Since ChatGPT release in 2022, Large Language Models (LLMs) have become essential for societal progress by improving information access and simplifying complex issues for audiences. This advancement integrates into cryptocurrency by addressing challenges from the advisory level, including research support, risk detection, to high-friction activities like autonomous onchain agents.
Understanding Large Language Models
Large language models are a type of artificial intelligence model designed to understand, interact and generate human-like text based on the input.
Some famous LLMs include:
- GPT from OpenAI
- Gemini from Google
- LLaMA from Meta (formerly Facebook)
- Claude from Anthropic
- Grok from xAI
Currently, GPT (OpenAI) leads in terms of market share based on traffic. However, the gap with Gemini (Google) is closing due to the search giant’s distribution advantages (Figure 1).
Figure 1. Comparing Generative AI Model Traffic. Source: Delphi Intelligence.
There are four main stages in an LLM’s development process:
a) Data processing: Diverse text sources web content, books, and code—are aggregated, cleaned, filtered for efficient pre-training processing.
b) Pre-training: Phase where model learns to predict the next token based on broad knowledge of language structure, facts, and reasoning through massive computational resources.
c) Fine-tuning: Model using supervised or reinforcement learning techniques, often incorporating human feedback to improve alignment, safety, and utility.
d) Evaluation, safety testing, and deployment: The model undergoes rigorous assessment for accuracy, bias, hallucinations, and robustness before integration into applications.
Figure 2. Four main stages of LLM training. Source from Turing.
Large Language Models Applications in Cryptocurrency
Since ChatGPT’s 2022 release, LLMs have boosted societal progress and now tackle key cryptocurrency challenges, from research to autonomous onchain agents.
Research & Market Insights Support and Trend Pattern Detection
LLMs excel at finding patterns in massive datasets. However, when it comes to crypto-specific activities like onchain data, it’s a completely different story. According to the Cyber Research (2025), general LLMs have less than 70% accuracy when responding to crypto-specific prompts (Figure 3). Consequently, today’s crypto research is often a fragmented and time-consuming endeavor that involves scrutinizing exchange graphs, hand-coding APIs, and developing potentially error-prone Dune queries.
Figure 3. Average accuracy across five evaluation runs using majority voting. Source from Cyber Research Team.
Taking advantage of LLMs, various Web3 projects integrated AI with its Web3 data to create it own services iike sentiment analysis, summarizing complex whitepapers, and even nowcasting short-term market conditions.
Currently, there are some notable players in this sector:
- Surf.AI: An advanced AI-powered platform for cryptocurrency research, market analysis, and trading insights, designed to simplify complex data into action. Surf aggregate and analyze data from social sentiment, onchain metrics, and news sources, creating deep dive query for crypto projects.
- LlamaAI & Nansen AI: LlamaAI (from DeFiLlama) and Nansen AI are two leading onchain analytics platforms that utilize vast onchain and offchain datasets to generate trading signals and predictive analytics for anomaly detection, such as wash trading or rug pulls, enhancing crypto research efficiency.
- Binance Wallet & OKX Wallet: Both apply LLMs to their Web3 wallet products, leveraging the huge user bases from their exchange platforms. Specifically, Binance’s AI Assistant provides token summaries, sentiment analysis, and narrative consolidation with Social Hype and Topic Rush. OKX analyzes onchain data, macro trends, and sentiment for research, detecting signals like onchain opportunities.
Noticeably, Herond Browser which owns both a browser and a Web3 wallet stand to gain a significant competitive edge by LLMs. This integration allows for hyper-personalized research support, real-time market insights, and advanced trend pattern detection tailored to individual user behaviors and preferences.
Smart Contract Auditing and Transaction Anomaly Detection
Smart contract security remains a hot topic in cryptocurrency as $3.4 billion has been hacked and exploited in 2025. LLMs address this by automating code reviews to detect patterns like reentrancy or logic flaws, and generating explanations for vulnerabilities and code repairing.
Some projects applying LLMs to smart contract auditing and onchain scrutiny:
- AuditAgent (Nethermind): An autonomous AI agent that scans smart contract repositories, performs risk analyses, and generates fix suggestions and reports.
- ChainGPT: A blockchain AI platform offering tools for code generation and security analysis of Solidity contracts.
- Elliptic: A blockchain analytics provider for compliance and risk management. Elliptic applies AI/ML to screen transactions, assign risk scores, and detect illicit activities with 100 billion points dataset.
Autonomous Agents for Onchain Activities
Onchain AI agents are autonomous, AI-powered programs that act in the blockchain world. Imagine a robot with its own wallet and pre-determined objective, it can interact with smart contracts, assets trading, or DAO voting. Unlike typical chatbots, these agents learn, think, and operate independently in the onchain economy, serving as your personal crypto assistant.
According to Sequoia Capital, over the next 5-7 years, society shift to an always-on economy where AI agents handle 24/7 activities in finance, healthcare, and more. Blockchain fit in this new scenario by the automation of the smart contract without middleman involvement. The infrastructure is already in place with various foundational primitives: Model Context Protocol (MCP), Agent-to-Agent (A2A), Agent-Payments-Protocol (AP2), and x402 standard (Figure 4).
Figure 4. Onchain AI Infrastructure. Source from Coinbase Venture.
Some notable projects working in this sector include:
- Virtual Protocol: A decentralized platform focused on building “society of AI agents” by enabling AI agents’ token creation and deployment. These agents can produce services or products, engage in commerce with humans and other agents, and handle tasks. Some notable x402 projects launched on Virtual frameworks include Aurra by Virtuals ($AURA), SANTA by Virtuals ($SANTA), and Ethy AI.
- Artificial Superintelligence Alliance: A decentralized platform for autonomous AI agents (uAgents) that can execute trades, manage smart contracts, and interact in DeFi. Agents have onchain wallets for holding/transferring tokens and can borrow against assets or settle trades in milliseconds.
- Spectral Labs: A platform to build AI agents with wallets, data feeds, and trading capabilities. Agents can autonomously invest in crypto, execute strategies, and interact with DeFi protocols.
In Conclusion
The integration of Large Language Models into the cryptocurrency represents a transformative shift, merging AI’s capabilities with blockchain to tackle key challenges. This includes enhancing market research and risk detection, where Herond Browser leverages its browser and Web3 wallet for personalized, data-driven advantages.
LLMs also strengthen security through automated smart contract auditing and transaction anomaly detection, while enabling autonomous onchain agents. As LLMs evolve, their synergy with cryptocurrency promises a more accessible, secure, and innovative ecosystem, driving societal progress.
About Herond
Herond Browser is a cutting-edge Web 3.0 browser designed to prioritize user privacy and security. By blocking intrusive ads, harmful trackers, and profiling cookies, Herond creates a safer and faster browsing experience while minimizing data consumption.
To enhance user control over their digital presence, Herond offers two essential tools:
- Herond Shield: A robust adblocker and privacy protection suite.
- Herond Wallet: A secure, multi-chain, non-custodial social wallet.
As a pioneering Web 2.5 solution, Herond is paving the way for mass Web 3.0 adoption by providing a seamless transition for users while upholding the core principles of decentralization and user ownership.
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