Today I'll show you exactly when I plan to start the engine.

Quick refresher for anyone who skipped it…

On next Thursday’s open, some 911 million shares of SpaceX come unlocked. The entire tradeable float right now is 639 million.

As Elon likes to say…

Let that sink in.

One Thursday morning hands the market 1.4 times the existing supply of a stock that's already rolled over from $200. Two days after the first earnings report in company history.

But here’s what I didn’t mention yesterday: that's only the first step.

Another 7% drips loose every couple of weeks through late October. Then another 1.3 billion shares after the Q3 report in November.

On December 8, everything left breaks the cage. Call it 4.6 billion shares between now and Christmas.

Historically, 15% to 40% of newly freed shares get sold in the first few weeks of each unlock. Given who's holding this, lean high.

Take, for example, the venture funds. They've been locked in for maybe ten years. They’re still up big.

And, for some, selling might not be a choice.

They owe money to pensions and endowments who've spent a decade asking one question—when do we get paid? August 6 is the first exit door anybody has offered them.

They need liquidity.

Then there are the employees.

Twenty-somethings sitting on eight-figure paper. As you read this, they’re calling realtors and scrolling through different colored Lambos.

Now consider something else:

The Forced Buyer is Exhausted

Russell 1000 added SpaceX in June. Nasdaq 100 took it on July 7. The biggest forced buying is already behind us.

And the S&P 500 requires GAAP profitability, which SpaceX doesn't have—it lost $5 billion last year—so the largest automated buyer on Earth sits this one out.

Even the bulls know the math.

Morgan Stanley carries a $300 target, the highest of the major banks. Their own note has SpaceX free-cash-flow negative until 2035 and needing roughly $84 billion a year of outside capital from 2027 through 2034.

That's the bull case.

My SPCX Strategy

None of this is a knock on SpaceX.

I want to own it. It’s a monster. One company flies more than 80% of what humanity puts into orbit. Someday, it will be the most valuable company on the planet.

So here's what I'm doing. $160 a day. Every trading day, no exceptions, no thinking. It keeps me in the story while I wait to start the truck.

But I'm not waiting for a specific number. I'm waiting for a specific moment—when the biggest lockups clear, the shorts start covering, and the tape stops making new lows.

That cluster is my signal. Best guess: it lands in November.

Here's the real opportunity…

The Fun Part

While SpaceX could get cheaper from here, the things SpaceX needs will not.

A company burning $84 billion a year has one way to keep its cost curve from eating it alive—own the bottleneck.

Buy the supplier. Vertically integrate the piece nobody else can make. Musk loves this move because it works.

And that short list exists. But it’s small.

One name on it has a market cap smaller than what SpaceX spends on propellant in a quarter.

At the same time, going public handed SpaceX something it never had—stock it can spend. So it buys the pieces it can't afford to keep renting.

James has been using AI to track Elon’s every move. And his AI system keeps pointing at one company.

It's $115 this morning.

You're not alone in the hole. 

Six members of Congress bought that same week, and they're underwater too. Cathie Wood has been buying more nearly every day, all the way down. 

So everybody holding this stock is asking the same thing. 

Do I sell.

Wrong question. Ask one you can actually answer today.

What did you buy?

Maybe you think you bought a rocket company. 

In reality, you bought a rocket company, an AI company, a satellite internet provider, and Twitter, all folded into one ticker over three years. 

And every one of those pieces answers to one man.

Not the board. Not the investors. Elon.

Next week, that simple fact is going to matter. And if you own SPCX—(and even if you don’t)—there are three dates you need to watch. 

First, let’s back up. 

The Rules Nobody Reads

Before a company sells a single share, somebody writes down the rules. How many shares exist. What each one is worth. Who decides what, and by how much.

That document is the corporate charter, and the founder writes it.

The law says one share, one vote. Then it says you can ignore that, and hands you the pen.

Zuckerberg took the pen. 

He wrote two kinds of stock into Meta's charter—the shares he sold to the public get one vote, the shares he kept get ten. 

Then he sold most of his stake and pocketed the money. Today he owns about an eighth of Meta and wins every vote in the building because his eighth is worth ten times that. 

Musk wrote the identical rule into SpaceX. Then he did the one thing Zuckerberg didn't.

He never sold.

He owns 42% of the company and controls 85% of the votes. No board to talk him out of anything. No shareholder who can force a question. No meeting he can lose.

He built it that way on purpose. 

