What Is an AI Moat, and What Does a Wide Moat Look Like in 2026?
What protects an AI product from rivals and from general models: the five moat sources, the two threats, and the markets whose products rate a wide moat today.
16 October 2026 · 9 min read · Data updated 18 October 2026
What is a moat for an AI product?
Based on FalcoScan data updated 18 October 2026, a moat is what stops a rival, or a general AI model, from taking a product’s customers. FalcoScan measures five sources of moat: switching costs, habit and retention, how hard the product is to copy, regulatory barriers, and how wired it is into other tools. It also tracks two threats: being a thin wrapper over a public model, and depending on another company’s platform. Across 7,583 live AI products, 21% rate a wide moat, 52% a narrow one and 27% none. A wide moat in 2026 looks like Security, Healthcare, Data and Finance: markets whose products are costly to leave and hard to rebuild on a public model.
What are the five sources of an AI moat?
The moat frameworks investors use (Morningstar's moat sources, Hamilton Helmer's seven powers) name more sources than a catalog can measure. FalcoScan rates the five it can see in its own data, and keeps two threats apart from the moat itself. Network effects, proprietary data, scale and brand are not measured, and the pages say so rather than guess.
| Source | In plain words | What it measures |
|---|---|---|
| Switching costs | Costly to leave | How much time, data and retraining a customer would lose by moving to a rival. |
| Habit and retention | Used every day | How deeply the product sits in a daily routine or workflow. |
| Hard to copy | Not a thin layer | Differentiation from rivals, and how few products are thin layers over a public model. |
| Regulatory barrier | Rules keep rivals out | Compliance work (health, finance, legal, privacy rules) a new entrant must clear first. |
| Ecosystem | Wired into other tools | Share of products with a public API and with ten or more integrations. |
The five moat sources, rated for every AI market against the other markets.
| Threat | What it measures |
|---|---|
| Wrapper exposure | Share of products whose core could be rebuilt on a public model in days. |
| Platform dependency | How much products rely on another company’s platform (a social network, a store, a search engine) to work. |
The two threats. Higher is worse for the moat.
What does a wide moat look like in 2026?
Four of the 29 AI markets FalcoScan rates average a wide moat, 75 or more out of 100: Security (79), Healthcare (78), Data (76) and Finance (76). In three of them, the main moat is regulatory barrier. Security, Healthcare and Finance are also protected by compliance rules a new entrant must clear before it can sell. Wrapper exposure is low in Security, Healthcare, Data and Finance: few of their products could be rebuilt on a public model in days.
| Market | Average moat | Rated products | Wide share | Main moat | Wrapper exposure |
|---|---|---|---|---|---|
| Security | 79 | 357 | 83% | Regulatory barrier | 0% |
| Healthcare | 78 | 246 | 91% | Regulatory barrier | 0% |
| Data | 76 | 393 | 81% | Switching costs | 2% |
| Finance | 76 | 387 | 76% | Regulatory barrier | 1% |
Average moat is the market's mean product moat score, 0 to 100. Wide share is the share of its rated products at 75 or more. Wrapper exposure is the share of products that could be rebuilt on a public model quickly. Source: FalcoScan catalog, data updated 18 October 2026.
Market averages hide the product. Here is the widest moat in each market where that product itself rates wide, as a name and a band: exact product scores are in FalcoScan plans, the API and the MCP server, never on a public page.
