> Are they unprofitable because they don't profit on inference, or because they reinvest all of the profit into scaling up?
They are scaling up using VC money, not revenue. As far as profit on inference goes, it's hard to separate it out from training: they cannot, at any given time, simply stop training because that would kill any advantage they have 6 months down the line.
For all practical purposes, you can't look at their inference costs independent of the training cost; they need to keep spending on both if they want to continue doing inference.
> Remember how long Amazon was unprofitable, by choice.
That was a very different scenario - AMZ was not spending their revenue on land-grabbing, they were spending their revenue on long-lived infra, while AI companies now are spending VC investment, not revenue, on land-grabbing.
The difference between spending your revenue on short-lived infra (training a new model, acquiring GPUs) and long-lived infra is that with long-lived infra, at any time, even after 10+ years, you can stop expanding your infra and keep the resulting revenue as profit.
With short-lived infra (models, GPUs), you can't simply stop infra spending and collect profit from the revenue, because the infra reached end-of-life and needs to be replaced anyway.
