The author observes that a call to GPT-5.6 Luna is only 4-5 orders of magnitude more expensive than grep, and then predicts that at current rates of progress, calling an LLM will soon be cheaper than a grep. I think this is a good time to invoke Stein's Law: "If something cannot go on forever, it will stop." These efficiency improvements won't continue forever. It's more likely that the per-call cost of high-quality, compiled software like grep will be a lower-bound that LLMs asymptotically approach, rather than a line that they blow past with perpetual exponential progress. (Barring a true breakthrough in something like quantum computing or room-temperature superconductors.)
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.
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.
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 :))
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.”
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.
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.
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
At some future point where LLM hardware is cheaper than simply running grep, then grep equivalent would benefit from those selfsame hardware improvements and be cheaper to run as well, probably still by the same ratio.
I also don't understand where the idea that frontier models are getting better efficiency comes from. The results are certainly improving, but that comes from feedback and multiplexing requests, which cost more.
Grep reads through the entire file looking for patterns.
An LLM scans its neural net (in ways that I don't understand) which is kinda-sorta like having a huge index.
You can improve over Grep if you have an index; and the LLM has an index.
Thus, it's plausible that an LLM can be more efficient at reading its neural net (IE, index) than Grep reading the whole file.
https://iepathos.github.io/ripgrep/performance/#work-stealin...
That’ll be just as true for an ASIC NN as it is for good ole grep.
NVMe v1 wasn't released until 2011 and there was no hardware available until 2012. Before that the fastest drive interface was 6 Gbps SAS. Then we got 4 GBps (32 Gbps) with NVMe v3, 8 Gbps (64 GBps) with v4, and 16 Gbps (128 Gbps) with v5. V6 is slated to double again. v7 is currently planned to double that. This is less than 15 years of progress.
In the 1990s, we went from 33 Mbps to 66, to 100, then 133 with IDE. We went from the 40 Mbps of 1986's SCSI 1 to 80 of SCSI 2 in 1994. Since then parallel SCSI has gone to 80, 160, 320, 640 (Ultra 2 wide), 1280 (Ultra 3), 2560 (Ultra-320), and 5120 (Ultra-640) Mbps.
SAS has also gotten faster, with 6 Gbps, 12 Gbps, and 22.5 Gbps. NVMe is still lower overhead and lower latency.
Memory bandwidth can also be addressed. Stock servers with EPYC are 16 channel DDR5. That's 409.6 GBps. Four channels of DDR3 was only 102.4 GBps. Video and accelerator card memory bandwidth is also increasing.
If the compiler invocation is sufficiently slow, the llm could consider outputting a binary directly?
For all we know matrix multiplications are a faster way to generate optimized machine code than branchy sequential compiler code with tons of heuristics and passes.
“are” or “could be”?
what if an LLM finds the answer early?
grep might continue to read everything, doing the wrong thing correctly.
Won't that also help grep and then move the asymptote down more?
From a computational standpoint this is obviously nonsense, but from an attentional one I'm not so sure. It may already be more attentionally expensive to use grep in some cases, such the moment you need to remember a non standard arg. And if this applies for performing a simple http operations, then it certainly applies going up the complexity chain.
Is running LLMs (or some other ML workload) on/with quantum computers expected to bring efficiency gains?
The only aspect that is poorly analyzed by the OP is business model viability. All players are investing insane amounts of money in infrastructure with the expectation that their future profits will justify all that investment. The winner or winners in the AGI race, they believe, will find the proverbial "pot of gold at the end of the rainbow."
The OP glosses over questions of business model viability with a brief qualitative discussion and very little hard data. For example, to earn an annual return > 10% on every trillion dollars of capital sunk into infrastructure, the owners of that infrastructure must earn free cash flow (operating profit less investment) in excess of $100 billion per year in perpetuity. Is that feasible? Why? How?
The OP does not really consider such questions.
