This report, written by Tiger Research, examines the widespread "fear of missing out" (FOMO) among cryptocurrency companies. From exchanges to security firms, they are all racing to launch AI-driven services. We will explore why they chose to act now. Key takeaways: Cryptocurrency companies across exchanges, security, payments, and research are launching AI services in tandem. Unlike previous cycles, proven profitable companies like Coinbase and Binance are leading the way. AI has moved from theory to a practical necessity. Adoption motivations differ across industries: exchanges aim to prevent churn; security companies aim to fill audit blind spots; and payment infrastructure targets the emerging agency economy. Having a feature and actually using it are two different things. FOMO and competitive pressure in the AI field are accelerating its adoption far beyond actual demand. Real demand and competitive anxiety are both at play. Distinguishing between value-creating adoption and mere labeling adoption is crucial. 1. Cryptocurrency companies are offering AI services. Artificial intelligence (AI) is the most watched area in the global market today. General-purpose tools like ChatGPT and Claude have become integrated into daily life, while platforms like OpenClaw have lowered the barrier to entry for building intelligent agents. While the cryptocurrency industry missed this wave, it is now integrating artificial intelligence across various verticals. What AI services do these companies offer? Why are they entering this market? 2. How Cryptocurrency Companies Adopt AI Technology 2.1 Research Cryptocurrency research suffers from structural problems: on-chain data, social sentiment, and key metrics are scattered across various platforms, making verification difficult. General AI often returns inaccurate answers to cryptocurrency queries. Projects like Surf address this by providing dedicated AI research tools for cryptocurrencies, which integrate fragmented data sources. Of all cryptocurrency AI applications, research has the lowest barrier to entry for ordinary users, requiring no programming or trading expertise. 2.2 Trading Exchanges are leading the application of AI in the trading field. Approaches vary. Some methods directly expose proprietary trading data to users; others allow users to issue natural language commands to AI agents, which then handle the entire process from analysis to execution in one step. Exchanges have been providing APIs for years. The difference today is the addition of a new layer: interfaces like MCP and AI Skills enable non-developers to access exchange functionality through AI agents.Tools once confined to developers are now accessible via natural language. This aligns with a broader trend of 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. This presents both an opportunity and a challenge for exchanges. As the number of AI users grows, user loyalty to a single exchange may decrease, as traders can execute trades anywhere. The reason exchanges are adopting AI is simple: to quickly attract users and maintain their activity on the platform. Trading involves real asset management, requiring a higher level of judgment and responsibility than research. However, with the lowering of barriers to entry, this field is also opening up to ordinary users. 2.3 Security/Auditing Traditional smart contract auditing relies on manual line-by-line code review, a slow, costly method with inconsistent review standards among different auditors. Now, AI has been integrated into the workflow: AI first scans the code, which is then subjected to targeted in-depth review by human auditors. This improves speed and coverage without replacing auditors. CertiK is a prime example. The company has previously faced criticism for its audit projects being maliciously exploited. However, these incidents occurred outside the scope of audits. Audits examine code at specific points in time and do not include continuous monitoring. CertiK uses artificial intelligence to fill this gap. It adds real-time post-audit monitoring capabilities and publishes monitoring results through a public dashboard. Because the expanded monitoring scope is driven by AI rather than human intervention, CertiK and the projects it audits benefit. In the security field, the application of AI is not about disrupting existing services, but rather expanding the scope of human work: improving the accuracy of audits and filling blind spots after audits. 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 need payment channels to participate in economic activities: such as paying API fees, purchasing data, and purchasing services from other agents. For agents, the most natural payment method is an on-chain wallet with stablecoins. Two models are emerging. The first is a general protocol that embeds payments into HTTP requests, allowing agents to automatically perform on-chain settlements when accessing paid APIs. The second is payment plugins for specific agents, where agents can only execute payments within manually preset permissions and limits.Payment infrastructure is the area most closely linked to stablecoins. However, because the payment agents are AI agents rather than humans, no fully functional models have yet emerged. USDC issuer Circle is also attracting significant attention. The company released a proposal to connect its Gateway payment infrastructure with the x402 protocol and invited developers and researchers to review and contribute. While not a mature market, it's already beginning to digest this trend. One of the key drivers of Circle's stock price increase is its AI agent payment model. The implementation of payment infrastructure will be slower than in the other areas mentioned above, but it has become one of the most prominent macro themes in the current market. 3. Why Cryptocurrency Companies Are Now Entering the AI Field When ChatGPT launched in November 2022, both AI and cryptocurrencies were still immature. While AI models were impressive, they couldn't reliably perform tasks. The cryptocurrency industry was suffering from the FTX crash and a widespread crisis of trust. Since then, AI has developed rapidly. The functionality and usability of all major models have improved significantly in the past year. In contrast, cryptocurrencies during the same period merely "utilized" artificial intelligence: a plethora of "meme coins" masquerading as AI, poorly functional AI agents, and marketing-driven hype. Decentralized AI infrastructure projects continue to emerge, but their quality is clearly inferior when objectively compared to native AI services of comparable quality. Now, the gap is widening further. In the AI industry, infrastructures like MCP (which enables agents to directly call external tools) and OpenClaw (which supports no-code agent construction) have already made the era of intelligent agents a reality. Cryptocurrency companies are only just beginning to take action. The difference this time lies in who is acting. It's no longer emerging startups touting AI, but rather established companies with mature profit models: Coinbase, Binance, and Bitget. These companies aren't launching AI services for marketing purposes; they're not driven by immediate profits, but by a fear of falling behind: FOMO (fear of missing out). Coinbase CEO Brian Armstrong's actions perfectly exemplify this sense of urgency. He issued an order to all engineers, requiring them to deploy AI coding tools within a week and firing employees who didn't comply. But keeping a clear head is also crucial.Take trading automation as an example. Agents can view prices and propose strategies, but how many users will truly trust them and entrust their funds to them for real-time trading? Furthermore, has the x402 protocol actually been implemented in the real world? Ultimately, the adoption of artificial intelligence in the cryptocurrency space is not about chasing trends. With the advent of the AI era, companies are actively working to avoid losing market position. Having a feature and actually utilizing it are two different 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 surfing teams. Nobody knows how high the water level will rise, or whether the pool will become an ocean. But cryptocurrencies won't be submerged in the flood. [Tiger Research]
AI-Driven FOMO: The Strategic Shift in Crypto’s Adoption of Artificial Intelligence
The crypto market is currently experiencing a profound structural shift as established players accelerate their integration of artificial intelligence. Tiger Research’s analysis reveals a critical evolution beyond the typical hype cycles: proven profitable companies like Coinbase and Binance are now leading the AI charge, transforming what was once theoretical into practical necessity. This represents a maturation of the industry that will likely reshape competitive dynamics and create both significant opportunities and substantial risks for investors.
