Comment by MangoCoffee
3 days ago
OpenAI and Anthropic are both seeking trillion IPOs, while Chinese labs are pumping out open-weight models that are free for US providers to host and monetize.
These Chinese models cost less of US SOTA models to run, even if they are less capable. Providers can just run them, offer cheap tokens, and pocket the margin.
I just don't see how you justify a trillion valuation for US AI labs when the underlying models are being commoditized this fast.
This is going to be catastrophic.
Whether AI works or is useful or not isn’t even the question anymore. It can fulfil every promise Sam Altman has been making and will still make no financial sense to justify these valuations.
I take it from [1] (transcript of recent DeepSeek CEO discussion with investors) that DeepSeek would disagree on the immediate catastrophic impact to the likes of OpenAI or Anthropic. The reason is even though technology parity mostly exists, only OpenAI, Anthropic et al have the inference capacity to gain market share and generate revenue. Chinese vendors don't have the chips needed to scale up inference and gain market share, and the DeepSeek CEO doesn't think this would happen in optimistic circumstances in the next 3 years, but thinks it might be possible in 5 years.
In summary, regardless of country of origin, availability of inference capacity is the moat protecting the likes of OpenAI and Anthropic, not technology superiority.
[1] https://www.fredgao.com/p/deepseeks-liang-wenfeng-breaks-his
That merely pushes the valuation onto the hardware makers, not the companies that have the temporary preferential access to their hardware.
That makes them at best temporary middlemen.
It only justifies their long term valuations if they can leverage that temporary monopoly for technological superiority (they can't) or lasting market share (they can't).
Chinese models prove there's no technical advantage, and the software side is heavily commoditized so there's not much advantages to market share either.
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I would add that it is not just capacity, but also negotiation ability. With scale comes the ability to negotiate better prices than everyone else. Even if you can find capacity for your smallish user base, your inference cost can not match these companies unless you have a technical advantage for your inference cases. Squeezing the hardware requires request batching and caching which are far easier at scale and sustained user activity.
Export controls have highly motivated China to figure out how to make state of the art chips entirely in country.
It’ll certainly take years but I would not bet against China’s ability to manufacture something.
Thanks for sharing.
Is lack of inference chips due to the trading blocks by trump administration? What if Trump agrees to sell chips to china, would they collapse then? That's not a very strong position to be at
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is that releveant if people can host their own models? that activity still undermines the valuation / diminishes the US companies 'moat' ?
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they have the capacity via market manipulation; so you know, they only have things they've bought on the governments future debt obligations.
so, you know, they're as vulnerable as utilities at this point, if only there were people who gave a shit more about society than greed.
I have already begun winding down my spend on claude and OAI to make room for infra budget. Anecdotal, but I have no doubt a lot of others are doing the same, I very much agree the US players have major issues looming. What an exciting time to be alive!
Not exciting for anyone directly or indirectly invested in a frontier lab or its partners. And that is a lot of people, including you.
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The car industry is also a trillion $$ market in the US. I don't see why that would go any differently from the Chinese cars ban.
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Why is anything going to be catastrophic? Companies can go bankrupt without catastrophes for the rest of us. Happens all the time.
I read it as catastrophic for the companies trying to IPO. It'll be great for the rest of us though.
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> Why is anything going to be catastrophic?
Many believe, including myself, that the market is currently propped by a massive AI bubble. Nearly a US $1 trillion is being spent this year, and more is planned for next year. All of this is for a "build up". There is no pay out. The major AI companies are taking in massive losses in the hopes that they will eventually be able to cash out.
The math is not looking good to me. The effect will be like the dotcom bubble. But much much bigger. Because the numbers are so much bigger.
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The US will just do what they did with Chinese EVs: ban the superior technology to protect US companies.
a lot harder to ban software than hardware the size of EVs
I seriously need to start considering the scenario in which this leads to next global financial crisis.
