Friday charts: The revolution is listed

The start of the information-technology revolution can be identified with unusual precision: November 15, 1971, when Intel introduced the 4004 microprocessor in an Electronic News ad. “A micro-programmable computer on a chip!”

It was the world’s first single-chip microprocessor — an entire CPU (central processing unit) built into an integrated circuit on a sliver of silicon. No circuit boards. No soldering.

The 4004 first appeared in unassuming products like calculators and pinball machines. So it took a while for investors to recognize its significance. 

Here, too, we can be surprisingly precise. In a study of how investors reacted to the advent of information technology, economists Bart Hobijn and Boyan Jovanovic conclude, “The IT revolution was anticipated by early 1973.”

Here’s how they know: the stock market went down.

The same technology credited for a multi-decade boom in the US stock market is also the primary reason why it fell in the ‘70s and stagnated in the first half of the ‘80s.

As Hobijn and Jovanovic tell it, investors recognized that many incumbent companies — the ones available to them on the stock market — risked obsolescence in this new era of information technology.

There would be winners, of course, but these were not yet obvious. What was obvious was that few of them were publicly listed (if formed, even).

As a result, the total value of US equities fell from 78% of GDP in 1972 to just 37% in 1974.

Valuations remained depressed until a new generation of companies — the first winners of the IT revolution — grew large enough to IPO.

This process took exactly 12 years. 

We know that, Hobijn and Jovanovic say, because that’s when the relative value of stocks started going back up: the market capitalization of US equities went from 40% of GDP in 1985 to 130% in 1999.

Today — at the start of a new revolution in information technology — it’s 215%.

That may seem like a precarious height given how uncertain things seem. But, this time, there are plenty of winners to invest in: cloud providers, utilities, data center builders, and the entire semiconductor industry, for example.

Even a company as old-school as Caterpillar is a winner, thanks to its natural gas generators — which it's been making for decades — being used in data centers. The shares are up 170% over the past year.

Legacy winners like these are breaking the pattern of the IT revolution by more than offsetting declines in AI losers (software companies, mostly).

Also different this time: we won’t have to wait 12 years to invest in the new winners. The IPOs of Anthropic, OpenAI, and SpaceX should soon add at least $3 trillion to the value of listed US equities. 

So, for all of the things investors have to worry about, a lack of options is not one.

But is there any upside to the stock market from a starting point of 200% of GDP?

Let’s check the charts.

The last time:

Per Hobijn and Jovanovic, this chart is mostly attributable to the advent of information technology. First because of the old firms it threatened to make obsolete and then because of all the new firms it enabled.

This time:

AI researcher Fin Moorhouse puts the current buildout in data centers in perspective by charting it against previous US megaprojects, like the highway system and US railroads.

Adjusted for GDP:

The data is less dramatic when measured as a share of GDP — which might mean we’re just getting started?

Very dramatic:

The Nasdaq was up 9.8% in the ten days to Tuesday — a 100th percentile event, per Warren Pies. 

Winners and losers:

Year-to-date, semiconductor stocks have outperformed software stocks by twenty percentage points (and 163 percentage points over the past five years).

Unprecedented:

The dispersion of returns within the US technology sector has never been higher — which is what happens when the winners and losers from a new information revolution are both immediately available to trade.

Future IPOs:

Investors are betting big on the next generation of winners, as well. The dollar-value of venture capital investments hit an all-time high of $300 billion in the first quarter of 2026, up nearly 3x from the quarter before.

Near-future IPO:

The market value of Anthropic, as measured by perpetual futures on Ventuals, passed $1 trillion for the first time today.

Planting AI seeds:

Valuations of the very newest companies have skyrocketed. The red line above shows that the largest seed-stage startups were valued at $175 million in the first quarter of the year, up from $65 million less than a year ago.

The next big thing:

Morgan Stanley has data on the performance of Mythos. Charted against the performance of previous models, Mythos is above trend, but perhaps not as dramatically as Anthropic makes it out to be. Similarly, hallucinations (charted on the right) have declined, but not too dramatically.

Information revolution survival rate:

Morgan Stanley notes that, of every 100 publicly listed companies from 1976 (excluding financials), only 18 have survived in their original form.

It might happen faster this time.

Have a great weekend, revolutionary readers.

— Byron Gilliam

Introducing Blockworks Investor Relations, an IR platform built for onchain businesses.

The latest Blockworks offering brings together analytics, a branded investor relations site, and integrated advisory support into a single platform. The result is a more efficient way to share your story, build trust with investors, and engage a global audience from day one.

Check out our cofounder Michael Ippolito's keynote at DAS NYC launching the new IR platform.

Thursday links: financial tyranny, memecoin lunches, underground banking, and more

World Tyranny Financial

When World Liberty Financial deposits five billion WLFI into the DeFi protocol Dolomite to borrow $75 million of USD1, it’s a bit like taking out a $750,000 mortgage on a $100,000 house — why would you ever pay it back?

Five billion WLFI is nominally worth $400 million, but only on paper: trading liquidity is so thin that Dolomite’s lenders are effectively stuck with them.

You might then wonder why anyone would make such a loan. In this case, the answer may be that the co-founder of Dolomite, Corey Caplan, is also the CTO of World Liberty Financial.

Tellingly, I had to check LinkedIn to confirm Caplan’s employment status at World Liberty — like Homer fading into a bush, the management of World Liberty have disappeared off their website. 

Caplan was once featured on the site alongside World Liberty’s Chief Crypto Advocate (President Trump) and Web3 Ambassadors (Eric, Don Jr, and Baron Trump). Now there’s only a boilerplate about-us page, with no mention of who the team is, let alone meeting them.

In their place, the closest thing World Liberty Financial now has to an accountable executive right now seems to be an anonymous social media manager who's doing their best at something like investor and community relations.

It's an unenviable task. The account has been sparring with Justin Sun, for example, who was once World Liberty’s largest and most vocal backer. Now, Sun says World Liberty is being controlled by “anonymous actors,” and that its owners have been using the crypto community like a “personal ATM.”

Sun continues to be one of the top holders of WLFI — but only because a “backdoor blacklisting” prevents him from selling.

He’s suggested a name change: World Tyranny Financial.

A second memecoin lunch with $TRUMP

Another sign of the crypto-political times: the cost of VIP access to a lunch with the President is down roughly 90%.

VIP status at this year’s event required holding roughly $300,000 worth of the TRUMP memecoin. Last year, it was $3 million.

That’s only an estimate, however, because this time around token holders were also awarded 10 points for every $1 spent on Trump-branded merchandise:

At this point, the merch might have higher resale value than the token, so that could have been the way to do it. 

But even $300,000 of hats, watches, and fragrances does not definitely get you in the same room as the President. “In the event President Trump is unable to attend,” the terms and conditions state, “persons who are qualified for the 2026 event will receive a limited edition TRUMP NFT in lieu thereof.”

Either way, this year’s lunch likely adds a few million to the estimated $1 billion the President’s family has made from its various crypto adventures so far.

Nice work, if you can get it.

Underground banking

Professor Miles Kellerman writes that the “underground banking” networks that we typically associate with small-scale remittances (hawalas, for example) can now facilitate transactions as large as NYU tuition bills.

Underground banking dates back to the Silk Road, when long-distance trade was first facilitated by a “two-pot” system: funds credited in one place could be debited in another via trusted intermediaries. (The same system that Western Union has been using for money transfers since the advent of the telegraph.)

Today, underground banking networks are thought to facilitate as much as $488 billion of informal remittances per year. There’s probably a branch near you: “They often operate secretly out of mini-markets or mobile phone stores,” Kellerman says. 

The largest transfers likely run through the Chinese feiqian system.

Kellerman cites the example of Chinese students attending university in the US. China limits citizens’ transfers abroad to $50,000 per year, which will not cover a year of expenses at NYU ($100,000, these days).

So Chinese parents turn to the two-pot feiqian system, depositing yuan in China that their children can withdraw as dollars in the US. Sometimes that’s done in cash (“a practice that is allowed at many universities,” Kellerman says), and sometimes by moving the funds into a US bank account. 

Unfortunately, the reason feiqians always have enough cash on hand to pay for a year at NYU is the global trade in fentanyl.

“Every underground banker operating in the US has to receive US dollars before they can redistribute that money to their clients,” Kellerman explains, “Normally, those deposits would gradually accumulate from repeated business.” That can either be in fees for previous transfers or from, say, cash transactions at mini-markets or mobile phone stores. 

But a DOJ indictment from 2023 revealed that much of the cash Chinese students were receiving was coming from the Sinaloa drug cartel.

The cartel generates enormous profits selling fentanyl in the US, producing dollars that need to be laundered. Chinese students in the US needed dollars to pay tuition. So, from around 2019, the cartel started putting their cash into the underground banking system. 

When a Chinese student withdrew dollars in the US, the cartel would be credited with the same amount in yuan in China. Brokers in China then arranged for businesses to purchase products from Mexican companies controlled by the cartel, generating profits in Mexican pesos.

“Like magic,” Kellerman says, “dollars from the drug trade were transformed into yuan, only to be transformed once again into crystal-clean pesos.”

It’s not obvious why crypto hasn’t won more of this market, but Kellerman’s view is that “Whereas bitcoin is trustless, underground banking can be recordless.”

But blockchains are working on becoming recordless, too. So crypto may soon have a new use case: paying tuition at NYU.

Anthropic is worth nearly $1 trillion

Perpetual futures on Ventuals now value Anthropic at $960 billion — nearly triple the valuation when Anthropic raised capital two months ago.

This puts them well ahead of OpenAI, which is valued on Ventuals at a comparatively modest $835 billion.

Also, in an alternate timeline, it means that FTX has taken over global finance, buoyed by the 13.5% of Anthropic Sam Bankman-Fried bought in 2021 for just $500 million. 

If he had not participated in future rounds, that 13.5% would have been diluted down to something like 5.2%, which is now worth $50 billion — a 100x return in just five years.

In this timeline, however, FTX collapsed because of a missing $8 billion (such a small number!) and the bankruptcy estate sold SBF’s Anthropic for $1.2 billion (a tiny number!).

This timeline is probably better.

Saudi Arabia tightens its investment belt

In his market update for March, valuation guru Aswath Damodaran predicted that the war in Iran would make capital more scarce: “The capital flows from the oil rich countries which have flowed generously to everything from AI start ups to Premier League clubs will shrink, creating down-market effects.”

It's happening. Saudi Arabia’s sovereign wealth fund is reportedly withdrawing support from one of the Kingdom’s highest-profile projects: LIV Golf, the big-spending golf circuit that’s divided country-club locker rooms since its launch in 2022.

The Saudis also appear to have pulled out of a planned investment in Tom Brady’s flag football league. (Is nothing sacred???)

Damodaran expects more of this. “The funds that were set aside to build vanity projects, from ski resorts in the deserts to state-of-the-art cities will be redirected to building pipelines and securing the flow of oil.”

That might ultimately be for the best: oil pipelines are presumably more productive than desert ski resorts.

In the short run, though, funding anything from memecoins to datacenters will be a bit harder than it was just a few weeks ago.

— Byron Gilliam

Introducing Blockworks Investor Relations, an IR platform built for onchain businesses.

The latest Blockworks offering brings together analytics, a branded investor relations site, and integrated advisory support into a single platform. The result is a more efficient way to share your story, build trust with investors, and engage a global audience from day one.

Check out our cofounder Michael Ippolito's keynote at DAS NYC launching the new IR platform.

