Lightmatter Raises $154 Million From Google Ventures, Fidelity Management & Research Co., Others

With the artificial intelligence (AI) boom in full swing, companies like Lightmatter — a startup using light for AI computing — are in a position of power when seeking funding. 

Lightmatter announced that it has raised $154 million from Google Ventures and Fidelity Management & Research Co. among others. With this raise, the company tripled its valuation. 

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What Does Lightmatter Do?

In simple terms, Lightmatter uses light for AI computing, making for faster and more energy-efficient solutions. 

“We use light to link computer chips together, and we also use light to do calculations for deep learning,” Lightmatter Co-Founder and CEO Nick Harris said. “The reason that we’re getting these customers and data center scale deployments with our interconnect is that the generative AI boom is driving high-end chips like crazy.”

Historically, light has been used in the telecommunications industry to transmit data globally via fiber optic cables. But today, major tech companies are exploring light-based technology to decrease power costs in their data centers. The main challenge — the one that Lightmatter is working to overcome — lies in shrinking the size of the devices that create or control light.

As computing chips become more sophisticated, their power consumption increases. Harris noted that the cost of powering chips can account for up to half of a data center’s operating expenses. Light transmits information more energy efficiently than electrical signals over wires​. 

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Lightmatter has developed a chip called Passage, which connects other chips by converting electrons to photons and vice versa. This chip is set to be mass-produced and deployed in data centers next year. The recent funding round will significantly aid in rolling out this product​. 

The company is also developing a more futuristic product called Envise, a system that uses light, instead of electrons, for computing. Although it won’t train them, Envise will be capable of running large AI models. The system is expected to be tested by customers in 2023. 

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