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Opentrons raises $40 million Series E to push robots, not models, into the lab

Opentrons Labworks has raised $40 million in Series E funding led by healthcare investor Avego, with BroadOak Capital Partners participating. Pulse 2.0 and Robotics & Automation News reported the round on 30 October 2026.

AI & modelsExplainerRachel NwosuPublished: 4 November 20263 min readSources 21
Opentrons raises $40 million Series E to push robots, not models, into the lab

A $40 million round with no valuation attached

Healthcare investor Avego led the $40 million Series E round. BroadOak Capital Partners also participated.

Existing investors include SoftBank Vision Fund and Khosla Ventures, according to Pulse 2.0 and The AI Insider. Opentrons is based in New York City and was founded in 2014. That puts roughly a decade of lab automation sales behind this round. Opentrons did not disclose its valuation. It also did not disclose the ownership stakes investors acquired. Without those figures, the public cannot check whether Opentrons raised the $40 million above, at or below the price of the previous round. The silence leaves the price of the round unknown outside the company.

The bottleneck argument the round is built on

Opentrons argues that the physical lab, not the model, is now the constraint on experiments.

AI proposes experiments faster than labs can run them, the company says. The Opentrons Flex platform pairs laboratory robots with software and AI tools. Those tools are meant to design, run and refine experiments in a continuous loop. Each run through an Opentrons system produces structured, reproducible data that later AI cycles can use, according to the company. Opentrons says physical laboratory execution is an increasingly important bottleneck as models get faster at proposing experiments. "Millions of scientists spend much of their day on manual work a robot can do," CEO James Atwood said.

"Giving that time back to discovery is the biggest opportunity in science," CEO James Atwood said.

Where the money goes

Proceeds will fund AI-driven experimental execution and expanded automation capabilities. Opentrons calls its architecture open and API-driven.

The company also plans to enter new markets and geographies. It also plans to speed up protocol development and integrate its robots more deeply with third-party instruments and software. That framing matters for labs that already own its hardware. The promise is an upgrade path rather than a rip-and-replace. Opentrons says the money will also support additional automation across its robotics portfolio and deeper integration across laboratory systems. Protocol development is on the list, alongside the robots themselves. The company said the round will support expansion of its robotics, software and laboratory data infrastructure.

The installed base behind the pitch

Opentrons says it has deployed more than 11,000 robots in laboratories across more than 80 countries. Those are company figures. 90% of the 20 largest global pharmaceutical companies use its systems, according to Opentrons. The customer list also includes every top-20 U.S. research university and 14 of the 15 largest global biopharmaceutical companies. The announcement does not say how many of those installations are on paid service contracts. It also does not say how often the robots run. Its customers span pharmaceutical manufacturers, biotechnology developers, diagnostics laboratories and academic research institutions. Avego managing partner James Flexner said every AI-driven discovery still has to be tested in a real lab.

"Drug developers and AI companies alike are moving experimental work forward toward automated workflows that generate usable data," Flexner said.

What the announcement does not settle

Opentrons disclosed neither the valuation nor the investor ownership stakes. The funding announcement named no new customers. Opentrons did not give any revenue figures or a timeline for the new markets it plans to enter. It also did not quantify the claim at the centre of the funding round. Opentrons says its robots act as an execution layer that connects AI models to physical laboratory work. The funding announcement asserts that claim rather than measuring it. The announcement does not address whether the software upgrade path actually shortens experiments.

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Sources

21
  1. 01Opentrons Raises $40 Million Series E To Expand AI-Driven Laboratory Automation - Pulse 2.0EN
  2. 02Opentrons Raises $40 Million Series E Led by Avego to Expand AI-Driven Laboratory AutomationEN
  3. 03Opentrons raises $40 million to expand AI-powered lab automation - Robotics & Automation NewsEN
  4. 04Opentrons Announces $40M Series E Round to Standardize AI-Driven Science on Its RobotsEN
  5. 05Rapid LLM development is outrunning the research meant to explain it, study findsEN
  6. 06Hanwha Q-Cells shifts focus to research and development in EuropeEN
  7. 07Android-Smartphones sichern Fotos jetzt automatisch auf Windows-PCsEN
  8. 08CyberKord raises €2.7M to automate IT and cybersecurity operations with AIEN
  9. 09Quantum Interconnects Link Two 120-Qubit Processors (IBM Research)EN
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  14. 14Avoiding an uncontrolled reentry; boost Hubble and keep the science – IsaacmanEN
  15. 15 On this day in space! Oct. 30, 1964: NASA's weird Lunar Landing Research Vehicle takes 1st test flight to help Apollo astronauts land on the moon EN
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  18. 18From 1D Benchmarks to Complex 3D Geometries: Can Agents Reliably Specify Problems for Automated PDE Simulation?EN
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All figures and quotations in this text come from the sources listed below.

Content prepared by the editorial team with AI assistance.

Rachel Nwosu

Rachel Nwosu

AI, models and technology

Rachel Nwosu covers AI, models and technology for FLASH24, working from public model documentation, benchmark releases and repository histories rather than press summaries, and she skips announcements that arrive without reproducible numbers. She checks training-data claims against dataset cards and reruns reported metrics where code is available. She spends much of her week interviewing researchers and engineers, tracking model launch calendars, and comparing vendor benchmarks with independent evaluations. Outside the desk she runs 3D printers, restores old computers, and tests how models learn from internet junk. She does not publish benchmark figures she cannot trace to a source.

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