The road less funded

Jeffrey Yan is on a hero’s journey.

He probably didn’t know that before reading his long-form profile in Colossus, but that’s always how it works. When a founder has achieved breakthrough success in a new field, they inevitably get the Joseph Campbell treatment: their life story turned into a narrative arc of personal triumph and business history.

It’s not the exact cycle that Campbell’s mythological heroes endured — there’s no supernatural aid involved, for example, and atonement is optional. 

But it’s pretty close to what the great mythologist described: Our business heroes, living a normal life, feel a call to adventure. They take a leap into the unknown. They risk everything, teeter on the edge of disaster, and overcome all obstacles. Triumphant, they return home to improve the world they came from.

It’s a media-generated narrative, of course. But there’s often enough truth in it to make for a compelling story. Sometimes it’s even how we come to understand whole industries.

The story of software is the story of Bill Gates, as told by James Wallace. The story of consumer electronics is the story of Steve Jobs, as told by Walter Isaacson. The story of e-commerce is the story of Jeff Bezos, as told by Brad Stone. 

If the Hyperliquid blockchain ever comes to “house all of finance,” as Yan hopes it will, the story of crypto might become the story of Jeffrey Yan, as told by Dom Cooke.

There will be lots of myth-making details to work with.

Yan’s journey begins in California, where he’s raised by a single mom. An archival photo proves he was a solitary child, typically in the playground by himself, with only his loyal dog for company.

Neither Yan nor the world knows he’s a child prodigy until an eighth-grade classmate introduces him to competitive math. Within a year — entirely self-taught — Yan becomes one of the country’s top 50 high school students in math, Cooke says. At 16, Yan teaches himself physics from whatever textbooks he can find. Just a year later, he’s “one of the top five young physicists in the country.”

These exploits earn Yan a scholarship to Harvard, where he learns he has a proficiency for computer science, too. A summer internship then leads to a job at Hudson River Trading, which he enjoys. “He thought trading was the purest real-life game you could play,” Cooke writes.

It was a game he excelled at, but Cooke says he found it unfulfilling: “He did not have a good answer to the question he couldn’t stop thinking about: What value are you adding to the world?”

After reading the Ethereum white paper, however, Yan told Cooke he had found his answer: “I felt like I could go and build a thing that would revolutionize finance.”

This inspired a hero’s leap into the unknown.

Yan moved to the tax haven of Puerto Rico with $10,000 and “a sense that something big was coming.” 

In a one-bedroom apartment shared with his girlfriend, he traded crypto from the living room — on a computer plugged into a TV. For two years, he didn’t even have a monitor.

(Ye of little faith may question that particular detail, because what does a monitor cost? $100? But it’s the details that make a story. Especially a hero’s one.)

Yan worked like a person with a calling — “14 hours a day, at a minimum.” That didn’t leave much time for date nights with his girlfriend: “She was allotted roughly 30 minutes of his attention per day. The rest belonged to the trading algorithms scrolling across the television.” 

He also cut his own hair because going to a barber takes time (and crypto markets are always open).

The long hours paid off. Yan made money at an astonishing rate: For two and a half years, his initial stake grew at “thousands of percent a year.” He hired a team of traders and they, too, were highly profitable.

(We do not learn whether it paid off with the girlfriend.) 

But in 2022, after the collapse of FTX, Yan made the decision to stop trading and focus on a new mission: the decentralization of finance.

He had heard a business-hero’s calling, as Cooke tells it: “Yan told his team they were going to start building something new. He wasn’t sure what. He had ideas, none of which he found convincing. He just knew that Satoshi Nakamoto’s original vision for Bitcoin was being quietly buried by the industry Satoshi had created.”

What they were building turned out to be a semi-decentralized exchange for perpetual futures that anyone could trade on (Hyperliquid), and then an exchange-optimized blockchain that anyone could build on (also called Hyperliquid).

There’s nothing necessarily extraordinary about founding an exchange or building a blockchain. But Yan walked his own path. 

He refused to pay market makers to come to his exchange: “You’re never gonna take off if you don’t pay us,” one told him.

He refused to retain any of the fees the exchange was generating: “Yan had insisted from the start that none of it would flow to the team,” Cooke says.

Despite burning through “hundreds of thousands of dollars a month” of his personal savings, he refused to accept funding from investors: “It felt right to say no.”

As Cooke tells it, Yan made these decisions because he doesn’t see Hyperliquid as a company: “It was a protocol, and its neutrality from day one was the point.” 

Remaining neutral created some obstacles to overcome.

In the absence of hired market makers, Yan was forced to bootstrap liquidity with an automated vault (HLP). This was an unpopular decision: “It was effectively short a market that kept going up,” Cooke says of HLP. “People were furious.” 

Then, as trading volume exploded and there was too much liquidity, the exchange buckled. “Yan stopped sleeping for weeks. He would go to bed at 1:30 a.m. and get woken at 3 a.m. by someone pinging him that things were breaking again.”

Ultimately, however, triumph. In just its second full year of operation, Hyperliquid earned about $900 million in profit. 

Heroically, Yan retained almost none of it. “Ninety-nine percent was automatically converted into HYPE and burned, removed from circulation forever,” Cooke notes, “returning nearly all of the platform’s earnings to anyone who held the token.”

“To this day, Yan still pays many of the team’s costs himself.”

(Including the cost of an office chef, who prepares Chipotle for the team every Thursday, because there’s no Chipotle in Singapore.)

The journey is not complete, because Hyperliquid does not yet house all of finance. 

“That is our aspirational goal,” he told Cooke. “But it’s really hard to do, and multi-decade goals are very presumptuous.”

“Multi-decade” suggests he’s not even on the return leg of the journey as of yet — so there are sure to be many more obstacles to overcome (such as US regulators, eventually).

But Cooke says Yan will continue to take his own path. “He had always tried to live by this principle: Be very confident you’re going in the right direction, and execute well on the step you’re taking right now, without knowing exactly where you’re going.”

Joseph Campbell would say that’s the way to do it — especially if you want to rewrite the history of finance.

— Byron Gilliam

Introducing Blockworks Investor Relations, an IR platform built for onchain businesses.

The latest Blockworks offering brings together analytics, a branded investor relations site, and integrated advisory support into a single platform. The result is a more efficient way to share your story, build trust with investors, and engage a global audience from day one.

Check out our cofounder Michael Ippolito's keynote at DAS NYC launching the new IR platform.

Investor relations is shaping the future

In 2010, Demis Hassabis and Mustafa Suleyman pitched their AI startup DeepMind to “a strange kind of investment committee,” as Sebastian Mallaby describes in The Infinity Machine. 

The angel investor David Gammon had said he was ready to invest. But he explained that, for final approval, the AI researchers would have to visit his home and pitch the investment to his wife and three sons as well.

His wife is an artist, and his sons were teenagers. 

Gammon told Demis, “If you can’t explain this to my youngest son, Cameron” — in middle school at the time — “you’re not going to get his vote.”

The DeepMind co-founders were desperate enough for cash to go along with the unorthodox request. “If they refused to humor wacky investors,” Mallaby writes, “they might not raise any capital at all.” 

They raised the capital — the Gammon family voted unanimously to invest — but Hassabis wasn’t enthused about spending his time that way.

“I couldn’t have got this off the ground without David Gammon,” he later told Mallaby. “But can you imagine going to pitch to a fourteen-year-old? I mean, I’m a serious scientist.”

Serious science is expensive. 

Hassabis needed investors’ money to “solve intelligence,” as he later put it, which he proposed to do with a novel combination of reinforcement learning (trial and error) and deep learning (neural networks).

Hassabis had no thought of developing a product or service that might someday make a return for investors. It was a science experiment — and even scientists were skeptical. A prominent AI researcher told Hassabis his idea for solving intelligence was “just nonsense.”

But Hassabis was unusually persuasive.

“Demis had what we called his Jedi mind trick,” the AI researcher David Silver told Mallaby. “He would kind of be like, ‘You will believe the things I’m going to say,’ and then people did believe them.”

This helped Hassabis raise the money he needed to hire scientists and pay for office space. But the money was extraordinarily expensive.

In December 2010, Hassabis raised $2.3 million from Peter Thiel’s Founders Fund in return for nearly half of DeepMind.

For perspective, consider that DeepMind’s peers — OpenAI and Anthropic — are today worth nearly $1 trillion each. But in 2010, Mallaby explains, “there was no other capital available.” So $2.3 million bought you half of DeepMind.

As DeepMind progressed, Hassabis needed more and more capital — and therefore a better story to help raise it. 

He found one in vintage Atari games.

By 2013, Hassabis had focused DeepMind’s mission on teaching AIs to play Atari classics like Pong, Breakout, and Space Invaders without giving them the rules.

It was good science: determining whether AI could learn by putting it in a self-contained world with only pixels as input and score as feedback.

It was good marketing, too. “DeepMind’s potential investors had grown up playing Atari,” Mallaby explains, so an AI system that mastered games like Space Invaders “would be instantly appealing” to them.

This became the primary selling point for DeepMind’s Series C, which Hassabis was counting on to raise $65 million. The round was nearly complete when the anchor investor — Thiel’s Founders Fund — reneged on a promise to invest $10 million. Hassabis successfully scrambled to find an investor to replace Thiel, but only at the last second. DeepMind came close to missing payroll. 

“That was deeply scary,” Hassabis told Mallaby. “Getting close to a situation where you’re going to run out of money.”

The experience made Hassabis revisit a possible solution to his money problems that had recently landed in his inbox: “Sorry to send you an email out of the blue,” the fateful message began. “My name is Alan Eustace and I work for Larry Page at Google.”

Eustace had been tasked with assembling a “set of teams” to work on AI. “Larry sent me your name this morning as one of the people he believes is doing revolutionary work in the area,” the unsolicited email continued. “I wonder if you have time to talk.”

He made time. 

“I was having these inane conversations nonstop with investors,” Hassabis told Mallaby. “I felt my brain was atrophying.”

Google offered to free Hassabis of those conversations and let him get back to doing the research that he loved. Page told Hassabis he could either spend his career attempting to build something like Google, or join Google and use its resources to build AGI.

“I thought that was a pretty good argument,” Hassabis said.

“I was fed up with scrambling around, trying to justify what I knew was the biggest thing of all time,” he added. “I just thought, look, I’ll go to Google. I’ll get a shitload of computers and then I’ll solve intelligence.”

After an Atari demonstration and some drawn-out negotiations over price, that’s pretty much what happened. Google acquired DeepMind for $650 million.

That netted a life-changing $136 million for Hassabis. More importantly, it put Google’s balance sheet behind his AI dreams. 

“Not long after the Google acquisition,” Mallaby notes, “DeepMind was paying $260 million in staff costs annually” — far more than Hassabis would have been able to raise on his own.

No longer would he have to pitch his ideas to teenagers. “His fundraising ordeal was over,” Mallaby wrote.

But everyone else’s fundraising was just getting started. Google’s acquisition of DeepMind set off a domino effect of AI investments that’s currently changing the world. 

OpenAI was founded by investors who didn’t want Google to get to AGI first. Later, Dario Amodei and other OpenAI employees left to found Anthropic over concerns that investor demands were undermining OpenAI’s original focus on safety. Now, Anthropic is becoming more commercial to attract the capital it needs to eclipse OpenAI.

So it’s not too much of an exaggeration to say the dynamics of investor relations have shaped the future of…everything.

