Opentrons raises $40 million to expand AI lab automation

Opentrons Labworks raised $40 million in a Series E round led by health care investment firm Avego, with participation from BroadOak Capital Partners. The company will use the proceeds to expand its robots, software and data infrastructure. The announcement says more than 11,000 of its robots have been deployed at research institutions and other organizations in over 80 countries, and that they are used by 90% of the world’s top 20 pharmaceutical companies.

Opentrons has a deployment base of more than 11,000 units and says the new funding will advance AI-powered experiment execution and integration with laboratory automation systems. Beyond the scale of its robot deployments, a key condition for expanding lab automation into AI-driven workflows is whether experimental results can be turned into structured, reproducible data that informs subsequent experiments.

The focus of the funding is not just the $40 million amount, but also that the proceeds will support robots, software and data infrastructure. Opentrons says its Flex platform supports a continuous process of designing, executing and improving experiments, and that the funds will support AI-powered experiment execution and expanded automation capabilities. The plan appears to involve not only increasing the number of robots, but also making experiment execution a workflow that AI can use and connecting the resulting data to subsequent experiments. However, the announcement does not provide a breakdown of the investment, so it is not possible to determine how the funds will be allocated among robots, software and data infrastructure. The company’s stated deployment of more than 11,000 units in over 80 countries, along with its claim that its robots are used by 90% of the world’s top 20 pharmaceutical companies, supports its position as having an established base for lab automation. The announcement’s new initiatives, however, are not simply about increasing the existing deployment base. They include AI-powered experiment execution, protocol development, expanded equipment capabilities and stronger integration with other laboratory automation systems. As a result, the business will likely be judged not only by the number of installations, but also by how consistently it can execute experimental procedures across different labs and equipment, and turn results into reusable data. A structural consideration is the role of software and data between AI that plans experiments and robots that carry them out physically. The official announcement says experiments performed with the company’s robots generate structured, reproducible data. How closely this data is linked to experimental conditions and results, and whether it can be used for AI-driven design and improvement of subsequent experiments, will distinguish simple automation from a continuous experimental loop. The announcement does not specify data formats or conditions for connecting to external systems, so the available information does not show whether the same approach can be used across an entire lab that includes other companies’ equipment. Items to watch include the scope of automation after the investment, supported equipment, the extent of software integration, and the standardization and terms of use for experimental data. The announcement does not specify the number of additional robots, the timing of individual initiatives or the specific mechanisms by which AI will use data to learn and improve. Evidence will also be needed to show how the scale of deployments translates into greater reproducibility or shorter protocol-development times.

Opentrons Flex combines robots and AI systems in a platform for continuously designing, executing and improving experiments. According to the official announcement, the funding will support the expansion of AI-powered experiment execution and automation capabilities, as well as entry into new markets and regions. The company also plans to accelerate the development of experimental protocols using software and AI, expand robot and equipment capabilities, and strengthen integration with the lab automation ecosystem. It did not disclose how the funds will be allocated among these initiatives.