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Bold Predictions for 2026 from the Intersection of AI and Web3: The Era of Agents with Wallets

#ai #web3 #blockchain #casual

Originally published on 2026-01-03
Original article (Japanese): AI×Web3から見える2026年の大胆予測: エージェントがウォレットを持つ時代

Reflecting on the AI and Web3 industries in 2025, each has undergone a unique evolution. However, what will truly become interesting in 2026 is when these two domains begin to interact.

AI agents will operate smart contracts, on-chain AI will run on decentralized infrastructure, and the token economy will accelerate AI development. With the technological foundations established by 2025, 2026 will be the year when this fusion transitions from "experimentation" to "practical application."

In this article, we will boldly predict the "future where AI and Web3 intersect," taking into account the recap of the AI industry in 2025 (A tumultuous year that began with the DeepSeek shock) and the recap of the Web3 industry (Technologies implemented as products).

Prediction 1: AI Agents Will Have Wallets and Conduct Economic Activities Autonomously ★★★★☆

The Technological Foundation is Ready

By 2025, the necessary elements for the fusion of AI agents and blockchain have been established.

Preparations on the AI Side:

  • The ability for agents to utilize multiple tools (e.g., MCP, A2A Protocol)
  • Long-duration autonomous execution (Copilot agent mode, Kiro autonomous agent)
  • Default inference capabilities (o3, Claude 4, Gemini 2.5, GPT-5)

Preparations on the Web3 Side:

  • Revolutionizing the wallet experience (Smart account for EOAs via EIP-7702)
  • Abstraction of gas fees (Sponsoring, session keys)
  • Simplification of cross-chain transactions (Superchain Interoperability, Intents)

What Will Happen in 2026

Q1-Q2: Introduction of Agent-Specific Wallet Protocols

Based on existing smart accounts (EIP-7702, ERC-4337), agent-specific wallet specifications will emerge. Key features include:

  • Budget Limits: Ability to set daily spending caps
  • Allowlist: Interaction only with specific smart contracts
  • Audit Logs: All transactions can be reviewed by humans
  • Emergency Stop: Ability to halt operations immediately in case of issues

Q3-Q4: Expansion of Use Cases

Examples of economic activities conducted autonomously by agents:

  1. DeFi Automated Operations: Moving funds across multiple protocols for yield optimization
  2. NFT Proxy Purchases: Bidding and purchasing on OpenSea or Blur based on user instructions
  3. Data Sales: Selling reports or models generated by agents on-chain
  4. Automated Reward Distribution: Automatically distributing rewards for tasks completed by multiple agents

Factors Accelerating Realization

Agent Collaboration via Model Context Protocol (MCP)

In December 2025, Anthropic donated MCP to the Agentic AI Foundation under the Linux Foundation. This will standardize wallet operations as an MCP server, allowing unified access from multiple AI agents.

Flexibility of EIP-7702

EIP-7702 allows existing EOAs to apply smart contract code "only during transaction execution." This enables agents to have advanced permissions only when necessary, minimizing security risks.

Prediction 2: Practical Implementation of On-Chain AI Inference Market ★★★☆☆

Maturity of Decentralized AI Infrastructure

In 2025, the proliferation of open-weight models (e.g., DeepSeek, Qwen, Kimi K2, gpt-oss) and inference engines (vLLM, SGLang) made the option to run AI in-house realistic.

Simultaneously, the Web3 side saw the marketization of shared security (EigenLayer AVS), enabling a system where "Ethereum validators ensure the security of middleware as a side job."

What Will Happen in 2026

Q2-Q3: Providing AI Inference as AVS

Using the Restaking mechanism of EigenLayer, AI inference services will emerge as Actively Validated Services (AVS).

Mechanism:

  1. Node operators prepare GPU clusters and host specific models (Qwen3, Llama, etc.) with inference engines (vLLM/SGLang)
  2. Staked ETH in EigenLayer serves as collateral to guarantee the accuracy of inference results
  3. Users send inference requests via on-chain and receive results
  4. Nodes providing fraudulent inferences will be slashed (stake confiscation)

Practical Examples:

  • DeFi protocols delegating price predictions and risk assessments to on-chain AI
  • NFT projects executing image generation in a verifiable on-chain manner
  • AI analyzing proposal content in DAO voting and providing summaries to voters

Realizing Verifiability

Combination with ZK Proofs

In August 2025, ZK Coprocessors like Axiom and Brevis were put into practical use. In 2026, this will also apply to AI inference.

  • Executing AI inference off-chain
  • Submitting only the ZK proof that the inference was conducted correctly on-chain
  • Ensuring verifiability while reducing gas costs

This will address the challenge of "Can we trust AI outputs?" through the verification mechanisms of blockchain.

Prediction 3: Token Economy Accelerating AI Development ★★☆☆☆

Clarification of Data and Model Ownership

Until 2025, the rights concerning training data and trained AI models were ambiguous. In 2026, clarification of ownership through Blockchain will progress.