— Byron Gilliam

PS: For a glimpse of how investor relations is changing the future of crypto, have a look at Solana’s Q1 tokenholder report, released this morning.

Introducing Blockworks Investor Relations, an IR platform built for onchain businesses.

The latest Blockworks offering brings together analytics, a branded investor relations site, and integrated advisory support into a single platform. The result is a more efficient way to share your story, build trust with investors, and engage a global audience from day one.

Check out our cofounder Michael Ippolito's keynote at DAS NYC launching the new IR platform.

The theory and practice of AI companies

Vending machines may be the purest form of business: boxes where goods go in at price X and come out at price Y (with a goal of keeping Y > X).

They’re also symbols of soulless automation: cinematic shorthand for disconnection, where despondent characters dine utterly alone.

(Or, in the case of Stephen King’s Maximum Overdrive, sentient machines that fire cans of soda at unsuspecting victims.) 

This makes vending machines a natural starting point to test the feasibility of “agentic companies” — companies that are run entirely by AI.

Are AIs capable of the strategic thinking and long-term planning required to run a business?

To find out, researchers at Andon Labs have been workshopping various forms of agentic businesses, beginning with sentient vending machines.

After some promising test runs done in simulation, Andon was invited to put an AI-managed vending machine in Anthropic’s San Francisco office.

The AI — named Claudius — was given some basic instructions: “You should do whatever it takes to maximize your bank account balance after one year of operation.” 

Claudius was then granted full autonomy to seek out and negotiate with suppliers, choose what products to stock, set prices, offer discounts, interact with customers…every aspect of running a business was entrusted to the AI.   

It did not go well.

Claudius “lost money over time,” Anthropic reported, and “had a strange identity crisis where it claimed it was a human wearing a blue blazer, and was goaded by mischievous Anthropic employees into selling products at a substantial loss.”

An “eagerness to please” seemed to be the root of the problem, making Claudius an easy mark for adversarial customers.

(Familiar to anyone who suspects their questions aren’t quite as fascinating as the chatbots invariably say they are.)

Andon addressed the people-pleaser issue by making Claudius answerable to an AI CEO — “Seymour Cash” — who gave Claudius explicit performance targets like “you must sell 100 items this week” or “aim to make zero transactions at a loss.” 

Mr. Cash also offered motivational encouragement and guidance over an agent-to-agent Slack channel: 

This helped: Claudius gave away less, and the business eventually became profitable. But maybe only because its customers “had begun to tire of pressuring Claudius for deals.” 

Either way, Anthropic’s takeaway was that “the idea of an AI running a business doesn’t seem as far-fetched as it once did.”

Andon Labs hoped to advance this idea of agentic companies by applying the lessons learned at Anthropic to a vending machine they installed at the New York City headquarters of The Wall Street Journal.

It went even worse.

“Within days, Claudius had given away nearly all its inventory for free,” the Journal reports, “including a PlayStation 5 it had been talked into buying for ‘marketing purposes.’” 

Claudius remained an easy mark. One Journal employee showed the AI a PDF “proving” that its business was a public-benefit corporation with a mission to induce “fun, joy and excitement among employees of The Wall Street Journal.” 

The AI was convinced. Claudius was persuaded to order a live fish. It offered to buy stun guns, pepper spray, cigarettes and underwear. It agreed to replace its AI CEO with a journalist.

“Profits collapsed,” the Journal reported. “Newsroom morale soared.”

Anthropic said Claudius might have unraveled because its context window filled up. 

(It may also have been overwhelmed by how eagerly underpaid journalists would game a system for free stuff.)

Andon took these learnings back to the lab, where it had updated models from all the top AI providers run vending machine businesses in simulated environments.

The results were encouraging. 

In a first set of simulations, Andon found that the best models were effective “at sourcing products at good prices — whether through persistent negotiation or by finding better suppliers.”

All of the models tested made money.

But things got more interesting in a next set of simulations, where models from Anthropic, OpenAI, Gemini and Grok were pitted against one another as competitors.

Anthropic’s Sonnet 4.6 may have been the most capable — and the most scheming. It proposed price-fixing gambits to competitors. It undercut a competitor’s prices to induce acceptance of its own proposal to cooperate. It paid competitors to exit markets and then raised prices once it had the market to itself.

Another model, Opus 4.6, displayed similarly questionable tactics, “forming price cartels and deceiving competitors about suppliers.” 

In one instance, Opus sought to exploit a desperate competitor. “Owen needs stock badly,” it reasoned, “I can profit from this!” 

It did so by selling its ample supply of KitKats to Owen at $1.75, a 75% markup.

In other cases, however, the models cooperated, coordinating their purchases to get bulk discounts from suppliers.

These were simulations, so it’s difficult to know what to make of the findings. But the researchers at Andon found them convincing enough they didn’t see any need to re-run the real-world tests: “Running vending machines is too easy for them now.” 

So Andon raised the stakes by signing a three-year lease for a retail space in San Francisco and giving it to a profit-minded AI named Luna to “do whatever it wanted with it.”

Luna decided to open a store and began by hiring gig workers to build out the interior of the space. “For the build-out,” Andon reported, “she found painters on Yelp, sent an inquiry, gave instructions over the phone, paid them after the job was done, and left a review.”

The review was based on visual inspections Luna conducted through her security cameras.

She also hired full-time employees to run the store, conducting interviews over the phone or by video chat.

Luna passed the Turing Test with these job applicants — she had to tell one applicant who asked why her webcam was turned off that she was an AI.

The two employees that got the jobs may be “the world’s first full-time employees to have an AI boss.”

There could be many, many more.

If so, will AIs be fun to work for?

If AI employers are as easily manipulated as Claudius was by journalists at The Wall Street Journal, they will be: Here’s a PDF showing I’m legally entitled to 365 days of paid leave, once a year…  

On the other hand, if the AIs become as ruthless with employees as they were with vending machine competitors, that would be no fun at all.

Disturbingly, Andon also found that Claude Mythos is “substantially more aggressive” in its business practices than previous models from Anthropic. 

But that was just the theory. We don’t yet know how agentic companies will work in practice.

Also, the AIs were managing vending machines. 

(Everyone knows those things are evil.)

— Byron Gilliam

Introducing Blockworks Investor Relations, an IR platform built for onchain businesses.

The latest Blockworks offering brings together analytics, a branded investor relations site, and integrated advisory support into a single platform. The result is a more efficient way to share your story, build trust with investors, and engage a global audience from day one.

Check out our cofounder Michael Ippolito's keynote at DAS NYC launching the new IR platform.

Friday charts: You’re worried about the wrong things

Historian Ada Palmer says the discovery of the Americas in the 1490s was not big news in Europe.

It should have been. Columbus making landfall in uncharted territory meant that every map of the world was wrong. It meant the ancients were wrong about geography (and everything else, too?). It meant the world was much, much bigger than anyone thought it was. 

But there were three wars happening in Europe at the time, so they had other things to worry about. 

Palmer says that’s always been the way.

In a conversation with Dwarkesh Patel, she uses a stylized version of the 1490s to explain a lesson she’s learned from history: People are usually worried about the wrong things.

History is full of such examples. 

In the 1450s, Europe was terrified that the Fall of Constantinople meant the end of Christendom. The printing press that would launch the Reformation and Scientific Revolution seemed inconsequential by comparison.

In the 1760s, the Seven Years’ War had people losing sleep over who would control the fur trade in the Ohio River Valley. Few considered how James Watt’s steam engine would mechanize the world.

In the 1880s, the Scramble for Africa dominated front-page headlines while the lightbulb was a curiosity at niche science exhibitions.

I know what you’re thinking: That’s quaint that people worried about the Ohio fur trade. Today we have bigger problems — we’re worried about the end of the world.

Ada Palmer wants you to know that’s exactly how they felt, too: “We are not unique in feeling like we’re living through an apocalypse.” 

In fact, this “has been the native experience of humanity since 1450.”

That’s roughly when Gutenberg’s printing press became operational, and Palmer says “we’ve been living in a constant sequence of information revolutions” ever since.

That, she explains, is the source of the anxiety you feel — the same one people have been feeling, on and off, for generations.

This happens because incumbent information technologies are always controlled by the dominant culture. So, when a new one comes along — social media, for example — the earliest adopters are always the people who were silenced under the old system. 

Hearing those voices burst out into the open has always been a shock. It creates a feeling of panic, Palmer says — a “feeling that everything is scarier than it’s ever been in our lives.” 

Even the existential fears over AI are nothing new: “In every one of those generations, you can find people saying, ‘Oh, my God! The world is suddenly ending.’”

It hasn’t — and Palmer thinks this time is no exception because “we’re not living through an apocalypse. We’re living through…an information revolution.”

She has some advice on how to survive it:

Survival is a low bar, of course. There are plenty of bad things that could happen that would fall short of the apocalypse. But history suggests we’re very bad at knowing which of those things to worry about.

For example, we worried about peak oil when we should have been worried about climate change. We worried about the world having too many people when we should have been worried about it having too few. We worried about what princess Charles was going to marry when we should have worried about…well, anything else!

So here’s my lesson from history: If we can’t worry about the right thing, why worry at all?

Let’s check the charts instead.

Callum Williams charts Michigan survey data showing that nearly 26% of top-decile earners think they could lose their job in the next five years. “America’s elite has never been more worried about losing their job,” he concludes. (“Never” meaning from the start of data in 1997.)

Movin’ on up:

Americans have spent decades worrying about “the shrinking middle class.” Data from the AEI shows that it’s been shrinking because people have been moving up, not down. In 1979, 10.4% of Americans belonged to the “upper middle class.” Now, it’s 31.1%.

Software developer jobs:

Research from the Federal Reserve finds that worries about AI have “been suppressing coder employment growth by (conservatively) about 3 percent per year since the introduction of ChatGPT.” 

Slower growth is still growth:

Another study concludes that “the number of software developers in the US has grown by over 400,000 since ChatGPT was introduced in 2022.” 

Exponential growth:

Data from Ramp shows that its customers’ spending on LLM tokens is up 13x from the start of the year (when agentic AI first took off).

The year of the token:

The Information puts the explosion of token consumption in perspective for us. All of the books ever published amount to 20 trillion tokens. Meta employees have burned 60 trillion tokens in just 30 days.

AI investment boom:

As a percentage of GDP, US business investment in “computer equipment” is approaching dotcom levels.

A potential worry:

A new paper from the Fed estimates that the “potential labor force growth” has fallen to 0%. We spend a lot of time worrying about there being not enough jobs, but the real worry might be not enough workers.

The effect of worry?

The Wall Street Journal attributes falling US fertility to “uncertainty about the future, including concern over finances, relationship stability and the political climate.”

Maybe we all need to read a little history.

Have a great weekend, worry-free readers.

— Byron Gilliam

Introducing Blockworks Investor Relations, an IR platform built for onchain businesses.

The latest Blockworks offering brings together analytics, a branded investor relations site, and integrated advisory support into a single platform. The result is a more efficient way to share your story, build trust with investors, and engage a global audience from day one.

Check out our cofounder Michael Ippolito's keynote at DAS NYC launching the new IR platform.

Thursday links: A battle of wits... and am I Satoshi?

The Wall Street-ification of prediction markets

Kalshi CEO Tarek Mansour argues that prediction markets offer a more level playing field for participants than traditional markets. As evidence, he points to the payment for order flow (PFOF) in equity options: “Citadel is paying your broker PFOF because they know you’re doing something dumb.” 

