Where's the shovelware? Why AI coding claims don't add up

There's also the questionable copyright/IP angle.

As an analogy, can you imagine being a startup that hired a developer, and months later finding out the bulk of the new Web app they "coded" for you was actually copy&pasted open source code, loosely obfuscated, which they were passing it off as something they developed, and to which the company had IP rights?

You'd immediately convene the cofounders and a lawyer, about how to make this have never happened.

First you realize that you need to hand the lawyer the evidence (against the employee), and otherwise remove all traces of that code and activity from the company.

Simultaneously, you need to get real developers started rushing to rewrite everything without obvious IP taint.

Then one of you will delicately ask whether firing and legal action against the employee is sufficient, or whether the employee needs to sleep with the fishes to keep them quiet.

The lawyer will say this kind of situation isn't within the scope of their practice, but here's the number of a person they refer to only as 'the specialist'.

Soon, not only are you losing the startup, and the LLC is being pierced to go after your personal assets, but you're also personally going to prison. Because you were also too cheap to pay the professional fee for 'the specialist', and you asked ChatGPT to make the employee have a freak industrial shredder accident.

All this because you tried to cheap out, and spend $20 or $200 on getting some kind of code to appear in your repo, while pretending you didn't know where it came from.

I actually just looked, if anything the PH data supports his theory, assuming the website I found is scraping this data accurately.

In fact it looks like there were less products launched last month on PH than the same period a year ago.

https://hunted.space/monthly-overview

It's a bit hard as they're not summing by months but it looks like less to me quickly scanning it.

And as Claude Code has only really been out 3/4 months you'd be expecting launches to be shooting up week-by-week right about now as all the vibe products get finished.

They're not, see the 8 week graph:

https://hunted.space/stats

> LLMs can be automated to do busy work and although they may take longer in terms of clock time than a human, the work is effectively done in the background.

What is this supposed busy work that can be done in the background unsupervised?

I think it's about time for the AI pushers to be absolutely clear about the actual specific tasks they are having success with. We're all getting a bit tired of the vagueness and hand waving.

> AI didn't speed me up at all until agents got good enough, which was April/May of this year.

That was 5 months ago, which is 6 years in 10x time.

I don't think so, although I think at that point experience heavily comes into play. With GPT-5 especially, I can basically point cursor/codex at a repo and say "refactor this to this pattern" and come back 25 minutes later to a pretty much impeccable result. In fact that's become my favourite past time lately.

I linked some examples higher up, but I've been maintaining a lot of packages that I started slightly before chatgpt and then refactored and worked on as I progressively moved to the "entirely AI generated" workflow I have today.

I don't think it's an easy skill (not saying that to make myself look good, I spent an ungodly amount of time exploring programming with LLMs and still do), akin to thinking at a strategic level vs at a "code" level.

Certain design patterns also make it much easier to deal with LLM code: state reducers (redux/zustand for example), event-driven architectures, component-based design systems, building many CLI tools that the agent can invoke to iterate and correct things, as do certain "tools" like sqlite/tmux (by that I mean just telling the LLM "btw you can use tmux/sqlite", you allow it to pass hurdles that would otherwise just make it spiral into slop-ratatouille).

I also think that a language like go was a really good coincidence, because it is so amenable to LLM-ification.