[root_axis]

> They could find a way to climb up the value chain and capture more of the consumer surplus.

Yes, this is exactly why OpenAI and Anthropic are hyping AGI. If LLMs ever become good enough to replace workers, the first sign will be frontier model companies launching competitor businesses. It doesn't make sense to sell the formula for gold when you can just use it yourself.

> There could be a paradigm shift in compute architecture/compute cost.

Possible, but no signs of this on the horizon. If it does happen, it's impossible to predict when it will.

> We could reach a limit of marginal utility, shifting consumption to legacy models, thereby lengthening the depreciation/utility of training.

I'm not sure market dynamics will allow this any time soon. We seem to have already achieved a marginal utility equilibrium in terms of model size, so training new models on trending use-cases (e.g. synthetic data targeting tool calls, agentic workflows, computer use, etc) is really the driving force behind product differentiation. Nobody wants to admit "training new models isn't profitable" because that deflates the AGI singularity narrative that all this investment hinges on.