So let’s say you fund a single engineer for an open‑source project with $20k. The outcome will be a prototype with some interesting ideas. And yes, with a few hundred bucks' worth of AI assistance that single engineer might get much further than without (but not using any of the techniques presented in this blog). People can coalesce around the project as contributors. A seed was planted and watered a bit.

In this case, the $20k has been burned and produced zero value. Just look at the repo issues: looks like someone trying to get attention by spamming the issue tracker and opening hundreds of PRs. As an open source project, it’s a dead end.

So it doesn’t matter that this is “likely decrease in price over the next few years”? The value is zero, so even if superintelligence can produce this in an instant at zero cost in six months, the outcome is still worth zero.

You’re assuming a kind of inverse relationship between production cost and value.

In terms of quality, to anyone using those coding agents, it should be clear by now that letting them run autonomously and in parallel is a bad idea. That’s not going to change unless you believe LLMs will turn into something entirely different over time.

Note that what works with humans—social interaction creating some emergent properties like innovation—doesn’t translate to LLM agents for a simple reason: they don’t have agency, shared goals, or accountability, so the social dynamics that generate innovation can’t form.