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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 - thei

by roughly 9mo ago

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 + ...). 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. 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. 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.

kuil009 9mo ago
Thanks for this. It put into words a lot of the discomfort I’ve had with the current AI economics.
bigyabai 9mo ago
> but nothing about the industry's finances add up right now. The acquisitions do. Remember Groq?
- wmf 9mo ago
  That may not be a good example because everyone is saying Groq isn't worth $20B.
  - jsheard 9mo ago
    They were valued at $6.9B just three months before Nvidia bought them for $20B, triple the valuation. That figure seems to have been pulled out of thin air.
    - minimaltom 9mo ago
      Speaking generally: It makes sense for a acquisition price to be at a premium to valuation, between the dynamics where you have to convince leadership its better to be bought than to keep growing, and the expected risk posed by them as competition. Most M&As arent done by value investors.
    - TurdF3rguson 9mo ago
      Maybe it was worth the other $13.1B to make sure their competitors couldn't get them?
petesergeant 9mo ago
> nothing about the industry's finances add up right now Nothing about the industry’s finances, or about Anthropic and OpenAI’s finances? 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. 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. 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.
- HNisCIS 9mo ago
  It's become rather clear from the local LLM communities catching up that there is no moat. Everyone is still just barely figuring out how this nifty data structures produce such a powerful emergent behavior, there isn't any truly secret sauce yet.
  - petesergeant 9mo ago
    I’d argue there’s a _bit_ of secret sauce here, but the question is if there’s enough to justify valuations of the prop-AI firms, and that seems unlikely.
  - riku_iki 9mo ago
    > local LLM communities catching up that there is no moat. they use Chinese open LLMs, but Chinese companies have moat: training datasets and some non-opensource tech, and also salaried talents, which one would need serious investment for if decide to bootstrap competitive frontier model today.
flyinglizard 9mo ago
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. 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. This entire market runs on sovereign funds and cyclical investing. It’s crazy.
- golem14 9mo ago
  For instance, I believe Callcenters are in big trouble, and so are specialized contractors (like those prepping for an SOC submission etc). 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). So, maybe, the callcenter troubles will take some time to materialize.
segmondy 9mo ago
> 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 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.
- binary132 9mo ago
  >go download a model GP was talking about commercially hosted LLMs running in datacenters, not free Chinese models. 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.
  - wahnfrieden 9mo ago
    GPT 5.2 is an incredible leap over 5.1 / 5
    - hadlock 9mo ago
      5.2 is great if you ask it engineering questions, or questions an engineer might ask. It is extremely mid, and actually worse than the o3/o4 era models if you start asking it trivia like if the I-80 tunnel on the bay bridge (yerba buena island) is the largest bore in the world. Don't even get me started on whatever model is wired up to the voice chat button. But yes it will write you a flawless, physics accurate flight simulator in rust on the first try. I've proven that. I guess what I'm trying to say is Anthropic was eating their lunch at coding, and OpenAI rose to the challenge, but if you're not doing engineering tasks their current models are arguably worse than older ones.
      - SoftTalker 9mo ago
        My impression is that software developers are the lions share of people actually paying for AI, but perhaps that's just my bubble world view.
re-thc 9mo ago
> I am of the opinion that Nvidia's hit the wall with their current architecture Not likely since TSMC has a new process with big gains. > The story with Intel Was that their fab couldn’t keep up not designs.
- frankchn 9mo ago
  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. 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.
nl 9mo ago
> I am of the opinion that Nvidia's hit the wall with their current architecture Based on what? 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 Inference tests: https://inferencemax.semianalysis.com/ https://inferencemax.semianalysis.com/ Training tests: https://www.lightly.ai/blog/nvidia-b200-vs-h100 https://www.lightly.ai/blog/nvidia-b200-vs-h100 https://newsletter.semianalysis.com/p/mi300x-vs-h100-vs-h200-benchmark-part-1-training https://newsletter.semianalysis.com/p/mi300x-vs-h100-vs-h200... (only H100, but vs AMD) > but nothing about the industry's finances add up right now 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. 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?
- Forgeties79 9mo ago
