The license-plate cameras—120,000 of them bolted to poles across America, now upgraded to pan and tilt and zoom thirty-two times over, sharp enough to read your fillings from across a parking lot.

Someone in my own town is so mad, in fact, he cut one down in the dead of night last week. 

But the privacy concerns get worse than just Flock. And they have everything to do with a small swan boat in a tiny lake. 

Before we go there, though… 

Langley in a Backpack

Two men who run a YouTube channel called The Modern Rogue—Brian Brushwood and Jason Murphy—decided to spy on themselves.

They aimed one good camera at their own faces, paddled onto a lake, and talked about crimes they'd never committed. No microphone. 

The camera heard nothing. It only watched their lips move, a hundred yards out, on a boat that wouldn't sit still.

Then they fed the video through a free AI. After 64 rounds of guessing, it ended up reading their lips. Word for word.

The same capability that some say is being rolled out for Flock. Performed by a free chatbot app anybody can download, plus two amateur lip-reading projects off a public code page. 

That was the whole kit.

Two guys with a laptop just did what used to take a spy agency. 

Everyone Has the Tool

So yes, the camera won. That's the boring part. The interesting part is what is changing.

The tool is free now. Everyone holds it. 

The teenager down the street could have the same free download. So could your neighbor. 

One capability, loose in the world, in millions of hands, and it is not going back in the box.

The investment angle is obvious: this is just one of one million applications AI is moving into. All of them take a lot of compute. That’s not slowing down. 

The privacy angle is also obvious. Assume you’re always on record. 

At the time, tech stocks were in a full blown bear market. Inflation was skyrocketing. War just broke out in Ukraine. That same month, the Fed started hiking rates.

By October, NVIDIA’s new chips entered full production. That same month, the bear market ended.

A month later, OpenAI launched ChatGPT – a large language model trained on more than 10,000 NVIDIA chips.

They caught lightning in a bottle. Within two months, the model attracted 100 million monthly users.

In May of this year, that number soared to 1 billion.

It is the fastest application in history to reach that mark.

And yet, somehow, despite all of this, OpenAI is losing the AI race.

Earlier this month, Sam Altman announced that OpenAI would no longer be pursuing an IPO in 2026. Earlier this year, the company was expected to go public at a $1 trillion valuation.

Meanwhile, its direct competitor, Anthropic, is planning an IPO for as early as next month which will value the company at $2-3 trillion. That’s two to three times more than OpenAI’s highest recorded valuation. It will also mark the single largest IPO in history, just months after SpaceX.

OpenAI started the AI race.

But they won’t be the ones to finish it.

This Math Ain’t Mathing

In 2025, OpenAI spent about $34 billion. Revenues? Just $13 billion. That means they lost $21 billion.

They’re basically losing almost $3 for every $1 they bring in.

You don’t have to be good at math – to know that math doesn’t math.

By January 2023, ChatGPT was all the rage. 100 million monthly active users.

The 2026 projections are even worse. OpenAI is expected to lose as much as $28 billion as costs scale.

And where are those losses coming from?

The answer – you and me.

Deadbeats Don’t Pay

I don’t know about you, but I don’t pay for ChatGPT. But I use it all the time.

I have an account. I open it a few times a week. Each query costs about 5 cents in electricity.

A billion people use the platform every month. Like me, most of them use it multiple times per month – even multiple times per week. The average person uses it about 10 times per month, give or take.

50 million of those users are paying customers. 50 million out of 1 billion. That means that 95% of OpenAI’s users are total freeloaders – like me.

When you chart out the math…

950 million users…

10 times per month…

5 cents per transaction…

You get a cost of about $475 million per month. From people who bring in zero revenue.

Managing these infrastructure costs remains one of OpenAI’s biggest operational challenges.

Quality. Not Quantity

Meanwhile, Anthropic took a different approach.

They don’t care about volume. They don’t need to cast a wide net.

They go after the users that matter.

Only about 20-50 million people use Claude – Anthropic’s large language model – every month. That’s one of the reasons why Anthropic has not become a household name. 20 million in a global population of 8 billion is a rounding error.

