Space and Time (SxT) Labs has announced the completion of a $20 million Series A funding round, led by Framework Ventures and supported by several prominent investors. SxT Labs disclosed that the funding aims to accelerate the company’s development of tools that bridge the gap between artificial intelligence (AI) and blockchain technology.
Space and Time Secures $20 Million in Series A Funding to Enhance AI and Blockchain
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Space and Time Closes $20 Million Series A to Enhance Verifiable Compute Layer
Space and Time (SxT) Labs, based in Los Angeles, has raised $20 million in a Series A funding round, bringing its total funding to $50 million. The company focuses on creating a verifiable compute layer that integrates AI with blockchain technology.
This latest funding round was led by Framework Ventures, with participation from investors including Lightspeed Faction, Arrington Capital, and Hivemind Capital. Microsoft’s M12 Ventures, DCG, and Okx Ventures were also involved.
The announcement details that the capital will be used to advance engineering, product development, and community engagement efforts. The SxT network leverages blockchain indexing, a data warehouse, and a zero-knowledge (ZK) coprocessor to offer verifiable data processing solutions.
Their Proof of SQL technology is designed to provide trustless data processing, which the company sees as crucial for the future of decentralized applications ( dapps). SxT claims to be working closely with Microsoft on developing AI-powered applications, aiming to solve infrastructure challenges in both decentralized finance ( defi) and traditional finance sectors.
“Space and Time is committed to empowering the community to own their future in an AI-powered world by providing the tools they need to build next-gen applications at the intersection of AI and blockchain,” Nate Holiday, the co-founder and CEO of SxT remarked on Tuesday.
What do you think about Space and Time raising $20 million? Share your thoughts and opinions about this subject in the comments section below.
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