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.
That's really interesting. I have no experience writing anything that involves GPUs/TPUs, but over the years I've consistently read that CUDA is the "real moat" of Nvidia, which I never totally believed, but the way you describe makes it seem like it's not actually a moat in the slightest. It just happens to be an ecosystem associated with hardware that is not only considered the gold standard but happens to be more open than potential competition. Could it be that Nvidia has been on top because none of the competition has actually tried kicking them where it hurts?
It's a totally reasonable question, and one that everyone asks when they're learning about CUDA. The frustrating answer to your last question is that lots of companies have shipped GPU dev environments that can theoretically be used instead of CUDA. AMD has ROCm, Apple has had a couple projects (OpenCL, Metal), Intel has some stuff, and there are newer efforts like TinyGrad + a generation of slightly higher level frameworks from AI companies, like Triton from OpenAI.
The basic problem is that CUDA has become something of a Schelling point. If you want to train a model right now, the highest performance you can get is almost certainly on CUDA. From the basic general matrix multiply operation, to specific NN architectures, CUDA is going to have incredibly optimized implementations out of the box. And it's going to make multi-GPU training so much easier. And all the dependencies you build on (those layers you import from PyTorch or Transformers or whatever) are going to work optimally right away on CUDA. And that weird random repo that you found with a unique optimizer--it runs on CUDA too. And now the cool new implementation that you're about to release is also going to be built for CUDA.
It's so tempting to think "Just write replacement software", but you also need to transition the entire ecosystem in large part to match CUDA's effectiveness, and you need to get comparable performance out of your chip/library combo as NVIDIA can get out of its cards with CUDA.
There's a whole story here to how effective NVIDIA has been at navigating this. Very early on, they heavily prioritized PyTorch and TensorFlow, getting involved in the projects as much as they could and making sure they always ran best on CUDA. But the TLDR is that yes, you're right, another company could write a CUDA competitor. But actually replacing CUDA is a much larger task.
I'm personally hopeful that with the rise of coding agents, we see more movement on this front with other projects moving into view. It will take some time for any ecosystem to start to emerge that can dislodge CUDA for researchers who don't want to dive that deep into the stack, but hopefully we start to see some momentum build.
Software has always been the moat but for some reason it's always hamstrung by upper management. The latest of the frenzies being replacing sane (or whatever we have) of development practices with AI-slop.
Management likes it because it removes software developers from the loop.
The 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.
I had a hard time understanding why didn’t AMD make a better developer experience for this two years ago, and am now even more baffled that even with all the LLMs they still don’t seem to have moved a single inch, despite this probably being a tens of billions dollars worth feature.
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.
What 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.
I 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.
What's really interesting is that if you scale it to higher densities (eg 3nm and stacked die) with ComputeInMemory for fp8 you can reasonably start to fit 30B-70B models. With MoE and multiple stacked die, just like HBM, you could fit an open weight near frontier 1T model (like GLM5.2) at similarly much lower power <10kW and high token rates >2ktps. For running a bunch of agents where fill rate and speed/latency are important it may not matter that you're 6-18mo behind on weights. The process for the chip design could be largely automated, and new silicon pumped out as new weights are available (with a 3-6mo delay).
Very few consumers are going to spend multiple thousands of dollars to save $10 per month. Companies absolutely will to save hundreds per month per employee, but that's not consumer hardware.
It could be quite a while before we reach that point though. 5+ years easily.
I've been keenly interested in the ability to run local models, but the hardware is just not there. Consumer RAM speeds and capacity will have to significantly increase before local models will be able to perform as well as even the lowest end GPT-5.6 Luna model.
This is on the backdrop of RAM becoming prohibitively expensive. And without the speed and quantity of RAM, it becomes impossible to generate tokens at interactive speeds, regardless of model. There is a fundamental dependency between calculating all of the active params with the given RAM speed.
