Aaron Watson 🎥

I’ve got bad news. The AI cycle is over—for now. I’ve been an unapologetic AI maximalist since the first time I tricked GPT-4 into writing a working Python back-test for a volatility strategy back in early 2023. I’m still convinced it will take the wider economy years—maybe decades—to fully digest the productivity shock we’ve already uncorked. But the curve we’ve been riding just flattened into a long plateau. The problem isn’t that the models stopped improving. It’s that the improvements we need are measured in orders of magnitude, not percentage points. Every step up the scaling laws now demands a city’s worth of electricity and a sovereign wealth fund’s worth of GPUs. You can still squeeze clever tricks out of mixture-of-experts or chain tiny specialists into something that looks like agency; that keeps the demo videos cinematic. It just doesn’t get us to super-intelligence. For that we need either an architectural miracle (unforecastable by definition) or a civil-engineering miracle (a decade-long sprint to build nuclear plants and 2-nanometer fabs). The first is luck. The second is politics. Both are scarce. Meanwhile the models we have remain, at their core, next-token roulette wheels. Chain enough spins together and tiny error probabilities compound into existential glitches. In domains where you can automatically verify an answer—unit tests pass, the protein binds—those glitches are an acceptable tax. You iterate until it works. In domains where judgment is qualitative, the tax becomes fatal. My portfolio example still stings: I asked Claude Opus to locate the optimal weight for a new asset in an already-levered book. Opus quietly renormalized all weights to 1.0, vaporizing the leverage assumption. A layperson would see clean numbers and move on. Scale that failure mode to law, medicine, or national security and you understand why “human-in-the-loop” isn’t a slogan it’s a ceiling. What comes next is not the next spectacular demo but the quiet absorption of today’s tools into the 80 percent of the economy that still runs on Excel and email. Code will ship with more AI-authored lines, but senior devs will still sign the diffs. Customer-support bots will escalate the five percent of tickets that matter; the other ninety-five percent will vanish so smoothly customers won’t notice. Radiologists will still stare at scans, except now a model will have pre-read every slide and flagged the one in a thousand the tired resident would have missed. And yes, you’ll still need a PhD to notice that the portfolio weights got renormalized but you’ll build the new clustering module in five minutes instead of an afternoon. The productivity gains are real; they’re just not cinematic. For founders this means stop chasing the next 0.3 percent on MMLU. Find a vertical where verification is cheap and margins fat, then build the scaffolding that lets domain experts ride the model instead of babysit it. For investors, treat “AI” the way we treated “mobile” circa 2011: infrastructure bets can still clear the hurdle rate if you triple the time discount, but the application layer is a graveyard of demos wearing revenue costumes. For policymakers, forget the AGI manifestos and write zoning rules that let utilities run high-voltage lines to data centers. To my fellow zealots: we are not going back to the pre-2022 world. The ceiling just got higher, but the ladder is longer than we thought. That isn’t failure; it’s physics. The next breakthrough will arrive; maybe from a grad student with a sparse attention kernel, maybe from a national lab running a ten-gigawatt reactor. Until then the boring work of integration is the only game in town. So breathe. Ship the eval harness. Close the ticket. And remember: exponential curves always look flat when you zoom in too close.