When Mark Zuckerberg and Priscilla Chan proposed curing all disease, top scientists didn't cite scientific limits. Instead, they pointed to operational failures: data silos, unpublished information, and non-scalable tools. This revealed the core problem was engineering and infrastructure, not just pure science.

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.

CZI focuses on creating new tools for science, a 10-15 year process that's often underfunded. Instead of just giving grants, they build and operate their own institutes, physically co-locating scientists and engineers to accelerate breakthroughs in areas traditional funding misses.

CZI set an audacious goal to cure all disease. When scientists deemed it impossible, CZI's follow-up question, "Why not?" revealed the true bottleneck wasn't funding individual projects, but a systemic lack of shared tools, which then became their core focus.

CZI identified a market failure in scientific funding. Government grants favor short-term, small-scale investigations. CZI fills this gap by building long-term, expensive, foundational tools (like imaging and virtual cell models) that accelerate the entire field, rather than just funding the "next best grant."

Instead of funding small, incremental research grants, CZI's philanthropic strategy focuses on developing expensive, long-term tools like AI models and imaging platforms. This provides leverage to the entire scientific community, accelerating the pace of the whole field.

CZI's goal to cure all diseases by 2100 is seen as unambitious by AI experts but overly ambitious by biologists. This difference in perspective forces biologists to define barriers and AI researchers to understand data complexities, fostering a more credible, grounded approach to innovation.

CZI strategically focuses on developing long-term scientific tools and platforms by operating its own labs. This addresses a funding gap left by government grants for individual investigators and public-health-focused philanthropies, aiming to accelerate research for all scientists.

The current scientific funding model rewards individual discoveries. A more effective approach for the AI era would be to treat critical inputs like datasets as public infrastructure, enabling thousands of research teams to solve many problems at scale, rather than just one.