  > Is that based just on the HN "it is lots of money so it can't possibly make sense" wisdom? 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. How many times have we seen speculative investment of this magnitude? It’s shifting entire municipal and state economies in the US. 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.
  - sothatsit 9mo ago
    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. 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. 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.
linuxftw 9mo ago
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. 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.
- SoftTalker 9mo ago
  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.
  - TurdF3rguson 9mo ago
    Anyone with a $200M marketing budget.
    - SoftTalker 9mo ago
      Throw it on YouTube and get a few key TikTokers to promote it.
  - adrianN 9mo ago
    Anybody had the ability to write the next great novel for a while, but few succeed.
    - fc417fc802 9mo ago
      There are lots of very good relatively recent novels on the shelf at the bookstore. Certainly orders of magnitude more than there are movies. 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. 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.
xnx 9mo ago
Remember that without real competition, Nvidia has little incentive to release something 16x faster when they could release something 2x faster 4 times.
bsder 9mo ago
We've seen this before. In 2001, there were something like 50+ OC-768 hardware startups. At the time, something like 5 OC-768 links could carry all the traffic in the world. Even exponential doubling every 12 months wasn't going to get enough customers to warrant all the funding that had poured into those startups. When your business model bumps into "All the <X> in the world," you're in trouble.
- roughly 9mo ago
  Especially when your investors are still expecting exponential growth rates.
ethbr1 9mo ago
> (at which point Intel typically fired their chip design group, hired everyone from AMD or whoever, and came out with Core or whatever) Didn't the Core architecture come from the Intel Pentium M Israeli team? https://en.wikipedia.org/wiki/Intel_Core_(microarchitecture)#Technology https://en.wikipedia.org/wiki/Intel_Core_(microarchitecture)...
- KiwiJohnno 9mo ago
  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
- roughly 9mo ago
  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.
  - ethbr1 9mo ago
    I think that's an overly reductive view of a very complicated problem space, with the benefit of hindsight. If you wanted to make that point, Itanium or 64-bit/multi-core desktop processing would be better examples than Core.
- branko_d 9mo ago
  Yes, and the newest Panther Lake too! https://techtime.news/2025/10/10/intel-25/ https://techtime.news/2025/10/10/intel-25/
hvb2 9mo ago
> 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. 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 ?
nextaccountic 9mo ago
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
- jamiejquinn 9mo ago
  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. 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.
- throw-qqqqq 9mo ago
  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. I think the software side of the story is underestimated. Nvidia has a big moat there and huge community support.
- yencabulator 9mo ago
  I believe Furiosa hardware is a variant of TPU, and that's pretty much why they can beat Nvidia on LLM inference. GPUs are more general purpose, which means Furiosa can cut some of the overhead with designs dedicated for matrix multiplication only. https://furiosa.ai/blog/tensor-contraction-processor-ai-chip-architecture https://furiosa.ai/blog/tensor-contraction-processor-ai-chip...
ironbound 9mo ago
What do I care if there's no profit in LLM's.. I just want to buy ddr5 and not pay an arm and a leg for my power bill!
jimbo808 9mo ago
> which is going to blow out the economics on inference 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!
alecco 9mo ago
> I am of the opinion that Nvidia's hit the wall with their current architecture Google presented TPUs in 2015. NVIDIA introduced Tensor Cores in 2018. Both utilize systolic arrays. And last month NVIDIA pseudo-acquired Groq including the founder and original TPU guy. Their LPUs are way more efficient for inference. Also of note Groq is fully made in USA and has a very diverse supply chain using older nodes. NVIDIA architecture is more than fine. They have deep pockets and very technical leadership. Their weakness lies more with their customers, lack of energy, and their dependency on TSMC and the memory cartel.
- cayleyh 9mo ago
  Underrated acquisition. Gives NVIDIA a whole lineup of inference-focused hardware that iirc can retrofit into existing air cooled data centres without needing cooling upgrades. Great hedge against the lower-end $$$-per-watt and watt-per-token competition that has been focused purely at inference.
  - alecco 9mo ago
    Also a hedge from the memory cartel as Groq uses SRAM. And a reasonable hedge in case Taiwan gets blockaded or something.
nsteel 9mo ago
What about Etched? https://techfundingnews.com/nvidia-rival-ai-chip-maker-etched-founded-by-harvard-dropouts-lands-500m-at-5b-valuation/ https://techfundingnews.com/nvidia-rival-ai-chip-maker-etche...
rajhlinux 9mo ago
Have you seen the specs? Consumer RTX 5090 is faster and cheaper than the Furiosa RNGD Gen 2. You gotta be mad stupid to buy something that performs worse and it is 5 times more expensive.

