❌

Reading view

↗️

Stripe Is Building the Toll Road for the Machine Economy

✇Technocracy News
By: Patrick Wood

Most Americans know Stripe as the checkout box on a website. That picture is five years out of date.

Stripe reports that businesses on its platform generated $1.9 trillion in total volume in 2025, up 34 percent in a single year. By the company’s own reckoning, that is roughly 1.6 percent of global GDP. It claims 90 percent of the Dow Jones Industrial Average and 80 percent of the Nasdaq 100 as customers.

A private company, owned by insiders and venture funds and answerable to no public shareholder, already clears a measurable share of the world’s commerce. Now it is building the payment system for customers that are not human.

Two Kinds of Token

This writer has spent the past year explaining tokenization: stocks, bonds, real estate, and dollars converted into programmable entries on a digital ledger. That is the first kind of token.

There is a second kind. Every artificial intelligence model consumes and produces “tokens,” the small fragments of text that are the unit of machine computation. Every query to an AI model is metered in tokens and billed in tokens.

Tokens of value and tokens of computation are different things. Stripe is the one company positioning itself to meter, price, and settle both.

Emily Sands, Stripe’s Head of Data and AI, is an economist by training. In a recent interview with Nate B. Jones, she said this about inference tokens:

“Tokens are not yet a currency—maybe they will be, we’ll see—but they are certainly a very valuable asset.”

Stripe’s own AI chief is saying publicly that units of machine computation could become money.

The Pieces, In Order

The factual record comes from Stripe’s own announcements.

  • Stripe acquired Bridge, a stablecoin platform. Its 2025 annual letter reports that stablecoin payments volume doubled to around $400 billion, that an estimated 60 percent of it was business-to-business, and that Bridge’s own volume more than quadrupled.
  • With the venture firm Paradigm, Stripe built Tempo, which it describes as “a blockchain purpose-built for payments.”
  • The Machine Payments Protocol. On March 18, 2026, Stripe launched the protocol alongside the Tempo mainnet. It lets a business tell an AI agent, in machine-readable form, what it sells, what it costs, and how to pay.
  • Stripe acquired Metronome, a usage-based billing company that meters consumption in real time.
  • Streaming payments. At its Sessions 2026 conference, Stripe combined the two into “streaming payments.” These are stablecoin micropayments on Tempo, tied to Metronome’s meter, so that a business can “get paid for every token, exactly at the moment it’s used.”

Stripe was candid about why the old system cannot do this: “The amounts are too small and the usage is too fast—existing systems aren’t capable of processing minute sums every few milliseconds.”

A credit card cannot move a penny, because the fees alone exceed the payment. A stablecoin on a purpose-built chain can. Sands was measured about it: “Stablecoins bring us a long way. It is not the only way… but so far stablecoins seem to be an important part of microtransactions.”

Why OpenRouter Matters

In August, the financial press reported that Stripe had agreed to buy OpenRouter for more than $7 billion.

OpenRouter is a switchboard. A developer sends it a task, and OpenRouter routes that task to whichever of some 400 AI models best fits the required cost, speed, and quality. Roughly eight million developers use it.

Every request through OpenRouter is a metered event, and every metered event is a bill. With the acquisition, the company that counts the tokens and the company that collects the money are the same company.

Stripe has always seen the revenue side of a business: who paid, how much, how often. OpenRouter shows it the cost side: which model was used, what it cost, and how well it performed. Sands said it directly:

“You want to maximize profit on both the revenue side and the true-cost side, which requires getting deep into the guts of the underlying intelligence in your product and agent.”

She also gave the acquisition a name: “the rails for intelligence—not the rails for money, but the rails for intelligence.”

Stripe president Will Gaybrick told Payments Dive that the company is evolving from a payments company into “an AI infrastructure player.” He predicted there will not be room “for 100 different AI orchestrators.”

That describes a company expecting the market to consolidate around a few orchestrators and planning to be one of them.