Even with a model that has been quantized all the way down to Q4, the DGX/RTX Spark chip with 128GB of RAM can only generate ~18 tokens/sec for a MoE model with only 30B active parameters. There haven't been any broadly useful models below 30B active parameters. And that is for a $5000+ piece of hardware that will be one of the best for running on-device models.
I really want to buy instead of rent my AI, but the economics are truly terrible.
It's also hard to predict how much money will be burned going down wrong avenues. The internet was the future, but it took a lot of failed companies to eventually land on a sustainable model that brought us the giants we have today.
Railways were also the future, but that didn't stop a rush to build out (often subsidized) lines that were ultimately uneconomical (either because they were corrupt or the planned settlements never arrived).
If AI is similar, then there's going to be a long slowdown on compute spend until the surplus is worked through. A good historical analogy could be the fiber optic buildouts of the late 1990s. The demand for data never really went down much, but the industry eventually commodified and took down some large companies (Nortel, especially)
Nvidia's great superpower is flexibility. You can easily run models of very different types on the same card; and their hardware is great for R&D.
However, at some point AI may be good enough for most people and then it makes sense to make an ASIC for the model (or group of models); and at that point you don't need Nvidia.
I suppose this scenario will happen in various moments at different levels.
Each of the hyperscalers has put like 250B each in the last year for infra. That means that they need to be writing AI profits to the tune of 20B per year just to keep up with the cost of the cash they burned.
We are not there. But they better figure it out soon. The cash flows dried up, and everyone is taking debt to support the capex. Google for the first time in its public history is cash flow negative. Amazon too.
They also have to be feeling the heat of the ASIC vendors. AMD just acquired Taalas and they work with Cerebras all the time on special projects. ASICs outgun nVidia's chips by an order of magnitude.
Plus on top of that 1st and 2nd order can be correct, but then the price is too high, meaning people lose money even if correct about the future, but over pay for it.
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.
It seems they had a head start but are now facing stiff competition on all fronts. Software moat, GPU's for gaming, and its distant cousin datacenter compute. They rightfully invested their insane profits into many ventures, and how many of those have turned around into profit?
They are also a robotics AI company with Omniverse. They are also an AI company with Nemotron. They are also a bleeding edge network equipment company after the Mellanox aquisition.
They stand to make a lot of money if they succeed in every venture. Good for Jensen taking risks and driving innovation, I hope they succeed in chewing even 50% of what they bit off.
Right, it's clear that nVidia is taking care and trying to position itself so that it can continue making sales if and when inference goes local. And it's in a much better intrinsic position to do that than the LLM SaaS vendors are: nVidia sells shovels to the army, but it also knows how to sell shovels to Walmart. Whether the financial relationships that Huang's got his company into will cause it problems if the market shifts is a different question, though.
I 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.
>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.
I don't have to believe. I've run KDE and GNOME on the Tegra boards, you get full-fat CUDA support without sacrificing Vulkan drivers. It's incredible.
You can believe all you want that good single-core performance will corner the edge compute market. It hasn't, Graviton has more buy-in than any Apple Silicon chip ever got.
Yes. The more capable SoC PCs become, the weaker the "everything has to run in the datacenter" argument gets. As more powerful SoC PCs from multiple vendors appear over the next few years, that advantage may shrink for many workloads.
How much in value could Nvidia safely drop and over what period of time for it be fine for greater economy? What level of correction would be manageable?
And I am pretty sure that their value will drop in 5 to 10 years.
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.
Even 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.
I 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
Nobody is saying they’re outright failing, but that they’re not going to be printing money the way they have been recently. Think about Intel circa 2010: most of their competitors like POWER or MIPS were marginalized, they owned the desktop and server markets with a bit of competition from AMD well contained, and their biggest desktop competitor (Apple) had just switched. A lot of analyst predictions … did not match what happened next. The same was true of Cisco a decade earlier. Both companies are still there but they don’t set the terms in their market segments.
I’m not predicting Nvidia will MBA themselves to death in the near future but I think there’s a tendency to overstate how profitable companies will stay. The more money Nvidia makes, the more motivated their competitors will be to get a piece of that market and the more customers will be looking for alternatives like the push into TPUs which the article discussed.
