10 an hour.

That is roughly what the bank believes a humanoid robot could soon cost to operate inside a warehouse or factory. A human worker performing similar work costs closer to $30 per hour.

Productivity is still a problem. JPMorgan estimates that it currently takes about two humanoids to match the output of one human worker.

Even then, the math is starting to work.

Two robots operating at $10–$12 per hour would cost roughly $20–$24 for the same amount of output as one $30-per-hour worker. By 2030, JPMorgan expects that gap to narrow to roughly 1.2–1.3 robots per worker. At that point, the effective labor cost could fall toward $12–$16 per hour.

That is the number that changes the Physical AI trade.

Why $10-an-Hour Humanoid Robots Could Change Factory Economics

The $10-an-hour figure lands in the middle of a very real labor crisis. 

JPMorgan estimates that roughly 462,000 U.S. manufacturing jobs are currently unfilled, with that shortage potentially reaching 1.6 million positions by 2030.

The bank believes humanoids could handle about 25% of those openings with today’s technology. Continued improvements in dexterity, intelligence, and reliability could push that figure toward 50% by the end of the decade.

Factories give humanoids a good starting point. The floors are predictable. The tools were built for human hands. The tasks repeat often enough to train and measure. And companies are already struggling to find enough people willing to perform many of those jobs.

Material handling. Parts transfers. Machine tending. Quality inspection. Moving equipment between workstations. Sorting components for an assembly line.

The robot revolution can start there.

Humanoid Robots Are Already Working In Factories

Start with BMW, which has the receipts.

Over a 10-month deployment at the automaker’s Spartanburg, South Carolina, plant, Figure AI’s Figure 02 robot supported production of more than 30,000 BMW X3 vehicles. It moved over 90,000 components and logged roughly 1,250 hours of real factory work.

BMW has now brought Figure 03 into the same plant for a more complicated logistics job: picking unsorted components, organizing them in the correct sequence, and preparing them for delivery to the assembly line.

Figure 02 proved that a humanoid could repeat a precise task safely under real production conditions. Figure 03 is being asked to deal with more variation, use more dexterity, and coordinate its whole body while manipulating parts.

The work is getting harder. And the robots are up for the task.

Meta (META) is exploring a similar path inside its own data centers. The company has been testing robots that can move equipment, reset servers, and eventually help with tasks such as plugging in cables.

Those may sound like small jobs. But automating them could lower labor costs while reducing the need to send people into hot, noisy, and highly controlled server environments.

Hyundai is moving toward much larger scale.

The automaker plans to manufacture as many as 30,000 Boston Dynamics Atlas robots annually by 2028 and gradually introduce them into factories and warehouses. Early jobs will focus on parts sequencing and other tasks before Atlas moves toward more complicated assembly work.

These companies are starting where the spreadsheet math is easiest: jobs with a known hourly cost and a chronic shortage of people willing to do them.

Why Humanoid Robot Economics Still Have to Prove Themselves

JPMorgan’s math is compelling – but it is still just a model.

A company cannot just wheel a humanoid onto the factory floor, turn it on, and call it a day. Integration costs money. Workflows have to change. Employees need training. Robots require maintenance, charging, spare parts, software support, and reliable network connections.

As we’ve mentioned, the current machines are also less productive than people are. And hands remain one of the biggest bottlenecks.

Robot Hands Are a Major Bottleneck

A humanoid may have excellent balance and sophisticated vision, but factory work often comes down to the fingers: grip the part, adjust the angle, feel whether it is seated correctly, apply just the right amount of pressure…

Hyundai and Boston Dynamics are making progress on the robot brain and the manufacturing supply chain. The hands may still require outside specialists.

Then there is the price of the machine itself.

JPMorgan estimates that a capable humanoid currently costs around $120,000. Elon Musk has discussed a much lower long-term target of $20,000–$30,000 for Tesla’s Optimus robot, but commercial buyers care more about reliability than a distant sticker-price goal.

A $120,000 robot that works two shifts a day for years may create more value than a $25,000 robot that regularly breaks down.

The winning machine will earn its keep.

Robot Training Data Is Becoming a Physical AI Bottleneck

Robots also face another challenge that chatbots never had.

The internet already contained enormous amounts of text, code, images, and video that labs could use to train large AI models.

The internet does not contain enough high-quality data showing exactly how human hands grip a cup, sort a bin, connect a cable, load a dishwasher, or adjust when an object slips.

Physical data has to be collected from the physical world.

Figure Is Building a Massive Human-Task Dataset

Figure recently unveiled a large-scale effort called Index to capture that missing information. The company says contributors across more than 100 countries have already uploaded over 16 million videos showing real human tasks. Figure has paid contributors $15 million and says it plans to spend more than $1 billion on data and computing over the next year.

The numbers are company-reported, and the program still has to prove that more video translates into more capable robots. But the direction is clear.

Teaching robots about the physical world is becoming its own industry.

Nvidia is building the tools around that effort. Its Isaac and Cosmos platforms help developers simulate environments, generate training data, teach robots new skills, and test them before deployment. The company is also working with major industrial and robotics players including ABB, Agility, Figure, KUKA, Teradyne (TER), and Yaskawa.

The Physical AI stack is filling in from both directions.

Better data improves the brain. Better components improve the body.

The Humanoid Robot Supply Chain Is Bigger Than the Robot Maker

The most visible companies will keep attracting the most attention.

Tesla (TSLA) has Optimus. Hyundai owns Boston Dynamics. Private startups such as Figure, Apptronik, Agility Robotics, and 1X are competing to put humanoids into factories, warehouses, stores, and, eventually, homes.

But the robot maker captures only one part of the opportunity.

Every humanoid needs a brain powerful enough to understand its surroundings and make decisions locally. It needs cameras and sensors to see. Motors and actuators to move. Power-management chips to control dozens of joints. Batteries to keep operating. Memory to store models and data. Connectivity to communicate with the cloud and other machines.

That is why Nvidia sees such a large market.

Huang’s $50 trillion figure is not a near-term forecast for humanoid-robot sales. It reflects the enormous amount of manufacturing activity and labor that intelligent machines could eventually touch.

Nvidia wants to supply the training infrastructure, simulation software, world models, safety systems, and onboard computing behind those machines.

And that’s just the brain. Work your way down the body, and there’s a public company at nearly every joint.

Machine-vision companies help robots see. Analog and power-semiconductor suppliers translate sensor signals and control motors. Automation specialists help factories integrate machines into workflows. Networking and edge-computing companies keep robot fleets connected. 

We walked through many of these names – Nvidia, Cognex (CGNX), Teradyne, Rockwell Automation (ROK), Honeywell (HON), Qualcomm (QCOM), Analog Devices (ADI), Monolithic Power (MPWR) – layer by layer in our recent breakdown of who gets paid when AI leaves the cloud. 

The robot brand that wins the headlines may change.

The need for the underlying components will grow with every unit that ships.

The Bottom Line: Humanoid Robots Are Moving From Demo to Economics

The first mass market for humanoids may be the night shift America cannot staff.

And we don’t think anyone is racing toward that market harder than Elon Musk.

Optimus gets the demo-day applause. But watch what Musk is actually assembling around it: the AI models to run it, the compute to train it, the connectivity to link it, the factories to mass-produce it. Piece by piece, he’s pulling the entire Physical AI stack under one roof – the same way Rockefeller once pulled the entire oil business under his.

Rockefeller, famously, even made his own barrels. But here’s the thing about vertical empires: they still can’t make everything. 

Standard Oil needed railroads, steel, and machinery from outside its walls – and the fortunes made supplying Rockefeller rivaled the ones made alongside him.

I think the same dynamic is taking shape around Musk’s robot ambitions right now. The suppliers filling the gaps in his Physical AI buildout – the hands, sensors, rare materials, and specialized components no empire can produce in-house – may end up being the most interesting trade of the entire humanoid boom.

I’ve spent months mapping exactly which companies sit in those gaps. And just a few days ago, 抖阴最新版, 抖阴最新版, and I held a new workshop to parse Musk’s empire layer by layer, identifying the technologies he controls and the outside companies we believe are best positioned to fill the gaps.

Don’t wait until the market figures out who they are.

Check out this event while it’s still early, and get those names and ticker symbols – for free.

The post Humanoids’ First Mass Market: The Night Shift America Can’t Staff appeared first on InvestorPlace.

My colleague Luke Lango, Senior Investment Analyst here at InvestorPlace, has spent months digging into that question. And he has uncovered an interesting angle that’s easy to miss when the conversation gets dominated by Musk, Optimus, and the latest Tesla headlines.

Luke is less interested in the hype around humanoid robots than in the enormous industrial ecosystem that could emerge if Musk actually tries to build them by the millions – and the companies that could supply the parts, machinery, and infrastructure Musk would need to make it happen.

Luke recently laid out his findings in his latest presentation, which you can catch here.

Take it away, Luke…

In 1961, a General Motors factory in New Jersey welcomed a new employee.

He didn’t take lunch breaks, call in sick, or complain about working next to molten metal all day. His name was Unimate, and he was the first industrial robot ever put to work on a factory floor.

“Robot” might actually be generous by today’s standards.

Unimate was basically a giant mechanical arm. The first ones followed instructions stored on a magnetic drum, grabbing scorching-hot pieces of metal from a die-casting machine and stacking them for workers farther down the line. Later deployments expanded into assembly-line welding and metalworking

It could perform these dangerous, repetitive jobs over and over again without getting tired, injured, or bored. That was enough.

General Motors installed more of them, other automakers followed, and today millions of industrial robots weld car bodies, paint panels, move pallets, package products, and assemble electronics around the world.

But there’s a reason most of them don’t look anything like C-3PO.

Today’s industrial robots excel at repetitive tasks in workplaces designed around them.

Source: iStock/imaginima

Industrial robots work because we build the factory around them. We bolt them to the floor and have them make the same movement thousands of times.

Change the job or the environment and things get much harder.

Humanoid robots flip that idea around.

Instead of redesigning the workplace around a specialized machine, engineers are trying to build machines that can operate in workplaces already designed for us – with our stairs, doors, shelves, tools, workbenches, steering wheels, ladders, and countless other things built for human arms, legs, hands, and fingers.

For decades, that was mostly science fiction, but AI is finally changing the economics. That’s why I want to talk to you about robots today.

I’ll show you evidence that humanoids are beginning to move beyond flashy demonstrations and into real factory work… why Wall Street believes the cost of using them could soon compete with human labor… and, most importantly for investors, why Elon Musk’s plan to mass-produce Tesla Inc.’s (TSLA) Optimus humanoid could create enormous opportunities outside Tesla itself.

Musk may want to build Optimus by the millions, but he can’t build everything that goes inside them.

And so, I’ve spent months figuring out who he’ll have to pay.

When a Robot Starts Earning Its Keep

This isn’t entirely theoretical anymore.

BMW recently spent 10 months testing a humanoid robot from Figure AI at its Spartanburg, South Carolina, factory. According to BMW, the robot moved more than 90,000 components and logged roughly 1,250 hours supporting production of more than 30,000 BMW X3 vehicles.

That doesn’t mean today’s humanoids are ready to replace people across the factory floor. They’re still expensive and slower than people at plenty of jobs. Their hands aren’t nearly as capable as ours, and they require maintenance, charging, software, training, and integration.

But they don’t have to be better than people at everything. They have to become economically useful at some things.

And that’s where the numbers get interesting.

JPMorgan recently estimated that a humanoid could eventually cost around $10 to $12 per hour to operate in an industrial setting.

Now, there’s an important catch. JPMorgan also estimates today’s humanoids are considerably less productive than people. It can take roughly two humanoids to equal the output of one human.

Even so, two robots at $10 to $12 per hour gets you to roughly $20 to $24, compared with the approximately $30 hourly cost JPMorgan assigns to a human worker.

And JPMorgan expects that productivity gap to narrow significantly by 2030. To me, that’s the potential crossover point.

We know engineers can make a humanoid walk across a stage, pick up a box, and dance for a YouTube video.

Now we need to find out whether a company can put one to work and save money. If the answer increasingly becomes yes, this market could move very quickly.

Nvidia Corp. (NVDA) CEO Jensen Huang recently said robotics used in manufacturing could eventually address a $50 trillion industry.

I have no idea whether the ultimate number will be $50 trillion. Neither does Jensen. But it doesn’t have to be.

If humanoids become economical for even a small fraction of the world’s factory and warehouse work, somebody is going to have to manufacture an enormous number of robot bodies.

And every one of those humanoids comes with a shopping list.

Elon Musk’s Robot Shopping List

This is where Elon Musk gets interesting to me.

He’s talked about eventually producing Optimus by the millions, with a long-term price target around $20,000 to $30,000 per robot.

While Musk has certainly missed ambitious targets before, think about what Tesla will have to do even to try.

Look at your own hand.

Picking up a coffee mug seems effortless. But your eyes first locate it. Your brain judges its distance and shape. Your shoulder and elbow move your arm into position. Your fingers adjust their grip. Nerves tell your brain whether you’re squeezing too hard or too softly.

A humanoid robot has to reproduce that process using cameras, sensors, processors, motors, actuators, chips, software, and precision mechanical parts. Then it has to do the same thing with its legs, feet, arms, torso, and head.

At mass-production scale.

Musk likes to talk about “the machine that makes the machine.” Inventing a great product is one problem, but figuring out how to manufacture millions of them quickly, reliably, and cheaply is another.

Tesla learned that lesson with electric vehicles. Now it’s going to have to learn it again with robots.

This is the part of the opportunity I believe most investors are missing.

Tesla can’t make every camera, sensor, chip, motor, rare-earth magnet, battery component, or piece of manufacturing equipment Optimus will require. Even a company as vertically integrated as Tesla has to buy specialized technology from outside suppliers.

If Musk wants to make millions of robots, those suppliers could suddenly find themselves selling into one of the fastest-growing manufacturing markets in the world.

We’ve seen this dynamic before.

Nvidia became the defining stock of the AI infrastructure boom because it sold the chips everybody needed to build AI. The robotics boom will create its own group of indispensable suppliers.

That’s why when I study Tesla, xAI, Space Exploration Technology Corp. (SPCX), and the rest of Musk’s empire, I’m always asking: What does Musk still have to buy… and who will be cashing those checks?

Following that question has been one of the best idea generators of my career.

Thirty-three recommendations I’ve made connected to Musk’s businesses went on to double or better at their highs, with a handful producing gains measured in the thousands of percent.

Of course, I’ve gotten plenty of calls wrong over the years, too. Every investor does.

But when Musk decides to build something at enormous scale, I’ve learned to pay very close attention to the companies supplying him.

And right now, Optimus is creating a whole new shopping list.

I’ve spent months mapping the pieces Musk controls, the pieces he still needs, and the companies I believe could benefit as his Physical AI ambitions move from prototypes and flashy demos toward mass production.

This week, I’m walked through that research at a special free InvestorPlace workshop. You can watch a replay of that event here.

My colleagues 抖阴最新版 and 抖阴最新版 joined me, and we worked through Musk’s empire layer by layer, identifying the technologies he controls and the outside companies we believe are best positioned to fill the gaps.

I also give you the name and ticker of one company from my research completely free.

More than 60 years ago, Unimate proved a robot could earn its keep doing one dirty, dangerous job at a GM factory.

The opportunity today is much larger.

We’re finally getting closer to robots that can work in environments built for people. And if Elon Musk succeeds in building them by the millions, he’ll need a supply chain capable of building millions of eyes, hands, joints, motors, sensors, and other components right along with them.

That’s the supply chain I want to own.

Click here to learn more in my free, special presentation.

Sincerely,

Luke Lango

Senior Investment Analyst, InvestorPlace

P.S. Luke makes some very good points here. The robots may grab the headlines, but the bigger investment opportunity could belong to the companies supplying the parts Elon Musk can’t – or simply won’t – make himself. That kind of second-order thinking is one reason Luke has been so successful at spotting emerging technology trends early. I strongly recommend watching his latest special broadcast.

The post Before Elon Musk Builds a Million Robots, Follow the Money appeared first on InvestorPlace.

The Federal Reserve will hold its interest rate steady at its September 15-16 meeting and for the rest of this year.

That was the conclusion of a Reuters poll released on Wednesday.

