How I Am Thinking About Moats in 2026

Moats are not dead.

They just moved.

That is how I am thinking about it right now. In 2026, the moat is not just, “I built software.” AI has made building cheaper, faster, and easier to copy. A lot of the first version of a product can now be generated, cloned, rebuilt, or at least approximated faster than it could a few years ago.

If your product is just a UI wrapped around an API, that is probably not much of a moat.

If it is just a thin workflow on top of a model, that is probably not much of a moat either.

But this does not mean defensibility disappeared. It means defensibility is moving into deeper layers. Data. Cost. Workflow. Integrations. Distribution. Hardware. Operating secrets. The things underneath the visible product.

That is where I think the real question is.

Where does the moat actually live now?

What Is a Moat Now?

A moat is the thing that makes it hard for someone else to copy you, catch you, or make your product irrelevant.

That is the simple definition.

For a long time, just building the software was a meaningful barrier. If you could build the thing, ship the thing, and make it work, you already had some advantage. Not always a huge one, but something real.

Now that advantage is weaker.

AI did not remove the value of software. Software still matters. Good products still matter. Taste still matters. But the first version of software is less scarce than it used to be. More people can build. More people can copy. More people can get to a prototype.

So the moat has to sit deeper than the prototype.

The visible product is only the surface. The question is what sits underneath it that other people cannot easily reproduce.

The Software Itself Is Usually Not Enough

The issue with software in 2026 is that a lot of it can be copied at the surface level.

Somebody can look at your landing page. They can look at your onboarding flow. They can look at your feature set. They can use AI to generate a similar interface. They can connect to the same model. They can use the same database patterns. They can ship something that looks close enough.

That does not mean it will be good.

But it does mean the visible product is less defensible by itself.

If the product is a standard interface, plus a standard AI model, plus a standard database, then the question becomes simple: what is underneath the interface that they cannot copy quickly?

That is where the moat starts.

1. Data Moats

Data is still one of the clearest moats.

Not all data. Generic data is not enough. A random dataset sitting in a folder somewhere is not automatically valuable.

The valuable data is specific, fresh, hard to recreate, and tied to real behavior.

Think about a software company that identifies a valuable data source early. It builds software around collecting that data. Every customer interaction makes the data better. Every workflow adds more examples. Every day the system is running, the company learns something new.

After a few years, that company may be sitting on a gold mine.

You can copy the UI.

You can copy the workflow.

You can build a competitor.

But you cannot instantly buy five years of lived customer data.

That is the difference. The moat is not just that the company has data. The moat is that the data came from real usage, specific customers, labeled behavior, and feedback loops that are hard to recreate from the outside.

In AI products, this gets even more important. The product that sees more edge cases, captures more corrections, and learns from more workflows can improve in a way that a copycat cannot immediately match.

The strongest data moats are usually not broad. They are narrow and deep.

They are tied to a specific problem.

2. Model And Cost Moats

The model itself can also be a moat, but maybe not in the way people normally think.

Sometimes the moat is having the best model.

But sometimes the moat is having the best output at the best cost.

That distinction matters.

If one company has a frontier model that is slightly better, that is valuable. But if another company has a Chinese model, an open-source model, or a smaller specialized model that is close enough and 10x or 20x cheaper, that can be a real advantage too.

The best product is not always the product with the smartest model. Sometimes it is the product that can produce the right output reliably, quickly, and cheaply enough that the business actually works.

As the gap between models becomes less dramatic for many normal tasks, cost starts to matter more. Deployment starts to matter more. Routing starts to matter more. Knowing which model is good enough for which job starts to matter more.

This is where the moat can move into fine-tuning, inference optimization, model routing, context design, and the ability to use smaller models in smarter ways.

The question becomes less, “Who has the smartest model?”

The question becomes, “Who can produce the needed output at the lowest cost without the user feeling the difference?”

That is a different kind of moat.

3. Agent Harness Moats

I think the harness around the model is one of the most interesting moats right now.

By harness, I mean everything around the model that helps it produce useful work. The editor. The context system. The retrieval. The tools. The permissions. The review loop. The way the product understands the user’s environment.

The model matters, but the environment around the model shapes what the model can actually do.

Cursor is a good example of this.

Cursor’s advantage is not only that it calls an LLM. A lot of products can call an LLM. The advantage is the development environment around the model. The way it understands files. The way it edits code. The way it keeps context. The way it fits into the actual workflow of building software.

That harness is hard to copy because it is not one thing.

It is a thousand product decisions stacked together.

Competitors can reverse engineer parts of it. They can copy visible workflows. They can add similar buttons. But if Cursor keeps moving, the competitor is often copying yesterday’s version while Cursor is already thinking about the next thing.

That is what makes agent harnesses interesting. The moat is not just the model. The moat is the full system that turns the model into useful work.

4. Integration Moats

Integrations can also become a moat.

Some products look simple from the outside because the interface is clean. But underneath the interface, they are connected to a lot of messy systems.

Customer workflows are messy.

APIs are messy.

Permissions are messy.

Data models are messy.

Edge cases are messy.

If your product is deeply integrated into a business process, copying the UI does not copy the product. The competitor still has to understand the customer’s systems, connect to the same tools, handle the same weird cases, and make the workflow reliable.

