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Nvidia’s $589B DeepSeek rout

• AlgorithmicTime 2y ago
  [dead]
• einrealist 2y ago
  Nice to see a pullback within this AI bubble. Wake me when I don't have to double-check the results of generative AI, even with DeepSeek.
  • lm28469 2y ago
    > Wake me when I don't have to double-check the results of generative AI, even with DeepSeek That's gonna be a looong nap
• nabla9 2y ago
  Nvidia -13% in Frankfurt stock market just now. Valuations of private unicorns like OpenAi and Anthropic must be in free fall. DeepSeek spends $6 million in old H800 hardware to develop open source model that overtakes ChatGPT. AI gets better, but profit margins sink with strong competition. Chinese AI startup DeepSeek overtakes ChatGPT on Apple App Store https://news.ycombinator.com/item?id=42839656 https://news.ycombinator.com/item?id=42839656 Edit: Nvidia now -15% in Frankfurt.
  • gostsamo 2y ago
    Consider that the chinese might be misrepresenting their costs. A newsletter was implying that they might do it to undermine the sanctions justifications. Agree that the AI bubble should pop though and the earlier, the better.
    • nabla9 2y ago
      Their model is open and they published paper describing it https://arxiv.org/pdf/2412.19437 https://arxiv.org/pdf/2412.19437 The can't be far off, or it would be noticed. Even if they are heavily government subsidized for energy and hardware, I don see how the cost of training in the US would be more than double.
      • imdsm 2y ago
        Let's wait for reproduction first
        • jredwards 2y ago
          Posted elsewhere: https://xyzlabs.substack.com/p/berkeley-researchers-replicate-deepseek https://xyzlabs.substack.com/p/berkeley-researchers-replicat...
      • KeplerBoy 2y ago
        People on the internet can lie. Especially when such a lie could cause the nasdaq to dip multiple percent points. Not saying they are lying, but there incentives.
        • BoredPositron 2y ago
          Huggingface is reproducing it live on their blog…
        • pizza 2y ago
          Much easier to identify the incentives of the people who just lost a lot of money who were betting on the idea that it was their money that was going to make artificial intelligence intelligent. Everyone’s already begun trying this recipe in-house. Either it works with much less compute, or it doesn’t. For instance, HKUST just did an experiment where small weak base models trained with DeepSeek’s method beat stronger small base models being trained with much more costly RL methods. Already this seems like it is enough to upend the low end models niche market, things like haiku and 4o-mini. Be really skeptical why the people who should be making tons of money by realizing actually it was all a mirage and that they can now get the real stuff for even cheaper, would spend so much effort shouting about this, in order to undercut their own profitability..
      • logicchains 2y ago
        They express their cost in terms of GPU hours, then convert that to USD based on market GPU rental rates, so it's not affected by subsidies. It's possible however they lied about GPU hours, but if that was the case an expert should be able to show they lied by working out how many flops are needed to train based on the amount of tokens they say they used vs the flops of the GPUs they say they used.
  • esperent 2y ago
    > DeepSeek spends $6 million in old H800 hardware to develop open source model that overtakes ChatGPT. DeepSeek claims that's what they spent. They're under a trade embargo, and if they had access to any more than that it would have been obtained illegally. They might be telling the truth, but let's wait until someone else replicates it before we fully accept it.
    • belter 2y ago
      They have a lot of H100: https://www.reddit.com/r/NVDA_Stock/comments/1iadc0s/evidence_that_h100_nvidia_gpus_are_in_china_dated/ https://www.reddit.com/r/NVDA_Stock/comments/1iadc0s/evidenc...
      • msoad 2y ago
        four GPUs are very convincing indeed! :D
        • belter 2y ago
          You missed the black unwrapped boxes in the background....
        • bayindirh 2y ago
          That's 8 (not 4), on a NVIDIA platform board to start with. You can't buy them as "GPU"s and integrate them to your system. NVIDIA sells your the platform (GPUs + platform board which includes switches and all the support infra), and you integrate that behemoth of a board to your server, as a single unit. So that open server and the wrapped ones at the back are more telling than it looks.
    • senko 2y ago
      There are already some (limited) reproductions that suggest they're not completely lying (ie that there are indeed perf benefits).
    • sekai 2y ago
      [flagged]
  • nolok 2y ago
    They were massively overhyped though, it feels more like a correction (and a partial one at that) than a fall.
    • benterix 2y ago
      Of course they are overhyped but in spite of this Altman is always asking for more money. And we know financially they are just burning money. So when someone finally brings a cheap but good model for the masses, this is where money should go. (This will also help all small AI startups.)
  • negamax 2y ago
    The bigger correction will be in tech stocks that are overly exposed to datacenter investments to accommodate for ever rising AI demands. MSFT, AMZN, META they are all exposed
    • myth_drannon 2y ago
      MSFT is down 7%
      • voidfunc 2y ago
        It's kind of silly. It's not like MSFT and the other hyper-scalers dont need the capacity build out for other reasons too. This should be an easy pivot if DeepSeek turns out to be as good as promised.
  • SebFender 2y ago
    Taking out the popcorn early today.
  • scrlk 2y ago
    Not a good day for those who decided to hold 3x Nvidia ETPs - down 40% earlier.
  • chad1n 2y ago
    This is not exactly right, they said they spent $6M on training V3, there aren't numbers out there related to the training of R1, I can feel it will be cheaper than o1, but it's hard to tell how much cheaper. I can guess that overall deepseek spent way less than openai to release the model, because I have the feeling that the R&D part was cheaper too, but we don't have the numbers yet. Anyway, we can assume that deepseek and Alibaba will try to get the most out of their current GPUs however.
  • thelastgallon 2y ago
    [flagged]
    • GuB-42 2y ago
      The issue here is not that DeepSeek exists as a competitor to GPT, Claude, Gemini,... The issue is that DeepSeek have shown that you don't need that much raw computing power to run an AI, which means that companies including OpenAI may focus more on efficiency than on throwing more GPUs at the problem, which is not good news for those in the business of making GPUs. At least according to the market.
      • paganel 2y ago
        One of the questions about this is that of the US’s human capital, i.e. does the US (still) have enough capable tech people in order to make that happen?
        • mike_hearn 2y ago
          Lol, yes. The US is still very much at the forefront of this stuff. DeepSeek have presented some neat optimizations, but there have been many such papers and optimizations get implemented quickly once someone has proven them out.
          • paganel 2y ago
            > The US is still very much at the forefront of this stuff Doesn't look like it, because the some of the biggest US tech companies now active (including Meta and Alphabet) couldn't come up with what this much-smaller Chinese company has. Which begs the question, what is that companies like Meta, Alphabet and the like do with the (already) hundreds of billions of dollars that they invested in this space?
            • vmbm 2y ago
              Best guess is that they were all caught up in the arms race to try and make a better model, at whatever the cost. And if you work in this space you were probably getting thrown fistfuls of money to join in on it. I read somewhere on reddit that anyone trying to push for efficiency at these places was getting ignored or pushed aside. DeepSeek had an incentive to focus on efficiency because of the chip embargo. So I don't think this is necessarily a knock on US AI capabilities. It is just that the incentives were different and when stock prices are going to the moon regardless of how much capex was getting spent, it was easy for everyone to just go along with it. With that said, I think all of these companies are capable of learning from this and implementing these efficiency improvement. And I think the arms race is still on. The goal is to achieve super human level of intelligence, and they have a ways to go to get there. It is possible that these new efficiency improvements might even help them take the next step as they can now do a lot more with a lot less.
  • kvgr 2y ago
    Not sure if its as hot as it looks. Valuation of those private AIs is stupid. This looks like Chinese propaganda. Say we dont need that hardware, but it looks like they need it. https://wccftech.com/chinese-ai-lab-deepseek-has-50000-nvidia-h100-ai-gpus-says-ai-ceo/ https://wccftech.com/chinese-ai-lab-deepseek-has-50000-nvidi...
  • impossiblefork 2y ago
    It's a very strange result. I believe that NVIDIA is overvalued, but if DeepSeek really is as great as has been said, then it'll be even greater when scaled up to OpenAI sizes, and when you get more out you have more reason to pay, so this should if it pans out lead to more demand for GPUs-- basically Jevon's paradox.
    • franktankbank 2y ago
      The architecture doesn't keep yielding better results, Jevon's paradox doesn't apply.
      • impossiblefork 2y ago
        But surely it can be scaled up, or is this compression thing something making the approach good only for small models (I haven't read the Deepseek papers (can't allocate time to it))?
      • joak 2y ago
        Anyhow, if you can deliver more with less, this is huge good news for AI industry. After some readjustment we can expect AI companies to start using the new method to deliver more. Science fiction might happen sooner than expected. Buy the dip.
    • lmm 2y ago
      If the top-tier premium GPUs aren't the difference-maker they were thought to be then that will hurt NVIDIA's margins, even if they make some of it up on volume.
    • alecco 2y ago
      The limit is high quality data, not compute.
      • belter 2y ago
        > The limit is high quality data If, as some companies claim, these models truly possess emergent reasoning, their ability to handle imperfect data should serve as a proof of that capability.
  • qwytw 2y ago
    > overtakes ChatGPT That's arguable, though. I mean it's much cheaper and reasonably competitive which is almost the same but IMHO DeepSeek seems to get stuck in random loops and hallucinates more frequently than o1.
  • mgh2 2y ago
    Let's see how the open vs close ecosystem wins in AI
• jamil7 2y ago
  Shouldn't this news be bullish long-term for them?
  • blitzar 2y ago
    The $500 billion data center can now be $50 billion. That is excellent news, unless you were the company that was expected to sell $450 billion of GPUs with a 95% gross margin to that project.
    • Jlagreen 2y ago
      $50 billion can be afforded by WAY WAY more sites and companies so Nvidia will simply delivered to >10x more data centers. Or instead of shipping 100k GPUs to Meta, they will ship 100k GPUs to 10 different customers. For Nvidia, it is great news because now finally, the concentration of GPUs at Hyperscalers will end and every Fortune company can finally get their local data center to train their AI Models. Because if training AI models becomes more efficient and easier then the ones being that business are at risk so basically Big Tech. Nvidia isn't in that business but in the business of providing tools to train. Fortunately, Big Tech can easily do something to prevent ANYONE for competing. They simply buy all available GPUs. Oh wait, haven't they been doing it for years? Excatly! People really don't get what an arms race and market competition race is. How do I prevent disruption? I simply buy all the tools the competition needs to disrupt me. See, if Fortune 500 companies want to build large data centers but can't because all Hyperscalers buy the GPUs then eventually they will rent from cloud as otherwise they can't get the GPUs.
      • blitzar 2y ago
        > $50 billion can be afforded by WAY WAY more sites and companies Spending $50 billion to do $6 million worth of Ai training seems like a good way to trigger a golden parachute and "spend more time with your family" as a CEO.
        • throwup238 2y ago
          How about spending $50 billion in capex to do $100 billion worth of inference? Everyone is hung up on the cost of training R1 but it’s 685 billion parameters. We still need all of those GPUs to actually use the model.
• xyst 2y ago
  Buy the dip
  • elaus 2y ago
    Grab for the falling knife
  • testrun 2y ago
    The problem is to know where the dip is.