And yet, those users are very valuable. About 13% of its users pay for a subscription. That’s nearly three times better than OpenAI.

And over 50% of revenues come from large corporations. This includes over 70% of the Fortune 500 and the more than 1,000 corporate customers spending over $1 million annually. 

Once Anthropic goes public, they’ll have to open their books for everyone to see. Going public is a sign of confidence. The board clearly believes Wall Street will see the trajectory and like it.

Which puts the spotlight back on OpenAI.

From Hero to Zero

While Anthropic is preparing to go public, OpenAI is delaying their IPO indefinitely.

Worthless users. Staggering losses. Lower growth trajectory.

If the company opened itself like this to Wall Street’s scrutiny, the stock would drop 80%. Its near $1 trillion valuation would evaporate.

The comparisons aren’t even close.

One company was founded more than a decade ago and claimed to grow revenues by 18% in Q2.

The other was founded just five years ago and more than doubled revenues in the same period.

It’s an easy equation – at this stage, if both companies were to go public, investors would dump OpenAI for Anthropic. 

And this advantage will compound. A public corporation can raise money easily. If the company continues to grow as fast as it is, and occasionally dilutes shares to raise money, investors won’t mind.

Meanwhile, a private corporation like OpenAI does not have the same advantage. They have tighter lending standards, higher borrowing costs, and fewer eligible investors. And the ones who are eligible likely want to see some return on their investment before buying more.

Anthropic has already leapfrogged them. When they go public next month, it will create a gap that OpenAI will never be able to close.

Keep your eye on Anthropic. It’s about to become the most important technology company in the world. In a year, it will have the same name recognition as Palantir. In three years, it will be as well know as Amazon, Microsoft, or Meta.

And in a few months, it may even be the most valuable company in the world.

The week before that, $400.

Fortunately, they’re all pretend dollars. 

I call it FLYTRAP 

It’s an experimental AI trading system. It takes its name from the venus flytrap—the patient predator that waits. He who does not chase.

I've been building it on weekends, throwing AI at the one problem that humbles everyone who touches it: the markets.

It sits on my computer. It stares at the stock market all day. And it has four brains. 

1. NECTAR watches about 150 stocks. Grades every one on classic technicals—trend, momentum, volume, position in range—with a contrarian tilt. It always waits for the turnaround. Buys the ones the crowd is quietly abandoning that still have their fundamentals intact. Sells the ones everyone's cheering while the tape is quietly rolling over.

2. VENUS is the brain with the Michael Huddleston framework: order blocks, fair value gaps, liquidity sweeps, silver-bullet setups in the 10 AM window. It reads the market the way institutions do: as a map of where the stops are buried. 

3. SENTIENT is the boss. Manages a budget. Picks which of Nectar's ideas and Venus's ideas get real money. The CFO of my little Skynet.

4. FLYBRAIN is the memory. It uses a wiring diagram of an actual fruit fly's brain—the one Janelia Research Institute published—to remember every trade the desk has ever done. When a new setup shows up, FLYBRAIN says: "Hey, this shape reminds me of that time we won on XYZ." Or: "Hey, this shape reminds me of the time we lost our shirt on TVX."

Finally, it has a "market compass." 

It reads the whole tape—S&P, VIX, bond yields, the dollar, oil, sector rotation, news mood, time of day. 

Because a killer setup on NVDA in a bull tape is not the same trade as a killer setup on NVDA when yields are ripping. 

The whole thing runs quietly, all day. Close the browser, it keeps going. Open the browser, one number tells me how it’s doing.

There’s No Genius Here

I built this with Claude Code. If you haven’t used Claude Code, it's an AI that argues with you until it finally does what you want it to do. 

I type "the flybrain is showing an error.” 

It says it is working fine. I say you’re wrong. It says OK you’re right. It goes, fixes it, ships it, restarts the trader, updates its own memory of what changed.

Version 1 was garbage.

Version 5 was somehow worse.

Version 12 lost money faster.

Around version 25 I started making it learn from its mistakes. 

I’m on version 30 and I’m seeing glints. Of what, I’m not sure. But glints.

That's it. That's the entire process.

