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Nvidia's Risky Business
- dh2022 2mo ago[flagged]
- zaphar 2mo agoHe was referencing a book that made that case if you "squint". If you read that as actually a serious "this caused world war 1" statement rather than. This looks to have gotten some dominoes rolling that may have contributed to WW1 then that says more about you than it does about the article itself.
- dh2022 2mo agoRe: "This looks to have gotten some dominoes rolling that may have contributed to WW1 " - people who believe this should read some history about beginning of WW1. I recommend starting with "The Guns of August".
- simonw 2mo ago> Blaming some railroad bankruptcy for starting WW1 is where I stopped reading. What a weird reason to stop reading an article.
- dh2022 2mo agoI am assuming the rest of article is as well researched as the, rather long and incredibly incorrect, introduction. Bayesian inference and all....
- tomhow 2mo agoPlease don't post grouchy comments like this on HN. The guidelines make it clear we're trying for something better here. https://news.ycombinator.com/newsguidelines.html https://news.ycombinator.com/newsguidelines.html
- dh2022 2mo agoI did not mean to be snarky. But still, the proposition that a railroad bankruptcy “led to world war” (direct quote from the article) 40 years later is ridiculous.
- tomhow 2mo agoWe just don’t want denunciatory rhetoric like “is where I stopped reading” or “this guy should re-read what he puts out” or “ridiculous”. If someone is wrong just point out where they’re wrong. Educate us, don’t fulminate.
- tolugenius 2mo agoMore 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.
- wongarsu 2mo agoEven in the west, Nvidia's dominance is bound to weaken. There is a notable uptick of articles on HN about people running large models on AMD hardware. And while I don't know official sales figures, I know we have trouble getting our AMD system delivered AMD's software story is still a lot worse than Nvidia's. But patching up vllm to run one or two models you care about on AMD hardware is a much easier proposition than using them in most other fields of AI.
- moralestapia 2mo agonVidia will fail right after reaching an 8 trillion valuation and supplying 80% of the world's hardware! Trust me guys, it's over!
- doctorwho42 2mo agoI think this is a great example of the disconnect people have in these types of conversations. You can both become a company that supplies 80% of the world with your type of product, and then still have your stock go down in value. All it takes is over evaluation by the stock market. Then a course correction from unsustained growth on growth (second order). So even if you continually replace YoY 80% of the world's hardware on a rotating business, but you don't increase market share or increase demand (aka growth)... Your business looks stagnant to the stock market, and there isn't really anything you can do about it. The best you can do is track inflation +/- 2%. And that's why a lot of older established companies were dividend stocks. You don't expect to growth anymore, but that's not where the value is anymore... The value is in the reliable sales that will happen after infinitum because your company controls a majority share of the business... And that's ok! Unfortunately, silicon valley has created a philosophy of 'you gotta expand into new fields or your on the decline' - aka neo-monopolization
- u1hcw9nx 2mo agoEver 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?
- tguedes 2mo agoThe website from this article. It's not free. Ben Thompson releases 1 free article a week but the other 3 articles published each week requires a $15/month subscription. Ben Thompson is also very influential in Silicon Valley and the overall tech/media industry.
- u1hcw9nx 2mo agoI know him, I subscribed for a while. Even his paid content is lacking. I'm looking more Valens Research kind of analysis. SemiAnalysis is also good in the higher tier. ps. Being influential in Silicon Valley just means you are influential, it does not mean substantial. Leopold is still influential and gets money thrown at him at $100s of million despite having no substance.
- kaonwarb 2mo agoCriticism with no justification behind it is cheap.
- ey5e5uer5ur 2mo agoirony
- claytonjy 2mo agoyou might be looking for SemiAnalysis? I only read the free portions of articles but they have various paid options, mostly targeting investors with information and tools. https://semianalysis.com/ https://semianalysis.com/
- RustaIsBest 2mo ago[flagged]
- cmpxchg8b 2mo agoThat has literally nothing to do with the article.
- mgrunwald_ 2mo agoNice trolling.
- Erikun 2mo agoI see we have reached the stock market phase of Universal Paperclips.
