What data here is false? How do you gish gallop in writing?
1) Companies are spending a ton on capex for future AI compute
2) Current levels of AI revenue are not enough to recoup that capex spend
3) Revenues won’t increase enough in the future to recoup that capex spend
Almost anyone, bubbler or not, would agree with points 1 and 2. But Ed cites dozens of numbers from different sources to repeat and reinforce them. It feels to me like an effort to overwhelm the reader with data to support his overall argument. That’s what I would call a gish gallop.
The third point is a prediction. He cites a lot of facts and numbers here too, but ultimately whether you believe his prediction is going to depend on your assumptions.
The thing is, I really would love to see a detailed analysis of capex spend and amortization. Capex spent on the future is a big unknown. But the big labs have claimed they are profitable on inference. How much capex was invested to create the capacity to serve current models? How much revenue is coming from serving those models? What does the full profitability picture look like? What does that imply for future demand needs?
Regarding Gish galloping, I don’t think you can Gish Gallup in writing. The point as you said is to rapidly overwhelm an opponent. That’s not possible in writing as the points can be argued one by one at the responders leisure.
As the accountants say, profit is an opinion, but cash flow is a fact.
Like, if these companies are profitable on inference (depends on how you count training expenses I suspect), then they shouldn't need to raise more money.
For reference, Anthropic appear to have raised about $130bn, which is a lot. Assuming that OpenAI have raised about the same.
Even to get a 2x return on this investment they'd need to start making about $50bn per annum (profit, not revenue) for 10 years. That's Google level net income, on a very very different business (google's business is much more capital efficient).
I personally find that very unlikely, but we'll see I guess.
> a detailed analysis of capex spend and amortization
This is kinda irrelevant unless they are making money (which the hyperscalers are, and the pure play model companies are not).
Obviously more detailed data isn't easily available to report on, but I'd like to know:
- What are the labs spending on compute specifically to serve models? Are they really profitable "on inference" of existing models? If so, how long is the payback period to recoup their training costs for those existing models only? If not, how much would prices need to rise to be profitable?
- How much capex have the hyperscalers invested in just the compute being used to serve those existing models? Are they making money "on inference" when accounting for just that amortized capex? If so, what are the margins like?
- What share of hyperscalers' AI revenue is from training vs inference? (Presumably training is more dependent on VC investment and inference is more self-sustaining.)