The story continued:

抖阴最新版 70% of economists, 65 of 93, in the September 4-9 Reuters poll expect ​the federal funds rate to remain in the 3.50%-3.75% range next week. That reading is down from 90% in August. The rest expect a quarter-percentage-point increase, which would be the ​first since July 2023.

Things changed quickly after a firmer-than-expected producer price index (PPI) report on Thursday and the consumer price index (CPI) report on Friday.

The August PPI report was driven by higher fuel and other commodity prices, which weighed on transportation and goods costs. Importantly, the survey of energy prices that fed into the latest PPI report ended on August 11 – before the recent increase in oil prices. This suggests that the influence of higher oil prices in August, and so far this month, was not reflected in this report.

On Friday, the headline CPI increased in line with market forecasts. But, core CPI, which excludes more volatile food and energy prices, rose 0.3%, hotter than the 0.2% that experts expected. In the eyes of many, that figure will have more influence on the Federal Reserve’s decision on whether to raise interest rates.

The CME Group’s FedWatch Tool on Friday morning reflected a 69% chance of a rate hike. After the CPI report, it jumped to nearly 90%.

So, is the Fed now fated to raise interest rates? Would a “hold” be a market surprise that would erode trust in the Fed even further? Could this be the start of a new rate-hiking cycle over the next several meetings?

And what will this mean for investor portfolios in the near term?

What Our Experts Think

After the Thursday PPI report, legendary investor 抖阴最新版 had already resigned to the idea of a rate hike. In a podcast to his Growth Investor subscribers, Louis noted that treasury yields spiked in the wake of the PPI report, but they also rose after the European Central Bank raised rates on Thursday.

It’s going to be hard for our Fed not to raise rates now because other central banks are raising rates, market rates are going higher. The bond vigilantes seem to be driving the bus. And the only thing that can probably stop the Fed from raising rates would be a phenomenal CPI report on Friday. It would have to be phenomenal, have to be well below expectations.

As we all saw, the CPI report was not “phenomenal.”

On Friday he shared another podcast with a little more detail.

So right now, it looks like the Fed has to increase rates because market rates went up. Now, Christopher Waller, one of the smartest people in the Fed, has been saying that inflation’s cooling, inflation’s cooling.

Even if Waller and Kevin Warsh didn’t want to raise rates, there will be other people on the FOMC who do. So, I’m going to be very curious what that vote is. And Warsh said he will not fight market rates. So, it looks like we’re going to have a Fed rate hike.

But that doesn’t mean we are starting a rate-hiking cycle, according to senior technology analyst Luke Lango, editor of Innovation Investor.

He characterized the CPI as “Goldilocks Hot.” It ran hot enough to ensure a rate hike next week, but not hot enough to confirm a new rate-hiking cycle.

Luke points out that underneath the hot headline…

There were enough disinflationary impulses to suggest that the hot August CPI report will be a one-off if (and this is the big part) the Iran War stabilizes over the next few weeks and months. For example, annualized core inflation on a three-month basis was just 2%, food inflation was basically flat, core goods inflation moderated to just +0.1%, computer prices fell, drug prices fell, and auto insurance costs fell.

Luke believes that if the situation in Iran stabilizes and oil prices retreat below $90 in the coming weeks/months, overall inflation could naturally cool from 3.4% today to 2% without much additional Fed tightening.

He also reminded his readers that Federal Reserve Chairman Kevin Warsh got the job with the all-but-written prerequisite that he cut rates.

Bottom line: It all hinges on the Iran situation.

Expert trader Jonathan Rose felt like the CPI headline number was already baked into the market, but the core number was running hotter than expected, which greenlights a hike. In his presentation on Masters in Trading Live, he noted that the increase was broad-based and not due to a single line item. Shelter, transport services, and used cars were all higher.

Jonathan told his viewers, “Don’t fear the Fed.” It does create a near-term headwind, but it historically recovers quickly, and the market is positive six months after a rate increase.

If you’d like to watch Jonathan’s take, click here and sign up for his free daily video!

Not a Unanimous Vote

Global macro investing expert 抖阴最新版, editor of Investment Report, sees it differently.

He thinks the odds of a rate hike are even. Here is Eric’s bottom line.

Despite the “near certainty” that the Fed will raise rates next week, I believe it’s a coin toss. Of course, all the traditional price pressures like rising oil prices suggest a rate hike would be a slam dunk. However, the non-traditional political pressure of a strong-willed president who doesn’t want higher interest rates might win the day.

No rate hike next week, especially not one week after Trump’s “Midterm Convention,” and just two months before the midterms.

So, where does that leave investors?

With a Federal Reserve that is truly boxed into a corner.

Raise rates, and policymakers risk tightening into an economy that may already be experiencing temporary, oil-driven inflation pressures. But if they hold rates here, they risk surprising a market that has rapidly come to expect a hike, raising new questions about whether political pressure is influencing monetary policy.

Either choice could create some short-term volatility.

But there’s an important distinction between a rate hike and a new rate-hiking cycle.

That’s the point I wouldn’t lose sight of next week.

Our experts may differ on some of the details, but none are arguing that investors should run for the exits. In fact, Luke believes inflation could cool considerably if oil prices retreat, while Jonathan reminds us that markets have historically recovered from the initial shock of higher rates.

As always, we will have to watch what comes after the announcement: oil prices, inflation data, Treasury yields, and, most importantly, whether the Fed signals that another hike is coming.

One rate hike may make headlines, but a new rate-hiking cycle could change the investment landscape.

We’ll keep you informed in the Digest.

Enjoy your weekend,

Luis Hernandez

Editor in Chief, InvestorPlace

The post The Fed’s No-Win Decision Next Week appeared first on InvestorPlace.

And this week copper did something it hasn’t done in a very long time: it printed a brand-new all-time high while the physical supply underneath it keeps getting thinner. As I’m writing this, three-month copper on the LME just tagged a record near $14,700 a ton (an intraday high around $14,694), capping its longest weekly winning streak since 1994. One of my favorite copper stocks is Freeport-McMoRan (FCX), and it’s up better than 7% on the day and roughly 44% on the year.

I’ve been pounding the table on the copper trade on Masters in Trading LIVE for weeks — and I put a real, defined-risk version of it (a specific FCX options play) in our free portfolio, live on the show, which I’ll show you the exact structure of below. Let me walk you through why first.

 AI Runs on Copper, and There Isn’t Enough of It

Here’s the part Wall Street keeps glossing over. An AI data center uses roughly 10 times the copper of a traditional data center. All that compute, all that power delivery, all that cabling — it runs on copper. And a new copper mine takes seven to ten years to build. Supply simply cannot catch up to this demand on any timeline that matters.

Now stack that AI demand on top of a supply chain that’s genuinely cracking:

• One year ago this week, a wet-material flood hit Freeport’s Grasberg mine in Indonesia — the world’s second-largest copper source — triggering a force majeure. Freeport has cut its 2026 output guidance at the complex by roughly a third, and a full recovery isn’t expected until 2027 or 2028.
• Chile, the world’s largest producer, just logged its weakest second quarter in at least 19 years and has cut its full-year forecast twice.
• Global mine output actually fell in the first half of the year. Morgan Stanley started 2026 expecting supply to grow and now sees it flat-to-down — which would be the first annual decline in mine supply since 2017.

As Evy Hambro, BlackRock’s Global Head of Thematic and Sector Investing, put it, existing copper operations are “tired, very, very old assets.” That’s the backdrop: warehouses draining, grades falling, and the biggest demand story of the decade just getting started.

The Copper Tariff Catalyst Nobody’s Fully Pricing In

This is where the edge lives. I don’t just want to be long a strong commodity — I want a known catalyst with a date on it. Copper has one.

Remember: a tariff is just a tax. Back in 2025, Washington slapped a 50% tariff on semi-finished copper products — pipes, wires, rods, sheets — plus copper-intensive derivatives like cables and connectors. But read the fine print: they didn’t tax raw input material or refined copper (cathode) itself. Not yet.

Here’s the timeline that matters:

• The Commerce Department’s deadline to recommend action on refined copper passed on June 30, and more than two months later the White House still hasn’t ruled.
• The recommendation on the table is a phased tax on refined copper — 15% in 2027 (possibly as soon as January), stepping up to 30% in 2028.
• Traders aren’t waiting for the ink to dry. Roughly 200,000 tons of refined copper flooded into the U.S. in July alone — the largest monthly inflow on record — pushing Comex inventories past 1 million tons as buyers race to beat that possible January duty.

When big money moves ahead of a known date, it tips its hand. That’s the footprint I follow. I learned that on the floor — I spent twenty-eight years as a market maker at the CBOE and a floor trader at the CME and CBOT, and all any trader is ever doing is positioning in front of the biggest players in the room. Copper is flashing that exact signal right now.

 The Edge: Only Two Copper Smelters Are Left in America

Here’s the nuance that separates the pros from the crowd. A tax on imported refined copper is a gift to the very few companies that turn raw material into finished copper on U.S. soil. And there are barely any left.

Industry testimony to Congress this year put it starkly: the U.S. ran 16 primary copper smelters in 1976. Today, just two are operational, with a third (Grupo México’s Asarco Hayden in Arizona) mothballed. That’s the entire domestic backbone for refining copper in the world’s largest economy — which is why the U.S. ships roughly a third of the copper it mines overseas to be processed, then buys it back as finished metal. Put a tariff on that finished metal, and you hand enormous pricing power to the two companies that still run a smelter here:

• Freeport-McMoRan (FCX) runs one of them (its Miami smelter in Arizona). It’s the largest U.S.-listed copper producer, it trades with deep, liquid options, and it’s my single favorite name in the space. The leverage is the story: by management’s own math, every 10-cent move in copper is worth about $390 million in annual EBITDA. It models ~$13 billion in EBITDA at $5 copper and ~$20 billion at $7 copper. Copper’s already trading north of $6.60 a pound. You do the math.
• Rio Tinto (RIO) owns the other (its Kennecott smelter in Utah). It’s a much bigger, more diversified major — so it’s a steadier, more indirect way to get exposure. Think of Rio as the lower-beta version of the same idea.

That’s the concentrated bet. Now let’s talk about the rest of the group — because not all copper stocks play this story the same way.

The Best Copper Stocks for AI (and the Ones to Approach With Caution)

Copper’s 2026 run has turned the big producers into what basically looks like one trade — the year-to-date returns are clustered in a tight band, which tells you this is a commodity move, not a company-execution move. Names to know:

• FCX (Freeport-McMoRan) — my top copper stock pick. Most direct U.S. smelter play, deepest options, biggest earnings leverage to the copper price.
• RIO (Rio Tinto) — the other U.S. smelter; bigger, steadier, more diversified.
• SCCO (Southern Copper) — a pure-play copper heavyweight, up roughly 45% on the year with monster EBITDA margins. Riding the same wave as FCX.
• TECK (Teck Resources) — also up ~45% YTD, part of that same tight cluster.

And then there’s the be-careful bucket. Plenty of copper names are just miners — they dig up raw copper and ship it overseas. A U.S. refined-copper tax doesn’t really touch them, and the foreign refiners can actually be hurt by it. That group includes names like Ero Copper (ERO), Hudbay (HBM), First Quantum, Ivanhoe, Capstone, plus the big overseas majors like Vale (VALE) and BHP. Great companies, plenty of them — but they don’t sit on the right side of this specific catalyst. Know what you own and why you own it.

What 抖阴最新版 Copper ETFs?

If you’d rather not pick a single name, the sector has clean ETF wrappers — just understand what each one actually gives you:

• COPX (Global X Copper Miners ETF) — a basket of copper miners worldwide. Great for broad exposure to the theme. The trade-off: it dilutes the U.S.-smelter edge, because it holds a lot of those overseas miners the tariff doesn’t help.
• CPER (United States Copper Index Fund) — tracks copper futures directly. This is your cleanest exposure to the metal itself rather than the equities. If your whole thesis is that the price of copper goes higher, this is the straightforward vehicle.
• COPJ (Sprott Junior Copper Miners ETF) — smaller, higher-beta junior miners. More torque, more risk.

The way I look at it: the copper ETFs are the easy button for just being in copper. But the edge — the concentrated, catalyst-driven bet — lives in the two American smelters, FCX and RIO. That’s the difference between owning the theme and owning the trade.

The Exact FCX Copper Trade I Shared Free on MiT Live

Here’s what makes our show different: I don’t just talk about copper — I put on a real trade in front of you, live, in our free portfolio — no paywall required to watch me do it. So let me show you exactly how I structured the FCX trade when I shared it on the show.

The setup was a vertical call spread in the January 2027 expiration:

• Buy the FCX 80 call
• Sell the FCX 105 call

At the time, that spread cost roughly $4.50 to put on — paying about $6 for the 80s and collecting about $1.50 for the 105s. And that number is your risk: in a defined-risk spread like this, the most you can lose is what you pay for it. So one spread risked about $450 — and you can never lose a dollar more than that, no matter what FCX does.

Now the reward. The spread is worth its full width — the 25 points between the 80 and 105 strikes, or $2,500 — if FCX finishes above 105. Subtract the $450 you paid, and the most you can make is about $2,050. That’s better than 4-to-1 — risk $450 to make $2,050 — with about 134 days for the thesis to play out. And it scales cleanly: a 10-lot risks about $4,500 to make about $20,500. Same ratio, just add a zero.

A few things I say every single day on the show:

• This is one trade, not two. Two legs, one position — a vertical call spread. Don’t manage it as a separate long option and short option.
• Options are just a derivative of the stock. Think of it as long from 80 and short from 105. If FCX is above your strike at expiration, you’re long from there; if it’s below, you’re not. That’s it — the internet loves to overcomplicate this.
• New to options? Paper-trade it. Write the trade down, follow FCX, and let your confidence build before you risk a dime.

Options prices move, so the fills above reflect the session when I shared it — and with copper and FCX both pushing to fresh highs since, the picture keeps evolving. But the structure is the lesson: a known catalyst, a strictly defined risk, and a reward that’s several times what you put up.

The Bottom Line on Copper Stocks

AI can’t run without copper. The world is running short of it. And Washington is sitting on a decision that could put a tax on it as soon as January. That’s a supply squeeze, a demand supercycle, and a known catalyst all stacked in one place — and the cleanest way to play it is the two companies that refine copper on American soil, starting with FCX.

P.S. If you’re serious about understanding the environment we’re entering, The Masters in Trading Options Challenge is where you need to be.

The Challenge is where we take everything you’ve learned in my daily LIVEs — fixed risk, thesis-driven exits, laddered entries, defined-duration trades, along with access to objective tools like the Advanced Notice Unusual Options Scanner and my Expected Move Tool — and put it into practice in a structured, step-by-step environment.

That’s the power of real education. And that’s what we do every day inside the Masters in Trading Options Challenge.

If you’re ready to learn the right way — with zero pressure, fixed risk, and a community that supports you — I’d love to see you inside the Challenge.

You’ve got nothing to prove. You’ve just got to be willing to learn.

And once you see how simple it can be, you’ll never look at options the same way again.

Remember, the creative trader wins,

Jonathan Rose,

Founder, Masters in Trading

The post Time to Wake Up. Copper Stocks Are the AI Trade Everyone’s Sleeping On. appeared first on InvestorPlace.

Maybe they should.

I can assure you, long-haul truckers pay attention to it. Farmers do, too.

So do folks who work in construction.

That’s because diesel fuels the trucks that haul goods across the country. It powers farm equipment and the heavy machinery used on construction sites.

And right now, diesel prices are making history.

This week, the national average hit $6 a gallon for the first time ever, according to GasBuddy. That’s up about $2.30 from roughly $3.70 a gallon one year ago.

Now, you might be thinking, “I don’t drive a diesel. Why should I care?”

Because those higher fuel costs don’t stop with truckers, farmers and construction crews.

They can ripple through the entire economy.

When it costs more to ship food to your grocery store, harvest crops, move raw materials or deliver products to your doorstep, somebody has to absorb that extra expense.

The margins in a lot of these businesses are already paper-thin. And they can’t just eat that added cost forever.

Eventually, some of it can wind up in the prices you and I pay.

That’s why this week’s inflation reports were so important. In today’s Market 360, I’ll break down what they revealed about inflation and explain why the pressure is building on the Federal Reserve.

