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.

When government funding for science is volatile, the biggest long-term risk is losing a generation of talent. Nonprofits can provide stability by funding postdoctoral fellows and junior faculty. This shores up the scientific foundation and prevents a loss of talent that can't be undone later.

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 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.

The concentration of AI power in a few tech giants is a market choice, not a technological inevitability. Publicly funded, non-profit-motivated models, like one from Switzerland's ETH Zurich, prove that competitive and ethically-trained AI can be created without corporate control or the profit motive.

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.

Unlike for-profits with direct customer feedback, NGOs must please funders, who are not the beneficiaries. This misaligns incentives away from pure impact, creating a market inefficiency. For impact-maximizing professionals, this systemic weakness represents an opportunity to deliver significant value in a less-optimized space.

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 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.