A couple of comments here are circling the power question, so one angle from the infrastructure side.
In the State of the AI Economy deck, slide 36 carries the cost of consumed electricity: the $594m energy line, which is reasonable on its own. What it does not separately identify is the cost and lead time to make a GW of IT load actually available at the site. On-site power and cooling sit in the facility line, but utility-side interconnection, transmission, substations, queue timing, and fixed-capacity commitments appear only inside a $33m "land + utility" line, if at all.
That matters because large-load power is not purely usage-based. Demand charges, minimum-billing demand, and take-or-pay commitments keep much of the bill fixed even when utilization runs below plan. And slides 32-34 frame "headroom" as revenue after depreciation, not after OpEx, so energy is in the slide 36 unit cost but outside the "paying back" claim.
Slide 16's data center examples show both paths. Rainier in New Carlisle takes the grid route: a ~2.3 GW build phased over years, with a $150m+ transmission and 345kV substation buildout. xAI's Colossus took the other route, self-supplying ~1.2 GW of gas off-grid because the interconnection queue was too slow; per the SpaceX IPO filings it now runs ~1 GW of IT compute across three buildings, well past the chart's early-2025, single-building 300 MW marker.
Forward-looking, the swing on cost per token is less the marginal electricity price and more when, how, and under what fixed commitments a site gets powered.
Hi Corey! Yes, I think your lead-time point is very relevant: buying compute and building the DC doesn't help if it isn't connected and you haven't planned for behind-the-meter power generation. And delays put even more pressure on expended CapEx as it's spent without generating revenue.
On slide 36 we account for the substation CapEx depreciation charge in the land + utility line, and it constitutes $20m of the total $33m annual cost (we had to balance brevity/clutter on this so couldn't be as comprehensive in describing as we wanted to!). But this doesn't cover interconnection queue position, transmission upgrades beyond the substation, or the fixed-capacity commitments because they are site- and utility-specific (hard to represent in a stylised steady-state model).
And you're exactly correct on the headroom point: this is just to cover the pure depreciation charge. The remaining 19%/32% of revenues (slide 34) currently has to cover all DC OpEx as well as all the costs of generating value-add (from the foundation model & app layers) on top.
Thanks for coming back on this in detail. Good to know the $20m is the substation depreciation, and I get the brevity-versus-clutter tradeoff makes sense for this type of model. Agreed that interconnection position, transmission beyond the substation, and fixed-capacity terms are genuinely site-specific and hard to represent in a steady-state model. That's the part I find most interesting going forward.
Thanks! First study of this kind I've seen; looking forward to future reports and trend analysis. Some of my encounters with AI enhanced interfaces are very iffy. Is there away in the future to separate out user learning curve improvements?
Thank you so much. Can you help explain how we should think about the impact of power costs embedded in this analysis, including on a forward looking basis? I need to take a closer read, but is it already embedded inside the token costs or are these incorporated elsewhere?
Thanks! Best place for power costs is in slide 36, where we look at the unit cost of tokens: energy costs are 2/3 of datacentre OpEx, but CapEx costs are the most significant driver (89%) of the token cost floor
Seems to me that the risk to the Frontier Models has been dramatically increased by the US Govt blocking access to Fable/Mythos 5.
The Chinese OpenWeight models are catching fast and priced at 10% of the Frontier and as majority of work is good enough on Opus or DeepSeek - the desire for most organisations to leverage Frontier (except Defence and high frequency FS) could decline rapidly.
The Energy issue is huge and IMHO is a battle only the Chinese can win. The buildout of US Data Center capacity is coming under increasing pressure from all over the country and when there is talk of 9GW Datacenters covering areas larger than NYC and using more power than the rest of the state, does it not become obvious that the exponential growth will run over a cliff in 2027, if not sooner.
The usable unit of value slide is the best one - question remains on what will be left for the rest of us.
Act as System Integrators? Harness Designers?
