There is ~30GW of LLM compute in operation worldwide. The AI industry says it needs to ~4x this number in 5 years. This is both physically impossible and deeply misguided.
The vast majority of improvements in AI do not come from scale at this point; they came from software engineering—training, harnesses, tool integration, loops. And even with all of this effort, what have we really gotten? Where are the productivity gains? Where are all the jobs it’s replacing? Where are all the scientific discoveries? Where are the products it’s made?
NONE of the things we were promised have happened. How is adding more chips going to make this business make sense?
Answer: It won’t.
The core problem for the industry is that engineering doesn’t move nearly as much money as capex. The industrial incentives all point towards building sheer scale rather than better software.
Jensen Huang needs data centers, not better chatbots. He couldn’t care less if it’s “AGI” or not. As long as he’s giving banks an excuse to finance his chips, that’s all that matters.
The hyperscalers and the frontier labs sold scale as the “moat.”
They convinced the market to make a $5T+ bet on it. That was dead wrong and we’re all about to see just how wrong it was.
LLMs are an experimental software feature that metastasized into the greatest misallocation of resources in all of human history.
I am now full doomer—not because chatbots are going to become “superintelligent,” but because we bet the entire economy on the absurd science fiction story that they will.
We are a deeply stupid species.
What do you notice about the other industries?
PHYSICAL INFRASTRUCTURE. All of those “bubbles” left infrastructure that added to GDP over many decades.
Before you say “data centers,” the vast majority of the cost is in GPUs—which begin depreciating rapidly after 2-3 years. Data centers aren’t railroad tracks; they are temporary money furnaces.
I don’t know how we get out of this without a great depression. It’s the most absurd mass psychosis in human history.