Moats in the Age of AI: What Actually Protects a Business Now

I have spent more than twenty years building marketing and growth systems, first inside large institutions like American Express, MetLife, and UBS, and now as the founder of a company whose entire premise is that discovery is changing. So I want to be careful here, because "AI changes everything" is the laziest sentence in business writing right now. Most things do not change. Customers still want to trust who they buy from. Great products still win more often than mediocre ones. Distribution still beats genius.

What is changing is narrower and more specific: the cost of certain kinds of advantage is collapsing. And a moat is nothing more than an advantage that is expensive for a competitor to copy. When copying gets cheap, the moat drains. When copying stays expensive, or gets more expensive, the moat deepens.

That is the whole framework. The interesting work is applying it honestly to your own business, especially to the advantages you are proud of, because pride is exactly where founders miss the water going out.

The uncomfortable starting point

For fifteen years, the default growth playbook had a predictable spine. You produced content, you ranked for it, someone searched, they clicked, they landed on your site, and you converted them. Every part of that chain assumed a human typing a query and choosing from a list of blue links.

That assumption is weakening. In our own State of AI Search 2026 research at AIrecommend.ai, we found that 37% of people now begin a product or service search with an AI tool rather than a traditional search engine, and that between 58% and 68% of searches end without a click to any website at all. I am giving you those as directional operator numbers, not gospel, but the direction is unmistakable to anyone watching their own analytics. The answer increasingly happens before the click. Sometimes the click never comes.

If a meaningful share of your business depended on being the intermediary between a question and an answer, the machine is now sitting in your chair. That is not a reason to panic. It is a reason to audit which of your moats were quietly built on that chair still being there.

Moats that are weakening

Let me name the ones I would treat with real suspicion.

Information asymmetry. For a long time, a great deal of professional value came from simply knowing things other people did not, and charging for the gap. Basic legal structures, standard marketing tactics, how-to knowledge in almost any field. A capable AI model will now explain most of that competently and for free. If your business is fundamentally "I know the thing and you don't," the gap you were selling is narrowing every quarter. The knowledge itself is no longer the moat. What you do with it, in a specific situation, under real accountability, still can be.

SEO rankings as a standalone asset. Ranking number one for a commercial keyword used to be close to owning a toll road. When more than half of searches resolve without a click, the toll road has fewer cars on it, and the cars that remain are often the low-intent ones the AI could not fully satisfy. Ranking still matters, but it is shifting from a destination to an ingredient. The question is no longer only "do I rank," it is "does the model that answers on my behalf know me, trust me, and cite me." That is a different discipline.

Generic content at scale. The content-marketing arms race was a war of volume. Publish more, cover more topics, capture more long-tail queries. AI has made undifferentiated content effectively free to produce, which means it is now effectively worthless as a differentiator. When everyone can generate a thousand competent articles, a competent article defends nothing. The floor rose and swallowed the moat. What survives is content that carries something a model cannot synthesize from the open web: proprietary data, genuine field experience, a named point of view someone will defend in public.

Hard-to-find expertise, sold as access. There is a category of business built on being the rare person who can be found for a particular skill. Some of that scarcity was real, and some of it was just search friction. As AI collapses the friction of finding a good-enough answer, the "hard to find" part erodes faster than the "genuinely excellent" part. Excellence endures. Mere findability does not.

Notice the through-line. In every weakening moat, the underlying advantage was some form of scarcity that AI manufactures away: scarce information, scarce visibility, scarce production capacity, scarce access. If your defense depends on something being scarce, ask whether AI is about to make it abundant.

Moats that are strengthening

Now the more hopeful half, and I think the more important half, because the same force that drains the shallow moats deepens the sound ones.

Brand and trust. When the volume of available content approaches infinity and the cost of producing plausible-sounding material drops to zero, the scarce resource becomes trust. Whom do I believe. In a world of infinite competent answers, people and machines both fall back on reputation as a filter. This is the great inversion of the AI era: abundance of information makes trust more valuable, not less. A brand people actively seek out, ask for by name, and recommend to others is harder to copy today than at any point in my career.