    • nejsjsjsbsb 2y ago
      Or just do boring sound financial planning based on investment goals and retirement age!
  • sgt101 2y ago
    hodl?
  • alecco 2y ago
    Hold the bag.
  • suraci 2y ago
    with what
• Discordian93 2y ago
  It's funny the bubble seems popping because the tech can be made better or cheaper instead due to loss of enthusiasm for the tech itself. AI is here to stay I suppose then.
  • InkCanon 2y ago
    If anything Meta and other foundation model companies should be dropping. What's the moat? DeepSeek is releasing stuff for free.
    • spacebanana7 2y ago
      Meta should actually go up from this. If deep seek is perfect they don't need to pay for an expensive Llama team. And even if deep seek isn't perfect, the low cost training strategies they've invented could be used by Meta to reduce the cost of Llama training. Although Meta develops models they don't sell them. So a world where foundation models are free is fine for them.
      • InkCanon 2y ago
        But if they don't sell them, are they spending 50 billion a year on open source goodwill?
        • diggan 2y ago
          No, it seems to me like a stopgap measure against others, rather than anything for themselves. If they were out after "open source goodwill" they'd actually release the models as open source (like let people use it without signing a license/terms agreement, and use models for whatever). As it stands today, they're tricking people into "open source goodwill" but it will eventually catch up with them.
        • spacebanana7 2y ago
          From Meta’s perspective, AI could be incredibly profitable in the context of generating adverts or interactive chat bots for businesses. They just don’t want to use OpenAI/Google models because they fear being screwed over by them with anti-advert terms of service or price increases. Similar to what they suffered with Apple.
• dogma1138 2y ago
  Good time to buy then, I don’t understand how stupid some traders can be. A more efficient model is better for NVIDIA not worse. More compute is still better given the same model. And as more efficient models proliferate it means more edge computing which means more customers with lower negotiating power than Meta and Google… This is like thinking that if people need to dig only 1 day instead of an entire month to get to their nugget of gold in the midst of a gold rush the trading post will somehow sell fewer shovels…
  • digitcatphd 2y ago
    This doesn't have anything to do with DS, despite what the media claims, it comes to a heightened sense of fear people have with playing with fire.
  • diegocg 2y ago
    Nvidia is way too overvalued regardless of deepseek or the success of AI. This is just some correction (not even too big even considering the current bubble), these traders are not stupid.
    • dogma1138 2y ago
      NVIDIA has a P/E ratio of 56 that’s double that of the S&P 500 but half that of AMD and the same one as Meta. And whether it’s overvalued or not isn’t relevant that selling a stock because the product the company produces is now even more effective is mind bogglingly stupid.
      • InkCanon 2y ago
        It's arguable how good a strategy it is to check against other P/E. During the tech bubble people would say X is cheap, because Y is trading at 100P/E instead of 200
        • dogma1138 2y ago
          Again that may reasonable but it’s a completely different argument. Whether there is a bubble or not and whether NVDA is overvalued is irrelevant to the subject at hand. If it’s cheaper to train models it means far more customers that will try their luck. If you reduce training requirement from a 100,000 GPUs to a 1000 you’ve now opened the market to 1000’s and 1000’s of potential players instead of like the 10 that can afford dumping so much money into a compute cluster.
• fire_lake 2y ago
  Fascinating. I think Meta is a big winner from this - they still control the content and now mining it for value has been proven even cheaper by DeepSeek.
  • ppsreejith 2y ago
    Agree. Apple should be as well. The only con I can think of would be their (Meta's) data center investments but seems this will make them more efficient?
  • rvz 2y ago
    Exactly this. This does not affect Meta as many people believe. The startups that are in trouble are the ones that have been screaming for "AGI" and buying up GPUs and close-sourcing their models. Open source was always going to win the race to the bottom. [0] [0] https://news.ycombinator.com/item?id=41671582 https://news.ycombinator.com/item?id=41671582
  • sitkack 2y ago
    Yours is the correct take, it isn't about who did it, it was that it was done at all. This shows how effective open source and open development can be. Best way to get a correct answer is ... https://www.phind.com/search?cache=ws3qq1xl8hj4yx1izd5oeda0 https://www.phind.com/search?cache=ws3qq1xl8hj4yx1izd5oeda0
    • p2detar 2y ago
      Not sure I understand how exactly open source plays a key role here in terms of project development. Looking at https://github.com/deepseek-ai https://github.com/deepseek-ai, those repos have a bunch of of contributors but unless I'm wrong I don't see any significant contributions. What am I missing?
      • sitkack 2y ago
        One could argue that by Meta and other companies releasing open weights and detailed got us to where we are now with R1. Even if it wasn't your race car that crossed the line first, everyone can now get a copy.
• homarp 2y ago
  see also https://news.ycombinator.com/item?id=42822162 https://news.ycombinator.com/item?id=42822162 "The Short Case for Nvidia Stock"
• tobyhinloopen 2y ago
  I was wondering why my whole portfolio was deeply red
• fancyfredbot 2y ago
  The people writing market commentary are simply making it up. The news about DeepSeek is not new and doesn't reduce the value of ASML. People are selling now because they are scared because the number went down.
  • knoopx 2y ago
    this, absolutely irrelevant to deepseek
  • sekai 2y ago
    Andrej Karpathy was tweeting about DeepSeek a month (!) ago. "DeepSeek (Chinese AI co) making it look easy today with an open weights release of a frontier-grade LLM trained on a joke of a budget (2048 GPUs for 2 months, $6M)." https://x.com/karpathy/status/1872362712958906460 https://x.com/karpathy/status/1872362712958906460
    • igleria 2y ago
      this same forum ignored deepseek one month ago, save for a few... open minded people.
      • Cumpiler69 2y ago
        "That forum", just like the mobs in ancient Rome, doesn't operate on logic, it operates on emotional feelings and groupthink.
        • igleria 2y ago
          Indeed, although like you say, that is a property of forums rather than a unique trait of HN. I do love HN most of the time.
      • throwup238 2y ago
        Deepseek: The quiet giant leading China’s AI race (27 days ago, 465 comments): https://news.ycombinator.com/item?id=42557586 https://news.ycombinator.com/item?id=42557586 Are we talking about the same forum? HN commenters have been raving about DeepSeek v3 for at least a month.
• pjc50 2y ago
  The whole AI valuation was based on being able to rent-seek a significant chunk of all white collar salaries, with a permanent monopoly moat because nobody else would pay hundreds of billions to train models. And yet again a cheaper Chinese product turns up and everyone loses their minds. Expect a ban incoming to preserve the AI valuations.
  • torginus 2y ago
    Banning wouldn't work imo - now that the cat's out of the bag, with the architecture being open source, anyone can replicate their results to a compete with them for a relatively small investment.
• rvz 2y ago
  Well this is very bad for the private AI companies but Big Tech will be fine, especially Meta and Apple. Everyone else who over-leveraged into the private AI companies (Anthropic, OpenAI) are going to have their valuations under scrutiny.
  • romanovcode 2y ago
    What does Apple have to do with AI
    • suraci 2y ago
      MSFT should be here instead of apple
    • rvz 2y ago
      Nothing. Apple (and Meta) are not directly impacted by AI even if the cost of training these AI models becomes cheaper and $0 free models get better. It actually affects the frontier AI companies (OpenAI, Anthropic, etc) who directly make money from their closed models AND spend hundreds of millions on training these models. Why pay $3/per million tokens (Claude 3.5 Sonnet) when DeepSeek R1 offers $0.14 / per million tokens and the model is on par with (OpenAI o1) and R1 itself is released for free? $0 free AI models are eating closed AI models lunch.
      • nejsjsjsbsb 2y ago
        Yes. The shovels (Nvidia) should be happy with this. The competition gold diggers should be scared of the alchemists. This analogy ain't working. But yeah agree with you!
      • pickledoyster 2y ago
        > Meta) are not directly impacted by AI even if the cost of training these AI models becomes cheaper and $0 free models get better. cheaper spam and impersonation engines (LLMs) have a direct impact on the ad network that is strategically downsizing its moderation efforts
    • 2y ago
• fintechie 2y ago
  The parallels with the dotcom bubble are clear... Tech is magical. Tech becomes cheap/free. Stock holders panic. For some time it's been clear that there's an AI bubble and this may be what finally pops it.
  • impossiblefork 2y ago
    I don't think there has to be an AI bubble, but valuations overall have to come down to something in accordance with the interest rates and expected long-term profit rates.
  • baobabKoodaa 2y ago
    According to you there's a bubble in AI and this... legitimate AI research breakthrough... may be the thing that pops the bubble?
    • Deegy 2y ago
      Bubble's don't always imply fraudulent underlying tech. The dot com bubble was a real thing, and yet the internet has gone on to be one of humanities most valuable innovations.
      • baobabKoodaa 2y ago
        I agree with that. It doesn't refute my point, though. Point being, I would expect something else to pop a bubble.
• zo1 2y ago
  Wow, literally bought more Nvidia shares last week. Just goes to show the stock market is 80% gambling. Me believing in the value of a company is overshadowed by the hype of growth and "future valuation".
  • asddubs 2y ago
    the price hasn't even dropped to as low as it was last week
    • qwytw 2y ago
      I don't think it has been this low (~$126) since October?
      • asddubs 2y ago
        yeah, I looked it up at the time. No idea if it fell further since my comment or if I was looking at outdated info at the time
  • MadSudaca 2y ago
    If this is gambling, what isn’t gambling? Sometimes things just don’t go the way you want. A Chinese company coming up with a cheaper alternative to a cutting-edge technology out of nowhere, is an outcome that is hard to predict. In hindsight, betting on Nvidia maintaining its monopoly on a resource crucial for such an important technology as AI, might not be the best of ideas, but then again, who knows.
    • rsanek 2y ago
      buying an individual stock is gambling. buying an broadly-diversified index is not.
  • nejsjsjsbsb 2y ago
    But it doesn't matter because you got them at a price you deemed they were worth based on some analysis right? :)
• fvv 2y ago
  based on meta declarations , amd is better at customized inference than nvda , meta uses amd mi300x for all inference and which bought 173.000 gpu ( 40% of their total gpu count ) according to that report https://www.cnbc.com/2023/12/06/meta-and-microsoft-to-buy-amds-new-ai-chip-as-alternative-to-nvidia.html https://www.cnbc.com/2023/12/06/meta-and-microsoft-to-buy-am...
• gsky 2y ago
  It doesn't matter whose model is better. AI is here and it won't go anywhere. As a result companies that feed AI will see better valuations
• Sol- 2y ago
  I find it interesting because the DeepSeek stuff, while very cool, doesn't seem invalidate that more compute wouldn't translate to even _higher_ capabilities? It's amazing what they did with a limited budget, but instead of the takeaway being "we don't need that much compute to achieve X", it could also be, "These new results show that we can achieve even 1000*X with our currently planned compute buildout" But perhaps the idea is more like: "We already have more AI capabilities than we know how to integrate into the economy for the time being" and if that's the hypothesis, then the availability of something this cheap would change the equation somewhat and possibly justify investing less money in more compute.