Market Transformation: From Hype to Practical Implementation
Unlike previous cycles where AI in crypto consisted largely of marketing buzz, the current wave is characterized by tangible implementations across key verticals. The distinction between “having a feature” and “actually using it” has never been more crucial. Exchanges like Coinbase are deploying AI coding tools with mandatory deadlines, indicating a strategic imperative rather than a marketing play. This shift from speculative startups to established players with proven business models fundamentally alters the risk-reward calculus for investors.
Impact on Token Valuations: We’re likely to see a bifurcation in market performance. Projects that successfully implement AI-driven features with clear user adoption will likely outperform those engaging in superficial “AI washing.” Exchange tokens (COIN, BNBB) may benefit from increased user engagement and trading volume, while security-focused tokens could see valuation increases as AI-enhanced audit capabilities reduce systemic risks. Payment infrastructure projects facilitating AI agent transactions may experience particularly strong growth if the “agency economy” materializes as predicted.
Sector-Specific Analysis
Research: Democratization of Market Intelligence
AI-powered research tools like Surf are addressing a critical pain point in crypto analysis: fragmented data sources. The low barrier to entry for these tools represents a significant market opportunity. However, the research sector faces intense competition from both crypto-native projects and established AI companies. Investors should prioritize platforms with unique data access or superior algorithms that create genuine information advantages.
Trading: The Double-Edged Sword of Democratization
Exchanges are leveraging AI to transform trading from a developer-centric activity to one accessible through natural language interfaces. This democratization presents a paradox: while it expands the user base, it may also reduce platform lock-in as traders can execute strategies across multiple exchanges. The real value lies in exchanges that can create network effects around their AI trading tools, potentially developing proprietary algorithms that provide genuine alpha generation capabilities.
Investment Consideration: Exchange tokens with robust AI trading ecosystems may outperform, but the sustainability of this advantage depends on continuous innovation. We’re likely to see significant consolidation as exchanges acquire or develop superior AI capabilities.
Security: AI as Force Multiplier
The integration of AI in security auditing, exemplified by CertiK’s post-audit monitoring, represents perhaps the most immediately valuable application. AI enhances rather than replaces human auditors, addressing a critical pain point in smart contract security. This sector offers compelling risk-adjusted returns as security concerns remain a primary barrier to institutional adoption.
Payment Infrastructure: The Wildcard
AI agent payment infrastructure remains the most speculative but potentially transformative segment. The connection between AI agents and stablecoins could create unprecedented demand for digital currencies if the “agency economy” emerges as predicted. Circle’s involvement in this space adds institutional credibility, but the timeline for meaningful adoption remains uncertain.
Risks and Overlooked Challenges
The current AI frenzy is not without significant risks. The report correctly identifies FOMO as a driving force, potentially leading to overinvestment in capabilities with unclear ROI. Several critical risks demand investor attention:
- Implementation Gap: Many announced AI features may fail to deliver promised value, leading to disappointment and potential sell-offs.
- Security Vulnerabilities: AI systems introduce new attack vectors. Manipulated AI trading algorithms or compromised AI security assessments could create systemic risks.
- Regulatory Uncertainty: The intersection of AI and trading creates complex regulatory challenges that could impact specific segments of the market.
- Market Saturation: As established players enter the space, startups may struggle to differentiate, leading to valuation pressure.
Strategic Investment Opportunities
For sophisticated investors, the current AI integration wave presents several compelling opportunities:
- Vertical Specialists: Companies focusing on specific AI applications (security, research, trading) with clear technical advantages and established user bases offer attractive risk-reward profiles.
- Data Advantage Platforms: Projects with unique data access or superior analytical capabilities may develop sustainable competitive moats.
- Payment Infrastructure Pioneers: While higher risk, projects successfully facilitating AI agent payments could capture significant value as the agency economy develops.
- Established Player Ecosystems: Exchange tokens and infrastructure tokens from companies with proven execution capabilities and substantial resources may benefit from the broader trend.
Conclusion: Strategic Imperative or Temporary Fad?
The integration of AI into crypto infrastructure represents a fundamental evolution rather than a passing trend. The involvement of established players with proven business models adds credibility and increases the likelihood of meaningful adoption. However, investors must distinguish between value-creating implementations and mere marketing efforts.
The most significant opportunity lies in identifying projects where AI integration directly addresses core market inefficiencies while creating sustainable competitive advantages. As the market matures, we expect to see a shakeout where genuinely valuable AI capabilities drive returns, while superficial implementations fall by the wayside. For investors, the key is maintaining a clear head amid the FOMO-driven frenzy and focusing on projects with tangible execution capabilities and demonstrated user adoption.