The AI industry will transition from a net-loss phase (due to exponential compute costs) to a profitable phase where individual models generate revenue, but cumulative R&D investments sustain losses until scaling and efficiency improvements drive profitability.
The point is it doesn't equilibrate to perfect competition with zero margins. If there's three firms in the economy and all are kind of independently behaving rationally, it doesn't equilibrate to zero. Help me understand that, because right now we do have three leading firms and they're not making profit. So what is changing? Again, the gross margins right now are very positive. What's happening is a combination of two things. One is that we're still in the exponential scale-up phase of compute. A model gets trained. Let's say a model got trained that costs $1 billion last year. Then this year it produced $4 billion of revenue and cost $1 billion to inference from. Again, I'm using stylized numbers here, but that would be 75% gross margins and this 25% tax. So that model as a whole makes $2 billion. But at the same time, we're spending $10 billion to train the next model because there's an exponential scale-up. So the company loses money. Each model makes money, but the company loses money.