I disagree with the author's interpretation of many of their points.

> Skill Atrophy

Very true, but in the same way moving from assembly -> scripting languages degraded the skill of programmers to manage memory. AI is another tool (albeit one that's on a different level than any other transformation we've had); what matters is whether the programmer understands the intent of the code, not the 0's and 1's that it turns into. That intent is still there, we're just writing it at a much higher level than before.

> Artificially low cost

This heavily depends model to model. IMO OpenAI/Anthropic are likely the outliers here, and I do agree that it's unlikely they'll recoup the training costs for their specific models, but they also legitimized an industry - something that's hard to tangibly price. Many of the models out of China however will almost certainly recoup cost. Qwen 3 had a training price of around $1.6m USD. In the first quarter of this year OpenRouter processed around 5 trillion tokens from it, landing at around 2.5m in revenue (very rough numbers). Assuming a 30% margin, that's already 40% of their training cost in revenue for a quarter from a single platform - their usage in china is almost certainly higher.

The reality is training costs are getting cheaper. I agree that currently the top providers are heavily subsidizing costs, but that doesn't mean you can't drive revenue, they just choose not to as having the "best" model right now gives them clout.

Yeah, I agree. The first point depends on the second: skill atrophy is only an issue if you expect that coding LLMs will essentially disappear or become much more expensive. But they won't. There are companies offering GLM-5 at a profit, and it's affordable, and a great model.

The third point is moot if you run agents in a container and review their code, which you can and should do.

The fourth point is real, but the chance of it actually ending up harming people who use LLMs to write code is IMO remote.