You can burn anything* into an ASIC to make it cheaper per-call.

non-backreferencing grep is not very difficult to implement in an ASIC either. But it's probably not worth it because of how relatively rarely you use it and of the data transfer costs.

LLMs are great candidates for ASIC-burning because they're slow compared even to network speeds and run all the time. The issue is that you don't want to burn a specific model or architecture that then becomes obsolete.

So you've got two possible futures, and both guarantee large price drops: (a) LLMs keep getting better and better and better, so ability/$ keeps rising; or (b) LLMs plateau in ability, in which they will start getting ASIC'd.

To be clear, I agree with the overall premise of the article!

But I would probably take a long horizon bet that the grep implementation on my machine will remain cheaper than an equivalent ai task, even though I think those ai tasks will become far cheaper over time.

I just think the original comment's model of asymptotic approach is probably more likely to be accurate than the model of the line blowing through this grep-like cost level.

> LLMs plateau in ability, in which they will start getting ASIC'd.

They don't need to plateu for that to happen. There are companies already building AI on ASIC, and IIRC they were approach 12 months lead time. A 12 months old frontier model (Sonnet 4.5, GPT-5, Kimi K2) for 1% of the price is still a rather good value proposition.

This whole story really reminds me of crypto coins. Like.. going from mining one coin, or lets say token, to millions of fractions like 0.00000000001 bitcoin a week.