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Comment by ngruhn | Hacker News Reader

> I think the models are being optimized for wealth extraction from users and companies, instead of solving problems.

I don't think so. Expect that in a market with high vendor lock-in but that's not the case here. The market is extremely competitive and switching cost are near zero. Anthropic can't afford to pull shit like this and sacrifice quality.

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You don't have LLM-based processes if you think there is no lock-in. There may be no lock-in for coding if you enforce decent rules (but still some ambiguous docs can be interpreted differently), but any non-trivial pipeline/system, these models are not stupid but each has some quirks. Sometimes for some reason they will ignore some instruction while all other models have no trouble following it. These things accumulate.

Plus there's subjective stuff even for coding, people learning how to deal with it. Even on HN you can already see cloude/codex camps each strongly convinced that one is better than the other.

My employer just finalized a contract with Anthropic, for enterprise Claude Code use. Which means that unless there is a _major_ downgrade in service quality, we are now locked in for the next few years (but at least for a year, although vendor contracts are renegotiated less frequently).

Just checked the dashboard, and we seem to have the exact same $200 credit as others, enterprise or not. Token inflation affects us just like everyone else.

It feels a bit like buying the same box of chocolates every day, but the size / weight of the box is shrinking... the price remains unchanged!

The disconnect between the reality of and the consumer sentiment of this particular realm of products seems to be one of the most dramatic and widespread I’ve personally ever seen.

You'd also expect that in a market where the players think they're in a bubble.

Anthropic can't afford to pull shit like this and sacrifice quality.

And yet, the Java language exists.

For market share, ANTHROP\C needs to optimize for the vast mediocrity that are mid-bell-curve users and enterprises.

This adaptation tends to come with significant drag for the right ends of the bell curve firms or teams.

Unless ANTHROP\C have a separate objective function by cohort and ensure that doesn't regress, improving results for the emerging middle will nerf tools from point of view of those with high in-domain expertise.

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