The bottleneck stopped being code generation. It's verification.

I've felt this for months and couldn't name it cleanly. Hacker News named it for me this week. The binding constraint in AI-assisted coding is no longer how fast you can generate code. It's how fast you can verify it.

The HN consensus crystallized around a specific shape: the developers getting real value orchestrate multiple bounded workflows instead of handing an agent one big autonomous task, and they treat pricing, session limits, context retention, and harness design as the product itself, not side issues. Traversal CEO Anish Agarwal put it the same way on the Stack Overflow podcast: AI made writing code cheap and made running it safely in production harder. The bottleneck moved to debugging and incident response.

Then the money agreed. Patronus AI raised $50M to build simulated "digital worlds" that stress-test autonomous agents, with investors citing nearly insatiable demand. Agent evaluation is now its own fundable infrastructure category. And the most human data point of all: Ford, to celebrate topping JD Power's quality ranking, admitted it had to rehire veteran "gray beard" inspectors after its automated quality systems fell short. Judgment-heavy verification work is proving genuinely hard to automate away. That story hit 595 points on HN for a reason.

So what do you do Monday morning? Stop optimizing for generation speed. You already have more code than you can check. Invest the marginal hour in your eval and verification harness, not your prompt library. Break big autonomous tasks into bounded workflows with explicit stop and verify conditions. If you're picking what to learn next, learn to build the thing that catches the agent's mistakes, because that's the scarce skill now. Timothy B. Lee framed it well: saying LLMs take no skill is like saying management has no learning curve because employees do what you say. The judgment is the job.