I guess gigawatts is how we roughly measure computing capacity at the datacenter scale? Also saw something similar here:
> Costs and pricing are expressed per “token”, but the published data immediately seems to admit that this is a bad choice of unit because it costs a lot more to output a token than input one. It seems to me that the actual marginal quantity being produced and consumed is “processing power”, which is apparently measured in gigawatt hours these days. In any case, I think more than anything this vindicates my original decision not to get too precise. [...]
Gigawatts seems like more a statement of the power supply and dissipation of the actual facility.
I’m assuming you can cram more chips in there if you have more efficient chips to make use of spare capacity?
Trying to measure the actual compute is a moving target since you’d be upgrading things over time, whereas the power aspects are probably more fixed by fire code, building size, and utilities.
Measuring data centers in watts is like measuring cars in horsepower. Power isn't a direct measure of performance, but of the primary constraint on performance. When in doubt choose the thermodynamic perspective.
I mean a single nuclear reactor delivers around 1GW, so if a single datacenter consumes multiple of those, it gives a reasonably accurate idea of the scale.
That these data centers can turn electricity + a little bit of fairly simple software directly into consumer and business value is pretty much the whole story.
Compare what you need to add to AWS EC2 to get the same result, above and beyond the electricity.
That's a convenient story, but most consumers' and businesses' use of AI is light enough that they could easily run local models on their existing silicon. Resorting to proprietary AI running in the datacenter would only add a tiny fraction of incremental value over that, and at a significant cost.
Sure but where the puck is going is long-running reasoning agents where local models are (for the moment) significantly constrained relative to a Claude Opus 4.6.
Their disclosed run rate was 14bn around the time of those filings IIRC, they started showing meaningful revenue around start of 2025, so if you just linearly extrapolate up that would give you ~7bn-ish actual revenue over that period. The more the growth is weighted towards the last few months the more that number goes down
So I don't think those numbers are really in tension at all
If your revenue doubles every month, then in the first month where you make $2.5B, your total lifetime revenue has been $5B ($2.5B this month, $1.25B the month before, etc. is a simple geometric series). But your current revenue run rate for the next year will be $2.5B x 12 = $30B.
They're not quite growing that fast, but there's nothing inherently inconsistent between these claims... as long as the growth curve is crazy.
1) It's in their interest to distort numbers and frame things that make them look good - e.g. using 'run-rate'
2) The numbers are not audited and we have no idea re. the manner in which they are recognising revenue - this can affect the true compounding rate of growth in revenues
The numbers are certainly audited by their investors. Anthropic isn't foreign to PR talk, but investors know what to look for in their book. They aren't stupid unlike how they are viewed on HN.
There are more investment money than Anthropic need. They can pick and choose.
I think you can argue that AI is going to explode and take over the economy, and it’s still a bubble.
I think one possible route is that cloud capacity just becomes totally commoditized and none of the hyperscalers will be able to extract the kinds of profit margins that would allow them to make a good return on their investment (model makers will fall victim to this too). Ultimately, what may happen is that market competition for everything explodes since AI and robots can do all the work, prices for everything (goods, services, assets) collapses, and no one is really any richer than anyone else.
Even if the AI frontier becomes "totally commoditized" it will still be reliant on a scarce factor, namely leading-edge chips. Chipmakers will ultimately capture that value, because competing it away would require expanding the industry and that's a very slow process involving billion-dollar expenses planned far in advance (multiple years, and that lead time can only expand further as the required scale gets even larger).
They won’t. They’ll just casually fade away from prior statements. Just like all the software engineers whose first take was that it’s just autocomplete.
Interesting to see Anthropic investing in compute infrastructure. The bottleneck I keep hitting is not
raw compute but where that compute lives — EU customers increasingly need guarantees their data stays
in-region. More sovereign compute options in Europe would unlock a lot of enterprise AI adoption.
There's no limit to the algorithms. People dont understand yet. They can learn the whole universe with a big enough compute cluster. We built a generalizable learning machine
the question is will we experience resource constraints before we get there? what if the step up to post-scarcity is gated by a compute level just out of our reach?
Edit: What we have built is a natural language interface to existing, textually recorded, information. Transformers cannot learn the whole universe because the universe has not yet been recorded into text.
Transformers operate on images and a variety of sensor data. They can also operate completely on non-textual inputs and outputs. I don't know what the ceiling on their capabilities is, but the complaint that they only operate on text seems just obviously wrong. There are numerous examples but one is meteorological forecasting which takes in a variety of time series sensor inputs and outputs e.g. time-series temperature maps. https://www.nature.com/articles/s41598-025-07897-4
100% agreed. Sadly, lots of people out there with the "trust me bro, just need more compute". Hopefully we don't consume all the planet's resources trying.
I reevaluated my priors long ago when I saw that scaling laws show no sign of stopping, no sign of plateau.