What He Does With It

A man who can't be outvoted doesn't negotiate. 

He gets what he wants. When Musk needs something, he buys the company that makes the thing.

And that’s how he’s always worked. 

He paid $109 million for a German automation shop in 2017, for the machines that build his machines. He paid $218 million for a battery company in 2019, kept the one process he wanted, and runs it in Texas today. 

He bought a thirty-person satellite startup in 2021 for its engineers, and their thruster flies on Starlink now. 

He also bought Twitter for $44 billion and blew a hole in his own net worth. He sold xAI to SpaceX for a quarter of a trillion dollars—the seller and the buyer, same man, same afternoon.

That's what 85% buys you. The freedom to do all of it and not beg for permission. 

Now here’s why this is important. 

Three Dates to Watch

Tuesday, SpaceX reports earnings for the first time as a public company. Twenty-four years private, and analysts finally get to ask their questions out loud.

Thursday, $123 billion in insider stock unlocks, and everyone who got in early decides whether to run.

Both are out of your hands. The third one isn't.

Because before either date, something else is moving, and nobody's watching it.

This Sunday at 7 p.m. Eastern, James goes live to make his case for the company he believes is Musk’s next big buyout—a name a two-hundredth the size of SpaceX, holding the one thing Musk won't rent.

He's calling it Elon's Big Buy.

The event is free to attend, but here’s the problem.

Musk doesn't announce on a schedule. He's made a career of moving before anyone's ready.

So we'd rather you not depend on an email you might catch too late. On the next page, you can also opt into text alerts, which we don't open up on every event. (Only if you want them.)

There’s only one way to join the event:

Click here and sign up so your seat’s locked in. 

Ten and a half hours. 1.3 million tokens. Working physics. Crisp graphics. Every art asset generated out of thin air.

Total cost: sixty dollars.

I’m bringing it up again today for two reasons. 

One, because I want you to understand how remarkable this is. 

Second, because it has everything to do with why I jumped on Mark Jeffrey’s podcast Hash Rate this week. 

First things first. 

Why This is Crazier Than Anyone Realizes

A “token” is about three quarters of a word. 

So the AI thought its way through a million words to hand me a video game. That’s about the length of the complete Harry Potter series. 

And it did it in about ten hours. 

I couldn't read a million words in ten hours. Nobody could. Let alone program a game from scratch. 

And how it did it is even more remarkable. 

I lifted the method from Matt Schumer. You tell the model to screenshot the real Call of Duty, build its own version, screenshot that, compare the two, split the work across agents working in parallel, and keep going until they match. 

I'm a coder by background. I remember trying to make games twenty years ago. It would’ve been impossible to make anything like this by myself, let alone in 10 hours. Even two years ago this project was a team of programmers, six months, a million dollars. I would have signed the check.

Today it costs sixty bucks. 

Everyone is arguing about inflation. Here's a number nobody has in a spreadsheet: one job, a million dollars down to sixty, in twenty-four months.

I got into all of this with Mark. And it went somewhere uncomfortable for many software stocks.

So What's Left? 

If I can clone a billion-dollar franchise in a day for the price of a decent dinner, then the code protects nothing. 

If the software doesn't protect a company anymore, something has to. 

Three things survive. 

Data. An AI is only as smart as what it's been shown. And there's information it will never see. A bank's fraud records. A hospital's scans. A police department's body cam footage. Nobody is emailing that to an AI lab. Whoever holds the most useful data holds something all the cheap intelligence in the world can't copy. 

Distribution. Being where the customer already is. I can build a better product this afternoon and never reach a single person using the incumbent. Ask anyone who built a better search engine.

Trust. The one everybody underrates. I could write my own tax software. I use H&R Block anyway. Mine would probably be better. But when the audit letter arrives, I want somebody else's name on the box. Nobody ever got fired for buying IBM.

Here's what all three look like in one company.

There's an AI project called Score that teaches machines to tell a forest fire from a guy with a flashlight on a camera feed. 

Boring. Enormously valuable. 

It works the way I just described—train on the specialized footage a general-purpose model will never be shown. And it reaches customers through PricewaterhouseCoopers, so it arrives wearing a suit clients already trust.

Score isn't a startup in Palo Alto.

It's a subnet on a crypto network called Bittensor.

The Trade

Bitcoin talked strangers all over the world into plugging in computers and burning electricity, and paid them in Bitcoin for the trouble. 

Though it’s down from all-time highs, few people can argue it hasn’t been a massive success. 