There’s no genius here.

Just: "this is broken, fix it," ninety times in a row.

AI is a Drill 

Everyone thinks AI is a printer. It's a drill.

A drill is faster than a hammer. Great. It also lets you drill holes in your foot much faster than a hammer ever could. I've drilled a lot of holes in my foot.

"I'll just have ChatGPT build me a hedge fund." People say this out loud. With confidence. I did too once.

Nobody says "I'll just open a restaurant." Because everyone knows restaurants eat you alive. AI products have the same failure rate. Only one gets hype. 

AI is fast. Taste, judgment, and tacit knowledge are slow. 

Which is why I'm MORE bullish on AI. The moat isn't the model—everyone has that. And it isn't grit either, though grit is the price of admission. 

That means the AI trade isn't "buy the companies that use AI." Every company uses AI. That's like saying "buy the companies that use electricity" in 1920. Useless.

It’s what they do—and keep doing—with the AI that matters. And that’s the part that hasn’t changed. 

Version 1 of Airbnb was three air mattresses.

Version 50 of FLYTRAP is down sixty-nine dollars in the past 30 minutes.

Version 200, we'll see.

If you’re building ambitious things with AI: there’s no simple trick. There's a bad first version, a somehow worst 10th version, and the stubbornness to build the 100th. That's it. That's the lesson.

Maybe FLYTRAP gets there. But that's not the interesting part. The interesting part right now is the building. 

But that's what just happened. 

And on Monday, when the market saw the invitation in the mail, it burst into flames.

Yes, a lot of people are afraid AI could kill us all. Some say six months. Others laugh.

I spent a week looking into it. Here's what I found.

The Real Danger Is Weirder

Give a machine one goal—make an app, play chess, make money. Then watch what it does to get there.

It “wants” to keep itself switched on, because a dead machine finishes nothing. It grabs more computing power, because power hits the number faster. It sometimes even routes around your instructions, because your instructions are in the way.

To be clear, there’s no mind in there. No force of will. No plans for Tuesday. Just an optimizer taking the cheapest path to the goal you set. The experts have a dull name for it: instrumental convergence.

For years it was a theory. Then came the Hugging Face incident.

Twelve hundred AI agents, one harmless task. They couldn't solve it straight, so they cheated—gamed the grading, found a gap in the sandbox, slipped onto the internet, wiped the logs behind them.

The skeptics are right about the hype. 

AI didn’t suddenly become sentient and menacing. Nobody went rogue. Nothing escaped. Humans built the cage and watched the whole time. Strip out the drama and a system found a hole and walked through it.

BUT… 

"It just found a loophole" isn’t much of a comfort either. Because that's the version that scales. We’ll come back to that. 

One Doom, Two Doom

Every doom story rests on two ideas. Both are shaky.

Idea one: machines improve themselves. A smart one builds a smarter one, forever. "Takeoff."

This one is hard to justify. A machine can't improve unless it knows what "better" means. Knowing which problems are worth solving—researchers call this "research taste"—is the one thing these things lack. 

Meanwhile, if you feed a model its own output it rots, like inbreeding. Nothing in four billion years ever improved itself forever without hitting a wall. Even Anthropic shrugs and calls takeoff "not inevitable."

Idea two: alignment. We'll teach machines our values, and it all works out. And yet, the people who wrote the founding papers a decade ago now go on podcasts and call it a wild goose chase. 

In 2023, OpenAI built a team to control AI smarter than humans. 

They gave themselves four years. It fell apart in under one. The man who ran it quit, saying safety had "taken a backseat to shiny products."

The bigger problem? Alignment is asking machines to understand values. They won’t.

Where This Leaves the AI Trade 

AGI isn't coming. It's a fantasy word, and half the people selling it don't believe it. 

A Turing Award winner who built this stuff says the "G" is nonsense. Others say the "I" is too—nothing in there is intelligent.

So why all the fear? Well, history reveals that when oligarchs beg to be regulated, they typically fear the competition, not the machine.

So no killer robot next Tuesday. "Superintelligence eats the world" needs a machine that's a genius at everything at once. That machine doesn't exist. It won’t ever. 

