Decelerate by using Capitalism Itself | Joseph Perla

If you want AI to slow down, stop asking anyone to slow down. Charge them for the data.

Everyone in the safety world says capabilities are outpacing alignment. It has been repeated so many times that it functions as a throat-clearing noise rather than a claim. Here is the version with a mechanism in it: the speed of capability gain is set by a competitive process that has no line item for whether anybody understands what is being built. Not a small line item. There is no line in any lab's financial model called interpretability debt. A firm that slows down to close that gap loses share to a firm that doesn't, and the firm that doesn't gets the capital, the compute, the talent, and the next training run. This isn't villainy. It's what every market does when the externality is unpriced, and this externality is unpriceable, because the party who would sue you is a future that may or may not exist.

So be precise about the dangerous variable. Capability isn't doom. Rate is doom. A world that gets to transformative AI in 2045 with twenty years of accumulated interpretability, formal methods, hardened infrastructure and institutional practice is a different world from one that gets there in 2029 with a pile of evals and some vibes. Same destination, wildly different survival odds. The thing we want is not stop. It's slow. And slow is the one intervention our toolkit is worst at delivering.

Look at what we've tried. A voluntary pause requires unanimity among parties who have every reason to defect and no way to detect each other defecting; whoever pauses gets replaced by whoever didn't, and the 2023 letter is now a cultural artifact instead of a policy. Regulation is slow, bounded by jurisdiction, captured with impressive speed, and double-edged, because compliance is a fixed cost and fixed costs favor incumbents — a regime that demands a $50M safety apparatus is a regime where only frontier labs can operate. You have thinned the race without slowing it. Compute governance is the most serious proposal in the family, but it routes through export control, which routes through geopolitics, which routes through a rival state that has correctly worked out that this technology is strategically decisive. Asking a Chinese lab to slow down over p(doom) is asking a nation to accept permanent subordination on the basis of an argument it doesn't accept. And moral suasion is the ugly one: it works best on the labs that already take safety seriously, which means its net effect is to move the frontier toward labs where it has no purchase at all. Suasion disarms the careful.

Every one of those levers asks people to want something other than what they want. That's why they lose. Markets beat arguments.

Meanwhile look at the flywheel they're up against. Take Anthropic, not because it's the worst actor but because it's the hardest case — it publishes its scaling policy and funds a real interpretability team, so if the structural argument holds there it holds everywhere. The disclosed run-rate sequence goes roughly $9B at the end of 2025, $14B in February 2026, $19B in March, $30B in April, $47B alongside the Series H in mid-May, with third-party trackers putting late-summer ARR in the high $60s and The Information doing the arithmetic on $100B annualized inside the calendar year. They filed confidentially for an IPO on June 1 at a reported $965B. Argue about run-rate versus trailing revenue if you like; the gap between them is the entire point of an exponential. Revenue funds compute, compute funds capability, capability funds revenue, and the loop runs as fast as capital markets will allow. Capital markets are enthusiastic.

Now the part that should bother you more than the revenue curve. The labs are being paid to receive their scarcest input.

In every extractive industry that has ever existed, the firm pays for the raw material. Oil companies pay for leases, smelters pay for ore, pharma pays trial participants. The AI industry invented something better: its customers pay it for the privilege of supplying the material that trains its successor. And the material is not marginal. Public text is running out, which is the stated premise of published frontier research, not a guess. What's left in quantity is private: internal code, internal documents, expert corrections, tool traces, failed attempts, and the record of which answer a domain expert finally accepted. That last category is expert preference data at scale, generated for free, in a transaction where the expert is the one paying. Every enterprise API call is a revenue event and an acquisition event at the same time. Nobody designed this. It's the best flywheel in the history of capital formation.

The contractual promise around it is real, and I don't think the big labs are lying. But notice the promise is about rows. Read Generative Data Refinement: Just Ask for Better Data (Jiang et al., Google DeepMind) as an...