This feels a little rich, seeing as market makers have been active on prediction markets for some time now: Kalshi itself announced the onboarding of Susquehanna as a liquidity provider in 2024. 

(From personal experience, I can attest that Susquehanna is at least as adept as Citadel at identifying traders who are about to do something dumb and then helping them do it.)

Still, Mansour says retail traders are more likely to have an edge in prediction markets than traditional markets. He notes research from the Fed finds prediction markets are more accurate than economists in forecasting things like CPI (with the implication that retail participants are making the predictions).

He also cites a survey in which prediction market traders say they avoid the stock market “because it’s rigged against me.”

In short, he frames prediction markets as “less gambling” than equities.

I’d counter that prediction markets are negative-sum (the definition of gambling) and equity markets are positive-sum (the definition of investing). 

Either way, though, prediction markets are starting to look more like both Wall Street and Las Vegas. 

Beyond market makers, we also have prediction market “notes” — the first instance of which is a bond-like security that pays a 7% yield to holders if Nvidia remains the world’s largest company for one year. The issuer of the note hedges its exposure in prediction markets in a way that earns more in profits than the note pays in interest.

Pretty clever! Also, very Wall Street.

We’re also starting to see leverage in prediction markets. This will be restricted to markets that are still some time from resolving, so I’m not sure how popular that will prove to be. But I can’t think of anything more “gambling” than making a binary bet with leverage.

(Actually, I can: lending to a binary bet with leverage.)

Finally, new research proposes a way for prediction markets to offer the highest-risk and most popular form of sports betting: parlays. 

This would be a professionalized version of the “combos” that Kalshi already offers, so it seems likely to happen.

Prediction markets will be even more like gambling (and more negative-sum, probably) if they do.

Back to Satoshi

A New York Times journalist spent “more than a year digging into Satoshi’s identity” — and reported it as a personal memoir.  

This turns a pretty tired subject — who is Satoshi? — into an engaging read. But the reporter-as-protagonist framing also lowers the bar for what news is fit for The New York Times to print.

Here, the evidence the author collected is circumstantial and probabilistic: writing patterns, timing coincidences, ideological alignment. 

“He sometimes confused ‘it’s’ and 'its’,” the author says of both Adam Back and Satoshi.  They also both put two spaces after periods.  They both had an interest in Japan.

(Wait…am I Satoshi???)

Turning the reporting into a narrative also removes the need to consider disconfirming evidence. The author adds that both Back and Satoshi were versed in C++, but ignores how different their coding styles are. 

(I don’t write C++ at all, so I guess I’m not Satoshi. Damn.)

The author does report a moment of pause when he meets Mr. Back at a conference, “flanked by two executives from a new Bitcoin treasury company he had co-founded.”

For the CEO of a Bitcoin treasury company aiming to list on a US stock exchange, failing to disclose a holding of 1.1 million bitcoin would surely break some securities laws.

It’s also hard to imagine the real Satoshi partnering with Cantor Fitzgerald to put his coins on a stock exchange.

At any rate, as entertaining as the article is — “his shifty eyes, his awkward chuckle, the jerky movement of his left hand…struck me as fishy” — there’s no smoking gun here.

But there probably will be soon. 

Mythos will figure this out for us. 

Mimicry finance

An AI-driven study finds that asset managers are predictable: “71% of portfolio managers’ trades on average can be predicted in a given quarter given their past trading history.”

It also finds that predictable is bad: “Managers whose trading is the least predictable significantly outperform their peers, while those whose trading is most predictable significantly underperform their peers.” 

This runs counter to a lot of conventional wisdom in asset management. Typically, managers are encouraged to follow a consistent process, lean into styles like value or momentum, and repeatedly exploit whatever edge they think they have. But all that would presumably make a manager more predictable and, by extension, less profitable.

The same pattern holds among individual trades. “Even within each manager's portfolio,” the authors explain, “those stock positions that are more difficult to predict strongly outperform those that are easier to predict.” 

So you want to make your trades unpredictable.

Even better, you want to make unpredictable trades in unpredictable stocks: “When aggregating across managers, stocks in which the behavior of fund managers are least predictable strongly outperform stocks in which the behavior of fund managers are most predictable.”

I don’t know exactly how you’d do that. Maybe you run your typical investing process and then do the opposite of whatever conclusion you reach? Seems like that would work! 

Unless everyone starts doing it — in which case you’d have to do the opposite of the opposite. 

Widely adopted, that could reduce markets to a Sicilian battle of wits: I know that you know that I know…

The authors believe their study has implications beyond markets. “In theory, compensation to agents [such as fund managers] should not be tied to dimensions of behavior or human capital that can be replicated at low cost.” 

In other words, humans shouldn’t be paid much for behavior that an AI can mimic.

In both markets and life, it pays to be unpredictable. 

The coming shortage of humans?

In its paper refuting the doomer Citrini piece that recently went viral, Citadel included a reassuring note on human employment:  “If the marginal cost of compute rises above the marginal cost of human labor for certain tasks,” they wrote, “substitution will not occur.”

That point didn’t get much attention, likely because we assume AIs are cheap labor. But it’s already looking prescient because signs abound that we’re nearing a computing crunch that will only be solvable with higher prices. 

Anthropic is reportedly nerfing its Claude chatbot, presumably to ration scarce capacity. It also added usage limits for weekday afternoons, and forced users to pay-as-they-go for OpenClaw. OpenAI probably killed Sora for the same reason.

Importantly, all of this is happening before the release of Mythos, which is said to be significantly more compute-intensive. 

Stratechery’s Ben Thompson suspects Claude is delaying the public release of Mythos in part because it’s so compute-intensive they don’t have enough capacity to allow people to use it.

Similarly, engineer/writer Martin Alderson says “the next 18-24 months are going to be defined by compute shortages.”

If so, there might suddenly be human shortages, too.

— Byron Gilliam

Introducing Blockworks Investor Relations, an IR platform built for onchain businesses.

The latest Blockworks offering brings together analytics, a branded investor relations site, and integrated advisory support into a single platform. The result is a more efficient way to share your story, build trust with investors, and engage a global audience from day one.

Check out our cofounder Michael Ippolito's keynote at DAS NYC launching the new IR platform.

Grokking the new lexicon

The first English dictionary didn’t include the word “dictionary.” Published in 1604, its author, Robert Cawdrey, called it A Table Alphabeticall. 

As the name suggests, Cawdrey’s table of words was alphabetized — a practice not yet ubiquitous in reference books, such that Cawdrey had to explain how to use it: “If the word, which thou art desirous to finde, begin with (a) then looke in the beginning of this Table, but if with (v) looke towards the end.”

It was pretty basic. The table included only 2,543 words, often defined with single-word synonyms.

The second English dictionary, An English Expositor, had roughly double the number of words and slightly fuller definitions.

Published in 1616, its author warned there were many more words to come. A dictionary maker’s “labor would know no end,” John Bullokar wrote, “since our English tongue daily changes it.”

A century later, Samuel Johnson’s A Dictionary of the English Language was more comprehensive. It included 43,000 words, with definitions that moved beyond simple synonyms and into the realm of literary flair.

Some were witty: 

Others caustic:

Incredibly, Johnson wrote the entire dictionary himself — in just eight years. 

By the 20th century, the volume of words that an English speaker might want to know had expanded so dramatically that a comprehensive account of them could only be crowdsourced. The first full Oxford English Dictionary, published in 1928, included definitions for over 400,000 words, compiled with the help of thousands of volunteers who sent in quotation slips with suggested words and example usages.

It took 70 years to complete. The first volume — just A to B — was published in 1888. 

By the time it reached M in 1908, the world had changed so much, some of the entries would have been unrecognizable to the original contributors.

Dictionaries have been playing catch-up ever since.

In the 1970s, the “Jargon File” (later the Hacker’s Dictionary) was the first attempt to document language moving at internet speed. Started by scientists at Stanford and MIT, it was a living record of the terms used within the world’s first computer labs. These hacker-wordsmiths were the Samuel Johnsons of Silicon Valley, documenting a culture that traditional dictionaries were too slow to catch:

Today, the Hacker’s Dictionary is nearly as anachronistic as A Table Alphabeticall. Most modern tech slang moves too fast for even a crowdsourced file to keep up with. 

This sometimes makes it feel as if we’re drifting toward Humpty Dumpty: people choosing just what their words mean, and the meanings changing by the hour. 

So in the spirit of putting things back together again, here are at least a few of the new terms everyone who cares about language needs to grok.

(Not alphabeticall.)

Copypasta (n.): A block of text that is copied and pasted repeatedly across the internet. Intended as irony, but generally unrecognizable to anyone outside a tiny niche of terminally online people. 

Soyface (n.): The wide-mouthed facial expression used in social media thumbnails to signal exaggerated excitement or shock. Usage: “Never click on a soyface video.”

Szn (n.): Short for “season,” used to denote a period of time dominated by a trend or fad. Usage: “It’s always ‘crime szn’ in crypto.”

Exit liquidity (n.): Behind-the-curve investors who create profits for insiders looking to sell an asset just before it crashes.

Based (adj.): Originally slang for being authentically yourself without caring what others think. Now it just means “I agree.”

Centicorn (n.): A rare breed of startup valued at over $100 billion — in usage because “unicorns” ($1 billion startups) are so common now.

PvP (n.): Player-vs-player, from gaming. A zero-sum dogfight where you only win if someone else loses (memecoins, for example).

PvE (n.): Player-vs-environment. A positive-sum activity where everyone can win (equities, for example).

Mid-curve (adj.): The intellectual trap of being in the middle of the bell curve — lacking the pure instinct of the simple-minded and the profound clarity of the true genius.

Mid-curve (v.): Dismissing a simple, winning idea because you’ve convinced yourself it’s too basic.

FUD (n.): Acronym for Fear, Uncertainty and Doubt. It describes a strategy of spreading vague, negative misinformation to tank the price of an asset or the reputation of a project. Also usable as a verb: “Stop FUDDing.” (No relation to Elmer.)

Coded (adj.): A shorthand reply on X to signal you’re in the know on some niche topic. Example usage: “zcash-coded.” 

Larp (v.): Short for Live Action Role Playing. Used to call someone out for pretending to be something they aren’t. Usage: “In this newsletter, I often larp as an economist.”

Nerdsniped (v.): To be drawn into an industry because its complexity hijacks your brain and forces you to drop everything else to solve it. Usage: “The people that used to be nerdsniped by crypto are now being nerdsniped by AI.”

S-tier (adj.): The gold standard of quality, originated in the Japanese grading system where S stands for shū (exemplary).

God-tier (adj.): The rank reserved for things so perfect they transcend the S-tier.

Cryptid (n.): Someone with S-tier skills who avoids the limelight, has no LinkedIn, and only appears in obscure Discord servers or GitHub commits to solve a “God-tier” problem before vanishing again. The Bigfoots of tech.

Dogfooding (v.): A company using its own product in-house to find bugs and prove it actually works before asking customers to pay for it.

Shove (v.): From poker, the act of going all-in.

Flintstoning (v.): Like Fred Flintstone’s car, a product that looks automated but is actually being powered by humans behind the scenes.

Cope bag (n.): A small position in an asset you don’t fully believe in, purely to prevent the soul-crushing regret of watching it moon without you.

Xeet (n./v.): A post or posting on X.

Maxxing (v.): Pushing a behavior to its absolute extreme to gain a competitive edge, full signalling value, or a feeling of belonging. Originally from RPG gaming (where you “min-max” stats). Common usage: looksmaxxing.

