And even “product engineers” often do not have experience going from zero to post sales support on a saas on their own.

It is a skill set of its own to make product decisions and not only release but stick with it after the thing is not immediately successful.

The ability to get some other idea going quickly with AI actually works against the habits needed to tough through the valley(s).

a) there are likely many more active, eager contributors all of a sudden, and

b) there's suddenly a huge amount of new papers published every week about algorithms and techniques that said contributors then eagerly implement (usually of dubious benefit).

More cynically, one might also hypothesize that

c) code quality has dropped, so more frequent releases are required to fix broken programs.

I think this might be more of an comment on software as a business than AI not coding good apps.

200 years ago text was much more expensive, and more people memorized sayings and poems and quotations. Now text is cheap, and we rarely quote.

I’ve done some experiments with reading gedcom files, and I think I’m quite close to a demoable version of a genealogy app.

Biggest thing is a tool for remotely working musicians. It’s about 10000 lines of well written rust, it is a demoable state and I wish I could work more on it but I just started a new job.

But yeah, this wouldn’t have been possible if I hadn’t been a very experienced dev who knows how to get things live. Also I’ve found a way to work with LLMs that works for me, I can quickly steer the process in the right way and I understand the code thats written, again it’s possible that a lot of real experience is needed for this.

    > So, let’s ask again, why? Why is this jump concentrated in software about AI?...Money and hype

Don't get me wrong: there are real productivity gains to be had, but the reality is that building small one-offs and personal tools is not the same thing as building, operationalizing, and maintaining a large system used by paying customers and performing critical business transactions.

A lot of devs are surrendering their critical thinking facilities to coding agents now. This is part of why the hype has to exist: to convince devs, teams, and leaders that they are "falling behind". Hand over more of your attention (and $$$) to the model providers, create the dependency, shut off your critical thinking, and the loop manifests itself.

The providers are no different from doctors pushing OxyContin in this sense; make teams dependent on the product. The more they use the product, the more they build a dependency. Junior and mid-career devs have their growth curves fully stunted and become entirely reliant on the LLM to even perform basic functions. Leaders believe the hype and lay off teams and replace them with agents, mistaking speed for velocity. The more slop a team codes with AI, the more they become reliant on AI to maintain the codebase because now no one understands it. What do you do now? Double down; more AI! Of course, the answer is an AI code reviewer!. Nothing that more tokens can't solve.

I work with a team that is heavily, heavily using AI and I'm building much of the supporting infrastructure to make this work. But what's clear is that while there are productivity gains to be had, a lot of it is also just hype to keep the $$$ flowing.

A lot of the real value shows up as workflow compression instead. Internal tools, one-off automations, bespoke research flows, coding helpers, things that would never have justified becoming a product in the first place.