- doctorwho42 2mo agoHonestly, that game with a few changes would be very on the nose today :D
- Theodores 2mo agoPhew. I thought we were just about to leave it, albeit not quite getting to space, just dissolving the financial system so our AI overlords can make some more harvester drones.
- jcfrei 2mo agoIn 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.
- onlyrealcuzzo 2mo agoWhat makes this insanely hard to predict is that the compute needed for the same quality output has roughly gone down 90% every 18 months for ~5 years. 1) We don't know how long that trend will continue, but you do know where to look for when it may end (if smaller sized models continue to compress the knowledge effectively of larger models). 2) We don't know when the appetite for higher cost models might go down and by how much if smaller models get "good enough" and price becomes far more important. It is entirely possible that 5 years from now, there's >100x LLM inference going on - but demand for AI chips (including memory) is only 2x or less. It is also entirely possible that at some size - LLMs pick up some emergent capability that doesn't scale well to smaller sizes - and that there's an incredible boost to demand to get that capability. It's just very hard to predict.
- mattnewton 2mo agoI think efficiency is unlikely to result in lower demand for compute, instead more useful compute per watt increases the value of that compute; and we are not going to run out of economically useful things to do with it anytime soon on the demand side. The harder thing to forecast for me is if we hit a wall on increasing efficiency, either on the model weights side or silicon side, with current approaches. If we have to switch to something like burning the model weights into silicon to continue to make gains, then the current math on general purpose accelerators might be upside down.
- cmiles8 2mo agoNothing 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.
- pelotron 2mo agoWhat if we build our whole economy on that belief?
- CodesInChaos 2mo agoA lot of things go up forever, as long as you denominate them in an inflationary currency ;-)
- cmiles8 2mo agoThere’s a massive difference between “going up forever” and “point B is higher than point A.” The current setup can’t sustain a downturn, even if yes 20 years from now point B is likely to be higher than present. That’s the danger. Those that are going to get wiped out by the AI bubble burst aren’t wrong about AI being huge long term, they just put themselves in a position to not survive the storms that happen between points A and B.
- rglover 2mo agoThis is the best articulation of this problem/paradox I've read yet.
- barbecue_sauce 2mo ago
- KaiMagnus 2mo agoIMO 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.
- yaportmax 2mo agoThis is what so many people on HN and the market are constantly missing. Jason Kottke almost didn't found his blog in 1998, famously quoted as saying: "I thought I was too late, that no one would be interested." Needless to say, the internet was a tiny joke in 1998 compared to what it is now. We are just barely scratching the surface of what's possible with AI, both in terms of the leading edge and in the 'torso' of the economy (the portion you're describing). Folks from Silicon Valley working in AI-forward companies have a skewed perception of how many people have adopted this technology so far. Codex recently celebrated hitting 10 million users. This is a great milestone and all, but to put it in context, Microsoft office has a billion users. Sure, many people use Claude Code and or some other harness and the growth is staggering, but the overall scale is tiny compared to software as a whole. Costs of serving and usage are still very high, prohibitively so for many, so we aren't even close to market saturation. And even at the leading edge, people who do work in those AI-forward companies; models are still slow, require hand holding, and produce suboptimal outcomes sometimes. Imagine the value when instead of needing to prompt it once per 30 mins, you prompt it once per day. Then once per week. Then once per month. Imagine all this running not on 3 trillion parameter models, not on 10 trillion, but 100 trillion. What kind of computer infra will be needed then? Certainly more than we have today.
- RyanOD 2mo agoI've always marveled at how one can pick any year since the internet went mainstream and in that year people thought, "Oh my goodness, the internet is amazing!" Then, move five years forward from that year and look back. In every case, people think, "Hah! The internet was so simple then!" In 2031, I suspect we'll say the same about 2026.
- echelon_musk 2mo agoIs this just an ad for a new book about trains? Disappointed by the lack of Tom Cruise.
- Altaba 2mo agoBen 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)
- motoxpro 2mo agoI think more interesting take here would be WHEN this will happen. I don't think Ben, or anyone else, thinks we wont have some sort of correction or stabilization in supply/demand (he has said as much) But when will that occur? 2 months? 2 years? 20 years?