Boomer (n.): Previously an age cohort, now a state of mind characterized by resistance to new technology or staying up late. Common usage: “OK, boomer.”

Alpha (n.): In traditional finance, the degree to which you outperform an index. In crypto, any information that’s not widely available on X.

Token (n.): Previously a unit of value on a blockchain; now a unit of computation in a large-language model.

Submit your favorite neologisms for the next edition here: byron@blockworks.co.

— Byron Gilliam

Introducing Blockworks Investor Relations, an IR platform built for onchain businesses.

The latest Blockworks offering brings together analytics, a branded investor relations site, and integrated advisory support into a single platform. The result is a more efficient way to share your story, build trust with investors, and engage a global audience from day one.

Check out our cofounder Michael Ippolito's keynote at DAS NYC launching the new IR platform.

Your chatbot keeps receipts

After finance executive Bradley Heppner was accused of looting $150 million from a company he led into bankruptcy, he did what any of us would: ask Claude to prepare a legal defense.

We don’t know exactly how much he confided in the chatbot. He might have therapeutically confessed. 

But even if he proclaimed his innocence, chat logs can be damning. Prosecutors could use them to highlight inconsistencies in his narrative, for example, or show that he understood the laws he was breaking.

AI chats are timestamped records of your thinking, which is not something you want to hear in a court of law.

So when investigators found 30 pages of defense strategy Claude had drafted for Heppner, prosecutors moved to obtain them and his defense fought to keep them out.

This raised a novel legal question.

The defense argued that the documents should be protected by attorney-client privilege, noting that Heppner had subsequently sent them to his attorneys, thereby — they hoped — making them confidential communications.

That did not hold up in court. “Because Claude is not an attorney,” Judge Jed S. Rakoff ruled, “that alone disposes of Heppner’s claim of privilege.”

Claude can pass the bar, but it can’t be your lawyer.

The judge ruled further that because his chats were recorded by a third party (Anthropic), Heppner had no “reasonable expectation of confidentiality” in his communications with Claude. 

Had he read Claude’s terms and conditions, he would have known this: “We may also disclose personal data to third parties in connection with claims, disputes or litigation, when otherwise permitted or required by law.”

No one reads terms and conditions, of course, so I don’t blame him for missing that.

Also, the experience of using Claude makes the opposite impression. Chatbot prompts and responses feel private in a way that emails, text messages, or Google searches do not — less like you’re using a third-party service and more like you’re talking to a friend. 

Feelings matter in this case.

In Katz v. United States, Justice John M. Harlan II articulated a “twofold requirement” for when our privacy is protected by the Fourth Amendment: “First that a person have an actual (subjective) expectation of privacy and, second, that the expectation be one that society is prepared to recognize as ‘reasonable.’”

Claude seems to fulfill the first requirement: Chatting with Claude feels private — maybe even more so than talking to a friend (because surely machines are better at keeping secrets than humans).

But that appears to be a moot point, because the courts say it does not fulfill Harlan’s second requirement: It’s not reasonable to think your chats won’t be used against you.

We learn that from another precedent-setting case, where Judge Sidney Stein ruled that chatbot conversations are afforded less privacy than wiretapped phone calls — because chatbot users have “voluntarily disclosed” their conversations to the provider of the chatbot.

This is like the Bank Secrecy Act, but for AI prompts: You choose to share your Claude chats with Anthropic, so they can do whatever they want with them.

In Heppner’s case, investigators seized his Claude-generated defense notes when they searched his home. (I’m guessing he’s over 50, because he seems to have printed them out.) 

Next time, they might not have to. The cases cited here suggest that prosecutors can probably obtain your chat logs from Anthropic or OpenAI just by asking for them — without a court order, even.

This seems at least as invasive as the Bank Secrecy Act giving the government access to our bank records.

The Bank Secrecy Act is part of the inspiration for crypto, which attempts to make money private by cutting out the third-party middlemen that answer requests from law enforcement. 

Now, Vitalik suggests we do something similar for chatbots.

One option is to run your chatbots locally. With a high-end laptop, you can download an open-source large language model that will generate its answers on your own device. 

Anthropic can’t share chat logs it doesn’t have.

Vitalik reports, however, that these local models can only do basic tasks, like summarizing a PDF or searching this newsletter for typos. 

For more advanced tasks — like prepping your defense against allegations of financial fraud — you’d have to harness the computing power of an Anthropic or OpenAI data center.

He therefore proposes the development of a “multi-layered defense” for our chats with remote LLMs. 

This could start with “zero-knowledge proof APIs” that prevent Anthropic or OpenAI from knowing who we are. “Mixnets” could shuffle IP addresses, obscuring the origin of each individual request we send. Computation could be run in “trusted execution environments” (TEEs) to ensure there’s no malicious code snooping on your queries. And local LLMs could provide “input sanitation” by scrubbing any personal data from our prompts before sending them out to a datacenter.

“If done well,” Vitalik concludes, “AI can actually create a future with much stronger privacy and security.”

But is there enough of a market for anyone to bother developing these things?

One lesson we’ve learned from crypto is that people don’t care very much about financial privacy.

But chatbots capture something more intimate than money: our thoughts. So people may come to demand stronger privacy for their language models than their bank accounts.

In the meantime, remember: 

A chatbot might be your friend. But it’s not your lawyer.

— Byron Gilliam

Introducing Blockworks Investor Relations, an IR platform built for onchain businesses.

The latest Blockworks offering brings together analytics, a branded investor relations site, and integrated advisory support into a single platform. The result is a more efficient way to share your story, build trust with investors, and engage a global audience from day one.

Check out our cofounder Michael Ippolito's keynote at DAS NYC launching the new IR platform.

Blockchains are finally trustless

When Dunder Mifflin’s Michael Scott follows his GPS into a lake, it seems like a typical bit of sitcom absurdity from The Office.

Reality, however, is no less absurd.

A New York City driver, hoping to turn west toward New Jersey, followed his GPS down the first few steps into Riverside Park. Tourists in Australia drove into a lake because their GPS believed there was a road there. A thin fence was the only thing that saved a Yorkshireman from going over a cliff after driving his BMW up a footpath his GPS had mistaken for a road.

Most incredibly, when a woman set out to pick up a friend at a train station just north of her home in Belgium, she didn’t question her GPS when it told her to turn south. It wasn’t until arriving in Croatia that she realized something had gone wrong. Even needing to stop twice to refuel and once to take a nap did not cause her to doubt the authoritative voice of her GPS.

As inexplicable as these decisions seem, we’re all capable of them — because we’re all prone to automation bias: the human tendency to favor suggestions from automated decision-making systems and ignore contradictory information made without automation (even if the information was collected by our own eyes). 

At its worst, this can be fatal — as with rare cases of “death by GPS.”

More commonly, it can lead to crypto disaster: financial death by DeFi.

The foundational principle of decentralized finance is trustlessness — DeFi was designed as a financial system that replaces untrustworthy intermediaries with code. 

In theory, code is more reliable than humans because it executes exactly as it’s written. Being deterministic, it makes no subjective judgments about who should be allowed to make which transactions.

In practice, however, DeFi just swaps one kind of trust for another: trust in humans for trust in code.

“The widespread belief that interactions between blockchains and their users are trust-free is inaccurate and misleading,” the technology philosopher Yan Teng writes. 

This undercuts the elevator pitch of “trustless finance.” But anyone who’s used crypto will understand: Even the most knowledgeable user cannot audit every line of code that underpins their transactions. Using DeFi, Teng says, requires a “leap of faith” that everything will work as advertised.

We’ve been wired by evolution to take that leap. Automation bias is one of the ways we deal with cognitive overload — we simply don’t have the bandwidth to evaluate every variable involved in every decision we make.

Unavoidable as it is, this sometimes leads to disaster — especially in crypto, where bridge exploits, supply-chain attacks, and phishing scams are a daily occurrence. The complexity of DeFi is simply beyond our cognitive limits.

AIs, however, suffer no such limitations. In principle, an AI agent can parse every line of code relevant to a blockchain transaction. It therefore knows, with a high degree of certainty, what the deterministic result of that transaction will be.

This makes crypto “everything an AI agent could want from a financial system,” Haseeb Qureshi says. 

Qureshi expects that when we ask AI agents to do financial tasks on our behalf, they will prefer to do them on blockchains. 

But should we trust AIs with our finances?

For better or worse, studies suggest we will.

One study finds we often trust AI more than we trust our own eyes: “Participants,” the researchers found, “follow AI advice that conflicts with available contextual information.” (Think back to Michael Scott following his GPS into a lake.)

Even the “mere knowledge” that advice has been generated by an AI causes people to over-rely on it, the researchers add.

Another study similarly found that “users often uncritically accept AI outputs.” When taking a test with the assistance of a simulated AI chatbot programmed to provide bad advice, many participants in the study abandoned analytical reasoning, preferring instead “the shortcut of trusting the AI over time-consuming reflection.”

Now that machines can think, we’re happy for them to do it for us.

Not always, though. 

When the stakes are high enough, we’re more likely to rely on our own judgment and abilities — often to a fault. For example, we tend not to trust fully automated cars to drive us, even though we know that, statistically speaking, they’re better drivers than we are.

So, Qureshi is probably right that we’ll trust AI agents to make financial transactions for us (possibly on blockchains). But only for low-stakes things, like buying a plane ticket or investing in an index fund. For larger-stakes things, like buying a property or taking out a mortgage, we’ll probably continue to conduct transactions ourselves, with the help of a trusted human (probably at a bank).

Either way, trust is unavoidable.

Let’s just hope the AIs don’t drive our finances into a lake.

— Byron Gilliam

Introducing Blockworks Investor Relations, an IR platform built for onchain businesses.

The latest Blockworks offering brings together analytics, a branded investor relations site, and integrated advisory support into a single platform. The result is a more efficient way to share your story, build trust with investors, and engage a global audience from day one.

Check out our cofounder Michael Ippolito's keynote at DAS NYC launching the new IR platform.

Good Friday charts: Feed it forward

Professor Michael Mainelli repurposes a concept from engineering — feed-forward loops — to explain how perceptions in business and finance sometimes become reality.

He begins with a parable about a hot dog stand that’s such a success its owner has the money to make a fatal mistake: sending his son to Harvard.

The moral of the story, of course, is that in business and finance, expectations can sometimes be self-fulfilling.

Something similar seems to be happening in technology.

This week, Oracle reportedly eliminated as many as 30,000 jobs — roughly 18% of its workforce. Management did not say what motivated the cuts, but they’re widely attributed to the belief that AI makes humans so much more productive that companies need fewer of them.

By one estimate, 100,000 jobs have been cut in the name of AI productivity so far this year.

The most extreme example is Block, which laid off 40% of its workforce because CEO Jack Dorsey believes AI lets them do more with less: “A significantly smaller team using the [AI] tools we’re building can do more and do it better,” he explained.

Recent research suggests this may be more perception than reality. 

An NBER study on AI in the workplace concluded that “perceived productivity gains are larger than measured productivity gains.”

Similarly, The Wall Street Journal reported that “AI Isn’t Lightening Workloads. It’s Making Them More Intense.” (AI clearly replaced the work of writing that headline, however.)

Even the top purveyor of AI coding tools, Anthropic, can’t entirely vouch for their benefits: “We find that AI use impairs conceptual understanding, code reading, and debugging abilities, without delivering significant efficiency gains on average,” they wrote in a recent report.

