I am of the opinion that Nvidia's hit the wall with their current architecture in the same way that Intel has historically with its various architectures - their current generation's power and cooling requirements are requiring the construction of entirely new datacenters with different architectures, which is going to blow out the economics on inference (GPU + datacenter + power plant + nuclear fusion research division + lobbying for datacenter land + water rights + ...).<p>The story with Intel around these times was usually that AMD or Cyrix or ARM or Apple or someone else would come around with a new architecture that was a clear generation jump past Intel's, and most importantly seemed to break the thermal and power ceilings of the Intel generation (at which point Intel typically fired their chip design group, hired everyone from AMD or whoever, and came out with Core or whatever). Nvidia effectively has no competition, or hasn't had any - nobody's actually broken the CUDA moat, so neither Intel nor AMD nor anyone else is really competing for the datacenter space, so they haven't faced any actual competitive pressure against things like power draws in the multi-kilowatt range for the Blackwells.<p>The reason this matters is that LLMs are incredibly nifty often useful tools that are not AGI and also seem to be hitting a scaling wall, and the only way to make the economics of, eg, a Blackwell-powered datacenter make sense is to assume that the entire economy is going to be running on it, as opposed to some useful tools and some improved interfaces. Otherwise, the investment numbers just don't make sense - the gap between what we see on the ground of how LLMs are used and the real but limited value add they can provide and the actual full cost of providing that service with a brand new single-purpose "AI datacenter" is just too great.<p>So this is a press release, but any time I see something that looks like an actual new hardware architecture for inference, and especially one that doesn't require building a new building or solving nuclear fusion, I'll take it as a good sign. I like LLMs, I've gotten a lot of value out of them, but nothing about the industry's finances add up right now.
> I am of the opinion that Nvidia's hit the wall with their current architecture<p>Based on what?<p>Their measured performance on things people care about keep going up, and their software stack keeps getting better and unlocking more performance on existing hardware<p>Inference tests: <a href="https://inferencemax.semianalysis.com/" rel="nofollow">https://inferencemax.semianalysis.com/</a><p>Training tests: <a href="https://www.lightly.ai/blog/nvidia-b200-vs-h100">https://www.lightly.ai/blog/nvidia-b200-vs-h100</a><p><a href="https://newsletter.semianalysis.com/p/mi300x-vs-h100-vs-h200-benchmark-part-1-training" rel="nofollow">https://newsletter.semianalysis.com/p/mi300x-vs-h100-vs-h200...</a> (only H100, but vs AMD)<p>> but nothing about the industry's finances add up right now<p>Is that based just on the HN "it is lots of money so it can't possibly make sense" wisdom? Because the released numbers seem to indicate that inference providers and Anthropic are doing pretty well, and that OpenAI is really only losing money on inference because of the free ChatGPT usage.<p>Further, I'm sure most people heard the mention of an unnamed enterprise paying Anthropic $5000/month per developer on inference(!!) If a company if that cost insensitive is there any reason why Anthropic would bother to subsidize them?
> Their measured performance on things people care about keep going up, and their software stack keeps getting better and unlocking more performance on existing hardware<p>I'm more concerned about fully-loaded dollars per token - including datacenter and power costs - rather than "does the chip go faster." If Nvidia couldn't make the chip go faster, there wouldn't be any debate, the question right now is "what is the cost of those improvements." I don't have the answer to that number, but the numbers going around for the costs of new datacenters doesn't give me a lot of optimism.<p>> Is that based just on the HN "it is lots of money so it can't possibly make sense" wisdom?<p>OpenAI has $1.15T in spend commitments over the next 10 years: <a href="https://tomtunguz.com/openai-hardware-spending-2025-2035/" rel="nofollow">https://tomtunguz.com/openai-hardware-spending-2025-2035/</a><p>As far as revenue, the released numbers from nearly anyone in this space are questionable - they're not public companies, we don't actually get to see inside the box. Torture the numbers right and they'll tell you anything you want to hear. What we _do_ get to see is, eg, Anthropic raising billions of dollars every ~3 months or so over the course of 2025. Maybe they're just that ambitious, but that's the kind of thing that makes me nervous.
