Grayscale Highlights Blockchain’s Expanding Role in AI Infrastructure as PEARL Surges 580%

Digital asset management firm Grayscale is turning its attention to the intersection of artificial intelligence and blockchain technology, spotlighting three distinct crypto projects addressing critical pillars of the AI boom: training data collection, hardware financing, and computing revenue. Among them, a project known as PEARL has captured market attention with a reported 580% price gain over a three-month period, underscoring the growing intersection between decentralized networks and the physical infrastructure powering modern artificial intelligence.

The focus on these projects highlights an ongoing evolution in how blockchain applications interact with the broader industrial supply chain. On October 23, Zach Pandl, head of research at Grayscale, released an analysis examining how decentralized protocols are solving real-world bottlenecks in the AI sector. Pandl specifically pointed to PEARL, DATA Network, and USD.AI as prime examples of blockchain utility extending deep into the AI infrastructure buildout. While these protocols offer innovative models for revenue generation, data sourcing, and capital deployment, Pandl also injected a note of market realism regarding their long-term trajectory, emphasizing that their sustained relevance will ultimately depend on widespread adoption and competitive economic viability.

PEARL’s 580% Gain Puts AI Computing Rewards in Focus

The most immediate market reaction to Grayscale’s focus has centered on PEARL, a blockchain project explicitly designed to merge high-performance computing with decentralized ledger technology. According to Coingecko data cited in Grayscale’s analysis through October 22, PEARL recorded a dramatic 580% gain over a three-month span, accompanied by a notable 93.5% surge in its network hash rate.

PEARL operates by enabling graphics processing units—commonly referred to as GPUs—to perform complex artificial intelligence workloads while simultaneously securing a proof-of-work blockchain network. Because modern GPUs are engineered to execute thousands of calculations in parallel, they are uniquely suited for both intensive AI model processing and the cryptographic computations required by blockchain ledgers.

Through this dual-purpose architecture, hardware operators and miners can potentially diversify their revenue streams, earning blockchain network rewards alongside traditional commercial payments for rendering AI computing services. This convergence of decentralized consensus mechanisms and high-utility hardware is increasingly viewed as a viable path toward offsetting the immense capital expenditure required to run modern AI data centers.

DATA Targets Paid Contributions to AI Training

Beyond raw computational power, the artificial intelligence industry faces an escalating bottleneck regarding the availability, quality, and legal provenance of training data. Large language models and computer vision systems rely heavily on massive datasets—comprising human-contributed photographs, video files, and voice recordings—to learn patterns and improve their predictive capabilities.

To address this challenge, DATA Network is building a decentralized infrastructure designed to streamline the collection, licensing, and tracking of human-generated training material. The protocol aims to bridge the gap between AI laboratories requiring high-quality inputs and individuals who supply the raw data, ensuring that contributors are directly compensated for their materials. According to Grayscale’s snapshot, the DATA Network has already verified more than 280 million records within its ecosystem.

To scale its operational capacity, DATA announced plans on October 12 to migrate its core protocol—the governing software and rules that dictate network operations—to the Sui blockchain. Additionally, the project designated Walrus, a decentralized file-storage network, as its default storage layer. By leveraging decentralized data storage and high-throughput blockchain architecture, DATA hopes to provide AI developers with verifiable, transparently sourced datasets while rewarding contributors through tokenized incentives.

USD.AI Connects Blockchain Funding With GPU Loans

The massive capital expenditure required to procure advanced hardware remains one of the primary hurdles for artificial intelligence startups and cloud providers. High-performance graphics processing units are notoriously expensive and difficult to secure, creating a heavy reliance on specialized financing. USD.AI, a blockchain-based lending platform, is attempting to bridge this gap by providing loans secured directly by physical GPUs and the cash flows they generate.

According to the platform’s financing terms, USD.AI offers funding of up to 80% of total deployment costs, subject to rigorous credit reviews and borrower eligibility assessments. Under this arrangement, borrowers are required to contribute at least 20% of the equipment cost upfront, with the principal repaid over a standard three-to-five-year window. By pooling capital on-chain, decentralized finance participants can fund these loans and potentially generate attractive yields.

However, this model also exposes lenders to traditional financial risks, including borrower defaults, collateral depreciation, and potential smart contract software failures. Despite these inherent risks, institutional interest in crypto-native hardware financing has continued to expand. In August, major crypto exchange Bullish announced a $100 million credit facility dedicated to USD.AI, designed specifically to finance GPU-backed loans. This arrangement effectively channels stablecoin capital from the digital asset ecosystem directly into the physical machinery driving the global AI economy.

The broader movement toward institutional hardware financing is also visible in agreements outside the immediate scope of USD.AI. In April, Vertical Data finalized a $43 million infrastructure and financing agreement with Alpha Compute Corp.—formerly known as Alphaton Capital and traded on the Nasdaq under the ticker ALP. The deal centered on cutting-edge infrastructure utilizing Nvidia’s B300 chip model, which is specifically engineered for intensive AI workloads and privacy-preserving confidential computing services.

This integration of traditional finance and crypto-backed liquidity mirrors massive capital-mobilization efforts across the broader technology landscape. In August, Nvidia announced proposed partnerships with six major financial institutions aimed at mobilizing more than $500 billion for AI infrastructure over time. While those institutional platforms leave final financing decisions to traditional entities assessing customer demand, cash flow, utilization rates, and residual equipment values, the parallel rise of projects like PEARL, DATA Network, and USD.AI demonstrates that blockchain technology is carving out an increasingly prominent role across every layer of the modern AI supply chain.