Coding AI printed money. Every other AI agent use case is so far behind. The reason is stupidly simple.

The gap is genuinely wild. Cursor went from ~nothing to billions in revenue in about a year, fastest-growing software product ever, and SpaceX reportedly agreed to buy it for $60B. Meanwhile everywhere else: 95% of enterprise AI pilots return $0 measurable value, most agent pilots never reach production, companies are abandoning some of the agentic initiatives. Same models. Same money. Totally different outcomes. So what's the actual variable? I don't think it's the model. I think it's this: code can grade itself. You run it, it passes or fails, instantly, for free. That means you can train it on millions of auto-checked attempts with no human bottleneck. Karpathy basically called this, AI gets superhuman fast only where you can automatically verify the output. Best example of the flip side: there's a customer-service benchmark where you run the same task 8 times. Top models get it right all 8 less than 25% of the time. That's not a product, that's a slot machine. Not because the model's dumb, but because "good support" has no test that turns green. And the enterprise failure reports back it up: projects die on "couldn't define success" and "inconsistent outputs," not model quality. So my take: every "AI agent for X" startup is secretly betting on whether X can be checked, and most don't realize it. The model isn't the moat. The verifier is. Coding didn't win because devs are early adopters, it won because software is the one job where the truth is free. The play outside coding isn't a better model. It's building a feedback loop where none exists. People of the Internet! spill the beans... What job looks unverifiable now but secretly has a cheap grader nobody's built yet?