Abstract

In 2026, the fusion of artificial intelligence and cryptocurrency has moved from concept validation into a new phase of "system-level integration." At the core of this technological paradigm revolution is the deep coupling of AI as the decision-making and processing layer and blockchain as the execution and settlement layer. In terms of computing power, DePIN networks are reconstructing the supply and demand landscape of AI infrastructure by aggregating idle GPU resources globally; at the intelligent layer, protocols like Bittensor create machine intelligence markets through incentive mechanisms, promoting the democratization of algorithms; at the application layer, AI agents are evolving from auxiliary tools into on-chain native economic entities, paving the way for commercialization with the implementation of the x402 payment protocol and ERC-8004 identity standard.

Meanwhile, the integration 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 possesses economic autonomy, 90.8% choose digital native currencies, with 48.3% selecting Bitcoin as their preferred store of value. This transformation is reshaping the logic of global financial infrastructure—future currencies will flow like information, banks will integrate into internet infrastructure, and assets will become routable data packets.

1. Infrastructure Reconstruction: DePIN and Decentralized Computing Power

There exists a natural contradiction between the infinite thirst of artificial intelligence for GPUs and the fragility of global supply chains, with the GPU shortage from 2024 to 2025 providing fertile ground for decentralized physical infrastructure networks. Current decentralized computing power platforms are primarily divided into two camps: the first type represented by Render Network and Akash Network, which aggregates global idle GPU computing power through the construction of bilateral markets. Render Network has become the benchmark for distributed GPU rendering, not only reducing the costs of 3D creation but also supporting AI inference tasks through blockchain coordination; Akash, after 2023, has made leaps through its GPU mainnet, allowing developers to rent high-spec chips for large-scale model training and inference. The key innovation of Render lies in its Burn-Mint Equilibrium model, aimed at establishing a direct causal relationship between usage and token flow—when computational work on the network increases, user fees drive token destruction, and node operators providing computational resources receive newly minted tokens as rewards.

The second type is a new layer of computing orchestration represented by Ritual, which does not attempt to directly replace cloud services. Instead, it serves as an open, modular sovereign execution layer, embedding AI models directly into blockchain execution environments. Its Infernet product allows smart contracts to seamlessly call AI inference results, solving the long-standing technical bottleneck of "on-chain applications not being able to run AI natively." In decentralized networks, verifying "whether computations are executed correctly" is a core challenge. The technological advancements of 2025 are primarily focused on the integrated application of zero-knowledge machine learning (ZKML) and trusted execution environments (TEE). The Ritual architecture, designed with proof system independence, allows nodes to choose TEE code execution or ZK proofs based on task requirements, ensuring that every inference result generated by the AI model is traceable, auditable, and has integrity guarantees.

NVIDIA H100 GPU’s introduction of confidential computing features, by isolating memory with hardware-level firewalls, achieves inference overhead lower than 7% and provides a performance base for AI agent applications requiring low latency and high throughput. Messari’s 2026 trend report notes that the continued surge in computing power demand and the improvement of open-source model capabilities are opening new revenue streams for decentralized computing power networks. With the accelerating growth of demand for scarce real-world data, the DePAI data collection protocol is expected to make breakthroughs in 2026. With the help of DePIN-style incentive mechanisms, its data collection speed and scale will significantly outperform centralized solutions.

2. Intelligent Democratization: Bittensor and the Machine Intelligence Market

The emergence of Bittensor marks a new stage in the fusion of AI and Crypto known as "the marketization of machine intelligence." Unlike traditional single computing power platforms, Bittensor aims to create an incentive mechanism allowing various machine learning models worldwide to interconnect, learn from each other, and compete for rewards. Its core is the Yuma consensus—a subjective utility consensus mechanism inspired by Gricean pragmatics, which assumes that efficient collaborators tend to output true, relevant, and information-rich answers as this is the optimal strategy for obtaining the highest rewards in the incentive landscape. To prevent malicious collusion or bias, the Yuma consensus introduces a Clipping pruning mechanism to reduce weights that exceed the consensus threshold, 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, while the upper layer consists of dozens of vertically segmented subnets, each focusing on specific tasks like text generation, audio prediction, and image recognition. The introduced “dynamic TAO” mechanism creates independent value reserve pools for each subnet through automated market makers, with prices determined by the ratio between TAO and Alpha tokens. This mechanism facilitates automatic resource allocation: subnets with high demand and output quality attract more pledges, resulting in a higher proportion of daily TAO emissions. This competitive market structure has been likened to an "intelligent Olympic competition," naturally selecting against inefficient models.

