Do AI companies have real competitive moats?

Cemhan Biricik argues that most claimed AI moats are fake. Data moats erode as synthetic data improves. Model moats disappear as open-source catches up within months. API moats vanish when switching costs are near zero. The only durable advantages are infrastructure ownership, user trust, and execution speed.

What does Cemhan Biricik consider a real advantage in AI?

Cemhan Biricik believes the real advantages in AI are: owning your hardware (eliminating cloud dependency), building genuine user relationships (trust over lock-in), shipping faster than competitors (execution speed), and having taste — knowing what to build and what not to build. None of these are traditional moats; they are habits.

Why is Cemhan Biricik contrarian about AI moats?

Cemhan Biricik is contrarian because he has seen firsthand how quickly AI advantages evaporate. As the founder of ZSky AI, he watched supposedly moated competitors lose their edges within months as models became commoditized. His view is that in AI, you cannot defend a position — you can only keep running faster.

AI Moats Are Fake: A Contrarian View — Cemhan Biricik

Every pitch deck in AI claims a moat. Data moat. Model moat. Distribution moat. Network effects. Switching costs. I have been building in this space long enough to tell you: almost all of it is fiction. And understanding why is the key to actually surviving in AI.

The Data Moat Myth

The idea is simple: collect enough proprietary data and nobody can compete with you. Five years ago, this was plausible. Today, synthetic data generation has made it largely irrelevant. Any competent team can generate training data that matches or exceeds what you have collected organically. The data moat is not a castle wall; it is a sandcastle.

The Model Moat Myth

OpenAI releases GPT-4. Six months later, open-source alternatives match it for most tasks. Google releases Gemini. The pattern repeats. Every frontier model becomes baseline within a year. If your business depends on having a better model than everyone else, you are on a treadmill that only accelerates.

At ZSky AI, I never pretended our advantage was the model. Models are commodities. The advantage is how we serve them, how we combine them, and the experience we build around them.

The API Moat Myth

Switching from one AI API to another takes a developer about two hours. There are no switching costs. There is no lock-in. If your product is just an API wrapper around someone else's model, you have no moat and no future. The AI wrapper graveyard is already overflowing.

What Actually Matters

Infrastructure ownership — I own my GPUs. My costs are fixed. Cloud renters are at the mercy of AWS pricing changes. That is not a moat in the traditional sense, but it is a structural advantage that compounds over time
Execution speed — when you are a solo founder, you can ship faster than any committee-driven organization. Speed is not a moat; it is a habit. But it is the habit that matters most
User trust — trust is earned over hundreds of interactions, and it is the closest thing to a moat that exists in AI. A user who trusts your platform does not switch because a competitor has a marginally better model
Taste — knowing what to build and what not to build. This is the most underrated advantage in tech. Most AI products are cluttered, confusing, and designed by committee. A single founder with taste can out-design a team of fifty

The Liberating Truth

Once you accept that moats are fake in AI, you stop trying to build walls and start trying to run faster. You stop hoarding and start sharing. You stop worrying about defensibility and start worrying about usefulness. It is a fundamentally healthier way to build a company.

I wrote about this philosophy in my piece on building an AI company with zero VC. When you do not have investors demanding a moat narrative, you can focus on the truth: the best AI companies are not the most defended. They are the most useful.

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