Tokens Too Cheap to Meter

76 pointsposted 6 hours ago
by teoruiz

48 Comments

jetrink

2 hours ago

> Tokens become cheaper than tool calls

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.)

sanderjd

40 minutes ago

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.

serbuvlad

23 minutes ago

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.

scotty79

12 minutes ago

depends on what you are grepping ... greapping a large file might be more expensive one day than generating n-th token with LLM that works fully in hardware

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

FranOntanaya

an hour ago

LLM is spicy memoizing, so it can potentially be faster than a tool call. But people will spend a month tweaking and testing to ensure they have the level of determinism they need, which means it's more expensive, and that they should have used actual memoization in the first place.

arijun

an hour ago

It might never beat out grep, but it could beat some more expensive to call tools, similar to how heuristics will often be faster than exact answers. Rust Analyzer can be slow at times, I could see an AI tool taking over a subset of its work.

14u2c

23 minutes ago

> calling an LLM will soon be cheaper than a grep

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.

cs702

2 hours ago

I found the OP insightful and worth a read. Thank you for sharing it on HN.

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.

sanderjd

33 minutes ago

I think the article's analysis is basically right in a vacuum. That is, I think it's clear that inference is a viable business model. But what isn't clear is whether it will be such a profitable business model for any given company that it will justify the investment that company has taken. I kind of think the winners might be a follow-on generation of companies that focus on this commodity inference business model instead of the invent-machine-god-first "business model" and thus are wiser about their level of investment and capital costs.

cs702

24 minutes ago

> I think it's clear that inference is a viable business model.

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.

twoodfin

4 minutes ago

A phrase comes to mind: "Your margin is my opportunity."

BenzeneDream

an hour ago

They are already turning profits and inference has shown to be a cash cow. And they've already secured compute for the next several years.

cs702

an hour ago

Some frontier labs are reporting positive "adjusted EBITDA" (earnings before interest, taxes, depreciation, and amortization, with extra adjustments to make the figure positive).

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.

0cf8612b2e1e

22 minutes ago

EBITDA might make sense for the resellers who package up open weight models and sell inference. It is not appropriate for the labs who have billions in debt for RAM, new data centers, gobbling up competitors, etc.

Those real debt obligations are going to want to be paid back.

sanderjd

30 minutes ago

Definitely. The question is: Is it enough to recoup the enormous capital costs and justify the level of investment they've received. I think there's a decent chance that it will be. But maybe not. And the longer they keep focusing on training new models more so than on inference, the more uncertain I become that it's all going to work out.

metalliqaz

an hour ago

Who is the "they" that are turning profits?

alpineidyll3

2 hours ago

Exactly. "Cost-to-distill" is a critical parameter. Right now usage of frontier models for all tasks is both subsidized and irrationally popular even at the subsidized price. Deepseek would solve most tasks faster and 10x cheaper. I agree with the author that just as Deloitte exists, frontier labs will exist. But not because their products are proprietary technical marvels or gods, but rather because of branding.

ido

28 minutes ago

DS wouldn't be 10x cheaper than the subsidized subscription plans from openai/anthropic. Although it is of course much cheaper than the enterprier/API pricing- I think if you're on the subscription plans, you can't beat that on performance per price.

abirch

29 minutes ago

Too Cheap to Meter reminds me of the promise of Nuclear Power in 1954

"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.

etatester

19 minutes ago

It's really quite unfortunate that the promise was not delivered, mostly for political reasons. I hope that a new wave of reactors and the dire need for clean energy restarts the nuclear race.

bryanlarsen

2 hours ago

> NVIDIA will still boom

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.

npilk

2 hours ago

There seems to be a mistake in the cost comparison between 2025 and 2026. The 2025 chart axis is the cost to run the entire "intelligence index", and the 2026 version is a weighted average cost per task.

I don't disagree with the thesis here, I just don't think costs are coming down quite that quickly.

foob

an hour ago

A much deeper analysis on the falling price per task was published yesterday by Epoch AI [1]. It's a real statistical analysis and comes to more defensible and grounded conclusions. The headline takeaway is:

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

empw

an hour ago

GPU case doesn't seem that strong? The graph is impossible for me to reason about at least. You could draw basically any trend line through that GPU graph and it would look equally plausible to me. The main takeaway I get is that the NVidia H100 from four whole years ago is barely different in efficiency from the state of the art, which is surprising to me, and seems to indicate the exact opposite of what the article says.

rwolf

2 hours ago

for the first chart (sourced from https://epoch.ai/data/machine-learning-hardware?view=graph&y...), what is the audience supposed to think about that trend line? there's a step after you slap a regression on some points where you evaluate whether there's a real trend or noise, right? i don't see that either in the article or the linked source.

hermitcrab

2 hours ago

It is hard to see how the environmental side effects of this aren't going to be somewhere between bad and disastrous.

empath75

2 hours ago

I think this is a case where just drawing a "line goes up" extrapolation is incredibly misleading because there is _tremendous_ economic pressure to get costs down, and costs are very tightly tied to energy use. All of these systems are incredibly inefficient right now and have a lot of room to go down in energy use. I'd guess that the absolute _floor_ is burning model weights directly to silicon and that's like a 90+% reduction in energy use.

xienze

2 hours ago

No it'll be fine as long as you do your part and not drive a car, or have AC, or eat meat, or have children, or live in detached housing, or...

simianwords

23 minutes ago

We have people suggesting that ai is so costly to run that all labs are secretly subsidising tokens and we can expect a reprice soon.