You may be right. I'm not so sure. Inference looks like a viable business model for those operators that have SOTA infrastructure in place, but the investment required to have it is enormous, and appears to be never-ending, because if an operator stops investing aggressively, its infrastructure quickly becomes non-competitive, and customers will quickly leave for alternatives. SOTA infrastructure is a moving target.
Why would it be any different for inference? If we believe OP, it'll just become part of regular compute infra, and thus part of the renting-out-compute business model.
I think it's an open question if the current generation of inference investment will pan out, but in the long term, there'll be a balance between investment cost and margin, just as in every other industry.
Edit to add: Or or might just be AWS / GCP / Azure that benefits from this business model. They're already pretty good at selling commodity infrastructure.
But yeah, I definitely don't have high confidence in any of this!
Only if you also have the model thats better than anyone else's.
As soon as models are free, or there are no newer models (assuming thats going to happen, and thats not a given) then the only thing you can compete on is price.
This means that the only thing you have to differentiate is either price, speed or ease of use. (or regulatory capture...)
We are at pets.com level of spend currently. Unless model development becomes cheaper, then we are going to run out of novel debt but not really debt mechanisms.
One is how much does it cost OpenAI to train the model.
The other is, if I stole OpenAI's model how much would it cost for me to run it?
R&D costs versus operational costs. Operational costs are very likely profitable. R&D is catastrophically expensive currently.
Free cash flow (operating profit less investment), actual cash coming in, is deeply in the red.
EBITDA can be a sensible measure of profitability when there isn't much need for additional investment. That doesn't seem to be the case with these operators. They need to invest aggressively to avoid losing customers to competitors. All of these operators have made multi-year commitments to invest more in infrastructure. In addition, they have guaranteed quite a bit of debt to fund it.
Maybe it all will work out fine (and I sure hope it does!), but I didn't see any hard data from the OP, or from you, supporting that view.
Those real debt obligations are going to want to be paid back.
We're in a situation where AI isn't going to go away, but whatever financial mode we're in right not is not going to work.
"It is not too much to expect that our children will enjoy in their homes electrical energy too cheap to meter,..." Lewis Strauss
https://en.wikipedia.org/wiki/Too_cheap_to_meter#Origins
Oddly enough my power bill was metered and big.
Of course, perilous territory for future irony depending on how your prediction plays out.
Later, you were billed for time connected to the "internet" (compuserve or aol or whatever)
Around when the iphone came out, software went from tens or hundreds of dollars to pennies, then free.
On the other hand legal advice has always been expensive, because a good answer is worth it.
Medical advice is worth it. Investing advice is worth it.
(That said, I wonder if with home solar and batteries if electricity will ever "generally" go down in price to normal people)
Texting was charged per character.
And if you have the capabilities to install your own solar it can pay itself back in 6 years. Not sure what that looks like with 100% battery coverage.
[0] https://en.wikipedia.org/wiki/Zwentendorf_Nuclear_Power_Plan...
Are we still going to make the same error over and over again? The day China says "stop" the price per watts will explode, just like when Russia and Iran said "stop".
Given current found reserves and the current rate of use, the world has about 40 to 50 years of natural gas. Thorium used in molten salt thorium reactors would provide electricity for 60 billion years or so if we could actually extract all of it. That's 10 billion years or more if it provided all human energy consumption. Of course there's a limit to extraction, but it is over three times as common as uranium.
Also, besides thorium one can mix in partial amounts of other fuels, including uranium and plutonium. There is no runaway meltdown risk, as the fission is actively managed rather than actively suppressed. Fuel is spent more completely. The waste products are smaller, less radioactive, and have far shorter half-lives.
Then of course we're always getting slightly closer to productive fusion reactors.
These technologies along with solar PV, solar thermal, hydro, wind, geothermal, wave power, and batteries likely all have a place in the future.
There's a decent chance that at some point in the future residential customers will pay for the connection and only commercial or industrial customers will actually be metered. That's not because companies want to give up additional revenue. It's because at some point the cost of meters, tracking usage, and competitive advertising about who has the cheapest plans costs more than the power the typical customer uses above the base charge.
