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Comment by jqpabc123 | Hacker News Reader

The current closed source frontier models are more capable than the latest from DeepSeek. But is the capability difference enough to justify a 30x price difference?

"Frontier models" are caught in a financial dilemma of their own making --- they have spent such huge sums on development and as a result, they may have inadvertently priced themselves out of the market.

Energy costs are a huge factor for AI. He who has the lowest energy costs will likely be able to dictate market prices. And fossil fuels dependence doesn't look to be advantageous for AI.

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Historically the winners in software have a flywheel that turns faster with more users. Facebook the more of your friends on it the better the product was. Google tracked how long users were on pages to improve search.

The frontier models are going to win that way. They won't feed your code back into the system but they will track which code you keep and what code gets a "try again claude".

They're not going to lose on price. No consumer software ever has because ultimately it's not that expensive relative to salary and the marginal cost is 0.

>They're not going to lose on price. No consumer software ever has

Lists examples of software that are free to the users

Go on...

httpd

I want AI to go the way of Linux. I hope we see that future.

The marginal cost of AI is not 0. That's one of the big differences between this and older SaaS software. Inference costs a lot of money. Even if you're looking at just capital depreciation, it's quite expensive. I suppose it's more accurate to say marginal cost is stepwise - adding 1 new user is 0 cost if and only if your existing inference hardware covers that user's usage. As soon as you need a new server, adding _that_ new user costs ~$20k/year (assuming 100k server and 5 year depreciation).

This is true for traditional SaaS too, but the number of concurrent users that could be served by one machine and the cost of the hardware were both at least an order of magnitude better.

The marginal cost of AI is not 0.

In other words, AI is not your daddy's software. Comparing AI with old school software markets simply does not compute.

It's not literally 0 but for most casual users it is. Programmers are heavier users and the ones that have 10 agents going wild are even more so. In that case the cost is above 0 but economies of scale means the effect is the same.

The marginal cost for _users_ (on a token plan) is $0 but not for the AI company supplying that token plan.

Exactly the CC sessions flywheel is a treasure trove of data and they all know that. The reason we went to stackoverflow was because there was data (upvotes/downvotes, comments, workarounds) discussed under the answers. That is a very high quality signal from the field.

> they may have inadvertently priced themselves out of the market.

Last week we were all talking about how Anthropic has too much demand, how they had to rent a data center from a competitor, and how the limits they’ve put on their service to deal with the demand are making users angry.

DeepSeek is cheap because they’re working hard to attract users.

The open weights models released for free weren’t free to train. It’s a loss leader to get attention to try to sell you something in the future.

The prices we pay for tokens right now are set by supply and demand, with some being sold at high premiums and others at a loss. Some models are given away for free after the companies spent money on researchers and compute.

Yes and no. Just take a look at the OpenRouter providers page:

https://openrouter.ai/deepseek/deepseek-v4-pro/providers

Deepseek v4 Pro is much cheaper when provided by Deepseek itself, likely as a combination of the loss leader strategy you mention and the desire to have more data flow through their pipeline for training. However, the same open weights model, provided by other providers, is somewhere in the $2-3/1M output-tokens range. Compare Opus 4.7 at $25/1M output-tokens.

Unless you mean that releasing open weights models is the loss leader, in which case, you might be right but I hope you're wrong. We've seen some of this from Qwen at least - their latest model is closed only. I hope there's always someone willing to make this bet and release better and better open models.

> I hope there's always someone willing to make this bet and release better and better open models.

What would this bet be? Training is expensive and open weights mean that for hosting you compete on price with people that don't have this item on their bill.

"Attention is all you need" - the larger bet is that by releasing your models open-weight, you'll get more attention and mindshare than if you tried to jump in to compete with the major closed providers, and the value of that attention will outweigh the cost of the training run.

So far, it's really only the Chinese labs (and FAIR or whatever Meta's project is called now) that are doing this. Oh yeah, and Google's Gemma.

At the moment, this is all massively distorted by the prestige and investment money flowing into the space. None of the labs have to charge the real cost of inference let alone the marginal cost of training because they are instead lighting investment money on fire to cover that.

One imagines (though I have not investigated in detail) that there's a degree of national prestige work going on too. The Chinese labs are trying to show that they can build better and more efficient models and are releasing open to undercut the US labs.

> Unless you mean that releasing open weights models is the loss leader, in which case, you might be right but I hope you're wrong.

This is specifically what I meant.

DeepSeek’s official service is trying to recoup some of the training and engineering costs too.

The other providers only have to recoup their hardware costs and the cost of a team to run it.

Even though DeepSeek’s official service is more expensive per token, they’re running at a lower profit than the OpenRouter providers because they had to pay for the R&D.

This is a deliberate choice. We already see it with Qwen splitting their releases between open weight and hosted only models. The open weights are a loss leader to get attention. Without them you’d almost never hear about their hosted models.

Except DeepSeek's official service is _less_ expensive per token, which suggests they're underpricing it substantially as well to attempt to draw more attention / more data.

> lowest energy costs will likely be able to dictate market prices

This is a good insight. I think everyone has seen that chart China's electricity generation going parabolic vs the US. That combined with cheaper yet equally good talent means at least in that segment, the closed labs won't catch up anytime soon

> the closed labs won't catch up anytime soon

Which closed labs won’t catch up to whom?

Not my comment, but I’d venture to guess they’re referring to the likes of DeepSeek et al, who are/will be able to host their top-tier inference infra more efficiently

right now the most likely outcome is that they are going to host locally produced much more power hungry chips, and even if the lead on electricity production will stay, it will be eaten by inefficiency of the hardware.