| Product | Market | Band | Strong on |
|---|---|---|---|
| Apiiro | Security | Wide | switching costs, habit and retention, hard to copy, regulatory barrier and ecosystem |
| Butterfly IQ AI Ultrasound | Healthcare | Wide | switching costs, habit and retention, hard to copy and regulatory barrier |
| Cube Metrics Layer | Data | Wide | switching costs, habit and retention, hard to copy and ecosystem |
| Ramp Finance AI | Finance | Wide | switching costs, habit and retention, hard to copy, regulatory barrier and ecosystem |
| Harvey Legal AI | Legal | Wide | switching costs, habit and retention, hard to copy, regulatory barrier and ecosystem |
| JAX | AI Models | Wide | switching costs, habit and retention and hard to copy |
| Rippling AI Workforce | Human Resources | Wide | switching costs, habit and retention and ecosystem |
| UBTECH Walker S | Automation | Wide | switching costs, habit and retention, hard to copy and regulatory barrier |
| Northbeam Attribution | Ecommerce | Wide | switching costs, habit and retention, hard to copy and ecosystem |
| Gong | Sales | Wide | switching costs, habit and retention and hard to copy |
| Sierra AI Conversations | Support | Wide | switching costs, habit and retention, hard to copy and ecosystem |
| Archistar Property AI | Real Estate | Wide | habit and retention, hard to copy and ecosystem |
| Sierra | Agents | Wide | switching costs, habit and retention and hard to copy |
| Suno Studio | Voice | Wide | habit and retention and hard to copy |
| 6sense | Marketing | Wide | habit and retention, hard to copy and ecosystem |
| DJI | 3D & AR/VR | Wide | switching costs, habit and retention, hard to copy and regulatory barrier |
| Stripe | Coding | Wide | habit and retention, hard to copy and ecosystem |
| Nvidia DLSS 4 AI | Gaming | Wide | habit and retention, hard to copy and ecosystem |
| DeepL | Productivity | Wide | habit and retention and hard to copy |
| Webflow Designer | Design | Wide | habit and retention, hard to copy and ecosystem |
| Lila Sciences | Research | Wide | switching costs, habit and retention, hard to copy and regulatory barrier |
| Readwise | Learning | Wide | habit and retention and hard to copy |
| Descript Full | Video | Wide | habit and retention and hard to copy |
| Adobe Firefly 4 Pro | Image | Wide | habit and retention, hard to copy and ecosystem |
"Strong on" lists the moat sources where the product clears the strong line for that source. Source: FalcoScan catalog, data updated 18 October 2026.
How are AI products spread across the three bands?
21% of the 7,583 rated products have a wide moat, 52% a narrow one and 27% none. The bars count products in each five-point band of the moat score; the wide band starts at 75 and the narrow band at 55. 858 of the rated products carry scores estimated from their market's peers until the FalcoScan model scores them.
Count of products per band of the 0 to 100 moat score. Unit: products. Source: FalcoScan catalog, data updated 18 October 2026.
Which AI markets have the widest and narrowest moats?
Every AI market FalcoScan rates, widest average moat first. Each market links to its own moat page.
| # | Market | Average moat | Band | Wide | Narrow | None | Main moat |
|---|---|---|---|---|---|---|---|
| 1 | Security | 79 | Wide | 297 | 57 | 3 | Regulatory barrier |
| 2 | Healthcare | 78 | Wide | 223 | 22 | 1 | Regulatory barrier |
| 3 | Data | 76 | Wide | 317 | 64 | 12 | Switching costs |
| 4 | Finance | 76 | Wide | 294 | 88 | 5 | Regulatory barrier |
| 5 | Legal | 71 | Narrow | 20 | 103 | 1 | Regulatory barrier |
| 6 | AI Models | 68 | Narrow | 79 | 342 | 32 | Hard to copy |
| 7 | Human Resources | 68 | Narrow | 17 | 111 | 0 | Switching costs |
| 8 | Automation | 67 | Narrow | 14 | 341 | 5 | Switching costs |
| 9 | Ecommerce | 67 | Narrow | 49 | 217 | 29 | Habit and retention |
| 10 | Sales | 67 | Narrow | 69 | 180 | 29 | Switching costs |
| 11 | Support | 66 | Narrow | 54 | 143 | 34 | Switching costs |
| 12 | Real Estate | 65 | Narrow | 3 | 90 | 8 | Hard to copy |
| 13 | Agents | 63 | Narrow | 38 | 294 | 78 | Hard to copy |
| 14 | Voice | 62 | Narrow | 28 | 196 | 70 | Hard to copy |
| 15 | Marketing | 61 | Narrow | 21 | 208 | 55 | Ecosystem |
| 16 | 3D & AR/VR | 60 | Narrow | 3 | 50 | 21 | Hard to copy |
| 17 | Coding | 60 | Narrow | 38 | 319 | 138 | Habit and retention |
| 18 | Gaming | 60 | Narrow | 1 | 55 | 17 | Hard to copy |
| 19 | Productivity | 60 | Narrow | 22 | 206 | 102 | Habit and retention |
| 20 | Design | 56 | Narrow | 17 | 177 | 149 | Hard to copy |
| 21 | Research | 56 | Narrow | 4 | 102 | 37 | Hard to copy |
| 22 | Learning | 54 | No moat | 6 | 144 | 112 | Habit and retention |
| 23 | Avatars | 52 | No moat | 0 | 26 | 41 | Hard to copy |
| 24 | Search & SEO | 51 | No moat | 0 | 23 | 69 | Hard to copy |
| 25 | Music | 50 | No moat | 0 | 13 | 48 | Hard to copy |
| 26 | Video | 49 | No moat | 2 | 113 | 206 | Hard to copy |
| 27 | Image | 47 | No moat | 2 | 120 | 293 | Hard to copy |
| 28 | Social | 44 | No moat | 0 | 53 | 193 | Ecosystem |
| 29 | Writing | 37 | No moat | 0 | 57 | 263 | None |
Wide, narrow and none count the market's rated products in each band. Source: FalcoScan catalog, data updated 18 October 2026.