  • Dalewyn 2y ago
    The stock market is not the economy, Wall Street is not Main Street. You need to look at this more macroscopically if you want to understand this. Basically: China tech sector just made a big splash, traders who witnessed this think other traders will sell because maybe US tech sector wasn't as hot, so they sell as other traders also think that and sell. The fall will come to rest once stocks have fallen enough that traders stop thinking other traders will sell. Investors holding for the long haul will see this fall as stocks going on sale and proceed to buy because they think other investors will buy. Meanwhile in the real world, on Main Street, nothing has really changed. Bogleheads meanwhile are just starting the day with their coffee, no damns given to the machinations of the stock market because it's Monday and there's work to be done.
    • jart 2y ago
      Not really. The Magnificent Seven are the only thing propping up the whole US economy. If they go down, you go down.
      • SebFender 2y ago
        Well said.
      • nullocator 2y ago
        Isn't this a good reason to break these companies up and mitigate the risk?
• JSR_FDED 2y ago
  I saw the interview with Dario Amodei. Look at 0:48 https://m.youtube.com/watch?v=7LNyUbii0zw https://m.youtube.com/watch?v=7LNyUbii0zw He seems adamant that there are no diminishing returns to scaling AI. I don’t want to stir up conspiracy theories but I do think that currently all the big AI players have a vested interest in the message that the current scaling paradigm is the right one, and that this is a supremacy issue wrt China. It drives so much investment and valuation that I doubt they can truly be objective.
  • igleria 2y ago
    > I don’t want to stir up conspiracy theories but I do think that currently all the big AI players have a vested interest in the message that the current scaling paradigm is the right one, and that this is a supremacy issue wrt China. It drives so much investment and valuation that I doubt they can truly be objective. 500 Billion is a lot of money. Expect even crimes to be commited in order to make it happen.
  • Jlagreen 2y ago
    The issue is it feels like we came to a stop but Hyperscalers are simply waiting for Blackwell. That's all. Why buy 100k Hoppers if 20k Blackwell offer the same compute so then it's better to buy 100k Blackwells right? Backwell will increase cluster scaling easily by 10x performance and if you buy 10x of them then your compute on a cluster will be 100x than before. If it takes you to wait 6-12 months for that then so be it. You will easily make up the time in the end with the speed up.
• igleria 2y ago
  I will never forget HN's not-made-in-the-US bias https://news.ycombinator.com/item?id=42514633 https://news.ycombinator.com/item?id=42514633
  • alecco 2y ago
    I think part of the problem was many/most previous models coming out of China were trained on eval data to cheat in the rankings. Quite an uphill battle for Deepseek.
    • blitzar 2y ago
      I think part of the problem was many/most previous models coming out of the US were trained on eval data to cheat in the rankings.
    • igleria 2y ago
      Assuming that is true, which I don't have a reason to believe otherwise, only gave an excuse to the average reader to disregard without reading more than the title. You can see a similar bias in academia with work originating outside EU/USA. before someone thinks something strange regarding me, I can only tell you I'm not chinese, but Argentinian :)
  • 2024user 2y ago
    Which replies in particular? I read that thread and didn't see any bias?
  • noxs 2y ago
    this one too https://news.ycombinator.com/item?id=41999151 https://news.ycombinator.com/item?id=41999151 people are just more focusing on the political side of it
    • suraci 2y ago
      this happens in EVERY thead about Chinese tech news it's always 'but tiananmen square'
• junon 2y ago
  Not sure what the fuss is. I tried Deepseek earlier today for the first time and it was even worse than o1 when it came to reasoning skills and following my requests for how I wanted to engage with it. o1 at least gives it to me straight. When I ask it to engage in more back and forth before assuming what I'm after, it tends to follow through. Deepseek seemed immediately eager to (very slowly) feed me a bunch of made up information thinking that's what I wanted. I feel as though a lot of people get hung up on these sort of "micro benchmarks" whereas trying to get practical work done is severely under tested. I'm not a fan of openai at all but I don't have the spare compute to run anything locally so o1 suffices for now. Still don't see how this is anything but a win for Nvidia though.
  • LittleTimothy 2y ago
    The excitement isn't the capabilities of the model, it's how efficiently it was created. One of the major lessons in AI in the last couple of years was that scale mattered - you would want to throw more and more compute at a problem and that has turned into incredible share prices for Nvidia and incredible investments in data centre and energy generation. If it turns out that actually we didn't need quite such incredible scale to get these results and actually we were just missing some really quite basic efficiency optimizations then the entire investment cycle into Nvidia, data centres and energy generation is going to whipsaw in an incredible way.
    • nejsjsjsbsb 2y ago
      Which doesn't make sense. If anything it has the chance to make AI finally profitable and financially sustainable. Maybe that is the fear!
    • Balgair 2y ago
      Essentially, Deepseek is showing that there is a lot of room for improvement with AIs. To paraphrase Orwell, AIs are a lot more like Alarm Clocks and a lot less like Manhattan Projects.
  • lm28469 2y ago
    It can be a win for nvidia but bad for nvidia stock if the win is smaller than the initially expected win. The business is doing good, the wannabe traders not so much
• rhubarbtree 2y ago
  This is an example of a recurrent phenomenon in politics. Trump comes in with big plans, this and that, threatening countries, deporting people, a clear agenda to take America to the hard right. Isolationism, crush China, screw every other country over, etc. etc. And then - something completely unexpected, a total curveball - arrives a week into office and everything changes. Your agenda collapses and you enter reactive mode. AI bubble could pop. Valuations drop. Crypto might get hit. Where is Project Stargate now? It might become a joke. A more cynical observer might suggest that _the timing was no coincidence_.
  • suraci 2y ago
    I said in other threads about how the establishment passed Trump a ticking time bomb – if he were smart, he should've made it blow up early himself to avoid becoming the next Herbert Hoover
    • rhubarbtree 2y ago
      He's inherited a bubble. If he chooses to stoke that bubble...
  • refurb 2y ago
    > Your agenda collapses and you enter reactive mode. Where has Trump's agenda collapsed? I might have missed the press release. And why would a curveball on AI throw off an agenda around trade, immigration and military engagement? I don't follow. China could take the lead on AI and I don't see it would impact any of those things. Isn't DeepSeek open source? The US already has access to it, so what leverage could China possible have? > A more cynical observer might suggest that _the timing was no coincidence_. Do you feel the Chinese government closely controls AI research and timed this response? Any evidence for that?
    • rhubarbtree 2y ago
      > Where has Trump's agenda collapsed? I might have missed the press release. Well, it's too early to say if it has happened in this case. But we've seen it happen again and again, so it will not be a surprise if it happens. > And why would a curveball on AI throw off an agenda around trade, immigration and military engagement? I don't follow. Well, trade restrictions against China have just backfired spectacularly. So further trade restrictions may not seem as good an idea. And to start trade wars, you need a strong economy. And at the moment America's economy is entirely driven by the AI bubble, as it is the value of the AI stocks that has separated the economy from the European trend. It is very likely that military engagement will be driven by, or affected by, developments in AI. And there's no doubt that Taiwan's situation is heavily affected by chip production. > Do you feel the Chinese government closely controls AI research and timed this response? No evidence, but I am not naive enough to think it's out of the range of possibility. I don't follow your reasoning, more likely the timing of the release is all they had to modify - which is quite trivial for any government. To not question that would be very naive. They have _every motive_.
• canterburry 2y ago
  Right now it seems like DeepSeek should splurge on some more GPUs from NVidia because all my requests are timing out.
  • alecco 2y ago
    Deepseek API works fine and it's dirt cheap.
• niemandhier 2y ago
  So a quant company releases a model and that causes stocks of an overvalued company to go down drastically? Maybe this was the point of creating this model all along ?
  • alephnan 2y ago
    OpenAI is not publicly traded
    • gsky 2y ago
      They must have shorted NIVIDA
• yani 2y ago
  Could NVIDIA be in hot water if it's proven they sold restricted hardware to Chinese companies?
  • alecco 2y ago
    H800s were made to match Biden's export restrictions. They were banned in late 2023 but a lot were sold to China. Having 2k is quite small compared to the bigger players like BABA (200k employees) and Tencent (100k employees). And those sure have access to the few H100s that were smuggled. But unlikely for a tiny company like High-Flyer/Deepseek (160 employees).
  • lmm 2y ago
    No, they're already too big to fail. They'll get a slap on the wrist at most.
• deleted 2y ago
  [deleted]
• dageshi 2y ago
  Well this thread got nuked
  • dangexplainer 2y ago
    [flagged]
    • kccoder 2y ago
      Right, everyone should be focused on the rapid dismantling of the government; stuffing his cabinet to the gills with incompetent and dangerous sycophants; pardoning of violent criminals, especially those which nearly killed several officers, and the leaders of the organizations who directed the coup attempt; and the capricious way we are treating our historical allies. Everything else is a distraction.
• pizza 2y ago
  Traders are saying not doing multitoken prediction, not using Sharpe ratio adjusted rewards, using reward models, and not compressing KV cache tokens by >90%, were supposed to be worth hundreds of billions of dollars of future expected revenue flow, at least according to other traders. I say to the traders: you should have just stuck to reading arxiv, TPOT, and jhana twitter for the past 2 years, rather than listening to other traders, if you were trying to understand the utter spread of low hanging fruit that just hasn’t been picked up yet!
  • igleria 2y ago
    > you should have just stuck to reading arxiv, TPOT, and jhana twitter for the past 2 years Traders expect to be able to trade based on regurgitated headlines made by tech influencers who don't know ANYTHING about NOTHING.
  • djtango 2y ago
    Who is jhana?
    • pizza 2y ago
      It’s a part of twitter that is nominally about advanced forms of meditation but is also a surprisingly overrepresented by ai researchers lol
  • nejsjsjsbsb 2y ago
    You need to be very solvent to stay rational.
  • danielmarkbruce 2y ago
    The low hanging fruit thing is 100% correct. Anyone reading papers saw it everywhere, on every dimension. And it's not to say the authors didn't see it either, they did - they just had to get something out now. I'd guess folks in semi conductors saw the same things for ages.
• msoad 2y ago
  We are at multiple trillion dollar investment territory, all purely based on the idea that "to make AI you need lots of GPU and power"
• plaidfuji 2y ago
  The part of this that doesn’t jibe with me is the fact that they also released this incredibly detailed technical report on their architecture and training strategy. The paper is well-written and has a lot of specifics. Exactly the opposite of what you would do if you had truly made an advancement of world-altering magnitude. All this says to me is that the models themselves have very little intrinsic value / are highly fungible. The true value lies in the software interfaces to the models, and the ability to make it easy to plug your data into the models. My guess is the consumer market will ultimately be won by 2-3 players that make the best app / interface and leverage some kind of network effect, and enterprise market will just be captured by the people who have the enterprise data, I.e. MSFT, AMZN, GOOG. Depending on just how impactful AI can be for consumers, this could upend Apple if a full mobile hardware+OS redesign is able to create a step change in seamlessness of UI. That seems to me to be the biggest unknown now - how will hardware and devices adapt? NVDA will still do quite well because as others have noted, if it’s cheaper to train, the balance will just shift toward deploying more edge devices for inference, which is necessary to realize the value built up in the bubble anyway. Some day the compute will become more fungible but the momentum behind the nvidia ecosystem is way too strong right now.
