What If There Never Was a Moat?

In venture, we love to ask founders: “What’s your moat?”

In AI, that question has become almost theological. Is it the model? The data? The infra? The agents? Everyone’s hunting for the one magical defensive layer that will protect their startup from OpenAI, Anthropic, or the next well-funded competitor.

But what if we’ve been asking the wrong question all along?

Instead of “What is your moat?” we should be asking:

“How long will your moat last, and what does it buy you time to do next?”

Because in technology generally-and AI specifically-there are no permanent moats. There are only time‑bound temporary advantages and what you build on top of them.

Moats Were Never Permanent (Even in Buffett’s World)

Warren Buffett popularized the idea of an economic moat: a durable competitive advantage that protects a company’s “castle” from competitors and preserves high returns on capital over long periods. Classic examples include:

• Cost advantages (GEICO, Costco)
• Brands and intangibles (Coca‑Cola, Apple)
• Network effects and switching costs (American Express, enterprise platforms)

Buffett’s own framing, though, is often misquoted. He doesn’t say “find a moat and you’re done.” He focuses relentlessly on durability,whether the moat will still matter in 10–20 years and explicitly warns that many moats turn out to be Roman candles: bright, short, and quickly extinguished.

A more honest paraphrase of modern competitive dynamics is:

All moats are temporary. The only question is how temporary-and whether you reinforce or replace them before they fail.

In other words, moat = time. Not permanent safety.

Apple: The Castle That Keeps Moving

If any company should have a “real” moat, it’s Apple. Yet their history is one long story of reinventing the product lines before the old moats run dry:

• Early 2000s: the iPod dominates portable music and helps drive Apple’s turnaround.
• Late 2000s: the iPhone effectively eats the iPod and then the broader smartphone market.
• 2010s–2020s: Apple systematically builds new engines—App Store, services, wearables, silicon—rather than relying on any single device.

The iPod’s “moat” (design, integration, iTunes, content relationships) was real. It just wasn’t permanent. Apple’s real defensibility is the compounding system around the products and network effects - brand, distribution, ecosystem, developer and customer loyalty. Their willingness to cannibalize their own castles before someone else does keeps them ahead.

The lesson: products are disposable; capabilities and compounding systems are the only things that approximate a long‑term moat.

Moats, Drawbridges, and Siege Engines

If we go back to the original medieval metaphor, it’s actually more helpful than most pitch decks give it credit for.

In reality:

• Many moats were just dry ditches, not crocodile‑infested lakes.
• Attackers routinely got across by:
  • Filling the moat with earth and debris over time
  • Building temporary bridges or barges
  • Tunneling under walls to collapse them (sapping)
  • Sitting outside and starving the defenders out
• Eventually, gunpowder and artillery made classic castles obsolete; you could just sit on a hill and pound stone walls into dust.

Defenders responded by updating the whole system, not just the ditch: drawbridges, portcullises, layered gatehouses, murder holes, and angled bastions.

There are a few useful translations for startups and investors:

• Your “model moat” today is just a filled‑with‑water version of a ditch. In AI, attackers can fill it in (catch up on model quality or distill from it), go over it (use a better API), or tunnel under it (change the abstraction layer).
• Drawbridges are tactics, not strategy. Pricing, rate limits, API access policies—these control who crosses, not whether the castle is worth defending.
• Eventually, a new “artillery” (e.g., a drastic cost curve shift, new architecture, or regulatory change) makes the old wall design obsolete. Sticking with “but we have a moat” at that point is how you get turned into a heritage site.

The right question isn’t “do we have a moat?” It’s:

“Given that attackers (competitors) historically always get across moats, what’s our plan for the siege engines that will show up 6–24 months from now?”

Cursor vs. Claude Code: A Case Study in Time‑Bound Moats

Take Cursor, the AI‑native code editor that’s gone vertical. In  just a few years:

• Cursor forked VS Code, infused AI into every layer, and became a beloved IDE for developers.
• It scaled to billions in ARR and valuations in the tens of billions, with very high Fortune 500 penetration.
• Its primary moats, at least on paper, looked like: UX / DX, deep integrations, and speed. In reality these were all means to the end - brand and developer loyalty.

Then the foundation model providers moved up the stack:

• Anthropic launched Claude Code, an agent‑first coding environment where developers offload entire tasks rather than just individual completions.
• OpenAI and others followed with their own agentic coding products. Suddenly the “AI pair programmer” wasn’t a niche; it was a strategic focus for the very frontier labs Cursor depends on.

Cursor reacted by shipping Cursor 3: an agent‑management console that demoted the IDE to a fallback surface, explicitly betting on a world where supervising agents matters more than writing code line by line. In parallel, it started training its own Composer models, effectively cannibalizing its own editor, much the same way Apple let the iPhone eat the iPod.

