The Moat Problem
What the Best AI Companies Are Building Right Now
Every week I talk to brilliant founders building AI companies. The demos are impressive. The technology works. The vision is compelling.
And when I ask “What’s your moat?” the answer is some version of:
“We’re automating [workflow X] with AI.”
Or: “We’re building [product Y], but with voice.”
Or: “We’re adding AI to [company category X]—the same way everything went from offline to online.”
I understand why this feels like enough. You’re solving a real problem. You’re using cutting-edge technology. You have customers who want this. And yes, there was a massive wave where every category got an “online version.”
But here’s what I see: automation alone isn’t a moat. Neither is adding AI to an existing product category. And the offline-to-online transition isn’t the right analogy.
Because six months from now, a dozen competitors will launch similar automation. The foundation model providers will add those capabilities natively. And the “better UX” advantage you’re counting on? It gets copied the moment it works.
This isn’t meant to discourage—it’s meant to redirect. Because there are companies building genuine defensibility in AI. I see them. I back them. The patterns are clearer than most founders realize, and more achievable than you might think.
These moats aren’t mysterious. They’re architected from day one. And they compound with every customer you serve.
What the Winners Are Actually Building
The companies that will reach venture scale aren’t just automating workflows or adding AI features. They’re building systems that become more valuable, more embedded, more irreplaceable with every customer they serve—and they’re designing for that compounding from the first line of code.
Here’s what they’re building:
1. Workflow Integration That Creates True Switching Costs
The strongest moat I see? Become infrastructure, not just a tool.
This means your AI doesn’t just complete a task—it learns the customer’s edge cases, remembers their context, understands their processes so thoroughly that removing it would disrupt their entire operation.
One company I work with built the back office infrastructure for medical practices. Not just scheduling or billing—the complete operational system. After six months, the system knows that Dr. Martinez always books follow-ups on Thursdays, that Medicare Advantage plans from Anthem need a specific prior-auth sequence, that Sarah at the front desk needs alerts formatted differently than the office manager does. Institutional knowledge that would take a competitor three years to rebuild—if they could rebuild it at all.
That’s not a product you compare against alternatives. That’s infrastructure your business runs on.
How to build this:
Design for deep integration from day one, not surface-level automation
Capture context and edge cases specific to each customer
Make your product the system of record, not just a layer on top
Build features that connect across workflows, not just automate individual tasks
You’ll know it’s working when: Retention climbs above 95%. Net Revenue Retention exceeds 120%. Customers who say “we can’t imagine running without this.”
2. Proprietary Data Flywheels That Compound
The distinction between commodity AI and defensible AI: does your product get smarter for each customer over time?
Not “we collect usage data”—with every interaction, does the AI become more attuned to this customer’s needs in ways competitors couldn’t replicate for years?
Netflix doesn’t just have viewing data. Every second you watch, every pause, every 2am binge, every show you quit after ten minutes, makes their recommendations for you better. After five years of this intimate data accumulation, switching to a competitor means starting over. They don’t know that you watch cooking shows when stressed, that you skip intros but not credits, that Tuesday nights are for documentaries. Netflix does.
The same principle applies in B2B AI. If your product learns from usage patterns, remembers interactions, and adapts in real-time to each customer’s workflow, you’re building irreplaceable institutional knowledge.
How to build this: Design your product to learn from every interaction, not just log it. Create personalization specific to each customer’s context—their language, their edge cases, their rhythms. Make the AI remember decisions and patterns. Ensure that customer A’s usage creates unique value for customer A, not just for your aggregate model.
The test: Could a competitor with the same technology but no usage history replicate what your product knows about a customer after 6 months? If yes, you don’t have a flywheel yet.
3. Distribution & GTM That Scales Itself
Sarah Guo talks about this constantly, and she’s right: distribution is the most underrated moat in AI.
The best AI products don’t just work well—they’re embedded in places where customers already are. They spread through usage. They create network effects where each customer makes the product more valuable for others.
What this looks like in practice:
Embedded distribution: You’re not competing for attention—you’re inside the tools people already use. GitHub Copilot lives in VS Code, where developers already spend eight hours a day. Grammarly sits in Gmail, Google Docs, Slack—everywhere you type. Superhuman’s AI doesn’t ask you to open a new app; it’s woven into the email workflow you’d do anyway.
Viral by design: Each user’s success creates visibility for others. The product gets better as more people use it, creating genuine network effects.
Sales that compound: Your customers become your sales force. Each implementation generates 2-3 more opportunities because results are visible.
Platform partnerships: You’ve locked in distribution through partnerships that make you the default choice for an entire ecosystem.
How to build this:
Start where your customers already are (don’t ask them to go somewhere new)
Design for visibility—make successes shareable and observable
Build referral mechanics into the product itself, not just marketing
Create genuine network effects where possible (not just user growth)
Watch for this signal: Customer acquisition cost declining as you scale, not increasing. Organic growth outpacing paid acquisition.
4. Momentum Through Relentless Shipping
In AI, velocity itself becomes a competitive advantage.
When you’re shipping so fast that competitors can’t keep pace, when early wins generate organic traction, when each launch compounds into the next—you’re not just ahead. You’re building a gap that’s increasingly difficult to close.
