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Nvidia’s $589B DeepSeek rout

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Re: Nvidia’s $589B DeepSeek rout

#951

Post by 00N8 »

esafak wrote: post #626
yo-cuddles wrote: post #448

Earlier quoted context omitted.

Probably not. If the price of Nvidia is dropping, it's because investors see a world where Nvidia hardware is less valuable, probably because it will be used less. You can't do the distill/magnify cycle like you do with alphago. LLM models have basically stalled in their base capabilities, pre training is basically over at this point, so the news arms race will be over marginal capability gains and (mostly) making th…

So you're saying we're close to AGI? Because the game doesn't stop until we get there.

I think LLMs are getting us closer to AGI in the same way that Madame Tussauds wax museum got us closer to human cloning

Re: Nvidia’s $589B DeepSeek rout

#952

Post by bdelmas »

sek wrote: post #781
eigenvalue wrote: post #567

90% of the comments in this thread make it clear that knowing about technology does not in any way qualify someone to think correctly about markets and equity valuations.

The crash is absolutely rational; the cascading effect highlights the missing moat for companies like OpenAI. Without a moat, no investor will provide these companies with the billions that fueled most of the demand. This demand was essential for NVIDIA to squeeze such companies with incredible profit margins. NVIDIA was overvalued before, and this correction is entirely justified. The larger impact of DeepSeek is mo…

The crash of NVIDIA is not about the moat of OpenAI.

But because DeepSeek was able to cut training costs from billions to millions (and with even better performance). This means cheaper training but it also proves that OpenAI was not at the cutting edge of what was possible in training algorithms and that there are still huge gaps and disruptions possible in this area. So there is a lot less need to scale by pumping more and more GPUs but instead to invest in research that can cut down the cost. More gaps mean more possibility to cut costs and less of a need to buy GPUs to scale in terms of model quality.

For NVIDIA that means that all the GPUs of today are good enough for a long time and people will invest a lot less in them and a lot more in research like this to cut costs. (But I am sure they will be fine)

Re: Nvidia’s $589B DeepSeek rout

#953

Post by derangedHorse »

nindalf wrote: post #933
rhubarbtree wrote: post #749

Earlier quoted context omitted.

I think that’s unfair unless you give specific examples and clear evidence he’s wrong. I disagree with PG on economics and politics, but much of his writing on that is subjective.

Happy to. Here's PG misrepresenting a wealth tax - https://nindalf.com/posts/wealth-tax/

> No one is proposing a tax that would apply to all your assets

Yes they are, that’s literally what a wealth tax is.

How were you defining wealth?

Re: Nvidia’s $589B DeepSeek rout

#954

Post by bdelmas »

aurareturn wrote: post #608
eigenvalue wrote: post #567

90% of the comments in this thread make it clear that knowing about technology does not in any way qualify someone to think correctly about markets and equity valuations.

I think you’re wrong and Wallstreet got Deepseek’s impact wrong. You say DeepSeek should decrease Nvidia demand. Wallstreet agreed today. I say DeepSeek should increase Nvidia’s demand due to Jevon’s Paradox.

I agree but in the short/medium term, I think it will slow down because companies now will prefer to invest in research to optimize (training) costs rather than those very expensive GPUs. Only when the scientific community will reach the edge of what is possible in terms of optimization that it will be back at pumping GPUs like today. (Although small actors will continue to pump GPUs since they do not have the best talents to compete).

Re: Nvidia’s $589B DeepSeek rout

#955

Post by gilbetron »

sghiassy wrote: post #917

I feel there’s a gap missing in this thread (or I may be the one missing it) DeepSeek proved knowledge distillation works very well and cheaply https://en.m.wikipedia.org/wiki/Knowledge_distillation But they didn’t show how to build a new frontier model cheaply. So, you still need massive investments to build new frontier models. But the bad part, is they can be replicated cheaply

I think you are missing it:

https://stratechery.com/2025/deepseek-faq/

That has a great overview - this is a new model, but also a distillation. They used new techniques to make it really cheap (comparatively).

Re: Nvidia’s $589B DeepSeek rout

#956

Post by coryfklein »

See the 2-year NVDA chart[0] for some perspective.

* Price has dropped to the level it was at four months ago in October

* The 1-year is up 142%

* The 2-year is up 530%

If you're just going by the market's assessment, this is not a "crash" and the "game has not changed".

[0] https://www.tradingview.com/x/b42zFn05/

Re: Nvidia’s $589B DeepSeek rout

#957

Post by snake_doc »

optimiz3 wrote: post #697
snake_doc wrote: post #669

Earlier quoted context omitted.