SpaceX/xAI announced a deal that gives it the option to acquire Cursor for $60B or pay $10B just to “work together” and plug Cursor into xAI’s Colossus supercomputer. That tells you two things. First, the scarce asset isn’t raw model capability — it’s product + distribution into expert developers, which is exactly the moat Cursor compounded. Second, this is Cursor doubling down on cannibalizing its own old moat: using xAI’s compute to train its own models and reduce dependence on Anthropic/OpenAI, the same way Apple let the iPhone eat the iPod. In a world where model-level advantages compress fast, infra players will buy distribution moats.

The takeaway is that it’s better to cannibalize your own moat before someone else does.

What’s interesting here isn’t whether Cursor “wins,” but rather what this proves:

1. Its product moat (better coding UX) was always time‑bound. As soon as agentic coding matured and the frontier labs prioritized it, that surface became contested.
2. Cursor’s real advantage now looks to be its brand and the developer loyalty therein. It’s the fact that a critical mass of engineers already live in it, have built habits there, and talk about Cursor as “how I work.”

Even in a world where everyone codes through Claude Code‑style agents or Codex‑style tools, Cursor will be far ahead of the next startup deciding to build “an AI IDE”. They’ve already built:

• A distribution channel into dev teams
• Deep understanding of workflows and edge cases
• A brand that means “serious AI‑assisted dev”

Brand, distribution, workflow depth— are the moats that compound. The specific interface (IDE vs. agent console)  happens to be the drawbridge configuration of the moment.

From “What Is Your Moat?” to “How Long Does Your Moat Last?”

So what does a better question look like for founders and investors in AI and infra?

1. How long is your current moat?

Concretely:

• Model or feature lead: Are you 3, 6, or 12 months ahead of others before they can replicate your capabilities using the same foundation models?
• Distribution: Do you have a 2-3 year head start in landing the right users (e.g., devs, analysts, specific verticals) and embedding into their workflows?
• Economics: How long until advantageous cost curves become available to others (via newer models or better infra) and thus flatten your advantage?

If the honest answer is “six months,” The follow‑up is: what can you build in those six months that extends the advantage?

2. What compounding systems are you building while the moat is in place?

Use this window to invest in things that  become more defensible with time:

• Data flywheels: proprietary feedback, labeled edge cases, domain‑specific corpora that competitors can’t easily replicate.
• Workflow lock‑in: you become the default system of action, not just a button in someone else’s tool.
• Brand and trust: especially in AI, where hallucinations, privacy, and safety are of utmost importance, trust can be a stronger moat than raw performance.
• Ecosystems: integrations, plugins, and partner networks that make you the obvious home for a class of work.
• A GTM machine: a repeatable sales, marketing, and partnerships engine that compounds distribution and makes you the default choice for your ICP.

This is exactly what vertical AI and infra theses now argue: early speed is the wedge; data, usage loops and brand/lock‑in are the long‑term moats.

3. What’s your next moat?

Assume your current advantage will get crossed—the moat  will be filled, the drawbridge bypassed, and that the artillery will soon show up.

Then ask:

• When (and not if) that happens, what new advantage will you be already in position to create, precisely because you owned this phase?
• What expansions become obvious only after you have distribution, data, and brand?
• How will you self‑cannibalize your own product before someone else does?

Think of it as sequential castle building. Each moat buys you time and capital to build the next, hopefully at a higher level of abstraction and value, and with more structural defensibility.

The First Moat Still Matters

Not all first moats are created equal. Two products can look identical from the outside but give you very different options for “moving the castle.”

Think of a retail banking sales agent that performs great and has amazing UX, but sits on a thin integration layer vs. one that ships with a serious data spine: deep connections into core banking, transaction history, risk systems, KYC, etc.

The first moat is performance + UX. The second moat is performance + UX on top of a real data layer.

The first version might win some logos, but it leaves you with very few degrees of freedom for the next castle; you don’t own the substrate you’ll need for underwriting, fraud, product expansion, or cross-institution workflows. The second version may be slightly worse on day-one UX, but it bakes in the infrastructure that lets you move up a level later.

The Real Moat: Being Willing to Move the Castle

If you insist on treating your current product as the castle, you’re going to design the wrong defenses.

Historically:

• Castles that clung to stone walls in the age of cannons became ruins and museums.
• Castles that evolved—adopting star forts, new materials, new doctrines—stayed relevant much longer.

In tech and AI, the analog is clear:

• Features and models are walls.
• Brand, distribution, data, trust, and the ability to ship new “castles” on top of the old ones are the only things that can even pretend to be moats.

So instead of interrogating founders with “what’s your moat?” we should be asking:

1. How long do you think this moat will last, realistically?
2. What advantages are you compounding while it does?
3. When the siege engines ultimately arrive, are you the one rolling out the next castle, or the one giving tours of the old one?

In other words: in AI, there was never really a moat in the Buffett, 30‑year sense. There was only time—and what you chose to build with it.