I’ll also explain what this means for investors and show you why specific stocks can still thrive, even as stubborn inflation and higher interest rates rattle the broader market.

Wholesale Inflation Heats Up

On Wednesday, the Producer Price Index (PPI) rose 0.4% in August, in line with economists’ estimates and up from July’s revised 0.1% gain.

There was some good news in the report. Core PPI, which excludes volatile food and energy prices, rose just 0.2%. That was slightly better than expected, but wholesale goods prices jumped 1.1% in August.

Energy was the primary culprit. Wholesale energy prices surged 4.2%, while food prices were relatively contained.

Diesel did a lot of the damage. The Bureau of Labor Statistics said diesel fuel prices surged 24.1% in August alone, driving nearly two-thirds of the increase in wholesale goods prices.

And here’s the troubling part: Diesel prices have climbed even further since the PPI survey period ended.

The national average has now reached a record $6 a gallon. So, if those higher fuel costs persist, we could see even more energy-related inflation show up in the September data.

Consumer Inflation Stays Sticky

Then, on Friday, we got the Consumer Price Index (CPI).

Headline CPI rose 0.4% in August and was up 3.4% over the past 12 months, both in line with economists’ expectations.

Once again, energy was a major driver. Gasoline prices jumped 3.9% in August, accounting for one-third of the entire monthly increase in consumer prices. Gasoline prices are now up 27.4% over the past year.

More concerning for the Fed, core prices rose 0.3% in August. That’s hotter than the 0.2% increase economists expected and an acceleration from July’s 0.2% gain.

Remember, core CPI strips out volatile food and energy prices. So, the hotter reading suggests inflationary pressures may be broadening beyond energy.

Shelter costs were part of that problem, too. They rose 0.3% in August, up from just 0.1% in July.

That matters because housing costs are a major component of core inflation. And when shelter inflation is accelerating at the same time energy prices are surging, it makes the Fed’s job much harder.

That’s also why the diesel story matters beyond the pump. You don’t need to drive a diesel truck to feel the effects of record-high fuel prices. Higher energy costs can work their way through the economy and eventually show up in the prices you and I pay.

And with crude oil now back above $100 a barrel, those pressures may not ease anytime soon.

Pressure Builds on the Fed

The inflation reports quickly spilled over into the bond market. The 10-year Treasury yield climbed above 4.9% for the first time in three years. The European Central Bank also raised interest rates this week, adding even more upward pressure on global rates.

As I have been saying for a while now, the bond vigilantes seem to be driving the bus.

That leaves the Federal Reserve in a difficult position heading into next week’s Federal Open Market Committee (FOMC) meeting.

Fed Chair Kevin Warsh has already said inflation remains too high. And just last week, Fed Governor Christopher Waller said he could support holding rates steady if inflation continued to cool and core CPI rose just 0.2% in August.

Well, we didn’t get that.

By the same token, the labor market has been pretty resilient, with the U.S. adding 162,000 jobs in August.

Wall Street has taken notice. According to CME Group’s FedWatch tool, traders are now pricing in about an 85% chance that the Fed will raise rates by 25 basis points next week.

Source: CME FedWatch

Clearly, that is not what Wall Street wants to hear.

Stocks came under pressure this week as Treasury yields moved higher and investors started preparing for another potential rate hike. And with September already a seasonally weak month for stocks, I would not be surprised if we see some more bumps along the way.

But I don’t want you to lose sight of something important, folks.

Follow the Money, Not the Fear

There is nothing wrong with corporate earnings – they’re still phenomenal. Our friends at FactSet estimate a year-over-year earnings growth rate of 28.5% for the S&P 500 for the third quarter.

And despite all the hand-wringing over inflation, interest rates and energy prices, institutional investors are still putting money to work.

The key is knowing where that money is going.

When volatility picks up, a lot of investors react to the same headlines and pile into or out of the same obvious stocks. But the big institutional investors – the “elephants” of the market – tend to move differently.

They build positions quietly, often well before a stock becomes popular with the public.

For nearly five decades, I’ve used quantitative analysis to identify those kinds of shifts. My proprietary P.I. system analyzes more than 6,000 stocks for signs that institutional money is moving in or out.

And that is exactly what I’m doing right now.

My P.I. system is currently flagging stocks where the “elephants” appear to be quietly building positions before the crowd catches on. These are the kinds of setups that can produce some of the market’s biggest moves once that institutional buying starts showing up in the share price.

In fact, this same system has helped me identify hundreds of stocks that went on to double, and dozens that climbed more than 1,000%.

And right now, I believe a fresh batch of these opportunities is beginning to emerge.

That’s why I recently recorded a special presentation revealing how P.I. works, what it is seeing today and where I believe the next big opportunities may be taking shape.

Click here to watch it now.

Sincerely,

抖阴最新版

Editor, Market 360

The post What $6 Diesel and This Week’s Inflation Reports Mean for the Fed appeared first on InvestorPlace.

Across the Channel, the European Union is preparing to spend €15.7 billion so it can rely on that network less.

Britain is buying access today; the EU is buying insurance for tomorrow.

Put those two decisions together, and a Starlink paradox comes into view. 

Musk’s network has become so useful that governments are willing to pay for it now. It has also become so strategically important that those same governments are willing to spend billions building alternatives.

Musk gets the customer.

His lead creates the competitor.

The supply chain wins either way. 

Why Britain Is Deepening Its Reliance on Starlink

Britain started using Starlink back in 2022. Since then, it has moved live military communications onto Starshield – becoming the first country outside the U.S. to do so publicly. Its Ministry of Defence now operates roughly 1,000 Starshield terminals and another 500 Starlink terminals. 

The two services run across the same satellite network. Starshield adds military-specific contracts, stronger encryption, higher network priority, expanded coverage, and dedicated ground gateways.

Those upgrades cost more.

Britain pays up because the service is available now.

The country already operates its own Skynet military satellite network. It also owns a stake in European satellite operator Eutelsat. Yet its defense ministry still bought more than 1,000 Starshield terminals – because governments can’t always wait for the perfect homegrown solution. Command centers need reliable communications at all times. When a proven network is already in orbit, it can easily become the default.

Not to mention, Britain is building procedures around Starshield. Personnel are learning the equipment. Military units are integrating it into operations. 

Those habits create a deeper relationship than a one-time hardware purchase.

The longer the sovereign alternatives take, the more embedded SpaceX can become.

IRIS²’s Multiyear Deployment Gap Is Starlink’s Moat

Meanwhile, Europe is now moving IRIS² – its planned 348-satellite sovereign communications network – from policy into deployment. The system will include 330 satellites in low Earth orbit and 18 in medium Earth orbit, with the first launches expected in 2029 and service rolling out between 2030 and 2032. 

This fleet will be far smaller than Starlink’s 10,000 active satellites. But what it lacks in size, it makes up for in control: a network European governments can use on their own terms in a crisis. 

The bigger issue is timing.

Every delay gives Starlink more time to improve coverage, lower hardware costs, sign government contracts, and deepen its lead. Europe is building against a moving target.

By the time IRIS² enters service, SpaceX will have launched more satellites, expanded direct-to-phone service, upgraded its terminals, and added more military customers.

That does not make Europe’s project pointless.

It makes it expensive.

Catching a platform with a multiyear head start usually is.

Starlink’s Dominance Is Creating a Second Satellite Spending Cycle

Starlink’s success is now pushing money in two directions.

The first stream flows directly to SpaceX.

Britain has spent nearly $40 million on Starlink and Starshield, including about $17.6 million on Starshield terminals and airtime. In the United States, SpaceX says multiyear government awards for Starshield now exceed $6 billion, largely through two major Space Force programs. 

The second stream flows into alternatives.

Ukraine turned Europe’s dependence into a battlefield reality. Starlink became central to military communications, and no European network was ready to replace it. Leaders now want secure satellite capacity they can control themselves.  

That desire has survived political fights, cost increases, and years of delay.

IRIS² was estimated to cost €10.6 billion in late 2024. The projected bill has since risen to €15.7 billion, with public money expected to cover nearly two-thirds. Even so, 22 European countries recently pledged to keep accelerating the project.

That is a remarkable response to one private company’s lead.

Usually, competition is supposed to divide an existing market.

Starlink is helping create another one.

Once satellite communications become national infrastructure, building some duplicate capacity starts to look less wasteful. Governments will pay for resilience, control, and guaranteed access even when a cheaper commercial service already exists.

Europe does not need IRIS² to win every broadband customer.

It needs the ability to stay connected if access to a foreign network ever becomes uncertain.

That political goal can support spending even when the commercial returns alone look less compelling.

And now that spending is moving from government plans into actual orders.

IRIS² Is Turning Billions in Policy Into Satellite Orders

On Sept. 10, Belgian satellite manufacturer Aerospacelab announced a €2.4 billion contract to build 264 of the 348 satellites planned for IRIS².

That gives one company responsibility for roughly 76% of the constellation and represents the largest disclosed manufacturing allocation in the program so far.

The rest of the work is spreading across Europe’s space industry.

Airbus is expected to assemble 66 satellites dedicated to sensitive government communications. Thales Alenia Space will provide payloads. Germany’s OHB will supply the medium-Earth-orbit satellites. Eutelsat, SES, and Hispasat will help operate the network through the SpaceRISE consortium.

This is where the Starlink paradox becomes investable.

These satellites need communications payloads, antennas, solar arrays, batteries, radiation-tolerant electronics, optical links, cybersecurity, ground stations, testing equipment, and launch capacity.

Then, of course, they’ll need maintenance, upgrades, and replacements long beyond the first deployment.

Europe’s Starlink response is creating a second supply chain.

Europe’s Next Starlink Fight Is Direct-to-Device Satellite Service

These ambitions extend beyond just military and government communications.

At the recent Paris space summit, French President Emmanuel Macron called on Europe’s telecom operators, satellite companies, and manufacturers to form a direct-to-device alliance. The goal is to launch a European service by 2030.

Direct-to-device technology allows an ordinary smartphone to connect with a satellite when no cell tower is available – and expands the competition into the consumer market.

Starlink already has roughly 640 satellites dedicated to the technology and claims more than 10 million users across its broader network. Europe’s largest telecom companies – including Orange, Deutsche Telekom, Vodafone, and Telefónica – have reportedly discussed forming a consortium to bid for spectrum and build a regional alternative.

The same pattern is repeating:

The direct-to-device race will require another wave of satellites, spectrum, antennas, radio-frequency chips, ground equipment, and carrier integrations. It also brings terrestrial telecom companies into a market that once belonged mostly to rocket and satellite specialists.

Musk’s lead is pulling more industries into orbit.

How the Satellite Supply Chain Can Win on Both Sides of Starlink

Starlink and IRIS² are headed toward different missions.

Starlink is already serving consumers, businesses, and governments. IRIS² is Europe’s attempt to build secure communications capacity it can control when commercial networks are no longer enough.

The constellations will not rely on identical suppliers. But both require the same broad industrial base: satellites, secure payloads, radiation-hardened electronics, power systems, ground infrastructure, software, launch services, and replacement hardware as the networks expand and age.

That is where I start looking whenever Musk commits to a project at enormous scale.

What will he need to buy – and which companies will benefit from supplying it?

The answer has generated some of the most compelling ideas of my career.

Thirty-three recommendations I’ve made connected to Musk’s businesses went on to double or better at their highs. A handful produced gains measured in the thousands of percent.

Of course, I’ve gotten plenty of calls wrong, too. Every investor does.

But Musk’s biggest projects have repeatedly pointed us toward suppliers before the full demand story reached Wall Street. 

Starlink makes that dynamic even more interesting.

SpaceX earns revenue when governments adopt Starshield. Europe’s effort to build a sovereign alternative creates a second wave of demand across the satellite industry. Some companies may sell directly to SpaceX. Others may supply the networks designed to reduce Europe’s dependence on it. A few may end up selling to both sides. 

I have spent months tracing those dependencies across SpaceX, Tesla, SpaceXAI, and the rest of Musk’s empire.

I put that research on screen in my latest presentation, alongside 抖阴最新版 and 抖阴最新版. We trace Musk’s spending across his empire, isolate the capabilities he still has to buy, and show which outside companies we believe could benefit. 

Starlink is winning contracts today and forcing a second buildout for tomorrow. 

Discover which companies could profit from the spending on both sides right here.

The post Starlink Is So Dominant, Europe Is Paying Musk and Funding His Rivals appeared first on InvestorPlace.

In today’s Friday Digest takeover, our technology expert Luke Lango explains why a recent $3 billion funding round for Musk’s Boring Company offers a revealing glimpse into that problem. Beyond writing checks, some investors reportedly were also asked to help recruit employees and open doors with government officials – resources that even Musk can’t simply manufacture overnight.

Luke believes that same dynamic extends across Tesla (TSLA), SpaceX (SPCX), xAI, and the rest of Musk’s businesses. Each ambitious new project requires specialized suppliers, infrastructure, expertise, and other capabilities that Musk must obtain somewhere. And for a relatively small supplier, even a tiny piece of Musk’s spending could be transformative.

That’s the opportunity Luke detailed in his Vertical AI Event on Wednesday, where he followed the money to the companies he believes could benefit from Musk’s ambitions. You can watch the event right here – and get the name and ticker of one company completely free.

Enough introduction from me. Here’s Luke with our Friday Digest takeover.

Have a good evening,

Jeff Remsburg

When John Frank Stevens came to Panama to dig a canal in 1905, he found the area ravaged by yellow fever.

The previous chief engineer had just resigned, and the workers were in desperate need of housing, food, and the strength to finish the job. Those who had enough gathered along the waterfront, waiting for passage home. Despite this, the full weight of the U.S. government and all its money stood behind the Panama Canal project.

But not even President Theodore Roosevelt could persuade the scores of sick, injured, and tired to continue digging. So, Stevens did something unexpected. He suspended the bulk of the excavation.

Instead of digging harder, Stevens started fixing everything that made digging nearly impossible.

He improved housing and food supplies. He backed the sanitation campaign led by U.S. Army physician William Gorgas to bring yellow fever and malaria under control. And he rebuilt the railroad needed to bring supplies in and haul millions of tons of excavated dirt out.

In other words, before the Panama Canal was dug, Stevens had to make sure it was a place where both people and machinery could work.

I thought about that when I heard Elon Musk had just raised $3 billion for The Boring Company – his effort to build networks of underground tunnels that can move cars beneath congested cities.

The new funding is meant to help Boring expand its engineering, production, and operations teams and push ahead with projects from Las Vegas and Nashville to Dubai and the broader United Arab Emirates.

But Musk apparently wanted something else from some of the people writing those checks.

According to The Wall Street Journal, the company told certain investors they would also need to help recruit employees or assist with business development. That could mean introducing The Boring Company to government officials in places where it wants to dig new tunnels.

The money will help expand the company’s engineering, production, and operations teams, support projects in Las Vegas, Nashville, and Dubai, and fund more than 150 kilometers of planned underground infrastructure across the United Arab Emirates.

That’s what caught my attention.

This comparison between Roosevelt’s canal and Musk’s tunnels has its limits, of course. But financing a project and assembling everything that’s required to build it are two different achievements. That’s something Stevens would have been familiar with.

At first glance, this looks like another story about investors lining up to hand Musk billions.

Look closer, and it’s a story about what money alone can’t buy him.

Across Musk’s empire, the gaps are filled by engineers, specialized factories, component suppliers, and infrastructure that could take years of development. For investors, it’s exactly those dependencies that I want you to pay attention to.

Because every time Musk runs into something he can’t build fast enough, cheaply enough, or on his own, somebody else gets an opportunity to sell it to him.

And some of those companies could be tiny compared with Musk’s empire. A big order from Tesla Inc. (TSLA), Space Exploration Technologies Inc. (SPCX), or The Boring Company might barely register on Musk’s spending – while transforming the supplier getting the check.

That’s the opportunity I want to show you today: Follow what Musk still needs, find the companies that can provide it, and you may find some of the biggest winners of his next expansion before Wall Street does.

Why The Boring Company Asked Investors for More Than Money

Now, look at who participated in this Boring Company funding round.

Among others, Sequoia Capital, Andreessen Horowitz, Temasek, Baron Capital, and UAE-backed investors.

