Investing in Phylo

Co-leading Phylo's $13.5M seed round

| America | Tech | Opinion | Culture | Charts |

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For years, leaders at the frontier AI labs – Sam Altman, Dario Amodei, Demis Hassabis – have been remarkably consistent about one thing: AI’s biggest long-term impact will be in science.

We agree. But before AI impacts science, science will need to shape how AI products are built.

Despite dramatic model progress, most AI products are still optimized for dialogue, not discovery. Chatbots and generalist agents are powerful for answering questions, but the scientific method doesn’t fit cleanly within a chat window. Science is a process – it’s iterative, stateful, error-prone, and deeply contextual.

So if AI is going to meaningfully accelerate biology, it has to work the way scientists work.

Science needs an integrated environment

Modern biology exists in a fragmented state. When it’s not paywalled, scientific literature lives in PDFs. Data is stored in spreadsheets. Analysis is accessible via R and Python scripts. Databases are locked behind clunky web portals. Results get stitched together manually, often irreproducibly, by humans acting as emergency room surgeons.

Other technical professions solved this long ago. Designers have Figma. Analysts have Excel. Software engineers have Integrated Development Environments (IDEs). But biology never got its equivalent.

What’s missing is an Integrated Biology Environment (IBE) – a single place where hypotheses are generated, experiments are planned, data is analyzed, models are run, and results are produced in a way that’s auditable and reproducible.

That’s the category Phylo is building.

“Phylo,” a prefix derived from the Greek phylon meaning “class”, denotes the scientific classification of organisms and echoes throughout evolutionary biology.

Phylo is building the AI Scientist: an IBE as an agentic orchestration layer that coordinates tools, data, and models across the full scientific workflow, positioning the human scientist as the conductor and reallocating the mind and time toward higher-order science.

We’re proud to co-lead Phylo’s $13.5M seed round and partner with a team particularly suited to build a native environment for scientific discovery – an AI Scientist built by and for scientists.

Building on a movement

When we first met Phylo’s co-founder, Kexin Huang, it was clear his graduate work at Stanford wasn’t a response to recent AI hype. Kexin has spent nearly a decade pursuing this idea of an AI scientist, long before it was a defined category, shaped by his experience across academia and applied AI. That conviction led him to partner with co-founder Yuanhao Qu, a cancer biology PhD student whose experimental and computational biology background complemented Kexin’s AI training, and together they built Biomni – a popular open-source biomedical research agent that became the first concrete step toward Phylo’s IBE platform.

Biomni spread quickly and organically. Researchers adopted it across academia and, soon after, in hospitals, biotech, and pharma. Scientists were returning to it and using it for real-world research workflows, validating both the depth of the problem and the pull of the product.

Today, it is used by more than 7,000 labs across academia and industry, already supporting tens of thousands of scientists and saving millions of research hours.

Phylo is now taking that foundation and delivering Biomni Lab, an enterprise-grade environment built on top of Biomni, with the security, traceability, and workflow infrastructure required for production scientific use.

As scientists use Biomni Lab, the system improves through direct feedback from real research, enabling learning that is grounded in how science is actually done.

This is an evolution in biology. We believe Biomni Lab can become the landing page on every scientist’s laptop in every lab – the one tab that’s always open. When a single environment becomes the window through which all science is done, the pace and coherence of discovery fundamentally change.

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