I almost forgot: The reason why “business leaders” want to see AI everywhere is because they think it’ll be just like the adoption of every other technology to this point: It gets cheaper over time (not always better, but usually so).

The assumption is that if they “beat their competitors” to be the first ones using AI efficiently, they’ll utterly destroy them (economically; they won’t be able to compete). It’s a very bad assumption.

So far, “Big AI” is getting better but at costs that scale geometrically with the amount of “better”. That is: You can improve reliability (e.g. reduce hallucinations, increase accuracy, improve outputs in various ways, etc) but only by drastically inflating the cost and at reduced speed and efficiency.

We’re starting to learn that LLMs need about two generations of hardware advances before they’re going to be cost efficient for the types of “human productivity enhancement” that business leaders want. It’s only affordable now because “Big AI” is subsidizing the costs, trying to get customers hooked. The assumption being that if they’re hooked on AI, they’ll be able to raise prices to reflect actual costs. Just like the business leaders, this is a very bad assumption.

Instead, what’s going on is that the open weights AI models are starting to get “good enough” for most tasks (that you’d want to use them for, e.g. coding or agentic automation stuff). That means that all the billions and billions being spent by Big AI is just building up debt that will never be repaid and having this side effect of using up all the chip/memory capacity in the entire world.

It’s an absurd situation and the world will eventually look back on this time like we do the dotcom era.