Bittensor pointed the same trick at AI. Run the machines through subnets (which are small AI companies on Bittensor), do useful AI work, get paid in a token called TAO. Best work earns the most. 

Meanwhile, Kimi K3 released its open weights on Monday. These stories are related. 

Because the moat everyone assumed the frontier labs would enjoy for years evaporated in about three seconds. State-of-the-art intelligence is rapidly becoming a commodity.

Commodities have no brand loyalty and no network effects. The cheapest producer wins. Full stop.

So who's cheapest? Bittensor. 

There's another subnet on Bittensor running frontier models on consumer graphics cards instead of million-dollar racks, on the cheapest power on Earth. Cheapest anywhere. Market cap of about twenty-five million dollars.

Meanwhile OpenRouter carries a valuation around ten billion. A Bittensor subnet called Chutes supplies a great deal of what routes through OpenRouter and books roughly a million in revenue.

Bittensor, the network all of these subnets run on, has been dead money around $200 for over a year, waiting on a breakout nobody has delivered yet.

Cheap frontier intelligence might be it.

And here's what makes it interesting at its current price. 

Bittensor is 128 AI companies wearing one ticker. Vision. Translation. Bank fraud detection. Several selling raw AI horsepower cheaper than anyone on Earth. 

Together they're valued at roughly two billion dollars, at a moment when a single AI company can carry a trillion. 

That’s the opportunity. 

I say “unique” because it's not about inflation, or wars, or recessions, or flash crashes, etc. The market has spent centuries bouncing back from those things and it will continue to do so. 

But what's unique is that AI, in just a few years, has 2 billion paying customers and many trillion dollar companies. 

Out of nowhere. Imagine that. 2 billion users. Trillion dollar companies. And the innovation that (hopefully) will come from that. And the news cadence is so fast that the whole AI story gets confusing from week to the next. 

Here’s what we’ve seen just in the past week or so. These are just bullets because each requires big summaries and discussion. 

• The rise of Kimi 3. Unlike the Deepseek moment a year ago, this shows that light weight, open source AI models could be just as good as the trillion dollar companies. 

What does this mean for trillion dollar valuations on Anthropic, OpenAI, and even SpaceX? 

What does this mean for all infrastructure plays if I don’t need as many data centers or chips? 

• The Chinese ASML. A trillion dollar monopoly … no longer a monopoly? 
• The NVDA / Open AI circular deal? Is this what bubbles are made of? 
• The Chinese Micron. Does Micron deserve , then, its trillion dollar valuation? 
• The release of Claude Fable. All of intelligence just became a software issue. And Fable is the software. What does this mean for every single company, from Adobe, to H&R Block, to Intuit, to Microsoft, when Claude can do you better. 

Some more on that last one: 

On Saturday night I put in this prompt into Claude Opus 5, using Claude Code on ultracode mode. 

I want you to build a first-person shooter at the level of the most recent Call of Duty games. It should be utterly perfect, visually beautiful, with every single thing done at AAA quality—from textures to physics to anything you could think of.

Fan out sub-agents and have sub-agents tackle each one individually so that the game is utterly perfect. You should /loop on each item and have a separate sub-agent check it visually to ensure it looks triple A. That separate sub-agent should be a really harsh critic, and if it doesn't look triple A, it should keep going.

Don't stop until each sub-agent is utterly wowed with the quality when compared with the actual Call of Duty game. It should literally compare them side by side blind and say which one looks better. Do this in ThreeJS. /loop until it's utterly perfect. Fan out sub-agents and ultracode.

I let it run for 10 hours. 

The result was a complete first person shooter game at arguably the quality level of Call of Duty. 

I stopped it at ten hours. If I had let it run for 20 hours it would be even better. 

I posted a video about the game and the game itself to play at https://operation-blackout-trailer.vercel.app/

The reason I did it was to: 

1. Learn. Since I just gave you the prompt, you guys should try it also. 
2. It shows me that software that once cost millions to develop can now be developed for less than $100.
   
   

In other words, software is disposable. 

The entire way we value companies is going to change then.

Because all intelligence has also become a software question. For instance, instead of hiring an ad agency, NIke can just use a future version of Claude by saying, “Make me the best super bowl commercial”. And NIke will save millions and get a better result. 

Will this happen? I’ve spoken to Nike’s head of marketing and I’ve spoken to their ad agencies. The first is excited and the second is scared. 

The world will be a better place. 

But, first, there are going to be many valuation hiccups as we absorb all of this news plus who knows what news will be coming next week. 