The Permission Layer

The payments are the visible part of the system. The reputation scoring that precedes them matters more.

Sands described how Stripe helped the AI coding company Cursor with a new kind of theft: fraudsters opening accounts to steal free computing credits. No payment had occurred, so there was no transaction to flag. Stripe solved it anyway:

“That allowed us to move from scoring transactions to identifying abusive or risky customers.”

Because Stripe sees across millions of businesses, it can judge a person at the moment he creates an account, before he has bought anything. It then passes those scores to sellers, so that, in Sands’ words, “businesses can make decisions.”

Stripe’s Link wallet, which Sands puts at about 300 million consumers, is becoming “the wallet for agents.” Your identity is attached to it. Your agent spends through it. The human owner sets the spending limits, and every purchase traces back to a named person.

The stated purpose is fraud prevention, and the fraud is real. But a cross-merchant reputation score, run by one private company and applied before any transaction exists, is a permission system whatever its purpose. This writer has documented the same architecture at the Bank for International Settlements and the largest banks. Stripe arrived at it from the commercial side, with no mandate and no public debate.

What This Means for the Individual

Sands made two admissions that deserve attention.

First, on pricing: “There is probably no longer such a thing as a SKU.” The fixed, posted price, the same for every buyer, is giving way to pricing that adjusts continuously by customer and by outcome.

Second, on negotiation between machines:

“Agents are relentless. They are willing to negotiate as long as they need to negotiate… You could imagine consumer surplus being eroded relatively quickly.”

Consumer surplus is the economist’s term for the good deal, the difference between what you would have paid and what you did pay. An economist at the center of this system expects that difference to shrink.

My Thoughts

My inference is as follows. Stripe has assembled, under one private roof, a wallet bound to identity, a reputation score that precedes the purchase, a meter on machine intelligence, a stablecoin, and a blockchain to settle it. Each piece has a reasonable business justification. Together they amount to the ledger, the meter, and the gate for machine commerce.

Technocracy was defined in 1938 as the science of social engineering, with the measurement and accounting of all production and consumption at its core. The original Technocrats had no instrument that could do it. Stripe’s stack, built for ordinary commercial reasons, can.

Whoever owns the meter sets the terms of commerce, and Stripe now owns the meter.

References

CoinDesk, “Coinbase-Backed AI Payments Protocol Wants to Fix Micropayment but Demand Is Just Not There Yet,” CoinDesk, March 11, 2026. Cites Artemis data: about $28,000 daily volume, about 131,000 daily transactions, average payment about $0.20, roughly half of activity self-dealing or wash trading.

Forrester, “Why Stripe’s Machine Payments Protocol Signals a Turning Point for Micropayments,” Forrester Blogs, 2026. Machine Payments Protocol launched March 18, 2026, with the Tempo mainnet, co-created with Paradigm.

Nate B. Jones, “AI Agents Are Starting To Buy. Stripe Is Building How They Pay,” interview with Emily Sands, Head of Data and AI, Stripe, YouTube, 2026. Source of all Sands quotations.

Payments Dive, “Stripe Acquisition May Bolster Its AI Strategy,” Payments Dive, 2026. Reported price above $7 billion (Bloomberg) and up to $10 billion (Wall Street Journal); about 8 million developers and 400 models; Gaybrick statements.

Stripe, “Stripe Publishes 2025 Annual Letter and Announces Tender Offer,” Stripe Newsroom, 2026. $1.9 trillion total volume, up 34 percent; roughly 1.6 percent of global GDP; 90 percent of the Dow and 80 percent of the Nasdaq 100; stablecoin volume around $400 billion; Bridge volume more than quadrupled; Tempo.

Stripe, “Stripe Builds Out the Economic Infrastructure for AI with 288 Launches,” Stripe Newsroom, Sessions 2026. Streaming payments; Metronome metering with stablecoin micropayments on Tempo; Link wallets for agents.

