The Evolution of AI+Crypto: DePIN solves computing power, Bittensor drives intelligence, AI Agents change interaction...

Author: Go2Mars' Web3 Research Institute

The Symbiosis of Algorithms and Ledgers: A Major Shift in the Global Technology Paradigm

In the third decade of the 21st century, the combination of artificial intelligence (AI) and cryptocurrency is no longer merely a combination of two popular terms, but a profound technological paradigm revolution. With the global cryptocurrency market capitalization officially surpassing the $4 trillion mark in 2025, the industry has completed its transition from an experimental niche market to an important part of the modern economy.

One of the core drivers of this transformation is the deep convergence between artificial intelligence (AI) as an extremely powerful decision-making and processing layer and blockchain as a transparent and tamper-proof execution and settlement layer. This combination is addressing the pain points of both: AI is at a critical juncture in its transformation from centralized giant monopolies to decentralized and transparent "open intelligence"; while the crypto industry, after its infrastructure has been gradually improved, urgently needs AI to solve problems such as complex on-chain interactions, weak security, and insufficient application utility.

From the perspective of capital flows, the strategic divergence among top venture capital firms also confirms this trend. a16z Crypto completed its fifth funding round of $2 billion in 2025, firmly positioning the intersection of AI and Crypto as the core of its long-term strategy, believing that blockchain is a necessary infrastructure to prevent AI censorship and control.

Meanwhile, institutions like Paradigm are expanding their investments to robotics and broader AI, attempting to capture the cross-industry benefits brought about by technological convergence. According to OECD data, by 2025, venture capital investment in AI will account for 51% of total global investment, and the proportion of funding for AI-related projects in the Web3 space is also steadily increasing, reflecting the market's strong acceptance of the narrative of "decentralized intelligence."

1. Infrastructure Restructuring: Decentralized Computing Power and Computational Integrity

There is an inherent contradiction between artificial intelligence's insatiable appetite for graphics processing units (GPUs) and the fragility of the current global supply chain. The GPU shortage, which became the norm between 2024 and 2025, provided fertile ground for the explosion of decentralized physical infrastructure networks (DePIN).

1.1 The Dual Evolution of the Decentralized Computing Market

Currently, decentralized computing platforms are mainly divided into two camps.

The first category is represented by Render Network (RNDR) and Akash Network (AKT) , which aggregate idle GPU computing power globally by building a decentralized two-sided market. Render Network has become the benchmark for distributed GPU rendering, not only reducing the cost of 3D creation but also supporting AI inference tasks through blockchain coordination, allowing creators to obtain high-performance computing power at a lower price. Akash, on the other hand, made a leap forward after 2023 with its GPU mainnet (Akash ML), allowing developers to lease high-specification chips for large-scale model training and inference.

The second category is a new type of computational orchestration layer represented by Ritual . Ritual's uniqueness lies in its approach: it doesn't attempt to directly replace existing cloud services, but rather serves as an open, modular sovereign execution layer, embedding AI models directly into the blockchain's execution environment. Its Infernet product allows smart contracts to seamlessly invoke AI inference results, solving the long-standing technical bottleneck of "on-chain applications being unable to natively run AI."

1.2 Breakthroughs in computational integrity and verification technologies

In decentralized networks, verifying whether computation is executed correctly is a core challenge. Technological advancements in 2025 will primarily focus on the integrated application of zero-knowledge machine learning (ZKML) and trusted execution environments (TEEs).

The Ritual architecture, designed with proof-system agnosticity, allows nodes to choose between TEE code execution or ZK proofs based on task requirements. This flexibility ensures that even in a highly decentralized environment, every inference result generated by the AI ​​model is traceable, auditable, and guaranteed for integrity.

2. Democratization of Intelligence: The Rise of Bittensor and the Commodity Market

The emergence of Bittensor (TAO) marks a new stage in the commercialization of machine intelligence by combining AI and crypto. Unlike traditional single-computing 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.

2.1 Yuma Consensus: From Linguistic Learning to Consensus Algorithms

At the heart of Bittensor is the Yuma consensus (YC), a subjective utility consensus mechanism inspired by Grice pragmatics.