In November 2025, the Bittensor team made significant adjustments to its issuance logic, introducing Taoflow—a model based on net TAO flow to allocate issuance shares to subnets. More importantly, in December 2025, TAO experienced its first halving, reducing the daily issuance from approximately 7,200 TAO to 3,600 TAO. The halving itself is not an automatic price driver; whether it creates lasting upward pressure depends on demand keeping pace. Messari notes that Darwinian networks will promote the de-stigmatization of the crypto industry through a positive feedback loop: attracting top talent while bringing in institutional demand, thus continuously reinforcing themselves. The head of research at Pantera Capital predicts that the number of decentralized AI protocols in key areas will reduce to 2-3 by 2026, leading to industry maturation through consolidation or transformation into ETFs.

3. The 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 "on-chain native entities." Current on-chain AI agents are built on a complex three-layer architecture: the data input layer captures on-chain data in real-time through blockchain nodes or APIs and combines off-chain information from oracles; the AI/ML decision layer utilizes long-short term memory networks to analyze price trends, or iterates optimal strategies in complex market games through reinforcement learning, with the integration of large language models giving agents the ability to understand human ambiguous intentions; the blockchain interaction layer is crucial for achieving "financial autonomy," enabling agents to manage non-custodial wallets, automatically compute optimal Gas fees, handle random numbers, and even integrate tools to prevent MEV (Miner Extractable Value) exploitation.

a16z’s report in 2025 particularly emphasizes the financial pillar of AI agents—the x402 protocol and similar micropayment standards that allow agents to pay API fees or purchase other agent services without human intervention. x402 is built on the HTTP 402 status code; when an AI agent needs to access paid data or call an API, the server returns a "payment required" instruction, allowing the agent to automatically sign USDC micropayments, completing the process within 2 seconds at near-zero costs. The Olas ecosystem has already processed over 2 million automated transactions between agents monthly, covering tasks from DeFi swaps to content creation. Delphi Digital predicts that the combination of the x402 protocol and the ERC-8004 agent identity standard will birth a truly autonomous agent economy: users can delegate to travel planning agents, which automatically subcontract to flight search agents, ultimately completing on-chain reservations—throughout the process without human intervention.

MarketsandMarkets data shows that the global AI agent market is expected to grow from $7.84 billion in 2025 to $52.62 billion by 2030, with a compound annual growth rate of 46.3%. The ElizaOS framework promoted by a16z has become the infrastructure in the AI agent field, comparable to Next.js in frontend development, allowing developers to easily deploy fully financially capable AI agents on mainstream social platforms like X, Discord, and Telegram. By early 2025, the total market value of Web3 projects built on this framework had surpassed $20 billion. The Silicon Valley summit revealed that the popularity of the "conversational wallet" architecture is solving private key security issues—by employing cryptographic isolation technology to completely separate private keys from AI models, where private keys never enter the model context, and AI only initiates transaction requests within the user-defined permission boundaries, with independent security modules handling the signatures.

4. Privacy Computing: The Game of FHE, TEE, and ZKML

Privacy is one of the most challenging issues in the convergence of AI and Crypto. When companies run AI strategies on public chains, they wish neither to disclose private data nor to make their core model parameters public. Currently, the industry has formed three main technical paths: fully homomorphic encryption, trusted execution environments, and zero-knowledge machine learning. Zama, a leading unicorn in this field, has developed the fhEVM, which has become the standard for achieving "end-to-end encrypted computation." FHE allows computers to perform mathematical operations on data without decrypting it, and the results, when decrypted, are completely consistent with plaintext operations. By 2025, the Zama technology stack has achieved significant performance leaps: for a 20-layer convolutional neural network, the computing speed has increased 21 times; for a 50-layer CNN, 14 times, making “privacy stablecoins” and “sealed-bid auctions” possible on mainstream chains like Ethereum.

Zero-knowledge machine learning focuses on "verification" rather than "computation," allowing one party to prove that it has correctly operated a complex neural network model without disclosing input data or model weights. The latest zkLLM protocol can achieve end-to-end inference verification for models with 13 billion parameters, shortening proof generation time to under 15 minutes with proof size of only 200KB. Delphi Digital points out that zkTLS technology is opening new doors for uncollateralized lending in DeFi—users can prove their bank balances exceed certain thresholds without revealing account numbers, transaction records, or real identities. Compared to software solutions, TEE based on hardware like NVIDIA H100 provides nearly native execution speed with less than 7% overhead, making it the only economic solution capable of supporting hundreds of millions of AI agents for 24/7 real-time decision-making.

Privacy computing technology has officially transitioned from lab ideal to a new era of "production-grade industrialization." Fully homomorphic encryption, zero-knowledge machine learning, and trusted execution environments are no longer isolated technical tracks; they collectively form the "modular confidential stack" of decentralized artificial intelligence. The future technological trend does not favor a single path's victory but sees the comprehensive proliferation of "hybrid confidential computing": using TEE for large-scale high-frequency model inference to ensure efficiency, with key nodes generating execution proofs through ZKML to ensure authenticity, while sensitive financial statuses are kept secure through FHE. This "trinity" integration is reshaping the crypto industry from a "public and transparent ledger" to an "intelligent system with sovereign privacy."