- Altaba 2mo agowell its typically when it becomes clear that the private equity firms taking the risk decide they can not get the returns they need, forcing the backstoppers such as Nvidia to take that burden, and the whole ecosystem collapses.
- motoxpro 2mo agoI guess my point is that until that collapse, returns are very very very good. This can (and probably will) go on for a decent amount of time more, regardless of the inevitable things you point out. My guess is at least another 2 years, as most people don't use AI yet, or maybe more precisely, AI is not used in the underlying workflows (which are invisible to the consumer) that make up most people's jobs. Who knows if I am right. My original post was just pointing out that what you say is about timing, not whether it's true or not, because of course its true.
- Altaba 2mo agoYes, agreed, it is timing. I think a sign we are getting closer is the new equity issuances, which kind of are leveraging the current environment and the retail excitement.
- dzonga 2mo agoNvidia 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.
- pletnes 2mo agoNvidia sell iot boards with unified architecture. Would not be shocked if they launch pc/laptop/server boards at some point.
- synergy20 2mo agothat undercuts their core business, so it will be a defensive play at most to fend off mac and amd's local inference offerings
- bigyabai 2mo agoI don't know how people can say this with a straight face. Nvidia was selling desktop-grade ARM SOCs before Apple Silicon was ever announced, specifically for edge robotics, computer vision and ML. The absolute fastest desktop Mac GPUs cannot beat an Nvidia laptop GPU in prefill or inference speeds. Apple Silicon is a non-entity for professional datacenter deployment and arguably unusable for frontier models at agentic context sizes. AMD is Nvidia's primary worry, and they're not doing much better in terms of GPGPU SOC compute.
- officeplant 2mo ago>Nvidia was selling desktop-grade ARM SOCs before Apple Silicon was ever announced You can believe all you want that the dinky little jetson boards were desktop grade when historically the ARM SoC portion of a jetson board couldn't even keep up with broadcom/rockchip SoCs. It's taken until recently for the actual arm compute portion of Nvidia SoC's to be worth a damn at all, and they still fall far behind Apple let alone the rest of the pack like Qualcomm/Samsung.
- clarkmoody 2mo ago> 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?
- Dardalus 2mo agoTend 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.
- HDThoreaun 2mo agoI think the linked semi-analysis piece is about right. Google is more focused on protecting its 4.2 trillion dollar golden goose than pushing cutting edge which leads to a bureaucratic nightmare that researchers simply dont need to engage with when openAI/anthropic are offering even more money. Why waste time dealing with bureaucracy at google when you can be top dog at openAI or anthropic?
- mrandish 2mo ago> Google cannot possibly hope to compete with these other companies... It's possible Google has intentionally decided to take a more conservative blended approach than purely competing at the bleeding edge of the frontier. If so, they obviously have no incentive to state it publicly but the recent departures and financials are consistent with the idea. It also makes sense that a company so much bigger, longer-term and (somewhat) more diversified than pure-play frontier labs would play the game to align with their strengths (capital, balance sheet, breadth, etc). In their position, why not take an 'arms supplier' strategy in the near-term while drafting behind the frontier labs as a fast follower in AI, essentially betting the AI race is more akin to the Indy 500 than a quarter-mile drag race. If they're wrong and it IS more like a drag race, Google is in a better position to absorb and adjust than a frontier lab, for whom current valuations and capex spending requirements nearly require this to be a relatively short, winner-take-all race.
- petesergeant 2mo ago> 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.
- petesergeant 2mo ago> 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.
- znnajdla 2mo agoThere'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.
- YuechenLi 2mo agoNvidia'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.
- HeWhoLurksLate 2mo agoI mean they had/have the Coral but that's in an entirely different market segment
- bigyabai 2mo agoCoral and the TPUs are ASICs, and therefore are barely reprogrammable. It doesn't really compare to the complexity and flexibility of CUDA ALUs.
- musebox35 2mo agoThe biggest advantage of tpus is the high bandwidth fiber optic interconnect between them that allows distributed computing on pods with thousands of tpus and the co-design of cooling systems that go with their racks. I do not think that we will see personal tpus any time soon.
- dnnehgf 2mo agoso 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.
