I think for the same model wall time is probably a more intuitive metric; at the end of the day what you’re doing is renting GPU time slices.

Large outputs dominate compute time so are more expensive.

IMO input and output token counts are actually still a bad metric since they linearise non linear cost increases and I suspect we’ll see another change in the future where they bucket by context length. XL output contexts may be 20x more expensive instead of 10x.

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'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.

"we’re going to have a leaderboard of who used the most"

Yeah I've seen stuff like that and it's a bit bewildering for me. Feels a bit like AWS is new and we're competing to see who can deploy the most EC2 instances.

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.

Curious - what’s this court filing?

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.

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.

Broadcom designs it as well [0], though GCP also works on design as well.

[0] - https://www.broadcom.com/products/custom-silicon

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.

I feel like that’s a bit glib?

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.

Somehow we must be doing this wrong.

"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...

none of the three are even remote moat

human ingenuity will solve this

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

Based on a glance at their other comments: not satire.

AFAIK the data does not need to be text.

It’s more than likely not.

Poe's (c)law?

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.