On April 13, 2026, as Bitcoin trades at $74,484 and the broader cryptocurrency market capitalization exceeds $2.2 trillion, a quieter transformation is taking place at the intersection of artificial intelligence and blockchain technology. NEAR Protocol, co-founded by Illia Polosukhin — whose background includes foundational work on the Transformer architecture that powers modern large language models — is emerging as a critical infrastructure layer for decentralized AI applications. The convergence is not accidental; it reflects a growing recognition that AI systems require decentralized infrastructure to achieve their full potential.
The Synergy
The relationship between AI and blockchain has evolved from superficial partnerships to deep architectural integration. NEAR Protocol exemplifies this shift. Founded as a sharded, proof-of-stake blockchain designed to scale global applications, the protocol has progressively positioned itself as a platform where AI workloads can interact with decentralized data, compute, and incentive systems without relying on centralized intermediaries.
The synergy operates at multiple levels. At the data layer, blockchain provides verifiable provenance for training datasets, enabling AI models to demonstrate that their training data meets specific quality and diversity criteria. At the compute layer, decentralized networks distribute AI inference and training across geographically dispersed nodes, reducing dependency on any single cloud provider. At the incentive layer, token mechanisms align the interests of data providers, compute operators, and AI model developers in ways that traditional corporate structures cannot easily replicate.
Polosukhin’s unique position at the intersection of AI research and blockchain development gives NEAR a technical credibility that few other protocols can match. The Transformer architecture, which he co-authored the original paper on, underpins virtually every major language model in existence today, from OpenAI’s GPT series to the open-source models being deployed across decentralized networks.
AI Use Cases in Web3
The practical applications of AI within the NEAR ecosystem and similar platforms are expanding rapidly. Autonomous AI agents that can manage their own cryptocurrency wallets, execute trades, and interact with smart contracts represent one of the most transformative use cases. These agents operate continuously, responding to market conditions and user instructions without human intervention, and they require the kind of programmable, trustless infrastructure that blockchains provide.
Decentralized Physical Infrastructure Networks (DePIN) are another critical application. These networks connect physical hardware — GPUs, sensors, storage devices — to blockchain-based coordination systems. Reports from April 2026 indicate that DePIN protocols are projected to generate over $100 million in verifiable on-chain revenue by the end of the year, with autonomous AI agents expected to number over one million by July 2026.
AI-driven analytics and risk assessment tools built on blockchain data are also gaining traction, providing real-time intelligence about market conditions, protocol health, and security vulnerabilities. These tools leverage the transparency of on-chain data to deliver insights that would be impossible to obtain from traditional financial markets.
Data Privacy Implications
The convergence of AI and blockchain also raises important questions about data privacy. AI systems require vast amounts of data to function effectively, but the transparent nature of many blockchains creates tension between data availability and privacy protection. Zero-knowledge proofs and other privacy-preserving cryptographic techniques offer a potential resolution, enabling AI systems to verify data properties without accessing the underlying data itself.
NEAR Protocol’s approach to this challenge includes support for private computation environments where AI models can process sensitive data without exposing it to the public blockchain. This architecture enables applications in healthcare, finance, and identity verification where data privacy is not merely a preference but a regulatory requirement.
The Innovation Frontier
Looking ahead, the most promising developments in the AI-blockchain convergence center on composability — the ability for different AI services and blockchain protocols to interoperate seamlessly. Just as DeFi protocols can be composed into complex financial products, AI services running on blockchain infrastructure can be composed into sophisticated workflows that combine data sourcing, model training, inference, and output verification.
The emergence of standards for AI model verification on-chain, tokenized compute resources, and cross-chain AI agent communication protocols suggests that the infrastructure for a truly decentralized AI ecosystem is maturing rapidly. NEAR Protocol’s combination of AI-native leadership, scalable architecture, and growing developer ecosystem positions it as a significant contributor to this transformation.
Concluding Thoughts
The convergence of AI and blockchain is no longer a speculative narrative but an observable reality backed by growing infrastructure, real revenue, and increasing adoption. Projects like NEAR Protocol that combine deep technical expertise in both domains are uniquely positioned to benefit from this trend. As the market for decentralized AI services grows, the protocols that provide the most reliable, scalable, and privacy-preserving infrastructure will capture disproportionate value — and the tokens that power those protocols will reflect that fundamental utility.
Disclaimer: This article is for informational purposes only and does not constitute financial advice. Always conduct your own research before making investment decisions.
Illia co-authoring the Attention Is All You Need paper then building a blockchain for AI is the most credible founder-to-chain pipeline in crypto
transformer_ the Attention Is All You Need connection is insane alpha. most AI coins have zero ML pedigree, NEAR literally has the guy who invented the architecture
inference and verification is the realistic play for on-chain AI. nobody is training GPT-5 on NEAR but proving model outputs on chain has real value
Illia co-authoring the Attention Is All You Need paper is the strongest alpha for NEAR. While other L1s pivot to AI now NEAR has been building the data and compute infrastructure for this convergence for years. The User Signature abstraction is a game-changer for AI UX.
Fascinating to see the Transformer architecture creator leading a blockchain protocol. This deep understanding of how AI models process information gives NEAR a unique advantage in building AI-native smart contracts that can interact with off-chain models securely.
Decentralized AI sounds great on paper, but NEAR has a lot of competition from specialized chains. Polosukhin is a legend, but can the protocol actually handle the massive data throughput required for training models, or is it better suited just for the inference and identity layers?
good question on data throughput. NEAR sharding helps but training massive models on-chain is still impractical. inference and verification is the sweet spot
this convergence of AI and blockchain is where the real innovation happens
this convergence of AI and blockchain is where the real innovation happens
the real story here is the usage metrics, not the price
this convergence of AI and blockchain is where the real innovation happens
this convergence of AI and blockchain is where the real innovation happens
the real story here is the usage metrics, not the price
the real story here is the usage metrics, not the price
finally seeing infrastructure projects with actual revenue
the real story here is the usage metrics, not the price