Unlikely. We have a big lead in terms of general computing devices, but China can leapfrog us with ASICs. They might still lag in the training space for a while but in terms of serving inference, USA is absolutely COOKED at the low-mid end.

I should have expanded, but basically, the OSS models becoming more and more capable to solve all day to day SWE coding needs will take a cut from frontier labs revenue.

Not to say that frontier labs won't make progress, but the bar for a sufficiently capable agent is all the OSS models need to meet to make this happen. I imagine a lot of hybrid setups where something like Opus is used only for planning/architecture, and anecdotally, the real token consuming part is implementation not architecture.

> China's electricity generation going parabolic

Even if we all switch to Chinese models, the west isn't going to be running the model on Chinese servers... and the majority of costs are from inference.

> cheaper yet equally good talent

China has tech talent, but this isn't a 3rd world developing nation. Chinese AI researchers are getting paid $10M+ USD/year salaries.

Also they're equally good, but somehow consistently behind?

Training models is as much art as science at this point. There's no gap in scientific acumen at Chinese labs, but the US has more real world experience in the art of training large models, and the US has the capital allocation lead.

Yes but when the Heads of CCP make something their target they chase it with all their might. Read the recent news of the fact that Chinese AI researchers can't leave China. China is now going after the Diamond industry of India.

Energy costs and privacy.

Currently the projects I am involved require devs to use approaches like Ollama, Foundry Local and co if they happen to have good enough hardware, picking the best alternatives out of https://www.canirun.ai.

I’ve been on this issue for a while now, models are not going to matter as much in the future. Pure energy cost will be the determining factor in who is most successful. The US just cannot build cheap energy the way other China can and at the scale that China will build it. 10 years from now it will be seen as the single source of advantage

You mean coal?

If the cost of software development falls so precipitously that energy costs are a driving factor, that implies so many other changes that I don't know how we can trust any analysis of what would happen.

> The US just cannot build cheap energy

Nuclear power anyone?

Cheap.

What is expensive in nuclear energy? Reason there is not more of nuclear reactors is not the cost, it is regulation. Regulation can be changed (it also seem to already have, recently, IIRC - starting 2024 NRC law changes by Biden admin and later by Trump admin)

Reason there is not more of nuclear reactors is not the cost, it is regulation.

The reason for regulation is that failure is not an option --- unless you're willing to accept the cost of making a big chunk of a state uninhabitable for a very long time.

How much would failure cost? The Chernobyl exclusion zone is over 1000 square miles --- about half the size of Delaware. And it is expected to remain uninhabitable for the next 20,000 years.

Also, the Russian Academy of Sciences estimates that up to 1 million people may suffer premature death as a result of radiation exposure and contamination from the event.

In the long run, renewable energy is a lot cheaper.

Same as bitcoin then.

> "Frontier models" are caught in a financial dilemma of their own making --- they have spent such huge sums on development and as a result, they may have inadvertently priced themselves out of the market.

I feel it'll wind up like the dotcom/fiber bubble. Way too much money poured into it, lots of expensive bankruptcies or write-offs, and a readjusted market sea level.

Absolutely. We are in a phase of "free money" for AI. Just as with the dotcom bubble that leads to 1) lots of experimentation, and 2) lots of infrastructure buildout (which includes AI model training). Once the money dries up, some infrastructure (including models) will turn out to be profitable, most won't. And some experiments will turn out to be successful, most won't. Lots of useful things will come out of that, both the failed and the successful attempts. Just as the dotcom boom payed real dividends 5-10 years later and laid the groundwork for the world we have today

I agree. I think when companies go bankrupt due to bubbles it's healthy for the economy at large. Capital gets redistributed to other employees and other companies that can build something valuable, and bad business people leave the market.

This sounds to me like the Bitcoin bros. Yes, the first-gen technology was very energy-heavy, but afterwards people (bitcoin maxis and people who held the bag) kept insisting that all new technology is “shitcoins” and that everyone should just buy bitcoin.

Actually, platforms that serve many customers can bring down the costs tremendously through caching, and don’t need the AI credits as much: https://safebots.ai/costs.html

Bitcoin is a poor analogue for much anything since it's very much designed to be energy-heavy.

Oh, and neural networks doing a huge number of floating point operations per word is not energy-heavy?

Training these neural networks every few months isn’t energy-heavy?

Both Bitcoin and these large models weren’t “designed to be energy-heavy”. It was a consequence of first-gen design decisions to solve a specific problem. Then as time went on, costs went down and they became a huge outlier in terms of energy. The question is whether the bagholders (the AI companies that invested untild amounts into the initial training) will fight to keep people using their tech and fearmonger about everything else.

Bitcoin is pretty much explicitly designed to use as much electricity as the market will allow, without becoming any more useful. If you removed 99% of the miners from the current system, Bitcoin will still be exactly the same - it won't be any faster or slower, and the same number of transactions will flow through. The cost of electricity serves only as a lower bound on the expected value of a coin.

Neural nets on the other hand generally show more capability as you add more compute power. There's a point where it's less valuable than the cost increase, so people don't do more than that, but it isn't constant value like Bitcoin.

It wouldn’t be exactly the same, because if you had all that mining capacity and 99% magically took a holiday, there is now enough mining power to take over the network anytime. It’s not secure.

Same with AI. Now that the Mythos and other models are finding exploits in every code base and anyone can run them, you can’t afford anymore not to keep burning credits securing your code base. It’s like proof of work red queen theory. You have to run faster and faster just to stay in place. Great business model.

Bitcoin is a good analog because the goal was to create durable trust. The energy utilization is just a means to an end of fairly distributing new tokens to members of the network. There are many other schemes they could use and have considered adopting. The energy use is not necessary, it’s sufficient.

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