The Only Question That Matters After “Does It Work?”

You’ve built something. People are using it. Revenue is growing. And then, inevitably, someone shows up doing the same thing. Maybe a little cheaper, maybe a little shinier, maybe backed by more money than you’ll ever see. This is the moment most founders aren’t prepared for, and it’s the one that decides whether your company becomes something real.

I’ve been fortunate enough to start a few companies and to help build many more through Subvisual’s Ventures. There’s a pattern I keep seeing: founders obsess over building something that works and forget to consider what would stop someone else from building the exact same thing.

That “what stops them” is what investors and strategists call a moat. Not just investors and strategists. We all do. Understanding moats is one of the most underdiscussed aspects of building a company. It’s often treated as an exercise in speculation, which always sits wrong with me.

The term gets thrown around a lot, often loosely. Warren Buffett popularised it, and every VC on Twitter seems to have an opinion on what counts as one. But there’s a framework by Jerry Neumann, a VC and Columbia professor, that I think cuts through the noise quite well. He published it in a piece titled A Taxonomy of Moats, which organizes the concept around four structural sources of competitive advantage: State-Granted Advantages, Special Know-How, Scale, and System Rigidity.

What I like about Neumann’s framework is that it doesn’t romanticise moats. It treats them as structural, almost mechanical. Either the structure of your business makes it hard for competitors to copy you, or it doesn’t. Your feelings about it don’t matter. I find that refreshing, because in the startup world we tend to confuse passion for defensibility, and they’re not the same thing at all.

Let me walk you through each category and share what I’ve seen play out in practice.

State Granted: the moat you probably don’t have

The first category is the one most people think of when they hear “competitive advantage”: patents, regulation, licensing. Government-granted protections that literally make it illegal for someone to copy you.

The truth is, most startups don’t have this, and most shouldn’t waste time trying to get it. Patents are expensive, slow, and in the tech world, they’re often worked around faster than they’re granted. That said, there are industries where regulatory moats are extraordinarily powerful. If you’re building in fintech, healthcare, or anything that touches compliance, the cost and complexity of obtaining a license can be quite a daunting barrier to entry. The irony is that the very regulation founders curse in their first year becomes essentially a shield in their third year.

There’s a subtler version of this category that’s worth paying attention to: control of scarce resources through contracts. Exclusive agreements with suppliers, distribution partners, or key employees. Non-competes and NDAs are a part of this equation. These aren’t glamorous, but they can be effective. I’ve seen startups lock in early partnerships that competitors could not replicate, simply because they were first to the table and wrote a good contract.

Special Know-How: harder to copy than you think

This is where things get interesting for most tech companies. Special Know-How splits into two flavours: the closely held kind, like trade secrets, and the tacit kind—the stuff that lives in your team’s heads and processes rather than in any document.

• Trade secrets are straightforward. If you’ve developed a proprietary algorithm, process, or dataset, and you keep it confidential, that’s a moat. But it’s fragile. People leave, they talk, they start competing companies. The protection depends entirely on your ability to keep the secret, which gets harder as you grow.
• The tacit side is far more compelling. Experience curves, procedural knowledge, institutional memory, deep customer insight. These are things that accumulate slowly and can’t be copied quickly. This is why a team that has been working together for years in a specific domain is genuinely hard to replicate, even with more money. It’s also why I believe so deeply in the value of team stability and knowledge sharing within companies.

At Subvisual, we’ve seen this play out with our ventures. The teams that build the deepest understanding of their users and domain develop a somewhat compounding advantage. Every conversation, every failed experiment, every support ticket adds a layer of understanding that a new competitor simply doesn’t have. You can’t buy that. You gotta earn it. This a moat that will change with AI and I will be very interested to see how. Everyone’s first instinct is to say that this moat will become shallower, but I think there’s an interesting case for the exact opposite.

Scale: the moat everyone wants

Network effects, economies of scale, platform dynamics. This is a big one. This is the category that makes VCs salivate. And for good reason. When scale-based moats kick in, they’re arguably the deepest.