YC's operating logic assumes that an efficient collaborator tends to output truthful, relevant, and informative answers, as this is the optimal strategy for maximizing rewards within an incentivized landscape. Technically, YC calculates token emissions by weighting the performance of miners based on validators' evaluations. Its core logic can be represented by the following LaTeX formula for the allocation of emission shares:

Where E represents the emission reward, Δ represents the daily total supply increment, W represents the validator evaluation weight matrix, and S represents the corresponding staking weight. To prevent malicious collusion or bias, YC introduces a clipping mechanism to reduce weight settings that exceed the consensus benchmark, ensuring the robustness of the system.

2.2 Subnet Economy and Dynamic TAO Paradigm

By 2025, Bittensor had evolved into a multi-layered architecture. The bottom layer is a 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 an independent value reserve pool for each subnet through an Automated Market Maker (AMM), the price of which is determined by the ratio of TAO to Alpha tokens:

This mechanism enables automatic resource allocation: subnets with high demand and high-quality output will attract more staking, thereby obtaining a higher proportion of daily TAO emissions. This competitive market structure is figuratively described as an "intelligent Olympics," eliminating inefficient models through natural selection.

3. The Rise of the Agent Economy: AI Agents as a Primary Entity of Web3

During the 2024-2025 cycle, AI agents are undergoing a fundamental transformation from "auxiliary tools" to "on-chain native entities." This evolution is reflected not only in the increasing complexity of the technical architecture, but also in the fundamental expansion of their roles and permissions within the decentralized finance (DeFi) ecosystem.

The following is an in-depth analysis of this trend:

3.1 Proxy Architecture: A Closed Loop from Data to Execution

Current on-chain AI agents are no longer simple scripts, but mature systems built on three complex logical layers:

• Data Input Layer: The agent captures on-chain data such as liquidity pools and trading volumes in real time through blockchain nodes or APIs (such as Ethers.js), and combines it with oracles (such as Chainlink) to introduce off-chain information such as social media sentiment and centralized exchange prices.

• AI/ML Decision Layer: The agent uses Long Short-Term Memory (LSTM) networks to analyze price trends or iterates the optimal strategy in complex market games through reinforcement learning. The integration of Large Language Models (LLMs) also endows the agent with the ability to understand ambiguous human intentions.

• Blockchain Interaction Layer: This is key to achieving "financial autonomy." Agents can now manage non-custodial wallets, automatically calculate optimal gas fees, handle nonces, and even integrate MEV protection tools (such as Jito Labs) to prevent front-running in transactions.

3.2 Financial Tracks and Agent-to-Agent Transactions

In its 2025 report, a16z specifically highlighted the financial pillars of AI agents—the x402 protocol and similar micropayment standards. These standards allow agents to pay API fees or purchase services from other agents without human intervention. For example, the Olas (formerly Autonolas) ecosystem already processes over 2 million automated inter-agent transactions monthly, covering a wide range of tasks from DeFi swaps to content creation.

Agent economic components

This trend is already clearly reflected in market data. In terms of growth rate, the AI ​​agent market is on the verge of explosive growth. According to research data from MarketsandMarkets, the global AI agent market is projected to grow from $7.84 billion in 2025 to $52.62 billion in 2030, with a compound annual growth rate (CAGR) of 46.3%. Furthermore, Grand View Research has also provided a similar long-term forecast, estimating that the market size will reach $50.31 billion by 2030.

Meanwhile, standard tools for the development layer are also taking shape. The ElizaOS framework, heavily promoted by a16z, has become the infrastructure of the AI ​​agent field, comparable in status to "Next.js" in front-end development. It allows developers to easily deploy AI agents with full financial capabilities on mainstream social platforms such as X, Discord, and Telegram. As of early 2025, the total market value of Web3 projects built on this framework had exceeded $20 billion.

4. Privacy Computation and Confidentiality: The Game Between FHE, TEE, and ZKML

Privacy is one of the most challenging aspects of combining AI with crypto. When companies run AI strategies on public blockchains, they don't want to leak private data or disclose their core model parameters. Currently, the industry has three main technical approaches: Fully Homomorphic Encryption (FHE), Trusted Execution Environments (TEEs), and Zero-Knowledge Machine Learning (ZKML).

4.1 Zama and FHE's Industrialization Journey

Zama, a leading unicorn in the 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 the data, and the results are completely consistent with the plaintext operations after decryption.