Q2-Q3: Data NFTs and Model NFTs

  • Data NFTs: Tokenizing training datasets and licensing usage rights
  • Model NFTs: Tokenizing trained models and selling access rights to inference APIs
  • Contributor Tokens: Automatically distributing rewards to data providers and annotators based on usage

Example Scenario:

  1. A hospital providing a medical image dataset registers as the owner of the data NFT
  2. An AI startup trains a model using that data
  3. Each time the model is commercialized, a smart contract automatically distributes royalties to the hospital

Sustainability of Open Source AI

In 2025, DeepSeek-R1 and Qwen3 were released as open-source, but recovering development costs remained a challenge.

Q3-Q4: "Open but Monetized" Model

  • The model is open-weight (available to anyone)
  • For commercial use, a mechanism to pay licensing fees via on-chain is established
  • Payments are automated (via smart contracts) and highly transparent

This allows for a balance between "being open" and "sustainable development."

Prediction 4: AI Accelerating UX Improvements in Web3 Products ★★★★★

Challenges in 2025

The biggest challenge for Web3 was the complexity of UX. While technical solutions are being developed through EIP-7702 and Intents, psychological barriers remain, such as "not knowing what can be done" and "fear of failure."

What Will Happen in 2026

Q2-Q4: AI Assistants Becoming Web3 "Translators"

Use Case 1: Natural Language DeFi Operations

User: "Buy $1000 worth of this token and operate it in a safe pool."

AI Agent (internal processing):

  1. Selecting the optimal DEX (e.g., UniswapX / CowSwap based on intents)
  2. Calculating slippage and gas fees
  3. Presenting to the user for approval
  4. Executing the transaction (automatically retrying in case of failure)

Use Case 2: Automating Risk Explanations

Before executing a smart contract, AI:

  • Performs static analysis of the contract code
  • References past transaction history (via ZK Coprocessors)
  • Explains risks in natural language (e.g., "This pool is audited but has a low collateral ratio.")

Use Case 3: Simplifying Cross-Chain Operations

User: "I want to buy an NFT on Optimism with USDC from Arbitrum."

AI Agent:

  1. Constructs a cross-chain transaction using Superchain Interoperability
  2. Optimizes gas fees (L2 bridge vs direct swap)
  3. Completes with a single signature

Supporting Technologies

Application of Vibe Coding

In February 2025, Andrej Karpathy proposed Vibe Coding, emphasizing "conveying intent" over "writing code."

In 2026, this concept will be applied to Web3, allowing users to simply express "what they want to do," and agents will construct the optimal transaction.

Prediction 5: Implementation of Collective Intelligence through "Decentralized AI + DAO" ★★★☆☆

AI Supporting DAO Decision-Making

By 2025, DAO voting participation rates were low (often below 10%), and the quality of decision-making was a concern.

Q3-Q4: AI-Powered Governance

  1. Proposal Summarization: AI automatically summarizes lengthy proposals, reducing the burden on voters
  2. Impact Analysis: Running simulations of the financial impact and changes in tokenomics if a proposal is passed
  3. Individual Recommendations: Recommending proposals that align with each voter's past voting history

Implementation Example:

  • An AI like NotebookLM analyzes DAO proposals
  • A research AI like Deep Research collects related past discussions and external information
  • Results are stored on-chain for voter review

Training Decentralized AI

Q4: Federated Learning × Blockchain

Federated Learning, where multiple nodes collaborate to train AI models, will be managed on the blockchain.

Mechanism:

  1. Each node learns with local data (data is not shared)
  2. Only the learning results (gradients) are submitted on-chain
  3. Smart contracts aggregate gradients and update the global model
  4. Token rewards are distributed based on contribution

Benefits:

  • Enables training large models while maintaining data privacy
  • Contributions are transparently recorded, making incentive design easier

Conclusion: 2026 Will Be the Year of "Fusion"

By 2025, AI and Web3 have each built a practical foundation in their respective domains.

  • AI has evolved from a "smart answerer" to an "agent that integrates research, operation, and generation."
  • Web3 has progressed from "specification formulation" to "products operating on the mainnet."

In 2026, these two will interact and create practical value.

Summary of Predictions:

  1. AI agents will have wallets and conduct economic activities autonomously ★★★★☆ (Q1-Q2)
  2. Practical implementation of the on-chain AI inference market ★★★☆☆ (Q2-Q3)
  3. Token economy accelerating AI development ★★☆☆☆ (Q2-Q3)
  4. AI accelerating UX improvements in Web3 products ★★★★★ (Q2-Q4)
  5. Implementation of collective intelligence through "decentralized AI + DAO" ★★★☆☆ (Q3-Q4)

Of course, these are all "bold predictions." Many technical challenges (security, scalability, regulation) remain.

However, what we see from the reflections of 2025 is that rather than announcements of "models released" or "specifications decided," it is the actual implementations that run on the mainnet and change user experiences that will drive the next change.

Let’s witness together the transition of the fusion of AI and Web3 from "experimentation" to "practical application" in 2026.

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