These are some of the biggest, best-connected investors in the world. They have plenty of money. But they also know people. They know engineers, business leaders, and government officials. And I think that’s a big part of what Musk is trying to bring into the company here.

You can build a really good tunneling machine but still run into red tape getting permission to put it underneath a city. You need engineers, local partners, and government approvals. And those can be harder to come by than another billion dollars.

So when I look at this round, I see Musk both raising money and recruiting a network that could help him put that money to work.

The UAE-linked investors are a good example. The Boring Company plans more than 150 kilometers of underground infrastructure there, including its Dubai Loop – a planned network of underground tunnels and stations designed to move passengers around the city in Tesla vehicles.

Bringing in well-connected local investors could help Boring recruit people, find partners, and navigate the dozens of approvals required before the first tunnel gets dug.

To me, that explains why Musk wants more from these investors than a check. He needs help turning a funded project into a working tunnel.

Dubai’s 48 Permits Show Why Capital Is Not Enough

Look at what needs to happen in Dubai.

The first phase of the Loop is expected to cover about 6.4 kilometers and include four passenger stations connected by underground tunnels, with Tesla vehicles carrying riders between them. Before digging can begin, The Boring Company says it needs to seek roughly 48 permits and no-objection certificates from around 10 different entities.

That’s just the first phase. And it’s in an extremely business-friendly jurisdiction. Imagine what Musk would need to dig in Chicago or Paris.

Now, Musk can build a faster tunneling machine. But how fast that machine digs doesn’t matter much if you’re still waiting for permission to put it in the ground.

Utility lines have to be mapped. Roads and buildings above the route have to be accounted for. Safety requirements have to be met. And all of it requires people who understand how to get a complicated infrastructure project approved and built in that particular market.

You can’t create that kind of expertise overnight.

And that’s the larger point. Across Musk’s empire, he keeps running into things that money alone can’t produce quickly – capabilities that other people and companies have spent years building.

For investors, that’s where this story gets much bigger than The Boring Company.

Across Musk’s Empire, Capital Is Only the Starting Point

Take SpaceX. Musk can build a more powerful rocket, but to launch it more often, he needs manufacturing capacity, specialty materials, advanced electronics, and trained workers. He also needs permission to launch.

You see the same problem at xAI. Musk can spend billions building enormous data centers packed with AI chips, but those chips need electricity, cooling systems, and high-speed networking to work. If you’re waiting on a grid connection, buying another thousand chips doesn’t solve the problem.

Then look at Tesla Inc. (TSLA). Musk wants to mass-produce robotaxis and humanoid robots. That means taking technology that works in development and turning it into something you can manufacture, deploy, and service at enormous scale.

For robotaxis, you need regulatory approvals, charging infrastructure, and people who can keep the fleet operating. For humanoids, you need sensors, semiconductors, precision components, and manufacturing partners that can deliver them reliably.

And remember, the suppliers have to scale right alongside you. If you want to build a million robots, you need a supply chain capable of supporting a million robots.

That’s where this gets really interesting for investors.

Musk can raise billions almost overnight. But he can’t build a new power plant, qualify a new factory, train thousands of workers, or create years of manufacturing expertise overnight.

Other companies already have some of those capabilities. And as Musk’s ambitions get bigger, I want to know which ones can deliver what he needs, when he needs it.

Because Musk’s next expansion could become their next major order.

Follow Musk’s Spending to the Companies Getting Paid

When I study Musk’s empire, I keep coming back to one question: What does he need that somebody else is better positioned to deliver?

It could be a utility with power available where he wants to build. A manufacturer with capacity ready to go. A supplier whose components have already passed years of testing.

The next question is: How much could that business grow if Musk starts buying more?

Because an order that represents a small part of his spending could make a meaningful difference to a smaller supplier’s revenue. That’s the opportunity I’ve spent months researching across his AI, robotics, and space businesses.

And I reveal a lot of that research during my Vertical AI Event.

In that workshop, I map Musk’s empire on screen and trace his spending into the outside companies he still depends on. My colleagues 抖阴最新版 and 抖阴最新版 join me to examine the opportunity, and you’ll get the name and ticker of one stock poised to benefit completely free.

You can watch it now, but access is available for a limited time.

You’ll see where we believe Elon Musk’s most important supply constraints are developing, which companies could help resolve them, and why I’ve circled September 24 as a potential catalyst for Musk’s next move.

More than a century ago, the Panama Canal couldn’t be built with money and ambition alone. John Frank Stevens needed the people, equipment, transportation, and infrastructure that could turn President Roosevelt’s enormous project into something that actually worked.

Musk faces his own version of that problem today. He has the money. He has the ambition. What he doesn’t have is everything required to build it all himself.

That’s where I see the opportunity. I want to show you the companies that could get paid to supply what Musk still needs.

Watch the Vertical AI Event now and get your free stock pick before access closes.

Sincerely,

Luke Lango

Senior Investment Analyst, InvestorPlace

P.S. If you take one thing from Luke’s essay, make it this: Elon Musk can raise billions, but he still can’t build everything himself. That gap is where Luke believes some of the most interesting opportunities may be hiding. In his Vertical AI Event, Luke, Louis/I, and Eric/I follow Musk’s spending into the companies that could get paid to fill those gaps. Watch it here – and get one stock name and ticker completely free.

The post Elon Musk Raised $3 Billion – Here’s Who Could Profit Next appeared first on InvestorPlace.

More than a century ago, the builders of the Panama Canal learned the same lesson. Before they could finish one of the world’s greatest engineering projects, they first needed the people, equipment and infrastructure to make it possible. And Luke believes Musk is running into a similar challenge today as he pushes ahead with everything from AI and robotics to rockets and underground tunnels.

That’s why Luke has spent months tracking the outside companies Musk still needs to turn those ambitions into reality. I recently joined Luke and 抖阴最新版 to walk through that research – including one stock name and ticker you can get completely free. Click here to watch the event and get the full story.

In the meantime, I’ll turn things over to Luke to explain why Musk’s latest $3 billion funding round offers another important clue…

*

When John Frank Stevens came to Panama to dig a canal in 1905, he found the area ravaged by yellow fever.

The previous chief engineer had just resigned, and the workers were in desperate need of housing, food, and the strength to finish the job. Those who had enough gathered along the waterfront, waiting for passage home. Despite this, the full weight of the U.S. government and all its money stood behind the Panama Canal project.

But not even President Theodore Roosevelt could persuade the scores of sick, injured, and tired to continue digging. So, Stevens did something unexpected. He suspended the bulk of the excavation.

Instead of digging harder, Stevens started fixing everything that made digging nearly impossible.

He improved housing and food supplies. He backed the sanitation campaign led by U.S. Army physician William Gorgas to bring yellow fever and malaria under control. And he rebuilt the railroad needed to bring supplies in and haul millions of tons of excavated dirt out.

In other words, before the Panama Canal was dug, Stevens had to make sure it was a place where both people and machinery could work.

I thought about that when I heard Elon Musk had just raised $3 billion for The Boring Company – his effort to build networks of underground tunnels that can move cars beneath congested cities.

The new funding is meant to help Boring expand its engineering, production, and operations teams and push ahead with projects from Las Vegas and Nashville to Dubai and the broader United Arab Emirates.

But Musk apparently wanted something else from some of the people writing those checks.

According to The Wall Street Journal, the company told certain investors they would also need to help recruit employees or assist with business development. That could mean introducing The Boring Company to government officials in places where it wants to dig new tunnels.

The money will help expand the company’s engineering, production, and operations teams, support projects in Las Vegas, Nashville, and Dubai, and fund more than 150 kilometers of planned underground infrastructure across the United Arab Emirates.

That’s what caught my attention.

This comparison between Roosevelt’s canal and Musk’s tunnels has its limits, of course. But financing a project and assembling everything that’s required to build it are two different achievements. That’s something Stevens would have been familiar with.

At first glance, this looks like another story about investors lining up to hand Musk billions.

Look closer, and it’s a story about what money alone can’t buy him.

Across Musk’s empire, the gaps are filled by engineers, specialized factories, component suppliers, and infrastructure that could take years of development. For investors, it’s exactly those dependencies that I want you to pay attention to.

Because every time Musk runs into something he can’t build fast enough, cheaply enough, or on his own, somebody else gets an opportunity to sell it to him.

And some of those companies could be tiny compared with Musk’s empire. A big order from Tesla Inc. (TSLA), Space Exploration Technologies Inc. (SPCX), or The Boring Company might barely register on Musk’s spending – while transforming the supplier getting the check.

That’s the opportunity I want to show you today: Follow what Musk still needs, find the companies that can provide it, and you may find some of the biggest winners of his next expansion before Wall Street does.

Why The Boring Company Asked Investors for More Than Money

Now, look at who participated in this Boring Company funding round.

Among others, Sequoia Capital, Andreessen Horowitz, Temasek, Baron Capital, and UAE-backed investors.

These are some of the biggest, best-connected investors in the world. They have plenty of money. But they also know people. They know engineers, business leaders, and government officials. And I think that’s a big part of what Musk is trying to bring into the company here.

You can build a really good tunneling machine but still run into red tape getting permission to put it underneath a city. You need engineers, local partners, and government approvals. And those can be harder to come by than another billion dollars.

So when I look at this round, I see Musk both raising money and recruiting a network that could help him put that money to work.

The UAE-linked investors are a good example. The Boring Company plans more than 150 kilometers of underground infrastructure there, including its Dubai Loop – a planned network of underground tunnels and stations designed to move passengers around the city in Tesla vehicles.

Bringing in well-connected local investors could help Boring recruit people, find partners, and navigate the dozens of approvals required before the first tunnel gets dug.

To me, that explains why Musk wants more from these investors than a check. He needs help turning a funded project into a working tunnel.

Dubai’s 48 Permits Show Why Capital Is Not Enough

Look at what needs to happen in Dubai.

The first phase of the Loop is expected to cover about 6.4 kilometers and include four passenger stations connected by underground tunnels, with Tesla vehicles carrying riders between them. Before digging can begin, The Boring Company says it needs to seek roughly 48 permits and no-objection certificates from around 10 different entities.

That’s just the first phase. And it’s in an extremely business-friendly jurisdiction. Imagine what Musk would need to dig in Chicago or Paris.

Now, Musk can build a faster tunneling machine. But how fast that machine digs doesn’t matter much if you’re still waiting for permission to put it in the ground.

Utility lines have to be mapped. Roads and buildings above the route have to be accounted for. Safety requirements have to be met. And all of it requires people who understand how to get a complicated infrastructure project approved and built in that particular market.

You can’t create that kind of expertise overnight.

And that’s the larger point. Across Musk’s empire, he keeps running into things that money alone can’t produce quickly – capabilities that other people and companies have spent years building.

For investors, that’s where this story gets much bigger than The Boring Company.

Across Musk’s Empire, Capital Is Only the Starting Point

Take SpaceX. Musk can build a more powerful rocket, but to launch it more often, he needs manufacturing capacity, specialty materials, advanced electronics, and trained workers. He also needs permission to launch.

You see the same problem at xAI. Musk can spend billions building enormous data centers packed with AI chips, but those chips need electricity, cooling systems, and high-speed networking to work. If you’re waiting on a grid connection, buying another thousand chips doesn’t solve the problem.

Then look at Tesla Inc. (TSLA). Musk wants to mass-produce robotaxis and humanoid robots. That means taking technology that works in development and turning it into something you can manufacture, deploy, and service at enormous scale.

For robotaxis, you need regulatory approvals, charging infrastructure, and people who can keep the fleet operating. For humanoids, you need sensors, semiconductors, precision components, and manufacturing partners that can deliver them reliably.

And remember, the suppliers have to scale right alongside you. If you want to build a million robots, you need a supply chain capable of supporting a million robots.

That’s where this gets really interesting for investors.

Musk can raise billions almost overnight. But he can’t build a new power plant, qualify a new factory, train thousands of workers, or create years of manufacturing expertise overnight.

Other companies already have some of those capabilities. And as Musk’s ambitions get bigger, I want to know which ones can deliver what he needs, when he needs it.

Because Musk’s next expansion could become their next major order.

Follow Musk’s Spending to the Companies Getting Paid

When I study Musk’s empire, I keep coming back to one question: What does he need that somebody else is better positioned to deliver?

It could be a utility with power available where he wants to build. A manufacturer with capacity ready to go. A supplier whose components have already passed years of testing.

The next question is: How much could that business grow if Musk starts buying more?

Because an order that represents a small part of his spending could make a meaningful difference to a smaller supplier’s revenue. That’s the opportunity I’ve spent months researching across his AI, robotics, and space businesses.

And I reveal a lot of that research during my Vertical AI Event.

In that workshop, I map Musk’s empire on screen and trace his spending into the outside companies he still depends on. My colleagues 抖阴最新版 and 抖阴最新版 join me to examine the opportunity, and you’ll get the name and ticker of one stock poised to benefit completely free.

You can watch it now, but access is available for a limited time.

You’ll see where we believe Elon Musk’s most important supply constraints are developing, which companies could help resolve them, and why I’ve circled September 24 as a potential catalyst for Musk’s next move.

More than a century ago, the Panama Canal couldn’t be built with money and ambition alone. John Frank Stevens needed the people, equipment, transportation, and infrastructure that could turn President Roosevelt’s enormous project into something that actually worked.

Musk faces his own version of that problem today. He has the money. He has the ambition. What he doesn’t have is everything required to build it all himself.

That’s where I see the opportunity. I want to show you the companies that could get paid to supply what Musk still needs.

Watch the Vertical AI Event now and get your free stock pick before access closes.

Sincerely,

Luke Lango

Senior Investment Analyst, InvestorPlace

The post What the Panama Canal Can Teach Us 抖阴最新版 Investing With Elon Musk appeared first on InvestorPlace.

According to The Wall Street Journal, the tunneling company told certain investors they would also need to help recruit employees or assist with business development. That could mean introducing The Boring Company to government officials in places where it wants to build new tunnels.

In some cases, failing to produce viable job candidates could give the company the right to repurchase part of an investor’s stake.

Elon Musk responded to the report with just one word: “True.”

Then, on Sept. 9, The Boring Company announced a $3 billion Series D funding round that valued the business at $23 billion. That is roughly four times its $5.7 billion valuation from 2022.

The money will help expand the company’s engineering, production, and operations teams, support projects in Las Vegas, Nashville, and Dubai, and fund more than 150 kilometers of planned underground infrastructure across the UAE.

At first glance, this looks like another story about investors lining up to hand Musk billions.

Look closer, and it’s a story about what money alone can’t buy him.

The Boring Company is borrowing its investors’ networks to recruit engineers and open doors with local officials. Across Musk’s other businesses, outside suppliers provide the industrial equivalent: factories, components, power, and expertise he cannot create on demand.

Those outside networks are where Musk’s spending becomes other companies’ revenue.

Why The Boring Company Asked Investors for More Than Money

The Boring Company’s latest round included some of the largest and best-connected investors in the world.

Sequoia Capital. Andreessen Horowitz. Temasek. Baron Capital. UAE-backed entities.

These firms have plenty of money – and The Boring Company appears to want something more. 

It wants hiring pipelines, local connections, and introductions to the people who control where projects can be built.

That makes this round look almost like a business-development network wrapped around a financing deal.

And the UAE’s involvement makes the strategy especially clear.

UAE-linked investors supplied the lead capital. The country is also where The Boring Company plans to deploy more than 150 kilometers of underground infrastructure, building on the Dubai Loop project already under contract.

The investors, customers, local institutions, and future construction sites are beginning to overlap.

That is useful when your product requires permission to tunnel underneath a major city.

Every new Loop requires a fresh set of engineering studies, utility maps, safety reviews, construction plans, local partners, and government approvals.

Dubai’s 48 Permits Show Why Capital Is Not Enough

The first phase of the Dubai Loop is expected to include roughly 6.4 kilometers of tunnel and four stations. Before digging can begin, The Boring Company says it must seek approximately 48 permits and no-objection certificates from around 10 different entities.

Musk may be able to design a faster tunneling machine – but he cannot make 10 separate agencies disappear.

The company still has to map underground utilities. It has to account for existing buildings and roads. It has to satisfy environmental, transportation, and public-safety standards and coordinate with officials who may never have approved a system like this before.

