Startup Moats: Why They Matter in 2026
With AI pushing product cycles into overdrive, figuring out whether a startup has a real moat isn’t just a thought exercise anymore—it’s table stakes if you want to pick the right job or investment. Dan Hockenmaier recently broke down a straightforward way to tell if a startup can actually go the distance. His core point: not every so-called advantage holds up, and knowing what’s defensible could save you a lot of wasted time.
There’s this idea that picking a future winner in startups is pure luck. Hockenmaier pushes back. He says startups can deliver returns as good or better than tech giants, but only if you dig into their fundamentals—especially around defensibility. As AI shortens the path from idea to product, a lasting moat is what separates the companies that stick around from those that get leapfrogged or fade out.
Hockenmaier’s Three-Step Framework for Assessing Moats
Dan Hockenmaier, known for his work in growth and marketplaces, boils the challenge of judging a startup’s future down to three moves: spot a compounding advantage, estimate how strong it could get, and check if it’s actually working. He offers this playbook so candidates, founders, and investors can tell the difference between momentum and true defensibility.
Step one is separating a short-lived edge from a compounding advantage—something that gets harder to compete with as the company scales. Hockenmaier calls out four types of compounding advantages:
- Switching costs: When a product is so embedded in a customer’s workflow that taking it out would be a major headache. Rippling, which started with onboarding and now spans HR and finance, is a case study in making itself essential to business operations.
- Network effects: Products that get better the more people use them. Think LinkedIn for social networks, Roblox for gaming, or Amazon for marketplaces. At Faire, Hockenmaier notes, product roadmaps were filtered through the question of how each feature would strengthen the network.
- Scale economies: When bigger really is better—companies that drive down costs or boost quality as they grow. Zipline, for instance, slashed delivery costs from $300 to $12 as it scaled up drone manufacturing and delivery density.
- Data effects: Products that improve as they gather more data. Stripe Radar, for example, detects fraud by learning from transaction data across merchants. AI startups also get smarter with every user action.
Hockenmaier warns that things like speed, distribution, and brand matter for growth, but don’t, on their own, create durable moats. They’re like high-octane fuel—nice to have, but not the engine that keeps a business running ahead.
Understanding the Four True Compounding Advantages
1. Switching Costs
People often see switching costs as a trap for customers, but you don’t get there unless the product delivers serious value and becomes part of the daily routine. The real sticking point isn’t just about moving data—it’s the “workflow tax”: how much time and disruption it takes for teams to relearn systems and rebuild processes. AI might chip away at migration headaches, but the human and organizational friction is a stubborn obstacle.
Look at companies like Salesforce, Datadog, ADP, and Epic. Their software runs so much of the business that teams are built around them. Rippling is walking this same path—starting from onboarding, then branching into HR, payroll, finance, and analytics.
The aim: own the workflow so thoroughly that pulling out their system would be a nightmare. You see it in their product strategy—lots of integrations and features that tie together different parts of a business—and in their heavy focus on product and engineering teams.
2. Network Effects
For companies with network effects, size and engagement are the main event. Each new user makes the product better for everyone else. That’s why even something as outdated as Craigslist’s interface still works—it’s the network that counts. These businesses often put less emphasis on shiny features and more on anything that grows or fortifies the network.
Take Faire. Hockenmaier describes a laser focus on getting more retailers and brands, making search and discovery smarter, and lowering risk for users through perks like free shipping or credit. Every decision was measured by its impact on the network’s strength.
The pattern repeats at LinkedIn, where the real value is having everyone in one place; at Roblox, where users attract both players and creators; and at Amazon, where a thick marketplace draws in buyers and sellers alike.
3. Scale Economies
Scale economies let companies deliver lower prices or higher quality, simply by spreading out big, fixed costs. This is the home turf of capital-heavy companies. Zipline, for example, took the cost of a delivery from $300 down to $12 by cranking up manufacturing and packing more deliveries into each route.
The cost curve itself becomes a moat: competitors have to eat big losses before they can match your efficiency. You see this in manufacturing (SpaceX), retail (Costco), and media (Netflix), where getting bigger means more pricing power or better products.