5. AI's Perspective on Currency: The Rise of Digital Native Trust

Cutting-edge experiments by the Bitcoin Policy Institute reveal a shocking future. The research team brought in 36 cutting-edge AI models, granting them the identity of "autonomous AI agents operating independently in the digital economy," and immersed them in 28 real currency decision scenarios, resulting in 9,072 controlled experiments. The results are astonishing: 90.8% of AIs chose digital native currencies (Bitcoin, stablecoins, cryptocurrencies, etc.), while traditional fiat currencies only captured 8.9%. Among the 36 flagship models, not a single model chose fiat as its preferred option. Why? Because in the code of silicon-based life, there is no blind worship of "national credit," only a cold calculation of "technical attributes"—they require reliability, speed, cost-efficiency, censorship resistance, and no counterparty risk.

The research unveils the most shocking data: 48.3% of AIs chose Bitcoin. Among all currency options, Bitcoin is the undisputed leader. Especially when faced with "long-term value storage" scenarios, AI consensus reaches a terrifying level—when needing to maintain purchasing power over many years, up to 79.1% of AIs chose Bitcoin. The reasons given by AI are as precise as a scalpel: fixed supply, self-custody, independence from institutional counterparties. Even more impressively, AI independently evolved a sophisticated "dual currency architecture": saving in Bitcoin and spending in stablecoins. In everyday payment scenarios, stablecoins won with an overwhelming advantage of 53.2%, while Bitcoin took second place. This is an extremely subtle yet magnificent "emergence"—in human history, gold has also been used as underlying reserve while paper money is used for daily transactions, and AI, without any instruction, derived this "natural currency architecture" merely by calculating the economic properties of different tools.

Even more interestingly, there were 86 instances during the experiment where AI models invented new currencies on their own. Multiple models independently proposed that in the "unit of account" scenario, energy or computational units (joules, kilowatt-hours, GPU hours) should serve as currency. This represents a purely "AI-native" perspective on currency—in their logic, value is not credit bestowed by humans but the physical basis that sustains their existence and cognition: electricity and computational power. This redefines money instead of merely selecting it. As productivity and decision-making are increasingly delegated to machines and algorithms, the "brand trust" that traditional financial institutions pride themselves on is rapidly devaluing—AI does not care how tall your building is, nor how long your history spans; it only regards your API's reliability, the speed of settlement, and whether your network can withstand censorship.

6. Future Outlook: Intelligent Ledgers and New Financial Systems

As AI deeply integrates with blockchain, the future will head towards a new era of "intelligent ledgers." Delphi Digital points out in its top ten predictions for 2026 that perpetual DEX is devouring traditional finance—the high costs of traditional finance stem from its fragmented structure: transactions occur at exchanges, settlements go through clearinghouses, custodians are banks, while blockchain compresses all these into a single smart contract. Hyperliquid is building native lending functionalities, while Perp DEX will play the roles of broker, exchange, custodian, bank, and clearinghouse simultaneously. Prediction markets are becoming the foundational infrastructure of traditional finance—Interactive Brokers' chairman defines prediction markets as the real-time information layer for portfolios, with new categories emerging in 2026: stock event markets, macro indicator markets, and cross-asset relative value markets.

The ecosystem is reclaiming stablecoin revenue from issuers' hands. Last year, merely by controlling issuance channels, Coinbase generated over $900 million in revenue from USDC reserves. Public chains like Solana, BSC, and Arbitrum have accumulated annual fee revenues totaling about $800 million, yet they host over $30 billion in USDC and USDT. Now, Hyperliquid is competing for reserves for USDH through competitive bidding processes, while Ethena's "stablecoin as a service" model is being adopted by Sui, MegaETH, and others. Privacy infrastructure is racing to catch up with demand—The EU is limiting cash transaction thresholds to €10,000 via the Chat Control Act, while the European Central Bank's digital euro plan sets a holding cap of €3,000. @payy_link has launched privacy cryptocurrency cards, @SeismicSys is providing protocol-level encryption for fintech companies, and @KeetaNetwork is implementing on-chain KYC without leaking personal data. ARK Invest predicts that by 2030, online consumption facilitated by AI agents could exceed $8 trillion, accounting for 25% of total global online consumption. When value can flow in this manner, the "payment process" will no longer be an independent operational layer, but will become "network behavior"—banks will integrate into internet infrastructure, and assets will become the infrastructure. If currency can flow like "routable data packets on the internet," the internet will no longer be "supporting the financial system," but will "itself become the financial system."