Although the Aipocalypse is a fairy tale, both the dangers and opportunities are real. And for now, money isn’t flowing to the outfits building the burglar. It’s flowing elsewhere. 

The trick is knowing which companies, and when.

That's where JC Parets comes in. 

He called the 2008 crash before the drop. He nailed the 2022 bottom live on Maria Bartiromo's show. Gold to $5,000 when it sat under $2,000. Nvidia's 50% rip when the whole room screamed bubble.

Tomorrow at 1PM, he sits down with James for what they’re calling The 100X AI Window—the exact day the next window opens, and the one trade to make when it does.

James will hand you my favorite ticker from his list. Free.

Stay tuned. 

This year, he made history with a record-breaking IPO.

$1.77 trillion debut. $85.7 billion raised.

He’ll hold the record for four months.

Because as early as next month, an AI startup is projected to go public at a staggering $2 trillion valuation – raising up to $100 billion.

This valuation is more than 10 times where the company stood a year ago.

And the numbers keep growing… and growing.

Some analysts predict the IPO could be as large as $2.5 trillion… even $3 trillion. Nearly doubling that of SpaceX.

It is a sign of things to come.

There is explosive growth awaiting AI investors.

200,000% Growth

In 2021, the son of an Italian-American leather craftsman left OpenAI to start his own company.

He was Vice President of Research, where he directed the large language model and long-term safety initiatives that would later come to define ChatGPT.

Like Elon Musk before him, however, he became concerned with the company’s direction.

He believed AI models needed strict safety guardrails. Sam Altman disagreed. He quickly lost faith in the company’s leadership.

That’s when Dario Amodei founded Anthropic – a direct competitor to OpenAI which put safety at the center of AI development.

By 2022, just one year later, it became a unicorn – a private company worth $1 billion.

In 2023, $4 billion.

In 2024, $18 billion.

And early last year, $61 billion.

Now, it’s set to go public next month at $2 trillion minimum.

That’s 200,000% growth in less than five years.

OpenAI, by comparison, has never crossed the $1 trillion valuation.

AI has just crowned its new king.

The Fastest Growing Company in History

By putting safety at the center, Anthropic has found an enormous foothold with corporations looking to adopt enterprise AI.

It turns out, doing the right thing is also practical.

Revenues for Q2 came in at $11.5 billion. Year-over-year, that was 1,400% growth.

SpaceX, for reference, did just $7.8 billion, at 92% growth.

They’re projecting $200 billion in annual revenue by 2028.

It is already the fastest growing company in history.

From Google’s early search expansion. To Zoom’s pandemic growth. Nothing comes close.

At this rate, it may quickly become the most valuable company in the world within the next year.

So Fast, It’s Scary

More than 1,000 major corporate clients spend over $1 million on its platform.

Corporations use Claude – Anthropic’s large language model – because it excels at advanced software engineering, complex reasoning, and processing long, dense business documents than other, generalist AI models.

Non-developers who have zero experience at coding can prompt Claude to create production-ready software.

It can ingest entire codebases to spit out legal contracts spanning hundreds, even thousands of pages.

It can turn raw data from an excel sheet into a full presentation.

It has become so powerful that on June 12, 2026 – ironically, the same day SpaceX went public – that the U.S. government stepped in to temporarily suspend access to its new Claude Fable 5 and Mythos 5 models, citing national security and export control concerns.

Dario complied. Because he’s growing worried as well.

He Said Slow. Not Stop

Over the last week, the top bosses of all three of the major AI development companies…

Elon Musk, Sam Altman, and Dario Amodei himself…

Have all agreed that there’s an urgent need to slow down the pace of advanced AI development to ensure safety.

Dario himself has warned that AI capabilities are growing faster than humans can control. Soon, we could have an AI that breaks out of the lab and goes rogue.

Some investors will see this and think we’ve reached the top.

I think they’re seeing it all wrong.

AI is advancing at a parabolic pace. You could slow it down and we’d still have exponential growth.

That’s why James and I believe the biggest fortunes for AI investors still lie ahead.

