CuspAI raises $450 million and launches a materials discovery network with 45+ partners
CuspAI raises $450 million and launches a materials discovery network with 45+ partners
On July 20, Cambridge-based CuspAI announced a $450 million Series B round at a $2.6 billion valuation and launched the AI Materials Foundry. The network includes more than 45 companies and research organizations, including NVIDIA, Meta, Samsung, Hyundai, Applied Materials, and Lam Research.
Finding a substance with the required property is not enough to make a chip or battery. Researchers must also synthesize it in a laboratory, measure its properties, and manufacture it on the producer's equipment using available raw materials. At each of these stages, a chemical formula either becomes a specific material or is rejected.
CuspAI describes the AI Materials Foundry as a network of data, laboratories, computing resources, and scientific expertise. The company aims to connect agentic AI with industry data and customer requirements. The network is intended to identify materials while accounting from the outset for where and how they will be produced.
In this work, manufacturing data and customer requirements matter as much as computational analysis because they determine whether a material is suitable at all. CuspAI is bringing organizations with computing resources, scientific expertise, and manufacturing problems into the Foundry.
The round was led by Kleiner Perkins and NEA, with participation from Bezos Expeditions. According to The Guardian, CuspAI links the network's work to semiconductors, energy grids, and batteries. The company plans to expand its operations in the United States, the Asia-Pacific region, and Europe.
The $450 million gives CuspAI the resources to organize materials discovery around laboratories and manufacturing requirements, rather than around computation alone. The value of the Foundry will now depend on whether its participants can move a candidate from computational analysis to a laboratory sample that meets the requirements of a specific manufacturing process.