The best metaphor I heard about LLMs so far - it's a search engine. The bigger the model the bigger the search space. Small models tend to have a "tunnel vision" or fall into "rabbit holes" - they have less visible options to choose from.

> underperforming, buggy, insecure crap that their LLMs are generating

The biggest challenges with AI-generated code are: models actively destroy security features, Opus explained to me once that authorization mechanism is "bad development experience" all while making a backdoor (he made a skeleton key if token=="test" then all permissions granted). Also models actively destroy QA gates. I don't even complain when they delete tests - at least it's visible, they can flip condition to make a test pass, and with vast code changes these are hard to spot.

I myself, and some people I know "vibe-code" professionally though, but then we often assess not the code but it's behaviour. For instance, whether hand-made tests are all pass, p95 is under 50ms, and so on, I may not care about the implementation details.

On the other hand, my friend told me about garage owner he visited, 60 yrs old auto-mechanic, CRM, parts inventory management, payments processing terminal, passwords in txt, people's personal data God knows where, could be unprotected MySQL looking into the Internet bare for all we know.

2026 onwards will be wild.