> far-fetched

Remember Google?

Once it was far-fetched that they would make the search worse just to show you more ads. Now, it is a reality.

With tokens, it is even more direct. The more tokens users spend, the more money for providers.

> It’s in the interest of the model builders to solve your problem as efficiently as possible token-wise.

Unless you’re paying by the token.

It’s not that crazy. It could even happen by accident in pursuit of another unrelated goal. And if it did, a decent chunk of the tech industry would call it “revealed preference” because usage went up.

> But switching costs are pretty low

Switching costs are currently low. Once you're committed to the workflow the providers will switch to prepaying for a year's worth of tokens.

To be clear I don't think that's what they're doing intentionally. Especially on a subscription basis, they'd rather me maximize my value per token, or just not use them. Lulling users into using tokens unproductively is the worst possible option.

The way agents work right now though just sometimes feels that way; they don't have a good way of saying "You're probably going to have to figure this one out yourself".

This is a good point. For example if you have access to a bunch of slot machines, one of them is guaranteed to hit the jackpot. Since switching from one slot machine to another is easy, it is trivial to go from machine to machine until you hit the big bucks. That is why casinos have such large selections of them (for our benefit).

As a rational consumer, how would you distinguish between some intentional "keep pulling the slot machine" failure rate and the intrinsic failure rate?

I feel like saying "the market will fix the incentives" handwaves away the lack of information on internals. After all, look at the market response to Google making their search less reliable - sure, an invested nerd might try Kagi, but Google's still the market leader by a long shot.

In a market for lemons, good luck finding a lime.

A lot of people are getting good results using it on hard things. Obviously not perfect, but > 50% success.

That said, more and more people seem to be arriving at the conclusion that if you want a fairly large-sized, complex task in a large existing codebase done right, you'll have better odds with Codex GPT-5.2-Codex-XHigh than with Claude Code Opus 4.5. It's far slower than Opus 4.5 but more likely to get things correct, and complete, in its first turn.

I think a lot of it comes down to how well the user understands the problem, because that determines the quality of instructions and feedback given to the LLM.

For instance, I know some people have had success with getting claude to do game development. I have never bothered to learn much of anything about game development, but have been trying to get claude to do the work for me. Unsuccessful. It works for people who understand the problem domain, but not for those who don't. That's my theory.

Don’t use it for hard questions like this then; you wouldn’t use a hammer to cut a plank, you’d try to make a saw instead