The logic is simple, really. If your product becomes more valuable as more people use it, every new user makes it harder for a competitor to catch up. User networks, two-sided marketplaces, platforms. You know exactly what I’m talking about because you too use Amazon and Google products and have a significant part of your digital life in their ecosystems. Think about how hard it would be to compete with their marketplaces, not because the technology is complex, but because the other side of the market is already there.

But here’s what Neumann’s framework clarifies, and may seem obvious but can elude founders: scale moats don’t exist at the beginning. You can’t have a scale advantage before you have scale. This sounds unspeakably obvious when I write it out, but the number of pitch decks I’ve seen that claim “network effects” as their moat before they have a hundred users is sobering.

There’s another dimension to scale that’s less discussed but equally important: variance reduction. As companies grow, their cost of capital drops, their internal hurdle rates decrease, and their willingness to experiment increases. A large company can afford to run ten experiments, where a startup can only afford one. This gives incumbents a compounding advantage in learning and iteration. It’s a little daunting to think about, but essential if you want to compete intelligently.

System Rigidity: the moat you build against

This last category is, in my view, the most relevant for startups. System rigidity is essentially the idea that existing systems resist change, and you can either be trapped by that rigidity or use it strategically. There are three levels/layers to it:

• Intra-company level: this includes business model innovation, value chain innovation, and what Clay Christensen famously called disruption. Incumbents have organisational structures, cultures, and business models that make it hard for them to respond to new approaches. This is the startup’s great equaliser. You can do things differently precisely because you don’t have legacy structures.
• Ecosystem level: here it’s all about switching costs, bundling, brand, and complementary assets. Once your product is woven into a customer’s workflow, the cost of switching, which isn’t just financial, mind you, but cognitive and operational, becomes a moat in itself. This is why integrations matter so much. Every API connection, every workflow dependency, every piece of data that lives in your system is a thread that ties the customer to you.
• And then there’s the societal layer: traditional, cultural, even moralistic forces that shape markets. These are the slowest to change and the hardest to predict, but they’re real. Consumer habits, cultural preferences, and regulatory norms create structural advantages for those who understand them.

Here’s the part that Neumann’s framework really put in perspective for me, and that I think every founder needs to sit with: most startups don’t have a moat when they start. They can’t. Network effects require users. Scale advantages require scale. Deep tacit knowledge requires time. Even patents require you to have invented something first.

You don’t have a moat. Any moat. It’s your job to pick up a shovel and dig one. But why would you? What protects you in the early days?

The answer, which you may love or hate, is uncertainty.

The very thing that makes startups terrifying is the fact that nothing is proven, that the market is unclear, that the technology might not work. This is also what keeps bigger, better-resourced competitors from bothering to enter. Established companies don’t chase uncertain markets. They wait. And in that window of waiting, smart founders build their moat.

This reframe has stuck with me because I’ve been through this more than once. Quite a few times, really. Uncertainty is a strategic asset if you teach yourself to use it. The founders who succeed are the ones who enter uncertain territory, learn faster than anyone else, and use that learning to build structural advantages before the market becomes obvious enough for everyone else to pile in.

If you’re building something right now, here’s what I’d ask you to consider. What is your moat going to be? Not what it is today (you don’t have one!) but what are you building towards?

• Are you accumulating tacit knowledge that will be hard to replicate?
• Are you building network effects into your product design?
• Are you creating switching costs through deep integrations?
• Are you exploiting system rigidity by doing something that incumbents structurally can’t?

The honest answer might be “I don’t know yet,” and that’s fine. But the question needs to be on the table. Because the brutal truth of the startup world is that building something that works is necessary but insufficient. If you can’t defend what you’ve built, someone with more resources will take it from you. Not because they’re better, but because they can afford to be good enough.

I’ve seen it happen too many times. Don’t let it happen to you.

*This article was inspired by Jerry Neumann’s excellent A Taxonomy of Moats on Reaction Wheel, which I highly recommend reading in full.