- rcr-anti 2mo agoFor awhile I've found two things hard to square, that the hardware and software making up current gen AI will bring us to a socioeconomic singularity, and the reality the thing they're mostly trying to emulate is a few pounds of meat and fat running on tens of watts equivalent. On one hand the current AIs are obviously super human in some tasks, get completely dunked on in others by far simpler organisms. My cat can catch a bug out of the air, Fable 5 in Cowork can lack the dexterity to make a slideshow because I had LibreOffice instead of Microsoft Office. Not even close to analogous, but point being they appear to have pretty fundamental differences in how they can interface with the world that the economic thesis seems to gloss over.
- atom_arranger 2mo agoAnother interesting discrepancy is that people think current GPUs are maybe capable of running AGI but they still can barely manage photorealistic rendering of a single room in realtime, or simulate something like a shirt thrown into a pile of laundry. They can generate a video of it based on millions of existing videos, but not do a real simulation of light and physics in realtime.
- TheBicPen 2mo agoAn AGI doesn't need that level of detail to do most tasks effectively. To use the OP's example, a cat catching a bug out of the air does not need to run a fluid dynamics simulation of airflow over the bug's wings to be able to catch it. A cheap approximation of the flight path is sufficient. Perhaps some physical tasks will need that level of detail but many will not.
- TonyStr 2mo agoIs the human brain capable of doing real simulations of light and physics in realtime? Or does it hallucinate the details and represent some low-resolution mental image? You may be overestimating the capabilities of flesh-based neural networks and underestimating the capabilities of silicon-based neural networks.
- 2mo ago
- gizajob 2mo agoLaughable to cite and reiterate the idea that Google is cooked where it comes to SoTA and AI in general when they operate, reliably and successfully for decades, one of the largest computing infrastructures on Earth and will likely continue to usefully serve the 90% of AI requests that don’t involve managing large codebases. Also seems strange to suggest that Google would need to Aquihire a company like Thinking Machines when it could spin up their AI model in a couple of weeks on its own TPUs if it felt like it. Demis likely wants to focus on his specific interest at the junction of biochemistry, neurobiology and computation which is more specific and unique to Demis than building a general purpose Q&A search model.
- epolanski 2mo agoI actually agree with the article stating that not aiming for SOTA is actually a benefit for Google. Gemini is already good for enterprise users, most companies I know are using Gemini 3.1 to interact with their email and sheets and creating presentations on the fly.
- davedx 2mo agoPeople and pundits have been dooming and bearing on Nvidia for as long as it's been around. It increased in intensity when gpus were used for large scale crypto mining and it became material to their operations, and continued as AI ("the bubble") started to really take off. Over those years, my NVDA stock has been by far my biggest winner. I'm now up more than 1500% on it. Let the dooming continue
- gigatexal 2mo agoThis is fine. everything is on fire it’s not a bubble. ;-)
- thelastgallon 2mo agoI wonder why Google doesn't create an open-source CUDA alternative. Google released Kubernetes to stay relevant/competitive in the cloud wars, they were a distant third. They now have an opportunity to create an open source industry standard. Or the companies spending trillions of dollars can do a Manhattan Project (Or X-Prize) and let a thousand startups work on it. One will succeed. Between Google, Amazon, FB, Microsoft, Apple, AMD, Qualcomm, Intel (and dozens of other companies) there is enough economic incentive to do it. Also, isn't this what AI is supposed to be extremely good at, CUDA experts can continue to write CUDA (without having to learn anything new), a translation layer will rewrite it. If software can be one-shot from markdown files, this can't be impossible.
- alexpotato 2mo agoBack in during the dotcom boom, there were multiple examples of companies being bought for ridiculous amounts. Often as the result of a bidding war. Even back then, some economists used the "hidden wallet auction" as an example of how this could happen. To summarize: - there is a wallet - you don't know how much is in the wallet - you bid on amount to buy the wallet - if you get the highest bid you win - crucially, if you lose then you still have to pay This is often cited as a game that you do not want to play b/c it's a. hard to predict the upside, b. the downside is huge. That being said, people still got into these auctions and because of sunk cost fallacy, decided to keep bidding even if they might lose. The hyperscaler race feels a bit like the above but no one seems to ant to admit it.