Shameless self promotion: I put together an AI infra built-out financials data-vis project/page recently. It includes graphs and tracks updates to stuff like GPU depreciation schedules like this.
The real question IMO is building capex. Those depreciate over a longer timeline, but if demand were to drop in 5 years and we have an excess of datacenter space, that's going to hurt. We're approaching the physical limits of what's possible with silicon and fiber optics. Unless there are new architectures or break throughs in algorithms, the only way to make AI more productive today is to build larger clusters, or much longer training/RL runs.
Bears are gonna eat.
My thinking is there wouldn’t be downward pricing pressure on data center compute if utilization was actually this high - it would mean providers could jack up prices and walk with larger margins rather than burn money for acquisition, right?
Plus the market wouldn’t increase the price of borrowed money if utilization was that high; they’d see the same signal. But the cost to borrow money for datacenters has roughly doubled year on year (might just be for riskier ventures? Buildings? Is this aggregate?) - those two signals wouldn’t disagree, it’s nonsensical (I mean, right?)
Edit: it’s just “the buildings”, and it makes sense: maximal utility comes from installing compute in a building already setup to house compute (cooling, infra, power). I actually take this as a negative signal: we’re seeing downward pricing pressure already, AND new projects aren’t fully booked, AND money is now twice as expensive. Somebody’s going to lose their shirt unless OpenAI’s Omnissiah comes online and converts us all.
did you make this with ai? if so, which model?