[SecretDreams]

> But inference costs are dropping dramatically over time, and that trend shows no signs of slowing. So even if a task costs $8 today thanks to VC subsidies, I can be reasonably confident that the same task will cost $8 or less without subsidies in the not-too-distant future.

I'd like to see this statement plotted against current trends in hardware prices ISO performance. Ram, for example, is not meaningfully better than it was 2 years ago, and yet is 3x the price.

I fail to see how costs can drop while valuations for all major hardware vendors continue to go up. I don't think the markets would price companies in this way if the thought all major hardware vendors were going to see margins shrink a la commodity like you've implied.

[santadays]

I've seen the following quote.

"The energy consumed per text prompt for Gemini Apps has been reduced by 33x over the past 12 months."

My thinking is that if Google can give away LLM usage (which is obviously subsidized) it can't be astronomically expensive, in the realm of what we are paying for ChatGPT. Google has their own TPUs and company culture oriented towards optimizing the energy usage/hardware costs.

I tend to agree with the grandparent on this, LLMs will get cheaper for what we have now level intelligence, and will get more expensive for SOTA models.

[lelanthran]

Google is a special case - ever since LLMs came out I've been pointing out that Google owns the entire vertical.

OpenAI, Anthropic, etc are in a race to the bottom, but because they don't own the vertical they are beholden to Nvidia (for chips), they obviously have less training data, they need constant influsx of cash just to stay in that race to the bottom, etc.

Google owns the entire stack - they don't need nvidia, they already have the data, they own the very important user-info via tracking, they have millions, if not billions, of emails on which to train, etc.

Google needs no one, not even VCs. Their costs must be a fraction of the costs of pure-LLM companies.

[viraptor]

> OpenAI, Anthropic, etc are in a race to the bottom

There's a bit of nuance hiding in the "etc". Openai and anthropic are still in a race for the top results. Minimax and GLM are in the race to the bottom while chasing good results - M2.1 is 10x cheaper than Sonnet for example, but practically fairly close in capabilities.

[lelanthran]

> There's a bit of nuance hiding in the "etc". Openai and anthropic are still in a race for the top results.

That's not what is usually meant by "race to the bottom", is it?

To clarify, in this context I mean that they are all in a race to be the lowest margin provider.

They re at the bottom of the value chain - they sell tokens.

It's like being an electricity provider: if you buy $100 or electricity and produce 100 widgets, which you sell for $1k each, that margin isn't captured by the provider.

That's what being at the bottom of the value chain means.

[viraptor]

I get what it means, but it doesn't look to me like they're trying that yet. They don't even care that people buy multiple highest level plans to rotate them every week, because they don't provide a high enough tier for the existing customers. I don't see any price war happening. We don't know what their real margins are, but I don't see the race there. What signs do you see that Anthropic and Openai are in the race to the bottom?

[lelanthran]

> I don't see any price war happening. What signs do you see that Anthropic and Openai are in the race to the bottom?

There doesn't need to be signs of a race (or a price-war),only signs of commodification; all you need is a lack of differentiation between providers for something to turn into a commodity.

When you're buying a commodity, there's no big difference between getting your commodity delivered by $PROVIDER_1 and getting your commodity delivered by $PROVIDER_2.

The models are all converging quality-wise. Right now the number of people who swear by OpenAI models are about the same as the number of people who swear by Anthropic models, which are about the same as the number of people who swear by Google's models, etc.

When you're selling a commodity, the only differentiation is in the customer experience.

Right now, sure, there's no price war, but right now almost everyone who is interested are playing with multiple models anyway. IOW, the target consumers are already treating LLMs as a commodity.

[flyinglizard]

Gmail has 1.8b active users, each with thousands of emails in their inbox. The number of emails they can train of is probably in the trillions.

[brokencode]

Email seems like not only a pretty terrible training data set, since most of it is marketing spam with dubious value, but also an invasion of privacy, since information could possibly leak about individuals via the model.