I will compare this to the Internet Bubble in the 90s. Not the bubble part but the fact that we are seeing the rise of a technology that is still at its inception that is going to drastically change the world in ways we can’t even predict from week to week. 

The occasion was the Pennsylvania Defense and Innovation Summit. Jamie Dimon. Blackstone's Jon Gray. The CEOs of Lockheed, General Dynamics, RTX. Ten billion in pledged investment.

Trump wanted to talk about aircraft carriers.

The USS Gerald R. Ford launches planes with electromagnetic catapults instead of steam. He'd met a catapult operator who told him steam was better. 

You can fix steam with a hammer and a blowtorch, the guy said. Electromagnetic launch needs an MIT graduate, and there's rarely one on a flight deck at three in the morning.

Then the elevators. Also magnetic. Also bad.

"That makes you want to get sick," he said.

(Then: "I hope I'm not boring anybody.")

Nine minutes later, wedged between a coal-waste-to-jet-fuel venture and a list of Pennsylvania factory expansions:

"I hope you're gonna do magnets. Somebody out there, I hope, you're all brilliant people, magnets. Do magnets, okay? I'll tell you how to make money. Do magnets."

Then: "It's not complex, not hard. We have the material. For some reason, they just don't do it."

Magnets are a menace to the Navy and the greatest business opportunity in America. Both true. Same podium. Nine minutes apart.

The man contains multitudes.

He’s Not Wrong

On the substance, he's right. 

Steam catapults, designed in the 1950s, have proven insanely reliable. Electromagnetic Aircraft Launch Systems (EMALS)? Not so much. 

He's right about the second thing too. America should make magnets again. 

America stopped making them because it stopped being profitable. America can't start again easily because it's the exact opposite of "not complex, not hard."

What's gone is the chemists who tune a separation cascade, the furnaces that turn oxide into metal, and the floor knowledge of how to sinter a magnet that survives a missile. None of that was regulated away. It was sold.

But now there’s a time limit. 

Starting January 1, 2027, every defense contractor must certify that no stage of magnet production touched China, Russia, Iran, or North Korea. Not the magnet. Not the metal. Not the mine.

The rule reaches an estimated 78% of Pentagon weapons programs. It exists because investigators found Chinese samarium-cobalt inside the F-35's turbomachine pump and stopped deliveries.

Five days after Carlisle, Trump signed an executive order removing the escape hatch. "We couldn't find a domestic supplier" stops working unless you can prove you were funding somebody to become one.

The speech was a trailer. The movie came out Monday.

The 4% Problem

The global magnet market runs roughly 260,000 tons a year. America’s about 10,000 tons of it.

Four percent.

Meanwhile, ask how many end up inside things Americans actually buy and the number is closer to 50,000.

The growth categories are the ones we haven't lost. Defense, data centers, humanoid robotics. Nobody offshores a Tomahawk guidance package to Ningbo.

But four percent was a market outcome. January 1 is a legal requirement. 

Which is the whole bet, but it's worth remembering how these things can go. 

The Synthetic Fuels Corporation was going to free America from OPEC. 

Congress wrote into the statute that it could not be terminated before September 1992. Reagan killed it in April 1986. Of $88 billion authorized, roughly $960 million ever got spent, across four projects, none of which survived. 

Congress has moved the magnet deadline before, and there's already talk on the Hill of phasing it rather than enforcing it cold. 

But notice what actually killed synfuels. Oil got cheap. The premise evaporated on its own and no vote could have saved it.

The magnet version would be a China you could count on. Nobody's betting on that. The F-35 still can't fly without one. This mandate dies only if Washington decides to let it.

That's a failure mode we control. The only kind worth betting on.

Do magnets. But choose carefully. 

From a trading standpoint, my setup is straightforward. I want price above the 8-day and 21-day exponential moving averages (EMA). I want momentum to improve. And I want buyers initiating at higher prices instead of merely responding whenever Bitcoin or Ethereum falls. 

That setup is beginning to return.

But the charts are only part of what is starting to interest me. The other part is where investors may look for their next burst of momentum if the AI infrastructure trade begins to cool off. 

For much of the past year, the AI trade has been a vacuum cleaner for growth capital.

Semiconductors, memory, optical networking, power equipment and the rest of the data-center complex have offered something crypto could not. They had accelerating revenue, visible shortages and earnings estimates moving higher. As long as those stocks kept delivering both growth and velocity, there was little reason for momentum investors to go searching for another trade.

