This notion that "we don't have enough compute" does not cleanly reconcile with the fact that labs are burning cash faster than any cohort of companies in history.

If I am a grocery store that pays $1 for oranges and sells them for $0.50, I can't say, "I don't have enough oranges."

There is a major logic flaw in what you're saying.

'If I am a grocery store that pays $1 for oranges and sells them for $0.50, I can't say, "I don't have enough oranges."'

How about 'if I'm a grocery store and I see no limit on demand for oranges at $.50 but they are currently $1, I can say 'if oranges were cheaper I could sell orders of magnitude more of them'.

Buying oranges for $1 and selling for $0.5 is an investment into acquiring market share and customer relationships and a gamble on the price of oranges falling in the future.

The grocery store analogy works if compute is the orange.

But labs arent buying oranges — theyre buying the only orchard on the island, hoping it yields a fruit no ones grown yet. Burning $1B to net $500M isnt "I have too few oranges." Its "Im betting the farm Ill find a new one."

Both can be irrational. Theyre irrational in different ways.

You can if you're exhausting the global production of oranges.

If there were more oranges you’d pay less to buy them and your economics would work out.

You misunderstand.

"I built a ship to go to the Indies and bring back tea."

"Bro, the ship cost 100,000 pounds sterling and only brought back 50,000 pounds of tea. I don't care if you paid 12,500 pounds for the tea itself, you're losing money."

There is a very rational reason labs are spending everything they can get for more compute right now. The tea (inference) pays 60%+ margins. And that is rising. And that number is AFTER hyper scalars make their margins. There is an immense amount of profit floating around this system, and strategics at the edge believing they can build and control the demand through combined spend on training and inference in the proper ratios.