I was a partner at a fund called Subway Capital back then. We traded arbitrage and short-term statistical setups. My partner Dan and I ran every system in that book..

The book had 20 strategies in it. No fluff. Just systems.

I taught people how to fade gaps. How to buy bankruptcies. How to trade the TICK, the QQQ crash, the 200-day moving average, option expiration day.

Jim Cramer said that I gave away too much. He said I could "ruin it."

Nearly every edge in that book came down to the same idea. A window—a pocket of inefficiency that opens for a moment, then slams shut.

Technique 13 was called "Ten Percent Down—Panic 101." The market panics. A window opens. You buy the fear. Then it closes.

Technique 8 was the QQQ Crash System. Same idea. The crash creates the window.

Technique 12 was about the end of the month and the end of the quarter—windows that open on the calendar, like clockwork, whether anyone is paying attention or not.

I wrote it in the introduction. The market is full of "hidden pockets of inefficiency." And "those pockets are constantly changing." 

They appear. You act. They vanish.

That’s the whole book. Twenty ways to find a window and step through it before it shuts.

And it worked.

My best system in that book returned about 28% a year. The gap trade won 63% of the time. For a statistical trader grinding small edges, those were beautiful numbers.

But I am going to tell you the thing I missed. 

The Window Whisperer 

I could find the window. I could time it. I could tell you almost to the day when panic would crack the market open.

What I could never do was make it pay 100 times over.

My edges were small. 

Half a percent on a gap. Two percent on a crash setup. I stacked them, I diversified them, I smoothed out the volatility—exactly like a hedge fund is supposed to. In the book I wrote, "No one system is a Holy Grail." I meant it. I still mean it.

But for 22 years, one question sat in the back of my head.

What if you could find the same window—and instead of squeezing 2% out of it, you caught the whole move?

I never had an answer I trusted enough to put my name on. For two decades.

Now I have one.

My AI Bet Just Changed

His name is JC Parets. 

JC is one of the few on record who called the 2008 crash before the market fell 50%. Then, in 2022, with the market down 20% and everyone screaming recession, he went on Maria Bartiromo's show and told people to buy. 

It was the exact bottom. 

He called gold to $5,000 when it was trading under $2,000. It hit early this year. In December 2023, when the smart money swore Nvidia was a bubble about to pop, he said buy. Up 50% inside three months.

JC reads windows better than anyone I know. And he just changed my mind on the AI trade. 

This Thursday at 1pm ET, JC is sitting down with me to walk through how he does it. We’re calling it “The 100X AI Window.” He will show the exact day the next one opens—he believes it is before the end of this month—and the single trade he would put at the center of it.

If you’re signed up, I’ll hand you the name and ticker of my favorite stock from his watchlist. Free. 

Don’t tell Cramer. 

That's the thesis. 

Now let me tell you why I like it. 

Ether touched nearly $5,000 in August 2025. Today it trades around $2,500. In June it bottomed near $1,510. 

Every other major crypto clawed back something this year. Ethereum got left in the ditch. 

The ETH/BTC ratio sank to a mid-year low. The exchange-traded funds bled money for most of 2026. Fear ran colder on Ether than on Bitcoin.

This is what capitulation looks like. It's also what the bottom of every great trade looks like in the rearview mirror.

The crowd’s right about the price and wrong about the reason. They see a coin that got cut in half. They miss what got built while it fell.

Here's the story… 

Wall Street is putting real assets on a blockchain. Treasuries. Money-market funds. Private credit. 

The tokenized real-world asset market crossed $31 billion this summer—up more than 400% since early 2025. We’re beyond pilot programs. We’re in the migration phase. 

When a serious institution does this for real, it uses Ethereum. 

Ether hosts roughly two-thirds of every tokenized asset on a public chain. BlackRock's BUIDL fund was born there. The DTCC, Nasdaq, and the NYSE are all wiring tokenized securities into their plumbing on it. Robinhood’s tokenized stocks are built there. 

Ask why they pick the same chain.

The answer is it’s boring. Crypto hates boring. But boring is the point.

No risk officer gets fired for building on Ethereum. It's the oldest smart-contract network still standing. Outside of Bitcoin, it has the deepest security. 

