In H1 2026, cost of revenue is $2.3B. That is primarily inference compute, but also includes cost of training models (but not research).
In the same period, R&D was $9.4B. That is primarily spent on compute. That is what they are "holding back" already, in full public view.
This is not a company that is optimizing for OpEx or end user revenue. The only thing relevant to their plans is achieving AGI/ASI first, and controlling enough compute to leverage it to earn the majority of the current economy.
Likewise the singularity. While recursive self-improvement is theoretically trivial (just tweak a random subset of weights like a monkey with a typewriter, eventually you'll get something smarter), the resulting exponential improvement is still very hypothetical and feels unlikely. Unless you're ok with an exponent of 1.000000000000000001.
I agree we will only see it in hindsight, but I think that's always the case with any technology. It seems worse until it's suddenly better. I don't think it matters to argue the exact moment though. For example when did cars become clearly better than horses? No one cares about the exact date.