by roughly 9mo ago

kuil009 9mo ago

Thanks for this. It put into words a lot of the discomfort I’ve had with the current AI economics.

bigyabai 9mo ago

> but nothing about the industry's finances add up right now. The acquisitions do. Remember Groq?

wmf 9mo ago
That may not be a good example because everyone is saying Groq isn't worth $20B.
- jsheard 9mo ago
  They were valued at $6.9B just three months before Nvidia bought them for $20B, triple the valuation. That figure seems to have been pulled out of thin air.
  - minimaltom 9mo ago
    Speaking generally: It makes sense for a acquisition price to be at a premium to valuation, between the dynamics where you have to convince leadership its better to be bought than to keep growing, and the expected risk posed by them as competition. Most M&As arent done by value investors.
  - TurdF3rguson 9mo ago
    Maybe it was worth the other $13.1B to make sure their competitors couldn't get them?

petesergeant 9mo ago

> nothing about the industry's finances add up right now Nothing about the industry’s finances, or about Anthropic and OpenAI’s finances? 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. 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. 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.

HNisCIS 9mo ago
It's become rather clear from the local LLM communities catching up that there is no moat. Everyone is still just barely figuring out how this nifty data structures produce such a powerful emergent behavior, there isn't any truly secret sauce yet.
- petesergeant 9mo ago
  I’d argue there’s a _bit_ of secret sauce here, but the question is if there’s enough to justify valuations of the prop-AI firms, and that seems unlikely.
- riku_iki 9mo ago
  > local LLM communities catching up that there is no moat. they use Chinese open LLMs, but Chinese companies have moat: training datasets and some non-opensource tech, and also salaried talents, which one would need serious investment for if decide to bootstrap competitive frontier model today.

flyinglizard 9mo ago

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. 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. This entire market runs on sovereign funds and cyclical investing. It’s crazy.

golem14 9mo ago
For instance, I believe Callcenters are in big trouble, and so are specialized contractors (like those prepping for an SOC submission etc). 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). So, maybe, the callcenter troubles will take some time to materialize.

segmondy 9mo ago

> 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 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.

binary132 9mo ago
>go download a model GP was talking about commercially hosted LLMs running in datacenters, not free Chinese models. 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.
- wahnfrieden 9mo ago
  GPT 5.2 is an incredible leap over 5.1 / 5
  - hadlock 9mo ago
    5.2 is great if you ask it engineering questions, or questions an engineer might ask. It is extremely mid, and actually worse than the o3/o4 era models if you start asking it trivia like if the I-80 tunnel on the bay bridge (yerba buena island) is the largest bore in the world. Don't even get me started on whatever model is wired up to the voice chat button. But yes it will write you a flawless, physics accurate flight simulator in rust on the first try. I've proven that. I guess what I'm trying to say is Anthropic was eating their lunch at coding, and OpenAI rose to the challenge, but if you're not doing engineering tasks their current models are arguably worse than older ones.
    - SoftTalker 9mo ago
      My impression is that software developers are the lions share of people actually paying for AI, but perhaps that's just my bubble world view.

re-thc 9mo ago

> I am of the opinion that Nvidia's hit the wall with their current architecture Not likely since TSMC has a new process with big gains. > The story with Intel Was that their fab couldn’t keep up not designs.

frankchn 9mo ago
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. 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.

nl 9mo ago

> I am of the opinion that Nvidia's hit the wall with their current architecture Based on what? 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 Inference tests: https://inferencemax.semianalysis.com/ https://inferencemax.semianalysis.com/ Training tests: https://www.lightly.ai/blog/nvidia-b200-vs-h100 https://www.lightly.ai/blog/nvidia-b200-vs-h100 https://newsletter.semianalysis.com/p/mi300x-vs-h100-vs-h200-benchmark-part-1-training https://newsletter.semianalysis.com/p/mi300x-vs-h100-vs-h200... (only H100, but vs AMD) > but nothing about the industry's finances add up right now 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. 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?

Forgeties79 9mo ago
> Is that based just on the HN "it is lots of money so it can't possibly make sense" wisdom? 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. How many times have we seen speculative investment of this magnitude? It’s shifting entire municipal and state economies in the US. 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.
- sothatsit 9mo ago
  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. 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. 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.

linuxftw 9mo ago

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. 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.