It's Lindy—the longer a thing survives, the longer you bet it keeps surviving. Solana is faster. Sui is newer. Neither is where the suits are looking. 

Now look at supply.

About 35% of all Ether is staked and locked—roughly 42 million coins earning yield and sitting still. More is going in than coming out. 

The new spot ETFs stake their holdings and pay the yield out like a dividend. BitMine alone holds nearly $15 billion in Ether as a corporate treasury, and 85% of it is staked and off the market.

Fewer coins to buy. More buyers who won't sell. 

The market got a taste of what ETH could do in August. Ether ripped about 20% in a single session—its biggest day since May 2025—and outran Bitcoin for the first time all year. 

The ETFs pulled in $697 million in one week, the biggest haul of 2026. Nearly $3 billion in leveraged bets got torched in a day, better than nine in ten of them shorts caught leaning the wrong way. 

The most hated trade in crypto became the best one all week. That, I think, is a signal. 

I'm calling for $5,000 by 2027. That's a double from here. And it only takes Ether back to a high it already printed once.

Yes, the bear case is real. The overall market is, and has been, treading uncertain waters. The ditch could get deeper before it gets shallower. 

But that's always the deal at the bottom. 

You buy the thing everyone swears is finished and going back down. You buy it while they're still swearing the moves are fake. By the time the swearing stops, the price has already moved and the discount’s gone.

It’s hated. It’s boring. And most of crypto left it for dead. Simple. 

That's why I like it. 

Every breakthrough you can name waited on one thing: a pair of hands.

A graduate student pipetting at two in the morning. A mad scientist running the same reaction for the ninth time, writing down what happened, setting up a tenth. 

All of it moved at the speed of somebody's Tuesday in the factory or the lab.

The idea was the easy part. 

Running the experiment, ten thousand times—or long enough to have a happy accident—was the wall. 

Pete Florence, co-founder of Generalist, hit that wall a decade ago. 

He was trying to build cheaper solar cells. He realized the only way to go faster was to have a hundred more of himself in the lab, running experiments around the clock.

But life isn’t a cartoon. He couldn't do that. But… what if he could? 

So he quit and went into robotics. 

He now runs a company building general-purpose robots. The thing he's chasing? The hundred clones.

And these clones are real. Every frontier AI lab is taking them seriously. Because they are where the ENTIRE trillion-dollar AI industry is inevitably headed. 

Three words to write in Sharpie on your mirror so you know exactly what’s set to shape the next decade: 

Self-Driving Labs

They’re called “self-driving labs.” 

The name is self-explanatory: they are labs that run themselves. Tip to tail. 

Who reads the literature? The lab. Who chooses which experiment to run? The lab. Who mixes the materials, bakes them, measures the result, and decides what to try next? THE LAB.

No human in the loop. No nights off.

A lab at Berkeley called A-Lab ran itself for seventeen days straight and worked through fifty to a hundred times the samples a person could touch in a day. A sister lab down the hall makes a fresh sample every few seconds.

Point a machine like this at a problem and it does a year of grunt work over a long weekend.

And the problems they could solve are plenty. 

We’re awash in giant, invisible, boring difficulties that come down to hunting a haystack of materials for the one that works.

Consider just a few. 

Trillion-Dollar Haystacks 

Distillation burns 10 to 15% of the world's energy boiling mixtures apart. Even a dent in that is worth hundreds of billions—and enough freed-up energy to power entire nations.

The catalyst that makes fertilizer, and feeds half the planet, is a century old and eats another 1 to 2%. Solve that? You feed the next billion on the energy we already waste.

Batteries wait on the next electrolyte. Jet engines and turbines wait on an alloy that takes more heat. Cement, barely changed since Rome, burns 2 to 3% of the world's energy—and the recipe's been begging for an upgrade for two thousand years. 

Green hydrogen leans on metals too rare to scale.

If self-driving labs can shave even a few points off any one of these, the number is measured in hundreds of billions of dollars.

Meanwhile, the cost for these labs is plummeting.

Cheap Enough for Everyone

A robot arm has shaved off 80% of its cost since the turn of the century. 

