HN

Comments (67)

• YuechenLi
  Nvidia's biggest advantage in AI has never been only their hardware performance but how entrenched their software is in ML research that flowed down stream. However, if you've actually used CUDA C/C++, it's pretty one of the worst software development ecosystem imaginable: you get all the footgun of regular C++, plus GPU compute pretending to be C++ and but doesn't actually behave like C++ because CPU and GPU compute are fundamentally different, and the only reason people put up with it is because Vulkan and HIP C/C++ are even worse.Google's limitation is that they still don't offer TPUs in a PCI-E card/dev board that people can plug in to their PC for local development and sane low level API to develop against, instead you have to go through their cloud and their full software stack which greatly limits ecosystem growth. The minute that Google figures that out, that's when Nvidia's dominance would be challenged.
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  jcfrei
  In many investment theses - like Nvidia's bet that demand for compute will keep growing - the first order assumption is usually correct. Yes, demand for more compute, chips, infrastructure is huge and each year some additional data centers will be built. Where such investment bets usually fail is in the second-order assumptions: Ie. the expectation of the growth of demand. This is where there's a high chance that the current expectations are likely exaggerated. So: demand is likely to persist for the foreseeable future but not increase every year. And that can upend the whole investment story. That can be enough to make these bonds a huge burden for Nvidia in the end. Not because people stopped buying more compute but because they stopped buying more every year.
• dnnehgf
  so short them. if you think that the demand for skilled-labor-substitutive capital is saturable in the medium term or that improvements at the model level eat those at the hardware/cuda level or that nvidia just has the timing wrong, short them.
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  dzonga
  Nvidia has been playing a dangerous but profitable game since the Crypto boom.but now I think they probably have bitten more than they can chew.Apple already proved with their unified memory - that as long you have the capacity you can run capable models locally - thereby goes demand for inference if everyone is running some model locally.For training - Chinese models have proved that you don't need the latest & greatest in Nvidia hardware. Same as TPUs.only time will tell.
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  tolugenius
  More interesting take on Nvidia's position than I've come across before. One thing to be noted is 1) Nvidia is already making moves in robotics so even if their position in AI (moreso llms) diminished, they certainly have another big avenue arguably harder to just get into (although I'm not sure what efforts Google is doing for the tpu in robotics). Another point is Nvidia is still the main player in the west, that is, China certainly can and will create their own full stack without reliance on US companies. That puts Europe and other countries in an interesting, do you buy Nvidia because it's the only option or for security. That's to say I believe Nvidia's position relied on many different things being true at the same time, and we're moving towards an environment where those things are certainly being contested at (roughly) the same time.
• znnajdla
  There's another factor which Ben failed to consider. Which is that NVIDIA doesn't need to rely on demand for their proprietary CUDA stack or their GPUs growing -- they are already selling directly to the consumer, and likely capturing much higher margins. They are moving up stack, not down, where demand for raw compute matters less. With the DGX Spark and Jensen’s statement about “open models”, their next product is likely a strong hint: consumer devices to fulfill the Mac Mini demand craze. They are probably going to start burning LLMs durectly onto sillicon and then selling DeepSeek-in-your-home to individual developers. I bet that would sell even better than Anthropic Max coding plans and is not dependent on hyperscaler funded boom-bust cycles. So Ben’s analysis highlights the risk of their existing business not growing but they are likely planning new businesses.
• Dardalus
  Tend to agree with Ben's thesis RE Demis and DeepMind not really being focused on the agentic coding race. That being said, it remains to be seen whether Sergey and Koray can inspire the foot soldiers in the same way that Sama and Dario do. I'm not too optimistic, and that's to say nothing of the fact that Google cannot possibly hope to compete with these other companies on potential employee upside.
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  KaiMagnus
  IMO focusing on the hyperscalers is kind of misleading.Yes, for programmers and tech companies AI is kinda boring now, but AI integration in general is still kind of uncharted territory.There are so many small companies and individuals just getting started with AI today and I believe a large the customer base (and revenue) is still untapped. Hell, I’m discovering new use cases regularly still and the average mismanaged 30 people whatever SaaS vendor probably didn’t even get started yet.
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  Erikun
  I see we have reached the stock market phase of Universal Paperclips.
• clarkmoody
  > To translate such figures into comparable 2026 magnitudes, multiply by a factor of 1,200.Perhaps this has something to do with the economic dislocations and world wars between the 1870s and today?
• petesergeant
  > The subsequent bankruptcy of Jay Cooke & Company triggered the Panic of 1873, culminating in endless railroad bankruptcies across the country, a multi-year depression, multi-decade deflation, and, one could argue, the financial conditions that made Europe, four decades later, into a tinder box.American history education needs some dire reform.
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  cmiles8
  Nothing goes up and to the right forever. Nothing.Building a business model on the belief that “this time is different” always finds storms on the horizon.
• Altaba
  Ben is wrong; demand for compute, aka revenue backlogs, is mythical and will collapse, simply because of two reasons :1. Circular investment/spending.2. Too much capital in the system, so returns cannot be hit regardless because the barrier is too high. (Evidence being every capital cycle in history)
• petesergeant
  > After the departure of DeepMind CEO Demis Hassabis (technically promoted to chairman, but no longer in charge of day-to-day operations) and Gemini co-lead and former Chief Scientist Jeff Dean, along with a host of other prominent researchers, SemiAnalysis declared that Gemini is Cooked: "For all intents and purposes, we believe DeepMind is no longer a frontier lab"Counterpoint: xAI pooped out a frontier model based on nothing but capital and one man's desire to push a right-wing political narrative. Google has the talent, and the money, and the experience, they just need some leadership.
• echelon_musk
  Is this just an ad for a new book about trains?Disappointed by the lack of Tom Cruise.
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  RustaIsBest
  [flagged]
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  dh2022
  Blaming some railroad bankruptcy for starting WW1 is where I stopped reading. Seriously, this guy should re-read what he puts out before posting.
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  u1hcw9nx
  Ever free newsletter and talking head spouts narratives like this free. If you want something that quantifies and gives actionable information, you must do it yourself or pay for it. What are your below $2000/month sources for good analysis?