The current administration is definitely corrupt enough that you could imagine an anti-competitive deal of some sort but I don’t think there’s a way for even that to change matters because key competitors are well-connected American companies willing to play that game, too.
> The more money Nvidia makes, the more motivated their competitors will be to get a piece of that market and the more customers will be looking for alternatives like the push into TPUs which the article discussed.
But people have been saying this about CUDA for twenty years, and we are not any closer to a replacement GPGPU paradigm today.
The root comment in this thread was about Nvidia hedging their bet on lost AI market share. They recognize that a reduced pace in training and inference competition will undercut their business, but CUDA isn't a one-trick pony for LLMs alone. TPUs are - you can't even reuse the same architecture for training and inference, they're separate ASICs unlike CUDA cores/ALUs. Veterans of crypto mining will tell you that the ASICs lost in the end, as Nvidia was evolving their hardware faster than the ASIC manufacturers could iterate. When the crypto acceleration landscape diversified away from ETH/BTC into altcoins, Nvidia was still there making money hand-over-fist from mining hardware.
I guess you could argue that robotics, world models or computer vision won't be a trillion-dollar market. But Nvidia is positioned to be the first mover in all of these markets, and none of their competitors are even coming close to the integrated stack that they sell consumers.
For 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.
Another 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.
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.
This 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.
I'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!"
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.
Phew. 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.
Laughable 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.
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.
> 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.
> 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.
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)
I 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?
He 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.
Re: "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".
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?
The 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.
I 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.
you 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.
They are the marketing wing of the AI ecosystem. Their recent article on how SpaceX would drive 500B in data-center revenue was ludicrous-mode. Lets revisit this in a few years.
There’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.
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.
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.
“CUDA is the worst development ecosystem in existence. Except for all the others.”
The basic problem is that CUDA has become something of a Schelling point. If you want to train a model right now, the highest performance you can get is almost certainly on CUDA. From the basic general matrix multiply operation, to specific NN architectures, CUDA is going to have incredibly optimized implementations out of the box. And it's going to make multi-GPU training so much easier. And all the dependencies you build on (those layers you import from PyTorch or Transformers or whatever) are going to work optimally right away on CUDA. And that weird random repo that you found with a unique optimizer--it runs on CUDA too. And now the cool new implementation that you're about to release is also going to be built for CUDA.
It's so tempting to think "Just write replacement software", but you also need to transition the entire ecosystem in large part to match CUDA's effectiveness, and you need to get comparable performance out of your chip/library combo as NVIDIA can get out of its cards with CUDA.
There's a whole story here to how effective NVIDIA has been at navigating this. Very early on, they heavily prioritized PyTorch and TensorFlow, getting involved in the projects as much as they could and making sure they always ran best on CUDA. But the TLDR is that yes, you're right, another company could write a CUDA competitor. But actually replacing CUDA is a much larger task.
I'm personally hopeful that with the rise of coding agents, we see more movement on this front with other projects moving into view. It will take some time for any ecosystem to start to emerge that can dislodge CUDA for researchers who don't want to dive that deep into the stack, but hopefully we start to see some momentum build.
Management likes it because it removes software developers from the loop.
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.
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.
> If we have to switch to something like burning the model weights into silicon to continue to make gains
I think that's already being considered semi-seriously [0][1]
[0] https://taalas.com/products/
[1] https://ir.amd.com/news-events/press-releases/detail/1296/am...
I've been keenly interested in the ability to run local models, but the hardware is just not there. Consumer RAM speeds and capacity will have to significantly increase before local models will be able to perform as well as even the lowest end GPT-5.6 Luna model.
This is on the backdrop of RAM becoming prohibitively expensive. And without the speed and quantity of RAM, it becomes impossible to generate tokens at interactive speeds, regardless of model. There is a fundamental dependency between calculating all of the active params with the given RAM speed.