Several questions to be discovered and a lot to be developed towards the downstream.
cheers!
rogério
much to explore
A couple of comments here are circling the power question, so one angle from the infrastructure side.
In the State of the AI Economy deck, slide 36 carries the cost of consumed electricity: the $594m energy line, which is reasonable on its own. What it does not separately identify is the cost and lead time to make a GW of IT load actually available at the site. On-site power and cooling sit in the facility line, but utility-side interconnection, transmission, substations, queue timing, and fixed-capacity commitments appear only inside a $33m "land + utility" line, if at all.
That matters because large-load power is not purely usage-based. Demand charges, minimum-billing demand, and take-or-pay commitments keep much of the bill fixed even when utilization runs below plan. And slides 32-34 frame "headroom" as revenue after depreciation, not after OpEx, so energy is in the slide 36 unit cost but outside the "paying back" claim.
Slide 16's data center examples show both paths. Rainier in New Carlisle takes the grid route: a ~2.3 GW build phased over years, with a $150m+ transmission and 345kV substation buildout. xAI's Colossus took the other route, self-supplying ~1.2 GW of gas off-grid because the interconnection queue was too slow; per the SpaceX IPO filings it now runs ~1 GW of IT compute across three buildings, well past the chart's early-2025, single-building 300 MW marker.
Forward-looking, the swing on cost per token is less the marginal electricity price and more when, how, and under what fixed commitments a site gets powered.
Hi Corey! Yes, I think your lead-time point is very relevant: buying compute and building the DC doesn't help if it isn't connected and you haven't planned for behind-the-meter power generation. And delays put even more pressure on expended CapEx as it's spent without generating revenue.
On slide 36 we account for the substation CapEx depreciation charge in the land + utility line, and it constitutes $20m of the total $33m annual cost (we had to balance brevity/clutter on this so couldn't be as comprehensive in describing as we wanted to!). But this doesn't cover interconnection queue position, transmission upgrades beyond the substation, or the fixed-capacity commitments because they are site- and utility-specific (hard to represent in a stylised steady-state model).
And you're exactly correct on the headroom point: this is just to cover the pure depreciation charge. The remaining 19%/32% of revenues (slide 34) currently has to cover all DC OpEx as well as all the costs of generating value-add (from the foundation model & app layers) on top.
Thanks for coming back on this in detail. Good to know the $20m is the substation depreciation, and I get the brevity-versus-clutter tradeoff makes sense for this type of model. Agreed that interconnection position, transmission beyond the substation, and fixed-capacity terms are genuinely site-specific and hard to represent in a steady-state model. That's the part I find most interesting going forward.
Thanks! First study of this kind I've seen; looking forward to future reports and trend analysis. Some of my encounters with AI enhanced interfaces are very iffy. Is there away in the future to separate out user learning curve improvements?
Thank you so much. Can you help explain how we should think about the impact of power costs embedded in this analysis, including on a forward looking basis? I need to take a closer read, but is it already embedded inside the token costs or are these incorporated elsewhere?
Thanks! Best place for power costs is in slide 36, where we look at the unit cost of tokens: energy costs are 2/3 of datacentre OpEx, but CapEx costs are the most significant driver (89%) of the token cost floor
Thank you! Super interesting
This is an amazing analysis.
Seems to me that the risk to the Frontier Models has been dramatically increased by the US Govt blocking access to Fable/Mythos 5.
The Chinese OpenWeight models are catching fast and priced at 10% of the Frontier and as majority of work is good enough on Opus or DeepSeek - the desire for most organisations to leverage Frontier (except Defence and high frequency FS) could decline rapidly.
The Energy issue is huge and IMHO is a battle only the Chinese can win. The buildout of US Data Center capacity is coming under increasing pressure from all over the country and when there is talk of 9GW Datacenters covering areas larger than NYC and using more power than the rest of the state, does it not become obvious that the exponential growth will run over a cliff in 2027, if not sooner.
I think this could very well be a risk that will need to be managed.