But no trade moves straight up forever. At some point, even the best-performing stocks need to digest their gains. That can happen through a sideways price consolidation, valuation compression as earnings catch up with share prices, a moderate correction or an internal rotation from the most extended semiconductor names into other parts of the AI complex.

None of those outcomes requires the AI thesis to break. 

In fact, I remain convinced that the AI infrastructure buildout has years to run. But a great long-term theme can still become a tired near-term trade. And when the market's fastest horse stops running for a while, capital begins looking for something else with momentum and room to move.

Bitcoin and Ethereum are obvious candidates. 

Both are liquid enough for institutions, volatile enough to produce the velocity momentum traders want, and well below the levels that would signal broad speculative enthusiasm. More importantly, they increasingly sit on opposite sides of the AI economy. Bitcoin is the scarce monetary asset in a world where AI should make many other things more abundant. Ethereum is part of the financial infrastructure that could support tokenized assets, stablecoins, and transactions initiated by AI agents.

That does not mean money will mechanically flow out of NVIDIA or Micron and directly into Bitcoin. Every seller still needs a buyer. What changes is the marginal bid. If AI stocks stop rewarding good news while Bitcoin and Ethereum begin breaking resistance, portfolio managers, systematic funds, and momentum traders will notice. Performance attracts capital. Capital creates more performance. That is how a rotation can build on itself.

Ethereum currently has the cleaner chart. Price has moved above the 8-day and 21-day EMAs (light blue and red on the chart below), and the 8-day EMA has remained above the 21-day since early July. It is not something we need to bang the desk over, but I see it as short-term bullish.

More importantly, price and both exponential moving averages are above the now-flattening 50-day simple moving average (purple on the chart above). Price has also reclaimed $1,815, the area where selling was cut off in early and late February 2026.

As long as Ethereum remains above its short-term moving averages and the 50-day SMA, my trading bias is bullish. Continued rotation around $1,815 is probably the most likely near-term outcome, but I think price is headed toward the declining 200-day SMA (green on the chart above) near $2,180 over the next month or so.

I do not expect Ethereum to trade up to the 200-day SMA and simply blow through it. That would be too easy. While I am becoming more bullish on crypto in general, I suspect it will take some work for price to break above that level and sustain the move. Longer-term investors are probably better off waiting for Ethereum to hold above the 200-day SMA and begin moving higher before getting too aggressive.

But that 200-day moving average could become especially important if AI stocks are consolidating at the same time. A sustained breakout in Ethereum while the AI leaders move sideways would be our first meaningful sign that investors are not simply buying a crypto bounce. They may be rotating toward a new source of momentum.

Bitcoin's chart is similar, though not quite as clean.

Price is back above the 8-day and 21-day EMAs, along with the now-flattening 50-day SMA. But recapturing those moving averages is not what should concern the bears. It is the persistent responsive buying under $60,000 that should have sellers scrambling.

When Bitcoin broke through its February lows in early June, the bears had a clear shot at driving it well below $60,000. They tried repeatedly and failed. Selling continues to get cut off whenever Bitcoin trades under that level.

The monthly chart explains why. The area around $60,000 marked major tops in 2021 and sat near the center of Bitcoin's lengthy 2024 consolidation. For one reason or another, supply is being absorbed around that level.

The near-term line in the sand is $65,000. A sustained move above it should bring the declining 200-day SMA near $73,000 into play. We tested that moving average in May, and I suspect Bitcoin will test it again over the next month.

From there, I will be watching how Bitcoin behaves relative to the Nasdaq and the major AI infrastructure stocks. If Bitcoin can push through resistance while the AI infrastructure complex continues digesting its gains, the rotation thesis becomes more than an interesting story. It begins showing up in price.

This is not a new bull-market declaration.

Bitcoin and Ethereum still have declining 200-day moving averages overhead, and both have work to do before the longer-term trend becomes convincingly bullish. But for trading purposes, I am leaning long while they remain above their 8-day and 21-day EMAs and responsive buying continues to overwhelm initiative selling.

The buyers are finally giving us something to work with. If the AI trade gives investors a reason to look elsewhere, crypto may finally have the fuel to do something with it.

 Disclosure: Long IBIT, ETHA, BTC

The cause was a Chinese model called DeepSeek that cost $5.6 million to train. Wall Street did the math in nine seconds. Cheap models, fewer chips, Nvidia's a pumpkin.

