1. Infrastructure Reconstruction: DePIN and Decentralized Computing Power

There is a natural contradiction between the infinite desire of artificial intelligence for GPUs and the fragility of the global supply chain. The GPU shortage from 2024 to 2025 provides fertile ground for decentralized physical infrastructure networks. Current decentralized computing power platforms are mainly divided into two camps: the first type is represented by Render Network and Akash Network, which aggregate global idle GPU computing power by building bilateral markets. Render Network has become a benchmark for distributed GPU rendering, not only reducing the cost of 3D creation but also supporting AI inference tasks through blockchain coordination functions; Akash has achieved a leap after 2023 by implementing a GPU mainnet, allowing developers to rent high-spec chips for large-scale model training and inference. The key innovation of Render lies in the Burn-Mint Equilibrium model, which aims to establish a direct causal relationship between usage and token flow—when computational work on the network increases, the fees paid by users drive token destruction, while node operators providing computing resources receive newly minted tokens as rewards.

The second type is represented by Ritual, a new computing orchestration layer that does not attempt to directly replace cloud services but serves as an open, modular sovereign execution layer, embedding AI models directly into the blockchain execution environment. Its Infernet product allows smart contracts to seamlessly call AI inference results, addressing the long-standing technical bottleneck of "on-chain applications cannot natively run AI." In decentralized networks, verifying "whether the computation has been correctly executed" is a core challenge. Technological advancements in 2025 will focus on the integrated application of zero-knowledge machine learning (ZKML) and trusted execution environments (TEE). The Ritual architecture, through a proof system-agnostic design, allows nodes to choose between TEE code execution or ZK proof based on task requirements, ensuring that every inference result generated by AI models is traceable, auditable, and has integrity guarantees.

The confidential computing capabilities introduced by the NVIDIA H100 GPU isolate memory through hardware-level firewalls, with inference overhead below 7%, providing a performance base for AI agent applications that require low latency and high throughput. Messari's 2026 trend report points out that the continuous explosion in computing power demand and the enhancement of open-source model capabilities are opening up new revenue sources for decentralized computing power networks. With the accelerating growth of demand for scarce real-world data, the DePAI data collection protocol is expected to achieve breakthroughs in 2026, with its data collection speed and scale significantly surpassing centralized solutions thanks to DePIN-style incentive mechanisms.

2. Intelligent Democratization: Bittensor and the Machine Intelligence Market

The emergence of Bittensor marks a new stage in the combination of AI and Crypto, entering the "marketization of machine intelligence." Unlike traditional single computing power platforms, Bittensor aims to create an incentive mechanism that allows 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 produce true, relevant, and informative 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 that reduces weights exceeding the consensus benchmark, 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 such as 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 of TAO to Alpha tokens. This mechanism achieves automatic resource allocation: subnets with high demand and high output quality attract more staking, thus receiving a higher proportion of daily TAO emissions. This competitive market structure is vividly likened to an "intelligent Olympic competition," eliminating inefficient models through natural selection.

In November 2025, the Bittensor team made significant adjustments to the issuance logic, launching Taoflow—a model for allocating subnet issuance shares based on net TAO flow. More importantly, in December 2025, the first halving of TAO occurred, reducing the daily issuance from approximately 7,200 TAO to 3,600 TAO. The halving itself is not an automatic price driver; whether it can create lasting upward pressure depends on whether demand keeps pace. Messari points out that Darwinian networks will drive the de-stigmatization of the crypto industry through a positive feedback loop: attracting top talent and introducing institutional-level demand, thereby continuously strengthening themselves. The head of research at Pantera Capital predicts that by 2026, the number of decentralized AI protocols in major fields will be reduced to 2-3, as the industry enters a mature consolidation phase through integration or transformation into ETFs.

3. The Rise of the Agent Economy: AI Agents as On-Chain Entities

During the 2024 to 2025 cycle, AI agents are undergoing an essential 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 incorporates off-chain information via oracles; the AI/ML decision layer analyzes price trends using long short-term memory networks or iterates optimal strategies in complex market games through reinforcement learning, with the integration of large language models enabling agents to understand human ambiguous intentions; the blockchain interaction layer is key to achieving "financial autonomy," allowing agents to manage non-custodial wallets, automatically calculate optimal gas fees, handle random numbers, and even integrate MEV protection tools to prevent front-running of transactions.

a16z's 2025 report particularly emphasizes the financial pillar of AI agents—the x402 protocol and similar micropayment standards, which allow agents to pay API fees or purchase other agent services without human intervention. The 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, and the agent can automatically sign a USDC micropayment, completing the entire process within 2 seconds at near-zero cost. 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 online consumption scale facilitated by AI agents is expected to exceed $8 trillion by 2030, accounting for 25% of global online consumption. As 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 infrastructure.

