The question that really matters: is the net present value of each $1 investment in AI Capex > $1 (+ some spread for borrowing costs & risk).
We'll be inference token constrained indefinitely: i.e. inference tokens supply will never exceed demand, it's just that the $/token may not be able to pay back the capital investment.
> it's just that the $/token may not be able to pay back the capital investment.
the loss is private, so that's OK.
A similar thing happened to the internet bandwidth capacity when the dot-com bust happened - overinvestment in fibre everywhere (came to be called dark fibre iirc), which became superbly useful once the recovery started, despite those building these capacity not making much money. They ate the losses, so that the benefit can flow out.
The only time this is not OK is when the overinvestment comes from gov't sources, and is ultimately a taxpayer funded grift.
Investment in dark fiber was intentional and continues to this day. Almost all of the cost for laying fiber is in getting physical access to where you want to put the fiber underground. The fiber itself is incredibly cheap, so every time a telecom bothers to dig up mile upon mile of earth they overprovision massively.
The capital overhang of having more fiber than needed is so small compared to other costs I doubt the telecoms have really regretted any of the overprovisioning they've done, even when their models for future demand didn't pan out.
Every time someone says “but dark fiber”, someone else has to point out that graphics cards are not infrastructure and depreciate at a much, much higher rate. I guess it’s my turn.
Fiber will remain a valuable asset until/unless some moron snaps it with a backhoe. And it costs almost nothing to operate.
Your data center full of H100s will wear out in 5 years. Any that don’t are still going to require substantial costs to run/may not be cost-competitive with whatever new higher performance card Nvidia releases next year.
That is a fine point. However I am not sure if replacing the gpus themselves will be the bottleneck investment for datacenter costs. After all you have so much more infrastructure in a datacenter (cooling and networking). Plus custom chips like tpus might catch up at lower cost eventually. I think the bigger question is whether demand for compute will evaporate or not.
When the bubble pops the labs are going to stop the hero training runs and switch the gigawatt datacenters over to inference and then they're going to discover that milking existing GPUs is cheaper than replacing them.
Softbank investment funds include teacher pension plans and things like that. Private losses attached to public savings can very quickly become too big to fail.
Nobody forced a pension plan to invest in Masa's 300 year AI vision or whatever. Why it's even legal to gamble pensioners' money like that is beyond me.
I don't think merely building infrastructure at a loss is what's being described here - it's building infrastructure that won't get used (or used enough to be worth it). More of a bridge to nowhere situation than expecting to recoup the cost of a bridge with tolls or whatever.
Infrastructure building at a loss is very much not okay for a government and is usually the result of some form of corruption (e.g. privatize the profit), incompetence (e.g. misaligned incentives) or both.
However, the cost-benefit analysis on governmental projects typically includes non-monetary or indirect benefits.
We'll be inference token constrained indefinitely: i.e. inference tokens supply will never exceed demand, it's just that the $/token may not be able to pay back the capital investment.