Proprietary data. A general model knows what the internet knows. It does not know what only you know, the data you generate through your own operations, customers, transactions, and outcomes. That asset is not on the open web, cannot be synthesized, and compounds the longer you run. This is, in my view, the single most durable technical moat available to most companies. The catch is that you have to actually be collecting and structuring it, on purpose, rather than letting it evaporate.

Community and belonging. A model can answer a question. It cannot make you feel like you belong to something. Communities, real ones with identity, status, and mutual obligation, are expensive to build and nearly impossible to clone, because their value lives in the relationships between members, not in any content you could scrape. As information commoditizes, membership appreciates.

Real relationships and reputation. The trust between a specific buyer and a specific seller, built over years of showing up and delivering, is not something a competitor can prompt their way into. Referrals, repeat business, and word of mouth run on a currency AI does not print. If anything, as machine-generated outreach floods every channel, a genuine human relationship becomes more distinctive, not less.

Distribution. This one predates AI and outlasts it. Owning the audience, the list, the shelf, the relationship with the customer at the moment of decision, has always beaten owning the product. In an era where discovery is being intermediated by machines you do not control, owning a direct line to your audience, one that does not depend on any algorithm's goodwill, is worth more than ever. Every founder should be asking what they own outright versus what they merely rent from a platform.

Regulatory, physical, and structural moats. Licenses, physical infrastructure, exclusive supply, hard-won compliance, network effects with real switching costs. AI does not dissolve these. A model can tell you how to build a hospital, a bank, or a logistics network. It cannot hand you one. The moats made of atoms, obligations, and accumulated permission are, if anything, relatively more valuable now that the moats made of pure information are draining around them.

The reallocation

Here is the part that matters for what you do Monday morning. This is not an argument to abandon everything and chase the shiny new discovery channel. It is an argument to move resources deliberately from draining moats to deepening ones.

Concretely, that means a few shifts.

Stop over-investing in undifferentiated content and start investing in a small amount of content that no one else could write, grounded in your own data and a real point of view you will put your name on. One genuinely proprietary insight outperforms a hundred summaries of the obvious.

Treat your proprietary data as a strategic asset with an owner, a roadmap, and a budget, not as exhaust. If you are not deliberately capturing the data your operations generate, you are letting your most defensible advantage wash away.

Redirect energy from ranking for the algorithm toward being known by name. The goal is becoming the brand people and machines cite, not merely the page they might have clicked. When the answer is generated rather than listed, being the trusted source the model reaches for is the new front page.

Invest in the direct relationship. Build the audience you own, the community you host, the reputation you earn in public. These are slow, unglamorous, and nearly impossible to fake, which is exactly why they last.

And be honest about your physical and structural advantages. If you have them, lean in, because they are appreciating relative to everything digital and copyable around them. If your entire moat was informational, that is not a death sentence, but it is a wake-up call to build something more durable underneath it.

The one question worth asking

I will leave you with the test I now apply to every advantage we have, and every one I help a founder evaluate.

Could a competent competitor, armed with today's best AI tools and a few months, reproduce this?

If the answer is yes, it was never much of a moat, and it is getting shallower. If the answer is no, and specifically if the reason is trust, proprietary data, real relationships, community, distribution, or something physical and hard-won, then that is where your future defensibility lives. Pour resources there.

The companies that struggle in this transition will be the ones defending the moats that are already draining, mistaking the walls of information asymmetry for walls of stone. The ones that thrive will notice the water going out early, accept it without drama, and dig deeper where the ground still holds. The technology is genuinely new. The discipline of asking what is actually hard to copy is as old as competition itself.

About the author

Scott Tischler is the Founder & Chairman of AIrecommend.ai and a practitioner-authority on AI search and Answer Engine Optimization. With 20+ years in marketing technology — including American Express, MetLife, and UBS — and executive study at Wharton, Harvard, Yale, and Oxford, he helps businesses become the ones AI recommends.