> OpenAI has $1.15T in spend commitments over the next 10 years<p>Yes, but those aren't contracted commitments, and we know some of them are equity swaps. For example "Microsoft ($250B Azure commitment)" from the footnote is an unknown amount of actual cash.<p>And I think it's fair to point out the <i>other</i> information in your link "OpenAI projects a 48% gross profit margin in 2025, improving to 70% by 2029."
> "OpenAI projects a 48% gross profit margin in 2025, improving to 70% by 2029."<p>OpenAI can project whatever they want, they're not public.
Sounds like the railway boom.. I mean bond scam's
The fact that there's an incestual circle between OpenAI, Microsoft, NVidia, AMD, etc.. where they provide massive promises to each other for future business is nothing short of hilarious.<p>The economics of the entire setup are laughable and it's obvious that it's a massive bubble. The profit that'd need to be delivered to justify the current valuations is far beyond what is actually realistic.<p>What moat does OpenAI have? I'd argue basically none. They make extremely lofty forecasts and project an image of crazy growth opportunities, but is that going to ever survive the bubble popping?
> Yes, but those aren't contracted commitments, and we know some of them are equity swaps.<p>It's worse than not contracted. Nvidia said in their earnings call that their OpenAI commitment was "maybe".
GPUs are supply constrained and price isn't declining that fast so why do you expect the token price price to decrease. I think the supply issue will resolve in 1-2 years as now they have good prediction of how fast the market would grow.<p>Nvidia is literally selling GPUs with 90% profit margin and still everything is out of stock, which is unheard of before.
>Further, I'm sure most people heard the mention of an unnamed enterprise paying Anthropic $5000/month per developer on inference<p>Companies have wasted more money on dumber things so spending isn't a good measure.<p>And what about the countless other AI companies? Anthropic has one of the top models for coding so that's like saying there ins't a problem pre dot com bubble because Amazon is doing fine.<p>The real effects of AI is measured in rising profit of the customers of those AI companies otherwise you're looking at the shovel sellers
> Is that based just on the HN "it is lots of money so it can't possibly make sense" wisdom?<p>I mean the amount of money invested across just a handful of AI companies is currently <i>staggering</i> and their respective revenues are no where near where they need to be. That’s a valid reason to be skeptical. How many times have we seen speculative investment of this magnitude? It’s shifting entire municipal and state economies in the US.<p>OpenAI alone is currently projected to burn over $100 billion by what? 2028 or 2029? Forgot what I read the other day. Tens of billions a year. That is a hell of a gamble by investors.
The flip side is that these companies seem to be capacity constrained (although that is hard to confirm). If you assume the labs are capacity constrained, which seems plausible, then building more capacity could pay off by allowing labs to serve more customers and increase revenue per customer.<p>This means the bigger questions are whether you believe the labs are compute constrained, and whether you believe more capacity would allow them to drive actual revenue. I think there is a decent chance of this being true, and under this reality the investments make more sense. I can especially believe this as we see higher-cost products like Claude Code grow rapidly with much higher token usage per user.<p>This all hinges on demand materialising when capacity increases, and margins being good enough on that demand to get a good ROI. But that seems like an easier bet for investors to grapple with than trying to compare future investment in capacity with today's revenue, which doesn't capture the whole picture.
I am not someone who would ever be ever be considered an expert on factories/manufacturing of any kind, but my (insanely basic) understanding is that typically a “factory” making whatever widgets or doodads is outputting at a profit or has a clear path to profitability in order to pay off a loan/investment. They have debt, but they’re moving towards the black in a concrete, relatively predictable way - no one speculates on a factory anywhere near the degree they do with AI companies currently. If said factory’s output is maxed and they’re still not making money, then it’s a losing investment and they wouldn’t expand.<p>Basically, it strikes me as not really apples to apples.
Consensus seems to be that the labs are profitable on inference. They are only losing money on training and free users.<p>The competition requiring them to spend that money on training and free users does complicate things. But when you just look at it from an inference perspective, looking at these data centres like token factories makes sense. I would definitely pay more to get faster inference of Opus 4.5, for example.<p>This is also not wholly dissimilar to other industries where companies spend heavily on R&D while running profitable manufacturing. Pharma semiconductors, and hardware companies like Samsung or Apple all do this. The unusual part with AI labs is the ratio and the uncertainty, but that's a difference of degree, not kind.