Strangely some people on HN seem to desperately cling to the notion that it's all going to come to a halt. This is unscientific. What evidence do you have - any evidence - that the scaling laws are due to come to an end?
I suspect it's not that people do not see the progress, they fail to fully trust laws not truly backed by physics like the transistor laws. We empirically see that scaling works and continue to work.
I’d like to see something that indicates models are getting better without the need for more training data. I would expect most gains are coming from more and better labeled data. We’re racing towards a complete encyclopedia of human knowledge. If we get there that’s only a drop in the bucket of all knowable things.
Bro the planet is literally experiencing a climate disaster and you think the solution is to create more systems that are misaligned with the planet's ecosystem for humans?
I guess the great filter is a real thing and not just a thought experiment.
I assure you that voluntary meat consumption because "taste buds go brr" is a much bigger problem than AI that results in actual productivity gains (and potentially solve the very climate crisis you complain about.)
The issue people have isn’t some interpretation of scaling laws, it’s whether the planet’s ecology is goi g to be able to sustain this endeavour.
I shouldn’t have to say this out loud, but if the environment collapses, we will die, and no amount of “just a bit more scaling bro, just think of the gains” will matter.
People's voluntary dietary choices cause far more suffering and ecological damage than AI, and for much less return or economic output. But you tell people to switch to plant based foods and they lose their shit.
Because all the variables that go into performance / efficiency measurement of a model (processing power, algorithm efficiency, parallelization, etc) boil down to cost per token input and token output. And the tangible cost for a datacenter is power consumed. Of course, amortized capex costs are also part of the game.
There's at least a decent argument to be made that the limiting factor is actually the physical silicon itself (at least at cutting-edge nodes) not really the power. This actually gives AI labs an incentive to run those specific chips somewhat cooler, because high device temperatures and high input voltages (which you need to push frequencies higher) might severely impact a modern chip's reliability over time.
Surely, there should be some more critical questions posed by why just buying a bunch of GPUs is a good idea? It just feels like a cheap way to show that growth is happening. It feels a bit much like FOMO. It feels like nobody with the capital is questioning whether this is actually a good idea or even a desirable way to improve AI models or even if that is money well spent. 1 GW is a lot of power. My understanding is that it is the equivalent to the instantaneous demand of a city like Seattle. This is absurd.
It feels like there is some awareness that asking for gigawatts if not terrawatts of compute probably needs more justification than has been proffered and the big banks are already trying to CYA themselves by publishing reports saying AI has not contributed meaningfully to the economy like Goldman Sachs recently did.
kinda complicated though when you consider it fully. Power consumption only measures the environmental impact really, we come up with more clever ways to use the same amount of power daily.
it's kind of like an electrical motor that exists before the strong understanding of lorentz/ohm's law. We don't really know how inefficient the thing is because we don't really know where the ceiling is aside from some loosey theoretical computational efficiency concepts that don't strongly apply to practical LLMs.
to be clear, I don't disagree that it's the limiting factor, just that 'limits' is nuanced here between effort/ability and raw power use.
"Do you realize that the human brain has been liken to an electronic brain? Someone said and I don't know whether he is right or not, but he said, if the human brain were put together on the basis of an IBM electronic brain, it would take 7 buildings the size of the Empire State Building to house it, it would take all the water of the Niagara River to cool it, and all of the power generated by the Niagara River to operate it." (Sermon by Paris Reidhead, circa 1950s.[1])
We're there on size and power.
Is there some more efficient way to do this?
It's easy to think about. Google reported a global average power consumption of 3.7GW in 2024, so you can think of this deal as representing an expansion of something like 10-15% of that 2024 baseline, if you assume 50% capacity utilization.
I think it’s also important to add the context that Broadcom’s CEO, Hock Tan, went on CNBC in October and had a vacuous conversation with Jim Cramer about their OpenAI “deal” at the time [0]. Nothing of substance was said, it was just endless loops about the opportunity of AI. It is now 6 months later and there has been nary a peep from Broadcom about any updates.
I think Anthropic is a more grounded company than OpenAI because Sam Altman is insane, but it is still playing the same game.
The VMware s/w rental market has no relevance to this deal, any more than the IBM role in data processing in germany in the 1930s had any relevance to their business in Israel in the 60s, or Oracle's failure in the DC market impacts licencing of the database product.
It's just not material. Broadcom make devices they need, and Broadcom want to sell those devices and exclude another VLSI company from selling, so the two have an interest in doing business. That's all there is to it.
About the most you could say is that the lawyers drafting whatever agreement they sign to, will reflect on the contract in regard to future changes of pricing and supply, in the light of what Broadcom did with VMWare licencing costs.
> Costs and pricing are expressed per “token”, but the published data immediately seems to admit that this is a bad choice of unit because it costs a lot more to output a token than input one. It seems to me that the actual marginal quantity being produced and consumed is “processing power”, which is apparently measured in gigawatt hours these days. In any case, I think more than anything this vindicates my original decision not to get too precise. [...]
https://backofmind.substack.com/p/new-new-rules-for-the-new-...
