It still rely on nvidia hardware why would it trigger a sell-off?

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There could be some trickery on the training side, i.e. maybe they spent way more than $6M to train it.

But it is clear that they did it without access to the infra that big tech has.

And on the run side, we can all verify how well it runs and people are also running it locally without internet access. There is no trickery there.

They are 20x cheaper than OpenAI if you run it on their servers and if you run it yourself, you only need a small investment in relatively affordable servers.

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I’m not sure. That’s a very static view of the context.

While china has an AI advantage due to wider adoption, less constraints and overall bigger market, the US has higher tech, and more funds.

OpenAI, Anthropic, MS and especially X will all be getting massive amounts of backing and will reverse engineer and adopt whatever advantages R1 had. Which while there are some it’s still not a full spectrum competitor.

I see the is as a small correction that the big players will take advantage of to buy stock, and then pump it with state funds, furthering the gap and ignoring the Chinese advances.

Regardless, Nvidia always wins. They sell the best shovels. In any scenario the world at large still doesn’t have their Nvidia cluster, think Africa, Oceania, South America, Europe, SEA who doesn’t necessarily align with Chinese interests, India. Plenty to go around.

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Extra funds are only useful if they can provide a competitive advantage.

Otherwise those investments will not have a positive ROI.

The case until now was built on the premise that US tech was years ahead and that AI had a strong moat due to high computer requirements for AI.

We now know that that isn't true.

If high compute enables a significant improvement in AI, then that old case could become true again. But the prospects of such a reality happening and staying just got a big hit.

I think we are in for a dot-com type bubble burst, but it will take a few weeks to see if that's gonna happen or not.

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Well there is also threats of huge tariffs on all TMSC chips entering the US. That seems like the actual fuel for the selloff.

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Maybe, but there is incentive to not let that happen, and I wouldn’t be surprised if “they” have unpublished tech that will be rushed out.

The ROI doesn’t matter, it wasn’t there yet it’s the potential for it. The Chinese AIs are also not there yet. The proposition is to reduce FTEs, regardless of cost, as long as cost is less.

While I see OpenAi and mostly startups and VC reliant companies taking a hit, Nvidia itself as the shovel maker will remain strong.

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And you should, generally we are amidst the internet world war

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That's becoming less true. The cost of inference has been rising with bigger models, and even more so with "reasoning models".

Regardless, at the scale of 100M users, big one-off costs start looking small.

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But I’d imagine any Chinese operator will handle scale much better? Or?

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Give that statement to maybe not super techy investors, and that could spook them into the sell-off.

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True, but training is one-off. And as you say, a factor 100x less costs with this new model. Therefore NVidia just saw 99% of their expected future demand for AI chips evaporate

It might also lead to 100x more power to train new models.

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if, on a modern gaming pc, you can get breakneck speeds of 5 tokens per second, then actually inference is quite energy intensive too. 5 per second of anything is very slow

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i can also run it on my old pentium from 3 decades ago. I'd have to swap 4MiB of weights in and out constantly, it will be very very slow, but it will work.

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I doubt that will be the case, and I'll explain why.

As mentioned in this article,

SFT (supervised fine-tuning), a standard step in AI development, involves training models on curated datasets to teach step-by-step reasoning, often referred to as chain-of-thought (CoT). It is considered essential for improving reasoning capabilities. DeepSeek challenged this assumption by skipping SFT entirely, opting instead to rely on reinforcement learning (RL) to train the model.
This bold move forced DeepSeek-R1 to develop independent reasoning abilities, avoiding the brittleness often introduced by prescriptive datasets.

This totally changes the way we think about AI training, which is why while OpenAI spent $100m on training GPT-4, running an expected 500,000 GPUs, DeepSeek used about 50,000, and likely spent that same roughly 10% of the cost.

So while operation, and even training, is now cheaper, it's also substantially less compute intensive to train models.

And not only is there less data than ever to train models on that won't cause them to get worse by regurgitating other worse quality AI-generated content, but even if additional datasets were scrapped entirely in favor of this new RL method, there's a point at which an LLM is simply good enough.

If you need to auto generate a corpo-speak email, you can already do that without many issues. Reformat notes or user input? Already possible. Classify tickets by type? Done. Write a silly poem? That's been possible since pre-ChatGPT. Summarize a webpage? The newest version of ChatGPT will probably do just as well as the last at that.

At a certain point, spending millions of dollars for a 1% performance improvement doesn't make sense when the existing model just already does what you need it to do.

I'm sure we'll see development, but I doubt we'll see a massive increase in training just because the cost to run and train the model has gone down.

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Maybe? Depends on what costs dominate operations. I imagine Chinese electricity is cheap but building new data centres is likely much cheaper % wise than countries like the US.

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Thank you. Sounds like good news.

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It still rely on nvidia hardware why would it trigger a sell-off?