Why do thin wrappers rarely have a moat?
Wrapper exposure is high in Support (40% of products), Productivity (44% of products), Learning (30% of products), Social (45% of products) and Writing (64% of products). Those markets average a moat of 52, against 65 in the 16 markets where wrapper exposure is low. Wrapper risk counts against the moat score directly, and a product that is a thin layer over a public model is also easy to copy, so it loses on two inputs at once.
The opposite case is regulation. In Security, Healthcare, Finance and Legal, most products face high compliance friction, and those markets average 76 on moat against 62 across the catalog. The rules a new entrant must meet before it can sell are a barrier no public model removes.
How does FalcoScan calculate a moat score?
Each product's moat score blends four inputs the catalog already holds: switching cost (30%), stickiness (25%), differentiation (25%) and the inverse of wrapper risk (20%), each rescaled to 0 to 100. A score of 75 or more is a wide moat, 55 to 74 is narrow and below 55 is none. A market's rating is the average of its products, and its five sources and two threats are rated against the other markets. Regulatory barrier, ecosystem and platform dependency describe the market and are not part of a product's score. The full method is on the AI Moats page, with the wrapper risk input explained in the methodology. This page is rebuilt from the latest refresh, and the date at the top is the date of the data it used.
Where can you dig into the moats?
Start with AI Moats, which maps every market and lets you check a product of your own. Compare the widest and narrowest markets side by side: Security against Writing. For how crowded each market is, read the most competitive AI markets and the markets with the lowest saturation. The wrapper risk index goes deeper on thin products, and the Market Terminal compares every market at once.
Questions people ask
What is a moat for an AI product?
A moat is what stops a rival, or a general AI model, from taking a product’s customers. FalcoScan measures five sources of moat: switching costs, habit and retention, how hard the product is to copy, regulatory barriers, and how wired it is into other tools. It also tracks two threats: being a thin wrapper over a public model, and depending on another company’s platform.
What counts as a wide moat?
On FalcoScan, a moat score of 75 or more out of 100 is a wide moat, 55 to 74 is narrow and below 55 is none. Based on data updated 18 October 2026, 21% of 7,583 rated AI products have a wide moat.
Which AI markets have the widest moats in 2026?
Security, Healthcare, Data and Finance average a wide moat on FalcoScan, based on data updated 18 October 2026. Security is widest at 79 out of 100, with 297 of its 357 rated products rated wide.
Which AI markets have no moat?
Learning, Avatars, Search & SEO, Music, Video, Image, Social and Writing average below 55 out of 100. Writing is lowest at 37, and 82% of its products have no moat.
Do AI wrappers have a moat?
Rarely. Wrapper exposure is high in Support (40% of products), Productivity (44% of products), Learning (30% of products), Social (45% of products) and Writing (64% of products). Those markets average a moat of 52, against 65 in the 16 markets where wrapper exposure is low. Wrapper risk counts against the moat score directly, and a product that is a thin layer over a public model is also easy to copy, so it loses on two inputs at once.
How is the FalcoScan moat score calculated?
Each product's moat score blends four inputs: switching cost (30%), stickiness (25%), differentiation (25%) and the inverse of wrapper risk (20%), each on a 0 to 100 scale. A market's rating is the average of its products. Regulatory barrier, ecosystem and platform dependency describe the market and are not part of a product's score.
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Photo: ze k / Pexels. Colour-graded for FalcoScan.
Citing these numbers
Every figure here is read from the FalcoScan catalog, and this page refreshes daily. The numbers above are from the data updated on 18 October 2026. When you quote one, quote that date with it.
FalcoScan, “What Is an AI Moat, and What Does a Wide Moat Look Like in 2026?”, data updated 18 October 2026. https://falcoscan.com/articles/ai-moats-explained