Technocracy Inc., The Technocrat magazine, 1938. Definition of Technocracy as “the science of social engineering.”

💾

AI agents are becoming customers, and most businesses have already decided whether agents can buy from them without realizing it. I went to Stripe to ask Emi...
  •  
  • ↗️
↗️

Trump Blasts “Luddites” For Opposing Data Centers

✇Technocracy News
By: Patrick Wood

On Monday, the President of the United States told the people of Ohio, Michigan, Virginia, Arizona, and every other state now fighting hyperscale data centers exactly what he thinks of them. “The only reason that communities throughout the U.S.A. should not want Data Centers is if they want to end up being backward and poor,” he wrote on Truth Social. “If they want to be successful and rich, with far lower taxes and jobs all over the place, let Data Reign.” And then the warning: “If we kill the Golden Goose, you will only have yourselves to blame.”

The context is not subtle. Polling now shows a large majority of Americans oppose new data centers, with complaints centering on rising electricity bills, excessive water usage and industrial noise. Candidates in at least 21 races across 18 states have run television ads on the issue, split almost evenly between the parties. The National Republican Senatorial Committee has warned that data-center opposition in Ohio is, in its own words, the anchor hanging around Senator Jon Husted’s neck in his race against Sherrod Brown.1

A rational political actor facing that landscape might listen. Trump instead did what Technocrats have always done when the public gets in the way of the engineering plan: he redefined the objection as a moral defect. And to seal it, he tied every county commissioner voting against a 500-megawatt campus to Beijing. “China could not be happier with this anti-data center movement,” he wrote. “Actually, they can’t believe it is happening!”2

If that rhetorical move sounds familiar, it should. It is the secular translation of a sermon Peter Thiel has been preaching for two years.

The Antichrist as Safety Regulator

Beginning in the fall of 2024 and culminating in a four-part lecture series delivered to the ACTS 17 Collective in San Francisco in 2025, Thiel laid out a theological-political thesis that has since become the operating theology of the AI buildout. The Antichrist, Thiel argues, will not arrive as a reckless technologist. He will arrive as a safety-minded administrator, exploiting fears of nuclear war, climate collapse and artificial intelligence to install a one-world governance system. His slogan, drawn from 1 Thessalonians 5:3, is “peace and safety.”3

Thiel named names. In the twenty-first century, he told his audience, the Antichrist is “a Luddite who wants to stop all science. It’s someone like Greta Thunberg or Eliezer Yudkowsky.” Climate activists and AI safety researchers are, in his phrase, “legionnaires of the Antichrist,” not because they are consciously evil but because they are building the institutional architecture a future world-state would need. And on the technologies themselves, Thiel was candid: “Maybe these things are good or bad. Stopping them seems far, far worse.”4

Set that beside the Truth Social post and the structure is identical. In Thiel’s register, the person who wants to slow AI is doing the Antichrist’s work. In Trump’s register, the person who wants to slow the data center is “backwards,” “poor,” and objectively serving China. Neither framing permits the position that most of those Ohio voters actually hold, which is not “stop AI” but “do not make my family pay for your grid load.” Economic objections invite cost-benefit analysis and compromise. Apocalyptic and treasonous framings do not. You cannot negotiate with the Antichrist, and you cannot negotiate with a Chinese asset.

Technocrat-in-Chief

Courtenay Turner and I documented in The Final Betrayal (November 2025) how the second Trump Administration became the vehicle by which a cohort of billionaire arch-Technocrats, Thiel foremost among them, moved from the periphery of American power to its center. Thiel financed J.D. Vance’s political career from the ground up. His Palantir alumni and Founders Fund network populate the national-security and technology apparatus. The Dark Enlightenment’s contempt for democratic consent, which we traced through Thiel’s own writings and those of his intellectual circle, is no longer a fringe philosophy. It is staffing policy.