Then we have these articles that say tokens will get so cheap that labs won’t know how to make profit.

Who is correct?

jackb4040

13 minutes ago

They're not secretly subsidizing, they're openly subsidizing.

Token pricing was a small minority of customers up until this year, when all the labs started trying to force customers onto token-based billing. Within the last week, Anthropic repriced my team's plan from a temporary "50% extra tokens" to 25%: https://support.claude.com/en/articles/15910845-claude-code-...

The fact that all this is ongoing within such a short timeframe should make you suspicious of any analysis that claims to be observing "statistical trends" like they've discovered a new Moore's Law out of 6 months of pricing data from 2 companies.

automatic6131

2 hours ago

>We are likely to see LLMs running locally at current frontier-quality on commodity hardware in the next 3-6 years

Yeah okay bud, anyone checked in with the state of consumer hardware recently? Not the author, evidently.

>oh in 3-6 years this will all be over

Yeah I'm sure Samsung, Nvidia and sk hynix will all be very calm with lower volumes and lower margins.

sanderjd

26 minutes ago

Seems like 3-6 years is a pretty conservative timeline to me. Memory and interview chip production could ramp up massively in that amount of time.

samuelknight

2 hours ago

The article observes that the cost of frontier intelligence from 2025 has fallen 100x in the last year. It also notes that the energy to run models is also collapsing. Consumer hardware is borked right now because these new algorithms are revolutionizing the utility of a computer. Computing is technology who's cost has been collapsing for 90 years, and its a safe prediction that it will decrease again.

api

2 hours ago

> Yeah okay bud, anyone checked in with the state of consumer hardware recently? Not the author, evidently.

RAM prices will crash when demand drops even a little. They'll probably crash to a lower (inflation adjusted) level than before. This has happened before.

Industrial scaling in general often looks like a sawtooth: price spike, capacity investment, crash, repeat.

Part of what's keeping prices high a little longer is that everyone knows this and is a little reluctant to plow resources into chip fabs for fear of having the bottom fall out before they recoup or sell that to someone else to hold that bag.

Graph the average compute and RAM in a mid-high end laptop at an inflation adjusted price point for the past 40 years. It's very exponential and hasn't slowed down much.

yxhuvud

2 hours ago

No. Prices will crash when supply side expands to meet the increased demand. Because demand won't go down to pre-bubble times any time soon. Unfortunately the supply side has been very slow in increasing production, partly because most steps of the production chain are all maxed out.

On a long enough scale you are right that prices will likely normalize to a better level, but before 2030? That would mean the factories are built quickly once they begin.

bryanlarsen

2 hours ago

> everyone knows this and is a little reluctant to plow resources into chip fabs

except cxmt who is plowing resources in like crazy

csoups14

an hour ago

That has more to do with geopolitics than it does the current price of memory.

api

2 hours ago

This is the core of my belief that data center construction is a huge bubble.

AI is not a bubble, IMO, though we may see a retrench and some companies with sky-high valuations will crash to more reasonable ones. But data center demand is probably a bubble, and the main driver will be reduction in the actual amount of power and data center space required to serve escalating demand.

I think hardware and model improvements will pace or maybe outrun demand and then when demand starts to saturate will keep going and leave a lot of orphaned data centers.

preommr

an hour ago

> AI is not a bubble

When people say "AI is a bubble", they mean economically as a whole, which includes data centers.

Perhaps we need better terminology for "product useful; numbers nonsensical"

bryanlarsen

2 hours ago

Jevon's paradox says that if data centers can serve a lot more tokens per dollar or watt there will be increased demand for data centers.

automatic6131

2 hours ago

Jevon's paradox isn't a physical law, it doesn't magically apply to everything. Millions more copies of Atari's ET game didn't cause everyone to pickup a cheap copy, and cause extra demand for a garbage video game. Some times (actually, usually, I'd argue) things are made that will sell for less than the cost of construction because of irrationality, and they don't induce extra demand and they don't change the negative profit margins.

You can't simply wave Jevon's paradox at things. Thousands of miles of canals were dug in the UK that couldn't be sustained and were abandoned. Thousands of miles of railways were laid that could be sustained and were abandoned. And those are potentially durable investments, unlike cheap walls, pillars and roofs laid over a levelled concrete slab full of fast depreciating IT equipment.

jackb4040

7 minutes ago

> You can't simply wave Jevon's paradox at things

I'm so glad the tide here is turning on this talking point, brought on by exactly the same people beating us over the head with it for months while no progress is made towards it materializing.

Many, many people who post here are capable neither of real analysis nor distinguishing real analysis from memes. They aren't hackers, they are adherents of a cult that happens to focus on the same subject matter as hackers.

bryanlarsen

an hour ago

It's true that Jevon's paradox doesn't always apply, although this does seem like a classic case.

But yes, if sold for a negative margin Jevon eventually stops because the decreasing supply will drive up prices.

> things are made that will sell for less than the cost of construction

Price is set at the marginal cost. Capital costs aren't in marginal costs.

You'll need a better counter-example than UK railways which suffered from Parliament price-fixing.