Physical Superintelligence Launches AI Physics Lab With $58M Seed Round

Sep 1, 2026
Physical Superintelligence launched with $58 million in seed funding to build an AI physics lab and develop Emmy, a platform for virtual physicists. Its first commercial use targets data center optimization.

Physical Superintelligence launched in Cambridge, Massachusetts with $58 million in seed funding, the company announced in a press release. The round was led by Breakthrough Energy Ventures, with participation from Dragon Global, Robot Ventures, Solari, Susa, SV Angel, Valkyrie, Balaji Srinivasan, Anthony Scaramucci, and individual investors from OpenAI, NVIDIA, SoftBank Energy, Oracle, Hugging Face, JUMP Capital, and the a16z Scout Fund.

PSI was founded by Matt Pines, Alex Klokus, and Dr. Alexander Wissner-Gross. The company is building an AI physics research lab centered on Emmy, a platform described as a team of virtual physicists that uses a reasoning engine and curated simulations to build physics models and test hypotheses in parallel.

Emmy’s first commercial use is optimization for terrestrial and orbital data centers. The system targets design problems across power, cooling, networking, and compute, including planning before construction and retrofits for existing facilities.

PSI is also the founding technical partner for the Fermi Explorer Mission, a privately funded interstellar mission to Alpha Centauri. The company validated the mission physics, found a more efficient trajectory within mass and budget limits, and will add scientific instruments for the flight.

We hope you enjoyed this article

Consider subscribing to one of our newsletters like Daily AI Brief.

Also, consider following us on social media:

Free newsletter

Daily AI Brief

Daily report covering major AI developments and industry news, with both top stories and complete market updates

Whitepaper

Tensordyne Napier: What If One Rack Could Do the Work of Nine?

Tensordyne

This Tensordyne whitepaper presents Napier, an inference-focused AI processor and rack-scale system based on the company’s TDN Math logarithmic number system. It examines infrastructure requirements for large mixture-of-experts and agentic models, compares major inference architecture approaches, and details the TDN AIP processor, TDN72 pod, TDN Link fabric, and Napier Ultra configuration. The paper reports simulation-based performance, cost, and accuracy-validation results, including Tensordyne’s projected comparison of one Napier rack with a nine-rack Nvidia Rubin plus Groq deployment; the chip is reported as taped out and in fabrication.

Read more
Free, six days a week

Daily AI Brief: the AI news that matters, in your inbox.