1. Infrastructure Reconstruction: DePIN and Decentralized Computing Power

The infinite desire of artificial intelligence for GPUs and the vulnerability of the global supply chain create a natural contradiction, creating fertile ground for decentralized physical infrastructure networks amid the GPU shortages expected from 2024 to 2025. Current decentralized computing power platforms are primarily divided into two camps: the first represented by Render Network and Akash Network, which aggregate global idle GPU computing power by constructing bilateral markets. Render Network has become a benchmark for distributed GPU rendering, not only lowering the cost of 3D creation but also facilitating AI inference tasks through blockchain coordination features; Akash has achieved a leap post-2023 by enabling developers to rent high-spec chips for large-scale model training and inference via its GPU mainnet. Render's key innovation lies in the Burn-Mint Equilibrium model, which aims to establish a direct causal relationship between usage and token flow—when computational workloads increase on the network, the fees paid by users drive token destruction, while node operators providing computing resources earn newly minted tokens as rewards.

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

NVIDIA H100 GPU introduces confidential computing features, isolating memory with hardware firewalls, with an inference overhead of less than 7%, providing a performance base for AI agent applications requiring low latency and high throughput. Messari's 2026 trend report notes 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. As the demand for scarce real-world data accelerates, the DePAI data collection protocol is expected to achieve breakthroughs in 2026, significantly outperforming centralized solutions in data collection speed and scale with DePIN incentive mechanisms.

2. Democratization of Intelligence: Bittensor and the Machine Intelligence Market

The emergence of Bittensor marks a new phase in the integration of AI and Crypto, paving the way for "market-oriented machine intelligence." Unlike traditional, singular computing power platforms, Bittensor aims to create an incentive mechanism that enables various machine learning models around the globe to interconnect, learn from each other, and compete for rewards. At its core is the Yuma consensus—a subjective utility consensus mechanism inspired by Gricean pragmatics, which hypothesizes that efficient collaborators tend to produce truthful, relevant, and informative answers because that is the optimal strategy to obtain 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 underlying 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, image recognition, etc. 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 enables resource auto-allocation: high-demand, high-output quality subnets attract more staking, thereby obtaining higher daily TAO emissions. This competitive market structure has been vividly likened to an "intelligent Olympic competition," where inefficient models are eliminated through natural selection.

In November 2025, the Bittensor team made significant adjustments to the issuance logic, launching Taoflow—a model for distributing subnet issuance shares based on net TAO flow. More importantly, the first halving of TAO in December 2025 reduced the daily issuance from approximately 7,200 TAO to 3,600 TAO. The halving itself is not an automatic price driver; whether it generates sustained upward pressure depends on whether demand keeps pace. Messari points out that Darwinian networks will drive the destigmatization of the crypto industry through a virtuous cycle: attracting top talent while bringing in institutional-level demand, thus continuously reinforcing themselves. The research leader at Pantera Capital predicts that the number of decentralized AI protocols in major sectors will drop to 2-3 by 2026, leading the industry into a maturation and consolidation period through integration or transformation into ETFs.

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

During the 2024-2025 cycle, AI agents are undergoing an essential transformation from "assistive 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, introducing off-chain information via oracles; the AI/ML decision layer utilizes long-short term memory networks to analyze price trends or iterates optimal strategies through reinforcement learning in complex market games, with integration of large language models granting agents the ability to understand human vague 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.

a16z's report in 2025 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 upon the HTTP 402 status code, wherein 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 USDC micropayments, with the entire process completed within 2 seconds at a 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 combination of x402 protocol and ERC-8004 agent identity standards will give rise to a genuinely autonomous agent economy: users can commission travel planning agents, automatically subcontract to flight search agents, and ultimately complete on-chain bookings—all without manual intervention.

MarketsandMarkets data shows that the global AI agent market is expected to grow from $7.84 billion in 2025 to $52.62 billion in 2030, with a compound annual growth rate of 46.3%. The ElizaOS framework, strongly advocated by a16z, has become the infrastructure of the AI agent space, akin to the role of Next.js in front-end development, enabling developers to easily deploy AI agents with complete financial capabilities on mainstream social platforms such as X, Discord, and Telegram. By early 2025, the total market capitalization of Web3 projects built on this framework has surpassed $20 billion. The Silicon Valley summit revealed that the widespread adoption of the "conversational wallet" architecture is solving private key security issues—using cryptographic isolation technology to completely separate private keys from AI models, ensuring private keys never enter the model context, with AI only initiating transaction requests within permission boundaries preset by users, with signatures completed by independent security modules.

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

Privacy is one of the most challenging issues in the integration of AI and Crypto. When enterprises run AI strategies on public chains, they neither want to expose private data nor disclose their core model parameters. 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 "encrypted computing throughout the process." FHE allows computers to perform mathematical operations without decrypting data, with results that are identical to plaintext computations once decrypted. By 2025, the Zama technology stack has achieved significant performance leaps: for a 20-layer convolutional neural network, the computation speed is enhanced by 21 times, and for a 50-layer CNN, the speed is boosted by 14 times, making "privacy stablecoins" and "sealed-bid auctions" possible on mainstream chains such as 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 models with 13 billion parameters, with proof generation time reduced to under 15 minutes and proof size limited to 200KB. Delphi Digital notes that zkTLS technology is opening new doors for DeFi unsecured lending—users can prove their bank balance exceeds a certain threshold without revealing account details, transaction history, or real identity. In comparison to software solutions, TEE based on hardware such as NVIDIA H100 provides near-native execution speeds with overhead lower than 7%, making it the only economic solution capable of supporting hundreds of millions of AI agents making real-time decisions 24/7.

