This report is written by Tiger Research, and cryptocurrency companies universally face "fear of missing out" (FOMO). From exchanges to security firms, they are all racing to launch AI-driven services. We will explore why they have chosen to take action at this time.

Key Takeaways

• Cryptocurrency companies in areas such as exchanges, security, payments, and research are synchronously launching AI services.
• Unlike previous cycles, companies like Coinbase and Binance, which have proven to be profitable, are leading the trend. AI has transitioned from a theoretical concept to a practical necessity.
• The motivations for adoption vary across different industries: exchanges aim to prevent user churn; security firms aim to fill audit blind spots; and payment infrastructure targets the emerging gig economy.
• Having a feature and truly utilizing it are two different things. The FOMO and competitive pressure in the AI field are accelerating its application, far exceeding actual demand.
• True demand and competitive anxiety are both at play. Distinguishing between adoption that creates value and mere label adoption is the key issue.

1. Cryptocurrency Companies are Offering AI Services

Artificial intelligence (AI) is the most watched field in the global market today. General tools like ChatGPT and Claude have integrated into daily life, while platforms like OpenClaw have lowered the barriers to building intelligent agents.

Although the cryptocurrency industry missed this wave, it is now integrating AI across various verticals.

What AI services do these companies offer? Why are they entering this market?

2. How Cryptocurrency Companies are Adopting AI Technologies

2.1 Research

Cryptocurrency research faces structural issues: on-chain data, social sentiment, and key indicators are scattered across various platforms, making verification difficult. General AI often returns inaccurate answers to cryptocurrency queries.

Projects like Surf address this issue by providing AI research tools dedicated to cryptocurrency, which can integrate scattered data sources. Among all cryptocurrency AI application scenarios, research has the lowest entry barrier for average users, requiring no programming or trading expertise.

2.2 Trading

Exchanges are leading the application of AI in trading.

Approaches vary. Some methods publicly expose proprietary trading data to users; others allow users to issue natural language commands to AI agents, with the AI completing the entire process from analysis to execution in one step.

Exchanges have offered APIs for many years. The difference now lies in the addition of a layer: interfaces like MCP and AI Skills enable non-developers to access exchange functionalities through AI agents. Tools that were once only for developers can now be accessed through natural language.

This aligns with the broader community trend. Non-developer users are increasingly using AI agents to build automated trading strategies without writing any code. They only need to describe the strategy, and the agents will build and execute the algorithms.

For exchanges, this is both an opportunity and a challenge. As the number of AI users grows, user loyalty to a single exchange may decrease because traders can execute trades anywhere. The reason exchanges are adopting AI is simple: to quickly attract users and keep them active on the platform.

Trading involves real asset management, requiring higher judgment and accountability compared to research. However, with lowered entry barriers, this field is also opening up to average users.

2.3 Security/Audit

Traditional smart contract auditing relies on manual line-by-line code reviews, which are slow, costly, and inconsistent among different auditors. Now, AI has been integrated into the workflow: AI first scans the code, followed by targeted in-depth reviews by human auditors. This improves speed and coverage without replacing auditors.

CertiK is a typical example. The company has previously faced criticism for its audits being later exploited maliciously. However, these events occurred outside the scope of the audits. Auditing examines code at a specific point in time and does not include continuous monitoring.

CertiK uses AI to compensate for this shortcoming. It adds real-time monitoring features post-audit and publishes monitoring results through a public dashboard. Because the extended monitoring range is driven by AI rather than human operation, both CertiK and the projects it audits benefit.

In the security field, the application of AI does not disrupt existing services but expands the scope of human work: improving accuracy during audits and filling post-audit blind spots. For blockchain security companies, AI is not a new business area but a tool to address existing security vulnerabilities.

2.4 Payment Infrastructure

AI agents require payment channels to participate in economic activities: for example, paying API fees, purchasing data, and buying services from other agents. For agents, the most natural payment method is on-chain wallets paired with stablecoins.

Two models are emerging. The first is a general protocol that embeds payments into HTTP requests, allowing agents to automatically settle on-chain when accessing paid APIs. The second is payment plugins for specific agents, allowing agents to execute payments only within predefined permissions and limits set by humans.

Payment infrastructure is the area most closely linked to stablecoins. However, since the payment subjects are AI agents rather than humans, a fully operational model has not yet emerged.

The issuer of USDC, Circle, is also attracting attention. The company has released a proposal to connect its Gateway payment infrastructure with the x402 protocol and invited developers and researchers to review and contribute.

This is not a mature market, but the market has begun to digest this development trend. One of the key drivers of Circle's stock price increase is its AI agent payment model. The implementation speed of payment infrastructure will be slower than that of the other areas mentioned, but it has become one of the most prominent macro themes in the current market.

3. Why Cryptocurrency Companies are Entering the AI Field Now

When ChatGPT launched in November 2022, neither AI nor cryptocurrency was mature. While AI models were impressive, they could not reliably perform tasks. The cryptocurrency industry was severely impacted by the collapse of FTX and a comprehensive crisis of trust.

Since then, AI has undergone rapid development. In the past year, the functionality and practicality of all mainstream models have significantly improved. In contrast, the cryptocurrency sector has merely "hopped on" the AI trend during the same period: it is filled with "Meme coins" labeled with AI, poorly functioning AI agents, and marketing-driven hype. Decentralized AI infrastructure projects continue to emerge, but when objectively compared to equivalent native AI services, their quality clearly pales in comparison.

Today, the gap is further widening. In the AI industry, infrastructures like MCP (which enables agents to directly call external tools) and OpenClaw (which supports no-code agent building) have made the era of agents a reality. In contrast, cryptocurrency companies are just getting started.

What distinguishes this time is who the actors are. No longer are the actors emerging startups flying the AI flag but established companies with proven profitable models: Coinbase, Binance, and Bitget. These companies are launching AI services not for marketing purposes, as they are not driven by immediate profits but by FOMO (fear of missing out).

The urgency is epitomized by Coinbase CEO Brian Armstrong's actions. He issued a directive to all engineers to launch AI coding tools within just one week and fired employees who did not comply with the directive.

However, it is also crucial to stay clear-headed. For instance, in automated trading, agents can view prices and propose strategies, but how many users will actually trust the agents and entrust them with funds for real-time trading? Furthermore, has the x402 protocol truly been applied in the real world?

Ultimately, the adoption of AI in the cryptocurrency sector is not about chasing trends. With the arrival of the AI era, companies are taking active steps to avoid losing market position. Having a feature and truly utilizing that feature remain two distinct issues. However, who is taking action is critical.

Imagine the AI industry as a swimming pool being filled with water. Those who jumped in before were just pretending to swim. Now, those who jump in are former national surfing team members. No one knows how high the water level will rise or whether this swimming pool will become an ocean. But cryptocurrency will not drown in the current.

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