  • fspeech 2y ago
    You are not taking into account why people are willing to pay exceedingly high prices for GPUs now and that the underlying reason may have been taken away.
  • gitfan86 2y ago
    What has changed is the perception that people like OpenAI/MSFT would have an edge on the competition because of their huge datacenters full of NVDA hardware. That is no longer true. People now believe that you can build very capable AI applications for far less money. So the perception is that the big guys no longer have an edge. Tesla had already proven that to be wrong. Tesla's Hardware 3 is a 6 year old design, and it does amazingly well on less than 300 watts. And that was mostly trained on a 8k cluster.
    • plaidfuji 2y ago
• reacharavindh 2y ago
  What I’d like to know is.. If a good model can be trained with much fewer GPUs using a breakthrough technique, can the breakthrough technique be used by OpenAI, MSFT et al who has loads of GPUs to train a model that is orders of magnitude better than their state of the art today? We’ve been getting the impression that the limiting factor was the number of GPUs right? If so, this reduces that bottleneck and frees them up to do even better right?
  • ReptileMan 2y ago
    From my understanding the limiting factor is the quantity and quality of data available for training.
• afavour 2y ago
  Seems bad! To me this highlights a failure in the US tech industry. Silicon Valley is theoretically a triumph of entrepreneurship, where the best and best thinking wins. In reality investors picked OpenAI as the winner right out of the gate, gave it more funding than most of us can imagine so that it could dominate the market, sat back and told themselves job done. Meanwhile in another tech industry a startup had to think lean and innovate its way around resource restrictions. And OpenAI looks like a mess now.
  • whalesalad 2y ago
    The innovators dilemma.
  • dralley 2y ago
    And this is why competition is a good thing. Silicon Valley keeps trying to create monopolies for the purpose of maximal profit extraction to the detriment of American technological development, or they use money and brute force as a substitute for actual innovation and get worse results.
• nialv7 2y ago
  Oh my, so much cope and denial here...
• aaron695 2y ago
  [dead]
• aurareturn 2y ago
  Deepseek should cause Nvidia and TSMC stocks to go up, not down. I'm buying more Nvidia and TSMC today. 1. More efficient LLMs should lead to more usage, which means more AI chip demand. Jevon's Paradox. 2. To build a moat, OpenAI and American AI companies need to up their datacenter spending even more. 3. DeepSeek's breakthrough is in distilling models. You still need a ton of compute to train the foundational model to distill. 4. DeepSeek's conclusion in their paper says more compute is needed for next break through. 5. DeepSeek's model is trained on GPT4o/Sonnet outputs. Again, this reaffirms the fact that in order to take the next step, you need to continue to train better models. Better models will generate better data for next-gen models. I think DeepSeek hurts OpenAI/Anthropic/Google/Microsoft. I think DeepSeek helps TSMC/Nvidia.
  • NicoJuicy 2y ago
    Ugh. No, we can now run a decent model through CPU. Not an expensive video card. Just try out the standard deepseek-r1 or even the deepseek-r1:1.5B through ollama. No need for expensive hardware anymore locally. My PC ( without Nvidia card/expensive hardware) runs the a deepseek 1.5 b query fast enough - 2 - 9 seconds until it's finished.
    • aurareturn 2y ago
      The world isn't satisfied with a "decent model". The world is trying to reach AGI. Further more, reasoning models require more tokens. The faster the GPU, the more thinking it can do in a set amount of time. This means the faster the hardware, the smarter the model output. Again, that reinforces the need for faster hardware. More thinking = smarter models Faster hardware = more thinking Therefore, faster hardware = smarter models
    • Jlagreen 2y ago
      I would shoot myself if I had to wait 9 seconds for a query. I sometimes even would like to kill by browser taking seconds to open a page...
      • NicoJuicy 2y ago
• aoeusnth1 2y ago
  A huge leap in the cost effectiveness of AI capabilities only paves the way for faster timelines to ASI. I'm not sure why that would reduce the economic value of Nvidia. Pretty sure this is a reaction in the wrong direction. Nvidia should be popping.
• deleted 2y ago
  [deleted]
• j7ake 2y ago
  This may be bad for tech stocks, but this is actually good for technology because the supposed monopoly in ML is deteriorating.
• dboreham 2y ago
  Because nobody would ever have imagined that clever people would optimize AI software so it doesn't need billions of expensive chips?
• bee_rider 2y ago
  ASML plunge indicates a hysterical/irrational component to the response, right? They aren’t going anywhere. If it turns out training is easier than expected, they make the devices that make the devices that do inference too… If the field is going to produce anything useful, cheap training gets us there faster.
  • maxglute 2y ago
    Not making the news in western media yet is PRC claims to have started mass producing their indigenous 28nm litho (70% of global wafer use) this month... the estimated cost is 1/30th of ASML machines. Extrapolate and they're on trend of produce 14nm machines at comparable fraction cost in next few years.
    • bee_rider 2y ago
      It seems hard to extrapolate there—the 30nm-10nm range is where Intel really started to start having trouble, right? Anyway, this seems like a bigger problem for companies whose business model is actually selling those chips. It couldn’t be the case that much of ASML’s valuation is based on people continuing to use their old 28nm machines, right?
      • bgnn 2y ago
        Not that hard for China since they have hired a lot of TSMC top emgineers, like the head of TSMC finfet program. TSMC seems to treat them like dirt while they are treated like superstars in China. Main deficiency they have is in the litho machines.
  • hintymad 2y ago
    I'm guessing the sentiment is not about training, but about the R&D capability of China. If Chinese can figure out how to build a good enough model faster and cheaper, they may be able to come up with an ASML competitor as well.
    • maxglute 2y ago
• hintymad 2y ago
  I don't quite understand the sentiment. Lowered cost of training and inference means more companies can join the game, and therefore more demand for the hardware. Basically Jevon's paradox in play. What will more likely fall instead in the long-term should be OAI's valuation, if they don't have other killer products.
  • maxglute 2y ago
    IMO reaction to short term semi cycle - a lot of reshored/redundant fab expansions last few years outside of TW. If compute costs goes down 10-50x there may not be enough use case to fill Jevon in next infra hardware cycle of ~5 years. People are doing more with same, but maybe use case not sufficiently more to go fill/justify acquiring more hardware in near term time frame.
• russli1993 2y ago
  Next episode in this is Chinese companies produces HBM3 (they are already shipping HBM2), 7nm class GPU products competitive against A100 from multiple vendors, EUV. The US will realize what a monster they created.
• lostmsu 2y ago
  I wish I would not already be so leveraged.
• hintymad 2y ago
  Is it possible that the key here is all about where top talent goes? America's best minds from Math and Informatics Olympiads gravitate to Wall Street for financial gains. Similarly, DeepSeek's top talent are mostly IMO and IOI medalists and they originally aimed for finance too (The parent company of DeepSeek is a successful quant trader), but strict government regulations pushed them towards AI instead. Ironically, this unintended pivot turned out to be quite significant
• Palmik 2y ago
  Massive overreaction in my opinion (not financial advice). People are forgetting how much compute is being used for inference. This is going to be further accelerated: - Reasoning models are going to generate orders of magnitude more tokens. - Synthetic an augmented data is more and more prevalent, and if you need to process pre-training scale dataset, you will need a lot of inference. - etc.
  • dumbfounder 2y ago
    No one will ever need more than 640k tokens.
  • physicsguy 2y ago
    Distillations are showing (and have been for a while) though that you don't necessarily need big hardware for decent inference. If that's the case, and you can run it on your Macbook trivially or even your smartphone.
  • fspeech 2y ago
    Inference can be done on much more diverse hardware.
  • mrbungie 2y ago
    When put into context one could say the OG overreaction was thinking 500B USD in infrastructure investments and restarting nuclear plants were rational decisions rather than working on efficiency.
• scoofy 2y ago
  I've been into investing for my entire adult life, and base my strategy mostly on John Neff's work on total return. I have missed out on a lot of investments in the QE period, because many of them seem like "if this mid-level company becomes the biggest company in the world, you'll make a reasonable return," which has always seemed insane to me, but we've seen it happen again and again. I realize that we are probably in a place where insider trading is much more prevalent that we expect, and that the point of an IPO has been turned on it's head, but these type of potential blowups of high PE stocks is something I've never really come to terms with.
• grajaganDev 2y ago
  This could be an extinction level event for some VCs.
  • echelon 2y ago
    Only if their portcos don't build products people buy. Making a pure-research, foundation model company is silly. Make a product company that sells products.
    • grajaganDev 2y ago
      Google and Microsoft both bundled their AI offering into their office suites because they weren't getting traction as an add-on. Startups don't have that option.
      • echelon 2y ago
        They did that because 1) Their initial AI offerings weren't real products customers would use or pay for 2) They weren't seeing sufficient adoption to justify the expense 3) They have insane levels of distribution in their existing product lines and can incrementally add AI features This is entirely orthogonal to whether or not other startups can build AI-first products or whether they can position themselves to compete with the giants.
        • grajaganDev 2y ago
          Yes as you point out in #1, they failed to develop an AI product and #2 they failed to get traction in the market. What moat does an AI startup have that will prevent them from being crushed by big tech?
          • esafak 2y ago
            Same moat as any non-AI startup? What do you consider an AI startup?
    • deleted 2y ago
• 2OEH8eoCRo0 2y ago
  Still overvalued IMO. Their market cap remains ludicrous.
  • m3kw9 2y ago
    Everything is overvalued if you think in terms of earning multiples, p/s, p/e should be 1:1
    • andrewmcwatters 2y ago
      Who's out here buying businesses for 1x the sales revenue volume? What a silly concept. If businesses could be so cheap, you'd just double down every single year until you owned every business on the planet.
    • dinkblam 2y ago
      1:1? so that dividends cover the share price in the first year of ownership?
    • justahuman74 2y ago
      Earning multiples choose an arbitrary time length of 1 year. What you're really trying to purchase is a machine that creates more money than it uses. You need to guess at if that machine will do its job at an arbitrary point in the future, and how well it will do it. Those factors are only loosely correlated with current PE
    • phyzix5761 2y ago
      Discounted future cashflows. If you buy an asset and every year it produces $100 profit for you it's worth more than $100. You're buying the ability to produce profits in the future not the profits its produced in the past. Those profits belong to the shareholders who have cashed that out already (through dividends or reinvestment).
    • saulpw 2y ago
      No, that means that they're earning enough in one year to cover their entire valuation. You want something like 10:1 p/e which means that the next 10 years earnings are factored in to cover their present valuation.
• h1h1hh1h1h1 2y ago
  So the Chinese graciously gift a paper and model which describes methods that radically increase the efficiency of hardware which will allow US AI firms to create much better models due to having significantly more AI hardware and people are bearish on US AI now?
  • baal80spam 2y ago
    Don't look for logic in the market, I suppose.
  • magic_hamster 2y ago
    I think the idea that SOTA models can run on limited hardware makes people think that Nvidia sales will take a hit. But if you think about it for two more seconds you realize that if SOTA was trained on mid level hardware, top of the line hardware could still put you ahead, and DeepSeek is also open source so it won't take long to see what this architecture could do on high end cards.