"Between digital economics and the wholesale embrace of King's Gillette's experiment in price shifting, we are entering an era when free will be seen as the norm, not an anomaly. How big a deal is that? Well, consider this analogy: In 1954, at the dawn of nuclear power, Lewis Strauss, head of the Atomic Energy Commission, promised that we were entering an age when electricity would be "too cheap to meter." Needless to say, that didn't happen, mostly because the risks of nuclear energy hugely increased its costs. But what if he'd been right? What if electricity had in fact become virtually free? The answer is that everything electricity touched—which is to say just about everything—would have been transformed. Rather than balance electricity against other energy sources, we'd use electricity for as many things as we could—we'd waste it, in fact, because it would be too cheap to worry about."
... What Mead understood is that a psychological switch should flip as things head toward zero. Even though they may never become entirely free, as the price drops there is great advantage to be had in treating them as if they were free. Not too cheap to meter, as Atomic Energy Commission chief Lewis Strauss said in a different context, but too cheap to matter. Indeed, the history of technological innovation has been marked by people spotting such price and performance trends and getting ahead of them."
My issue is that datacenter energy costs are being prioritized for commerce over residential use, so the average consumer is paying more for electricity, because a datacenter needs more electricity and they're getting tax breaks. Assuming this all improves efficiency for new products like automated robotics, there is a debatable benefit. Jevon's Paradox has no ceiling, except the environment, and people's 401ks.
"Had the atomic bomb turned out to be something as cheap and easily manufactured as a bicycle or an alarm clock, it might well have plunged us back into barbarism, but it might, on the other hand, have meant the end of national sovereignty and of the highly-centralised police State. If, as seems to be the case, it is a rare and costly object as difficult to produce as a battleship, it is likelier to put an end to large-scale wars at the cost of prolonging indefinitely a “peace that is no peace”."
It seems, especially with open weights, that the AI is much more like the alarm clock and not the battleship. $20/mo would have been about $1 in 1944
https://www.orwellfoundation.com/the-orwell-foundation/orwel...
The "most attractive quadrant" is completely meaningless. The whole point of a Pareto curve is that each point on the curve is better than everything else on at least one dimension, and that you can make these comparisons without placing a value judgement on the relative importance of the different metrics. If you make a composite score of the two metrics (any monotonically non-decreasing function, e.g. a weighted sum with non-negative weights), that score will always be maximized by one of the points on the Pareto frontier.
So going by the numbers in the 2nd chart (1st AA chart) from TFA alone:
- there's no reason one would choose Deepseek V4 Pro 0813 (max) even though it's in the "most attractive quadrant", because GLM-5.3-Flash is both cheaper and scores better.
- Claude Fable 5.1 (max with fallback) on the top right* could be your most attractive option if you need the best scoring model and don't care about cost, even though it isn't in the "most attractive quadrant"
- The un-shown model off the left side of the chart could be your most attractive option if you just need lots of cheap tokens and don't care about quality.
* I also dislike the way they place the labels, and that grey line connecting the label to the point is way too subtle.
I think Nvidia is under the same pressure as Anthropic/OpenAI. Nvidia will dominate research and probably keep dominating training, but the real volume is in inference. And for inference Nvidia's lead is only a few months, similar to the lead frontier labs have over open source. Nvidia will sell a lot of Rubin CPX's, but their margin on that will be a lot smaller than B200 because there is so much more competition in that space.
The cost of a given level of performance often falls fastest right after that level is first achieved, that is, when it is state of the art (SOTA). We see this pattern on three of our five main benchmarks of AI capability. Averaging across all five, cost falls 66% per quarter (75× per year) for performance that has just debuted as SOTA. Two years later, prices fall half as fast, at 32% per quarter (4.7× per year).
but the analysis itself has more nuance and is a quite interesting read.
[1] https://epoch.ai/publications/the-plunging-price-of-thought
I don't disagree with the thesis here, I just don't think costs are coming down quite that quickly.