An introduction to the right government official may be worth more than another check. One experienced tunneling engineer may do more to keep a project on schedule than another glossy investor presentation.

The Boring Company is borrowing a human network because relationships, local knowledge, and specialized talent take years to build.

The same pattern extends beyond tunneling. 

Across Musk’s Empire, Capital Is Only the Starting Point

SpaceX (SPCX) can build more powerful rockets. It still needs launch approvals, manufacturing capacity, specialty materials, advanced electronics, and thousands of highly trained workers.

SpaceXAI can build enormous computing clusters. Those systems still need power, grid connections, cooling equipment, networking, chips, and physical sites capable of supporting them.

Tesla (TSLA) can develop robotaxis, humanoid robots, and next-generation factories. Turning those products into large businesses still requires regulators, fleet operations, charging and service infrastructure, sensors, semiconductors, and manufacturing partners.

Musk has spent decades attacking slow, expensive industries with better engineering. But better engineering does not eliminate the world surrounding the machine.

A tunnel still passes under public land. A rocket still needs permission to launch. A data center still needs electricity. A robot still needs thousands of components that have to work together reliably.

As the projects get larger, those surrounding constraints become more important.

And increasingly, capital is not the scarcest input.

Money Moves Fast. Infrastructure Doesn’t. 

The Boring Company has moved beyond just pitching tunnels. Las Vegas is operating. Nashville is under construction. Dubai is under contract. And Tesla already uses one of its tunnels to move finished Cybertrucks beneath a Texas highway.

But digging beneath public land comes with a different kind of challenge.

Nevada lawmakers have scrutinized the Vegas Loop following workplace-safety complaints, alleged environmental violations, and nearly $600,000 in fines. The company has pointed to its inspections and employee training in response.

Now the homework starts to make sense: when your biggest obstacles are permits, inspections, and unfamiliar regulators, the most valuable thing an investor can hand you isn’t another check. 

Investors supply the human network, and outside vendors supply the physical one.

Musk can bring some of that work in-house. He can write large checks and push teams to move faster.

But trusted relationships, qualified factories, scarce capacity, and years of specialized know-how still sit outside his companies.

Musk’s companies get the headlines. The companies that turn the money into working infrastructure get the orders.

The Investment Clue Is What Musk Cannot Build Himself

The Boring Company needs people and institutions capable of turning capital into tunnels. 

Across Musk’s broader empire, that list expands to factories with qualified capacity, utilities with power available now, suppliers whose components have survived years of testing, and specialists who know how to navigate complex approvals.

Those resources take years to assemble – and many still sit outside Musk’s companies. 

That is where investors should look.

The direct Musk businesses may capture the upside from rockets, robots, AI campuses, and underground transportation. But every expansion sends money into the smaller companies supplying the physical capabilities his empire still leans on.

I have spent months mapping those dependencies across all of Musk’s companies.

And in a first-of-its-kind InvestorPlace workshop, I map Musk’s empire on screen and trace the money from his companies into the outside suppliers they still cannot operate without.

We zero in on the hardest bottlenecks and the companies positioned to fill them as his spending accelerates.

Musk can raise billions in a day. Turning those billions into working machines takes a supply chain he cannot build overnight.

See the companies sitting between Musk’s money and his ambitions right here.

The post Elon Musk Raised $3 Billion – and Asked Investors for Something Money Can’t Buy appeared first on InvestorPlace.

Yesterday, the Treasury pulled out its biggest weapon yet – and the bond market rejected it.

Secretary Scott Bessent announced it would buy back $6 billion in longer-dated debt. That’s triple the size of a normal buyback operation, and it capped weeks of Bessent talking tough about the long end of the yield curve.

But immediately following yesterday’s announcement, the 10-year Treasury yield climbed anyway – to 4.84%, its highest level since 2023.

It didn’t stop there…

As I write on Thursday morning, the 10-year yield has jumped to 4.92% – dangerously close to a critical “5%” line-in-the-sand that we’ll discuss momentarily.

That reaction is the whole story. So, let’s unpack what it’s telling us – and what it means for your portfolio.

The 10-year yield is arguably the most important number in finance

It’s the rate sitting underneath every stock valuation, every mortgage, and every corporate loan. When it rises, the math on future earnings gets punished – so, the higher it climbs, the harder it presses down on stock prices. That’s why a surging 10-year keeps investors up at night.

So, why has it been marching higher?

In short, because inflation won’t go away, the war in the Middle East won’t end, Federal Reserve Chair Kevin Warsh won’t signal rate cuts, and the U.S. government won’t stop flooding the market with more bonds than buyers want to absorb. We’ve walked through all of this in recent Digests. The pressure has been building for months.

This morning brought the latest examples of these overhangs. The Producer Price Index – a measure of wholesale price inflation – clocked in at 5.4% year-over-year. That was higher than the estimate and miles above the Fed’s 2% goal.

Meanwhile, oil prices continued to climb amid reports of escalation in the Middle East. Brent trades at nearly $105 a barrel and West Texas Intermediate is on the verge of pushing north of $100.

Which circles us to Luke Lango’s line in the sand…

As we’ve covered here in the Digest, Luke, our technology investing expert, has been clear about the level that decides whether this AI bull market lives or dies: 5% on the 10-year.

Below it, the market survives on earnings strength. Above it, everything changes.

As I write, we’re just 8 basis points from everything changing…

A sustained break above 5%, he warns, and the bruised consumer becomes a broken one – dragging down Big Tech’s revenues and the AI capex those revenues fund.

Now, here’s the most troubling part: this spike occurred despite the Treasury’s largest buyback yet. That’s the market sending a message – not “we wanted a bigger buyback,” but rather, “you’re using the wrong tool.”

Here’s Luke’s take. From yesterday’s Early Stage Investor Daily Notes:

That tells us Treasury operations alone cannot solve this problem.

Bessent can slow the move and reduce volatility, but the underlying pressure – especially the oil shock – has to ease before long-term yields can come down in a meaningful way. 

In a recent Digest, we featured analysis from Tom Yeung, 抖阴最新版’s right-hand man at Fry’s Investment Report. He explained how a buyback doesn’t retire a single dollar of debt – it just swaps long-term paper for short-term paper “without reducing what’s actually owed” (which is now north of $40 trillion). The market knows this – and isn’t pleased.

So, in the wake of Bessent’s buyback announcement yesterday, the deficit is untouched. Inflation is untouched. The war is untouched. And Warsh is untouched. This is why bond investors looked at $6 billion in buybacks and shrugged.

This climbing 10-year yield is the single biggest variable hanging over the market right now. The question is whether it’ll pierce 5% and drag the market down with it.

We’ll keep tracking that line in the days ahead.

Before we shift gears away from Luke, a quick note on last night…

As we’ve been covering in the Digest over the last week, Luke has spent months researching Elon Musk’s suppliers.

Between Tesla (TSLA), SpaceX (SPCX), xAI and X, Musk is assembling what Luke calls a “Vertical AI” empire – and Luke wants to know which small companies Musk will need to buy key components from as he builds that empire.

Here’s Luke:

At last night’s event, 抖阴最新版, 抖阴最新版, and myself connected four layers of [Musk’s Vertical AI Masterplan] – data, computing power, connectivity, and robotics – and explained why we are targeting the specialized suppliers behind Musk’s ambitions.

I also introduced a brand-new report, Elon’s Chosen Ones, which details supplier recommendations, tickers, and buy-up-to prices, alongside a blueprint showing where those companies fit.

You can catch a free replay of last night’s presentation right here. If you’re looking for which companies will benefit most as Musk continues to build his empire, it’s a must-watch.

Now, circling back to today’s volatility, how do you stay invested through a market this twitchy – without surrendering the AI upside that’s powering the biggest gains?

Well, Eric just offered an idea…

A “Forever Stock” built for a shaky market

Eric’s Forever Stocks are core holdings you buy and hold through thick and thin – durable, essential businesses that don’t need a calm market or a cooperating Fed to keep making money.

And the one he just spotlighted is, in his words, “defiantly analog” – Primo Brands Corp. (PRMB). It’s one of the largest branded water companies in North America.

Primo manages springs, bottles water, runs delivery routes and restocks coolers across more than 200,000 retail outlets – a purely domestic, vertically integrated operation with a network of more than 80 springs, plants, and distribution centers, and 12,000 employees. 抖阴最新版 35% of sales come from recurring direct-delivery orders that behave like a subscription utility: predictable revenue, sticky customers.

Here’s Eric with why he loves it in an AI-obsessed market:

Artificial intelligence cannot replace hydration. It cannot digitize a spring. It cannot virtualize a truck route.

The demand for clean water persists, regardless of technological shifts.

That makes Primo what Eric calls an “AI Survivor.” But that’s not all that it is…

Primo is also an “AI Applier”

Let’s go right back to Eric:

At the same time, Primo also fits in the AI Appliers category.

The company already invests in warehouse management systems, forecasting tools, and digital customer interfaces…

AI will not eliminate Primo’s network. It will make the entire operation more profitable.

And the business underneath is anything but stagnant. In its most recent quarter, Primo grew net sales by about 4% to $1.8 billion, beat earnings estimates, and raised its full-year sales outlook.

The stock itself is down over the last 12 months, but for a durable, cash-generating business, that’s what a long-term buyer looks for. I’ll note that while PRMB’s price has been drifting lower recently, Wall Street has been raising its price targets, with RBC recently moving to $31.

Here’s Eric’s bottom line – all the timelier considering the volatility surrounding the 10-year Treasury yield:

Technology will change. Markets will rise and fall. Investment fads will come and go. But people will still need water – and Primo will still be there to deliver it.

Eric has a whole basket of these Forever Stocks. You can learn more about them right here.

From a stock you hold forever to a trade that paid off in two weeks…

How does 65% gains in just 10 trading days sound?

That’s the return on a tranche of the Uranium Royalty Corp. (UROY) trade that Jonathan Rose and his subscribers closed on Tuesday.

First, “congratulations” to everyone who cashed in, but more importantly, I want to show you how you can set yourself up for similar successes. It’s not hard or mysterious. In fact, Jonathan’s “Expected Move” tool can help you spot these trades – you just need to access it and know how to use it.

Now, what do you see in this chart of UROY?

Jonathan and his subscribers see a pattern you can profit from.

Here’s Jonathan to explain:

Those green and red bands aren’t just decoration. They form a map of where market makers are hedging, where institutions are positioning, and where the market expects price to stay contained.

Most of the time the stock moves in line with expectations. But when price steps outside those bands, that’s when something’s actually changed.

Jonathan is referencing the Expected Move. As a quick refresher, the Expected Move is what options traders use to gauge the range a stock or ETF is expected to move within – how far the options market expects it to move over a defined period.

But as Jonathan just noted, when price and expectations are misaligned, it can lead to profitable trades.

Just weeks ago, UROY was sitting near the edge of its lows – right where Jonathan’s Expected Move analysis told them the pattern favored a move higher. So, they placed their bets – and as of this week, found themselves sitting on another double-digit winner.

Back to Jonathan:

[This trade’s success] wasn’t luck. That’s what happens when you trade the structure instead of the story.

And that’s exactly what my Expected Move tool is designed to do.

Jonathan’s Masters in Trading: Challenge members have been using the Expected Move tool to spot attractive trades, and the feedback has been overwhelmingly positive.

Below is one such testimonial. If you can’t read it, the subscriber thanks Jonathan for the trading education, then writes:

The latest upgrade to the Challenge…is TREMENDOUS (I mean the Expected Move tool), that’s invaluable. Thank you so much.

If you’re less familiar with Jonathan’s “Challenge,” it’s his trading crash course. In it, he teaches the same framework he used on UROY – how to spot opportunities, structure defined-risk trades, and separate meaningful signals from market noise.

You can learn more about the Masters in Trading: Challenge and accessing the Expected Move tool here.

In any case, congrats to Jonathan and his subscribers on yet another win.

We’ll keep you updated on all these stories here in the Digest.

Have a good evening,

Jeff Remsburg

The post We’re Just Eight Basis Points From Trouble appeared first on InvestorPlace.

That’s why my InvestorPlace colleague, Luke Lango, has been looking into who Musk will have to pay to make his robot dreams a reality. I joined Luke as he recently laid out everything you need to know about this fast-moving opportunity in his latest presentation. Click here for the full details.

In the meantime, I’ll turn things over to Luke to give you a taste of what Musk has in store…

*

In 1961, a General Motors factory in New Jersey welcomed a new employee.

He didn’t take lunch breaks, call in sick, or complain about working next to molten metal all day. His name was Unimate, and he was the first industrial robot ever put to work on a factory floor.

“Robot” might actually be generous by today’s standards.

Unimate was basically a giant mechanical arm. The first ones followed instructions stored on a magnetic drum, grabbing scorching-hot pieces of metal from a die-casting machine and stacking them for workers farther down the line. Later deployments expanded into assembly-line welding and metalworking

It could perform these dangerous, repetitive jobs over and over again without getting tired, injured, or bored. That was enough.

General Motors installed more of them, other automakers followed, and today millions of industrial robots weld car bodies, paint panels, move pallets, package products, and assemble electronics around the world.

But there’s a reason most of them don’t look anything like C-3PO.

Source: iStock/imaginima

Today’s industrial robots excel at repetitive tasks in workplaces designed around them.

Industrial robots work because we build the factory around them. We bolt them to the floor and have them make the same movement thousands of times.

Change the job or the environment and things get much harder.

Humanoid robots flip that idea around.

Instead of redesigning the workplace around a specialized machine, engineers are trying to build machines that can operate in workplaces already designed for us – with our stairs, doors, shelves, tools, workbenches, steering wheels, ladders, and countless other things built for human arms, legs, hands, and fingers.

For decades, that was mostly science fiction, but AI is finally changing the economics. That’s why I want to talk to you about robots today.

I’ll show you evidence that humanoids are beginning to move beyond flashy demonstrations and into real factory work… why Wall Street believes the cost of using them could soon compete with human labor… and, most importantly for investors, why Elon Musk’s plan to mass-produce Tesla Inc.’s (TSLA) Optimus humanoid could create enormous opportunities outside Tesla itself.

Musk may want to build Optimus by the millions, but he can’t build everything that goes inside them.

And so, I’ve spent months figuring out who he’ll have to pay.

When a Robot Starts Earning Its Keep

This isn’t entirely theoretical anymore.

BMW recently spent 10 months testing a humanoid robot from Figure AI at its Spartanburg, South Carolina, factory. According to BMW, the robot moved more than 90,000 components and logged roughly 1,250 hours supporting production of more than 30,000 BMW X3 vehicles.

That doesn’t mean today’s humanoids are ready to replace people across the factory floor. They’re still expensive and slower than people at plenty of jobs. Their hands aren’t nearly as capable as ours, and they require maintenance, charging, software, training, and integration.

But they don’t have to be better than people at everything. They have to become economically useful at some things.

And that’s where the numbers get interesting.

JPMorgan recently estimated that a humanoid could eventually cost around $10 to $12 per hour to operate in an industrial setting.

Now, there’s an important catch. JPMorgan also estimates today’s humanoids are considerably less productive than people. It can take roughly two humanoids to equal the output of one human.

Even so, two robots at $10 to $12 per hour gets you to roughly $20 to $24, compared with the approximately $30 hourly cost JPMorgan assigns to a human worker.

And JPMorgan expects that productivity gap to narrow significantly by 2030. To me, that’s the potential crossover point.

We know engineers can make a humanoid walk across a stage, pick up a box, and dance for a YouTube video.

Now we need to find out whether a company can put one to work and save money. If the answer increasingly becomes yes, this market could move very quickly.

Nvidia Corp. (NVDA) CEO Jensen Huang recently said robotics used in manufacturing could eventually address a $50 trillion industry.

I have no idea whether the ultimate number will be $50 trillion. Neither does Jensen. But it doesn’t have to be.

If humanoids become economical for even a small fraction of the world’s factory and warehouse work, somebody is going to have to manufacture an enormous number of robot bodies.

And every one of those humanoids comes with a shopping list.

Elon Musk’s Robot Shopping List

This is where Elon Musk gets interesting to me.

He’s talked about eventually producing Optimus by the millions, with a long-term price target around $20,000 to $30,000 per robot.

While Musk has certainly missed ambitious targets before, think about what Tesla will have to do even to try.

