AIs excel at exploring millions of problems at a surface level (breadth), a scale humans cannot match. Human experts provide the depth needed to tackle the difficult "islands" AIs identify. Science must shift from its current depth-focused model to one that first uses AI to map entire fields and clear away low-hanging fruit.

Knowledge workers are using AI agents like Claude Code to create multi-layered research. The AI first generates several deep-dive reports on individual topics, then creates a meta-analysis by synthesizing those initial AI-generated reports, enabling a powerful, iterative research cycle managed locally.

Information scientist Don Swanson showed novel discoveries lie hidden in existing literature. If one paper shows A implies B and another shows B implies C, a new link (A implies C) can be found. AI can now scale this process of recombining old knowledge.

Beyond productivity gains, AI's most transformative impact may be automating R&D to accelerate scientific discovery. This could lead to breakthroughs in health and wellness, solving problems that might otherwise take decades and fundamentally improving quality of life, not just GDP.

AI's true power in science isn't autonomous discovery, but process compression. It acts as an expert guide, allowing motivated individuals to navigate complex fields like drug discovery and assemble workflows that once required multiple specialized teams, blurring the line between professional research and individual effort.

The "AI vs. Dog Cancer" story shows that current AI's power is not autonomous discovery, but its ability to act as a research assistant, enabling motivated non-experts to orchestrate complex scientific projects by finding and coordinating with human experts.

The ultimate goal isn't just modeling specific systems (like protein folding), but automating the entire scientific method. This involves AI generating hypotheses, choosing experiments, analyzing results, and updating a 'world model' of a domain, creating a continuous loop of discovery.

While many focus on AI's business applications, its most profound benefit will be in science. Leaders like Google's Demis Hassabis believe AI will solve humanity's hardest problems in math, physics, and biology, with the potential to cure all diseases within a decade.

AI's key advantage isn't superior intelligence but the ability to brute-force enumerate and then rapidly filter a vast number of hypotheses against existing literature and data. This systematic, high-volume approach uncovers novel insights that intuition-driven human processes might miss.