The AI boom is creating huge demand for training data and the expensive computers that power it. Grayscale has identified three cryptocurrencies targeting these growing needs. Their prices, however, tell very different stories.
In an October 23 research note, Grayscale Head of Research Zach Pandl highlighted DATA Network (DATA), USD.AI (CHIP), and Pearl (PEARL).
Each addresses a different part of AI infrastructure, from supplying training data to financing computer chips and earning revenue from computing power.
1. DATA Network (DATA): Selling the Data AI Needs
AI models learn from images, videos, and voice recordings. DATA aims to collect this material with permission, license it to AI developers, and reward contributors.
On October 12, the Sui Foundation announced a $30 million investment in the network. DATA also plans to migrate to Sui, transferring existing tokens one-for-one.
Yet investors have seen little relief. DATA has fallen 42.07% over 90 days to $0.15243, including a 34.65% decline over the past month.
2. USD.AI (CHIP): Financing Expensive AI Chips
AI requires powerful graphics processors, or GPUs. USD.AI helps computing companies borrow money against this hardware and its future income, using blockchain-based lending pools.
In August, Bullish announced a $100 million financing facility supporting USD.AI’s GPU loans.
CHIP has gained 47.98% over 90 days to $0.04386, although it remains roughly 69% below its April peak.
3. Pearl (PEARL): Making GPUs Earn Twice
Pearl proposes using the same computing capacity for AI workloads and blockchain mining. Providers could earn AI revenue while collecting tokens for securing the network.
PEARL is the standout performer, surging 528.24% in 90 days to $2.00. However, it has retreated about 19% from its October 21 record of $2.46.
Supply also poses a risk. Around 1.2 million new tokens are estimated to enter circulation daily, while its $4.2 billion fully diluted valuation far exceeds its $713 million market cap.
Grayscale sees potential in all three projects, although their success remains uncertain.
“Their long-term relevance will depend on adoption and competitive economics. We are keeping an eye on them,” Pandl said.
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