> But when you just look at it from an inference perspective, looking at these data centres like token factories makes sense.<p>So if you ignore the majority of the costs, then it makes sense.<p>Opus 4.5 was released on November 25, 2025. That is less than 2 months ago. When they stop training new models, then we can forget about training costs.
I'm not taking a side here - I don't know enough - but it's an interesting line of reasoning.<p>So I'll ask, how is that any different than fabs? From what I understand R&D is absurd and upgrading to a new node is even more absurd. The resulting chips sell for chump change on a per unit basis (analogous to tokens). But somehow it all works out.<p>Well, sort of. The bleeding edge companies kept dropping out until you could count them on one hand at this point.<p>At first glance it seems like the analogy might fit?
Someone else mentioned it elsewhere in this thread, and I believe this is the crux of the issue: this is all predicated in the actual end users finding enough benefit in LLM services to keep the gravy train going. It's irrelevant how scalable and profitable the shovel makes are, to keep this business afloat long term, the shovelers - ie the end users - have to make money using the shovesl. Those expectations are currently ridiculously inflated. Far beyond anything in the past.<p>Invariably, there's going to be a collapse in the hype, the bubble will burst, and an investment deleveraging will remove a lot of money from the space in a short period of time. The bigger the bubble, the more painful and less survivable this event will be.
Inference costs scale linearly with usage. R&D expenses do not.<p>That's not to mention that Dario Amodei has said that their models actually have a good return, even when accounting for training costs [0].<p>[0] <a href="https://youtu.be/GcqQ1ebBqkc?si=Vs2R4taIhj3uwIyj&t=1088" rel="nofollow">https://youtu.be/GcqQ1ebBqkc?si=Vs2R4taIhj3uwIyj&t=1088</a>
> I mean the amount of money invested across just a handful of AI companies is currently staggering and their respective revenues are no where near where they need to be. That’s a valid reason to be skeptical.<p>Yes and no. Some of it just claims to be "AI". Like the hyperscalers are building datacenters and ramping up but not all of it is "AI". The crypto bros have rebadged their data centers into "AI".
What about TPUs? They are more efficient than nvidia GPUs, a huge amount of inference is done with them, and while they are not literally being sold to the public, the whole technology should be influencing the next steps of Nvidia just like AMD influenced Intel
TPUs can be more efficient, but are quite difficult to program for efficiently (difficult to saturate). That is why Google tends to sell TPU-services, rather than raw access to TPUs, so they can control the stack and get good utilization. GPUs are easier to work with.<p>I think the software side of the story is underestimated. Nvidia has a big moat there and huge community support.
My understanding is all of Google's AI is trained and run on quite old but well designed TPUs. For a while the issue was that developing these AI models still needed flexibility and customised hardware like TPUs couldn't accomodate that.<p>Now that the model architecture has settled into something a bit more predictable, I wouldn't be surprised if we saw a little more specialisation in the hardware.
> <i>(at which point Intel typically fired their chip design group, hired everyone from AMD or whoever, and came out with Core or whatever)</i><p>Didn't the Core architecture come from the <i>Intel</i> Pentium M Israeli team? <a href="https://en.wikipedia.org/wiki/Intel_Core_(microarchitecture)#Technology" rel="nofollow">https://en.wikipedia.org/wiki/Intel_Core_(microarchitecture)...</a>
Correct. Core came from Pentium M, which actually came from the Israeli team who took the Pentium 3 architecture, and coupled this with the best bits from the Pentium 4
Yes, and the newest Panther Lake too!<p><a href="https://techtime.news/2025/10/10/intel-25/" rel="nofollow">https://techtime.news/2025/10/10/intel-25/</a>
Yeah, that bit was pure snark - point was Intel’s gotten caught resting on their laurels a couple times when their architectures get a little long in the tooth, and often it’s existential enough that the team that pulls them out of it isn’t the one that put them in it.
I think that's an overly reductive view of a very complicated problem space, with the benefit of hindsight.<p>If you wanted to make that point, Itanium or 64-bit/multi-core desktop processing would be better examples than Core.
