AI modeling transforms drug development from a numbers game of screening millions of compounds to an engineering discipline. Researchers can model molecular systems upfront, understand key parameters, and design solutions for a specific problem, turning a costly screening process into a rapid, targeted design cycle.

Wet lab experiments are slow and expensive, forcing scientists to pursue safer, incremental hypotheses. AI models can computationally test riskier, 'home run' ideas before committing lab resources. This de-risking makes scientists less hesitant to explore breakthrough concepts that could accelerate the field.

The ambitious goal of discovering a high-temperature superconductor isn't just a scientific target; it's a strategic choice. Achieving it requires building numerous sub-systems like autonomous synthesis and characterization, effectively forcing the creation of a general-purpose AI for science platform.

Google DeepMind's AI has expanded the catalog of known stable crystals from 40,000 to over 400,000. These AI-predicted materials are now being lab-tested and could lead to breakthroughs in physics-limited industries by enabling technologies like better electric vehicle batteries and superconductors.

Early AI models advanced by scraping web text and code. The next revolution, especially in "AI for science," requires overcoming a major hurdle: consolidating and formatting the world's vast but fragmented scientific data across disciplines like chemistry and materials science for model training.

AlphaFold's success in identifying a key protein for human fertilization (out of 2,000 possibilities) showcases AI's power. It acts as a hypothesis generator, dramatically reducing the search space for expensive and time-consuming real-world experiments.

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.

Experiments are not just for validation; they are a form of computation. By treating nature as a 'Physics Processing Unit' (PPU) working alongside digital GPUs, we can integrate physical experimentation directly into the computational loop, creating a powerful hybrid system for materials discovery.

Instead of screening billions of nature's existing proteins (a search problem), AI-powered de novo design creates entirely new proteins for specific functions from scratch. This moves the paradigm from hoping to find a match to intentionally engineering the desired molecule.