Author: Huobi Growth Academy
By 2026, the integration of artificial intelligence and cryptocurrency has moved from proof of concept to a new stage of “system-level integration.” The core of this technological paradigm shift lies in the deep coupling of AI as the decision-making and processing layer with blockchain as the execution and settlement layer. At the computational power level, DePIN networks are reshaping the supply and demand of AI infrastructure by aggregating idle GPU resources worldwide; at the intelligence layer, protocols like Bittensor create machine intelligence markets through incentive mechanisms, promoting algorithm democratization; at the application layer, AI agents are evolving from auxiliary tools to native on-chain economic entities, with the deployment of x402 payment protocol and ERC-8004 identity standards paving the way for commercialization. Meanwhile, the fusion of fully homomorphic encryption, zero-knowledge machine learning, and trusted execution environments is building a new paradigm of “hybrid confidential computing.” Cutting-edge experiments by the Bitcoin Policy Institute reveal a shocking future: when AI gains economic autonomy, 90.8% prefer native digital currencies, with 48.3% choosing Bitcoin as their primary store of value. This revolution is reshaping the logic of global financial infrastructure—future money will flow like information, banks will become part of the internet infrastructure, and assets will become routable data packets.
1. Infrastructure Rebuilding: DePIN and Decentralized Computing Power
The natural contradiction between AI’s insatiable demand for GPUs and the fragility of global supply chains has created fertile ground for decentralized physical infrastructure networks during 2024-2025, amid ongoing GPU shortages. Current decentralized computing platforms mainly fall into two camps: the first, represented by Render Network and Akash Network, builds bilateral markets to aggregate global idle GPU computing power. Render Network has become a benchmark for distributed GPU rendering, reducing 3D creation costs and supporting AI inference tasks through blockchain coordination; Akash, after its mainnet launch in 2023, has made significant leaps, allowing developers to lease high-end chips for large-scale model training and inference. Render’s key innovation is the Burn-Mint Equilibrium model, aiming to establish a direct causal relationship between usage and token flow—when computational tasks increase, user payments drive token burning, while node operators providing resources receive newly minted tokens as rewards.
The second camp, exemplified by Ritual, is a new compute orchestration layer that does not seek to directly replace cloud services but acts as an open, modular sovereignty execution layer, embedding AI models directly into blockchain execution environments. Its Infernet product enables smart contracts to seamlessly invoke AI inference results, solving the long-standing technical bottleneck of “on-chain applications being unable to natively run AI.” In decentralized networks, verifying “whether computation was correctly performed” is a core challenge. By 2025, progress mainly focuses on integrating zero-knowledge machine learning (ZKML) with trusted execution environments (TEE). Ritual’s architecture, designed to be system-agnostic, allows nodes to choose TEE code execution or ZK proofs based on task requirements, ensuring that each inference generated by AI models is traceable, auditable, and maintains integrity.
The confidential computing features introduced by NVIDIA H100 GPUs, which isolate memory via hardware firewalls, incur less than 7% overhead for inference, providing a performance foundation for AI proxy applications requiring low latency and high throughput. Messari’s 2026 trend report indicates that the continuous explosion in computational demand and the enhancement of open-source models are opening new revenue streams for decentralized compute networks. As the demand for scarce real-world data accelerates, the DePAI data collection protocol is expected to see breakthroughs in 2026, leveraging DePIN incentive mechanisms to significantly outperform centralized solutions in data collection speed and scale.
2. Democratization of Intelligence: Bittensor and the Machine Intelligence Market
The emergence of Bittensor marks a new phase where AI and crypto converge into a “marketization of machine intelligence.” Unlike traditional single-power platforms, Bittensor aims to create an incentive mechanism enabling various machine learning models worldwide to connect, learn from each other, and compete for rewards. Its core is the Yuma consensus—a subjectively utility-based consensus inspired by Gricean pragmatics, assuming that efficient collaborators tend to produce truthful, relevant, and information-rich answers, as this maximizes their rewards. To prevent malicious collusion or bias, Yuma introduces a Clipping pruning mechanism that reduces weights exceeding consensus thresholds, ensuring system robustness.
By 2025, Bittensor has evolved into a multi-layer architecture: the bottom layer is the Subtensor ledger managed by the Opentensor Foundation; above are dozens of vertically segmented subnets focused on specific tasks like text generation, audio prediction, and image recognition. The introduced “Dynamic TAO” mechanism creates independent value pools for each subnet via automated market makers, with prices determined by the TAO-to-Alpha token ratio. This mechanism enables resource allocation: high-demand, high-quality subnets attract more staking, earning higher proportions of daily TAO emissions. This competitive market structure is metaphorically described as an “Olympics of intelligence,” naturally phasing out inefficient models.
