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As AI proliferates and things on the internet are easier to manipulate, there\u2019s a need more than ever to make sure data and brands are verifiable, said Scott Dykstra, CTO and co-founder of Space and Time<\/a>, on TechCrunch\u2019s Chain Reaction podcast<\/a>.<\/p>\n \u201cNot to get too cryptographically religious here, but we saw that during the FTX collapse,\u201d Dykstra said. \u201cWe had an organization that had some brand trust, like I had my personal life savings in FTX. I trusted them as a brand.\u201d<\/p>\n But the now-defunct crypto exchange FTX was manipulating its books<\/a> 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.<\/p>\n And this transcends beyond FTX, into other industries, too. \u201cThere\u2019s an incentive for financial institutions to want to manipulate their records \u2026 so we see it all the time and it becomes more problematic,\u201d Dykstra said.<\/p>\n 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 \u2014 without revealing the origin data itself.<\/p>\n \u201cIt has a lot to do with whether there\u2019s an incentive for bad actors to want to manipulate things,\u201d Dykstra said. Anytime there\u2019s 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.<\/p>\n 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\u2019s correct. For example, if I wanted to know whether someone\u2019s credit score was above 700, if there\u2019s one in place, a ZK proof \u2014 prover \u2014 can confirm that to the verifier, without actually disclosing the exact number.<\/p>\n 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.<\/p>\n Dykstra\u2019s most recent concern is that AI data isn\u2019t really verifiable. \u201cI\u2019m pretty concerned that we\u2019re not really efficiently ever going to be able to verify that an LLM was executed correctly.\u201d<\/p>\n 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.<\/p>\n There needs to be a \u201cdecentralized, but globally, always available database\u201d that can be created through blockchains, Dykstra said. \u201cEveryone needs to access it, it can\u2019t be a monopoly.\u201d<\/p>\n For example, in a hypothetical scenario, Dykstra said OpenAI itself can\u2019t be the proprietor of a database of a journal, for which journalists are creating content. Instead, it has to be something that\u2019s owned by the community and operated by the community in a way that\u2019s readily available and uncensorable. \u201cIt has to be decentralized, it\u2019s going to have to be on-chain, there\u2019s no way around it,\u201d Dykstra said.<\/p>\n This story was inspired by an episode of TechCrunch\u2019s podcast Chain Reaction. Subscribe<\/a> to Chain Reaction on Apple Podcasts<\/a>, Spotify<\/a> or your favorite pod platform to hear more stories and tips from the entrepreneurs building today\u2019s most innovative companies.<\/em><\/p>\n Connect with us:<\/strong><\/p>\n\n
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