Just keep in mind that it can take a whole for things to play out. I’m someone who believe the US AI industry is completely unsustainable and built on sand, and will crash even if the current AI itself turns out to be very successful. But that doesn’t mean everything will burn to the ground next week. In a history book things will look very sudden but at normal speed that can easily take months to years to fully play out.
Also, take in consideration that the AI trade infected a lot of other trade in the economy, if you decide at some point to move your money to a place that is safe in case of a downturn be sure to carefully evaluate that’s actually the case
These crises are manufactured by the central banks.
Compare and contrast how the dot-com bust did _not_ lead to global financial crises. Nor did Black Monday, nor the recent string of bank failures in the US.
('Manufactured' above means that central banks are responsible. I make no judgement on intent here. Around 2008 it was incompetence by the Fed and ECB as far as I can tell. The Fed started paying interest on excess reserves and the ECB even increased rates. Twice. Amongst quite a few other missteps.)
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A lot of the performance of these open source models might come from distilling the closed frontier models. If those can't raise the funds anymore to train newer and better models then the whole improvement cycle might slow down.
Does stealing from a thief still amount to theft?
Another interesting potential market here will be 'LLM in a box'. All the hardware and other tooling in a prebuilt, but modular, package ready to go. Pay one up-front cost, get a system running [whatever open LLM] with a token rate of [x], optionally configured to be immediately ready for distributed usage. Basically the opposite of cloud stuff: no rent, no dependency, 100% guaranteed uptime, guaranteed security/privacy (at least subject to your own actions), and so on.
Palantir already offers a "turnkey AI datacenter", i.e. a rack with "NVIDIA Blackwell Ultra systems with eight NVIDIA Blackwell Ultra GPUs and NVIDIA Spectrum-X™ Ethernet networking for AI training and inference".
It is said that it comes with all hardware and software required to run inference or training with an open weights LLM.
The existence of this product, which competes with cloud-based offerings like those of OpenAI and Anthropic, is presumably the reason why the Palantir CEO criticized very harshly some time ago the business model of OpenAI/Anthropic.
While I doubt that the ethics of Palantir is any better than of OpenAI/Anthropic, in this particular case I have to agree with Alex Karp about "Sovereign AI", i.e. that only losers will make their business completely dependent on an external entity like OpenAI or Anthropic, who are certainly not trustworthy.
I'm not sure a data center run by ... Palantir of all organizations is what people have in mind when they worry about data sovereignty.
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How is this different from buying a supermicro rack? Better support?
“100% guaranteed downtime when you least can afford it and the support tickets are your problem.”
We’ve a hybrid shop, including hosting our own ML infra, and we save a ton from cloud spend with local ML. Easily one million USD over past three years. But it’s not “free”, you are shifting a lot of labor into your plate.
And with that also gain institutional knowledge, skill up your workers and attract talent that wants to work on this stuff.
All boils down to short-term/long-term thinking.
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> 100% guaranteed uptime
Disagree there but I think this is an interesting idea. We would need to find some more cost-efficient hardware to run it on than Nvidia GPUs.
It will come... all big hardware players (Intel, AMD, Broadcom) and dozens of startups (Tenstorrent, etc.) are working on it...
Exactly! As I've argued here on HN before, such an "LLM in a box" might end up being serviced/upgraded once or twice a year by a company very similar to the one servicing the coffee machine at the office. In contrast to databases, storage, etc. it doesn't matter much if the box breaks at some point – they'll just come by and replace it with a new one – and there's barely any software on the box to speak of, at least none that requires continuous development and feature upgrades, beyond rolling out security patches. This makes the business case drastically different from cloud and SaaS offerings, where most of the moat is in the software and the state maintenance (and the vendor lock-in of course). The LLM in a box is destined to become a commodity.
What makes that kinda complicated is that multi-user throughput of LLMs scale well but single-user performance often stays constant at low ends. If you could saturate e.g. 16 concurrent session-month of demand, you can just go buy 16 of 32GB GPUs and start charging monthly for inference. That could work if you had e.g. over thousand total employees with hundreds of devs eager to trying it out, but only if the company is also interested in a private inference experiment.