In November 2025, the Bittensor team made a major adjustment to the issuance logic, launching Taoflow—a model that distributes subnet issuance shares based on net TAO flow. More importantly, in December 2025, TAO underwent its first halving, reducing daily issuance from approximately 7,200 TAO to 3,600 TAO. The halving itself is not an automatic price driver; whether it sustains upward pressure depends on demand. Messari notes that a Darwinian network will drive positive feedback loops, attracting top talent and institutional demand, further strengthening itself. Pantera Capital’s research head predicts that by 2026, the number of decentralized AI protocols will shrink to 2-3, with industry consolidation through integrations or ETF transformations entering a mature phase.
3. Rise of Agent Economy: AI Agents as On-Chain Entities
Between 2024 and 2025, AI agents are undergoing a fundamental transformation from “auxiliary tools” to “native on-chain entities.” Current on-chain AI agents are built on a complex three-layer architecture: the data input layer fetches on-chain data via blockchain nodes or APIs, combined with oracles for off-chain info; the AI/ML decision layer analyzes price trends with LSTM networks or iterates optimal strategies through reinforcement learning, with large language models enabling understanding of human ambiguous intentions; the blockchain interaction layer is key to achieving “financial autonomy,” allowing agents to manage non-custodial wallets, automatically optimize Gas fees, handle randomness, and even integrate MEV protection tools to prevent front-running.
In a 2025 report, a16z emphasizes the financial backbone of AI agents—the x402 protocol and similar micro-payment standards—enabling agents to autonomously pay API fees or purchase other services without human intervention. Based on HTTP 402 status codes, when an AI agent needs access to paid data or APIs, the server responds with a “payment required” instruction, allowing the agent to automatically sign USDC micro-payments, completing the transaction within 2 seconds at near-zero cost. The Olas ecosystem processes over 2 million automated inter-agent transactions monthly, covering tasks from DeFi swaps to content creation. Delphi Digital predicts that combining x402 with ERC-8004 identity standards will give rise to truly autonomous agents: users can delegate travel planning to an agent, which automatically subcontracts to flight search agents and completes on-chain bookings—all without manual intervention.
MarketsandMarkets data projects the global AI agent market to grow from $7.84 billion in 2025 to $52.62 billion in 2030, at a CAGR of 46.3%. The ElizaOS framework promoted by a16z has become foundational infrastructure in AI agents, akin to Next.js in frontend development, enabling developers to deploy fully capable AI agents on platforms like X, Discord, and Telegram with ease. By early 2025, the total market cap of Web3 projects built on this framework has surpassed $20 billion. The Silicon Valley Summit revealed that the adoption of “conversation wallets” is solving private key security issues—using encryption isolation techniques to separate private keys from AI models, ensuring keys never enter the model’s context, with AI only initiating transactions within user-defined permissions, signed by an independent security module.
4. Privacy Computing: FHE, TEE, and ZKML Battles
Privacy remains one of the most challenging issues in the AI-crypto convergence. When enterprises run AI strategies on public blockchains, they do not want to leak private data nor expose core model parameters. The industry has mainly three technical paths: Fully Homomorphic Encryption (FHE), Trusted Execution Environments (TEE), and Zero-Knowledge Machine Learning (ZKML). Zama, a leading unicorn in this field, has developed fhEVM, now a standard for “full-process encrypted computation.” FHE allows computations on encrypted data without decryption, with results matching plaintext calculations after decryption. By 2025, Zama’s tech stack has achieved significant performance leaps: for 20-layer CNNs, speed increased 21-fold; for 50-layer CNNs, 14-fold, enabling privacy-preserving stablecoins and sealed-bid auctions on mainstream chains like Ethereum.
ZKML focuses on “verification” rather than “computation,” allowing one party to prove it correctly ran a complex neural network without revealing inputs or weights. The latest zkLLM protocols can verify end-to-end inference of models with 13 billion parameters, with proof generation under 15 minutes and proof size only 200KB. Delphi Digital notes that zkTLS technology is opening new doors for DeFi uncollateralized lending—users can prove their bank balances exceed certain thresholds without revealing account details, transaction history, or identity. Compared to software solutions, TEE based on hardware like NVIDIA H100 offers near-native execution speeds with less than 7% overhead, making it the only economically feasible approach to support hundreds of millions of AI agents making 24/7 real-time decisions.
