Yeah I bumped on that too. If it's possible to make llms cheaper than current grep, then it is also almost certainly possible to make grep cheaper.
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.
I did a toy project once implementing a limited version of grep on an FPGA and was able to get some speedup over GNU grep at the time, though marginal.
In any case, LLMs aren't IO bound :))
Put a Transputer in a Lego brick. Not "like a Lego brick", an actual Lego brick. Turn the notches into connectors for the serial link.
Plug'n'play.
And eventually hire people whose job it is to patch pipelines on demand for everyone in the office.
“Hey Jim, I’m gonna output the systemd logs of nginx on line five, can you assemble a grep pipeline for me to match all HTTP 500 status codes from /api/cart POST request log lines? Connect the filtered output to Tim’s desk, line 7. He’s there now, we are trying to figure something out.”
“Sure thing Bob, give me a moment.”
Baring it all to software: Raw machines | IEEE Journals & Magazine | IEEE Xplore https://share.google/nI9GyFJu4HvbYFrBf
(If you Google the name you will find free PDFs as well, the IEEE page is more useful as a summary and such.)
If your problem is grepping crazy amounts of data, the infrastructure for LLMs isn't a bad place to look for an example.
I wouldn't be surprised if that's what Apple is focused on for their next generation platforms – I wonder if more layers of caching between their SSDs and unified memory are on the cards.
Inference costs cannot keep falling forever, but they do still have a long way to go.
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.
Right now, I do actually use OpenAI's gpt-oss-safeguard-20b for somethings, was released 11 months ago, and is $0.075/M input / $0.30/M output now. I could see this model being in fairly widespread use at 10x speed and 1/10th cost if it was introduced today. Meaning, that for some usecases (moderation) i think dedicated chips can pan out today.
But for more general models, its tougher. Gemini 3 pro was launched in November, if ASICs brought it down 1/10th in cost, it would be $0.20/$1.2. GPT 6 Luna is $0.1/$0.50. Luna is better at a lot of things, but not everything. So 1/10th doesn't really make the ASICS investment worth it in my opinion, but if it brought it down to 1% ($0.02 / $0.12) it would be a really compelling model with a lot of use.
BUT, do i think something like Luna is probably generally capable of doing a huge amount of knowledge work. So if Luna came out at 1/10th the cost a year from now, it would probably be compelling for a while.
It all depends on the rate of improvement in cost/capability.
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.
The complexity gets faster as you get on with it.
With AI, the question is still open if this will actually turn out to be something useful or if it will in the end just be another way for the elites to make untold profits.
Yes, because it was a largely random undirected process.
Then you have to climb to another branch to get more fruit. The biggest issue with most problem space discovery is you're doing it blindfolded.
It accelerated from there rather than slowing down.
you could make hardware implementation of grep and store the file itself next to it in some ROM but that's not a very useful grep ... while hardware LLM is exactly as useful as software LLM only orders of magnitude faster
When the author wrote Llm can be as cheap as a tool, I read it as not equivalent. They even said the Llm can be embedded into a tool.
Their point was, the higher level use case — like classification — could become as cheap as grep. Which is quite well possible.