The AI world needs more data transparency and web3 startup Space and Time says it can help

As AI proliferates and things on the internet are easier to manipulate, there’s a need more than ever to make sure data and brands are verifiable, said Scott Dykstra, CTO and co-founder of Space and Time, on TechCrunch’s Chain Reaction podcast.

“Not to get too cryptographically religious here, but we saw that during the FTX collapse,” Dykstra said. “We had an organization that had some brand trust, like I had my personal life savings in FTX. I trusted them as a brand.”
But the now-defunct crypto exchange FTX was manipulating its books internally and misleading investors. Dykstra sees that as akin to making a query to a database for financial records, but manipulating it inside their own database.
And this transcends beyond FTX, into other industries, too. “There’s an incentive for financial institutions to want to manipulate their records … so we see it all the time and it becomes more problematic,” Dykstra said.
But what is the best solution to this? Dykstra thinks the answer is through verification of data and zero-knowledge proofs (ZK proofs), which are cryptographic actions used to prove something about a piece of information — without revealing the origin data itself.
“It has a lot to do with whether there’s an incentive for bad actors to want to manipulate things,” Dykstra said. Anytime there’s a higher incentive, where people would want to manipulate data, prices, the books, finances or more, ZK proofs can be used to verify and retrieve the data.
At a high level, ZK proofs work by having two parties, the prover and the verifier, that confirm a statement is true without conveying any information more than whether it’s correct. For example, if I wanted to know whether someone’s credit score was above 700, if there’s one in place, a ZK proof — prover — can confirm that to the verifier, without actually disclosing the exact number.
Space and Time aims to be that verifiable computing layer for web3 by indexing data both off-chain and on-chain, but Dykstra sees it expanding beyond the industry and into others. As it stands, the startup has indexed from major blockchains like Ethereum, Bitcoin, Polygon, Sui, Avalanche, Sei and Aptos and is adding support for more chains to power the future of AI and blockchain technology.
Dykstra’s most recent concern is that AI data isn’t really verifiable. “I’m pretty concerned that we’re not really efficiently ever going to be able to verify that an LLM was executed correctly.”
There are teams today that are working on solving that issue by building ZK proofs for machine learning or large language models (LLMs), but it can take years to try and create that, Dykstra said. This means that the model operator can tamper with the system or LLM to do things that are problematic.
There needs to be a “decentralized, but globally, always available database” that can be created through blockchains, Dykstra said. “Everyone needs to access it, it can’t be a monopoly.”
For example, in a hypothetical scenario, Dykstra said OpenAI itself can’t be the proprietor of a database of a journal, for which journalists are creating content. Instead, it has to be something that’s owned by the community and operated by the community in a way that’s readily available and uncensorable. “It has to be decentralized, it’s going to have to be on-chain, there’s no way around it,” Dykstra said.
This story was inspired by an episode of TechCrunch’s podcast Chain Reaction. Subscribe to Chain Reaction on Apple Podcasts, Spotify or your favorite pod platform to hear more stories and tips from the entrepreneurs building today’s most innovative companies.
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