In a world where the pace of data generation far outstrips our ability to process and understand it, scientific progress is increasingly hindered not by a lack of information, but by the challenge of navigating it. Today marks a pivotal shift in that landscape. FutureHouse, an ambitious nonprofit dedicated to building an AI Scientist, has launched the FutureHouse Platform, giving researchers everywhere access to superintelligent AI agents built specifically to accelerate scientific discovery. This platform could redefine how we explore biology, chemistry, and medicine—and who gets to do it.
A Platform Designed for a New Era of Science
The FutureHouse Platform isn’t just another tool for summarizing papers or generating citations. It is a purpose-built research engine that introduces four deeply specialized AI agents—each designed to tackle a major pain point in modern science.
Crow is a generalist agent, ideal for researchers who need quick, high-quality answers to complex scientific questions. It can be used through the platform’s web interface or integrated directly into research pipelines via API, allowing for real-time, automated scientific insight.
Falcon, the most powerful literature analysis tool in the lineup, conducts deep reviews that draw from vast open-access corpora and proprietary scientific databases like OpenTargets. It goes beyond keyword matching to extract meaningful context and draw informed conclusions from dozens—or even hundreds—of publications.
Owl, formerly known as HasAnyone, answers a surprisingly foundational question: Has anyone done this before? Whether you’re proposing a new experiment or investigating an obscure technique, Owl helps ensure that your work isn’t redundant and identifies gaps worth exploring.
Phoenix, still in experimental release, is designed to assist chemists. It’s a descendant of ChemCrow and is capable of proposing novel compounds, predicting reactions, and planning lab experiments with parameters like solubility, novelty, and synthesis cost in mind.
These agents aren’t trained for general conversations—they’re built to solve real problems in research. They’ve been benchmarked against leading AI systems and tested against human scientists in head-to-head evaluations. The result? In many tasks, such as literature search and synthesis, FutureHouse agents demonstrated greater precision and accuracy than PhDs. The agents don’t just retrieve—they reason, weighing evidence, identifying contradictions, and justifying conclusions in a transparent, auditable way.
Built by Scientists, for Scientists
What makes the FutureHouse Platform uniquely powerful is its deep integration of AI engineering with experimental science. Unlike many AI initiatives that operate in abstraction, FutureHouse runs its own wet lab in San Francisco. There, experimental biologists work hand-in-hand with AI researchers to iteratively refine the platform based on real-world use cases—creating a tight feedback loop between machine and human discovery.
This effort is part of a larger architecture FutureHouse has developed to model the automation of science. At the base are AI tools, such as AlphaFold and other predictive models. The next layer consists of AI assistants—like Crow, Falcon, Owl, and Phoenix—that can execute specific scientific workflows such as literature review, protein annotation, and experimental planning. On top of that sits the AI Scientist, an intelligent system capable of building models of the world, generating hypotheses, and designing experiments to refine those models. The human scientist, finally, provides the “Quest”—the big questions like curing Alzheimer’s, decoding brain function, or enabling universal gene delivery.
This four-layer framework allows FutureHouse to tackle science at scale, not only improving how researchers work, but redefining what’s possible. In this new structure, human scientists are no longer bottlenecked by the manual labor of reading, comparing, and synthesizing scientific literature. Instead, they become orchestrators of autonomous systems that can read every paper, analyze every experiment, and continuously adapt to new data.
The philosophy behind this model is clear: artificial intelligence shouldn’t replace scientists—it should multiply their impact. In FutureHouse’s vision, AI becomes a true collaborator, one that can explore more ideas, faster, and push the boundaries of knowledge with less friction.
A New Infrastructure for Discovery
FutureHouse’s platform arrives at a time when science is ready to scale—but lacks the infrastructure to do so. Advances in genomics, single-cell sequencing, and computational chemistry have made it possible to run experiments that test tens of thousands of hypotheses simultaneously. Yet, no researcher has the bandwidth to design or analyze that many experiments on their own. The result is a global backlog of scientific opportunity—an untapped frontier hiding in plain sight.
The platform offers a way through. Researchers can use it to identify unexplored mechanisms in disease, resolve contradictions in controversial fields, or rapidly evaluate the strengths and limitations of published studies. Phoenix can suggest new molecular compounds based on cost, reactivity, and novelty. Falcon can detect where the literature is conflicted or incomplete. Owl can ensure you’re building on solid ground, not reinventing the wheel.
And perhaps most importantly, the platform is designed for integration. Through its API, research labs can automate continuous literature monitoring, trigger searches in response to new experimental results, or build custom research pipelines that scale without needing to expand their teams.
This is more than a productivity tool—it’s an infrastructure layer for 21st-century science. And it’s free, publicly available, and open to feedback. FutureHouse is actively inviting researchers, labs, and institutions to explore the platform and shape its evolution.
With support from former Google CEO Eric Schmidt and a board that includes scientific visionaries like Andrew White and Adam Marblestone, FutureHouse is not simply chasing short-term applications. As a nonprofit, its mission is deeply long-term: to build the systems that will allow scientific discovery to scale both vertically and horizontally, enabling each researcher to do exponentially more—and making science accessible to anyone, anywhere.
In a research world overwhelmed by complexity and noise, FutureHouse is offering clarity, speed, and collaboration. If science’s greatest limitation today is time, FutureHouse may have just given some of it back.