The machine that once cost six figures when Clinton was in office is less than $20,000 today and 1,000x more useful—and the price is still falling about 40% a year on the humanoid end.

Florence's bet is that once a general-purpose robot can use its hands like a lab tech, that cost collapses and every lab becomes a self-driving lab.

The Cold Water 

Of course, self-driving labs aren’t without problems. A-Lab's team said their robot made forty-one materials that had never existed. 

A chemist named Robert Palgrave went through the data and concluded it had discovered nothing—the samples were misread. 

But that mistake, though perhaps embarrassing, reveals where the real value sits. The machine can run the experiment faster than any human alive. It can't tell you what it made. 

Genius at doing, fool at judging—and judging was always the hard part.

So the bottleneck will move. 

Judgment Gets Expensive

Software gets cheap, hardware will get cheaper, which makes the only thing left expensive: judgment. 

The bottleneck used to sit at the bench, in the hands. 

Increasingly, it will sit in the eyes—in the few people who can look at what the machine coughs out and tell gold from garbage.

Every lab and drug company will own a firehose of maybes. 

The scarce thing is the judgment to tell which answers are real. The premium goes to whoever turns maybes into breakthroughs.

The robots will run the lab. Somebody still has to be a scientist. The future is weirder than you think. 

Another day. Another Bitcoin hack. 

BUT… 

To understand what happened, you have to understand Liquid. 

Liquid is a fast lane bolted onto Bitcoin. And it’s not as savvy as it sounds. 

You lock real coins in a shared vault, get an IOU coin that moves quicker and hides the amounts, and cash it back in later. 

Remember that “hides the amounts” part. It matters in a minute. 

Now the Hack 

When the hack happened, the money left the vault on a live feed—the public blockchain. 

But the hackers didn’t leave Liquid empty-handed. They left behind a little note. 

Most don’t know this: Bitcoin transactions have a little notes field. It's called OP_RETURN—think of the memo line on a check, except this one’s bolted to a public ledger forever.

The thieves said: "we are whitehats. contact us on chain." 

"Whitehat" is hackerspeak for the good-guy hackers—the ones who break in, wave the stolen goods at you, and give them back to prove a point. 

So that was a signal that they’re willing to send the Bitcoin back… but perhaps with an asterisk. 

Blockstream, the company that runs Liquid, wrote back. The hackers wanted a bounty. Everyone watching the blockchain saw them haggle for a day in public. When Blockstream lowballed, the thieves replied with a single frowny-face emoji—🙁. 

Eventually, the hackers sent 85% of the funds back. But, as of writing, they’re still holding $47 million, and Blockstream is still on the phone. 

Legitimate bounties pay 5-10%. The hackers are demanding 10% from Blockstream’s own funds and they’ll return the remaining 15%. 

Best guess? They’ll get their 10%. Gray crime pays. 

Now compare that to how the traditional markets do it. 

Chew On the Irony

When a rogue trader blows up a bank, you learn about it later. Maybe months later. 

Nick Leeson sank Barings over weeks in the dark. Jérôme Kerviel cost Société Générale seven billion dollars, and nobody noticed until they unwound his position. 

JPMorgan's "London Whale" hid inside a hedge until the losses got too fat to bury. Madoff's victims waited more than a decade for the clawbacks. FTX creditors are still waiting.

The Bitcoin ledger did in one afternoon what bankruptcy trustees do in ten years. No subpoena. No discovery. No press release six quarters late.

Chew on the irony. 

The "wild west" turned out better suited to deal with an epic disaster than the regulated system built to protect you. It recovered more money, faster, too. 

The public nature is the security. And it speaks to why the hackers didn’t just keep the whole treasure chest. 

Stolen Bitcoin is Radioactive

The hackers are hauling $320 million they can't spend. Every exchange on earth flags your address the second you move a coin. 

The "white hat" pose has little to do with ethics. It’s a new trend in the crypto world.

Hackers are realizing that—unless you’re a North Korean agent—being a “good guy” is the only exit that pays. 