What we argued then, and what Monday’s outburst confirms, is that Trump has not merely hosted these men. He has become the Technocrat-in-Chief. The populist who promised to return power to forgotten communities is now instructing those same communities that their objection to a hyperscale facility next to their children’s school is evidence of backwardness.

Sorry, that is not populism. That is the voice of Howard Scott and Technocracy, Inc. in 1934, telling Americans that their opinions were irrelevant to the requirements of the energy system and that the Technate would allocate resources according to engineering necessity, not democratic preference.

The MAGA movement was told it was getting America First. What it is getting is a federal executive using the full weight of the presidency to override local self-government on behalf of the single most capital-intensive industry in human history.

Energy Is the Currency

The historical Technocracy movement made one claim that has never been refuted: the fundamental unit of value in an industrial society is the kilowatt-hour and not the dollar. Scott and his engineers proposed replacing money with energy certificates and allocating energy to every man,  woman, and child by expert decree. Thankfully, they failed to seize power in the 1930s. They did not fail to describe the twenty-first century.

Hyperscale data centers are the first industrial class whose product is literally computation purchased with electricity. When a facility draws hundreds of megawatts, the question is not abstract. It is whether the household or the hyperscaler has priority claim on the grid, and who pays for the transmission and generation built to serve the newcomer. Every rate case now pending before a state utility commission is a contest over exactly that. Trump’s post resolves the contest by fiat: the data center wins, and anyone who says otherwise is a traitor to prosperity.

This is resource control transferring from individuals to consolidated institutional actors, which is the definition of Technocracy I have used for two decades. It does not require a world government. It requires only that the people who own the compute also control the political apparatus that decides who gets the power.

The Inversion

Here is the irony Thiel’s disciples never confront. His stated nightmare is a distant regulator overriding local self-government in the name of safety. Yet the actual mechanism now in use is a national executive overriding local self-government in the name of the AI buildout. The tool he warns against is the tool being wielded, pointed the other way. The county that votes no is not the world-state. It is the last unwatched place in the system, and it is being told to shut up and sit down.

And the China card is doing double duty. Trump invokes Beijing as the beneficiary of American resistance in the very month he prepares to host Xi Jinping, the Technocrat-in-Chief of China, at the White House. A congressional committee has demanded an investigation into whether China is stoking the anti-data-center movement; Trump declined to repeat that charge, but said the Chinese “can’t believe it is happening.”5

China is the rhetorical enemy for domestic consumption and the strategic partner in the architecture. Readers who followed my analysis of the Beijing summit in May will recognize the pattern. American and Chinese Technocracy are not at war with each other. They are converging, and the American public is being disciplined to accept the merger.

Speak Up While You Can

I have repeated that Free Speech and the First Amendment are on the guillotine in 2026. This may be the last battle that decides our fate.

Certainly, the people of Ohio did not ask for this. They were not consulted. They are being told that their electric bills, their water tables, and their quiet nights are the acceptable price of a race with China whose terms they were never shown. And when they object, the most powerful man in the world calls them backward, foolish, and prone to self-harm.

They should say no anyway. Not because AI is evil, and not because the Antichrist is coming, but because the right of a community to decide what gets built inside its borders is the last practical check on Technocracy that ordinary Americans still hold.

Every local vote against a data center is a vote for the proposition that the people, not the engineers, decide. Thiel calls that heresy. Trump calls it treason. I call it self-government, and it is worth defending precisely because they want it gone.