Even with a model that has been quantized all the way down to Q4, the DGX/RTX Spark chip with 128GB of RAM can only generate ~18 tokens/sec for a MoE model with only 30B active parameters. There haven't been any broadly useful models below 30B active parameters. And that is for a $5000+ piece of hardware that will be one of the best for running on-device models.
I really want to buy instead of rent my AI, but the economics are truly terrible.
You can't save yourself rich.
Railways were also the future, but that didn't stop a rush to build out (often subsidized) lines that were ultimately uneconomical (either because they were corrupt or the planned settlements never arrived).
If AI is similar, then there's going to be a long slowdown on compute spend until the surplus is worked through. A good historical analogy could be the fiber optic buildouts of the late 1990s. The demand for data never really went down much, but the industry eventually commodified and took down some large companies (Nortel, especially)
However, at some point AI may be good enough for most people and then it makes sense to make an ASIC for the model (or group of models); and at that point you don't need Nvidia.
I suppose this scenario will happen in various moments at different levels.
We are not there. But they better figure it out soon. The cash flows dried up, and everyone is taking debt to support the capex. Google for the first time in its public history is cash flow negative. Amazon too.
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.
They are also a robotics AI company with Omniverse. They are also an AI company with Nemotron. They are also a bleeding edge network equipment company after the Mellanox aquisition.
They stand to make a lot of money if they succeed in every venture. Good for Jensen taking risks and driving innovation, I hope they succeed in chewing even 50% of what they bit off.
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.
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.
You can believe all you want that good single-core performance will corner the edge compute market. It hasn't, Graviton has more buy-in than any Apple Silicon chip ever got.
Nvidia dropping from being a $5 trillion company to a $242 billion company would be 1929 levels of bad.
Global economy end of days stuff, especially since Nvidia can't crash that hard without a lot of other stuff crashing with it.
And I am pretty sure that their value will drop in 5 to 10 years.
https://stockanalysis.com/quote/lon/VWRA/holdings/
That is an astounding figure and means markets are highly imbalanced, unless you think Nvidia represents 5% of the global economy.
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.
Trust me guys, it's over!
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
I’m not predicting Nvidia will MBA themselves to death in the near future but I think there’s a tendency to overstate how profitable companies will stay. The more money Nvidia makes, the more motivated their competitors will be to get a piece of that market and the more customers will be looking for alternatives like the push into TPUs which the article discussed.
The current administration is definitely corrupt enough that you could imagine an anti-competitive deal of some sort but I don’t think there’s a way for even that to change matters because key competitors are well-connected American companies willing to play that game, too.
Your margin is my opportunity - Jeff Bezos
The root comment in this thread was about Nvidia hedging their bet on lost AI market share. They recognize that a reduced pace in training and inference competition will undercut their business, but CUDA isn't a one-trick pony for LLMs alone. TPUs are - you can't even reuse the same architecture for training and inference, they're separate ASICs unlike CUDA cores/ALUs. Veterans of crypto mining will tell you that the ASICs lost in the end, as Nvidia was evolving their hardware faster than the ASIC manufacturers could iterate. When the crypto acceleration landscape diversified away from ETH/BTC into altcoins, Nvidia was still there making money hand-over-fist from mining hardware.
I guess you could argue that robotics, world models or computer vision won't be a trillion-dollar market. But Nvidia is positioned to be the first mover in all of these markets, and none of their competitors are even coming close to the integrated stack that they sell consumers.
https://deepmind.google/models/gemini-robotics/
Google is mostly the party behind the whole VLA principle.
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.
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.
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.
Perhaps this has something to do with the economic dislocations and world wars between the 1870s and today?
American history education needs some dire reform.
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.
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)
Disappointed by the lack of Tom Cruise.
What a weird reason to stop reading an article.
Bayesian inference and all....
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.
https://semianalysis.com/
They are the marketing wing of the AI ecosystem. Their recent article on how SpaceX would drive 500B in data-center revenue was ludicrous-mode. Lets revisit this in a few years.
Building a business model on the belief that “this time is different” always finds storms on the horizon.
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.