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.
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.
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
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.
The very exponential that you are relying on to give you runaway improvement is also giving exponentially increasing data to your competitors. All else being equal your competitors stay a step behind but you never develop a monopoly either. That's the best case for Anthropic/OpenAI. In reality, training data is just one variable, exponentials don't last forever, and your competitors will get better at capturing a bigger slice of training data.
a) indicative of problems that aren't already out there in the wild? (no) b) are the responses I'm getting so good and novel that the model can improve itself? (no)
It's the garbage in garbage out idea, just scaled up. If the model gave a bad answer, and I didn't catch it, and you now train on that I/O pair (my perhaps crappy prompt, the bad output), then you're not going to improve anything.
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.
When Xi Jinping did the announcement of their open weights push, they might as well cancelled their IPOs....
I close-out all my positions by end-of-trading everyday… so when the day came when there was a very clear and very scary indicator during early trading hours, quickly followed by SpaceX’s catastrophic fall right after opening bell, that was the end of my involvement….
And I fully expect oAI and anthro to be the same way. They’re being propped up with private loans, subsidies, and other tricky bookkeeping techniques. You would think their CEOs would pivot away from their current public personas. Ironically, they are like a poor man’s Elon Musk… and that doesn’t bode well for their companies
Second, humongous models are impractical even for them to deploy widely. They’re best used as teachers for smaller, more efficient models that can crank out the volume they need to sell.
Finally, there is a data wall. Sure, they can keep scaling RL on math problems and code. But with everything else, where will the supervision come from when they need several orders of magnitude more?
That's assuming the infrastructure needed to develop models stays available financially and supply wise. A lot of the services used to train and develop models are supplied and funded by people who are looking for multiple returns of investment. If/when OpenAI and Anthropic valuations fall and they inevitably get acquired, will Meta/Alphabet/Microsoft still want to spend lots of money for unclear returns in the short-term? Nvidia and co are on a one way train service to hype town. I don't think they will be happy to get on a coach to hype town Temu version. The shareholders likely won't.
Also, the backlash against LLMs is growing rapidly. AI content, data centres, etc is quickly gaining negative connotations outside of visual and music artists circles. While existing models are going nowhere, developing more advanced models is very quickly getting unpopular. LLMs Data centres increasing people's bills, Anthropic destroying old books, chat bots giving unethical advice to vulnerable people, etc. It won't be long before LLM infrastructure becoming an electoral issue.
Will a small research oriented community be big enough justify maintaining the apparatus needed to produce infra tech at a profitable level post OpenAI?
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
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.)
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.
It is just a dedicated computer system, which should be managed by its owner, like any other on-prem servers.
I doubt that it has a good price/performance ratio, but it is a solution for those who feel that they do not want to search, buy, assemble, install and configure every HW/SW component.
I'm under no NDA, if you actually want to know what's up.
For a lot of people (and orgs I'd guess) who just go and buy ≈$20 per month plans (or more for teams), they might not even need a fraction of that cost or capability. A lot of them don't even need it for coding or graphics. Even the API access based pricing aren't great from these frontier US AI houses. The distribution of "LLM being" offered will also give rise to many open-router like offering but at the end point level - direct interfaces to the customers. Pick your vendor sort.
AI shouldn't become another "search means Google".
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.
All boils down to short-term/long-term thinking.
For our own model training we needed to do some large scale translation tasks of a large dataset (1M or so documents, 10 or so target languages), running full-size NLLB on-prem saved us an absurd amount of money vs Google Translate API.
(For reference doing 1M target docs into a single language in Google Translate API is roughly $120k list price. You can run full size NLLB on an 48GB NVIDIA A600 and the major difference for us was speed, but for this task time to completion wasn’t an issue.)
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.
Trump keeps calling his enemies “communists”… then turns around and ‘seizes the means of production’ himself.
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?
There’s an assumption that you can spin up the infra and acquire customers within that margin
It's been a few years. Has anyone done this successfully yet?
Only Nvidia and approved friends can at the moment. Nvidia can even backstop your loan required.
Market is irrational.
- 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.
Provoking war, this is how the empire "defends" itself, usually.
I just hope that this time it will get stuck in your throat.