Despite the rampant surge in AI deployments, fueled by companies like OpenAI, Anthropic, and Google DeepMind, most companies still lack a coherent corporate AI strategy. What exists instead is a patchwork of experiments, pilot programs, and internal tools that rarely translate into durable competitive advantage. The uncomfortable truth is that many organizations are not strategically integrating AI, they are merely reacting to it.
This is particularly evident in crypto and blockchain, where AI is increasingly positioned as a catalyst for everything from on-chain analytics to autonomous agents and decentralized trading. I contend strongly that without a grounded understanding of their own business model and industry trajectory, companies risk misapplying AI in ways that create noise rather than value, and in fast-moving markets like crypto, that misalignment has real consequences.
The illusion of AI as a universal solution
One of the primary reasons companies struggle to define an AI strategy is the tendency to frame AI as a universal solution. The narrative suggests that AI can be applied across industries to solve nearly any problem, from customer service to trading strategies to smart contract optimization.
There is some truth here. AI is very obviously a supremely powerful general-purpose technology, but treating it as a catch-all solution obscures a more important reality, that AI is only as effective as the problem it is applied to.
In crypto, this dynamic is especially pronounced. Startups routinely position themselves as “AI-powered” without clearly articulating what specific inefficiency they are solving. Is AI improving liquidity provision? Enhancing risk management in DeFi protocols? Detecting fraud in real time? Too often, the answer is pretty vague.
The result is a growing layer of “AI theater”, activity that signals innovation but lacks strategic grounding. Companies deploy models without a clear link to revenue, cost reduction, or defensibility. In public markets, this may be tolerated as narrative. In crypto markets, where capital is more reflexive, it can quickly lead to mispriced assets and unsustainable valuations.
FOMO drives reactive behavior
The second driver is more psychological, FOMO. AI is creating enormous concentrations of capital, talent, and attention. Frontier labs and superstar firms dominate headlines, while early-stage startups attract aggressive funding. This creates a perception that a paradigm shift is already underway, and that those who hesitate will be left behind.
For companies without deep AI expertise, this is destabilizing. The default response is not to do nothing, but to do something. Internal teams are tasked with “figuring out AI.” Hackathons are launched. Tools are tested. Partnerships are explored.
On the surface, this looks like progress, but in reality, it often reflects a lack of direction. This pattern is now visible in crypto infrastructure as well. Exchanges, wallet providers, and DeFi platforms are experimenting with AI-driven features, chat interfaces, trading copilots, automated yield strategies, without a clear view of how these features align with their core business.
Some readers may argue that experimentation is inherently valuable and that strategy can emerge bottom-up, while others might proclaim that in a nascent field like AI, over-planning is itself a liability. I acknowledge there is merit to both views, but without a clear understanding of where AI fits, experimentation risks becoming an end in itself.
Strategy should follow the business, not the other way around
The biggest mistake leaders can make is trying to force their organization to fit an AI strategy, rather than shaping AI around the business itself.
A credible AI strategy starts with fundamentals. What business are you actually in? What is your core moat? Where are your bottlenecks? Only after answering these questions should companies evaluate whether AI can meaningfully address those pain points.
In crypto, this requires an additional layer of analysis: how will the industry evolve as AI-native competitors emerge? If every trading platform has access to AI-driven execution, where does differentiation come from? If on-chain data becomes fully interpretable in real time, what happens to informational edges?
These questions have direct market implications. If AI commoditizes certain capabilities, like analytics or execution, then value may shift elsewhere, potentially toward distribution, liquidity aggregation, or proprietary data. Companies that misread this shift risk investing heavily in capabilities that quickly become table stakes.
Given the pace of change, large, rigid AI strategies are unlikely to succeed. The landscape is moving too quickly. Instead, companies should take a more pragmatic approach.
First, I would recommend businesses focus on a narrow set of use cases where AI can deliver clear, measurable value. This could be as simple as improving internal operations or enhancing a specific product feature. Second, invest in building internal AI literacy. The goal is not to turn every employee into a machine learning engineer, but to ensure teams understand what AI can and cannot do.
Third, stay closely connected to broader industry developments. This might involve investing in early-stage startups, forming partnerships, or creating sandbox environments to test new ideas.
The objective is not to predict the future perfectly. It is to remain adaptable. In a market as dynamic as crypto, the winners are unlikely to be those with the most ambitious AI narratives. They will be the ones who understand their business deeply, apply AI selectively, and position themselves to evolve as the technology, and the market, matures.
About the author
Chandler Fang is the co-founder of t54. Prior to t54, Chandler was the Lead Product Manager of Payments at Ripple. Before Ripple, as VP of Product Management, he was in charge of JP Morgan’s Cash Flow Forecasting AI product. He also served as a Venture Partner at FoundersX Ventures, investing in DeepTech and FinTech for close to a decade. Chandler holds an MS in Financial Engineering from UC Berkeley Haas.
