If you look at all the hyperscalers' earnings reports, they all report their data centers being capacity constrained, especially for GPUs. Then couple this with survey-based reports [1, 2] showing that so far a minority of people used AI regularly for work, and that too for a fraction of their workdays. This indicates huge demand with even bigger potential for growth.
This is why there is this huge gold rush for infrastructure, why these players have such sky-high valuations and why investors are scambling to pour in even more money. The focus on AGI and ASI is a distraction and only relevant to the frontier model labs (more on them later). Even if AGI/ASI is never achieved and all model development was frozen today, we have decades of growth ahead of us.
The only risk is that all these productivity gains are a mirage (cue that METR paper) and at some point people will realize it and the whole scheme will come tumbling down. However, studies like [1] contradict that premise and are already finding productivity gains that match various other RCT-based studies:
>This implies that each hour spent using genAI increases the worker’s productivity for that hour by 33%. This is similar
in magnitude to the average productivity gain of 27% from several randomized experiments of genAI usage (Cui et al., 2024; Dell’Acqua et al., 2023; Noy and Zhang, 2023; Peng et al., 2023)
Our estimated aggregate productivity gain from genAI (1.1%) exceeds the 0.7% estimate by Acemoglu (2024) based on a similar framework.
(Not to mention my own anecdotal experiences and the rising frequency of people posting about their successes with tools like Claude Code on HN and other social media.)
To me there is virtually no risk that this data center capacity will be unused. There probably is a bubble, but only as far as the frontier labs are concerned; given that models costing millions to train still get commoditized rapidly, it is not clear if they can capture the value produced by their models to sustain their valuations.
But those models require infra to run and that's exactly what the hyperscalers are stockpiling. The frontier labs will need to get in on that game to survive long term.
This is why there is this huge gold rush for infrastructure, why these players have such sky-high valuations and why investors are scambling to pour in even more money. The focus on AGI and ASI is a distraction and only relevant to the frontier model labs (more on them later). Even if AGI/ASI is never achieved and all model development was frozen today, we have decades of growth ahead of us.
The only risk is that all these productivity gains are a mirage (cue that METR paper) and at some point people will realize it and the whole scheme will come tumbling down. However, studies like [1] contradict that premise and are already finding productivity gains that match various other RCT-based studies:
>This implies that each hour spent using genAI increases the worker’s productivity for that hour by 33%. This is similar in magnitude to the average productivity gain of 27% from several randomized experiments of genAI usage (Cui et al., 2024; Dell’Acqua et al., 2023; Noy and Zhang, 2023; Peng et al., 2023)
Our estimated aggregate productivity gain from genAI (1.1%) exceeds the 0.7% estimate by Acemoglu (2024) based on a similar framework.
(Not to mention my own anecdotal experiences and the rising frequency of people posting about their successes with tools like Claude Code on HN and other social media.)
To me there is virtually no risk that this data center capacity will be unused. There probably is a bubble, but only as far as the frontier labs are concerned; given that models costing millions to train still get commoditized rapidly, it is not clear if they can capture the value produced by their models to sustain their valuations.
But those models require infra to run and that's exactly what the hyperscalers are stockpiling. The frontier labs will need to get in on that game to survive long term.
[1] https://www.stlouisfed.org/on-the-economy/2024/sep/rapid-ado... (full paper: https://s3.amazonaws.com/real.stlouisfed.org/wp/2024/2024-02...)
[2] https://arstechnica.com/ai/2025/07/so-far-only-one-third-of-...