Perplexity demonstrated this. Not one big launch, but a rhythm of releases. Weekly features. Community engagement. Constant iteration. By the time competitors took notice, they’d built momentum that was nearly impossible to match.
Why this works: Each launch attracts more users. More users generate richer feedback. Better feedback enables faster iteration. Faster iteration attracts more users. The companies that ship fastest learn fastest—and learning speed is the ultimate competitive advantage in AI.
How to build this:
Ship in days or weeks, not months or quarters
Build tight feedback loops with early customers
Create a cadence the market starts to expect
Use each launch to compound attention and learning
The signal: Your product evolves noticeably faster than competitors. Customers can feel the velocity.
5. Vertical-Specific Tribal Knowledge (Combined With Another Moat)
Here’s where nuance matters: deep domain knowledge is powerful—but only if combined with another defensibility layer.
Going deep in a vertical gives you initial traction. You understand the regulations, the edge cases, the unwritten rules. You build something that actually works in practice, not just in demos.
But tribal knowledge alone? Competitors can hire domain experts and close that gap in 18 months.
The winning play: Combine vertical expertise with workflow integration or data flywheels.
In healthcare, you don’t just understand billing codes—you build a system that learns each practice’s specific patterns. In construction, you don’t just know the industry—you capture project data that makes your product smarter for each contractor with every deployment. In legal, you don’t just understand precedent—you become the system of record for how each firm actually practices.
How to build this:
Start with genuine domain expertise (work in the field, not just research it)
Use that knowledge to identify which workflows to embed in
Design for the data flywheel that compounds your domain advantage
Ask: “Can competitors close this gap by hiring experts, or is our advantage structural?”
The signal: Customers say “you’re the only one who gets how we actually work.” And that advantage grows with each customer, not shrinks.
6. Capital Efficiency as Strategic Advantage
One more pattern among winners: they’re capital efficient by design, and that efficiency becomes its own moat.
In the AI era, you can build with smaller teams. Distribution can be more viral. Models are accessible via API. The companies that figure out how to scale without burning capital have staying power.
They can outlast competitors who raised too much at too high a valuation. They can make better strategic decisions because they’re not desperate for the next round. They maintain leverage with investors. They keep more equity.
How to build this:
Start with models via API, not training from scratch (unless you have a specific reason)
Build small, focused teams that ship fast
Design for viral distribution, not expensive sales
Reach profitability or near-profitability before scaling spend
The compounding effect: Efficiency buys you time. Time lets you build real moats. Real moats attract better terms. Better terms preserve value for founders.
A Note on Models: When They Matter (And When They Don’t)
Let me add nuance to the “models commoditize” point.
Models DO matter when:
You need frontier reasoning that only exists in the top 2-3 models
You have proprietary data you can’t send to third parties (healthcare, legal, finance)
You need domain-specific performance that general models can’t match at any scale
You’re at the scale where economics justify training
Models DON’T create moats when:
Your advantage is “we’re using GPT-4 and competitors aren’t”
You’re training a model but don’t have the data flywheel to keep it ahead
The capability you’re building will be in the next OpenAI release
The key question: If foundation models get 10x better tomorrow, does your product become more valuable or less valuable? The best AI companies answer “more valuable”—because their moat is in the workflows, data, and distribution, not the model itself.
The Timing Question
Even the best moats don’t matter if your timing is wrong.
Are you building for where the market is today, or where it will be in two years? Are you early? Late? Or exactly on time?
The best founders are right about the future—not just about their product, but about when the market will be ready for it. They understand that moats protect you once you’re in the game. But you have to be in the right game at the right time.
Watch for: Are enterprises ready to trust AI for this use case? Do users have the behavior change required? Is the infrastructure mature enough? Sometimes the right answer is “build now, but plan for adoption in 18 months.”
Building Your Moat: The Action Plan
If you’re early in your journey, here’s how to think about defensibility:
Start with workflow integration as a core design principle. Don’t just solve a problem—embed yourself deeply enough that switching costs become meaningful.
Design for the data flywheel from day one. What usage patterns make your product uniquely valuable as it’s deployed? How does each customer’s experience compound into lasting advantage?
Build for distribution that scales itself. Where are your customers already? How can your product spread through usage?
Ship with urgency. Speed of iteration creates advantages that compound. The companies that learn fastest win.
Combine multiple moats. The strongest companies layer workflow integration with data flywheels with distribution momentum. That’s not just defensible—that’s nearly unassailable.
Stay capital efficient. It gives you time to build real moats instead of burning cash to paper over the lack of them.
The companies that reach venture scale in AI won’t necessarily have the best models or the most capital or the strongest brand at launch.
They’ll be the ones who understood early: in AI, the moat determines everything.
Not the model. Not the funding. Not even the idea.
The moat—that compounding advantage you architect from day one—is what separates the companies that scale from the ones that stall at Series A.
You’re building in the most deflationary technology environment in history. Models get cheaper, features get copied, and first-mover advantage lasts ninety days.
Which means the only question that matters is: what are you building that gets stronger with time?
If you’re thinking through these questions for your company—or if you’re building something with real defensibility—I’d genuinely love to hear from you. Drop a comment below or reach out directly. These are the conversations I find most valuable.
Excellent article and very informative and helpful.
Thanks