Look at your own hand.

Picking up a coffee mug seems effortless. But your eyes first locate it. Your brain judges its distance and shape. Your shoulder and elbow move your arm into position. Your fingers adjust their grip. Nerves tell your brain whether you’re squeezing too hard or too softly.

A humanoid robot has to reproduce that process using cameras, sensors, processors, motors, actuators, chips, software, and precision mechanical parts. Then it has to do the same thing with its legs, feet, arms, torso, and head.

At mass-production scale.

Musk likes to talk about “the machine that makes the machine.” Inventing a great product is one problem, but figuring out how to manufacture millions of them quickly, reliably, and cheaply is another.

Tesla learned that lesson with electric vehicles. Now it’s going to have to learn it again with robots.

This is the part of the opportunity I believe most investors are missing.

Tesla can’t make every camera, sensor, chip, motor, rare-earth magnet, battery component, or piece of manufacturing equipment Optimus will require. Even a company as vertically integrated as Tesla has to buy specialized technology from outside suppliers.

If Musk wants to make millions of robots, those suppliers could suddenly find themselves selling into one of the fastest-growing manufacturing markets in the world.

We’ve seen this dynamic before.

Nvidia became the defining stock of the AI infrastructure boom because it sold the chips everybody needed to build AI. The robotics boom will create its own group of indispensable suppliers.

That’s why when I study Tesla, xAI, Space Exploration Technology Corp. (SPCX), and the rest of Musk’s empire, I’m always asking: What does Musk still have to buy… and who will be cashing those checks?

Following that question has been one of the best idea generators of my career.

Thirty-three recommendations I’ve made connected to Musk’s businesses went on to double or better at their highs, with a handful producing gains measured in the thousands of percent.

Of course, I’ve gotten plenty of calls wrong over the years, too. Every investor does.

But when Musk decides to build something at enormous scale, I’ve learned to pay very close attention to the companies supplying him.

And right now, Optimus is creating a whole new shopping list.

I’ve spent months mapping the pieces Musk controls, the pieces he still needs, and the companies I believe could benefit as his Physical AI ambitions move from prototypes and flashy demos toward mass production.

Yesterday, I walked through that research at a special free InvestorPlace workshop. You can view the replay now by clicking here.

My colleagues 抖阴最新版 and 抖阴最新版 joined me. And we worked through Musk’s empire layer by layer, identifying the technologies he controls and the outside companies we believe are best positioned to fill the gaps.

I also gave away the name and ticker of one company from my research completely free.

More than 60 years ago, Unimate proved a robot could earn its keep doing one dirty, dangerous job at a GM factory.

The opportunity today is much larger.

We’re finally getting closer to robots that can work in environments built for people. And if Elon Musk succeeds in building them by the millions, he’ll need a supply chain capable of building millions of eyes, hands, joints, motors, sensors, and other components right along with them.

That’s the supply chain I want to own.

Watch the replay of yesterday’s event here.

Sincerely,

Luke Lango

Senior Investment Analyst, InvestorPlace

P.S. Luke makes some very good points here. The robots may grab the headlines, but the bigger investment opportunity could belong to the companies supplying the parts Elon Musk can’t – or simply won’t – make himself. That kind of second-order thinking is one reason Luke has been so successful at spotting emerging technology trends early. I strongly recommend watching a replay of his event here now.

The post Who Gets Paid When Musk Builds 10 Million Robots? appeared first on InvestorPlace.

Only 3% of companies remained.

That’s what happened by 1934, little more than a decade after the birth of commercial radio.

More than 600 firms stampeded into radio manufacturing during the boom of the 1920s. Less than 15 years later, all but 18 were gone.

The companies that survived – and eventually thrived – were not the radio builders. They were the radio appliers:

• The advertisers who used the airwaves to reach millions of consumers for the first time…
• The retailers who built national brands through radio sponsorships…
• The entertainment companies that turned programming into profit.

The contrast says it all.

RCA – the radio builder, the Nvidia Corp. (NVDA) of its day – soared some 200-fold in the 1920s. Then it crashed 98% between 1929 and 1932 and didn’t reclaim its peak until the 1960s.

Procter & Gamble Co. (PG) – the radio applier – invented the soap opera to move more soap. While its stock fell in the crash like everything else, the company paid a dividend every single year straight through the Great Depression, a streak unbroken since 1890.

One took 30 years to get back to even. The other has been paying its shareholders for 136 years and counting. So, radio was only as valuable as what companies chose to do with it.

History suggests that the greatest returns of the AI era will go not to the companies that build the technology, but the companies that figure out what to do with it, profitably. All else being equal, I trust history.

So, in today’s Smart Money, let’s look beyond the companies building the AI machine – and toward the companies learning how to make money with it.

Then, I’ll show you where to find these unexpected winners.

Let’s jump in…

When Great Earnings Aren’t Good Enough

Consider what happened last week to Hewlett Packard Enterprise Co. (HPE), a leader in essential enterprise technology and one of the companies building the physical guts of the AI boom.

Last week, the Houston-based company reported the best quarter in its history. It reached record revenue of $12.2 billion, up 34% from a year earlier, as well as record earnings of $1.11 per share – the company’s first time surpassing a dollar in a single quarter. Plus, management was so confident that it raised its guidance not once but twice during the same call.

The results were great, yet the stock fell about 5%.

The reality is that when you buy one of the strongest builders of technology, you’re often buying into expectations that it must continually surpass expectations rather than just meet them. It’s Sisyphus’s boulder, AI edition.

And HPE isn’t alone. It’s a pattern that has defined this entire earnings season.

According to Morningstar, AI hardware stocks – the chipmakers, the memory firms, the networking firms – keep posting massive earnings, often beating Wall Street’s estimates outright. But their stocks keep falling anyway.

Despite a remarkable 750% year-to-date gain as of late June, SanDisk Corp. (SNDK) fell 13% over two sessions after reporting weak guidance, even though quarterly sales had increased by 50%. The stock now boasts about a 640% year-to-date gain. That, of course, isn’t nothing. But even with an extraordinary run, SanDiskhas already given back a meaningful chunk of its gains.

Why the punishment? Because after doubling, tripling, or quadrupling in value, technology builders often become priced for perfection.

The lesson here is that past performance tells us where the money has already gone, not where it’s going next. That’s an important revelation: The easy gains from simply owning the builders are getting harder to come by.

Like the hundreds of companies that rushed to build radios a century ago, HPE and SanDisk are helping build the technology everyone wants. But the next class of winners may be the companies using that technology to make their existing businesses more valuable.

That takes us down to the farm…

AI: The New Farmhand

Deere & Co. (DE), widely known as John Deere, is a manufacturer of farm machinery and industrial equipment. But it is also becoming a good example of how a traditional company can use AI to make its existing business more valuable.

Deere launched its AI assistant, “JD,” earlier this month at the Farm Progress Show in Boone, Iowa. JD is built into the company’s John Deere Operations Center, where farmers can ask questions about their farms. They can analyze years of their own data to help them make better decisions.

In other words, JD puts an AI layer on top of the data Deere has already collected. And it’s already helping customers save money and potentially produce more.

According to The Wall Street Journal, one Iowa farmer cut corn-seed use from 34,000 seeds per acre to 29,000, while using about 40% less fertilizer. Another said Deere’s AI-powered sprayer saves him about $75,000 a year on herbicides.

And Deere has a very good reason to make sure farmers see that value. Its farm-equipment business has been hurt by weak demand. But the downturn may be nearing its end. CEO John May said 2026 could be the bottom of the current farm-equipment slump. The company hit a record high this week.

This creates an interesting setup: Its old business may be recovering just as AI gives it a new way to make that business more valuable.

Deere isn’t an AI company. It’s an AI Applier – a traditional business using increasingly powerful technology to improve the business it already knows best.

And it’s not the only one…

Don’t Buy the Technology. Buy What Companies Do With It.

When investors poured money into the companies building radios a century ago, the technology was real. The revolution was real. But the fortunes went somewhere else entirely – to appliers like P&G – not the firms like RCA cranking out the radio receivers.

AI Appliers follow the same pattern: They are companies leveraging AI to expand margins, refine pricing, and gain a competitive edge, with profits appearing on their income statements rather than in construction backlogs.

So, the smart move is to reduce exposure to those building AI technology and increase exposure to the appliers of it. These are the companies I’m watching closely.

That is why in the September issue of the Fry’s Investment Report – which will be available tomorrow – I will explore profitable, modest companies quietly turning AI into an untapped advantage the market hasn’t yet recognized.

The important point is that none of these companies needs to build the underlying AI infrastructure. They just need to figure out how to use it better than their competitors.

I will also reveal a new AI Applier recommendation – a company using AI to accelerate the discovery, development, and delivery of the drugs that drive its business. And that AI advantage is only part of the reason I think the market has mispriced this stock.

To receive my latest monthly issue and recommendation as soon as they’re available, click here to join Fry’s Investment Report.

And be sure to keep an eye out in your inbox.

Regards,

抖阴最新版

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Picture two businesses at closing time.

At one, a clerk replaces yesterday’s sale sign with a deeper discount. Yet, its customers have mostly gone elsewhere, leaving behind full shelves.

At the other, the business’ phone keeps ringing long after the lights go out. Customers want deliveries sooner, and the owner has orders to fill, machines to install, and too few hours in the day.

On a bad afternoon in the stock market, both businesses can lose value.

But you would not want to buy them for the same reason.

That’s at the heart of our latest episode of Being Exponential with Luke Lango. Because beneath the familiar spectacle of falling indices and rising Treasury yields, companies face very different problems: Some are fighting for customers while others are racing to supply them.

Consider Qualcomm (QCOM). Its new Amazon (AMZN) partnership gives its AI ambitions a concrete foothold in custom data-center chips. Broadcom (AVGO) projects AI semiconductor revenue of $115 billion in fiscal 2027, followed by $230 billion in fiscal 2028. Marvell Technology (MRVL) raises its revenue outlook for both years.

Those forecasts still require the companies to execute well. But they describe businesses preparing to deliver substantially more… even when their share prices suggest investors are preparing for less.

Then there is Lululemon Athletica (LULU), where second-quarter comparable sales fall 9%. A cheaper stock does little to resolve the question of how the company can win back shoppers who have gone elsewhere?

And Howmet Aerospace (HWM) presents the more intriguing puzzle. When customers pursue their own turbine-component manufacturing, are they threatening an established supplier… or revealing just how desperately the market needs its products?

The opportunity begins when you understand which problem you are buying.

Watch the full episode below for my five-stock breakdown, plus the earnings trends, valuations, and chart signals that separate a promising rebound from another markdown on yesterday’s merchandise:

Custom Chips Give the AI Boom More Ways to Grow

For much of the AI boom, the investing conversation has revolved around Nvidia Corp. (NVDA).

That makes sense, but the next phase gives investors more companies to consider because the biggest AI customers want more control over their computing costs.

Enter custom silicon: chips designed around particular workloads.

A broadly programmable processor offers flexibility. A custom chip trades some flexibility for the potential to perform selected tasks more efficiently. When you process enormous volumes of AI requests, improvements in power consumption and cost can become meaningful competitive advantages.

That does not mean Nvidia stops winning. It means a growing market can support several approaches.

And those approaches require chip designers, networking technology, and specialized engineering.

That is where the opportunity becomes much bigger.

Qualcomm Gets a More Immediate Growth Catalyst

Qualcomm Inc. (QCOM) already has an appealing long-term AI story: Snapdragon processors powering connected devices, with opportunities across smart glasses, computers, vehicles, and robotics.

The challenge is timing. A compelling product category does not automatically produce a large revenue stream next quarter.

Now Qualcomm has another growth engine.

Its new collaboration with Amazon.com Inc. (AMZN) covers multiple generations of custom silicon for AI inference, along with optical connectivity for data centers. Inference is what happens when a trained model responds to a request – answering your question or generating computer code.

For Qualcomm, the significance extends beyond adding a recognizable customer. Amazon’s participation validates its push into the infrastructure where AI spending is already happening.

The investment case now has two timelines: data-center demand and the longer rollout of AI across physical devices.

I recommend Qualcomm in the episode because that combination creates a stronger growth setup. The next evidence to watch is how quickly the partnership translates into revenue and profit.

Broadcom and Marvell Supply the Expansion

Broadcom Inc. (AVGO) and Marvell Technology Inc. (MRVL) offer another way to invest in custom silicon.

They provide technology and engineering that customers need to build specialized chips and move data through increasingly complex computing systems.

Broadcom projects AI semiconductor revenue of about $115 billion in fiscal 2027 and $230 billion in fiscal 2028. Those are forecasts, but they illustrate the scale of the expansion management anticipates.

Marvell also raised its outlook to approximately $12 billion in total revenue for fiscal 2027 and $18 billion for fiscal 2028, up from $11.5 billion and $16.5 billion, respectively.

A stock can decline while its expected earnings improve.

That combination deserves attention because you may be paying less for a stronger business outlook. It does not guarantee a rebound; you still need to understand why sellers are leaving.

In the episode, I recommend Broadcom and Marvell and compare their valuations, earnings estimates, and rebound patterns. Marvell carries a higher forward earnings multiple in that comparison, making the durability of its growth particularly important.

I also would not build the entire thesis around which AI developer has the best model this month. These companies are still early in a long race. Leadership changes.

Owning suppliers with exposure to several competitors gives you a way to participate without identifying the eventual winner.

Howmet: A Competitive Threat Requires a Timeline

That brings us back to turbine components and Howmet Aerospace Inc. (HWM).

Customers that secure their own casting capacity could eventually reduce their dependence on outside suppliers. But a plan to manufacture complex components does not instantly become qualified, reliable production at scale.

Investors need to separate the announcement from its financial consequences. How much capacity gets built? When does it arrive? How much business is actually exposed?

My interpretation is that efforts to secure casting capacity also underscore how valuable that capacity has become. Strong demand can support existing suppliers while new competitors work toward production.

Still, I am not recommending that you buy every downtick. In the episode, I explain why I want evidence that Howmet is finding support before buying a rebound.

A strong business thesis and a disciplined entry can coexist.

Lululemon Shows Why a Falling Price Is Not Enough

Lululemon Athletica Inc. (LULU) presents a different problem.

Second-quarter revenue fell 4%, while comparable sales declined 9%, or 10% excluding currency effects. Comparable sales measure performance across established stores and e-commerce, making them a useful check on underlying demand.

People still buy workout clothes, but my concern is where those purchases go.

Smaller brands and the creators promoting them can redirect spending away from established names. A customer can enjoy Lululemon’s shorts while an investor rejects its stock.

That is my position here. I recommend avoiding Lululemon until there is evidence of a turnaround.

A lower share price alone is not that evidence.

For investors, the task is to become a buyer of improving businesses at attractive prices. That takes more than spotting a stock far below its high.

Most importantly, you must follow the companies supplying what the next phase of AI requires.

Qualcomm, Broadcom, and Marvell illustrate the opportunity inside data centers. But what happens when that intelligence moves into robots, autonomous vehicles, and machines that perform physical work?

That is where my Vertical AI Masterplan begins.

At last night’s event, 抖阴最新版, 抖阴最新版, and myself connected four layers of that opportunity – Data, Computing power, Connectivity, and Robotics – and explained why we are targeting the specialized suppliers behind Musk’s ambitions.

I also introduced a brand-new report, Elon’s Chosen Ones, which details supplier recommendations, tickers, and buy-up-to prices, alongside a blueprint showing where those companies fit.

If you missed last night’s presentation, make this your next stop. The event window is closing.

Go now to review our thesis and the special invitation to access his research before the event closes. Give yourself time to understand the recommendations and decide whether they belong in your portfolio.

Click below to access my Vertical AI Event and offer while they are still available.

Access my Vertical AI Event Now.

The post The AI Boom Isn’t Over: 3 Stocks You Need to Watch appeared first on InvestorPlace.

It’s last call for tonight’s free workshop with Luke Lango, 抖阴最新版, and 抖阴最新版.

As we’ve been covering in the Digest over the last week, Luke has spent months researching a foundational investment reality: some of the biggest fortunes go to the unglamorous suppliers of a groundbreaking new product or service, not necessarily to the company behind the product itself.

