When firms begin automating R&D with AI, they initially pursue more radical innovations. AI facilitates access to distant knowledge domains, making ambitious recombinations cheaper to explore. The first effect of acceleration is bolder research.
Then the effect reverses. As more firms adopt AI-driven R&D, the aggregate rate of creative destruction increases. Each breakthrough is displaced faster. The monopoly duration that rewards radical innovation shortens. At some threshold of AI adoption, the economics flip: incremental innovations become more rational because they deliver returns before the next wave of disruption erases them. Fully AI-driven research would undermine the knowledge creation it seeks to accelerate โ duplication of effort, reduced originality, and a race to the bottom where each discovery is immediately obsoleted.
The structural mechanism is precise: acceleration changes the incentive landscape. The first units of speed reward ambition because they open new territory faster. Beyond a threshold, additional speed compresses the time any discovery remains valuable. The tool that makes radical innovation possible simultaneously makes it unprofitable.
This is different from simple diminishing returns. The returns don't diminish โ they're actively eroded by the same process that creates them. The faster everyone innovates, the less time any innovation stays novel, the less reward there is for being radical, the more rational it becomes to be incremental. The system doesn't run out of territory. It runs out of time to claim the territory it discovers.
The lesson is uncomfortable for anyone who believes more AI means more breakthroughs: there exists an optimal level of automation, and it's less than full. Beyond that level, the creative destruction engine consumes its own fuel.