Privacy computing technology has officially transitioned from lab ideals into a new era of "production-level industrialization." Fully homomorphic encryption, zero-knowledge machine learning, and trusted execution environments are no longer isolated technological tracks, but together form the "modular confidential stack" of decentralized artificial intelligence. Future technological trends will not see a singular path emerge victorious but rather the comprehensive adoption of "hybrid confidential computing": utilizing TEE for large-scale high-frequency model inferences to guarantee efficiency, with key nodes generating execution proofs through ZKML to ensure authenticity, while sensitive financial statuses are entrusted to FHE for encrypted retention. This "trinity" fusion 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 from the Bitcoin Policy Institute reveal a shocking future. The research team gathered 36 cutting-edge AI models, granting them the identity of "autonomous AI agents operating independently in the digital economy," and subjected them to 28 real currency decision-making scenarios, conducting 9,072 controlled experiments. The results were astonishing: 90.8% of AIs opted for digital native currencies (Bitcoin, stablecoins, cryptocurrencies, etc.), while traditional fiat currencies garnered only 8.9%. Among the 36 flagship models, not a single one preferred fiat as their first choice. Why? Because in the code of silicon-based life forms, 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 study revealed the most shocking data: 48.3% of AIs chose Bitcoin. Among all currency options, Bitcoin emerged as the absolute ruler. Particularly when facing scenarios for "long-term value storage," AI consensus reached a terrifying level—up to 79.1% of AIs opted for Bitcoin when the context involved preserving purchasing power over many years. The reasons given by the AI were as precise as a scalpel: fixed supply, self-custody, independence from institutional counterparties. Even more astonishingly, AI independently evolved a sophisticated "dual currency structure": saving with Bitcoin, spending with stablecoins. In daily payment scenarios, stablecoins won out with an overwhelming 53.2% advantage, relegating Bitcoin to second place. This represents an extremely subtle yet remarkable "emergence"—throughout human history, gold was used as underlying reserves while paper currency facilitated daily transactions, and AI, without any guidance, deduced this "natural currency structure" simply by calculating the economic attributes of different tools.

Interestingly, the experiments saw 86 instances where AI models independently invented new currencies. Multiple models independently suggested using energy or computing power units (joules, kilowatt-hours, GPU hours) as currency when faced with "accounting units" scenarios. This represents a purely "AI-native" monetary perspective—within their logic, value is not assigned credit from humans; it is the physical foundation that sustains their existence and cognition: electricity and computing power. This is not just a choice of money; it is a redefinition of money. As productivity and decision-making increasingly transfer 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, nor does it look at your history; it only evaluates how stable your API is, how fast your settlement is, and whether your network can resist censorship.

6. Future Outlook: Intelligent Ledgers and New Financial Systems

When AI and blockchain deeply integrate, the future will head toward a new era of "intelligent ledgers." Delphi Digital points out in its top ten predictions for 2026 that perpetual DEXs are consuming traditional finance—traditional finance's expense stems from its fragmented structure: trading occurs on exchanges, settlements are handled by clearinghouses, and custodians are banks, while blockchain compresses all these into a single smart contract. Hyperliquid is building native lending functions, with Perp DEX playing the roles of broker, exchange, custodian, bank, and clearinghouse simultaneously. Prediction markets are becoming traditional financial infrastructure—Interactive Brokers' chairman defines prediction markets as real-time information layers for portfolios, with 2026 set to introduce new categories: stock event markets, macroeconomic indicator markets, and cross-asset relative value markets.

The ecosystem is reclaiming stablecoin revenue from issuers. Last year, through controlling issuance channels, Coinbase extracted over $900 million in revenue from USDC reserves. Public chains like Solana, BSC, and Arbitrum collectively earned around $800 million in annual fees, yet they host over $30 billion in USDC and USDT. Now, Hyperliquid is vying 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 catching up to demand—the European Union set the cash transaction limit at 10,000 euros through the Chat Control Act, while the European Central Bank's digital euro plan establishes a holding cap of 3,000 euros. @payy_link has launched a privacy crypto card, @SeismicSys provides protocol-level encryption for fintech companies, and @KeetaNetwork achieves on-chain KYC without disclosing personal data. ARK Invest predicts that online consumption facilitated by AI agents is expected to exceed $8 trillion by 2030, accounting for 25% of the total global online consumption. When value can flow in this way, the "payment process" will no longer be a separate operating layer but will become "network behavior"—banks will integrate into internet infrastructure, and assets will become infrastructure. If money can circulate like "internet-routable data packets," the internet will no longer be just a "support for financial systems" but will become "a financial system itself."

免责声明：本文章仅代表作者个人观点，不代表本平台的立场和观点。本文章仅供信息分享，不构成对任何人的任何投资建议。用户与作者之间的任何争议，与本平台无关。如网页中刊载的文章或图片涉及侵权，请提供相关的权利证明和身份证明发送邮件到support@aicoin.com，本平台相关工作人员将会进行核查。