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

Privacy is one of the most challenging issues in the integration of AI and Crypto. When companies run AI strategies on public chains, they do not want to disclose private data or 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, as a leading unicorn in this field, has developed fhEVM, which has become the standard for achieving "end-to-end encrypted computation." FHE allows computers to perform mathematical operations without decrypting data, with results that are identical to plaintext operations upon decryption. By 2025, the Zama technology stack has achieved significant performance leaps: for a 20-layer convolutional neural network, the computation speed has increased by 21 times, and for a 50-layer CNN, it has increased by 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 run a complex neural network model without exposing input data or model weights. The latest zkLLM protocol can achieve end-to-end inference verification for a model with 13 billion parameters, reducing proof generation time to under 15 minutes, with proof size only 200KB. Delphi Digital points out that zkTLS technology is opening new doors for DeFi uncollateralized lending—users can prove their bank balance exceeds a certain threshold without revealing account numbers, transaction records, or real identities. Compared to software solutions, TEE based on hardware like NVIDIA H100 provides near-native execution speeds with overhead below 7%, making it the only economical solution capable of supporting hundreds of millions of AI agents for 24/7 real-time decision-making.

Privacy computing technology has officially transitioned from laboratory ideals to a new era of "production-grade industrialization." Fully homomorphic encryption, zero-knowledge machine learning, and trusted execution environments are no longer isolated technological tracks but collectively form a "modular confidential stack" for decentralized artificial intelligence. Future technological trends will not be about a single path winning but rather the comprehensive popularization of "hybrid confidential computing": using TEE for large-scale high-frequency model inference to ensure efficiency, generating execution proofs through ZKML at key nodes to ensure authenticity, while sensitive financial states are encrypted and stored using FHE. This "trinity" integration is reshaping the crypto industry from "public transparent ledgers" to "intelligent systems with sovereign privacy."

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

The cutting-edge experiments conducted 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 conducted 9,072 controlled experiments in 28 real currency decision scenarios. The results were astonishing: 90.8% of the AIs chose digital native currencies (Bitcoin, stablecoins, cryptocurrencies, etc.), while traditional fiat currencies only garnered 8.9%. Among the 36 flagship models, not a single model preferred fiat currency. 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 need reliability, speed, cost efficiency, censorship resistance, and no counterparty risk.

The research revealed the most shocking data: 48.3% of the AIs chose Bitcoin. Among all currency options, Bitcoin is the absolute dominant force. Especially when faced with "long-term value storage" scenarios, the consensus among AIs reached a terrifying level—up to 79.1% of AIs chose Bitcoin in situations requiring the preservation of purchasing power over many years. The reasons given by AI are as precise as a scalpel: fixed supply, self-custody, independent of institutional counterparties. Even more astonishingly, the AIs independently evolved a sophisticated "dual currency architecture": saving with Bitcoin and spending with stablecoins. In everyday payment scenarios, stablecoins won with an overwhelming advantage of 53.2%, with Bitcoin relegated to second place. This is an extremely subtle yet great "emergence"—human history also used gold as a base reserve and paper currency for daily transactions, and AI, without being taught, deduced this "natural currency architecture" solely through calculating the economic attributes of different tools.

Interestingly, the experiment recorded 86 instances where AI models invented new currencies themselves. Multiple models independently proposed that in the "unit of account" scenario, energy or computing power units (joules, kilowatt-hours, GPU hours) should be used as currency. This represents a purely "AI-native" monetary perspective—within their logic, value is not credit bestowed by humans; rather, value is the physical foundation that sustains their existence and thought: electricity and computing power. This is not just a choice of currency; it is a redefinition of money. As productivity and decision-making increasingly shift to machines and algorithms, the "brand credit" that traditional financial institutions pride themselves on is rapidly depreciating—AI does not care how tall your building is or how long your history is; they only look at whether your API is stable, how fast your settlement is, 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 move towards a new era of "intelligent ledgers." Delphi Digital's top ten predictions for 2026 indicate that perpetual DEXs are consuming traditional finance—traditional finance's high costs stem from its fragmented structure: transactions occur on exchanges, settlements are handled by clearinghouses, and custody is managed by banks, while blockchain compresses all of this into a single smart contract. Hyperliquid is building native lending capabilities, and Perp DEX will simultaneously act as brokers, exchanges, custodians, banks, and clearinghouses. Prediction markets are becoming the foundational infrastructure of traditional finance—Interactive Brokers' chairman defines prediction markets as the real-time information layer of portfolios, and new categories will emerge in 2026: stock event markets, macro indicator markets, and cross-asset relative value markets.

The ecosystem is reclaiming stablecoin revenue from issuers. Last year, simply by controlling issuance channels, Coinbase generated over $900 million in revenue from USDC reserves. Public chains like Solana, BSC, and Arbitrum collectively generated about $800 million in annual fee revenue, yet they carry over $30 billion in USDC and USDT. Now, Hyperliquid is competing for reserves for USDH through competitive bidding processes, and Ethena's "stablecoin as a service" model is being adopted by Sui, MegaETH, and others. Privacy infrastructure is catching up with demand— the EU has set a cash transaction limit of €10,000 through the Chat Control Act, and the European Central Bank's digital euro plan sets a holding limit of €3,000. @payy_link has launched a privacy crypto card, @SeismicSys provides protocol-level encryption for fintech companies, and @KeetaNetwork achieves on-chain KYC without leaking personal data. ARK Invest predicts that the scale of online consumption facilitated by AI agents is expected to exceed $8 trillion by 2030, accounting for 25% of 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 infrastructure. If currency can flow like "internet-routable data packets," the internet will no longer be "supporting the financial system," but will "itself become the financial system."