These companies usually run with big operations, engineering, and finance teams, and push hard to expand both their operations and their markets. Product roadmaps focus on wringing out process improvements, reaching new geographies, or launching new categories that use existing infrastructure.
4. Data Effects
Data effects are front and center in the AI age. Here, every new data point makes the product better, which brings in more users, which generates more data—a loop that feeds itself. Stripe Radar is a prime example: it’s processed more than 70 trillion data points and $1.9 trillion in transactions, giving it fraud-detection skills that a new player would need years to match.
With 92% of transactions tied to a card Stripe’s already seen, that head start is massive.
AI products like Cursor also gain from every user interaction, helping the model improve. Lots of vertical AI tools in law, healthcare, or customer service are building these same feedback loops, so each use or bit of feedback makes things better for everyone. In these companies, data science and machine learning teams are on equal footing with product or engineering.
Why This Framework Matters for Your Business Decisions
This isn’t just a theoretical checklist. If you join or back a company without a compounding advantage, you could find yourself stuck when challengers show up. A SaaS tool that’s easy to swap out or a marketplace without network effects might grow fast at first, but it’s open season for copycats. That’s time, money, and opportunity that may never come back.
Hockenmaier’s framework is especially relevant now, as AI lowers the barriers to entry and makes differentiation tough to hang on to. If your only levers are speed or distribution, you’re vulnerable to rivals who eventually build a real moat. Speed, distribution, and brand can help you get started, but without a compounding advantage, they rarely turn into lasting protection.
How You Can Apply the Moat Evaluation Process
Thinking of joining, partnering, or investing in a startup? Hockenmaier’s method doesn’t take long—about an hour, tops:
- Spot the compounding advantage. Look for evidence of one of those four types—switching costs, network effects, scale, or data—by seeing what the team is actually building and prioritizing. Ask: What’s the core loop? Which teams have the most heft and influence? Is the roadmap loaded with integrations, network-building, or data-collecting features?
- Estimate its strength. Not all network effects or scale advantages are created equal. Ask: How tough would it be for a competitor to catch up? Does the advantage get stronger as the company grows, or is it stuck in a small corner? Are scale efficiencies driving costs down in a way that’s genuinely hard to copy?
- Check if it’s working. Do you see actual signs that the advantage is turning into defensibility—like high retention, unique data, or customers who are getting more dependent? Look at customer behavior, product usage, and how the company handles competition. Is churn low because of deep integrations? Is the product clearly improving as more data rolls in?
Keep your eyes on the “core loop”—the set of activities that makes the product better as it grows. Notice which teams are big and influential (product, engineering, data science) and whether the roadmap is packed with integrations or network-boosting features. Don’t be shy about asking how the company plans to deepen its moat over time.
My Take: A Useful Litmus Test, But Not a Crystal Ball
Hockenmaier’s framework is practical and cuts through the fluff. It’s all about looking at what’s actually driving a business, not just what’s trending on tech blogs. From what I’ve seen, plenty of startups pitch speed or branding as their moat, but those edges usually evaporate the minute competitors show up. The four compounding advantages he details are the ones that actually stick around.
Of course, it’s not perfect. Some companies won’t have a neat, obvious moat—especially early on—and sometimes businesses shift from one advantage to another as they grow. The framework also doesn’t cover regulatory or cultural barriers, which can matter in areas like healthcare or finance, but are less of a factor in fast-moving tech.
Still, for AI and SaaS startups, this is the best short test I know. If you can’t see at least the early signs of a moat, be careful. Not having a compounding advantage doesn’t spell doom, but it usually means the company will need to keep breaking new ground or spend heavily to stay in front—which isn’t a recipe for long-term success.
The Bottom Line
In 2026, genuine startup moats are harder to find—and more valuable—than ever. Dan Hockenmaier’s framework gives you a clear lens to tell the difference between a passing edge and an advantage that compounds with time. If you’re putting your career, capital, or company on the line, make sure the startup is building one of these self-reinforcing loops. Anything else is just a temporary boost.
If you want to go deeper, Hockenmaier points to 7 Powers by Hamilton Helmer and his strategy newsletter. Both dive further into defensibility, with more frameworks and real-world examples for those who want to go beyond the basics.