    • amazingamazing 2y ago
      there's no reason to believe that performance will continue to scale with compute, though. that's why there's a rout. more simply, if you assume maximum performance with the current LLM/transformer architecture is say, twice as good as what humanity is capable of now, then that would mean that you're approaching 50%+ performance with orders of magnitude less compute. there's just no way you could justify the amount of money being spent on nvidia cards if that's true, hence the selloff.
      • futureshock 2y ago
        Wait no, there is actually PLENTY of evidence that performance continues to scale with more compute. The entire point of the o3 announcement and benchmark results of throwing a million bucks of test time compute at ARC-AGI is that the ceiling is really really high. We have 3 verified scaling laws of pre-training corpus size, parameter count, and test time compute. More efficiency is fantastic progress, but we will always be able to get more intelligence by spending more. Scale is all you need. DeepSeek did not disprove that.
• Neonlicht 2y ago
  ASML warned that it was just a question of time for the Chinese to get their own tech industry up and running. The Americans were in denial. Ultimately someone in America will get desperate enough and start a war when they still have a chance to win. See the Earth-Mars conflict in the Expanse.
• skizm 2y ago
  I like the chart Bloomberg has of the top 10 largest single day stock drops in history. 8 out of the 10 are NVDA (Meta and Amazon are the other two).
  • culi 2y ago
    Why mention it if you're not gonna link it?
    • dgfitz 2y ago
      Because paywall.
      • redundantly 2y ago
        It can still be helpful. Some people are already on the other side of that wall, or someone else usually comes along with an archived, non-paywall version.
      • culi 2y ago
        https://github.com/bpc-clone/bypass-paywalls-firefox-clean https://github.com/bpc-clone/bypass-paywalls-firefox-clean
    • Jach 2y ago
      https://archive.is/Svzfz https://archive.is/Svzfz Paywall schmaywall
    • TeaBrain 2y ago
      The chart is within the below linked article. The below link I've provided is a gift link, so you should be able to access the article through it. https://www.bloomberg.com/opinion/articles/2025-01-27/deepseek-s-ai-model-bursts-nvidia-s-stock-bubble-m6fnhvoi?accessToken=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzb3VyY2UiOiJTdWJzY3JpYmVyR2lmdGVkQXJ0aWNsZSIsImlhdCI6MTczODAzNDA4OSwiZXhwIjoxNzM4NjM4ODg5LCJhcnRpY2xlSWQiOiJTUVJSVVdEV1gyUFMwMCIsImJjb25uZWN0SWQiOiI2MzAyNDM3NEJFNUM0NzE1OTY5MEZDMTY4MTE1QzZEQiJ9.J05Z5Guk9mvLTvcChP683im9bpTr2jiOPJF9oc3ljKE https://www.bloomberg.com/opinion/articles/2025-01-27/deepse...
• paxys 2y ago
  IMO this is less about DeepSeek and more that Nvidia is essentially a bubble/meme stock that is divorced from the reality of finance and business. People/institutions who bought on nothing but hype are now panic selling. DeepSeek provided the spark, but that's all that was needed, just like how a vague rumor is enough to cause bank runs.
  • onlyrealcuzzo 2y ago
    Hard to argue Nvidia is a meme stock and that Tesla is not a bigger meme stock. If meme stocks were imploding, why is Tesla fine? This is about DeepSeek.
    • paxys 2y ago
      What does any of this have to do with Tesla? Even if Tesla is a bigger bubble, not all bubbles have to pop at the same time.
    • coliveira 2y ago
      Tesla is playing the political game with Trump. They're riding that wave. Musk always find some new reason for people to believe the stock.
    • pokstad 2y ago
      The market can stay irrational longer than you can stay solvent.
  • tgtweak 2y ago
    Hype buyers are also Hype sellers - anything Nvidia was last week is exactly what it is this week - DeepSeek doesn't really have any impact on Nvidia sales - Some argument could be made that this can shift compute off of cloud and onto end user devices, but that really seems like a stretch given what I've seen running this locally.
    • zozbot234 2y ago
      The full DeepSeek model is ~700B params or so - way too large for most end users to run locally. What some folks are running locally is fine-tuned versions of Llama and Qwen, that are not going to be directly comparable in any way.
• antigeox 2y ago
  I just want to know if I can buy a gaming video card at a reasonable price or if i should hold off on it. I don't care about the AI shit. And yes I'd prefer nvidia because their closest competitor can't tape a box together nevermind develop and assemble a graphics card.
  • oblio 2y ago
    Unlikely. Companies have doubled down on AI so expect the fall to be long and slow (think 2 years).
• dtquad 2y ago
  DeepSeek has humiliated the entire US tech sector. I wonder if they will learn from this, fire their useless middle management and product managers with sociology degrees, and actually pivot to being technology companies?
  • browningstreet 2y ago
    "Again, just to emphasize this point, all of the decisions DeepSeek made in the design of this model only make sense if you are constrained to the H800; if DeepSeek had access to H100s, they probably would have used a larger training cluster with much fewer optimizations specifically focused on overcoming the lack of bandwidth." https://stratechery.com/2025/deepseek-faq/ https://stratechery.com/2025/deepseek-faq/
  • blitzar 2y ago
    Maybe you don't need a $5,000,000 car to build a charitable foundation.
• 66yatman 2y ago
  Under deepseek R1 running on 6 Mac mini alone sure Nvidia can feel the pressure
• lvl155 2y ago
  But Apple was up…indexing at its finest.
  • zozbot234 2y ago
    Not surprising there - a maxed out Mac Studio is a great AI homelab, giving you way more bang for the buck than nVidia offerings.
    • jhickok 2y ago
      Think that was the cause of their stock increase? I feel like investors use opportunities like this to pile money into safer bets rather than just bail on stocks altogether.
      • lvl155 2y ago
        No of course not. Indexing in 2025 is a whole different world. It was most likely people covering Apple short they had paired with long QQQ.
• kiratp 2y ago
  This is really dumb. Deepseek showing that you can do pure online RL for LLMs means we now have a clear path to just keep throwing more compute at the problem! If anything we made the whole "we are hitting a data wall" problem even smaller. Additionally, its yet another proof point that scaling inference compute is a way forward. Models that think for hours or days are the future. As we move further into the regime of long sequence inference, compute scales by the square of the sequence length. The lesson here was not "training is going to be cheaper than we thought". It's "we must construct additional pylons uhhh _PUs" Markets remain irrational and all that...
  • aurareturn 2y ago
    Nvidia chip demand should increase from DeepSeek's release. This market doesn't make any sense.
    • lerp-io 2y ago
      good time to buy
  • zozbot234 2y ago
    Didn't DeepSeek also show that pure RL leads to low-quality results compared to also doing old-fashioned supervised learning on a "problem solving step by step" dataset? I'm not sure why people are getting excited about the pure-RL approach, seems just overly complicated for no real gain.
    • deepsquirrelnet 2y ago
      If I’m understanding their paper correctly (I might not be but I’ve spent a little time trying to understand it), they showed you only need a small amount of supervised fine tuning “SFT” to “seed” the base model, followed by pure RL. Pure RL only was their R1-zero model which worked, but produces weird artifacts like switching languages or excessive repetition. The SFT training data is hard to produce, while the RL they used was fairly uncomplicated heuristic evaluations and not a secondary critic model. So their RL is a simple approach. If I’ve said anything wrong, feel free to correct me.
• grazing_fields 2y ago
  [dead]
• jaggs 2y ago
  The real hidden message is not that bigger compute produces better results, but that the average user probably doesn't need the top results. In the same way that medium range laptops are now 'good enough' for most people's needs, medium range (e.g. DeepSeek R1x) AI will probably be good enough for most business and user needs. Up till now everyone assumed that only giga-sized server farms could produce anything decent. Doesn't seem to be true any more. And that's a problem for mega-corps maybe?
  • jdietrich 2y ago
    >medium range (e.g. DeepSeek R1x) AI will probably be good enough for most business and user needs Except R1 isn't "medium range" - it's fully competitive with SOTA models at a fraction of the cost. Unless you need multimodal capability or you're desperate to wring out the last percentage point of performance, there's no good reason to use a more expensive model. The real hidden message is that we're still barely getting started. DeepSeek have completely exploded the idea that LLM architecture has peaked and we're just in an arms race for more compute. 100 engineers found an order of magnitude's worth of low-hanging fruit. What will other companies will be able to do with a similar architectural approach? What other straightforward optimisations are just waiting to be implemented? What will R2 look like if they decide to spend $60m or $600m on a training run?
    • jaggs 2y ago
      Yes absolutely. I guess I meant medium range in terms of dev and running costs. R1 is a premium product at a corner store price. :) People are also forgetting that High-Flyer's ultimate goal is not applications, it's AGI. Hence the open source. They want to accelerate that process out in the open as fast as they can.
      • int_19h 2y ago
        I don't think a "good enough" AI is happening for a while. People will see what the new models can do and want the same. So long as improvements are rapid and visible, the demand will keep rising.
• Kon-Peki 2y ago
  If you look at total volume of shares traded, this would be somewhere in the range of 200th highest. If you look at the total monetary value of those shares traded, this would be in the top 5, all of which have happened in the past 5 years. #1 is probably Tesla on Dec 18 2020 (right before it joined the S&P500). It lost ~6% that day. Don’t get me wrong, this is definitely a big day. Just not “lose your mind” big. It’s clear that most shareholders just sat things out.
• kd913 2y ago
  The biggest discussion I have been on having this is the implications on Deepseek for say the RoI H100. Will a sudden spike in available GPUs and reduction in demand (from efficient GPU usage) dramatically shock the cost per hour to rent a GPU. This I think is the critical value for measuring the investment value for Blackwell now. The price for a H100 per hour has gone from the peak of $8.42 to about $1.80. A H100 consumes 700W, lets say $0.10 per kwh? A H100 costs around $30000. Given deepseek, can the price of this drop further given a much larger supply of available GPUs can now be proven to be unlocked (Mi300x, H200s, H800s etc...). Now that LLMs have effectively become commodity, with a significant price floor, is this new value ahead of what is profitable for the card. Given the new Blackwell is $70000, is there sufficient applications that enable customers to get a RoI on the new card? Am curious about this as I think I am currently ignorant of the types of applications that businesses can use to outweigh the costs. I predict that the cost per hour of the GPU dropping such that it isn't such a no-brainer investment compared to previously. Especially if it is now possible to unlock potential from much older platforms running at lower electricity rates.
  • chatmasta 2y ago
    Why is there this implicit assumption that more efficient training/inference will reduce GPU demand? It seems more likely - based on historical precedent in the computing industry - that demand will expand to fill the available hardware. We can do more inference and more training on fewer GPUs. That doesn’t mean we need to stop buying GPUs. Unless people think we’re already doing the most training/inference we’ll ever need to do… “640KB ought to be enough for anybody.”
    • Jensson 2y ago
      Historically most compute went to run games in peoples homes, because companies didn't see a need to run that much analytics. I don't see why that wouldn't happen now as well, there is a limit to how much value you can get out of this, since they aren't AGI yet.
