By: Ekko, Ryan Yoon

Translated and Organized by: BitpushNews

FOMO (Fear of Missing Out) is looming over cryptocurrency companies. From exchanges to security firms, they are racing to launch AI-driven services. This article will explore why they are choosing to take action at this time.

Key Points

• Cross-industry Layout: Cryptocurrency companies in exchanges, security, payments, and research are simultaneously launching AI services.
• Led by Giants: Unlike previous cycles, companies like Coinbase and Binance with mature profit models are taking the lead. AI has shifted from being a "narrative" to a "business necessity."
• Divergent Motives: Exchanges aim to prevent user churn; security companies seek to fill auditing blind spots; payment infrastructure targets the emerging Agent Economy.
• Gap Between Ownership and Practical Use: "Having the capability" and "actual use" are two distinct issues. AI FOMO and competitive pressure are accelerating AI adoption, even exceeding proven practical demand.
• True Demand Coexists with Competitive Anxiety: Distinguishing between "value-generating adoption" and "label-only adoption" is a key issue.

1. Cryptocurrency Companies are Offering Comprehensive AI Services

AI is the most focused area in today's global market. General-purpose tools like ChatGPT and Claude have entered daily life, while platforms such as OpenClaw have lowered the threshold for building agents.

The cryptocurrency industry is starting late in this wave, but is now integrating AI into every vertical.

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

2. How Cryptocurrency Companies Adopt AI

2.1. Research

Source: Surf AI

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

Projects like Surf address this issue by providing AI research tools specifically designed for cryptocurrencies that can integrate disparate data sources. Among all AI use cases in the cryptocurrency field, research has the lowest barrier to entry, requiring no programming or trading expertise.

2.2. Trading

Source: Bitget

Exchanges are at the forefront of AI adoption in trading.

Methods vary: some exchanges directly provide proprietary trading data to users; others allow users to issue natural language commands to AI agents, which complete the entire process from analysis to execution seamlessly.

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

This aligns with the broader community transformation. Non-developer users are increasingly constructing automated trading strategies through AI agents without needing to code. They describe strategies, and the agents handle the building and execution of algorithms.

For exchanges, this presents both an opportunity and a threat. As AI-driven users grow, loyalty to any single exchange will diminish, as agents can execute trades anywhere. The reasons exchanges adopt AI are simple: to quickly attract users and keep platforms active.

Trading involves real asset management, requiring more judgment and responsibility compared to research. However, as entry barriers lower, this field is also opening up to ordinary users.

2.3. Security / Auditing

Source: CertiK

Smart contract auditing traditionally relies on manual line-by-line code review, a process that is slow, expensive, and lacks consistency among different auditors. AI is now integrated into the workflow: AI scans the code first, and then human auditors conduct targeted deep reviews. This increases speed and coverage without replacing auditors.

CertiK is a leading example. The company has previously faced criticism for projects audited that were subsequently attacked. However, those events occurred outside the auditing scope. The audit only checks the code's situation at a specific point in time and does not include continuous monitoring.

CertiK uses AI to fill this gap. It adds post-audit monitoring in real-time and delivers it through public dashboards. Since the extended coverage is AI-driven rather than labor-intensive, both CertiK and the audited projects benefit from it.

In the security field, adopting AI is not about disrupting existing services but expanding 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 line, but rather a tool to address existing weaknesses.

2.4. Payment Infrastructure

Source: Coinbase

AI agents require payment rails to engage in economic activities: API fees, data purchases, and services from other agents. For agents, the most natural payment method is on-chain wallets paired with stablecoins.

Two models are currently emerging. The first is a universal protocol that embeds payments into HTTP requests, allowing agents to achieve automatic on-chain settlement the moment they access paid APIs. The second is agent-specific payment plugins, where agents only execute payments within limits set by humans.

Payment infrastructure is the area most closely associated with stablecoins. However, because the payment initiators are AI agents rather than humans, full operational models are not yet fully mature.

Source: Circle

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

This is still not a mature market, but the market has begun to price this trajectory. A key driver of Circle’s rising valuation is the narrative surrounding AI agent payments. The time to implement payment infrastructure will likely be longer than in the aforementioned fields, but it has become one of the most significant macro themes in the current market.

3. Why Cryptocurrency Companies are Entering the AI Field Now

When ChatGPT was released in November 2022, neither the AI nor the cryptocurrency industry was prepared. AI models were impressive but could not reliably execute tasks; meanwhile, the cryptocurrency industry was struggling due to the FTX collapse and a widespread trust crisis.

Since then, AI has made significant progress. In the past year, all mainstream models have significantly enhanced their capabilities and possess practical applications. In contrast, the cryptocurrency industry during the same period was merely "leveraging" AI: meme coins branded with AI, dysfunctional AI agents, and marketing-driven slogans. While decentralized AI infrastructure projects have emerged, their quality is noticeably inferior when honestly compared to equivalent native AI services.

The gap is now widening further. In the AI industry, infrastructures like MCP (allowing agents to directly call external tools) and OpenClaw (supporting no-code agent building) have made the era of agents within reach. Cryptocurrency companies are only starting to take action now.

What’s different this time is who the actors are. It is no longer startups slapping AI labels on themselves, but companies with mature profit models: Coinbase, Binance, and Bitget. These companies have no reason to launch AI services merely as a marketing gimmick. What drives them is not today’s revenue, but the fear of falling behind: FOMO.

Source: FORTUNE

This urgency is clearly visible in the actions of Coinbase CEO Brian Armstrong. He issued a company-wide directive, demanding all engineers to start using AI programming tools within a week, and fired those who did not comply.

But we also need to remain clear-headed. Taking trading automation as an example: agents can check prices and suggest strategies, but how many users will genuinely trust agents to take their funds for live trading? Has the x402 protocol already been implemented in the real world?

Ultimately, the AI adoption in the cryptocurrency industry is not about chasing trends. With the advent of the AI era, companies are taking action to avoid losing ground. "Having the capability" and "actual use" remain two distinct issues. But who is taking action is crucial.

Imagine the AI industry as a swimming pool being filled with water. Those who jumped in before were just pretending to swim; now those jumping in are former national team surfers. No one knows how high the water will rise, and no one knows if this swimming pool will turn into an ocean. But the cryptocurrency industry will not drown at the center of the wave.