The power of AI for Novonesis isn't the algorithm itself, but its application to a massive, well-structured proprietary dataset. Their organized library of 100,000 strains allows AI to rapidly predict protein shapes and accelerate R&D in ways competitors cannot match.

Models designed to predict and screen out compounds toxic to human cells have a serious dual-use problem. A malicious actor could repurpose the exact same technology to search for or design novel, highly toxic molecules for which no countermeasures exist, a risk the researchers initially overlooked.

The next leap in biotech moves beyond applying AI to existing data. CZI pioneers a model where 'frontier biology' and 'frontier AI' are developed in tandem. Experiments are now designed specifically to generate novel data that will ground and improve future AI models, creating a virtuous feedback loop.

Professor Collins’ team successfully trained a model on just 2,500 compounds to find novel antibiotics, despite AI experts dismissing the dataset as insufficient. This highlights the power of cleverly applying specialized AI on modest datasets, challenging the dominant "big data" narrative.

AI finds the most efficient correlation in data, even if it's logically flawed. One system learned to associate rulers in medical images with cancer, not the lesion itself, because doctors often measure suspicious spots. This highlights the profound risk of deploying opaque AI systems in critical fields.

While AI can accelerate the ideation phase of drug discovery, the primary bottleneck remains the slow, expensive, and human-dependent clinical trial process. We are already "drowning in good ideas," so generating more with AI doesn't solve the fundamental constraint of testing them.

A key value of AI agents is rediscovering "lost" institutional knowledge. By analyzing historical experimental data, agents can prevent redundant work. For example, an agent found a previous study on mouse models that saved a company eight months and significant cost, surfacing data from an acquired company where the original scientists were gone.

MIT Professor Jim Collins estimates a $20 billion investment could fund the R&D and clinical trials for 15-20 new antibiotics, solving the crisis for decades. This cost is a fraction of recent tech investments, framing an existential threat as a solvable, relatively affordable problem.

The AI-discovered antibiotic Halicin showed no evolved resistance in E. coli after 30 days. This is likely because it hits multiple protein targets simultaneously, a complex property that AI is well-suited to identify and which makes it exponentially harder for bacteria to develop resistance.