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.

Unlike fields with finite demand, the appetite for scientific discovery is infinite. Therefore, automating science won't displace scientists. Instead, it will create more questions and opportunities, transforming the scientist's role into a manager or 'wrangler' of AI systems that explore hundreds of ideas simultaneously.

AI is engineered to eliminate errors, which is precisely its limitation. True human creativity stems from our "bugs"—our quirks, emotions, misinterpretations, and mistakes. This ability to be imperfect is what will continue to separate human ingenuity from artificial intelligence.

True creative mastery emerges from an unpredictable human process. AI can generate options quickly but bypasses this journey, losing the potential for inexplicable, last-minute genius that defines truly great work. It optimizes for speed at the cost of brilliance.

In high-stakes fields like pharma, AI's ability to generate more ideas (e.g., drug targets) is less valuable than its ability to aid in decision-making. Physical constraints on experimentation mean you can't test everything. The real need is for tools that help humans evaluate, prioritize, and gain conviction on a few key bets.

Scientific research is being transformed from a physical to a digital process. Like musicians using GarageBand, scientists will soon use cloud platforms to command remote robotic labs to run experiments. This decouples the scientist from the physical bench, turning a capital expense into a recurring operational expense.

Unlike pre-programmed industrial robots, "Physical AI" systems sense their environment, make intelligent choices, and receive live feedback. This paradigm shift, similar to Waymo's self-driving cars versus simple cruise control, allows for autonomous and adaptive scientific experimentation rather than just repetitive tasks.

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.

Contrary to the idea that AI will make physical experiments obsolete, its real power is predictive. AI can virtually iterate through many potential experiments to identify which ones are most likely to succeed, thus optimizing resource allocation and drastically reducing failure rates in the lab.