      • chatmasta 2y ago
• jmward01 2y ago
  So, what are investors thinking to warrant this? If it is 'DeepSeek means you don't need the compute' that is definitely wrong. Making a more efficient x almost always leads to more of x being sold/used, not less. In the long term does anyone believe we won't keep needing more compute and not less? I think the market believes that high end compute is not needed anymore so the stuff in datacenters suddenly just became 10x over-provisioned and it will take a while to fill up that capacity. Additionally, things like the mac and AMD unified memory architectures and consumer GPUs are all now suddenly able to run SOTA models locally. So a triple whammy. The competition just caught up, demand is about to drop in the short term for any datacenter compute and the market for exotic, high margin, GPUs might have just evaporated. At least that is what I think the market is thinking. I personally believe this is a short term correction since the long term demand is still there and we will keep wanting more big compute for a long time.
  • amazingamazing 2y ago
    selling more does not necessarily mean you make more money. more efficiency could lead to less margins even if volume is higher. moreover, even things are incredibly efficient, the bar to sufficiently good AI in practice (e.g. applications), might be met with commodity compute, pretty much locking nvidia out, who generally sells high margin high performance chips to whales.
  • acchow 2y ago
    But the SOTA models basically all suck today. If people don’t think they suck, definitely in 1 year they’ll look back and consider those older models unusably bad
    • jiggawatts 2y ago
      I recently went to the LLM chat arena and tried my "test input" against the latest frontier models that GPT 3 failed on. This test snippet simply repeats the same four-letter word in a paragraph many times using all of its various possible meanings simultaneously. The request to the AI is to put the meaning of each usage of the word next to it in brackets. None of the frontier models can do this perfectly. They all screw up to various degrees in various interesting ways. A schoolkid could do this flawlessly. This is not some contrived test with bizarre picture puzzles as seen in ARC-AGI or testing obscure knowledge about bleeding-edge scientific research. It's simple English comprehension using a word my toddler knows already! It does reveal the fundamental flaw in all transformer-based models: They're just shifting vectors around with matrices, and are unable to deal with many categories of inputs that cause overlaps or bring too many of the tokens too close to each other in some internal representation. They get muddled up and confused, resulting in errors in the output. I see similar effects when using LLMs for programming: They get confused when there are many usages of the same identifier or keyword, but with some subtle difference such as being inside a comment, string, or in a local context where the meaning is different. I suspect this will be eventually fixed, but I haven't seen any fundamental improvement in three years.
• fspeech 2y ago
  The key question is: has demand elasticity increased for Nvidia cards? An increase in elasticity means people are more willing to wait for hardware price to drop because they can do more with existing hardware. Elasticity could increase even if demand is still growing. Not all growths are equally profitable. Current high prices are extremely profitable for Nvidia. If elasticity is increasing future growth may not be as profitable as the projection from when Deepseek was relatively unknown.
• bgnn 2y ago
  This totally makes sense, since Nvidia and ASML alike had hugely inflated market cap. This is more of a return to normal.
• kristianp 2y ago
  I found the Stratechery and Nadella's takes to be interesting. Also the fact that news orgs say things like: "The situation is particularly remarkable since, as a Chinese company, DeepSeek lacks access to Nvidia’s state-of-the-art chips used to train AI models powering chatbots like ChatGPT." Whereas they don't mention the fact that Deepseek still used Nvidia chips. news orgs are implying they didn't. Stratechery points out that the reduced memory/communications bandwidth of the gimped H800 chips they have access to drove the MOE/MLA architecture developments to make their model possible on the less powerful chips. Nadella (on X) points out that by Jevons paradox [1], AI usage (and NVidia chip usage) will increase because deepseeks has reduced costs. One other point Statechery made is that DeepSeek likely distilled the output of leading models for V3 and R1. They have shown that they can replicate the leaders cheaply and quickly, but they can't produce a leading model without copying (yet). [1] https://x.com/satyanadella/status/1883753899255046301 https://x.com/satyanadella/status/1883753899255046301
  • flashman 2y ago
    what people are neglecting about Jevons is that fuel efficiency led to increased use of coal because you could use coal to displace more expensive fuels i wonder what is more expensive that cheap large language models can displace? is the problem with selling unreliable B.S. really its price?
• noname120 2y ago
  Time to buy some Nvidia
• ozten 2y ago
  NVIDIA sells shovels to the gold rush. One miner (Liang Wenfeng), who has previously purchased at least 10,000 A100 shovels... has a "side project" where they figured out how to dig really well with a shovel and shared their secrets. The gold rush, wether real or a bubble is still there! NVIDA will still sell every shovel they can manufacture, as soon as it is available in inventory. Fortune 100 companies will still want the biggest toolshed to invent the next paradigm or to be the first to get to AGI.
  • culi 2y ago
    Yeah but NVIDIA's amazing digging technique that could only be accomplished with NVIDIA shovels is now irrelevant. Meaning there are more people selling shovels for the gold rush
    • ozten 2y ago
      CUDA begs to differ.
    • dragonwriter 2y ago
      What about DeepSeek negates NVidia’s advantages over other GPU vendors?
      • koolhead17 2y ago
        What is stopping huawei or other Chinese vendors to make chips on deepseek specification and 1/10th NVIDIA cost and mass market it?
        • shawabawa3 2y ago
          > What is stopping huawei or other Chinese vendors to make chips on deepseek specification What is "deepseek specification"? Deepseek was trained on NVDA chips. If chinese vendors could build chips as good as NVDA it wouldn't have such a dominant position already, that hasn't changed
      • culi 2y ago
• flowerlad 2y ago
  A similar efficiency event has occurred in the recent past. Blackwell is 25x more energy-efficient for generative AI tasks and offer up to 2.5x faster AI training performance overall. When Blackwell was announced nobody said “great we will invest less in GPUs”. Deep Seek is just another efficiency event. Like Blackwell it enables you to do more with less.
• numba888 2y ago
  Looks like panic sell off, but main question is: do models with DeepSeek architecture still scale up? If yes, then bigger clusters will outperform in near future. NVidia wins as tide rises all boats, and them first. If not, then it's still possible to run several models in parallel to do the same, potentially big, job. Just like humans team. All we need is to learn how to do it efficiently. This way bigger clusters win again.
• mrinterweb 2y ago
  The vast majority of NVDA's value is based on the assumption they are the only game in town that can do AI. I'm still waiting for a competing tech to disrupt them further: * Intel, AMD, etc. could start making competitive GPUs that push the price down * A new ASIC chip specifically designed for LLMs * A new training or LLM runtime algorithm that uses the CPU * Quantum chip that can train or run a LLM If Nvidia lost its AI dominance, where would its stock be?
  • blitzar 2y ago
    > If Nvidia lost its AI dominance, where would its stock be? Around the AMD (200bn) / Intel levels (100bn) which is a ~90% reduction in share price from todays close.
  • paxys 2y ago
    The thing with Nvidia is that it doesn't have a large "sticky" customer base that is guaranteed to spend money year after year on new products. If you look at other large tech companies with similar valuations (Apple, Microsoft, Amazon, Google, Meta), none of them are in danger of their core business disappearing overnight. In Nvidia's case, if large tech companies decide they don't need to continue loading up on AI chips and building larger data centers then they are back to where they were in ~2020 ($100-150B market cap from selling GPUs to gamers and professionals working on graphics-intensive apps).
• 0n0n0m0uz 2y ago
  I am glad I saw this coming and profited nicely.
• m3kw9 2y ago
  What people are betting on by selling is there would be a short term pause or deceleration in the investment while they try to squeeze more from their current batch, could be some efficiency catchup
• TheAlchemist 2y ago
  Nvidia valuation = $3000B Nvidia profits during AI madness last year = $65B (last 4 quarters) Nvidia profits during normal year = $5B This stock could drop 90% from here, and still be expensive. The numbers are absolutely crazy and make no sense at all. Stargate project is aiming to invest $500B over 4 years. Those $500B are a pipe dream, but let's suppose for a second that all of that $500B will be Nvidia profits and that we will have another Stargate project in 4 years, again resulting in a direct profits of $500B for Nvidia. And you know what ? In that scenario Nvidia would STILL be overvalued by historical standards ! EDIT: Changed $30B from the fiscal year, to $65B for last 4 quarters.
  • epicureanideal 2y ago
    Exactly. Also, what he’s saying is if 500/4 =125B were NVDA yearly profits (and of course really that would just be revenue, not profit), it’d still mean NVDA should be more like 1875B market cap at a more reasonable 15x price to earnings ratio. If I understand the previous poster correctly.
  • outside1234 2y ago
    The Stargate project does not actual exist. It is just something they completely made up by lumping together data center investments from across the industry and rounded up to the nearest $500B
    • xbgaf 2y ago
      Indeed, perhaps the plan was to pump certain stocks after they saw the unprecedented post election rally of Tesla and Bitcoin. And these people complain about the Pelosi index, which is peanuts in comparison.
  • synergy20 2y ago
    but you forgot the 80B from MSFT and 60B from META in 2025 alone? I assume quite a chunk of that investment will be going to NVDA. Stargate wise, it's a joke, they have no money for their lip service.
• lispforlife 2y ago
  [dead]
• hindsightbias 2y ago
  If this keeps up we’ll have to have a bailout.
• synergy20 2y ago
  It's said the AI war is a war between Chinese, be it US or China, be it hardware and software. If that's true, normally, mainland will win, as the people over that side have more grit, are more eager to succeed, are working at 996 schedule, and have nothing to lose. They're hard to stop as long as their government will not interfere.
• vcryan 2y ago
  Gambling is hard
• jmyeet 2y ago
  What you find with any market news is that the optimism and pessimism are always overblown. Fear and greed. It happens all the time. Look at Linux. For those old enough to remember, there was a time where many (including Microsoft) were worried it would destroy the company (eg [1]). There were complaints about the market destruction caused by Linux. What actually happened? Microsoft is bigger than ever even though Linux is on billions of devices worldwide. IF DeepSeek's claims are real and this stands up, all that's happened is at worst the profit opportunity has simply move *as it was always going to do). This might be bad for OpenAI and Sam Altman but Big Tech will (IMHO) be fine. Remember that training LLMs for chatbots, which is something people focus on, is just one narrow slice of the potential AI market. Recommendation engines, industrial/commercial applications, medicine, etc. If there has been a aoftware breakthrough and training LLMs now costs a fraction of what it did last year, there's now an order of magnitude more potential appllications that have become economical. Consider this: if we can do today with a model 1/10th the size of what we needed last week, what applications will there be for a model 10x DeepSeek-R1's size? I'm also reminded of the invention of the cotton gin. This automated what used to be a highly manual process. At the time, there was concern this would diminish the need for slaves on cotton plantations. Instead the need exploded because cotton became so much cheaper [2]. Lastly, Stargate is largely meaningless. Companies spend a fortune on data centers. GPUs are just a fraction of that. A genuine software improvement just means you can do more with less. My point is: don't panic. Unless you're an OpenAI investor, maybe. [1]: https://www.zdnet.com/article/microsoft-linux-is-a-threat-it-may-mean-prices-cuts-and-less-business-for-us/ https://www.zdnet.com/article/microsoft-linux-is-a-threat-it... [2]: https://www.archives.gov/education/lessons/cotton-gin-patent https://www.archives.gov/education/lessons/cotton-gin-patent
• t_mann 2y ago
  Curious thought: could those large price movements have something to do with the fact that DeepSeek is financed by a hedge fund (rather than the more typical VC)? It is unclear how DS will make money from its current strategy of sharing much of the secret sauce that went into training as well as releasing the results under permissive licenses. But if the play was "short major tech stocks and then release surprising results in a way that maximally undermines their current growth story", then it could make a lot more sense.