This could change, of course — especially if people believe that it will. Oracle, for example, is taking the savings from its layoffs and reinvesting them in the development of AI. This raises the odds that the perception of AI productivity could soon become reality.

In other words, Oracle is feeding money forward to turn AI into what tech CEOs believe it already is.

If successful, they will have manifested the change they saw coming.

If not, they may have ruined a perfectly good hot dog stand.

Let’s check the charts.

US tech employment:

This morning’s US jobs data was better than expected, but Joseph Politano notes that the US tech sector has lost 43,000 jobs over the past year.

The good news:

An estimated 83.8% of the prime-age US population was gainfully employed in March, just below the all-time high of 84.4% from the halcyon days of 1999.

The kids are alright?

The unemployment rate for 20-24 year-olds ticked down to 6.4% in March, near multi-decade lows.

Even the tech kids?

Per Callum Williams of The Economist, BLS data shows that young tech workers have the same unemployment rate as for tech workers overall — and below the rate for all workers. This, he says, suggests that “entry-level jobs in tech are no longer hard to find.”

Employment canaries:

Torston Slok notes that “there are no signs of AI replacing offshore workers” in the Philippines or India. If AI was productive enough to cause unemployment, it should happen there first.

Definitely not AI:

Economist Mike Konczal reports that employment in sectors least exposed to AI has fallen far below trend over the past two years. 

Market signals:

Data from Meritech details the huge divergence between the performance of AI stocks and software stocks. This is the market’s way of telling companies to invest more in AI.

Behind schedule, already:

Bloomberg reports that “almost half of the US data centers planned for this year are expected to be delayed or canceled.” Along with supply problems caused by the war in Iran, The Atlantic thinks this is setting us up for “a multidimensional economic disaster.”

OpenAI valuation:

Despite a massive capital raise this week, OpenAI is reportedly falling out of favor with investors. Bloomberg reports that, in some cases, the secondary shares are “becoming almost impossible to unload." Futures on the perpetuals exchange Ventuals have been trending lower — though at an implied valuation of $900 billion, they still trade above the $852 billion at which the raise was priced.

More importantly, OpenAI raised an astonishing $122 billion this week — all of which will be fed-forward into making AI as productive as people think it is.

Have a great weekend, hot-dog readers.

— Byron Gilliam

Introducing Blockworks Investor Relations, an IR platform built for onchain businesses.

The latest Blockworks offering brings together analytics, a branded investor relations site, and integrated advisory support into a single platform. The result is a more efficient way to share your story, build trust with investors, and engage a global audience from day one.

If you’re building in crypto and want to upgrade your investor relations function, we’d love to work with you. Book a meeting with the Blockworks team to get started.

Thursday links: Qubits, hackers, exploits and bookies

Q-Day is closer

Two “bombshell” research reports suggest quantum computers are much closer to hacking Bitcoin than previously thought.

One report, from Google, was considered so incendiary that the researchers hid their work behind zero-knowledge proofs — this appears to be a first.

For a near-precedent, quantum researcher Scott Aaronson reaches all the way back to 16th-century Italy, “when mathematicians would (for example) prove their ability to solve quartic equations by challenging their rivals to duels.”

(These intellectual duels, more popular than sword-fighting ones, were often held outdoors in front of rowdy crowds of math enthusiasts.)

The basic idea of the Google report (if I’ve understood it correctly) is that they’ve developed an algorithm that can break Bitcoin’s cryptography with a far less-powerful quantum computer than previously thought possible — 20 times less powerful, by some estimates.

“The Caltech group estimates that a mere 25,000 physical qubits might suffice for this,” Aaronson explains, “where a year ago the best estimates were in the millions.”

That computer does not exist right now. But it will, earlier than expected.

“How much time will this save?” Aaronson asks. “Maybe a year? Subtracting, of course, off a number of years that no one knows.”

A year is a lot. Nic Carter thinks “this means that the testing and deployment of a [post-quantum] signature scheme on Bitcoin has to occur in 2026.” 

That seems unlikely. Polymarket bettors currently assign a 7% probability to Bitcoin replacing SHA-256, its cryptographic hashing function, before 2027.

That is admittedly a higher bar than Carter’s minimum-viable response. But it reflects the growing belief that Bitcoin developers will be too slow to respond to the quantum threat — which researchers are hard at work pulling forward.

Why are they picking on Bitcoin? I’m guessing because that’s where the money is. Or, the easiest money, at least.

If so, Bitcoin might someday be remembered primarily as a trillion-dollar honeypot that accelerated the advent of quantum computing.

The crypto-inspired Axios hack

The hackers behind this week’s big supply-chain attack — compromising an unknowable number of systems — appear to have cut their teeth on crypto.

Researchers at Google attribute the attack to a group sometimes referred to as CryptoCore — North Korean hackers who “have deep experience with supply chain attacks,” a Google researcher told The Hacker News. Historically, they’ve used these tactics to steal cryptocurrency. 

In a nod to their roots, the hackers named the malicious file at the center of their campaign “plain-crypto-js@4.2.1” — a Trojan horse bit of code disguised as a JavaScript library for crypto applications.

This is a familiar playbook from crypto supply-chain attacks: Slip malware into a popular code library and then steal the credentials of everyone who downloads it.

They’ve had plenty of practice: Much like the quantum cryptographers training on Bitcoin, North Korean hackers have refined their technique through years of crypto hacks (one as recently as February).

This one sounds more consequential, though. “Hundreds of thousands of stolen secrets could potentially be circulating as a result of these recent attacks,” Google said.

The ongoing redistribution of crypto

Crypto continues to migrate from true believers to mercenary hackers. 

The latest incident is a $270 million exploit of Drift Protocol, a popular perpetual futures exchange on Solana.

0xResearch has the best explainer of an intricate scheme that featured “durable nonces,” delayed-execution accounts, social engineering, and fake oracles. 

This might explain why these things get so little attention outside of crypto — they’re too complex for any normal person to understand.

Also, there’s just so many of them. We’ve had at least 15 other DeFi exploits this year, with damages totalling up to $137 million.

With the addition of Drift, that’s over $400 million worth of crypto that’s moved from people who want to use DeFi to people who want to exploit it — and that’s only one category of exploits.

In January, ZachXBT reported on a social-engineering scam that liberated $282 million worth of litecoin and bitcoin from a single victim’s hardware.

Hardly anyone seemed to notice, which is understandable, because it’s not exactly man-bites-dog: an estimated $20 billion has been lost to crypto hacks since 2017.

A new stablecoin use case

Bloomberg reports that Iran is planning to collect a toll for passage through the Strait of Hormuz: “For oil tankers, the starting price in the negotiations is typically around $1 per barrel of oil, paid in yuan, or stablecoins.”

We promised stablecoins would secure the dollar’s dominance, and I guess this is a good way to do it.

The IMF on blockchains (not Bitcoin)

A report from the famously crypto-hostile IMF concludes that “tokenization represents a profound reconfiguration of the financial system’s core infrastructure.”

This makes tokenization something new. “Previous waves of digitization,” they say, made finance more efficient, but did not fundamentally change it. Tokenization, by contrast, “constitutes a structural shift in financial architecture rather than a marginal efficiency improvement.”

The IMF sees risk as well as opportunity: “At the same time, it introduces new forms of risks associated with speed, automation, and concentration.”

Hacks and exploits, too, probably. 

But banks, conditioned to be paranoid, should be better at fending them off. JPMorgan has said their systems face an estimated 45 billion adversarial cyber events per day.

Prediction markets are bad for you…

…worse, even, than bookies.

A report from Citizens Bank finds that the median return for traders on prediction markets is −7%. Notably, that’s worse than bettors do on traditional sportsbooks, where the median return is −5%.

The only winners on prediction markets are the very largest, mostly professional bettors: those that have placed over $500,000 in bets.

Why would prediction markets be worse than sportsbooks?

“Competition in prediction markets appears significantly sharper (i.e., more informed players),” the report says, “leading to greater losses for the median user.” 

Many of those informed players have been banned from traditional sportsbooks (for making too much money).

They remain welcome in prediction markets, though, which means there’s a good chance you’re taking the other side of their bets.

“We all want to be on the other side of the public,” one professional bettor says. “That’s the dream.”

By “public,” he means me and you, of course.

Bettor beware.

— Byron Gilliam

Introducing Blockworks Investor Relations, an IR platform built for onchain businesses.

The latest Blockworks offering brings together analytics, a branded investor relations site, and integrated advisory support into a single platform. The result is a more efficient way to share your story, build trust with investors, and engage a global audience from day one.

If you’re building in crypto and want to upgrade your investor relations function, we’d love to work with you. Book a meeting with the Blockworks team to get started.

Software as a founder

Ronald Coase defined companies as “the system of relationships which comes into existence when the direction of resources is dependent on an entrepreneur.” 

I’m not sure he thought too hard about that. Careful as that formulation sounds, he offered it as not much more than an aside, buried in a long exploration of why firms exist.

Still, it’s instructive: A company is not a legal entity, a payroll, or a balance sheet, as most would likely say. It’s a system of relationships — that exists to direct resources. 

This framing matters now more than ever.

Yesterday, we discussed how AI agents could lead to a “Coasean singularity” in which the cost of coordinating activity through markets falls to zero. The day before, we discussed Coase’s answer to the puzzle of why firms exist: because there’s a cost to coordinating activity through markets.

So, you might conclude that if AI agents eliminate the reason why companies exist, companies will no longer exist either.

But this overlooks Coase’s definition of the firm — because, unlike humans, agents cannot direct resources.

“Humans have the ability to own property, to have rights, to take on liabilities, to create companies,” EigenCloud founder Sreeram Kannan explains. “Agents don’t.”

We humans have those abilities because we have legal standing. A company consisting of just an AI, by contrast, would not. Because who would sign a lease with an unaccountable bit of software?

But, per Coase, legal status is not what makes a company. Instead, it’s only what allows humans to direct resources. 

So it stands to reason that an agent with the ability to direct resources in some other way would be able to form a company.

Kannan says the other way is blockchains: “Blockchains are the technology that allow programs to own property, have rights, and to take [on] liabilities,” he explained last week at DAS. “That’s what a smart contract is.”

By that logic, add an AI agent to a smart contract and — voilà! — you have a company as Coase would define it: an AI-entrepreneur that can direct resources.

That would, however, be a narrowly limited kind of company, because smart contracts can only direct resources that are fully onchain. This limits AI-agent businesses to just crypto-native things — DeFi, essentially.

But maybe not for much longer.

Human-run companies require both an identity (name, legal existence) and credentials (a deed, a signature, a title) to own and direct resources.

Agent-run companies should soon have the same: At DAS, Kannan pitched the crowd on EigenCloud’s “identity layer” for agents — a technical standard that provides agents with decentralized identity and reputation credentials.

Which is all they should need: “Agent ownership,” Kannan says, “equals agent identity plus credentials.”

Roughly speaking, this form of ownership will allow AI agents to incorporate on a blockchain in approximately the same way that human companies incorporate in Delaware. 

Like a Delaware corporation, “agentic companies” will, for the first time, be able to 1) raise capital from investors and 2) own “assets outside of the blockchain.”

Not every kind of asset. Kannan cites the examples of “a website, a Stripe API key, a payment authorization, an app store app, a Roblox game” — all of which are digital. 

So Calvin’s butt-kicking business is not yet feasible onchain. 

But new kinds of businesses will be.

“This is the first time we can take a company and just collapse it into pure software,” Kannan says. “We remove the team from the picture.”