The other way is certainly also true. Your short piece is rational, but lacks insight into the inference and training dynamics of ML adoption unconstrained. The rate of ML progress is spectacularly compute constrained today. Every step in today’s scaling program is setup to de-risked the next scale up, because the opportunity cost of compute is so high. If the opportunity cost of compute is not so high, you can skip…

Everyone can say things that sound smart. When it comes to markets the only thing that matters is if your portfolio was green or red.

No, the only thing that matters is if the portfolio delivers returns in excess of your cost of capital.

If your portfolio is green, you can still be a poor performer.

Re: Nvidia’s $589B DeepSeek rout

#958

Post by grazing_fields »

energy123 wrote: post #391
paxys wrote: post #383

Earlier quoted context omitted.

Has anyone verified DeepSeek's claims about R1? They have literally published one single paper and it has been out for a week. Nothing about what they did changed Nvidia's fundamentals. In fact there was no additional news over the weekend or today morning. The entire market movement is because of a single statement by DeepSeek's CEO from over a week ago. People sold because other people sold. This is exactly how a p…

They have not verified the claims but those claims are not a "vague rumor". Expectations of discounted cash flows, which is primarily what drives large cap stock prices, operates on probability, not strange notions of "we must be absolutely certain that something is true". A credible lab making a credible claim to massive efficiency improvements is a credible threat to Nvidia's future earnings. Hence the stock got so…

[dead]

Re: Nvidia’s $589B DeepSeek rout

#959

Post by nailer »

daveguy wrote: post #932
nailer wrote: post #873

Earlier quoted context omitted.

> Credit card companies averaged over 22,000 transactions per second in 2023 without ever having to raise the fee. Oh good because the fee is insanely high. > How many is crypto even capable of processing? 1M a second including voting transactions, divide by 4 for non-voting TPS. https://youtu.be/8sl3RcN2Rdk?si=saRTd-fQqG1-L_kb Transaction fee for a simple transfer is a fraction of a penny. I’m not sure if your last…

Most money and most transactions aren't in Solana. Solana processes ~15B /day. Credit card companies, 15T. Solana has only ever reached 2k/s https://capitaloneshopping.com/research/number-of-credit-car... Come on now, you know I was referring to consumer fraud. And there is no protection, unless you go off chain . Transaction fees in the most common coin, Bitcoin, vary wildly. The spike rose to over $100 at one point…

Yes, you are right. The traditional financial system is indeed more popular than crypto. I’m not sure what your point is, but yes, I absolutely agree.

Obviously, that has no effect of the capacity of crypto to take over the volume of existing financial transactions and largely replace existing middle men.

Random old tech from 2015 also had wildly fluctuating transaction fees. Likewise I can’t run call of duty on my ZX spectrum. I’m not sure what your point is there either, but yes, I agree. Obviously old tech being old doesn’t affect the capabilities of new tech, and the vast majority of payments are done on Solana rather than these old networks.

> Come on now, you know I was referring to consumer fraud

No I didn’t. But it was late.

My point, that crypto already has the capacity and the low fees remains unscarred.

Re: Nvidia’s $589B DeepSeek rout

#960

Post by yo-cuddles »

Jlagreen wrote: post #751
yo-cuddles wrote: post #448

Earlier quoted context omitted.

Probably not. If the price of Nvidia is dropping, it's because investors see a world where Nvidia hardware is less valuable, probably because it will be used less. You can't do the distill/magnify cycle like you do with alphago. LLM models have basically stalled in their base capabilities, pre training is basically over at this point, so the news arms race will be over marginal capability gains and (mostly) making th…

Your implication is that we have unlimited compute and therefore know that LLMs are stalled. Have you considered that compute might be the reason why LLMs are stalled at the moment? What made LLMs possible in the first place? Right, compute! Transformer Model is 8 years old, technically GPT4 could have been released 5 years ago. What stopped it? Simple, the compute being way too low. Nvidia has improved compute by 10…

The age of pre-training is basically over, I think everyone acknowledged this and it's not to do with not having a big enough cluster. The bull argument on AI is that inference time scaling will pull us to the next step

Except o3 benchmarks are, seemingly, pretty solid evidence that leaving LLM'S on for the better part of a day and spending a million dollars gets you... Nothing. Passing a basic logic test using brute force methods and which falls apart on a marginally easier test that it just wasn't trained on.

The returns on computer and data seem to be diminishing with more and more exponential increases in inputs returning geometric increases in quality, and we're out of quality training data so that is now much worse even if the scaling wasn't plateauing.

All this, and the scale that got us this far seems to have done nothing to give us real intelligence, there's no planning or real reasoning and this is demonstrated every time it tries to do something out of distribution, or even in distribution but just complicated. Even if we got another crank or two out of this, we're still at the bottom of the mountain here. We haven't started and we're already out of gas

Scale doesn't fix this any more than building a mile tall fence stops the next break in. If it was going to work we would have seen to work already. LLM's don't have much juice left in the squeeze, imo

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