Two possible paths for AI-driven scientific innovation

In an April 2025 post, the author envisions two approaches to AI for science: models trained for particular problems, such as protein folding, and generalist systems that could optimize experiments within time and compute limits. The author expects both to have a role and thinks AI may reach superhuman performance first on tasks with clear rewards, before harder-to-grade work such as explaining why a phenomenon occurs.