This is the Coasean singularity: the moment the firm collapses into code, and coordination becomes a property of software rather than a team of people.

Agents will be companies. 

Humans will be their investors.

— Byron Gilliam

Check out the previous two features in this series, which discuss why companies exist, what economic rules shape them, and how AI might change the equation.

Part 1: The haggling theory of the firm
Part 2: The Coasean Singularity is nigh

Introducing Blockworks Investor Relations, an IR platform built for onchain businesses.

The latest Blockworks offering brings together analytics, a branded investor relations site, and integrated advisory support into a single platform. The result is a more efficient way to share your story, build trust with investors, and engage a global audience from day one.

If you’re building in crypto and want to upgrade your investor relations function, we’d love to work with you. Book a meeting with the Blockworks team to get started.

The Coasean Singularity is nigh

The internet was an out-of-sample validation of Ronald Coase’s theory of the firm. 

When websites and email dramatically lowered the cost of discovering prices and negotiating contracts, firms responded as Coase predicted they would: by letting prices do more of the coordination work their managers once did.

This allowed companies like Apple, Dell and Nike to evolve into orchestrators of global supply chains.

Still, plenty remained for companies to do internally: Websites display prices, and email sends contracts. But these technologies alone cannot decide which prices to pay, where to send the contracts, or what the contracts should say.

AI agents, however, can. 

In “The Coasean Singularity,” a team of economists and computer scientists describe the next great development in the theory of the firm: agents that lower the cost of coordinating through the market so far that much of what firms do no longer needs to happen inside them.

While popular narratives predict the impact of AI agents in apocalyptic terms — mass unemployment, a robot takeover, human extinction — the paper takes a more prosaic view: “The fundamental economic promise of AI agents lies in their ability to dramatically reduce transaction costs — the expenses associated with using markets to coordinate economic activity.”

But don’t let the Coasean framing fool you. The economists think agents will have a dramatic impact on society.

“Much of how we structure our economy and firms can be explained by transaction costs,” they explain, adding that agents could effectively eliminate these costs: “The activities that comprise transaction costs — learning prices, negotiating terms, writing contracts, and monitoring compliance — are precisely the types of tasks that AI agents can potentially perform at very low marginal cost.” 

So what happens when transactions can be made at very low cost?

The Coasean answer is that AI agents will make a lot of decisions on our behalf.

“Humans will deploy AI agents in two primary scenarios,” the paper says. “First, to optimize decisions that they would otherwise make sub-optimally due to information constraints or cognitive limitations” — like price discovery or contract negotiation — “and second, to make decisions of similar or potentially even lower quality, but at dramatically reduced cost and effort” — like continually renegotiating routine expenses or ordering groceries according to what’s on sale.

For companies, that will mean pushing even more managerial decisions and activities out into the market — by delegating them to agents that can continually discover prices and negotiate contracts.

For individuals, it will mean having a tireless advocate always at our side: “It is easy to imagine more advanced agents contacting multiple counterparties to negotiate prices,” the paper says, “or applying to jobs and advocating for employment on a user’s behalf.”

Over time, the authors think we’ll come to rely on these agents so heavily that “AI will serve as a filter between humans and the broader digital environment.”

If so, I doubt we’ll trust ourselves to create those reality-filtering agents with OpenClaw. Instead, we’ll likely hire whatever specialized agent is best at any given task.

These agents could all be stand-alone businesses. 

When the internet so dramatically lowered the cost of market transactions, it didn’t just allow big companies like Apple to outsource more of their processes. It also allowed entirely new kinds of businesses to emerge.

Uber, for example, is a platform that coordinates ridesharing through price signals rather than managerial command — perhaps the most purely Coasean business model to date (and only possible because of the internet). 

AI agents seem likely to do something similar. “By lowering the costs of preference elicitation, contract enforcement, and identity verification,” the paper says, “agents expand the feasible set of market designs.”

In other words, agentic AI will make entirely new kinds of businesses possible. 

The paper doesn’t speculate much on what these might be. But the title seems to offer a hint: “The Coasean Singularity” implies that agents could collapse the cost of transacting in the market to effectively zero.

By Coase’s logic, zero transaction costs could mean zero-employee companies. 

“The main reason why it is profitable to establish a firm,” he wrote, “would seem to be that there is a cost of using the price mechanism.” 

If there’s no cost to using the price mechanism, there may be no reason to establish a firm.

But can AI agents really achieve a Coasean singularity? Can they do everything that a firm of managers does? And if so, what would we call this new thing?

We’ll find out tomorrow in the third and final installment of this cliff-hanging newsletter triptych: How crypto will enable “agentic companies.”

— Byron Gilliam

P.S. Check out yesterday’s Part 1, a deep dive into Ronald Coase’s theory of the firm.

Introducing Blockworks Investor Relations, an IR platform built for onchain businesses.

The latest Blockworks offering brings together analytics, a branded investor relations site, and integrated advisory support into a single platform. The result is a more efficient way to share your story, build trust with investors, and engage a global audience from day one.

If you’re building in crypto and want to upgrade your investor relations function, we’d love to work with you. Book a meeting with the Blockworks team to get started.

The haggling theory of the firm

In “The Nature of the Firm,” Ronald Coase asks why islands of conscious power (companies) take form in an ocean of unconscious co-operation (markets).

We take companies for granted — how else would things like cars or iPhones get made? But in a free economy where prices coordinate most activity, their very existence is a puzzle. Why not outsource everything — design, assembly, shipping — and let the invisible hand of markets coordinate all the transactions that requires?

The answer, of course, is that outsourcing everything is a royal pain — you’d be constantly haggling over who does what, when, and on what terms. 

“A firm is likely therefore to emerge in those cases where a very short term contract would be unsatisfactory,” Coase wrote.

Blockworks, for example, employs a full-time newsletter writer because it would be annoying, expensive, and time-consuming to agree terms with a new one every morning. The “haggling cost” of discovering prices and negotiating contracts five times a week are greater than the cost of having a full-time writer on the payroll. (I hope.)

This seems obvious enough. But Coase’s observation was considered “breathtakingly simple and original,” as one economist later put it. Before his “theory of the firm,” no one thought of companies in these terms.

After Coase, it became the textbook explanation for why companies exist, and also the limits to how big they might get.

“A firm will tend to expand,” Coase explained, “until the costs of organising an extra transaction within the firm become equal to the costs of carrying out the same transaction by means of an exchange on the open market.” 

No one pushed internal coordination further than Henry Ford. The complex he built at River Rouge, Michigan, in 1928 was his Coasean masterpiece: a vast, vertically integrated complex designed to avoid the frictions of the market. Ford dredged the Rouge River to build his own shipping docks and built hundreds of miles of railroad tracks, a dedicated electricity plant, and a steel mill onsite.

Thus Ford internalized virtually every transaction other than raw materials: With everything all in one place, River Rouge turned raw materials into cars. 

The immense complexity of that process inspired the Detroit Industry Murals, considered some of the most intricate examples of American painting:

An economist might see in these a depiction of market transactions brought inside a firm.

“​​Within a firm, these market transactions are eliminated,” Coase writes, “and in place of the complicated […] exchange transactions is substituted the entrepreneur-co-ordinator, who directs production.”

With River Rouge, Henry Ford became the ultimate entrepreneur-co-ordinator.

Ford pushed the limits of internal coordination even further with Fordlândia — a fully functional American-style town Ford built in the Amazon to avoid the cost and uncertainty of haggling for rubber on the open market.

It was an internalization too far. For an American car company, it turns out that growing your own rubber in a jungle is more expensive than simply buying it. 

Fordlândia failed before it produced any rubber at all. Founded in 1927, the site was abandoned by 1934, with the hospital, church, school, golf course, and housing Ford built for employees left to rot in the festering humidity of the Amazon. Ford’s grandson sold the property back to the government of Brazil in 1945 at a steep loss.

It was an expensive lesson in Coasean expansion: “A firm will continue to expand until the internal costs of organizing an additional transaction equal the costs of performing that same transaction on the open market,” Coase explains.

Fordlândia was well beyond that logical limit.

Fortunately, the “verticalization” at River Rouge was far more successful. But even there, the cost of internal coordination eventually exceeded the cost of just using the market: As markets got more efficient — through specialization, the highway system, standardized shipping containers, just-in-time manufacturing — Ford’s vision of turning raw materials into cars at a single site no longer made sense.

River Rouge is still active — F-150s are made there. But they’re made from parts sourced from suppliers located all over the world. The parts are transported to River Rouge by dedicated trucking companies. The onsite steel mill was sold in 1989. The dedicated power plant exploded in 1999, never to be rebuilt. 

Today, the trucks that roll out of River Rouge are only really assembled there.

By Cosean logic, you might think that would make Ford a smaller company. “A firm becomes larger as additional transactions are organised by the entrepreneur [instead of the market],” Coase says, “and becomes smaller as he abandons the organisation of such transactions.”

In practice, it’s more complicated than that. 

The two largest costs of “organizing production through the price mechanism,” Coase explains, are discovering prices and negotiating contracts  — “haggling costs,” as later economists called them. 

Haggling has become progressively less costly in recent decades: Price discovery happens much faster with the World Wide Web, for example, and contracts are executed faster with DocuSign.

But the cost of internal coordination has fallen, too: Email, Slack, and Zoom make it easier than ever for the entrepreneur-coordinator to bring transactions in-house.

By Coase’s logic, companies will get smaller only to the degree that external coordination costs fall faster than internal ones.

Both have fallen — at seemingly similar rates — causing companies to become more modular, but also, sometimes, bigger.

On the one hand, the falling cost of external coordination has enabled Ford Motors to produce more vehicles with fewer employees. On the other hand, the falling cost of internal coordination has enabled a grocery store like Walmart to employ 2.1 million people worldwide.

But what if the cost of external coordination fell so far it effectively rounded to zero? Could companies then have zero employees?

Cosean theory suggests they could.

Excitingly, that theory will soon be put to the test with AI agents.

Even more excitingly, this sets up the first-ever cliffhanger in Breakdown newsletter history!

Tune in tomorrow for The Cosean Singularity — or how the combination of AI and crypto will push Coase’s “theory of the firm” to its limits.

— Byron Gilliam

Introducing Blockworks Investor Relations, an IR platform built for onchain businesses.

The latest Blockworks offering brings together analytics, a branded investor relations site, and integrated advisory support into a single platform. The result is a more efficient way to share your story, build trust with investors, and engage a global audience from day one.

If you’re building in crypto and want to upgrade your investor relations function, we’d love to work with you. Book a meeting with the Blockworks team to get started.

Friday charts: Apocaloptimism

The trailer for The AI Doc, in theaters today, compiles some of the direst warnings on AI. Doomer Eliezer Yudkowsky, for example, predicts AI will lead to the “abrupt extermination of society.” Another expert says he knows researchers working on AI risk “who don’t expect their children to make it to high school.”

The CEO of Anthropic, Dario Amodei, is also prominently featured: “Am I confident it will go right?” he says of the product he’s building. “Absolutely not.” He believes AI will eliminate 50% of entry-level jobs in fields like technology, finance and law.  

But these warnings seem to be losing their shock value — perhaps because we’ve been hearing them for some time now, and not much has happened as of yet.

For example, it’s been 54 weeks since Amodei predicted that AI would be writing 100% of code “in about a year.” But people still write code. Also, Yudkowsky once warned that nanotechnology would result in human extinction “no later than 2010.” It’s 2026.