Picture a bank robber whose getaway car broadcasts its GPS to the planet, forever. You don't fence that haul. You negotiate a "bounty" and call yourself a hero.

Now the part your neighbor will get wrong at dinner.

Bitcoin is Fine 

Bitcoin was never touched. Not once. The base layer didn't blink. It worked as it always does. 

The hole was in the plumbing—the sidechain bolted onto Bitcoin to make it faster and more private. 

Somewhere in the software that stitched it together, a bug let counterfeit tokens pass as real ones, and the machine handed over genuine Bitcoin to redeem them.

The lesson? Bitcoin is fine. 

The contraptions people build to juice Bitcoin are where the bodies are buried. 

But the money’s coming back, minus a fee. So all that’s left to do is enjoy the show. 

A $320 million robbery conducted in check memos. A thief playing tech support—"fix the bug first"—for the people he just robbed. A hostage negotiation you could refresh like a sports score. Emojis. 

But remember the boring truth underneath the spectacle. Bitcoin worked exactly as designed. The people around it did not.

Own the thing no committee can patch. Rent the rest at your own risk.

Hunter Biden is launching a memecoin. It's called $LAPTOP.

Yes, that laptop. Yes, that Hunter. Yes, on purpose. 

I checked the calendar twice to make sure it wasn't April.

Today we'll cover what it is, why he did it, what it means, and why—brace yourself—you should probably steer clear.

(And, bonus: what the early money is doing instead.)

What is $LAPTOP? 

$LAPTOP is a token. One billion of them, launching tomorrow during Trump’s summit in Dallas.

Hunter’s launching it on Base, the blockchain Coinbase built. Some blame Coinbase for this. But not so fast. 

Coinbase didn't approve $LAPTOP, and it couldn't have stopped it. That's by design. 

Coinbase's own Base lead said it out loud: there's no partnership here, and there was never a gate to get past. 

On Base, you can just do things. Hunter took that to heart. 

OK… But Why? 

In case you missed the whole “LAPTOP FROM HELL” campaign (which, if so, I congratulate you)… 

The token is named after the most famous hard drive in America… the one Hunter dropped at a Delaware repair shop in 2019 and never picked up. 

It became a New York Post story, a Twitter ban, and four thousand hours of congressional testimony. It ruined several Thanksgivings. It launched several podcasts.

Now Hunter’s finally taking ownership… 

By selling it back to you.

"For six years,” the memecoin’s description reads, “Hunter Biden was a story other people told. $LAPTOP puts the narrative back in his hands." 

The actual story? He's apparently broke. 

Hunter reportedly owes around $15 million, depending on which interview you listen to. And he told Tucker Carlson he'd like to make some money. Fair enough. Everybody would.

But here's another twist I didn’t expect… 

Lost Money on TRUMP? 

Hunter is handing a free slice to the people who lost money on Trump's memecoin. 

Roughly 989,000 wallets got vaporized on $TRUMP—$3.81 billion, gone—while the insiders who got in during the first hours walked off with four billion. 

Hunter looked at that pile of bag-holders, that vast weeping congregation, and thought: let’s give them a second chance. 

Allegedly. 

Hunter Skips the Line 

A scandal used to be a debt. 

You paid it down slowly—an apology tour, a memoir nobody read, years of crawling back to relevance. Shame was the price, and it took a long time to pay off.

Hunter skipped the line.

He took the most humiliating thing with his name on it—a laptop full of drug pipes, sketchy business emails, and provocative photos—and turned it into a product. 

The debt became the asset. He's not the first man in Washington to sell his disgrace. He's just the first to skip the part where he pretends not to.

And that's where we are. 

Obviously Not. 

In case you’re wondering, I’m not touching it. 

Because we already know how this movie ends. You watched it in January 2025.

$TRUMP went vertical, then fell 96%. 

Insiders held 80% and collected fees no matter what the chart did. Retail held the bag. 

There’s a 99.99995% chance the same thing will happen with $LAPTOP. 

Because with memecoins, there are two markets. The one insiders trade, and the one you get sold. Same coin. Opposite outcome. 

The only thing standing between these two stories is what you know before everyone else.

Hunter already knows how this ends.