As startups build on commoditized AI platforms like GPT, product differentiation becomes less of a moat. Success now hinges on cracking growth faster than rivals. The new competitive advantages are proprietary data for training models and the deep domain expertise required to find unique growth levers.

Instead of building AI models, a company can create immense value by being 'AI adjacent'. The strategy is to focus on enabling good AI by solving the foundational 'garbage in, garbage out' problem. Providing high-quality, complete, and well-understood data is a critical and defensible niche in the AI value chain.

Instead of simply adding AI features, treat your AI as the product's most important user. Your unique data, content, and existing functionalities are "superpowers" that differentiate your AI from generic models, creating a durable competitive advantage. This leverages proprietary assets.

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.

Rather than selling single products, Novonesis designs custom blends or "cocktails" of different enzymes and microbes. This tailor-made approach solves specific customer problems so effectively that it makes the solution highly unique and difficult for competitors to replicate.

The future of valuable AI lies not in models trained on the abundant public internet, but in those built on scarce, proprietary data. For fields like robotics and biology, this data doesn't exist to be scraped; it must be actively created, making the data generation process itself the key competitive moat.

The true enterprise value of AI lies not in consuming third-party models, but in building internal capabilities to diffuse intelligence throughout the organization. This means creating proprietary "AI factories" rather than just using external tools and admiring others' success.

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.