Power infrastructure that might need to be built is gas generators or power plants, substations, whatever the buildings themselves need. Generators are apparently added even when not on-paper strictly necessary, as backup power. They are also faster to setup than GW-scale grid interconnection, so could be important for these sudden giant factories where nobody is quite sure 4 years in advance that they will be actually built at a given scale.
Datacenter infrastructure friction and cost will probably both smooth out the slowdown and disappear as a funding constraint for AI companies in the years following the slowdown. Compute hardware is rotated every few years, so at some point you don’t need new datacenters and accompanying infrastructure to setup a new generation of compute hardware, you just reuse an existing datacenter site that hosted old hardware. Also, any related datacenters that didn’t have excessive inter-site dark fiber will at some point set it up, so even increasing the scale will be less dependent on having everything at one site. This makes the infrastructure costs a much smaller fraction of the cost of a frontier AI training system, and there will no longer be friction.
The infrastructure or even hardware costs in principle don’t need to be paid by the AI company upfront, but either the market as a whole or the specific AI company (as a tenant) need to sufficiently assure the developer (that builds and owns the non-IT infrastructure) and the cloud provider (that installs and owns compute hardware) to commit to the project. My sense is that the estimates for the cost of a year of GPU-time for frontier compute end up at about a third of the cost of compute hardware. So access to a new $200bn training system that has $140bn worth of compute hardware (which only remains cutting edge for 2 years) will cost the tenant $45bn per year, even though the total capital expenditure is $100bn per year during the initial infrastructure buildout, and in later years after slowdown (when new infrastructure no longer needs to be built as much) it’s still $70bn per year to keep installing the newest hardware somewhere, so that some datacenter site will end up having it available.
Thus a few years after slowdown, we get about 2x more compute supported by the same level of funding (from $100bn per year to $45bn per year for the same compute, or keeping to $100bn per year for 2x the compute). But since 2x in compute corresponds to 2 years of compute hardware price-performance progress, and the relevant anchor is the 2000x of 2022-2028 training compute scale-up, that is just playing with about 2 years in the 2028-2045 period when another 2000x compute scaleup happens, mostly due to increasing price-performance of compute, and a level of growth similar to that of the current tech giants in the past. So not a crucial update.
Power infrastructure that might need to be built is gas generators or power plants, substations, whatever the buildings themselves need. Generators are apparently added even when not on-paper strictly necessary, as backup power. They are also faster to setup than GW-scale grid interconnection, so could be important for these sudden giant factories where nobody is quite sure 4 years in advance that they will be actually built at a given scale.
Datacenter infrastructure friction and cost will probably both smooth out the slowdown and disappear as a funding constraint for AI companies in the years following the slowdown. Compute hardware is rotated every few years, so at some point you don’t need new datacenters and accompanying infrastructure to setup a new generation of compute hardware, you just reuse an existing datacenter site that hosted old hardware. Also, any related datacenters that didn’t have excessive inter-site dark fiber will at some point set it up, so even increasing the scale will be less dependent on having everything at one site. This makes the infrastructure costs a much smaller fraction of the cost of a frontier AI training system, and there will no longer be friction.
The infrastructure or even hardware costs in principle don’t need to be paid by the AI company upfront, but either the market as a whole or the specific AI company (as a tenant) need to sufficiently assure the developer (that builds and owns the non-IT infrastructure) and the cloud provider (that installs and owns compute hardware) to commit to the project. My sense is that the estimates for the cost of a year of GPU-time for frontier compute end up at about a third of the cost of compute hardware. So access to a new $200bn training system that has $140bn worth of compute hardware (which only remains cutting edge for 2 years) will cost the tenant $45bn per year, even though the total capital expenditure is $100bn per year during the initial infrastructure buildout, and in later years after slowdown (when new infrastructure no longer needs to be built as much) it’s still $70bn per year to keep installing the newest hardware somewhere, so that some datacenter site will end up having it available.
Thus a few years after slowdown, we get about 2x more compute supported by the same level of funding (from $100bn per year to $45bn per year for the same compute, or keeping to $100bn per year for 2x the compute). But since 2x in compute corresponds to 2 years of compute hardware price-performance progress, and the relevant anchor is the 2000x of 2022-2028 training compute scale-up, that is just playing with about 2 years in the 2028-2045 period when another 2000x compute scaleup happens, mostly due to increasing price-performance of compute, and a level of growth similar to that of the current tech giants in the past. So not a crucial update.