> The reason this matters is that LLMs are incredibly nifty often useful tools that are not AGI and also seem to be hitting a scaling wall<p>I don't know who needs to hear this, but the real break through in AI that we have had is not LLMs, but generative AI. LLM is but one specific case. Furthermore, we have hit absolutely no walls. Go download a model from Jan 2024, another from Jan 2025 and one from this year and compare. The difference is exponential in how well they have gotten.
> exponential<p>Is this the second most abused english word (after 'literally')?<p>> a model from Jan 2024, another from Jan 2025 and one from this year<p>You literally can't tell the difference is 'exponential', quadratic, or whatever from three data points.<p>Plus it's not my experience at all. Since Deepseek I haven't found models that one can run on consumer hardware get much better.
There is a lot of talking past each other when discussing LLM performance. The average person whose typical use case is asking ChatGPT how long they need to boil an egg for hasn't seen improvements for 18 months. Meanwhile if you're super into something like local models for example the tangible improvements are without exaggeration happening almost monthly.
Random trivia are answered much better in my case.
> The average person whose typical use case is asking ChatGPT how long they need to boil an egg for hasn't seen improvements for 18 months<p>I don’t think that’s true. I think both my mother and my mother-in-law would start to complain pretty quickly if they got pushed back to 4o. Change may have felt gradual, but I think that’s more a function of growing confidence in what they can expect the machine to do.<p>I also think “ask how long to boil an egg” is missing a lot here. Both use ChatGPT in place of Google for all sorts of shit these days, including plenty of stuff they shouldn’t (like: “will the city be doing garbage collection tomorrow?”). Both are pretty sharp women but neither is remotely technical.
> Go download a model from Jan 2024, another from Jan 2025 and one from this year and compare.<p>I did. The old one is smarter.<p>(The newer ones are more verbose, though. If that impresses you, then you probably think members of parliament are geniuses.)
Yeah agreed, there were some minor gains, but new releases are mostly benchmark overfit sycopanthic bullshit that are only better on paper and horrible to use. The more synthetic data they add the less world knowledge the model has and the more useless it becomes. But at least they can almost mimic a basic calculator now /s<p>For api models, OpenAI's releases have regularly not been an improvement for a long while now. Is sonnet 4.5 better than 3.5 outside pretentius agentic workflows it's been trained for? Basically impossible to tell, they make the same braindead mistakes sometimes.
>go download a model<p>GP was talking about commercially hosted LLMs running in datacenters, not free Chinese models.<p>Local is definitely still improving. That’s another reason the megacenter model (NVDA’s big line up forever plan) is either a financial catastrophe about to happen, or the biggest bailout ever.
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> and the only way to make the economics of, eg, a Blackwell-powered datacenter make sense is to assume that the entire economy is going to be running on it, as opposed to some useful tools and some improved interfaces.<p>And I'm still convinced we're not paying real prices anywhere. Everyone is still trying to get market share so the prices are going to go up when this all needs to sustain itself. At that point, which use cases become too expensive and does that shrink it's applicability ?
> which is going to blow out the economics on inference<p>At this point, I don't even think they do the envelope math anymore. However much money investors will be duped into giving them, that's what they'll spend on compute. Just gotta stay alive until the IPO!
Thanks for this. It put into words a lot of the discomfort I’ve had with the current AI economics.
We've seen this before.<p>In 2001, there were something like 50+ OC-768 hardware startups.<p>At the time, something like 5 OC-768 links could carry <i>all the traffic in the world</i>. Even exponential doubling every 12 months wasn't going to get enough customers to warrant all the funding that had poured into those startups.<p>When your business model bumps into "All the <X> in the world," you're in trouble.
What do I care if there's no profit in LLM's..<p>I just want to buy ddr5 and not pay an arm and a leg for my power bill!
You’re right but Nvidia enjoys an important advantage Intel had always used to mask their sloppy design work: the supply chain. You simply can’t source HBMs at scale because Nvidia bought everything, TSMC N3 is likewise fully booked and between Apple and Nvidia their 18A is probably already far gone and if you want to connect your artisanal inference hardware together then congratulations, Nvidia is the leader here too and you WILL buy their switches.<p>As for the business side, I’ve yet to hear of a transformative business outcome due to LLMs (it will come, but not there yet). It’s only the guys selling the shovels that are making money.<p>This entire market runs on sovereign funds and cyclical investing. It’s crazy.