References

  1. Kevin Breuninger, “Trump says U.S. communities opposing AI data centers could end up ‘backwards and poor’,” CNBC, August 31, 2026, https://www.cnbc.com/2026/08/31/trump-data-centers-backlash-elections-congress.html. The NRSC memo language and the 21-races-in-18-states figure are reported there and in AFP coverage of the same day.
  2. Agence France-Presse, “Trump blasts US backlash against data centers,” August 31, 2026, syndicated at https://www.yahoo.com/news/politics/articles/trump-blasts-us-backlash-against-161603675.html. All Trump quotations are from his Truth Social post as reproduced by AFP.
  3. “Thiel’s Framing of Proponents of an AI Treaty as ‘Antichrist’ Collides with 60 Years of Catholic Doctrine,” Center for Bioethics and Public Affairs in AI (CBPAI), April 8, 2026, https://www.cbpai.org/blog-1/thiels-framing-of-proponents-of-an-ai-treaty-as-antichrist-collides-with-60-years-of-catholic-doctrine; and Fortune, “Peter Thiel just gave the public its closest look yet at his ‘Antichrist’ theory,” July 18, 2026, https://fortune.com/2026/07/18/what-is-antichrist-peter-thiel-pope-leo-ai-regulation/.
  4. Leaked recordings of the ACTS 17 Collective lectures, as reported by the Wall Street Journal and the Washington Post (September–October 2025) and analyzed in “The Eschatology of Deregulation: On Peter Thiel’s Antichrist,” OILab, https://oilab.eu/the-eschatology-of-deregulation-on-peter-thiels-antichrist/. The Thunberg/Yudkowsky remark is quoted in multiple contemporaneous accounts of the lectures.
  5. AFP, op. cit.; CNBC, op. cit. Trump’s upcoming White House meeting with Xi Jinping is noted in the AFP report.
  •  
  • ↗️
↗️

What? Most AI Is Now Written by AI?

✇Technocracy News
By: Patrick Wood

For the better part of the last two years, I have been tracking what appeared to be a reasonably predictable pattern in AI development: a doubling of capability roughly every 3.5 months. That figure came from METR’s time horizon benchmarks — measurements of how long an AI agent can work autonomously on a task before failing. Early 2024 through early 2026, the data held with uncomfortable consistency. If you plotted it, the curve bent upward with almost mechanical precision.

I say “appeared to be predictable” because that framing is now obsolete.

The variable that breaks every forecast model is Recursive Self-Improvement — the condition in which AI systems are no longer just tools that humans use to build AI, but active participants in building themselves. We crossed that threshold. The question of when is already behind us. The question now is what happens when a system that rewrites its own code, runs its own experiments, and optimizes its own training recipes starts doing so faster than any human team could direct it.

There is no clean answer. That is precisely the point.

What Has Actually Changed

Let me be specific, because vague gestures toward “exponential growth” have become their own form of intellectual laziness.

As of May 2026, Anthropic confirmed that more than 80 percent of the code merged into its own production systems was written by Claude — its own AI. Not assisted by Claude. Written by Claude. The company’s own engineers have described the shift as moving from doing work to managing a system that does the work. One Anthropic engineer publicly stated that 100 percent of his personal code output was AI-generated, with 22 pull requests shipped in a single day.

OpenAI’s announcement of GPT-5.6 in July 2026 included a data point that deserves more attention than it received: over the previous six months, the share of internal research compute devoted to AI coding inference grew one hundredfold, while internal agentic token usage — meaning AI agents working autonomously inside OpenAI’s own research infrastructure — increased twenty-two fold. OpenAI is now using its own frontier models to diagnose training failures, optimize training systems, run experiments, interpret results, tune computational kernels, and improve training recipes for the next model. The company described this as “quickly becoming standard.”

Google reported in early 2026 that 75 percent of all new code at the company is AI-generated, up from 25 percent in 2024.

These are not productivity statistics. These are evidence that the loop has closed. AI is now a primary agent in its own development cycle.

Why the 3.5-Month Doubling Figure Was Always a Floor, Not a Ceiling

When I cited the 3.5-month capability doubling figure, I was describing what the data showed for models built primarily by human engineers using AI as an accelerant. That is a fundamentally different situation from what is unfolding now.

RSI compresses timelines in a way that no benchmark extrapolation can capture in advance. Here is the basic mechanic: if AI doubles in capability every X months under human-directed development, and AI is now directing a significant fraction of its own development, then the effective time to the next doubling shrinks in proportion to how much of the development loop the AI controls.

