Tiger Research: What AI Services Do Crypto Companies Offer?

Bài học chính

• Cryptocurrency companies across sectors like exchanges, security, payments, and research are simultaneously rolling out AI services.
• Unlike previous cycles, proven, profitable companies like Coinbase and Binance are leading the charge. AI has shifted from a theoretical concept to a practical necessity.
• Adoption motives vary by industry: exchanges aim to prevent user churn; security firms seek to fill audit blind spots; payment infrastructure targets the emerging agent economy.
• Having a feature and actually using it are two different things. The “FOMO” and competitive pressure in the AI space are accelerating adoption far beyond actual demand.
• Both genuine need and competitive anxiety are at play. Distinguishing between value-creating adoption and mere label-sticking adoption is the key question.

1. Cryptocurrency Companies Are Offering AI Services

Artificial Intelligence (AI) is the most watched sector in global markets today. General-purpose tools like ChatGPT and Claude have integrated into daily life, while platforms like OpenClaw have lowered the barrier to building agents.

Trong khi mật mã industry missed the initial wave, it is now integrating AI across various verticals.

What AI services are these companies offering? And why are they entering this market?

2. How Crypto Companies Are Adopting AI Technology

2.1 Research

Cryptocurrency research suffers from structural issues: on-chain data, social sentiment, and key metrics are scattered across platforms and difficult to verify. General AI often returns inaccurate answers to crypto queries.

Projects like Surf address this by providing crypto-specific AI research tools that can integrate disparate data sources. Among all crypto AI use cases, research has the lowest barrier to entry for average users, requiring no expertise in programming or trading.

2.3 Trading

Trao đổis are leading the adoption of AI in trading.

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

Exchanges have offered APIs for years. The difference now is an added layer: interfaces like MCP and AI Skills enable non-developers to access exchange functionalities via AI agents. Dụng cụs once limited to developers are now accessible via natural language.

This aligns with a broader community shift. Non-developer users are increasingly building automated trading strategies through AI agents without writing any code. They simply describe the strategy, and the agent builds and runs the algorithm.

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

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

2.3 Security/Auditing

Traditional smart contract audits rely on manual, line-by-line code review, which is slow, costly, and inconsistent across auditors. Now, AI is integrated into workflows: AI first scans the code, followed by targeted deep review by human auditors. This improves speed and coverage without replacing auditors.

CertiK is a prime example. The company has previously faced criticism for auditing projects that were later exploited. However, these incidents occurred outside the audit scope. An audit is a point-in-time code check and does not include continuous monitoring.

CertiK uses AI to bridge this gap. It adds real-time post-audit monitoring and publishes results via a public dashboard. Since the expanded monitoring is AI-driven rather than manual, both CertiK and the projects it audits benefit.

In security, AI application isn’t about disrupting existing services but extending the scope of human work: increasing precision during audits and filling post-audit blind spots. For blockchain security firms, AI is not a new business area but a tool to address existing security vulnerabilities.

2.4 Payment Infrastructure

AI Agents need payment channels to participate in economic activities: e.g., paying API fees, purchasing data, and buying services from other agents. The most natural payment method for an agent is an on-chain wallet paired with stablecoins.

Two models are emerging. The first is a general-purpose protocol that embeds payment into HTTP requests, enabling agents to settle on-chain automatically when accessing paid APIs. The second is agent-specific payment plugins, where agents can only execute payments within pre-set human permissions and limits.

Payment infrastructure is the area most closely tied to stablecoins. However, since the payer is an AI agent rather than a human, fully operational models have yet to emerge.

Circle, the issuer of USDC, is also in the spotlight. The company published a proposal to connect its Gateway payment infrastructure with the x402 protocol, inviting developers and researchers to review and contribute.

This is not a mature market, but the market has begun pricing in this trend. One key driver for Circle’s stock price increase is its AI agent payment model. The realization of payment infrastructure will be slower than the other areas mentioned above, but it has become one of the most prominent macro themes in the current market.

3. Why Crypto Companies Are Moving into AI Now

When ChatGPT launched in November 2022, neither AI nor crypto were mature. AI models were impressive but couldn’t reliably execute tasks. The crypto industry was reeling from the FTX collapse and a comprehensive crisis of trust.

Since then, AI has advanced rapidly. Over the past year, the capabilities and utility of all major models have significantly improved. In contrast, crypto merely “leveraged” AI during the same period: flooded with AI-labeled “memecoins,” half-baked AI agents, and marketing-driven hype. Decentralized AI infrastructure projects kept emerging, but their quality paled in comparison to native AI services at a similar level.

Today, the gap is widening further. In the AI industry, infrastructure like MCP (enabling agents to directly call external tools) and OpenClaw (supporting no-code agent building) has made the agent era a reality. Crypto companies are just starting to move.

The difference this time is who is acting. It’s no longer fledgling startups flying the AI banner, but established companies with proven business models: Coinbase, Binance, and Bitget. These companies aren’t launching AI services for marketing purposes. They are driven not by immediate profit but by the fear of falling behind: FOMO.

Coinbase CEO Brian Armstrong’s actions perfectly illustrate this urgency. He issued a directive to all company engineers to launch an AI coding tool within a single week and fired employees who did not comply.

But maintaining a clear head is also crucial. Taking trading automation as an example, an agent can look at prices and suggest strategies, but how many users would truly trust an agent to handle their funds for live trading? And is the x402 protocol actually being used in the real world?

Ultimately, AI adoption in crypto isn’t about chasing a trend. With the arrival of the AI era, companies are acting to avoid losing their market position. Having a feature and genuinely utilizing it remain two different issues. But who is acting matters.

Think of the AI industry as a swimming pool being filled with water. Those who jumped in before were just pretending to swim. Those jumping in now are former national team surfers. No one knows how high the water will rise or if this pool will become an ocean. But crypto won’t be drowned in the flood.

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