SoftTalker 9mo ago
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.
- TurdF3rguson 9mo ago
  Anyone with a $200M marketing budget.
  - SoftTalker 9mo ago
    Throw it on YouTube and get a few key TikTokers to promote it.
- adrianN 9mo ago
  Anybody had the ability to write the next great novel for a while, but few succeed.
  - fc417fc802 9mo ago
    There are lots of very good relatively recent novels on the shelf at the bookstore. Certainly orders of magnitude more than there are movies. 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. 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.

xnx 9mo ago

Remember that without real competition, Nvidia has little incentive to release something 16x faster when they could release something 2x faster 4 times.

bsder 9mo ago

We've seen this before. In 2001, there were something like 50+ OC-768 hardware startups. At the time, something like 5 OC-768 links could carry all the traffic in the world. Even exponential doubling every 12 months wasn't going to get enough customers to warrant all the funding that had poured into those startups. When your business model bumps into "All the <X> in the world," you're in trouble.

roughly 9mo ago
Especially when your investors are still expecting exponential growth rates.

ethbr1 9mo ago

> (at which point Intel typically fired their chip design group, hired everyone from AMD or whoever, and came out with Core or whatever) Didn't the Core architecture come from the Intel Pentium M Israeli team? https://en.wikipedia.org/wiki/Intel_Core_(microarchitecture)#Technology https://en.wikipedia.org/wiki/Intel_Core_(microarchitecture)...

KiwiJohnno 9mo ago
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
roughly 9mo ago
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.
- ethbr1 9mo ago
  I think that's an overly reductive view of a very complicated problem space, with the benefit of hindsight. If you wanted to make that point, Itanium or 64-bit/multi-core desktop processing would be better examples than Core.
branko_d 9mo ago
Yes, and the newest Panther Lake too! https://techtime.news/2025/10/10/intel-25/ https://techtime.news/2025/10/10/intel-25/

hvb2 9mo ago

> 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. 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 ?

nextaccountic 9mo ago

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

jamiejquinn 9mo ago
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. 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.
throw-qqqqq 9mo ago
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. I think the software side of the story is underestimated. Nvidia has a big moat there and huge community support.
yencabulator 9mo ago
I believe Furiosa hardware is a variant of TPU, and that's pretty much why they can beat Nvidia on LLM inference. GPUs are more general purpose, which means Furiosa can cut some of the overhead with designs dedicated for matrix multiplication only. https://furiosa.ai/blog/tensor-contraction-processor-ai-chip-architecture https://furiosa.ai/blog/tensor-contraction-processor-ai-chip...

ironbound 9mo ago

What do I care if there's no profit in LLM's.. I just want to buy ddr5 and not pay an arm and a leg for my power bill!

jimbo808 9mo ago

> which is going to blow out the economics on inference 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!

alecco 9mo ago

> I am of the opinion that Nvidia's hit the wall with their current architecture Google presented TPUs in 2015. NVIDIA introduced Tensor Cores in 2018. Both utilize systolic arrays. And last month NVIDIA pseudo-acquired Groq including the founder and original TPU guy. Their LPUs are way more efficient for inference. Also of note Groq is fully made in USA and has a very diverse supply chain using older nodes. NVIDIA architecture is more than fine. They have deep pockets and very technical leadership. Their weakness lies more with their customers, lack of energy, and their dependency on TSMC and the memory cartel.

cayleyh 9mo ago
Underrated acquisition. Gives NVIDIA a whole lineup of inference-focused hardware that iirc can retrofit into existing air cooled data centres without needing cooling upgrades. Great hedge against the lower-end $$$-per-watt and watt-per-token competition that has been focused purely at inference.
- alecco 9mo ago
  Also a hedge from the memory cartel as Groq uses SRAM. And a reasonable hedge in case Taiwan gets blockaded or something.

nsteel 9mo ago

What about Etched? https://techfundingnews.com/nvidia-rival-ai-chip-maker-etched-founded-by-harvard-dropouts-lands-500m-at-5b-valuation/ https://techfundingnews.com/nvidia-rival-ai-chip-maker-etche...

rajhlinux 9mo ago

Have you seen the specs? Consumer RTX 5090 is faster and cheaper than the Furiosa RNGD Gen 2. You gotta be mad stupid to buy something that performs worse and it is 5 times more expensive.