If AI handles 80 percent of code output and that percentage is rising, you are not looking at a linear compression of the doubling time. You are looking at a feedback loop where each generation of AI produces a more capable successor faster than the previous generation did — and that successor inherits the full research infrastructure of its predecessor.

Google DeepMind’s June 2026 paper, From AGI to ASI, described the unconstrained version of this as potentially “hyperbolic” — meaning super-exponential, a curve that in theory races toward a singularity. The authors were careful to note that real-world resource constraints bend such curves into S-shapes before they go vertical. Compute costs money. Power requires physical infrastructure. Fabricating chips takes years of supply chain work. These are genuine friction points.

But friction points are not stopping points. They slow the curve. They do not reverse it.

Breakthroughs At Any Time

Here is where intellectual honesty requires admitting the limits of any analysis, including mine.

RSI does not just accelerate known processes. It creates conditions for qualitative leaps — the kind of change that looks, in retrospect, like it came from nowhere. The history of science is full of these moments. They are not random, but they are not predictable either. You cannot model the arrival of a genuinely new idea. You can create the conditions that make such ideas more likely, and an AI system running millions of research cycles per day, improving its own ability to run those cycles, is precisely such a condition.

What this means practically is that the question “when will ASI arrive?” is not answerable with a date. The consensus among serious forecasters — not YouTube thumbnails, but the AI-2027 team, the METR researchers, the Google DeepMind paper authors — clusters around AGI in the 2026-2027 range and ASI following within months to a few years. But those estimates assume that progress continues on a roughly continuous curve. RSI introduces the possibility of discontinuous jumps — moments where capability does not inch forward but lurches.

Nobody knows when those moments arrive. That is not a failure of analysis. That is the nature of the phenomenon.

What This Means Beyond the Lab

The implications of RSI extend well past questions of benchmark performance or corporate strategy. They reach into every institutional structure that assumes human cognitive supremacy — which is to say, every institutional structure that exists.

Legal systems assume that humans write laws, interpret them, and enforce them. Economic systems assume human judgment at key decision points. Democratic governance assumes a human electorate making decisions about a world they can understand. All of these assumptions are being stress-tested simultaneously by a technology that is now improving itself at a rate that exceeds the capacity of any regulatory body to track, much less manage.

The same politicians and regulators who failed to anticipate the social consequences of social media algorithms — a comparatively simple technology — are now being asked to govern RSI. This should concern everyone, regardless of their position on the political spectrum. This is not a left-right question. It is a question of institutional competence in the face of something genuinely unprecedented.

Sam Altman wrote in mid-2025 that “we are past the event horizon; the takeoff has started.” That is as close to a plain statement of fact as you will get from a sitting AI lab CEO. The event horizon metaphor is apt. Past a certain point, events inside cannot be communicated outward in a way that allows course correction.

We may or may not be past that point. What is certain is that the institutions charged with maintaining human oversight of this process were not built for it, are not staffed for it, and show no signs of being reformed fast enough to matter.

Conclusion

Anyone who tells you they know exactly how this unfolds is selling something. The honest position is this: AI capability is growing faster than any previous technology in history, the development loop has partially closed, and the factors that could produce discontinuous breakthroughs are now structurally embedded in the research infrastructure of every major AI lab on the planet.

The 3.5-month doubling was a data point describing yesterday’s trajectory. RSI means that the trajectory is now self-modifying. The curve is not just bending upward — it is bending the conditions that determine how fast it bends.

There are serious people who believe this ends well for humanity. There are equally serious people who believe it does not. What there is not, on either side, is certainty. The responsible course is to watch what the systems are actually doing — not what the press releases say — and to resist the temptation to normalize a situation that is, by any historical standard, abnormal.

The machine is rewriting itself. Pay attention.

  •  
  • ↗️
❌