You're talking about multi-session vs. single-session throughput. A single user can easily leverage multiple sessions via e.g. subagent swarms, especially on a lower-end setup where any single session is going to be quite slow. Saturating utilization during off-hours is harder but potentially quite feasible by assigning lower priority, unattended tasks/inference loops.
so something like this? https://tinygrad.org/#tinybox
I see ads for this all the time.
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I think at this point the question is: will the US government be willing and capable to justify the trillion dollar valuation for _one_ of the companies via regulatory capture? The US has a workforce of 170m, so 1.7 trillion would come down to 10k per person, or a discounted cashflow at 3% of 25 USD per month - not including private use, students etc.
Why would you restrict to the US workforce? ChatGPT has a billion users.
Because it would be the US taxpayers bailing them out.
It’s a common denominator if you want to do napkin-math for a whole national economy. Regulatory capture is like a tax on those people not on the beneficiary side, so if the government were to nationalize both supply (no export license for SOTA models) and demand (no foreign or self-hosted LLMs allowed), they’d end up making everyone else pay for it in some way or the other. The governmental utility function will then include only those using the services for direct economic benefit.
They have a stupid plan to buy ten to fifty percent of all the SOTA AI companies, and giving us all a fraction of the money.
Trump keeps calling his enemies “communists”… then turns around and ‘seizes the means of production’ himself.
It is impossible to justify the absurd private valuations they have given themselves in collusion with investors.
I wish they had tried to IPO because then we’d see the judgement of the market on this. But that’s why they didn’t this year. How long can they keep up the charade that their models are uniquely valuable and on the path to AGI?
> private valuations they have given themselves in collusion with investors.
What's the collusion?
Circular investment deals and investment deals at valuations which have no possible justification.
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US investors are desperate for the next hypergrowth opportunity. From what I can tell the US economic strategy is to outgrow its debt.
> US investors are desperate for the next hypergrowth opportunity
All investors.
> Providers can just run them, offer cheap tokens, and pocket the margin.
There’s an assumption that you can spin up the infra and acquire customers within that margin
Which is not unreasonable. Just hosting it in the EU and promising not to retain / sell the data let's you charge a healthy extra and compete in many areas other players can't.
> Just hosting it in the EU and promising not to retain / sell the data let's you charge a healthy extra and compete in many areas other players can't.
It's been a few years. Has anyone done this successfully yet?
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Keeping SLAs spinning isn’t this trivial
> There’s an assumption that you can spin up the infra and acquire customers within that margin
Only Nvidia and approved friends can at the moment. Nvidia can even backstop your loan required.
There could be soon AI safety regulations that will stop the US to host or use the Chinese models.
i doubt alot folks are very reliant on Chinese models
Most are not necessarily free to host and monetize. At least one of them has a license that says if you are re-hosting the model then you need a license with that company that made the model.
As an aside, if one of them nabbed Federal procurement, it would likely hit the equivalent of a trillion in revenue after a century.
I think that explains the race for IPO by the US AI labs, they know that the longer they wait, the less they will be worth.
"I just don't see how you justify a trillion valuation for US AI"
- military applications - financial applications - medical - applied science
In all those cases it is achievable for those who have needed training data, and Chinese are not going to get them easily. US AI Labs are showing: give us the data, we will do wonders, promising "singularity"-level future achievements.
> I just don't see how you justify a trillion valuation for US AI labs
Market is irrational.
Ok
I'm sure US billionaires will find a way to extract those trillions from the public. They're smart, they can handle it. After all, they can ask AI for advice on how to do it.
I suggest you think why OpenAI was worth billions before ChatGPT. The valuation is not about how the current set of models can be monetized.
Could you just tell us why you think they were worth billions before ChatGPT, instead of suggesting that we think on it? You seem to know the answer already, so please share it with the class.
I did. It is based off of future models that can be created with the people there.
Hmm.. how you justify?
Provoking war, this is how the empire "defends" itself, usually.
I just hope that this time it will get stuck in your throat.