Jensen Huang said the market misunderstood it.

Three weeks later the $589 billion was back. Then the business showed up: $215.9 billion in revenue, up 65%, and $97 billion in free cash flow.

He was right.

Now He's Doing It Again

Last week a Beijing startup called Moonshot released Kimi K3—2.8 trillion parameters, the largest open model ever built, benchmarking near the American frontier at a quarter of the price.

On July 27 they publish the whole thing, free. Anyone can download it and run it on their own machines with the door shut and the internet unplugged.

Washington is deciding whether to ban it. 

Agencies barred from using it. The labs behind it blacklisted. Treasury Secretary Scott Bessent has floated sanctions, claiming the Chinese copied American models to build their own.

Huang sat down with Axios and said the unthinkable: American companies should use them. 

And then he called them… excellent.

The objection came back fast, and it's the one Washington is acting on. Security.

The Security Argument

Yes, you can strip the safety guardrails off an open-weight model. Researchers at FAR AI proved it. Meaning, you can do things they’re not supposed to with them.

They also proved you can do it to OpenAI, Google, and Anthropic models—through those companies' own fine-tuning APIs, moderation systems and all. Fifteen bad examples was enough to break GPT-4.

The vulnerability belongs to fine-tuning. A closed model can at least be revoked. Open ones can never be recalled.

Which is exactly why banning Kimi accomplishes nothing. The model drops July 27. After that it sits on every hard drive on earth, permanently, whatever Washington decides in August.

The recall option expires before the debate finishes.

Ask Who's Doing The Asking

David Sacks ran White House AI policy until March. 

He said this:

"The leading closed-source labs—already a duopoly in terms of model revenue—are hoping the government will take out their open-source competitors."

Meanwhile, a source close to the administration told Axios that the labs or their allies arrive with a plan to ban open source every three to five months.

Every three to five months. That's a lobbying schedule.

The security worries are real and serious people hold them. But look at what the remedies accomplish. Procurement bans and blacklists do nothing about fine-tuning. 

They reduce competition.

Meanwhile… Nvidia has its own reasons. 

Nvidia Loves Fragmentation

Nvidia’s biggest customers are also its biggest threat.

Google, Amazon, Microsoft, Meta and OpenAI buy so many chips that it's become cheaper to design their own. All five are doing it. 

Google has TPUs. Amazon has Trainium. Microsoft has Maia. Meta has MTIA. OpenAI is building with Broadcom. Custom silicon is growing almost three times faster than the GPUs Nvidia sells.

Think of Nvidia as a landlord.

Five giant tenants rich enough to build their own house are a problem. Fifty thousand small tenants who never could are an annuity.

Free models create small tenants. When a hospital or a bank or a twelve-person firm can download something excellent for nothing, they can finally afford to run AI. And none of them will ever design a chip. They buy from Nvidia, or they rent from somebody who did.

Software people have a name for this. Commoditize your complement. 

Give away the thing next to your product, and everyone still comes to you for the part that costs money. Google gave away Android so Apple couldn't own the phone.

So the closed labs lobby Washington to kill open models. Huang defends them on television.

He's right. But he’s also selling shovels.

The pros. 

The hedge funds and pension funds, with their armies of analysts, their satellite photos of parking lots, and their standing dinner reservations with people who know things.

In short, the people who always know more than you do. (Allegedly.)

But the phrase is older than any of them. Older than Wall Street itself. 

It began at the track.

Long ago, "smart money" meant the bets placed by the sharps—the gamblers with a winning record, or the ones with a tip straight from the stable. 

And the best part? You didn't need to know a fetlock from a furlong. You just watched where the smart money went and shuffled in behind it.

Go back further and "smart money" meant something else entirely. 

Something darker.

In the 1690s it was the money paid to sailors and soldiers who got maimed on the job—from the old sense of "smart" meaning to sting or hurt, the way a wound smarts. 

By 1818, it was the money you paid to wriggle out of whatever unpleasant situation you'd gotten yourself into. In court it meant punitive damages. 

Money that hurts.

So the phrase reaches us today from two meanings: The clever insider's bet. And the painful price of being stuck.

In 2026, both of them are true. At the same time. About the same money.

The Edge Smart Money Can't Buy

The smart money is still the insider's bet—the trillions sloshing around inside the funds. 

But somewhere along the way it became the other kind too. The hurting kind. 

The smart money got so catastrophically well-fed, it’s simply too big to buy anything too small. 