For instance, I believe Callcenters are in big trouble, and so are specialized contractors (like those prepping for an SOC submission etc).<p>It is, however, actually funny how bad e.g. the amazon chatbot (Rufus) is on amazon.com. When asked where a particular CC charge comes from, it does all sorts of SQL queries into my account, but it can't be bothered to give me the link to the actual charges (the page exists and solves the problem trivially).<p>So, maybe, the callcenter troubles will take some time to materialize.
> I am of the opinion that Nvidia's hit the wall with their current architecture<p>Not likely since TSMC has a new process with big gains.<p>> The story with Intel<p>Was that their fab couldn’t keep up not designs.
If Intel's original 10nm process and Cannon Lake had launched within Intel's original timeframe of 2016/17, it would have been class leading.<p>Instead, they couldn't get 10nm to work and launched one low-power SKU in 2018 that had almost half the die disabled, and stuck to 14nm from 2014-2021.
Remember that without real competition, Nvidia has little incentive to release something 16x faster when they could release something 2x faster 4 times.
Based on conversations I've had with some people managing GPU's at scale in the datacenters, inference is an after thought. There is a gold rush for training right now, and that's where these massive clusters are being used.<p>LLM's are probably a small fraction of the overall GPU compute in use right now. I suspect in the next 5 years we'll have full Hollywood movies being completely generated (at least the specialfx) entirely by AI.
it's so weird how they spend all this money to train new models and then open sources it. it's gold rush but nvidia is getting all the gold.
Hollywood studios are breathing their last gasps now. Anyone will be able to use AI to create blockbuster type movies, Hollywood's moat around that is rapidly draining.
Have you....used any of the video generators? Nothing they create make any goddamn sense, they're a step above those fake acid trip simulators.
> Nothing they create make any goddamn sense,<p>I wouldn’t be that dismissive. Some have managed to make impressive things with them (although nothing close to an actual movie, even a short).<p><a href="https://www.youtube.com/watch?v=ET7Y1nNMXmA" rel="nofollow">https://www.youtube.com/watch?v=ET7Y1nNMXmA</a><p>A bit older: <a href="https://www.youtube.com/watch?v=8OOpYvxKhtY" rel="nofollow">https://www.youtube.com/watch?v=8OOpYvxKhtY</a><p>Compared to two years ago: <a href="https://www.youtube.com/watch?v=LHeCTfQOQcs" rel="nofollow">https://www.youtube.com/watch?v=LHeCTfQOQcs</a>
Anybody had the ability to write the next great novel for a while, but few succeed.
There are lots of very good relatively recent novels on the shelf at the bookstore. Certainly orders of magnitude more than there are movies.<p>The other thing to compare is the narrative quality. I find even middling books to be of much higher quality than blockbuster movies on average. Or rather I'm constantly appalled at what passes for a decent script. I assume that's due to needing to appeal to a broad swath of the population because production is so expensive, but understanding the (likely) reason behind it doesn't do anything to improve the end result.<p>So if "all" we get out of this is a 1000x reduction in production budgets which leads to a 100x increase in the amount of media available I expect it will be a huge win for the consumer.
Anyone with a $200M marketing budget.
> nothing about the industry's finances add up right now<p>Nothing about the industry’s finances, or about Anthropic and OpenAI’s finances?<p>I look at the list of providers on OpenRouter for open models, and I don’t believe all of them are losing money. FWIW Anthropic claims (iirc) that they don’t lose money on inference. So I don’t think the industry or the model of selling inference is what’s in trouble there.<p>I am much more skeptical of Anthropic and OpenAI’s business model of spending gigantic sums on generating proprietary models. Latest Claude and GPT are very very good, but not better enough than the competition to justify the cash spend. It feels unlikely that anyone is gonna “winner takes all” the market at this point. I don’t see how Anthropic or OpenAI’s business model survive as independent entities, or how current owners don’t take a gigantic haircut, other than by Sam Altman managing to do something insane like reverse acquiring Oracle.<p>EDIT: also feels like Musk has shown how shallow the moat is. With enough cash and access to exceptional engineers, you can magic a frontier model out of the ether, however much of a douche you are.
> but nothing about the industry's finances add up right now.<p>The acquisitions do. Remember Groq?