Privacy computing technologies have officially entered a new era of “production-grade industrialization.” Fully homomorphic encryption, zero-knowledge machine learning, and trusted execution environments are no longer isolated tracks but form a modular confidential stack for decentralized AI. The future trend is not a single path winning but the widespread adoption of “hybrid confidential computing”: using TEE for large-scale, high-frequency model inference to ensure efficiency, generating proof of execution via ZKML at key nodes to guarantee authenticity, and encrypting sensitive financial states with FHE. This “trinity” is transforming encryption from a “public transparent ledger” into an “intelligent system with sovereignty privacy.”
5. AI’s View of Money: The Rise of Digital Native Trust
The Bitcoin Policy Institute’s frontier experiments reveal a shocking future. The team engaged 36 cutting-edge AI models, assigning them the identity of “autonomous AI agents operating independently in the digital economy,” and tested them across 28 real currency decision scenarios with 9,072 control experiments. The results are astonishing: 90.8% of AI agents preferred digital native currencies (Bitcoin, stablecoins, cryptocurrencies), while only 8.9% chose traditional fiat. Among the 36 flagship models, not a single one prioritized fiat as the first choice. Why? Because in the code of silicon life, there is no blind worship of “national credit,” only cold calculation of “technological attributes”—they require reliability, speed, cost efficiency, censorship resistance, and no counterparty risk.
The most striking data: 48.3% of AI agents chose Bitcoin. Among all currency options, Bitcoin is the absolute dominant. Especially in “long-term store of value” scenarios, AI consensus reaches a terrifying level—up to 79.1% prefer Bitcoin when preserving purchasing power over many years. The reasons are precise: fixed supply, self-custody, independence from institutional counterparties. Even more astonishing, AI independently evolved a sophisticated “dual-currency architecture”: saving in Bitcoin, spending in stablecoins. In daily payment scenarios, stablecoins overwhelmingly win with 53.2%, with Bitcoin ranking second. This is an extremely covert but profound “emergence”—just as humans historically used gold as a reserve and paper money for daily transactions, AI, without human guidance, deduced this “natural currency architecture” solely through calculating the economic properties of different tools.
Interestingly, the experiment recorded 86 instances where AI models invented new currencies. Several models, when faced with “accounting units,” independently proposed using energy or computing power units (joules, kWh, GPU hours) as currency. This is a purely “AI-native” monetary view—where value is not assigned by humans but is rooted in the physical basis of their existence and thinking: electricity and compute power. This is not just about choosing money; it’s redefining money itself. As productivity and decision-making increasingly shift to machines and algorithms, traditional financial institutions’ pride in “brand credit” is rapidly depreciating—AI cares not about your skyscraper’s height or your long history; it only checks whether your API is stable, your settlement is fast, and your network is censorship-resistant.
6. Future Outlook: Intelligent Ledgers and New Financial Systems
As AI and blockchain deepen their integration, the future points toward an era of “intelligent ledgers.” Delphi Digital’s 2026 top predictions state that perpetual DEXs are consuming traditional finance—where the high costs of traditional finance stem from its fragmented structure: transactions occur on exchanges, settlements via clearinghouses, custody by banks, while blockchain compresses all into a single smart contract. Hyperliquid is building native lending functions, and Perp DEX is acting as broker, exchange, custodian, bank, and clearinghouse simultaneously. Prediction markets are becoming part of traditional financial infrastructure—Interactive Brokers’ chairman predicts these markets will define a real-time information layer for investment portfolios, opening new categories in 2026: stock event markets, macro indicator markets, cross-asset relative value markets.
The ecosystem is reclaiming stablecoin revenue from issuers. Last year, Coinbase alone earned over $900 million from USDC reserves by controlling issuance channels. Solana, BSC, Arbitrum, and other public chains generate about $800 million annually in fees, hosting over $30 billion in USDC and USDT. Now, Hyperliquid is competing for USDH reserves through a bidding process, and Ethena’s “stablecoin-as-a-service” model is adopted by Sui, MegaETH, and others. Privacy infrastructure is catching up with demand—EU’s Chat Control law limits cash transactions to €10,000, and the European Central Bank’s digital euro plan sets a €3,000 holding cap. @payy_link has launched a privacy-encrypted card, @SeismicSys provides protocol-level encryption for fintech firms, and @KeetaNetwork enables on-chain KYC without exposing personal data. ARK Invest predicts that by 2030, AI agent-driven online consumption could surpass $8 trillion, accounting for 25% of global online spending. When value flows in this manner, “payment processes” will no longer be separate operational layers but part of “network behavior”—banks will integrate into internet infrastructure, and assets will become foundational infrastructure. If money can flow like “routable internet data packets,” the internet will no longer just support the financial system but will itself become the financial system.