  • aptj 2y ago
    Sounds plausible. Can someone tell us how they can hide/get away with such stock shorting?
• thr0waway001 2y ago
  Is this Monday Black-ish?
  • robjan 2y ago
    Only for people who went all in on NVDA. The SP500 is just back to where it was last week.
• cAIctus480 2y ago
  A couple of things that caught my attention in context of the reactions from the AI community and investor community, but maybe im not fully read up. First, this seems to have been a surprise even amongst researchers but once released very transparent and open. Second, funded through a hedge fund.
• dang 2y ago
  Related ongoing thread: The impact of competition and DeepSeek on Nvidia - https://news.ycombinator.com/item?id=42822162 https://news.ycombinator.com/item?id=42822162 - Jan 2025 (423 comments)
• chenster 2y ago
  Word of the day - Rout
• yalogin 2y ago
  Nvidia has gotten lucky repeatedly. The GPUs were great for PC gaming and they were the top dog. The crypto boom was such an unexpected win for them partly because Intel killed off their competition by acquiring it. Then the AI boom is also a direct result of Intel killing off their competition but the acquisition is too far removed to credit it to that event. Unlike the crypto boom though, two factors make me think the AI thing was bound to go away quickly. Unlike crypto there is no mathematical lower bound for computation, and if you see technology's history we can tell the models are going to get better/smaller/faster overtime reducing our reliance on the GPU. Crypto was fringe but AI is fundamental to every software stack and every company. There is way too much money in this to just let Nvidia take it all. One way or another the reliance on it will be reduced
  • gradys 2y ago
    Is it luck, or is scaling arithmetic genuinely a useful capability to offer the world?
  • nailer 2y ago
    Money isn’t fringe, and the target for crypto is all transactions, rather than the existing model where you pay between two and 3.5% to a card company or other middleman.
    • daveguy 2y ago
      Credit card companies averaged over 22,000 transactions per second in 2023 without ever having to raise the fee. How many is crypto even capable of processing? Processing without the fee going up? What fraud protection guarantees are offered to the parties of crypto transactions?
      • nailer 2y ago
        > 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 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 question is regarding consumer fraud or cryptographic fraud proofs so I won’t answer it until you clarify.
• eigenvalue 2y ago
  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.
  • cageface 2y ago
    You could even say that overconfidence in abilities and knowledge outside of core expertise is the critical flaw of today's entire tech industry.
    • sympil 2y ago
      As a great example of this read Paul Graham’s essays that aren’t anout his core expertise.
      • haliskerbas 2y ago
        But every successful SV founder and or VC is not only a tech genius but also a geopolitical and socioeconomic expert! That’s why they make war companies, cozy up to politicians, and talk about how woke is ruining the world. /s
        • roenxi 2y ago
          In fairness, 'geopolitical experts' may not really exist. There are a range of people who make up interesting stories to a greater or lesser extent but all seem to be serially misinformed. Some things are too complicated to have expertise in. Indeed, while the existence of socioeconomic experts seems more likely we don't have any way of reliably identifying them. The people who actually end up making social or economic policy seem to be winging it by picking the policy that most benefits wealthy people and/or established asset owners. It is barely possible to blink twice without stumbling over a policy disaster.
          • benzible 2y ago
            So, you think the system is genuinely trying to identify expertise to achieve equitable outcomes, and just happening to fail at it? Rather than policy being shaped by personal networks and existing power structures that tend to benefit themselves?
• Nihilartikel 2y ago
  I'm not convinced that this is more than the market having a jump-scare at an incremental leap in model building technique. Say DeepSeek has worked out how to do more with less - that's great! I don't think it means that the market for Nvidia's silicon (or anyone else who can hop the CUDA moat) is going to shrink. I think that the techniques for doing more with less.. will be applied to _more_ hardware, to make even bigger things. AI is in its infancy, and frankly has a long way to go. An efficiency multiplier at this stage of growth isn't going to reduce the capital needed, it will just make it go further. There may be some concern about scaling the amount of training material, but I don't see that as the end of the road at all. After all, a human's mental growth is hardly limited by the amount of available reading material. After all written material is trained upon, the next frontier will just be in some other mimicry of biological metacognition. At least that's my armchair analysis.
• ezoe 2y ago
  I really don't understand the market thinks Nvidia is losing its value. If DeepSeek reduce the required computational resources, we can pour more computational resources to improve it further. There's nothing bad about more resources.
  • tonyhart7 2y ago
    - "I really don't understand the market thinks Nvidia is losing its value." because the less GPU need to train, the less money to be made - "If DeepSeek reduce the required computational resources, we can pour more computational resources to improve it further. There's nothing bad about more resources." thats why you are not hedgefund manager, these guys job is to ensure that the HYPETRAIN for company to buy as many nvidia gpu to sell no matter what, if we can produce comparable model without using B (as it stands billions of dollar), it means there are less billions of dollar to be made and the HYPETRAIN is near the end
  • skellington 2y ago
    Markets aren't rational. NVidia is currently a hype stock which means LOTS of speculation, probably with lots of leverage. So, the people who have made large gains and/or are leveraged are highly incentivized to panic sell on any PERCEIVED bad news. It doesn't even matter if the bad news will materially impact sales. What matters is how the other gamblers will react to the news and getting in front of them. :)
    • aunty_helen 2y ago
      Another thing, the markets had priced in X demand scaling @ ~145 and suddenly it's X/30 demand scaling and therefore the price should drop.
      • skellington 2y ago
        True, but current price isn't based on fundamentals, it's based on hype-value. nVidia is going to be a very volatile stock for years to come. I don't see deepseek changing nvidia's short term growth potential though. Efficiencies in training were always inevitable, but more GPU still equals smarter AI....probably.
• plaidfuji 2y ago
  Here’s a take I haven’t seen yet: If training and inference just got 40x more efficient, but OpenAI and co. still have the same compute resources, once they’ve baked in all the DeepSeek improvements, we’re about to find out very quickly whether 40x the compute delivers 40x the performance / output quality, or if output quality has ceased to be compute-bound.
  • BenFranklin100 2y ago
    That seems to be the key question.
  • deleted 2y ago
    [deleted]
  • xbmcuser 2y ago
    Yeah this was my first thought as well. If it got so efficient how good all the models will be 2-3 months from now
  • zeven7 2y ago
    I've missed the stories on this until now. Is it known (and is there an ELI5) how they were able to do it so much more efficiently?
    • riffraff 2y ago
      IIUC they released a paper, it's partially algorithmic improvements partially good old low level optimization.
      • CursedSilicon 2y ago
        There's something to be said about the idea that instead of just dumping oceans of money into buying Nvidia cards they just...optimized what they had I'd say the wider industry could learn a thing or two, but as other commentors have joked. The line must go up
        • 2y ago
• thw09j9m 2y ago
  I think the big picture that many people are missing here is the motivation that all these AI/tech companies have for buying up so many GPU's in the first place: achieving AGI/ASI. And while some still try to portray a dedication/duty to AI Alignment, I think most have either secretly or more publicly moved away from it in the race to become the first to achieve it. And I think, given that inference time compute is so much cheaper than pre-training, the first to achieve AGI might have enough compute on hand from having been to first to build it that they would not need to purchase many more GPU's from Nvidia. So at some point, Nvidia's revenues are going to decline precipitously. So the question is: how far away are we from AGI? Seems like most experts estimate 3-10 years. Did that timeline just shrink by 50x (or at least by some multiple) from these new optimizations from DeepSeek? I think Nvidia's revenue is going to continue to grow at its current pace (or faster) until AGI is achieved. Just sell your positions right before that happens. (Not an expert. Not even an amateur)
  • klodolph 2y ago
    > Seems like most experts estimate 3-10 years. That’s spitting distance from “we don’t know”. I think most of these valuations are made by people without the expertise to predict what the next one year of AI looks like, never mind 3-10 years.
  • YZF 2y ago
    It doesn't feel like DeepSeek has a big enough breakthrough here. This is just one of many optimizations we're going to see over the next years. How close this brings us to "AGI" is a complete unknown. The large investments were mainly for training larger foundation models, or at the very least hedging for that. It hasn't been that clear over the last 1+ years that simply increasing the number of parameters continues to lead to the same improvements we've seen before. Markets do not necessarily have any prediction power here. People were spooked by DeepSeek getting ahead of the competition and by the costs they report. There is still a lot of work and some of it may still require brute force and more resources (this seems to be true for training the foundation models still).
  • goatlover 2y ago
    Not that I believe it's likely to happen, but it seems incredibly fucking dangerous for there to be an ASI race with one winner. To the extent these companies believe it's possible, what are they hoping will be the outcome for humanity? That they get to be the trillionaires with an untouchable moat? Wouldn't this be like creating a Kwisitz Hadarach thinking you can control it, to borrow a Dune reference?
• DAGdug 2y ago
  All the references to Jevon’s paradox fail to account for three things: 1. There’s no good forecasting model to account for how aggregate demand moves as a function of efficiency gains in this space 2. Aggregate demand isn’t the same as Nvidia’s share of market, which could drop if alternative paradigms for training or inferencing gain traction 3. Forecasting time horizons matter for discounted cash flow/valuation calculations, which nobody has a good basis for IMO, there’s just a lot of uncertainty, and it’s fair for the market to discount the optimistic trajectory aggressively based on net new info.
  • ponty_rick 2y ago
    Summarizing in Kevin Malone style - Nvidia good now. Later? No know.
• deleted 2y ago
  [deleted]
• Too 2y ago
  Jeez chill... it’s just back to where it was 4 months ago and even after the drop it is still up 100% compared to this time last year! And it’s all fake inflated money. This unprecedented growth simply couldn’t continue forever.
• nicman23 2y ago
  waiting on 1500 5090
• robomartin 2y ago
  I feel that one of the most fundamental aspects of business reality is being skipped in all of these conversations, on and off HN. Everyone building an AI data center is likely using Nvidia technology. Sure, there's a 20% that is partially using other technology. The bulk of it is Nvidia. If your project is in the planning for the next, say, two years, you have already placed your orders with Nvidia or are going to in the next few months. Hardware has real lead times. You don't compile yourself 100K chips. They have to be made and you have to wait in line to get yours. For example, I remember when, during the pandemic, we had to place orders for chips with 40 to 50 week lead times. This means you have to make decisions today (or you already made them months ago) to get in line. Changes in training or inference efficiency should not change these orders or plans at all. If someone can train faster, they will benefit from the hardware in the pipeline. If they can make inference more efficient, they will be able to service more requests at reduced transactional costs. The orders are in the pipeline and will continue to be added to the pipeline. Nvidia isn't going to be shipping half the hardware because Wall Street, overnight, panicked. What the grocery store owner does with their stock portfolio because they panic has nothing whatsoever to do with reality. The same is true in the other direction. Wall Street has been going nuts with quantum stocks. Companies like Rigetti have exploded from nothing to see insane gains. This does not mean the company went from, well, shipping nothing to shipping real working solutions at scale. Today's market reaction was nothing less than sheep running scared because someone when "boo!". It has nothing whatsoever to do with business realities on the ground. Go build an AI data center without Nvidia chips (or with 10x less chips) and see how that goes when everyone is loading-up with them.
