The Chan Zuckerberg Initiative (CZI) announced a round of layoffs that affected about 70 employees — roughly 8% of its workforce — as the philanthropy pivots toward AI‑powered biomedical research centered on its Biohub network.
What changed and why
The cuts, concentrated at CZI’s Redwood City headquarters, are part of a strategic refocus that Mark Zuckerberg and Dr. Priscilla Chan began shaping in 2025. The couple signaled they want the organization to concentrate on “accelerating science,” especially through Biohub — a collaborative research network that partners with institutions such as UC San Francisco, Stanford, UC Berkeley, Northwestern and Columbia.
Zuckerberg has been candid about the shift. At a Biohub event in November he said, "I feel like the science work that we’ve done, the Biohub model in particular, has been the most impactful thing that we have done." In a November blog post the founders framed the new chapter as an all‑in push on "AI‑powered biology."
CZI’s move mirrors a broader trend among deep‑pocketed philanthropies directing capital into science and technology instead of earlier social programs. The shift also aligns CZI with the heavy AI investments Zuckerberg is making at Meta — a company budgeting tens of billions for AI development.
The human details
CZI gave affected staff a 60‑day notice and offered severance that includes 16 weeks of base pay, continuation of health insurance and a $10,000 stipend. Some employees were reassigned to other teams where possible, but the organization said it needs more research‑heavy expertise — computational biologists, data scientists and lab researchers — to meet its redirected mission.
Since Biohub started in 2016, the couple has directed roughly $4 billion to basic science work, and plans to increase that commitment over the coming decade. CZI maintains an operating budget in the neighborhood of $1 billion a year.
What Biohub wants to do next
Biohub’s stated ambition is large: to speed discoveries that could lead to preventing or curing many diseases. The network plans to lean on "frontier AI" and large‑scale biological models for tasks ranging from virtual cell simulation to immune reprogramming and disease prediction. That means hiring and investing in teams that straddle biology and machine learning.
This technical direction sits at the intersection of two fast‑moving areas — computational models for biology and new infrastructure demands for training them. Advances in model development, like the kinds described in recent AI product rollouts, have lowered barriers for ambitious research projects. For context on how research‑grade AI tooling is evolving, see Microsoft’s new imaging model MAI‑Image‑1 and how search and data indexing upgrades are changing what researchers can query in large repositories through efforts such as Gemini Deep Research.
Bigger questions
The refocus raises predictable questions. For one, what happens to the local and policy work CZI supported previously — education initiatives, criminal justice reform, housing and community grants? The organization says it will continue other philanthropic giving and that CZI will serve as infrastructure and support for initiatives, but the public signal is clear: science gets center stage.
There’s also the governance and ethics conversation. When billionaire funders concentrate power and capital behind high‑risk, high‑reward scientific projects, observers ask how priorities are set and who benefits from breakthroughs. CZI will be competing and collaborating with long‑standing health philanthropies, such as the Gates Foundation, which has long focused on global health and disease research.
What employees and the field should expect
If the plan proceeds, CZI will be hiring in a narrower technical band: computational biologists, systems biologists, machine‑learning engineers for biology, and data scientists who can both manage complex datasets and translate findings into testable experiments. For the Redwood City teams that remain, the next months will likely be a mix of rapid recruitment, investment in lab and compute infrastructure, and tighter programmatic goals.
This is a Philanthropy‑meets‑tech moment: big money, ambitious goals and AI as the accelerant. Whether the sharpened focus produces faster, safer progress against disease will depend on hires, partnerships, and how well machine learning and wet lab science are integrated — and on whether the shift can avoid leaving other social priorities starved of resources.