[palmotea]

> Email seems like not only a pretty terrible training data set, since most of it is marketing spam with dubious value

Google probably even has an advantage there: filter out everything except messages sent from valid gmail account to valid gmail account. If you do that you drop most of the spam and marketing, and have mostly human-to-human interactions. Then they have their spam filters.

[Terr_]

I'd upgrade that "probably" leak to "will absolutely" leak, albeit with some loss of fidelity.

Imagine industrial espionage where someone is asking the model to roleplay a fictional email exchange between named corporate figures in a particular company.

[SoftTalker]

> Google has ... company culture oriented towards optimizing the energy usage/hardware costs.

Google has a company culture of luring you in with freebies and then mining your data to sell ads.

[AdrianB1]

> if Google can give away LLM usage (which is obviously subsidized) it can't be astronomically expensive

There is a recent article by Linus Sebastian (LTT) talking about Youtube: it is almost impossible to support the cost to build a competitor because it is astronomically expensive (vs potential revenue)

[SecretDreams]

I do not disagree they will get cheaper, but I pointing out that none of this is being reflected in hardware pricing. You state LLMs are becoming more optimized (less expensive). I agree. This should have a knockon effect on hardware prices, but it is not. Where is the disconnect? Are hardware prices a lagging indicator? Is Nvidia still a 5 trillion dollar company if we see another 33x improvement in "energy consumed per text prompt"?

[zozbot234]

Jevon's paradox. As AI gets more efficient its potential scope expands further and the hardware it runs on becomes even more valuable.

BTW, the absolute lowest "energy consumed per logical operation" is achieved with so-called 'neuromorphic' hardware that's dog slow in latency terms but more than compensates with extreme throughput. (A bit like an even more extreme version of current NPU/TPUs.) That's the kind of hardware we should be using for AI training once power use for that workload is measured in gigawatts. Gaming-focused GPUs are better than your average CPU, but they're absolutely not the optimum.

[xpe]

> I fail to see how costs can drop while valuations for all major hardware vendors continue to go up. I don't think the markets would price companies in this way if the thought all major hardware vendors were going to see margins shrink a la commodity like you've implied.

This isn't hard to see. A company's overall profits are influenced – but not determined – by the per-unit economics. For example, increasing volume (quantity sold) at the same per-unit profit leads to more profits.

[PaulHoule]

It's not the hardware getting cheaper, it's that LLMs were developed when we really didn't understand how they worked, and there is still some room to improve the implementations, particularly do more with less RAM... And that's everything from doing more with fewer weights to things like FP16, not to mention if you can 2x the speed you can get twice as much done with the same RAM and all the other parts.

[SecretDreams]

Improvements in LLM efficiency should be driving hardware to get cheaper.

I agree with everything you've said, I'm just not seeing any material benefit to the statement as of now.

[sothatsit]

Inference costs falling 2x doesn’t decrease hardware prices when demand for tokens has increased 10x.

[PaulHoule]

It's the ratio. If revenue goes up 10x you can afford 10x more hardware if you can afford to do it all.

[hug]

> I'd like to see this statement plotted against current trends in hardware prices ISO performance.

Prices for who? The prices that are being paid by the big movers in the AI space, for hardware, aren't sticker price and never were.

The example you use in your comment, RAM, won't work: It's not 3x the price for OpenAI, since they already bought it all.

[mcphage]

> So even if a task costs $8 today thanks to VC subsidies, I can be reasonably confident that the same task will cost $8 or less without subsidies in the not-too-distant future.

The same task on the same LLM will cost $8 or less. But that's not what vendors will be selling, nor what users will be buying. They'll be buying the same task on a newer LLM. The results will be better, but the price will be higher than the same task on the original LLM.

[doctorpangloss]

> I fail to see how costs can drop while valuations for all major hardware vendors continue to go up.

yeah. valuations for hardware vendors have nothing to do with costs. valuations are a meaningless thing to integrate into your thinking about something objective like, will the retail costs of inference trend down (obviously yes)