• qz_kb 2y ago
  DeepSeek R1 just uses crappy PPO ("GRPO" is just using the sharpe ratio as a heuristic approximation to a value function) on top of distilled existing models, with tons of pipelining optimizations manually engineered in. I don't see this making leading edge research any less expensive, you won't get a "smarter" model - just a model that has a higher probability of giving an answer it could already give. If you want to try and do something interesting with the architecture the pipelining optimizations now slow down your iteration capability heavily. The RL techniques present will only work in domains where you can guarantee an answer is right (multiple choice questions, math, etc.). It doesn't really present any convincing leap forward in terms of advancing the capability of LLMs, just a strategy for compute efficient distillation of what we know already works. The fact this shitty PPO proxy works at all is a testament to the fact that DeepSeek is bootstrapping its capability heavily off of the output of existing larger models which are much more expensive to train. What DeepSeek R1 proves is you can distill a ChatGPT et al. into a smaller model and hack certain benchmarks with RL. If you could just do RL to predict the best next word in general this would have been done already - but the signal to noise ratio on exploration would be so bad you'd never get anything besides infinite monkeys at a typewriter. It's not a novel/complicated idea to anyone familiar with RL to try and improve probability of things you like, and whoever decided to do RLHF on an LLM surely thought of (and did) regular RL first - and found it didn't work very well with whatever pretrained model and rewards they had. it was like two weeks ago people were going crazy about O3 doing arc-agi by running the exact same kind of traces R1 is doing in "GRPO" at test time rather than train time. Doing this also isn't novel and also only helps on shitty toy problems where you can get a number to tell you good vs bad. There is no mechanism to compute rewards for general purpose language tasks - and in fact I think people will come to see the gains in math/coding benchmark problems come at a real cost to other capabilities of models which are harder to quantify and impossible to generically assign rewards to at internet scale. To explore the frontier of capability you will still need a massive amount of compute, in fact even more to do RL than you would need to do standard next token prediction - even if the LLM might have fewer paramters. You also can't afford to do all the optimizations as you try many different complex architectures.
• divbzero 2y ago
  Greater efficiency of light bulbs has led to more light bulb use, not less. More efficient training of LLMs could just as likely lead to more chip use, not less.
  • gniv 2y ago
    But did it not lead to net less electricity used for lighting?
    • BobbyJo 2y ago
      I'd wager there is more light today than before LEDs were invented. I'd wager a lot in fact.
    • divbzero 2y ago
      With more efficiency offset by more light bulbs, electricity used for lighting has been roughly flat since 2010: https://www.iea.org/data-and-statistics/charts/global-electricity-consumption-in-lighting-in-the-net-zero-scenario-2010-2030 https://www.iea.org/data-and-statistics/charts/global-electr... (For LLMs I wish that efficiency could lead to less electricity used for chips, but I think the best we can hope for is for electricity use to flatten out.)
      • gniv 2y ago
        From that graph it looks like residential went down quite a lot, which makes sense since that was the main usage of incandescents.
      • jes5199 2y ago
        we just have to get solar deployed sufficiently so that we no longer think of energy use as a waste of something precious
    • jstummbillig 2y ago
      Maybe, at some point. From what I can tell there's are mostly two options: Either AI is and will be useless or it's severely undersupplied. People, even those deeply technical, where AI has the most impact right now, still widely argue about if AI even offers any value. Adoption is far from anything that is plausible, if (not when) it became clear that it does. If you land on "does not", given the investments so far, commercial entities would obviously be overvalued already and any investment goes to 0 over time. But if we land on "does", how could Nvidia not be anything other than undervalued right now? No matter what frontier model: I can look at my screen, LLM generated characters visibly appearing in chunks, depending on the model after initially waiting for 10-20 seconds, for even benign queries — because that is the best we can do right now. And that's while most people still argue if AI will actually do anything and humanity at large does not really use it, neither personally nor societally. If AI does in fact do something valuable and that something gets better, everyone will want it and there will be demand for lots of chips.
• sakopov 2y ago
  Has the supposed DeepSeek training budget been verified in any way?
• shahzaibmushtaq 2y ago
  DeepSeek direct competitors[0] are OpenAI's models, Meta's Llama, Anthropic's Claude etc. NVIDIA isn't even DeepSeek indirect competitor. And no investor/expert in their right mind would compare a software company to a hardware company. [0] https://api-docs.deepseek.com/news/news250120 https://api-docs.deepseek.com/news/news250120
• captainhulllo 2y ago
  So the day this happened I didn't eat all day which was a weird thing. I don't know just one of those weird coincidences for no reason. Tomorrow I won't eat all day again and see what happens. Anyway I'm homeless so the not eating all day maybe kinda possibly having something to do with wiping out 600 billion dollars in stock market valuation just on the off chance that it might based on nothing more than wishful thinking? Nope. Not eating tomorrow. Wonder what will happen.
• butterisgood 2y ago
  One day does not a pattern make. Just bought some nvidia stock. Guess we shall see how it pans out…
• freddealmeida 2y ago
  funny thing is they prob had a position to make bank off that. smart.
• bigbacaloa 2y ago
  [dead]
• cultofmetatron 2y ago
  I dont' know what the surprise is here. the human brain consumes about 20 watts. literally a rounding error compared to what chatgpt uses. so we already know that there was plenty of room on the table to improve. incidentally, I love these kinds of market crashes. just moved a big chunk of my savings account into stocks last night :). Buy and hold. dont' sell during a dip lol
  • ludvigk 2y ago
    Why do you think the human brain and chatgpt are in any way related?
    • Satam 2y ago
      Because LLMs are based on the abstract ideas of neural nets from brains. Say what you wish, but some problems were completely unsolvable before we adopted this paradigm. On some level, we must've gotten some ideas close to the right ballpark.
  • lvl155 2y ago
    This is not a market crash. I don’t know how old you’re but you might have been conditioned to think this way by the market’s unrelenting march higher. But those of us from the dotcom days can tell you it crashes eventually and very painfully.
  • thrance 2y ago
    Maybe not that pertinent, but I remember someone computing joules per word written in humans vs chatgpt and found the former to be a bit higher.
• verytrivial 2y ago
  Is there evidence for the claim that the training was that much cheaper? Or is is 'apparent' by inspection of the verified ingestion architecture? I can't seem to find any evience beyond the team's statements. What have I missed?
  • Jasp3r 2y ago
    The hypothesis I have is that China has way more compute resources than they are willing to share. Compute resources they officially should not access to given export bans, where mentioning them might lead to their export ban bypass getting rolled up.
    • verytrivial 2y ago
      Maybe. They're under no obligation to tell the truth about this. Hypothetical: Take a large short position on NVDA, announce the market that you trained a massive model without using 10s of millions of rare-as-hen's-teeth NVDA-sentsitive resources. Settle postition then quietly settle giant compute bill. Difficult to know either way, but the market seems have taken the team at face value. I guess we'll know if and only if this reduced training cost methodology is replicated.
  • verytrivial 2y ago
    Well. https://www.tomshardware.com/tech-industry/artificial-intelligence/deepseek-might-not-be-as-disruptive-as-claimed-firm-reportedly-has-50-000-nvidia-gpus-and-spent-usd1-6-billion-on-buildouts https://www.tomshardware.com/tech-industry/artificial-intell...
• orwin 2y ago
  Is this linked to the fact that they bypassed CUDA, making Nvidia's moat way shallower that it was thought to be?
• fcanesin 2y ago
  Nvidia doesn't have a monopoly on GEMM and GEMV. There will be dozens of hardware vendors. It is TSMC that should be the most valuable company in the world.
  • farseer 2y ago
    By that logic TSMC is not the only one capable of 5nm. ASML should be the most valuable company in the world?
    • fcanesin 2y ago
      Certainly one of the most valuable, but I would still say TSMC as there are lots of other steps in the production besides photolithography (etching, ion implantation, vapor deposition, packing, ...).
• mustaflex 2y ago
  Not long ago Sam Altman was talking about how they were loosing money even on the paid version of chatgpt. Those incoming prices hikes going to be difficult to sell now.
• m101 2y ago
  1) to address frontier model company stock valuations (openai for instance): deepseek is creating stuff months after frontier companies. In the AI arms race it might make sense to burn billions to be 3 months ahead (think about how you use that superintelligence to prevent anyone else acquiring it) 2) to address Nvidia valuation: there is no cap to demand on intelligence (or, we're not close). People will never be satisfied with the intelligence achieved and just stop asking for more. So Nvidia will still sell the hardware as the demand side is uncapped. Unrelated note that I was considering and would like an opinion on: Nvidia is the software play in AI, and TSMC the hardware play. Nvidia has competitors like broadcom/AMD/TPUs but beats out on software. TSMC will be frontier on manufacturing everyone's hardware.
  • steve76 2y ago
    [dead]
• sghiassy 2y ago
  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 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
  • cubefox 2y ago
    This comment seems to be complete nonsense. See here https://arxiv.org/abs/2412.19437v1 https://arxiv.org/abs/2412.19437v1
    • sghiassy 2y ago
      The internet would be a little better, if people were a little nicer
      • cubefox 2y ago
        Sorry
        • sghiassy 2y ago
          Thanks for saying that :) I appreciate you
  • gilbetron 2y ago
    I think you are missing it: https://stratechery.com/2025/deepseek-faq/ 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).
    • sghiassy 2y ago
      Thank you for the link. I have a lot of respect for Stratechery. I learned a lot, and agree, I’m the one who was missing it haha
• d--b 2y ago
  NVidia stock is valued at $118 today. It was worth $50 in Jan 24. While the drop may sound huge, we're pretty far from a crash here.
• deleted 2y ago
  [deleted]
• ubermonkey 2y ago
  So I was aware of the basic story here from late yesterday, but I only actually looked at the numbers today. Focusing on the dollar figure here is misleading. Basically, if you look at only the stories, you get a dire picture -- like, "how will they make payroll????" However, if you look at the actual share price in context, you see . . . a not very interesting event. Look here: https://www.macrotrends.net/stocks/charts/NVDA/nvidia/pe-ratio https://www.macrotrends.net/stocks/charts/NVDA/nvidia/pe-rat... The graph definitely shows a share price adjustment, but it by no means erases the run-up Nvidia has enjoyed in the last 2 years. All that happened was the stock dropped back to it price of about the middle of last summer. This may be old news to most of you, but: in markets we also track a figure called the P/E ratio, which is the ratio of the price of the company vs. its profits. Old-school manufacturing firms -- what used to be "blue chip" stocks -- would be in the 18-22 range here. Apple which absolutely PRINTS cash, is at a high-flying 38. NVidia's P/E is still 58.8. The tl;dr here is that NVidia is still valued very, very highly. They're still what, the 3rd most valuable company in the world, with an enviable position in chipmaking. They still make money hand over fist. The weird part is that their firm is SO valuable that they could take a $600B haircut and it be almost a non-event.
• coryfklein 2y ago
  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/ https://www.tradingview.com/x/b42zFn05/