Lila Sciences Raises $235M to Build Autonomous AI Labs, Joins Unicorn Ranks
Lila Sciences has raised $235 million in Series A financing, a round that values the Cambridge-based startup at more than $1 billion less than a year after its launch. The company is using the money to pursue a sweeping goal: building what it calls scientific superintelligence. Lila’s platform combines AI models, robotics, and custom software to automate the entire scientific method, generating hypotheses, designing experiments, running them, and learning from the results.
The funding, co-led by Braidwell LP and Collective Global, adds to a $200 million seed round earlier this year from Flagship Pioneering, bringing Lila’s total raised to nearly $450 million.
At the heart of the effort are AI Science Factories: automated labs that integrate reinforcement learning, simulations, and robotics. According to the company, its first factory has already run hundreds of thousands of AI-driven experiments, yielding discoveries in life sciences, chemistry, and materials science. Lila argues that by closing the loop between reasoning and real-world verification, it can shrink research timelines from years to days.
Applications span multiple fields. In medicine, Lila says its AI agents can design novel proteins, nucleic acids, and diagnostics in hours instead of weeks. In materials science, its systems are being applied to catalysts, thin films, and ultra-stable metals, with implications for clean energy and advanced manufacturing. The company also sees opportunities in computing and energy systems, where new materials could unlock efficiency gains.
The Series A drew backing from a broad investor group including General Catalyst, March Capital, the Mathers Foundation, Modi Ventures, NGS Super, the State of Michigan Retirement System, Altitude Life Science Ventures, Alumni Ventures, ARK Venture Fund, Common Metal, Flagship Pioneering, and a wholly owned subsidiary of Abu Dhabi Investment Authority (ADIA). Endpoints News has reported the total could climb toward $300 million as additional tranches are finalized.
Lila is led by Geoffrey von Maltzahn, a Flagship partner, with Harvard geneticist George Church serving as Chief Scientist. The company plans to expand into Boston, San Francisco, and London, building additional Science Factories and hiring across AI research, robotics, biology, chemistry, and materials science.
Lila’s push to build what it calls “scientific superintelligence” reflects a wider momentum across the life sciences industry: the move toward autonomous or semi-autonomous research labs. The idea is simple in theory but radical in practice — closing the loop between AI-driven hypotheses and physical experiments so that machines can both reason and verify at scale.
Several companies are already testing versions of this model. Insilico Medicine, founded in 2014, has spent years developing what it brands as Pharma.AI, a suite of platforms for drug target discovery, molecule design, and clinical outcome prediction. More recently it has begun integrating those digital tools with a robotics lab designed to handle cell culture, high-throughput screening, sequencing, and imaging. The aim, much like Lila’s, is to create a feedback loop in which experimental results continually refine AI predictions.
Other models are emerging at different scales. Ginkgo Bioworks, working with Inductive Bio and Tangible Scientific, has been testing “lab-in-the-loop” workflows for small molecule drug discovery, tying predictive models directly to automated assays. Startups such as Potato are pursuing a broader vision of fully autonomous cycles, building robotic systems that can carry out benchwork as AI agents generate and prioritize experiments. These efforts remain in development, but they illustrate a common trajectory: reducing the friction between computational design and physical validation.
Beyond Legacy Tools
Lila’s arrival in the unicorn ranks signals how quickly capital is flowing into the convergence of AI and laboratory automation. Yet as industry analysts caution, the true measure of progress is not just in dollars raised or experiments run, but in outcomes that reach patients and markets.
A recent analysis by BioPharmaTrend highlights this tension. While companies such as Insilico Medicine, Recursion, Exscientia (acquired), and others have reported compressed discovery timelines in the past — in some cases cutting the journey from target identification to clinical candidate from several years to under 18 months — the number of AI-discovered drugs that have cleared late-stage clinical trials remains small. The report notes that many platforms are still best viewed as accelerators of particular stages of R&D, rather than as replacements for the entire pipeline.
That perspective underscores both the ambition and the challenge facing Lila. Its AI Science Factories embody the aspiration of what I call holistic drug development, a system where machine intelligence drives every step of the biopharmaceutical process, across multiple steps. But whether these factories can consistently deliver validated therapies, functional materials, or next-generation energy systems is still to be proven.
For now, Lila represents the frontier of a broader industry shift. If autonomous labs can reliably shorten discovery cycles, reduce costs, and expand the range of scientific problems machines can tackle, they could alter not only how drugs are developed but how experimental science itself is practiced in a broader sense.
For readers who want to keep track of how companies like Lila, Insilico, and others are reshaping the boundaries of science, the dynamics are shifting almost week by week. New funding rounds, technical milestones, and regulatory developments are constantly redrawing the picture. We follow these changes closely in our Where Tech Meets Bio newsletter, which offers weekly insights on the intersection of technology and the life sciences.
Topics: Biotech Ventures
