I’ve been thinking a lot about this. I think that AI software automation tools are disproportionately more useful in greenfield work done by small or tiny organizations. By an order of magnitude, maybe 2 in some cases.

What that means is anyone’s guess, but it seems like it should result in a Cambrian explosion of disruptive new companies, limited in scope by the idea space.

The thing about small teams is, with a few exceptions, the biggest challenges are typically funnels for users and product-market fit, overcoming and exploitation of network effects, etc… so even in small orgs, if you make 30 percent of the problem 4x faster/smaller you still have the other 70 percent which is now 92.5% of the problem.

This applies even more acutely in larger organizations… so for them, 99 percent of the problem remains.

Intangibles in an organization like reluctance, education, and organizational inertia fill the gap left by software acceleration, and in the end you only see tiny gains, if any.

What really happened, on an organizational scale, is that software development costs went down. We wouldn’t expect a wage collapse in coding to foment an explosive revolution in company profitability or dynamism. We shouldn’t expect those things of LLM assistance.

We should look at it as a reduction in cost with potentially dangerous side effects if not managed carefully, with an especially big reduction in r&d development costs.