The next major AI breakthrough will come from applying generative models to complex systems beyond human language, such as biology. By treating biological processes as a unique "language," AI could discover novel therapeutics or research paths, leading to a "Move 37" moment in science.

Modern AI systems can now 'speed run' a digital version of evolution. By combining an LLM's ability to rapidly generate hypotheses with an automated evaluation function, these systems can test ideas, discard failures, and pursue successful 'lineages' at a pace far exceeding biological evolution.

Current concerns focus on AI agents using existing bioinformatics tools. The more advanced threat is agentic AI that can code and create novel, personalized biological tools on demand, moving beyond a static toolset to a dynamic threat generation capability.

As biologics evolve into complex multi-specific and hybrid formats, the number of design parameters (valency, linkers, geometry) becomes too vast for experimental testing. AI and computational design are becoming essential not to replace scientists, but to judiciously sample the enormous design space and guide engineering efforts.

Research on bio-foundation models like EVO2 and ESM3 shows that strategically excluding key datasets (e.g., sequences of viruses that infect humans) dramatically reduces a model's performance on dangerous tasks, often to random chance, without harming its useful scientific capabilities.

The belief that nature represents the ceiling of pathogen danger is false. Just as humans engineer materials stronger than any found in nature, AI can be used to design viruses that are far more transmissible or lethal than their natural counterparts.

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.

In a specialized test (Virology Capabilities Test) assessing tacit knowledge, leading AI models doubled the scores of human experts in their own specialized areas. This challenges the long-held belief that practical 'know-how' is an insurmountable barrier for AI in biosecurity.

Futurist Freeman Dyson predicted biotechnology would follow computing's path, moving from large institutions to individual creators. AI is accelerating this shift by lowering the cognitive barrier to entry, potentially making biological design an accessible, decentralized craft. This counters the dominant narrative of AI as a purely centralizing force.