A tiny company could triple, quintuple, ten-bag, throw itself a parade—and it still wouldn't budge a hundred-billion-dollar portfolio by a rounding error. 

So they don't even look. They can't. 

Even Warren Buffett pointed out the edge he lost as he grew. 

"The highest rates of return I've ever achieved were in the 1950s,” he told Businessweek in 1999. 

“I killed the Dow. You ought to see the numbers. But I was investing peanuts then. It's a huge structural advantage not to have a lot of money. I think I could make you 50% a year on $1 million. No, I know I could. I guarantee that.”

If you’re reading this, you probably have the one thing all that smart money lost and can never buy back. 

You're relatively small. You can move. 

You can stroll through the little doorways the giants are wedged outside of and pick up the exact things they're incapable of touching.

"If I had $10,000 to invest,” Buffett went on, “I would focus on smaller companies because there would be a greater chance that something was overlooked."

And this is exactly where James and our Paradigm colleague Chris Cimorelli have been poking around.

The tiny stocks the smart money is overlooking. 

And they found one. 

Why They’re Giving it Away

It’s a $10 AI company, parked in the corner of the market the smart money can't reach. 

That won't last. 

The day it grows big enough for the giants to notice, they'll come thundering in at once—and the bargain will be a memory.

Until Thursday, James and Chris are giving it away free in a short 6-minute video. 

No paywall. No credit card. No small print you need a lawyer and a magnifying glass to survive.

They recorded a short video laying the whole thing out. 

Again, the ticker's free. You just have to watch it.

The smart money won’t. They’re too plump. You've got the opposite problem. And it’s the best problem in the business.

Don’t waste it. 

Watch the video here or click it below. They’ll give you the name. 

I haven’t really been to a doctor since I was 18. 

Recently, I hired a concierge doctor to run blood work. Everything was fine, but there was a small indication I might have pre-diabetes. 

So he put me on something called tirzepatide. 

It's a once-weekly shot that mimics two gut hormones—GLP-1 and GIP—the ones in charge of blood sugar and appetite. 

Everyone's on it. Now I'm everyone.

Then one day I doubled the dose, felt awful, and thought: this must be the peptides kicking in. This is terrible! 

I was wrong. I had COVID.

So, to figure out what to expect, I called Charles Duhigg. 

He wrote The Power of Habit. 

He's also lost 45 pounds on this stuff over two years. He explained what I'd actually signed up for.

Here's what I learned.

Gila Monster Venom

Peptides are all the rage right now. 

They're just short chains of amino acids, the same building blocks that make up proteins, only smaller. 

Your body already runs on them; they act as signals telling cells what to do. Tirzepatide is one of these. 

The longevity crowd uses "peptides" as a catch-all for a whole menagerie of these compounds, from the GLP-1 shots everyone's on to more exotic stuff people inject hoping to heal faster, sleep better, or outrun time. 

And the weirdest part: 

This whole class of GLP-1 drugs came from Gila monster venom. 

The lizard can shut its metabolism down for months. A researcher wanted to know why. That discovery became the drug exenatide (Byetta), the first GLP-1 medication, approved in 2005. 

Every GLP-1 drug since, including tirzepatide, builds on that lineage. 

Now, here’s what they do. 

Food Noise is Real 

Charles described sitting at a table with a basket of fries. Not hungry. Full, even. And still his hand reaches over. He eats them, barely aware he's doing it.

That reach is a habit. 

Every habit has three parts—a cue, a routine, a reward. The fries are the cue. They trigger a craving for salt and crunch. The craving pulls your hand. 

You obey.

What the GLP-1 does is quiet the cue. You see the fries and nothing fires. The noise in your head goes silent.

And into that silence, Charles said, something extraordinary becomes possible. A window opens. 

For a while—months—your habits go soft. Malleable. You can rewrite them. 

And if you rewrite them well, the new habits stay even after you stop the drug. If you don't, the weight comes roaring back the moment you quit. Most people quit within two years.

The drug isn't the miracle. The window is.

You Have Two Brains 

Some habits drag other habits behind them. Charles calls these keystone habits. 

Eat a little less and it's easier to run. Run and it's easier to eat the salad. Drink less and you sleep better. Sleep better and you focus at work. Focus at work and—

You see where this goes. One small change tugs a whole chain.

And the key part: Your brain isn't listening to what you say. It's watching what you do.

You have two brains. 

The front one sets goals: I want to lose 20 pounds. I want to run a marathon. That's your stated preference.