Is it priced that way, though? I assume next-gen TPU's will be more efficient?
And, that's silly, because API pricing is more expensive for output than input tokens, 5x so for Anthropic [1], and 6x so for OpenAI!
[1] https://platform.claude.com/docs/en/about-claude/pricing
[2] https://openai.com/api/pricing
I’m assuming you can cram more chips in there if you have more efficient chips to make use of spare capacity?
Trying to measure the actual compute is a moving target since you’d be upgrading things over time, whereas the power aspects are probably more fixed by fire code, building size, and utilities.
Compare what you need to add to AWS EC2 to get the same result, above and beyond the electricity.
I often get a 10x more cost effective run processing on my local hardware.
Still reaching for frontier models for coding, but find the hosted models on open router good enough for simple work.
Feels like we are jumping to warp on flops. My cores are throttled and the fiber is lit.
Feels like the lede is buried here!
So I don't think those numbers are really in tension at all
They're not quite growing that fast, but there's nothing inherently inconsistent between these claims... as long as the growth curve is crazy.
1) It's in their interest to distort numbers and frame things that make them look good - e.g. using 'run-rate' 2) The numbers are not audited and we have no idea re. the manner in which they are recognising revenue - this can affect the true compounding rate of growth in revenues
There are more investment money than Anthropic need. They can pick and choose.
Hahaha.
Mate nobody cares about that nor trusts it. Everyone is waiting in anticipation for the S-1 filing.
I cba but I read it before too. Its legit.
At what point would bubble-callers admit that they were completely wrong?
I think one possible route is that cloud capacity just becomes totally commoditized and none of the hyperscalers will be able to extract the kinds of profit margins that would allow them to make a good return on their investment (model makers will fall victim to this too). Ultimately, what may happen is that market competition for everything explodes since AI and robots can do all the work, prices for everything (goods, services, assets) collapses, and no one is really any richer than anyone else.
The magical thing about software is that efficiency gains can come pretty quickly relative to other industries.
Edit: What we have built is a natural language interface to existing, textually recorded, information. Transformers cannot learn the whole universe because the universe has not yet been recorded into text.
Strangely some people on HN seem to desperately cling to the notion that it's all going to come to a halt. This is unscientific. What evidence do you have - any evidence - that the scaling laws are due to come to an end?
I guess the great filter is a real thing and not just a thought experiment.
I shouldn’t have to say this out loud, but if the environment collapses, we will die, and no amount of “just a bit more scaling bro, just think of the gains” will matter.
Surely, there should be some more critical questions posed by why just buying a bunch of GPUs is a good idea? It just feels like a cheap way to show that growth is happening. It feels a bit much like FOMO. It feels like nobody with the capital is questioning whether this is actually a good idea or even a desirable way to improve AI models or even if that is money well spent. 1 GW is a lot of power. My understanding is that it is the equivalent to the instantaneous demand of a city like Seattle. This is absurd.
It feels like there is some awareness that asking for gigawatts if not terrawatts of compute probably needs more justification than has been proffered and the big banks are already trying to CYA themselves by publishing reports saying AI has not contributed meaningfully to the economy like Goldman Sachs recently did.
it's kind of like an electrical motor that exists before the strong understanding of lorentz/ohm's law. We don't really know how inefficient the thing is because we don't really know where the ceiling is aside from some loosey theoretical computational efficiency concepts that don't strongly apply to practical LLMs.
to be clear, I don't disagree that it's the limiting factor, just that 'limits' is nuanced here between effort/ability and raw power use.
"Do you realize that the human brain has been liken to an electronic brain? Someone said and I don't know whether he is right or not, but he said, if the human brain were put together on the basis of an IBM electronic brain, it would take 7 buildings the size of the Empire State Building to house it, it would take all the water of the Niagara River to cool it, and all of the power generated by the Niagara River to operate it." (Sermon by Paris Reidhead, circa 1950s.[1])
We're there on size and power. Is there some more efficient way to do this?
[1] https://www.sermonindex.net/speakers/paris-reidhead/the-trag...
Simo takes a medical leave. And there appears to be friction between the CEO and CFO.
I think Anthropic is a more grounded company than OpenAI because Sam Altman is insane, but it is still playing the same game.
[0] https://www.youtube.com/watch?v=pU2HhJ3jCts
It's just not material. Broadcom make devices they need, and Broadcom want to sell those devices and exclude another VLSI company from selling, so the two have an interest in doing business. That's all there is to it.
About the most you could say is that the lawyers drafting whatever agreement they sign to, will reflect on the contract in regard to future changes of pricing and supply, in the light of what Broadcom did with VMWare licencing costs.
[0] - https://www.broadcom.com/products/custom-silicon