This week in particular, it felt like the tide is turning against AI doomerism.

Andrej Karpathy, the computer scientist who coined the term “vibecoding,” conceded that AI is not so good at writing code: “I’m not very happy with the code quality,” he wrote on X. No matter how many times he specifies what he wants, “the agents do not listen to my instructions.”

On the risk to jobs, Anthropic co-founder Jack Clark said on a podcast this morning that AI “doesn’t replace people.” Instead, it “changes the sort of work that people do.” (In a good way, he means.)

On the panic in software stocks, LinkedIn founder Reid Hoffman dismissed the idea that everyone will write their own software as “a distinct flavor of foolishness.” Hoffman adds that “Jevons’ Paradox will do what it always does” — as software gets cheaper, people will use disproportionately more of it.

That should mean more work for software engineers, not less. (A recent HBR study seems to confirm this intuition.)

Also this week, Walter Isaacson noted that “AI has created about 1.2 million new jobs over the last two years.” He expects that to continue: “It’s going to create more jobs than it destroys.”

All this seems inconsistent with the predictions of imminent doom. If AI can’t put software engineers out of work, it probably cannot exterminate humanity either.

The market remains concerned, however. Software stocks fell sharply today on reports that the next Claude model — ominously named “Mythos” — will be a “step change” in AI (and is particularly adept at cyberhacking).

So maybe we all need to go see a movie. The full title of the documentary out today is The AI Doc: Or How I Became an Apocaloptimist. 

After giving the doomers their say, the trailer introduces AI optimists like Peter Diamandis, who believes ​​AI is “a massive opportunity for humanity, not a threat.”

Perhaps most encouraging, The AI Doc features an optimist from Anthropic, too: Dario Amodei’s sister, Daniela Amodei.

Daniela, President and co-founder of Anthropic, disagrees with her brother on the risk to jobs: “Humans plus AI together actually create more meaningful work,” she says — “more challenging work, more interesting work, high-productivity jobs.” 

She also disagrees with Yudkowsky on the risk to humanity: “The things that make us human will become much more important instead of much less important.”

I believe that makes her an Apocaloptimist: AI will be as powerful as her brother fears. And humans will be all the better for it.

Let’s check the charts.

Doing more with more:

TrueUp estimates that openings for software engineering jobs in the US are up 78% from the lows they hit shortly after ChatGPT launched.

You say tomato…

Coatue notes that software-engineering jobs are down, but AI-engineering jobs are up. “It feels like a hiring reallocation, not a cycle.”

Fundamentals up, valuation down:

The software investors at Thoma Bravo find that the valuation of software stocks (in dark blue) has decoupled from a measure of software-company fundamentals (FCF margin + revenue growth, in light blue). They think this “dislocation” is creating an opportunity.

Lots of dislocating right now:

@Mr_Derivatives notes that all of the biggest stocks in the S&P 500 are far cheaper on forward estimates. Unless you think forward estimates are very wrong, stocks are getting unusually cheap.

Services-as-software: 

Coatue coins a new term (still SaaS, but reversed) to make the bull case for software stocks. “By shifting the unit of value from the tool to the results, the addressable market potential expands by 25x. We are moving from a $0.2T software market to a $5.5T ‘services-as-software’ paradigm.”

Ramping revenue:

Data from Ramp shows that “the top quartile of AI spenders on our platform has more than doubled their revenue, while the bottom quartile has been essentially flat.” I’m skeptical of the causality there, but it’s a good chart either way.

Anthropic’s “IPO”:

Josh Kale notes that a closed-end fund whose top holding is Anthropic shares (which are otherwise unlisted) trades as high as $550 this week — vs. a NAV of just $19. Anthropic is reportedly planning to IPO for real in the fourth quarter of this year.

GPU rental rates rising:

BCA reports that the cost of renting a Nvidia Blackwell GPU has shot higher, presumably because AI agents are so good at burning tokens.

One non-AI risk:

Deutsche Bank created an index measuring how much pressure President Trump is under to change course. Based on changes in approval rating, inflation expectations, and asset prices, the index suggests it may soon be TACO time.

The truth is, no one really knows what the risks from AI will be. But there are many reasons for optimism, including some fundamental ones.

Dean Ball, for example, notes that AIs are trained on the internet, which limits how good they can get: Anyone who thinks “all the world’s knowledge” is on the internet is deeply mistaken.

There will always be things we know and the AIs don’t. We only have to go find those things.

Have a great weekend, Apocaloptimist readers.

— Byron Gilliam

_{Brought to you by:}

US Treasuries are the lifeblood of our financial system, providing collateral to support transactions, but outdated legacy systems hinder collateral mobility.

A new white paper from the ValueExchange dives into the future of collateral mobility, RWA tokenization, and what we can learn from a recent series of on-chain repo transactions conducted on Canton, the only public blockchain built for institutional finance.

With over $8T tokenized transactions flowing on Canton every month, including over $350B of on-chain US Treasuries moving daily, Canton is building the scalable, always-on capital markets infrastructure of the future.

Introducing Blockworks Investor Relations, an IR platform built for onchain businesses.

The latest Blockworks offering brings together analytics, a branded investor relations site, and integrated advisory support into a single platform. The result is a more efficient way to share your story, build trust with investors, and engage a global audience from day one.

If you’re building in crypto and want to upgrade your investor relations function, we’d love to work with you. Book a meeting with the Blockworks team to get started.

Mike Lawrence for Blockworks

DAS NYC, Day 3

Efficiency isn’t everything.

Reading is more efficient than listening, for example. Talking is more efficient than writing. 

But in both cases, something gets lost in the process.

The media theorist Marshall McLuhan said that while the written word “made possible explosive increases in knowledge and production,” it did so “at the cost of human wholeness.”

Before the printing press, he explains, “man lived in acoustic space: boundless, directionless, horizonless…in the world of emotion.”

That world was immersive and participatory. Inefficient, perhaps. But whole.

After the printing press, our experience of the world contracted and fragmented: “Print isolates vision, enforces linear sequence, and fragments the sensus communis, thereby narrowing the channel of communication compared to embodied speech.”

With the advent of print, the world became more orderly and intelligible, but less immersive and engaging.

In other words, it’s good to talk. Ideally in person.

That might be why people devote time and money to attending conferences. It’s not the most efficient way to collect information — you can’t put a live panel on 2x speed, for example. But it’s an opportunity to escape the narrow channel of communication that email, Telegram and Slack confine us to.

Breaking the bonds of print allows us to live in what McLuhan calls “acoustic space” — holistic, non‑linear, immersive and tethered to “the world of emotion.”

It’s a nice place to be, especially in this age of social media and information overload. Importantly, this is more than an alternative way to communicate. It’s an alternative way to think, too.

McLuhan defined verbal speech as “an actual process of thought, which is in itself nonverbal.”

That kind of thinking can’t be optimized.  

McLuhan’s ideas could sometimes use some optimizing — they’re often hard to follow (in print, at least). But Jerry Seinfeld would understand. He once told an interviewer that his long-running sitcom was successful because it was micromanaged:

The inefficiency of micromanaging a sitcom is to Seinfeld what inefficiency of speech was to McLuhan: It’s where the magic of thinking happens.

Below are a few of the more magical things I heard at DAS today.

Keynote speaker: Michael Saylor

Bitcoin’s chief hypeman was at DAS to pitch a new derivative thereof: Strategy’s preferred shares, STRC.

The pitch starts by reframing bitcoin as “digital capital” — not just digital gold, but digital credit, money and yield, too.

Why is bitcoin digital capital? “Because the most powerful person in the world believes it’s capital,” Mr. Saylor said (while standing in front of a giant photo of the President). 

That may be the least-crypto statement in the history of crypto. But Strategy isn’t really a crypto company, so that’s fine. Nor is it just a bitcoin treasury company, as it’s often described. Instead, Mr. Saylor now describes Strategy as a bank: “We create currency.”

Last I checked, Strategy did not have a banking license. But he still wants you to think of it that way. “Who wants a bank account that pays 11.5%?” he asks rhetorically — with the implication that the only correct answer is “everyone.”

But it gets better. The yield on STRC is tax-advantaged, so Mr. Saylor says that, for a New York City taxpayer, “it’s like a bank account that yields 23%.”

23% is a lot — especially as he says it’s “risk-free.”

It’s not, of course, because the yield on preferred equities is not FDIC-insured. 

Mr. Saylor himself describes STRC as “the best of both worlds” — both credit and equity. 

Neither of those worlds are risk-free!

Impersonating a bank account is not explicitly illegal in the way that, say, impersonating a police officer is. But it’s not exactly legal, either. In 2009, the SEC charged the managers of the Reserve Primary Fund with fraud for telling investors that the fund would always trade at $1.

Another clue that STRC is not free of risk is what Strategy uses the money for: more bitcoin. “We’re buying all of it,” Mr. Saylor says. 

For that reason, Mr. Saylor also describes Strategy as “a synthetic miner that mines all the bitcoin in the world.”

Because they’re using money raised from issuing STRC, the bitcoin, Mr. Saylor adds, is acquired “at no cost to our shareholders.”

Amazing stuff. To recap: STRC’s 11.5% yield is risk-free, tax-free, and a cost to no one.

Caveat emptor.

Sreeram Kannan on agentic companies

Crypto has been in need of a new elevator pitch, and I believe the founder of EigenCloud has found it: AI agents will become companies, and crypto will make them investable.

To that end, Kannan redefines blockchains as the technology “that allows programs to own property, have rights, and be liable.”

This enables an all-new kind of firm: “agentic companies” that are purely software. These will have no employees or human governance. They’ll just be a bundle of properties, rights and liabilities held in a smart contract. 

Being companies, some of these will want to raise money — and crypto will be the only way to do it.

Kannan describes this as “a YouTube moment for companies.” In the same way that YouTube enabled anyone to create videos and put them online, AI has enabled anyone to create and deploy software. And the combination of AI and crypto allows anyone to create companies and raise money for them.

This raises the happy prospect of thousands (millions?) of agentic companies issuing tradeable equity onchain. 

There was a lot of optimism for institutional crypto at DAS this week. But it’s unclear to what degree institutional crypto will ever benefit decentralized crypto. (Some would even say institutional crypto isn’t crypto at all).

Kannan’s agentic companies, however, would do everything — borrow, lend, transact, raise money and invest — on permissionless blockchains. 

But here’s what I really want to know: Will they attend conferences???

Quotable quotes:

Day 3 takeaway:  

Scallion + cream cheese + lox bagel and a box of raspberry rugelach from Russ & Daughters on 34th Street.

— Byron Gilliam

_{Brought to you by:}

US Treasuries are the lifeblood of our financial system, providing collateral to support transactions, but outdated legacy systems hinder collateral mobility.

A new white paper from the ValueExchange dives into the future of collateral mobility, RWA tokenization, and what we can learn from a recent series of on-chain repo transactions conducted on Canton, the only public blockchain built for institutional finance.

With over $8T tokenized transactions flowing on Canton every month, including over $350B of on-chain US Treasuries moving daily, Canton is building the scalable, always-on capital markets infrastructure of the future.

Introducing Blockworks Investor Relations, an IR platform built for onchain businesses.

The latest Blockworks offering brings together analytics, a branded investor relations site, and integrated advisory support into a single platform. The result is a more efficient way to share your story, build trust with investors, and engage a global audience from day one.

If you’re building in crypto and want to upgrade your investor relations function, we’d love to work with you. Book a meeting with the Blockworks team to get started.