[martinald]

I think the fibre optic analogy is a bad one. The key reason supply massively outstripped demand was that optical equipment massively improved in efficiency.

We are not seeing that (currently) with GPUs. Perf/watt has basically completely stalled out recently while tokens per user has easily increased in many use cases has went up 100x+ (take Claude code usage vs normal chat usage). It's very very unlikely we will get breakthroughs in compute efficiency in the same way we did in the late 90s/2000s for fiber optic capacity.

Secondly, I'm not convinced the capex has increased that much. From some brief research the major tech firms (hyperscalers + meta) were spending something like $10-15bn a month in capex in 2019. Now if we assume that spend has all been rebadged AI, and adjust for inflation it's a big ramp but not quite as big as it seems, especially when you consider construction inflation has been horrendous virtually everywhere post covid.

What I really think is going on is some sort of prisoners dilemma with capex. If you don't build then you are at serious risk of shortages assuming demand does continue in even the short and medium term. This then potentially means you start churning major non AI workloads along with the AI work from eg AWS. So everyone is booking up all the capacity they can get, and let's keep in mind a small fraction of these giant trillion dollar numbers being thrown around from especially OpenAI are actually hard commitments.

To be honest if it wasn't for Claude code I would be extremely skeptical of the demand story but given I now get through millions of tokens a day, if even a small percentage of knowledge workers globally adopt similar tooling it's sort of a given we are in for a very large shortage of compute. I'm sure there will be various market corrections along the way, but I do think we are going to require a shedload more data centres.

[Wowfunhappy]

> We are not seeing that (currently) with GPUs. Perf/watt has basically completely stalled out recently while tokens per user has easily increased in many use cases has went up 100x+ (take Claude code usage vs normal chat usage). It's very very unlikely we will get breakthroughs in compute efficiency in the same way we did in the late 90s/2000s for fiber optic capacity.

At least for gaming, GPU performance per dollar has gotten a lot better in the last decade. It hasn't gotten much better in the past couple of years specifically, but I assume a lot of that is due to the increased demand for AI use driving up the price for consumers.

Why wouldn't Moore's Law continue?

[Ekaros]

Difference is that with fiber you can put more data on same piece of glass or plastic or whatever just by swapping the parts at the end. And those are relatively small part of the cost. Most which is just getting the thing in place.

With GPUs and CPUs. You need to replace entire thing. And now they are the most expensive part of the system.

Other option is doing more with same computing power, but we have utterly failed with that in general...

[martinald]

It's been worse than that. Datacentres are needing basically completely rebuilt for especially Blackwell chips as they mostly require liquid cooling, not air cooling as before. So you don't need to just replace the hardware, you need to replace all the power AND provide liquid cooling, which means completely redesigning the entire datacentres.

[qcnguy]

Yeah, but the question is whether your demand for Claude Code would be as high as it is, if Anthropic were charging enough to cover their costs. Not this fake "the model is profitable if you ignore training the next model" stuff but enough for them to actually be profitable today.

[ThomasCloarec]

This is a crucial question that often gets overlooked in the AI hype cycle. The article makes a great point about the disconnect between infrastructure investment and actual revenue generation.

A few thoughts:

1. The comparison to previous tech bubbles is apt - we're seeing massive capex without clear paths to profitability for many use cases.

2. The "build it and they will come" mentality might work for foundational models, but the application layer needs more concrete business cases.

3. Enterprise adoption is happening, but at a much slower pace than the investment would suggest. Most companies are still in pilot phases.

4. The real value might come from productivity gains rather than direct revenue - harder to measure but potentially more impactful long-term.

What's your take on which AI applications will actually generate enough value to justify the current spending levels?

[Gigachad]

This reads like a chatgpt response.