The deeper one—the basal ganglia—ignores every word of it. It watches your behavior. 

You didn't run this morning. You ordered the hamburger. So it concludes: this person doesn't actually want those things. And it quietly builds you into the person you keep acting like, not the one you keep announcing.

But flip it. Skip the fries a few times. Take the shot every week. The deep brain notices. Oh. He's someone who takes care of himself. And slowly the identity shifts to match the behavior.

I told Charles this sounded like the placebo effect. Like Todd Herman's alter ego stuff—Kobe becoming the Black Mamba before a game. 

Charles said that's exactly it. You program the deep brain by acting like the person first.

The placebo doesn't change your biology. It changes who you think you are.

Hear About My Journey

I'm now three weeks into my GLP-1 journey. 

I'm keeping a journal. Because Charles warned me about the one thing that ruins all of this—you don't notice yourself changing unless you force yourself to watch.

If you want to learn more about these crazy drugs—and what Charles told me to expect—listen to the entire podcast.

Click here for the whole thing. 

William Stanley Jevons warned Britain was burning through its coal. Everyone said efficiency would save them. Better steam engines, less coal.

Jevons said the opposite would happen. He was right. Watt's engine made coal more useful, so Britain burned more of it. 

Efficiency expands the market. It doesn't shrink it.

Now the part everyone forgets about automation: ATMs.

The cash machine automated the teller's core job. Tellers per branch fell from about 20 to 13. 

Everyone predicted a bank teller bloodbath, but the opposite happened. Branches got cheap to open. So banks opened them everywhere. 

Branch count grew over 40%. Total teller jobs in America grew roughly 10%. And the tellers who remained spent their days on mortgages and small business loans instead of counting twenties.

That lasted fifty years. 

We’re about to see a similar shift with robots. And it flips everything we’ve been told about robots on its head. 

Hollywood Lied to You

How fast are we screwed?

That's the only question anybody actually has about robots. Everything else is throat-clearing.

The problem: The most powerful technology in human history arrives with the public relations profile of a villain’s origin story. 

The problem with that problem: we've been getting our robotics education from a genre where the machine has to turn evil or the plotline sucks. 

The problem behind both problems: we’ve all agreed, without discussing it, robots will replace muscle. 

But muscle is a commodity. You can hire it, train it, ship it, schedule more of it. Every economy on earth has a lever for getting more of it. 

Getting more brains, however? That’s the big bottleneck. 

Large retailers lose hours of productivity per employee per day. And not (always) because people are lazy. It’s largely because figuring out what should be priority is insanely difficult. 

The manager requires superhuman skills: one who can walk every aisle throughout the day with perfect memory and infinite patience. 

Those don’t exist. But robots that can do that already do. 

This isn’t limited to retail. 

Zooming out, every business in every sector runs the same loop: capture what's happening, analyze it, decide, execute. 

Execution is the part everybody can see, so it's the part everybody manages. Capture is the part nobody's ever been able to afford. 

You can't decide well about a store, a field, a warehouse, or a ward you can only see in fragments. Reality is always bigger than any one person's attention span.

So while the world obsesses over robots that can flex and execute, the actual value comes from the robot that can survey a building overnight and hand the staff a ranked list first thing in the morning.

The Contrarian’s Robot Thesis 

In short, most tasks worth automating probably aren’t the jobs people already do.

They're the jobs nobody could ever afford to hire for. The stuff too repetitive, too constant, too expensive to staff. The ones that don't even exist today. 

By 2030, analysts expect 100,000 store manager robots deployed in grocery stores alone. And 1 million by 2030. By next Christmas, several million people will probably see one in a store.

It’s likely few jobs will be destroyed in that process. 

Then that sequence will repeat. Warehousing. Logistics. Agriculture. Healthcare. Hospitality. 

Every one of these industries runs on a person making a hundred decisions a day with a tenth of the information required to make them well. Even the most experienced rely on guesswork.

Give that person the full picture distilled and the nurse gets her hours back, the agronomist gets to practice agronomy, and the blight gets caught on Tuesday instead of the following Tuesday.

Unfortunately, that adds up to a movie too boring for Hollywood. 

In reality, though, it means more stores, closer to where people live. Cheaper food. Better jobs. Somebody's kid gets a first summer job and somebody's mom gets a new career.

That's what abundance looks like. Nobody loses their dignity in the deal.That’s the contrarian side of the robot trade. And it’s the one I’m taking. More on how—in crypto and stocks—soon.