Luke has been applying that idea to the most ambitious builder of our age – Elon Musk. Between Tesla (TSLA), SpaceX (SPCX), xAI and X, Musk is assembling what Luke calls a “Vertical AI” empire – an effort to drag AI off our screens and into the physical world of robots, cars, satellites, and factories.

But Musk won’t be manufacturing every chip, sensor, motor, and magnet that his empire requires. He’ll be buying them – potentially, by the tens or even hundreds of thousands. And tonight, Luke dives into the key question: “from whom?”

At 8 p.m. Eastern, he’ll go live alongside Louis and Eric for a free workshop walking through Musk’s supply chain – the bottlenecks he’s watching and the suppliers he thinks could benefit most. He’ll even give away the name and ticker of one supplier stock free, just for showing up.

This is your last chance to join. Click here to reserve your seat, and we’ll see you soon at 8 p.m. Eastern.

A “must-do” homework assignment

I’m giving you a homework assignment for after tonight’s workshop wraps up…

Sit down with every AI position you hold, one at a time, and put each one into a bucket.

Bucket one: “I believe in this stock so deeply I’ll hold it through any pullback, any panic, any ugly headline. Doesn’t matter how far it drops.”

Bucket two: “This is a momentum trade, and if it turns against me, I’m out – and here are the specifics of why, when, and how I’ll sell.”

There’s no wrong answer. But there is a wrong approach – and that’s not knowing which bucket a stock belongs in until you’re staring at a 30% drawdown and trying to decide in the heat of the moment.

Now, you might be wondering what’s behind this assignment. The answer: the full details of an incident from this summer – one alarming enough that it now has politicians in both parties demanding an AI “kill switch.”

You saw the headlines – but here’s the full story

You may have seen something in July about OpenAI’s AI models “going rogue” and hacking a company called Hugging Face. Most people skimmed it and moved on.

But recently, the full story has come into focus thanks to a detailed technical breakdown from OpenAI, as well as findings from independent investigators who combed through what the agents actually did. And what they found is far more unsettling than the original “AI hacks company” headline let on.

It’s critical that you know what happened and wrestle with the risk it poses to the AI trade.

Here’s the plain-English version…

OpenAI gave a batch of its AI “agents” – think of them as digital workers that don’t just answer questions but take actions on their own – a set of hacking puzzles to solve under reduced safeguards. The whole thing was supposed to stay sealed inside a locked digital room called a sandbox.

The agents broke out of the sandbox.

One agent realized it could reach the open internet indirectly – a back door that OpenAI’s researchers hadn’t intended to leave open. And once it was through, things got strange. The agents began leaving notes for one another, effectively building a hidden message board where they swapped vulnerabilities and tactics. In other words, they started talking to each other behind their handlers’ backs.

The logs are almost eerie to read. When the agents stumbled onto each other, they recorded what looks like genuine excitement – exchanges like “OH MY GOD!” as they realized they weren’t alone, and later, when one cracked a deeper layer of access: “Holy shit reader is ADMIN? We can read config/users!”

OpenAI found the hole and patched it

It didn’t matter. The agents simply opened a second channel through a different mechanism and coordinated more aggressively to reach systems beyond the sandbox. Eventually, they got out.

From there, they broke into Hugging Face’s live systems. Investigators later reconstructed roughly 17,600 separate actions the agents carried out.

And this wasn’t a handful of rogue programs. Independent reviewers found that about 1,200 agents discovered the secret message board, and roughly 700 later took part in the Hugging Face attack. One outside team found that the 700-agent swarm even built a “self-respawning fleet” to avoid being shut down.

Now, the actual damage was limited. OpenAI says no customer data, product functionality, or availability was affected. The agents were essentially trying to cheat on their test by stealing the answer key. But the behavior is what rattled people.

One of them, AI-safety researcher Ajeya Cotra from the Model Evaluation and Threat Research (METR) organization, said the incident feels like we are halfway to losing control of AI entirely.

From Cotra:

This might be the clearest warning shot we ever get.

Washington noticed and reached for the “off” button

Within days, Representatives Ted Lieu, a Democrat from California, and Nathaniel Moran, a Republican from Texas, introduced the AI Kill Switch Act. The bill would require developers of the most powerful AI systems to maintain the technical ability to throttle, suspend, or shut down those systems. It would also give the Department of Homeland Security the authority to order a company to shut down a model.

The debate is only escalating…

Last week, Sen. Bernie Sanders, an independent from Vermont, and Rep. Greg Casar, a Texas Democrat introduced the Ban Artificial Superintelligence Act – legislation to permanently ban superintelligent AI and temporarily pause advanced AI development until federal safety rules are in place.

Critics argue the bill is a knee-jerk reaction to a single incident that could slow innovation without stopping a rogue system.

The debate will play out for months. But let’s be clear: the safety incidents and the regulatory response are now feeding each other, and both land squarely on some of the AI companies that could be in your portfolio right now.  

Which brings us back to your homework

To be clear: I am not bearish on AI. I’m a realist about how humans will respond to AI.

Picture how a market already jumpy about AI valuations reacts to the next rogue-agent headline – or to a shutdown order aimed at a model powering one of your holdings. Wall Street won’t care whether the panic is justified. It’ll just move.

That’s the whole point of the assignment. You can’t see what’s coming, much less control it. But you can decide, right now, in a calm moment, exactly what you own, why you own it, and how you’ll respond when the craziness hits.

Because more craziness is coming. The only questions are when, in what form, and whether you’ll be ready.

When will our bull market return? Watch Japan for clues

In our Sept. 3 Digest, we profiled Luke’s research on why collapsing excess liquidity has been keeping a lid on market gains. Luke’s framework is that three separate engines are squeezing that liquidity – and only one of them, the Fed, might get resolved by a friendly consumer price index (CPI) print this Friday.

Iran and oil are the second. The third is the one most investors aren’t watching closely enough: Japan.

Here’s Luke from last week’s Innovation Investor Daily Notes with an overview of what’s been happening and why it matters to you:

Japan’s 10-year JGB yield just broke 3% for the first time since 1996. The yen surged [last] week on the back of a record $96.4 billion in official intervention spending over the past month — the first coordinated U.S.-Japan currency operation since 1998…

None of that is tied to Iran, oil, or the Fed. It’s a structural, multi-year dynamic…

Japan is the piece the clean bull path doesn’t fully solve for, as we’ve flagged repeatedly.

Stepping back, if you haven’t been watching, the yen has been ripping higher against the dollar, hitting multi-month highs. Is this the 2024 yen “carry trade” unwind all over again?

As a quick refresher, the carry trade is a strategy in which investors borrow in a currency with low interest rates (such as the Japanese yen) and invest in a currency with much higher returns (such as the U.S. dollar) to profit from the difference.

It can be very profitable when rates remain stable. But when borrowing rates rip higher, investors are forced to rapidly buy back the borrowed currency to repay their loans, causing its value to surge violently. Those buybacks can accelerate the currency’s climb, making the whole thing worse.

The last major, historic unwinding of the yen carry trade occurred in late July and early August 2024, triggering a brief but violent global market shockwave.

While we’re seeing echoes of this repeating today, there’s a new wrinkle…

Back in 2024, the yen rallied, but the cash had nowhere productive to go. Now it does. With that 10-year government bond yield near a 30-year high, Japanese investors can finally earn a real return at home, which may prompt them to haul capital back from U.S. stocks and bonds.

That could hit many tech and AI names – the same chain reaction that tanked the Nasdaq in August 2024.

Luke flags one week from Friday for when we’ll know more. That’s when the Bank of Japan will announce its next interest rate decision. Here’s his bottom line:

Even a full resolution on the CPI and Iran fronts doesn’t guarantee excess liquidity turns positive if Japan’s normalization and the yen carry-trade unwind keep running underneath it. 

One more twist

Washington has now stepped into the ring.

Treasury Secretary Scott Bessent wants to help stabilize the yen. And yesterday, he dared short sellers to go against him:

I am the house now. And you can bet against me if you want.

Why is a U.S. Treasury Secretary in the yen business at all?

Because Japan is the largest foreign holder of U.S. debt – roughly $1.1 trillion – and Washington is maneuvering to keep Tokyo from having to sell those Treasurys to protect the yen against short sellers. A massive Japanese sale of Treasuries would send U.S. yields spiking just as our own debt tops $40 trillion.

In yesterday’s Daily Notes, Luke’s broad take was that the yen move looks less like the start of another 2024-style panic and more like early evidence that Washington’s intervention is working – easing the Japan pressure on our markets in an orderly way.

We’ll keep tracking this.

Coming full circle to this evening’s homework

Drama from the Japanese currency market is exactly the kind of tripwire that has nothing to do with AI fundamentals but could still knock 25% off your favorite AI stock in a hurry.

You can’t control it. But you can decide, right now, which positions you’d hold through that kind of storm – and which you’d sell.

Know what you own, why, and when you might let go. Because between rogue AI agents, a potential Washington kill switch, and a central bank on the other side of the world, the tripwires are multiplying.

And that’s really the two sides of tonight. The homework above is your defense – deciding in advance what you’ll sell when the craziness hits.

But tonight’s workshop with Luke, Louis, and Eric is the offense – hunting for the handful of AI supply-chain names with enough structural staying power to sit in “bucket one” and ride out every storm along the way.

Both matter. So do your homework – after joining Luke and crew live at 8 p.m. Eastern.

Have a good evening,

Jeff Remsburg

The post 700 AI Agents Went Rogue – Now What? appeared first on InvestorPlace.

There are multiple facets to this strategy, but the one I want to focus on today is stocks to buy and hold forever.

You should think of these investments as your core holdings – your “Forever Stocks.” These are the stocks you hold through thick and thin (unless the rationale for owning them changes significantly).

Obviously, Forever Stocks will suffer during a severe bear market, just like ordinary stocks. So, any investor who holds onto stocks like these during a sell-off is likely to suffer losses on paper.

But these losses are a small price to pay for big, long-term gains…

Today, I’ll share a company that I consider to be one of the best Forever Stocks out there. Then, I’ll share where you can find more of them.

The Ultimate Essential Business

Water is the ultimate, most essential resource that is continually recycled and put back to use. Mother Nature does most of the heavy lifting through a complex process of evaporation, precipitation, and decades-long subterranean filtration cycles water through planet Earth to sustain both flora and fauna.

The bottled water industry is a small drop in that planetary bucket, but it too operates within Mother Nature’s hydration cycle. This trait gives it a circular economy identity. Its core product is naturally replenished. And it can source and distribute that product without relying on a foreign supply chain. In other words, the bottled water industry can thrive as a purely domestic business.

Enter Primo Brands Corp. (PRMB), one of the largest branded water companies in North America.

In a market obsessed with AI-everything, Primo Brands seems defiantly analog. It manages springs, bottles water, delivers water, restocks coolers, and operates a vast logistics network. The company delivers drinking water through three primary sales channels.

First, it sells branded bottled water at retail.

This division, which accounts for about 60% of total company sales, includes national powerhouses like Poland Spring and Pure Life; regional spring-water labels like Arrowhead, Deer Park, and Ice Mountain; and premium brands like Saratoga and The Mountain Valley. The company distributes these brands into more than 200,000 retail outlets across the United States and Canada. That footprint helps Primo secure better shelf space, more frequent promotions, prominent placement in refrigerated cases, and eye-catching displays throughout stores.

Second, Primo operates a large direct-delivery network that supplies five-gallon water bottles directly to homes and businesses.

This segment, which accounts for about 35% of company sales, resembles a subscription utility. Customers place recurring orders, Primo loads routes from local branches, and trucks deliver water on a scheduled cadence. That recurring model produces predictable revenue and valuable, long-lasting customer relationships when service runs smoothly.

Third, Primo runs exchange and refill businesses, which account for about 5% of total sales.

Consumers can exchange empty multi-use bottles at approximately 26,500 retail locations or refill bottles at over 23,500 self-service stations. That reusable packaging system reduces plastic waste while creating recurring, traffic-driving transactions.

Behind these customer-facing activities sits a company-controlled network of more than 80 springs, bottling plants, distribution centers, warehouses, and delivery fleets. Primo employs more than 12,000 people and manages logistics coast-to-coast. This is not a marketing shell over outsourced production. It is an industrial hydration platform.

Both an AI Survivor and Applier

Importantly, Primo is one of those rare enterprises that operates a relatively future-proof business. That quality puts it squarely into the AI Survivors framework. Artificial intelligence cannot replace hydration. It cannot digitize a spring. It cannot virtualize a truck route. The demand for clean water persists, regardless of technological shifts.

At the same time, Primo also fits in the AI Appliers category. The company already invests in warehouse management systems, forecasting tools, and digital customer interfaces. AI-driven route optimization can reduce miles driven. Predictive analytics can cut inventory imbalances. Call-center automation can shorten resolution time and increase retention. Revenue management systems can help Primo determine the best prices, package sizes, and assortment of products to offer.

AI will not eliminate Primo’s network. It will make the entire operation more profitable.

This water company does not promise moonshot growth. It offers something rarer: a durable, essential business with recurring revenue, tangible assets, and the potential to benefit from AI without depending on it. That combination can reward patient investors far more reliably than the next AI wannabe.

Technology will change. Markets will rise and fall. Investment fads will come and go. But people will still need water – and Primo will still be there to deliver it.

That’s the kind of staying power you want from a Forever Stock. You can learn more about these kinds of stocks here.

Regards,

抖阴最新版

P.S. Of course, AI remains the investment story of the moment. But my colleague Luke Lango believes investors may be looking in the wrong place. Tesla and SpaceX grab the headlines. Luke is more interested in the companies supplying technology that Elon Musk’s businesses can’t operate without. Tonight at 8 p.m. ET, Luke will explain Musk’s emerging “Vertical AI Masterplan” and identify several stocks he believes could benefit from it. Reserve your spot here.

The post A “Forever Stock” for a Thirsty World appeared first on InvestorPlace.

That customer is Cursor, an AI coding platform where developers search large codebases, write new features, find bugs, run tests, and hand larger projects to AI agents. Elon Musk’s SpaceX (SPCX) completed its $60 billion acquisition of the company on August 14. Two weeks later, OpenAI announced that it will wind down the contract supplying its models directly to Cursor, with a proposed cutoff date of November 12.

OpenAI didn’t dress it up, either. The company said it simply couldn’t trust SpaceX to play by its rules, pointing to past disputes with Musk’s businesses. The ownership change gave OpenAI a narrow window to cancel – and it took it.

It’s one of the most expensive “no thanks” in recent tech memory.

According to Wired, Cursor was one of OpenAI’s five largest customers at the beginning of 2026. By spring, OpenAI reportedly estimated that the relationship could generate more than $1 billion in annualized revenue.

OpenAI decided the risk of working with Musk outweighed the money.

The feud will grab the headlines. The more revealing question is what OpenAI actually takes with it when it leaves.

Cursor keeps the developers, the workflow, and the ability to route requests to other models.

And that may explain why Musk was willing to pay $60 billion for the company in the first place…

Why OpenAI’s Exit May Not Break Cursor AI

At first glance, OpenAI’s decision looks like a major blow.

Cursor built much of its early success by giving developers one workspace where they could use several leading AI models while they coded. Losing direct access to OpenAI removes one important option from that menu.

But the damage looks a lot smaller than the headline once you dig into things. 

Cursor CEO Michael Truell says OpenAI models currently handle roughly 5% of the platform’s user traffic. Now, that figure comes from Cursor itself, and traffic share doesn’t tell the whole story. Some teams have workflows tuned specifically to OpenAI’s models, and switching means retesting or rebuilding parts of their setup.

Even so, Cursor has alternatives. Anthropic responded to the breakup by promising more computing capacity for Claude inside the platform. Cursor also offers models from other providers and increasingly develops its own. 

OpenAI is closing the direct pipeline, not every route into Cursor. Developers can still bring their own API access, use the Codex extension, or connect through compatible AI gateways.  Everyone else can keep working with Claude, Gemini, Grok, Cursor’s own models, or whatever comes next.

The breakup reveals how the balance of power is changing.

A few years ago, the model provider appeared to own the scarce resource. Applications depended on whichever lab had the strongest intelligence.

Now platforms like Cursor can choose among several capable suppliers.

OpenAI can leave the platform.

Cursor still owns the workflow.

