Opentrons raises $40 million Series E to push AI-run experiments on its lab robots

Opentrons Labworks, a New York company that builds benchtop robots for life-science labs, has raised $40 million in a Series E round. The announcement, distributed through Business Wire on October 29, frames the raise around a bet that AI-generated hypotheses will increasingly be tested by robots rather than by hand.

The round

Avego, a healthcare-focused investment firm founded in 2015 with offices in New York and Georgia, led the round. BroadOak Capital Partners joined, alongside existing investors SoftBank Vision Fund and Khosla Ventures. The company did not disclose a valuation or its total funding to date.

According to the release, the money will go toward scaling Opentrons' robotics, software and data infrastructure, investing in AI-driven experimental execution, extending automation across its instruments, speeding up protocol development with software and AI, integrating more closely with other lab automation tools, and entering new markets and geographies.

What Opentrons sells

Founded in 2014, Opentrons makes open, API-driven robots that automate repetitive bench work. SiliconANGLE describes two main machines: the Flex, which handles more samples at once and can use an optional gripper to move plates so that more steps run without a person, and the smaller, cheaper OT-2 for routine pipetting. Typical uses include DNA extraction, PCR, protein purification and ELISA assays.

The company says it has deployed more than 11,000 robots in over 80 countries and counts 90% of the top 20 global pharmaceutical companies as customers. Its own boilerplate gives a slightly different measure, citing 14 of the top 15 global biopharma companies and every top-20 US research university.

The pitch and its limits

The central argument is that the physical lab is becoming a bottleneck: AI models can propose experiments faster than scientists can run them. Opentrons positions the Flex, paired with its AI software, as a loop in which experiments are designed, executed and refined continuously. CEO James Atwood says the systems are built so that AI can run experiments directly, and Avego managing partner James Flexner argues that AI discoveries still have to be validated in real labs.

The release's claim that autonomous science is "standardizing" on Opentrons is the company's own characterization; it offers no market-share data to support it. Competitors in lab automation and the pace at which pharmaceutical labs actually hand experiment design to AI will determine whether that holds.