An AI model named EVO2 designed novel bacteriophage genomes from scratch. When created in a lab, these viruses were not only viable but also functioned better than the best-known natural phages at killing E. coli, marking a new era in biological engineering.

AI capabilities are rapidly advancing beyond theory. Today's frontier models can troubleshoot complex laboratory experiments from a simple cell phone picture, often outperforming human PhDs. This dramatically lowers the barrier to entry for conducting sophisticated biological research.

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.

AI performs poorly in areas where expertise is based on unwritten 'taste' or intuition rather than documented knowledge. If the correct approach doesn't exist in training data or isn't explicitly provided by human trainers, models will inevitably struggle with that particular problem.

Contrary to the focus of many safety frameworks, AI's biggest capability boost is not for novices, who remain incompetent, but for 'mid-tier' actors like PhD students. These individuals have foundational knowledge, making them the most dangerous recipients of AI assistance.

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.

Frontier AI models excel in medicine less because of their encyclopedic knowledge and more because of their ability to integrate huge amounts of context. They can synthesize a patient's entire medical history with the latest research—a task difficult for any single human. This highlights that the key to unlocking AI's value is feeding it comprehensive data, as context is the primary driver of superhuman performance.

AI's role in bioprocessing is not to replace scientists but to augment their abilities. It serves as a powerful tool providing predictive insights and autonomous optimizations. The ideal future is a partnership where humans guide strategy and interpret results, while AI handles the complex data analysis to make processes faster and more reliable.

Treat AI skills not just as prompts, but as instruction manuals embodying deep domain expertise. An expert can 'download their brain' into a skill, providing the final 10-20% of nuance that generic AI outputs lack, leading to superior results.