Grayscale Spotlights Three AI-Blockchain Projects
Grayscale has highlighted three blockchain projects targeting different parts of the artificial intelligence infrastructure market, drawing attention to the potential applications of blockchain technology in data licensing, GPU financing and computing monetization.
Frontier tech and crypto are converging.
Crypto projects part of the AI buildout:$DATA → Licensing and verifying AI training data.
$CHIP → Financing GPU hardware through onchain loans.
$PEARL → Merging proof-of-work with AI compute, letting miners earn rewards while powering compute.Read more on The Stack by @willhamilogden: https://t.co/7HTKvs9oCD
— Grayscale (@Grayscale) October 23, 2026
In an October 23 research article published on The Stack, Grayscale Head of Research Zach Pandl identified DATA, USD.AI and Pearl as examples of projects connecting blockchain-based systems with the growing requirements of AI development. Their approaches focus on different infrastructure needs, from obtaining training data to financing the hardware required to run computational workloads.
DATA Targets Human-made Training Material
DATA emphases on collecting, licensing and tracking human-contributed material, including photographs, videos and voice recordings. The project objects to allow applications to license these datasets to AI developers while reimbursing the people who contribute the material. Its approach centres on managing access to data and creating a framework for recognizing contributors.
On October 12, DATA announced plans to migrate its protocol to the Sui blockchain, with Walrus expected to serve as its default storage layer. The proposed transition also includes a planned $30 million investment from the Sui Foundation and a proposed migration of DATA tokens at a one-to-one ratio.
USD.AI & Pearl Take On Computing Power
USD.AI addresses another challenge in AI infrastructure: financing expensive computing equipment. The project connects cryptocurrency capital with loans backed by GPU equipment and related cash flows. Its onchain pools allow capital providers to fund these loans and potentially earn yield. This model aims to connect available digital capital with the financing requirements of AI computing infrastructure.
Pearl focuses on combining AI computing workloads with proof-of-work blockchain security. Computing providers may receive PRL token rewards alongside revenue generated from AI-related computing services. Its approach seeks to connect computational activity with a blockchain-based reward structure.
Together, the three projects illustrate different ways blockchain technology could be applied beyond conventional token trading and decentralized finance. Data licensing, access to computing hardware and the monetization of computing workloads are separate challenges, and each project addresses a different part of that landscape.
Grayscale’s examples highlight the intersection of blockchain and AI infrastructure, although the supplied report does not establish the projects’ commercial scale, adoption levels or future performance.
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