Space and Time is a multichain data platform that links verifiable onchain and offchain data for smart contracts and AI applications. Powered by its proprietary Proof of SQL cryptography, the platform allows developers to securely link analytical data to smart contracts, enabling advanced use cases in areas like DeFi, financial services, gaming, and enterprise. The solution ensures tamperproof data for blockchain and AI-driven projects.
Scott Dykstra is Co-Founder and Chief Technology Officer for Space and Time, and also serves as Strategic Advisor to several database and web3 technology startups including Sotero.
Scott has a tenured history of building and scaling large engineering teams around complex greenfield challenges and research-driven development. With a specialisation in enterprise-scale analytics, Scott previously served as a VP of Cloud Solutions at Teradata, where he spent nearly eight years bringing Teradata from on-premise deployments to next-gen, cloud-based SaaS.
Why did Space and Time build its own chain instead of using existing networks? How does this relate to Chainlink’s approach to data aggregation?
Space and Time, like Chainlink, aims to cryptographically secure indexed data offchain and then prove it to a smart contract on any popular chain.
The team chose to build a purpose-specific chain to better support data delivery rather than operate as a general layer 1 competitor.
The architecture is designed from the ground up, using a mix of zero-knowledge proofs and Byzantine Fault Tolerant consensus to ensure data integrity and support verifiable computation.
What is a ZK coprocessor, and how does Proof of SQL improve smart contract functionality?
A ZK coprocessor is a computing tool. It can be a single server or a network of servers that offloads processing from blockchains and uses ZK proofs to verify the results onchain. It’s a way to avoid redundant computations across a validator set.
Proof of SQL is the protocol Space and Time developed to verify the accuracy of SQL queries and data integrity on the SXT Chain.
The network’s Prover nodes handle queries and proof generation in under a second, and the results are then verified cryptographically, offering fast, scalable support for smart contracts.
How did building during several market cycles shape the development of the SXT mainnet?
The project began with a vision for a decentralised query engine even before all use cases were clearly defined.
The unexpected rise of EVM-compatible layer 2 networks led us to pivot to a rollup-centric approach for delivering data across multiple chains, rather than focusing solely on the Ethereum mainnet.
This expanded Space and Time’s integration possibilities and helped support a wider range of projects.
Now that SXT Chain is live, how has the EVM developer community responded?The mainnet launch has triggered strong interest from teams across the EVM ecosystem — Sophon in the ZKsync ecosystem being one example.
We recently announced a collaboration with Avalanche, aimed at giving their developers easier access to indexed data and the ability to send data back to smart contracts.
The volume of requests we’ve received for custom integrations has been overwhelming, and we’re excited to see the network support as many of them as possible.
The mainnet launch marked the point where Space and Time became truly trustless and secure, with 34 validators globally reaching consensus to secure incoming data and ZK proofs for outgoing data.
How has the Genesis Validator Rewards Program been received?
The response to the Space and Time Genesis Validator Rewards program has been very positive. We onboarded 34 validators in the first month.
How does Space and Time support onchain AI agents, and what trends have you seen there?
AI agents are becoming more active onchain, engaging in activities such as trading, yield optimisation, and smart contract auditing.
These AI agents require timely, accurate onchain data with cryptographic proof — something Space and Time is designed to provide. This includes metrics that aren’t typically available from centralised sources.
We also see a future where smart contracts function as controls or safeguards for AI agents, offering structure and limits to their activities.
Do you expect verifiable data to become a kind of programmable asset?
The team envisions a future where trusted data becomes monetisable.
In this “DataFi” model, contributors of high-quality datasets could earn fees when their data is queried.
This would create a market-driven incentive to publish accurate and useful data onchain.