Why Your AI Startup Doesn't Need a Moat (Yet)
Founders obsess over defensibility when they should obsess over learning velocity
The question comes up in every pitch meeting, usually within the first fifteen minutes. The founder has just finished explaining what they are building - an AI tool for contract review, or supply chain optimisation, or medical imaging - and the investor leans forward: "What's your moat?"
The founder, who has rehearsed this, launches into a prepared answer about proprietary data, or network effects, or a unique model architecture. The investor nods, makes a note. The conversation moves on. And both parties have just spent five minutes discussing the least important question available to them.
I have sat on both sides of this table. I have asked the moat question and I have answered it. And I have come to believe that for early-stage AI startups - pre-product-market-fit, pre-scale, often pre-revenue - the obsession with moats is not merely premature. It is actively harmful. It directs founders toward fortification when they have not yet determined whether they are standing on ground worth defending.
The Buffett Distortion
Warren Buffett popularised the moat metaphor in the 1990s, talking about Coca-Cola and See's Candies - businesses with decades of brand equity, distribution networks, and customer loyalty built up over generations. The metaphor was powerful, memorable, and precisely the kind of mental model that venture capitalists love to borrow. So they did, and they applied it to companies that had existed for eighteen months and had forty customers.
The category error should be obvious. Buffett was describing the characteristics of mature businesses that had already won their markets. He was not prescribing what those businesses needed in their first two years. Coca-Cola did not start by building a moat. Coca-Cola started by figuring out that people liked the drink. The moat - distribution agreements, brand recognition, shelf space dominance - accumulated over a century of operation. It was an outcome of success, not a precondition for it.
When an investor asks "what's your moat?" to a seed-stage AI startup, they are applying the diagnostic criteria of a fifty-year-old business to a company that is still searching for its first repeatable sales motion. The question is not wrong in the abstract. It is catastrophically wrong in the timing. And the damage is subtle: it redirects the founder's attention from the questions that actually matter at their stage toward a question that will not matter for years.
Learning Velocity Is the Real Advantage
The learning velocity test: How many experiments did your team run last month? How many produced a clear insight that changed what you built next? If you cannot answer both questions with specific numbers, your learning velocity is probably slower than you think.
If moats are the wrong obsession, what is the right one? Learning velocity: the speed at which the company converts experiments into durable knowledge about what works.
Every early-stage startup is running a search algorithm, whether it uses that language or not. It is searching the vast combination of possible products, customers, pricing models, and go-to-market strategies for a configuration that produces repeatable value. The companies that search faster win. Not because speed is inherently virtuous, but because the search is enormous and most configurations fail. The faster you eliminate dead ends, the sooner you find the path.
Learning velocity in AI startups has a specific character that distinguishes it from learning velocity in, say, a SaaS tool or a marketplace. An AI startup building a document analysis product needs to learn, simultaneously, which document types customers most urgently need analysed, what error rate is commercially acceptable, how much human review the workflow requires, and where the model fails in ways that destroy trust rather than merely reduce accuracy. These questions interact with each other. You cannot answer them in sequence. You answer them in parallel by shipping, watching, and iterating faster than anyone else in the market.
The startups I have watched win in AI were not the ones with the cleverest moat story for investors. They were the ones that shipped fastest, learned fastest, and adjusted fastest. They had tight feedback loops between what they built and what they discovered. Their advantage was not a wall around their position. It was a clock speed that competitors could not match.
Why AI Moats Are Weaker Than You Think
Even setting aside the timing question, the specific moats that AI founders invoke are weaker than they appear under examination.
Proprietary data is the most commonly cited moat in AI, and the most overrated. Yes, some companies sit on genuinely unique datasets that would be difficult or illegal to replicate. But most AI startups claiming a "data moat" have a modest head start that will evaporate within twelve to eighteen months of a well-funded competitor entering their market. Real data moats require either exclusive access to a source - a regulatory licence, an exclusive partnership, a physical sensor network - or such a volume lead that catching up is economically impractical. Most seed-stage companies have neither. They have a few thousand labelled examples and a hope that the head start will compound. It usually does not compound fast enough.
Model architecture is weaker still. The pace of open-source AI research means that any architectural innovation you develop today will be replicated or surpassed within months. The major labs publish their techniques. The open-source community reproduces them. If your defensibility story is "we fine-tuned the model in a clever way," you are in an arms race with every AI researcher and open-source contributor on the planet. The half-life of a model-level advantage is measured in months, not years.
Network effects are real but slow to build in AI and frequently misunderstood. A marketplace has network effects from day one: each new seller makes the platform more valuable to buyers, and vice versa. Most AI products do not work this way. Your contract review tool does not become more valuable to Firm A because Firm B also uses it - not automatically. Network effects in AI require deliberate product design to create feedback loops where usage improves the product for everyone. This is achievable. It is not something you have at the seed stage. It is something you build toward once you know what you are building.
When Fortification Begins to Matter
The moat was real. The castle was not.
I am not arguing that moats are irrelevant. I am arguing that they are a concern for a later stage of the company, and that treating them as an early-stage priority produces distorted decisions.
Moats begin to matter when three conditions converge. First, you have product-market fit - genuine, measurable evidence that a meaningful number of customers value what you have built enough to pay for it repeatedly. Second, you can identify the specific dimensions along which competition will be won or lost in your market. Is it speed? Accuracy? Integration depth? Price? Breadth of coverage? Third, you can see, concretely, how a specific investment will create a durable advantage along one of those dimensions.
Before product-market fit, moat-building is speculation dressed up as strategy. You are fortifying a position without knowing whether it is the right position. Every hour spent building defensibility is an hour not spent learning whether you are building the right thing. And the cost of premature fortification is not just wasted time. It is rigidity. Moats, by their nature, make you harder to move. They anchor you. For a company that may need to pivot - and the majority of early-stage companies do, at least partially - anchoring is precisely what you cannot afford.
I watched a well-funded AI startup spend eight months building a proprietary data pipeline that was genuinely impressive from an engineering standpoint. Exclusive data partnerships. Custom annotation tooling. A feedback loop that improved the model with every customer interaction. It was textbook moat-building. Then they discovered that their customers did not actually care about the problem they were solving - or more precisely, did not care enough to pay what the startup needed to charge to sustain the business.
What to Build Before the Walls
If you are an early-stage AI founder thinking about defensibility - and you should think about it, just not build for it yet - here is what I would focus on instead.
Build the tightest possible feedback loop between your product and your customers. Not analytics dashboards, though those are useful. Actual conversations. Understand why customers use your product, what they use it for that you did not anticipate, and where they build workarounds for its shortcomings. This understanding is not a moat. It is the raw material from which you will eventually construct one.
Ship fast enough that competitors cannot learn from your mistakes without making them first. If you are three months ahead of the nearest competitor in understanding your customer's actual workflow, that gap is worth more than any architectural advantage you could build. Knowledge compounds. The team that has run fifty customer experiments and shipped ten iterations understands the problem at a qualitatively different level of resolution than the team that has run five experiments and shipped two.
Design your product so that usage makes it better. This is the one moat-adjacent activity worth doing early. If every customer interaction generates data that improves the product for the next customer, you are planting the seed of a genuine flywheel - not because you have more data, but because you have more relevant data, structured in ways that directly improve the thing customers value.
And when the investor asks about your moat, try this: "We are the fastest team in this market at learning what works. In six months we will know things about this problem that nobody else knows, because we will have run more experiments and talked to more customers than anyone. That knowledge is where the moat comes from." Any investor worth partnering with will hear exactly what they need to in that answer. And if they do not, they are the wrong investor for your stage.