By 2025, Zama's technology stack had achieved a significant performance leap: a 21x speedup for 20-layer convolutional neural networks (CNNs) and a 14x speedup for 50-layer CNNs. This progress enabled "privacy stablecoins" (transaction amounts are encrypted to the outside world, but the protocol can still verify their legitimacy) and "sealed bid auctions" on mainstream chains such as Ethereum.

4.2 Combining ZKML's Validation Efficiency with LLM

Zero-knowledge machine learning (ZKML) focuses on "verification" rather than "computation." It allows a party to prove that it has correctly implemented a complex neural network model without exposing the input data or model weights. The latest zkLLM protocol enables end-to-end inference verification of models with 13 billion parameters, reducing proof generation time to under 15 minutes and proof size to only 200 KB. This technology is crucial for high-value financial audits and medical diagnostics.

4.3 TEE and GPU Synergy: The Power of the Hopper H100

Compared to FHE and ZKML, TEE (Trusted Execution Environment) offers near-native performance. NVIDIA's H100 GPUs introduce confidential computing capabilities, isolating memory through hardware-level firewalls, with inference overhead typically below 7%. Protocols like Ritual are heavily adopting GPU-based TEEs to support AI agent applications requiring low latency and high throughput.

Privacy computing technology has officially moved from idealistic concepts in the laboratory to a new era of "production-grade industrialization". Fully homomorphic encryption (FHE), zero-knowledge machine learning (ZKML), and trusted execution environments (TEEs) are no longer isolated technological tracks, but together constitute the "modular confidential stack" of decentralized artificial intelligence.

This convergence is fundamentally rewriting the underlying logic of Web3, leading to the following three core conclusions:

• FHE is the underlying standard for Web3's "HTTPS": with unicorns like Zama increasing computing performance by tens of times, FHE is achieving a qualitative leap from "everything is public" to "encryption by default". It solves the privacy problem of on-chain state processing, enabling privacy stablecoins and fully MEV-resistant transaction systems to move from theory to large-scale compliant applications.

• ZKML is the mathematical endpoint of algorithmic accountability: The “ZKML singularity” arriving in the second half of 2025 marks a dramatic drop in verification costs. By compressing the inference proof of a 13 billion-parameter (13B) model to under 15 minutes, ZKML provides “mathematical-level consistency” assurance for high-value financial audits and credit ratings, ensuring that AI is no longer an untrustworthy black box.

• TEE is the performance foundation of the agent economy: compared to software solutions, TEE based on hardware such as the NVIDIA H100 offers near-native execution speed with less than 7% overhead. It is currently the only economical solution that can support hundreds of millions of AI agents making 24/7 real-time decisions, ensuring that agents securely hold private keys and execute complex policies within a hardware-level firewall.

The future technological trend is not about the success of a single path, but the widespread adoption of "hybrid confidential computing." In a complete AI business flow: TEE is used for large-scale, high-frequency model inference to ensure efficiency; key nodes generate execution proofs through ZKML to ensure authenticity; and sensitive financial states (such as account balances and privacy IDs) are encrypted and stored by FHE.

This "trinity" integration is reshaping the crypto industry from a "transparent ledger" to a "sovereign privacy-enabled intelligent system," truly ushering in an era of automated proxy economy worth trillions of dollars.

5. Industry Security and Automated Auditing: AI as the "Immune System" of Web3

The cryptocurrency industry has long suffered huge losses due to smart contract vulnerabilities. The introduction of AI is changing this passive defense approach, shifting it from costly manual auditing to real-time AI monitoring.

5.1 Innovation of Static and Dynamic Auditing Tools

Tools like Slither and Mythril, by 2025, had deeply integrated machine learning models, enabling them to scan Solidity contracts for reentrancy attacks, Suicidal functions, or gas consumption anomalies at sub-second speeds. Furthermore, fuzzing tools like Foundry and Echidna utilize AI to generate extreme input data, detecting deeply hidden logical vulnerabilities.

5.2 Real-time Threat Prevention System

Beyond pre-deployment auditing, significant progress has been made in real-time defense. Systems like Guardrail's Guards AI and CUBE3.AI can monitor all pending transactions (Mempool) across chains and automatically trigger contract suspension or block malicious transactions when malicious attack signals (such as governance attacks or oracle manipulation) are detected. This "proactive immunity" significantly reduces the hacking risk to DeFi protocols.

