Their assumption is that training is a fixed cost: you'll spend the same amount on training for 5 users as you will with 500 million users.

Spending hundreds of millions of dollars on training when you are two guys in a garage is quite significant, but the same amount is absolutely trivial if you are planet-scale.

The big question is: how will training cost develop? Best-case scenario is a one-and-done run. But we're now seeing an arms race between the various AI providers: worst-case scenario, can the market survive an exponential increase in training costs for sublinear improvements?

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simianwords 5 days ago | parent [-]

They just won’t train it. They have the choice.

Why do you think they will mindlessly train extremely complicated models if the numbers don’t make sense?

	▲	crote 5 days ago \| parent [-]
		Because they are trying to capture the market, obviously. Nobody is going to pay the same price for a significantly worse model. If your competitor brings out a better model at the same price point, you either a) drop your price to attract a new low-budget market, b) train a better model to retain the same high-budget market, or c) lose all your customers. You have taken on a huge amount of VC money, and those investors aren't going to accept options A or C. What is left is option B: burn more money, build an even better model, and hope your finances last longer than the competition. It's the classic VC-backed startup model: operate at a loss until you have killed the competition, then slowly increase prices as your customers are unable to switch to an alternative. It worked great for Uber & friends.