That's only half the problem. OpenAI is contractually obligated, if you will, to believe that models will continue improving at an impressive rate for the foreseeable future (otherwise their valuation makes no sense).

If you believe that, then you should expect to get Sol-level performance out of a Luna-cost model within six months or a year. If you have a system with the weights baked in, that means you're going to end up serving that Sol-class model several times more expensively than it will take someone who comes along a few months later. (such as what recently happened with DeepSeek's update.)

And under that assumption of continuing advancement, baking things in doesn't make sense in general - it's a play you'd make if you think things are slowing down a lot. Which may be right but it's not OpenAI or anthropic's play.

Good enough will hit when the tech stops advancing quickly. You could have a "good enough" model but in 2 years if the general purpose chip can run it just as fast, there is no point having the single purpose one.

The whole point is that it's supposed to be more efficient. But models are also still getting absurdly more efficient every year, so you're likely nullifying much/most of the advantage. 18 months is a long time right now (and 18 is only time to tape out, not operational in data centers).

Even if the balance was net positive, you would also not be able to train them against new tools/harnesses or knowledge. How many years do you expect to keep using them?

The good enough level isn't ever arriving. We're in the first or second inning for LLMs. They will rapidly subdivide in complexity, they will not stagnate in the next decade.

Beyond the model, when would you freeze processor performance, such that it was good enough? Because that's exactly what freezing on Talaas is premised around.

The semiconductor technology will also continue to improve. You lose twice. Talaas is one of the dumbest ideas I've seen in semiconductors in decades.