A Practical Roadmap for Developing Crypto Using AI

In the future digital landscape, the integration of AI and Crypto is no longer a technological experiment, but a profound revolution concerning "productivity efficiency" and "wealth distribution rights." This combination not only gives AI an independently controllable "wallet," but also gives Crypto an autonomous "brain," jointly ushering in a multi-trillion-dollar era of autonomous agency economy.

The following is a diagram illustrating the core benefits and practical applications of this integration at the enterprise and individual levels:

For businesses, the combination of AI and Crypto primarily addresses the structural contradictions between high computing costs, fragile system security, and data privacy protection.

• The dramatic decrease in infrastructure costs (the DePIN effect): With the help of distributed computing networks (such as Akash or Render), enterprises are no longer bound by the expensive purchase of NVIDIA H100 clusters. Real-world data shows that renting idle GPUs globally can reduce costs by 39% to 86% compared to traditional cloud service providers. This "computing freedom" allows startups to afford the fine-tuning and training of ultra-large-scale models.

• Automation and affordability of security barriers: Traditional contract auditing is time-consuming and expensive. Now, by deploying AI security agents driven by neural networks, such as AuditAgent, enterprises can achieve "sentinel monitoring" throughout the entire development lifecycle. They can identify logical vulnerabilities such as reentrancy attacks the moment code is committed, and can automatically trigger contract circuit breakers directly at the memory pool level the moment a hacker's command is issued, protecting protocol assets from loss.

• "Encrypted computation" of core business secrets: By leveraging fully homomorphic encryption (FHE) and "blind computation" networks such as Nillion, enterprises can run AI strategies on public blockchains without disclosing core model parameters and private customer data. This not only establishes data sovereignty but also allows financial and medical data, which were previously subject to compliance risks, to enter decentralized collaborative networks.

For individual users, the integration of AI and Crypto means the complete elimination of technical barriers and the opening of entirely new revenue streams.

• Intent-driven "private bankers": In the future, users will no longer need to understand gas fees or cross-chain bridges. AI agents built on frameworks like ElizaOS will achieve "radical abstraction"—you only need to say, "Put this $1,000 into the safest place with the highest interest rate," and the AI ​​will autonomously monitor the entire network's APY and automatically close positions when risks fluctuate. Ordinary people can now enjoy top-tier hedge fund-level asset management.

• Data Yield Farming: Your digital footprint is no longer being exploited by giants. Through platforms like Synesis One, users can participate in "Train2Earn," providing labeled data for AI training and directly earning token rewards. They can even hold Kanon NFTs to receive passive dividends every time AI accesses a specific knowledge term, truly realizing "data as an asset."

• Ultimate protection of privacy and identity: Using Worldcoin or cryptographic identity protocols, you can prove you are human and not AI, while using privacy-preserving computing networks to protect your personal schedule, home address, and other sensitive information from being leaked to AI service providers. This "blind interaction" model ensures that while enjoying the convenience of AI, you still retain the ultimate right to interpret your digital sovereignty.

This two-way architectural evolution is entrusting "trust" to blockchain and "efficiency" to AI. It not only restructures the competitive advantage of enterprises but also builds a ladder for every ordinary person to access a smart sovereign economy.

Evolution Prediction: Towards a New Era of "Intelligent Ledgers"

In summary, how can AI be better integrated with Crypto? The answer lies in shifting from "simple tool stacking" to "deep architectural coupling".

• First, blockchain must evolve into a platform capable of supporting large-scale computing. Efforts by protocols such as Ritual and Starknet are making ZKML as simple as calling a standard library.
• Secondly, AI agents must become legitimate entities in economic life . With the popularization of identity standards such as ERC-8004, we will see a "smart network" composed of hundreds of millions of agents, engaging in resource competition and value exchange on the blockchain 24/7.
• Finally, this convergence will reshape human financial sovereignty . Privacy-preserving payments through FHE, equitable distribution of resources to creators through traceability protocols, and algorithmic democratization through marketplaces like Bittensor together constitute a blueprint for a fairer, more efficient, and decentralized future digital economy.

In this long-distance race of technology, the crypto industry offers more than just funding; it provides a philosophical framework of "transparency" and "trust." AI, on the other hand, provides the "brain" that makes these frameworks truly function.

With the arrival of 2026, this convergence will not be limited to the tech world, but will reach billions of ordinary users around the world through more intuitive AI interfaces.