Source: Azeem Azhar, Exponential View
The assumption that AI will accelerate scientific discovery by automating thinking overlooks a harder constraint: the operational, experimental, and validation infrastructure that turns theoretical insight into verified knowledge. Azhar's framing reorients the AI-in-science conversation away from compute-first narratives toward lab automation, data standardization, peer review capacity, and institutional incentives—suggesting that deploying smarter models at unsolved problems misses where friction actually lives. This shapes where venture capital and research funding flow, and why some AI labs' scientific breakthroughs remain isolated rather than cascade into new capabilities across fields.