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Companiescryptography

Nexus Laboratories raises $25 million from Pantera and Lightspeed to expand ‘zero-knowledge’ privacy tools

By
Niamh Rowe
Niamh Rowe
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June 10, 2024, 8:00 AM ET
Daniel Marin, left, the founder and CEO of Nexus, with chief scientist Jens Groth.
Daniel Marin, left, the founder and CEO of Nexus, with chief scientist Jens Groth.Nexus

Nexus Laboratories is a virtual machine—software that emulates a physical computer—that wants to bolster trust in the internet. To do so, Nexus is betting on a species of cryptography called “zero-knowledge proofs,” or zk-proofs, which allow one party to prove to another that a piece of data is true, without conveying the underlying data. A simple analogy: a driver’s-license scanner that reveals someone is 21 without disclosing their name or address. Nexus believes zk-proofs are powerful and wants to make them accessible to any developer.

Nexus took a step towards that goal on Monday, announcing it has raised $25 million in a Series A funding round. The round was co-led by Lightspeed Venture Partners and Pantera Capital. Faction Ventures, Dragonfly Capital, and Blockchain Builders Fund also participated, bringing the company’s total funding to $27.2 million.

Nexus envisions “a new future for the Internet where the integrity of computations and data are protected by proofs,” Daniel Marin, the company’s founder and CEO, said in a statement.

Whether it’s artificial intelligence, cloud computing, or blockchains, pretty much any data set can be verified with zk-proofs. For now, however, Nexus is focused on rollups, the term for a Layer 2 blockchain built atop a main, layer 1 network, like Ethereum. Rollups are designed to help the underlying network scale by batching transactions together, and sending them to the main blockchain in a single transaction.

“We expect that every decentralized network will see mission critical applications of zk-proofs,” said Lauren Stephanian, general partner with Pantera Capital. “We believe there will be thousands of rollups powered by zk-proofs in the very near future, all which will require proof generation.” 

So, why should we care about verified computation? Marin told Fortune that in a world that’s increasingly on-chain and powered by AI, the use cases are far-reaching. This might mean proof of personhood, verifying tax software, or even authenticating crucial yet confidential data in the defense industry. “Anything that requires a lot of security, you need proof for it,” he says. 

What, hypothetically, could be the largest dataset capable of being verified in the future? The Ethereum blockchain, he replied. This would mean proving all computation, from block zero to present, and compressing it into a single proof of maybe 100 bytes. “So, you could summarize all blockchain history into a single proof,” he explains, one that would get updated with every new block. But, he admits, we’re a long way off from that just yet.

Investor Haseeb Qureshi, managing partner with Dragonfly, said in a statement that the firm first met Marin when he was a computer science and cryptography student at Stanford. “My firm led the seed investment in Nexus when Daniel graduated because we were impressed with his expertise and vision for how to improve performance and lower barriers to the use of zero-knowledge proofs,” Qureshi said. 

Earlier this month, the company announced Nexus 1.0, the first major release of its zk-proof virtual machine. The software uses cryptography to “compact proof aggregation,” plus “optimize and parallelize verifiable computation” across a network of machines. 

With the funding, Nexus intends to expand its product offerings, support early users, and maintain its engagement with the scientific community. The company is also eyeing collaboration with the financing. It’s launching both an open-source developer community, and the first phases of a new volunteer computing network, that it hopes will be able to break the record for the largest computation ever performed.

It’s tricky to wrap one’s head around the possibility of what verifiability and truth mean in the computation, Marin admits, “but there’s a lot of possibilities here,” he insists.

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