US chip-maker Nvidia led a rout in tech stocks Monday after the emergence of a low-cost Chinese generative AI model that could threaten US dominance in the fast-growing industry.
The chatbot developed by DeepSeek, a startup based in the eastern Chinese city of Hangzhou, has apparently shown the ability to match the capacity of US AI pace-setters for a fraction of the investments made by American companies.
Shares in Nvidia, whose semiconductors power the AI industry, fell more than 15 percent in midday deals on Wall Street, erasing more than $500 billion of its market value.
The tech-rich Nasdaq index fell more than three percent.
AI players Microsoft and Google parent Alphabet were firmly in the red while Meta bucked the trend to trade in the green.
DeepSeek, whose chatbot became the top-rated free application on Apple’s US App Store, said it spent only $5.6 million developing its model – peanuts when compared with the billions US tech giants have poured into AI.
US “tech dominance is being challenged by China,” said Kathleen Brooks, research director at trading platform XTB.
“The focus is now on whether China can do it better, quicker and more cost effectively than the US, and if they could win the AI race,” she said.
US venture capitalist Marc Andreessen has described DeepSeek’s emergence as a “Sputnik moment” – when the Soviet Union shocked Washington with its 1957 launch of a satellite into orbit.
As DeepSeek rattled markets, the startup on Monday said it was limiting the registration of new users due to “large-scale malicious attacks” on its services.
Meta and Microsoft are among the tech giants scheduled to report earnings later this week, offering opportunity for comment on the emergence of the Chinese company.
Shares in another US chip-maker, Broadcom, fell 16 percent while Dutch firm ASML, which makes the machines used to build semiconductors, saw its stock tumble 6.7 percent.
“Investors have been forced to reconsider the outlook for capital expenditure and valuations given the threat of discount Chinese AI models,” David Morrison, senior market analyst at Trade Nation.
“These appear to be as good, if not better, than US versions.”
Wall Street’s broad-based S&P 500 index shed 1.7 percent while the Dow was flat at midday.
In Europe, the Frankfurt and Paris stock exchanges closed in the red while London finish flat.
Asian stock markets mostly slid.
Just last week following his inauguration, Trump announced a $500 billion venture to build infrastructure for AI in the United States led by Japanese giant SoftBank and ChatGPT-maker OpenAI.
SoftBank tumbled more than eight percent in Tokyo on Monday while Japanese semiconductor firm Advantest was also down more than eight percent and Tokyo Electron off almost five percent.
I believe it would have lowerr operational costs assuming the models the only thing different and they target the same size. Deepseek does the “mixture of experts” approach which makes it use a subset of parameters thus making it faster / less computational.
That’s said I have a basic understanding of AI so maybe my understanding is flawed.
But the models that are posted right now don’t seem any smaller. The full precision model is positively humongous.
They found a way to train it faster. Fine. So they need fewer GPUs and can do it on slower ones that are much, much cheaper. I can see how Nvidia takes a hit on the training side.
But presumably the H100 is still faster than the H800s they used for this and presumably running the resulting model is still just as hard. All the improvements seem like they’re on the training side.
Granted, I don’t understand what they did and will have to go fishing for experts walking through it in more detail. I still haven’t been able to run it myself, etiher, maybe it’s still large but runs lighter on processing and that’s noticeable. I just haven’t seen any measurements of that side of things yet. All the coverage is about how cheap the training was on H800s.
They aren’t necessarily smaller from my understanding. Say it has 600B parameters, it more efficiently uses them. You ask it a programming question, it pulls 37B parameters most related to it and responds using those instead of processing all 600B.
Think of it like a model with specialized submodels that a specific one may provide the best answer, and uses it.
Gotcha. Doesn’t quite answer the running cost question on the face of it, though. Has anybody published any benchmarks with comparisons? All I see are qualitative benchmarks on the output and that mythical six million figure for the training, but I haven’t found anything to justify the “Nvidia is doomed” narrative yet.
Ah yeah I haven’t seen anything on that. That’ll be next weeks headlines probably lol
600B is comparable to things like Llama 3, but r1 is competing with openAI’s o1 model as a chain of thought model. How big that is is classified but its thought that chatGPT4 was already in the trillions and that o1 was a big step beyond that.
Hell, maybe that’s the one real outright advantage of this weird panic. Maybe OpenAI is forced to share technical specs of their models again instead of working on a “trust me bro, it’s too dangerous for you to know” basis.
Absolutely, the big American tech firms have gotten fat and lazy from their monopolies, actual competition will come as a shock to them.
But that’s the thing, there was actual competition. It’s not like they weren’t competing with each other.
They are freaking out because the competition is Chinese, specifically. I seriously doubt the read of this situation would be that the bottom fell out of AI if one of the usual broligarchs had come up with a cheaper process for training.
Did the US accidentally generate an incentive for that to happen in China by shoddily blocking tensor math accelerators but only the really fancy ones and only kinda sorta sometimes? Sure. But both the fearmongering being used to enforce those limitations and the absolute freakout they are currently having seems entirely disconnected from reality.
Maybe we can go back to treating this as computer science rather than an arms race for a while now.
I dont doubt that a part of it is that they are Chinese, but I think a big part of it is that they are willing to undercut the current players by 10x on price. That has scared the crap out of the “broligarchy” (great term) who are used to everything being cozy and not competing on price with each other, only as a method to drive non-tech companies out of markets.
They see what deepseek is doing as equivalent of what Amazon did in online sales or uber in taxis, an agressive underpricing in order to drive competion out the market.
Yeah, I don’t know. I wonder.
The monetization scheme on all these AI applications was always confusing to me. It was certainly not seeking to break even, if their ravenous requests for funding are to be believed. Nobody is really looking at these based on token pricing, beyond other entrepeneurs hoping to make derivative content they can monetize themselves downstream.
They all seem to want to replicate the Google approach of giving stuff for free forever until you have a monopoly and my impression was that it really wasn’t working. I still have that impression today, regardless of the cost per token on each platform.
So who knows. I’m also entirely capable of believing that all these idiots sincerely thought they were building the singularity and were complete geniuses and nobody but them could figure it out. I am making no assumptions at this point.