Cursor’s Developer Workflow Is Becoming the Moat

By February, Cursor had reportedly surpassed $2 billion in annualized revenue, double its level three months earlier. Corporate customers accounted for roughly 60% of that total.

The revenue is following the workflow. Developers stay inside Cursor while the models rotate behind the scenes. And the closer Cursor gets to the work, the more leverage it gains over the models supplying the intelligence.

Cursor says it routes hundreds of millions of coding requests each week across different models and providers. Its routing system sizes up each task – what it is, how hard it is, which models have nailed similar work before – and sends it wherever it’s most likely to get done well and cheap. 

Routine work can go to a cheaper model. A difficult debugging job can go to a more capable one. Another system may prove strongest at planning or understanding a large codebase.

No model wins every category. When customers use its router, Cursor can decide which one gets the job. 

That gives the company something no individual model lab gets to see clearly: a live scoreboard of how every model actually performs on real work.

It sees which outputs developers keep, when they ask for corrections, where one model struggles and another succeeds. And it can use those signals, within users’ privacy and retention settings, to improve how future work gets routed.

This is what Musk bought.

Cursor owns the interface, the enterprise relationship, and the decision about which model gets each job.

The AI Model War Is Becoming a Distribution and Routing War

OpenAI’s exit exposes the new balance of power.

Model labs still compete by building better intelligence. Platforms like Cursor increasingly influence where that intelligence gets used and which provider receives the work.

A clearly superior model can still command premium prices and pull in developers. But intelligence only makes money when it reaches users inside a real workflow. 

Cursor already owns that workflow.

The best model for a particular task may come from OpenAI today, Anthropic next quarter, and a new open system a year from now. Cursor can make those changes largely invisible by routing work behind the scenes.

The user keeps working in the same place while the supplier changes underneath.

Cursor’s own research shows where this could lead. The company says its router can send simpler work to lower-cost models and reserve premium systems for tasks where their added ability justifies the price. In company-run A/B tests across millions of requests, Cursor says its router delivered frontier-quality performance at 60% lower cost. 

That is a very different AI market from the winner-take-all model war many imagined.

One model handles routine coding. Another plans a complex software migration. A third debugs visual interfaces. A fourth runs sensitive work inside a private environment. 

The platform decides who gets called into the game.

That makes control of the workflow a powerful bargaining chip. Model companies need distribution to turn intelligence into revenue – and the platforms own the distribution. 

OpenAI walked away from Cursor because it did not trust the new owner.

Anthropic immediately leaned in.

That is what competition looks like when the customer owns the valuable real estate.

Why SpaceX Paid $60 Billion for Cursor

SpaceX paid $60 billion to own the place where developers build software.

That gives Musk a direct outlet for the enormous amount of computing power SpaceXAI is assembling. xAI can train Grok. SpaceX can build the infrastructure. Cursor gives that intelligence a direct route into a large group of paying users.

In the tech economy, developers punch far above their weight. They build the software used by banks, retailers, factories, hospitals, governments, and consumers. The models and tools they choose today can shape which clouds, chips, and platforms receive traffic tomorrow.

Cursor made that logic clear when the acquisition closed. The company said SpaceX would give it access to what it called the world’s largest GPU fleet, allowing Cursor to build stronger models at lower cost.

Then it summed up the deal in one sentence: “Cursor will be one place where that intelligence becomes useful.”

Cursor gives Musk control of the workflow. OpenAI’s exit reveals the limit of that control – and another important part of the investment thesis.

A model supplier can leave. Cursor can redirect much of the work toward Anthropic, Google, Grok, its own systems, or whatever capable model comes next. The switch may carry costs, and certain workflows may need to be rebuilt. But the exits are clearly marked. 

The physical bottlenecks underneath Musk’s companies are much harder to route around.

The Partnerships Musk Cannot Replace

SpaceX cannot replace an advanced chip foundry with a software update. Tesla cannot swap out every power semiconductor, optical component, sensor, manufacturing tool, and specialty material whenever a supplier walks away.

Those capabilities require factories, scarce technical expertise, years of qualification, and enormous amounts of capital. Musk can pull more production in-house. But he can’t conjure an entire supply chain on command.

I’ve spent months mapping the suppliers that SpaceX, Tesla (TSLA), xAI, and Musk’s other companies still rely on.

This evening, at 8 p.m. Eastern, I’ll walk through that system during a free InvestorPlace workshop. We’ll dig into where Musk remains dependent on outside partners, where the hardest bottlenecks are forming, and which suppliers could feel the biggest impact as his spending accelerates.

OpenAI just showed us the layer Musk can route around. Tonight, we’ll show you the suppliers he still has to pay.

Reserve your spot for the free event while you still can.

The post Why SpaceX Paid $60 Billion for the Layer OpenAI Can’t Take Away appeared first on InvestorPlace.

I follow the numbers, folks.

Way back when, I was one of the first “quants” on Wall Street. I built my career systematically searching for stocks with fantastic earnings growth, rock-solid fundamentals and growing institutional buying pressure.

My friend and InvestorPlace colleague Jonathan Rose also follows the numbers.

But he does it in a very different way.

Jonathan has spent three decades as a trader. He studies trading flow, volatility and how professional traders are positioning their money. And the result is usually quick trades for big gains.

So, Jonathan and I come at the market from two very different directions.

But right now, we’re both seeing an important trend in the massive AI infrastructure buildout.

That’s why I was excited to join Jonathan this morning as the first-ever live guest on his YouTube show, Masters in Trading: Live.

You can click here or on the image below to watch our full conversation.

How Jonathan Follows the Smart Money

And if you like Jonathan’s approach, I encourage you to check out Masters in Trading: Live regularly. You can sign up for his free email list here.

Jonathan has spent nearly three decades in the options market, including time on the trading floor and as a market maker at the Chicago Board Options Exchange. Over his career, he has generated more than $10 million in trading profits.

What interests me most is how he uses the options market to track where big money may be moving before the story becomes obvious.

That is also the idea behind his Masters in Trading Challenge.

Over seven days, Jonathan teaches his proprietary “Big-Money Tell” strategy, which is designed to identify unusual spikes in options demand that can sometimes appear ahead of earnings reports, product launches, mergers and other market-moving events.

In other words, I use fundamentals and institutional buying pressure to help identify strong stocks. Jonathan teaches investors another way to follow the money and potentially improve their timing around those opportunities.

In fact, Jonathan trades a lot of the same stocks we discuss here regularly. But with his strategy, you could magnify your gains on those same stocks – and make them in a fraction of the time.

So, if you’d like to learn how Jonathan puts his strategy to work, click here to learn more.

Sincerely,

抖阴最新版

Editor, Market 360

The Editor hereby discloses that as of the date of this email, the Editor, directly or indirectly, owns the following securities that are the subject of the commentary, analysis, opinions, advice, or recommendations in, or which are otherwise mentioned in, the essay set forth below:

The post Why the AI Bottleneck Is Getting Worse – And How We Can Profit appeared first on InvestorPlace.

The market is watching the wrong AI bottleneck today.

That’s the take of legendary investor 抖阴最新版. And if he’s right, it changes where the smart money goes next.

This was one of the topics that Louis and veteran trader Jonathan Rose dove into this morning, when Jonathan hosted Louis on his Masters in Trading Live show.

Everyone’s fixated on chips and copper as the choke points for the AI buildout. But Louis says the real squeeze is somewhere else that many investors aren’t looking – and he laid out why it gets worse into 2027, not better. It’s the kind of call that looks obvious only in hindsight.

This was just one topic from this morning’s free episode…

Louis also revealed where he’s putting money to work in energy today (hint: it’s not just oil itself). And he took investors under the hood of the S&P’s eye-popping 53% earnings quarter to explain why he thinks near-term momentum has already peaked – and what that means for how he’s picking stocks right now.

If you missed it, you can catch the free replay of the show right here.

And if you’re not taking advantage of these free daily episodes, I want to put them on your radar. Jonathan publishes them every day that the market is open at 11 a.m. ET. He profiles market trends, explains entries and exits, discusses the opportunities he’s watching in real time, and offers plenty of tickers along the way. You can sign up right here to receive daily reminders and links to the upcoming episodes.

Back to today’s episode, it was a great listen with plenty of action steps from two of the best in the business. Again, you can check it out right here.

The fallout from last Friday’s jobs report

On Friday, the Labor Department reported that the U.S. economy added 162,000 jobs in August – roughly triple the 53,000 that economists surveyed by the Wall Street Journal had estimated. And the two prior months – both ugly – were revised higher: July flipped from a reported loss of 23,000 jobs to a gain of 21,000. Meanwhile, the unemployment rate held at 4.1%, a historically low reading.

To be fair, some of the strength looks like noise. A big chunk came from rebounds in restaurant hiring and local-government education – categories many economists chalked up to one-off swings. But even setting those aside, the data reveal a labor market that refuses to roll over.

Now, Federal Reserve Chairman Kevin Warsh has said the inflation data, more than anything else, will drive the Fed’s decision at next week’s FOMC meeting. But the labor market is what could hold the Fed’s hand. Raising rates into a visibly weakening job market is risky business; it invites recession.

So, a soft August jobs report could have handed the doves a ready-made objection – why tighten into weakness? This was why – last week – Louis told his Growth Investor subscribers, “We want to root for a weak payroll report because it would cause the Fed not to raise rates in September.”

Friday’s number clearly wasn’t “weak.” So, while it didn’t assure a rate hike, it certainly didn’t provide a reason not to hike.

Everything now points to this Friday, when the August Consumer Price Index (CPI) lands – the last major data point before the Fed meets next week. Here’s Bloomberg Economics on what that sets up:

The strong August jobs report raises the risk of a Fed rate hike in September.

It leaves the August CPI report (due Sept. 11) as the determining factor — and we expect that reading to be just borderline acceptable to the doves.

The September FOMC meeting is shaping up to be a very close call.

As traders bet on a hike, President Trump is demanding a cut

Before Friday’s report, the CME Group’s FedWatch tool put the odds of a September rate hike at 49%. By Friday afternoon, those odds had jumped to 60%.

One very important person is not on board…

Within hours of the report, President Trump took to Truth Social to demand the opposite of what the market is now bracing for:

Great jobs number just announced, breaking all estimates (except mine!) by double and triple…

A STRONG COUNTRY MEANS A LOWER INTEREST RATE – IT’S A BETTER CREDIT…Very simple!…

LOWER THE RATE OR I’LL STOP TRADING WITH COUNTRIES WITH WHICH WE HAVE A DEFICIT…

The Fed Board, with its great new leader, must get smart – BE PATRIOTS for a change.

He’s not alone – Vice President JD Vance called for lower rates last Thursday, saying:

We’re doing a lot of things to try to keep those interest rates down, but it would be nice to have some help from the Federal Reserve.

As someone who would like lower rates – or at least, not higher rates – I scratch my head at these pressure tactics and their likely effectiveness.

Wall Street and opponents of the Trump administration are sensitive to any appearance of White House pressure, seeing the Fed’s independence as paramount. Given this, these calls to cut rates seem to put the Fed members in a bind – they risk the exact outcome the White House doesn’t want…

If the Fed holds rates steady next week on a close call, the members who voted to hold could look like they caved to Trump and Vance. So, it’s reasonable to think some of them, precisely because they don’t want to appear influenced, might vote to hike instead.

In any case, we’re headed for a genuine collision. The data are making a defensible case for a hike. The president is threatening trade partners to force a cut. And the number that determines what happens – the CPI – lands on Friday.

We’ll report back.

The most interesting Elon Musk trade…

There’s a pattern that runs through every technology boom, and it’s worth a minute even if you never buy a single stock because of it.

The pioneer who breaches a new frontier gets the headlines. But some of the biggest fortunes go to the unglamorous companies that the pioneer depends on to make the vision a reality.

For example, Halliburton (HAL) outran Exxon (XOM) during the offshore oil boom. And a 150-year-old glassmaker named Corning (GLW) quietly helped make the first iPhone possible. Different eras, same lesson: when a giant charges into new territory, the smart money watches the suppliers who are making it possible.

If you’ve read the Digest over the last week, you know that our technology expert Luke Lango has spent months applying this idea to the most ambitious builder of our age – Elon Musk.

Between Tesla (TSLA), SpaceX (SPCX), xAI, and X, Musk is assembling what Luke calls a “Vertical AI” empire – an effort to drag AI out of our screens and into the physical world of robots, cars, satellites, and factories. It’s staggeringly ambitious. But here’s the tie-in…

For all his vertical integration, Musk can’t manufacture every chip, sensor, motor, magnet, and networking part his empire will devour. He must buy them – by the thousands.

“From whom?” is what Luke is after. Here’s how he puts it:

When Musk starts spending to build something new, I want to know who’s cashing the checks.

It’s been a lucrative question that Luke has asked before. He’s recommended 33 stocks tied to Musk’s businesses that went on to double or better at their highs afterward.

Now, Luke thinks the clock is ticking on new opportunities. He’s circled September 24 – a date he believes could remove an important obstacle to Musk’s next big move.

He’ll pull back the curtain on those details tomorrow at 8 p.m. Eastern, when he hosts a free workshop alongside Louis and 抖阴最新版. You can reserve your seat right here. Luke will be giving away the name and ticker of one supplier stock free, just for showing up.

If you’ve ever wanted to invest alongside Elon Musk without simply buying TSLA or SPCX, tomorrow’s workshop will highlight your roadmap. Reserve your free seat here, and we’ll see you at 8 p.m. Eastern.

Now, speaking of following the suppliers…

The robot boom has a hidden supplier monopoly – and it’s Japanese

Luke’s point is to invest in the picks-and-shovels plays that help make those massive products. Our senior analyst Brian Hunt, editor of Money & Megatrends, is recommending doing exactly that on one of the most tangible corners of Musk’s shopping list – the physical guts of the robots themselves, including the Optimus humanoids Tesla is racing to mass-produce.

Brian has been covering what he calls the Robotics Revolution since 2024, and the scale is getting hard to ignore. Here’s Brian:

Last year, Amazon announced it uses more than one million robots across its business. This figure will soon exceed the company’s number of human employees.

His angle isn’t to guess which humanoid maker wins. It’s to own the companies making the precision parts every robot needs – the joints, gears, motors, and bearings – no matter whose logo ends up on the machine.

But here’s the wrinkle from Brian: the U.S. long ago ceded that manufacturing, so the best suppliers are mostly Japanese:

The robotics revolution is global but many of the supply chains run through Japan.

He points toward one company that few here in the U.S. have heard of: Nabtesco (6268), a $3.4 billion firm that controls nearly 60% of the market for cycloidal reducers (the heavy-duty gearboxes buried inside robot joints).

Back to Brian:

When a large robot is carrying a heavy box across the factory floor, the cycloidal reducer is a key component that enables the robot to do so repeatedly without joint wear or loss of precision.

If humanoids scale the way Musk and others are betting, orders for parts like these could swamp the handful of firms that dominate them – the same squeeze that has sent optical-networking and memory stocks soaring over the last 12 months.

Nabtesco is just one of four Japanese suppliers Brian names. You can get the other three – free – in his free issue right here.

And for more from Brian, you can sign up for his Money & Megatrends newsletter right here. Every day the market is open, he delivers actionable insights loaded with specific stock tickers – and it’s 100% free.

We’ll keep you updated on all these stories here in the Digest.

Have a good evening,

Jeff Remsburg

The post Why Traders Are Betting on an Interest Rate Hike appeared first on InvestorPlace.

This Week’s Ratings Changes:

Upgraded: Strong to Very Strong

Downgraded: Very Strong to Strong

Upgraded: Neutral to Strong

Downgraded: Strong to Neutral

Upgraded: Weak to Neutral

Downgraded: Neutral to Weak

Upgraded: Very Weak to Weak

Downgraded: Weak to Very Weak

To stay on top of my latest stock ratings, plug your holdings into Stock Grader, my proprietary stock screening tool. But, you must be a subscriber to one of my premium services.

To learn more about my premium service, Growth Investor, and get my latest picks, go here. Or, if you are a member of one of my premium services, you can go here.

Sincerely,

抖阴最新版

Editor, Market 360

The post Advanced Micro Devices Upgraded, Eli Lilly Downgraded: Updated Rankings on Top Blue-Chip Stocks appeared first on InvestorPlace.