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.

CZI’s mission to cure all diseases is seen as unambitious by AI experts but overly ambitious by biologists. This productive tension forces biologists to pinpoint concrete obstacles and AI experts to grasp data complexity, accelerating the overall pace of innovation.

To vet ambitious ideas like self-sailing cargo ships, first ask if they are an inevitable part of the world in 100 years. This filters for true long-term value. If the answer is yes, the next strategic challenge is to compress that timeline and build it within a 10-year venture cycle.

Building the first large-scale biological datasets, like the Human Cell Atlas, is a decade-long, expensive slog. However, this foundational work creates tools and knowledge that enable subsequent, larger-scale projects to be completed exponentially faster and cheaper, proving a non-linear path to discovery.

CZI's Biohub model hinges on a simple principle: physically seating biologists and engineers from different institutions (Stanford, UCSF, Berkeley) together. This direct proximity fosters collaboration and creates hybrid experts, overcoming the institutional silos often reinforced by traditional grant-based funding.

The key to successful open-source AI isn't uniting everyone into a massive project. Instead, EleutherAI's model proves more effective: creating small, siloed teams with guaranteed compute and end-to-end funding for a single, specific research problem. This avoids organizational overhead and ensures completion.

OpenAI's non-profit parent retains a 26% stake (worth $130B) in its for-profit arm. This novel structure allows the organization to leverage commercial success to generate massive, long-term funding for its original, non-commercial mission, creating a powerful, self-sustaining philanthropic engine.

Moving from a science-focused research phase to building physical technology demonstrators is critical. The sooner a deep tech company does this, the faster it uncovers new real-world challenges, creates tangible proof for investors and customers, and fosters a culture of building, not just researching.

A critical flaw in philanthropy is the donor's need for control, which manifests as funding specific, personal projects instead of providing unrestricted capital to build lasting institutions. Lasting impact comes from empowering capable organizations, not from micromanaging project-based grants.