This is especially true in B2B software.

A shallow integration is not a moat. Connecting to one API and pulling basic data is not enough. But a deep integration into a painful customer process can be a moat because it becomes expensive to replace and difficult to reproduce.

Sometimes the defensibility is not in the feature.

It is in the coordination.

5. Operating Secret Moats

Some companies are hard to copy because of how they operate.

Not just what they sell.

How they operate.

This is the “secret sauce” kind of moat. I have worked around companies where the public product did not fully explain the advantage. The advantage was in the way the team did the work. The process. The internal tools. The service model. The way customers were onboarded. The way decisions were made.

People talk about the Coca-Cola recipe because it is a simple symbol of a secret process. The recipe is not the whole company, but it represents the idea that something valuable can live beneath the surface.

In software and services, the recipe might be a repeatable process nobody outside the company fully understands.

It might be a specific way of supporting customers.

It might be internal tooling.

It might be a cultural habit around speed, taste, quality, or attention to detail.

The important thing is that operating secrets are fragile if they only live in one person’s head. They get stronger when they are embedded into systems, training, tools, and culture.

A secret process that only one person knows is a risk.

A secret process that the company can repeat is a moat.

6. Distribution Moats

Distribution might be even more important in the age of AI.

Because if AI makes it easier to make products, then there will be more products. More tools. More wrappers. More agents. More dashboards. More things asking for attention.

That means trust becomes more valuable.

Even if someone can copy your product, they may not be able to copy your audience. They may not be able to copy your brand. They may not be able to copy your community, sales channel, partnerships, SEO, or relationship with customers.

A founder with an audience has leverage.

A company with trusted enterprise relationships has leverage.

A product with a strong community has leverage.

This is why distribution is often more defensible than code. Code can be copied faster than trust can be built.

Making the product is easier now.

Getting people to care is still hard.

7. Hardware Moats

Hardware is another place where moats can still be very real.

Software can move fast because it lives in a world of code. You can clone a repo, deploy a product, push an update, and change the experience quickly.

Hardware has to pass through the physical world.

Somebody has to make the thing.

Somebody has to manufacture it.

Somebody has to ship it.

Somebody has to deal with supply chains, quality control, inventory, distribution, repairs, certifications, and all the physical constraints that software people do not always think about.

AI can help with design. It can help with prototyping. It can help with simulation. But it does not remove the difficulty of getting a physical product into the world.

That friction can be a moat.

If you build something new and useful in hardware, a competitor cannot always just copy the idea and launch the next week. They have to build the supply chain. They have to make the object. They have to distribute it. They have to make it work in reality.

In software, copying can happen fast.

In hardware, copying still has to go through the physical world.

The Time Moat

There is another moat that is harder to name.

The time moat.

Sometimes the moat is not that nobody can copy you. Sometimes the moat is that by the time they copy you, you have already moved again.

This matters a lot in AI products because the space changes so quickly. The goal is not always perfect protection. Perfect protection may not exist. The goal is enough lead time to compound.

Compound what?

Product judgment.

Data.

User feedback.

Distribution.

Workflow knowledge.

Taste.

Speed alone is not a moat. Moving fast in the wrong direction does not help. But speed plus taste plus feedback loops can become a moat because the product keeps learning faster than the copycats.

That is the Cursor example again.

If competitors are always copying what you shipped months ago, and you are already on the next version of the workflow, then the gap can stay alive.

Not because you are impossible to copy.

Because you keep compounding faster.

Weak Moats In 2026

There are also weak moats.

A basic wrapper around a model is a weak moat.

A UI someone can clone quickly is a weak moat.

A prompt with no data, workflow, or distribution behind it is a weak moat.

A feature that probably belongs inside a larger platform is a weak moat.

Being first for a few weeks is a weak moat.

This does not mean those products cannot make money. They can. Sometimes speed to market is enough for a while. Sometimes a simple product solves a real problem and people pay for it.

But I would not confuse temporary advantage with a real moat.

The question is not, “Can someone copy this?”

The question is, “What happens after they copy it?”

If the answer is that nothing meaningful changes, then the moat probably was not very strong.

What Builders Should Ask

If I were building something in 2026, these are the questions I would ask:

1. What do we know or collect that competitors cannot easily get?
2. Can we produce the same or better output at a lower cost?
3. Is our harness or workflow meaningfully better than calling the model directly?
4. Are we deeply integrated into a painful customer process?
5. Do we have an operating secret that compounds over time?
6. Do we own a distribution channel or trusted relationship?
7. Are we building in the physical world where copying is naturally harder?
8. If competitors copy today’s product, what will we know or have by then that they will not?

Those questions feel more useful to me than asking whether AI killed all moats.

AI did not kill moats.

It exposed weak ones.

Conclusion

Moats in 2026 are less about hiding the idea and more about compounding the layers around the idea.

The idea can be copied.

The surface can be copied.

The first version can be copied.

But the deeper layers are harder.

The data you collect. The cost structure you build. The harness around the model. The integrations. The operating secrets. The distribution. The physical constraints. The speed at which you keep learning.

That is where I think defensibility lives now.

The product people can see is only the surface.

The moat is what keeps improving underneath it.