On Durability

I spend my time evaluating what is worth building for the long term — as an advisor, as an investor, and as an operator. That question has changed.

AI is collapsing the cost of building things. Teams that once required dozens of people can now be run by a handful. Entire products can be developed in weeks that used to take months.

When execution becomes cheap, a different question matters more: what cannot be easily replicated?

Not what is popular, or what is growing, or what is well-funded — but what is hard to replicate, resistant to abstraction, and worth defending over a long time horizon.

Durability Is Not a Moat

Start with a distinction. Durability is not the same as a moat.

Moats are competitive advantages — network effects, switching costs, economies of scale. AI weakens several of them. Scale advantages shrink when a five-person team can match the output of fifty. Switching costs drop when AI handles data migration and onboarding. Network effects hold, but only for systems that have already achieved density.

Durability is narrower and harder. A durable advantage solves a persistent problem using something that is difficult to replicate. Both conditions must hold. Solving a persistent problem with replicable tools is a commodity business. Holding a unique asset that does not solve a real problem is a science project.

When execution becomes cheap, the only advantages that survive are the ones competitors cannot execute into existence.

The Three Durable Assets

In the AI era, durable businesses increasingly come from three sources — the advantages that remain difficult to replicate even when execution becomes cheap.

Physical presence. AI cannot show up. It cannot occupy a location, maintain a machine, inspect a building, or shake a hand. Businesses built around physical assets — real estate, equipment, infrastructure, logistics — retain a durability that many software businesses do not. The limit here is atoms, and there are only so many of them. As the digital cost curve flattens, the relative value of what cannot be digitized increases.

Copart illustrates this dynamic. Its salvage vehicle auction marketplace is valuable, but the real advantage is the network of vehicle yards and logistics infrastructure built over decades. A competitor can replicate the software. Replicating the physical footprint — land, transport routes, and insurance relationships — is much harder. The irony of the AI era may be that the most defensible businesses are the least digital. This inverts the venture theses of the last thirty years of software — backed to the days of real risk and real assets. Not because software does not matter — it does, enormously, as an amplifier — but because the physical layer is the one layer that cannot be replicated by a competitor with a credit card and an API key.

Operational data from real work. The most defensible data is not scraped, licensed, or aggregated from public sources. It is generated through direct interaction with customers, operations, or environments — and it compounds over time. Most enterprise data that is not publicly available is created through real-world activity: machines running, workers performing tasks, systems moving goods, decisions being made.

Stripe's payments platform processes billions of transactions across its network, generating continuous feedback on fraud patterns, payment success rates, and merchant behavior. That data improves the system over time because it is generated through real economic activity, not scraped from public sources. The system becomes more valuable because it sits inside the flow of real-world activity. The data is not the product. The data is the byproduct of doing the work — and the accumulation of it becomes a structural advantage that AI amplifies rather than replaces. A new entrant can match your tools overnight. They cannot match your accumulated operational knowledge overnight.

Niche context that resists abstraction. Some markets are small enough, specific enough, or regulated enough that generalist AI tools cannot serve them well. A compliance workflow in specialty insurance. A procurement process for rare materials. A credentialing system for a licensed profession. A quality control protocol for a manufacturer with unusual tolerances. These problems do not scale horizontally. They go deep. Depth creates durability that breadth does not.

Veeva Systems illustrates how powerful this dynamic can be. Veeva dominates software for pharmaceutical companies not because CRM technology is difficult, but because the regulatory and operational context of drug development is extraordinarily specific. Generalist AI is powerful precisely because it is general. But generality becomes a weakness when the problem requires context that only comes from years of operating inside a specific domain. The key for niche businesses is capitalizing leanly enough to stay small and focused. The moment a niche business takes on capital that demands platform-scale returns, it is forced to dilute the depth that made it durable in the first place.

Where These Assets Converge

One place durable assets are starting to converge is robotics. Robotic systems operate in the physical world, which means they combine physical presence with the generation of proprietary operational data. Every robot deployed in a warehouse, factory, or hospital continuously generates information about how real environments behave. Over time that operational dataset compounds. And because robotics deployments are embedded inside specific industries — logistics, manufacturing, agriculture — they accumulate deep domain context as well.

The hardware creates presence. The operations generate data. The industry context creates specialization. Over time, deployment itself becomes an advantage — because systems operating in the real world continuously generate the data and context that competitors lack. Many durable marketplaces emerge from this same pattern. When supply is tied to physical systems, operational data, or deep expertise, the marketplace becomes the coordination layer around a scarce asset.

The Durable Asset Test

If you are building a business in this era, the test is simple: could a well-funded competitor with access to the same AI tools replicate what you have in twelve months?

If yes, you are likely in a commodity business — possibly a profitable one, but not one that compounds. If no, ask why. The reasons usually trace back to the durable assets: physical presence, operational data, niche context.

Durable companies of the next horizon will own at least one of these assets. The strongest will combine two. The rare ones will build systems where all three reinforce each other. In practice, this means many of the most durable AI companies of the next decade will not look like traditional software businesses at all. They will be embedded inside real-world systems where physical operations continuously generate proprietary data and domain expertise over time.

But economics is only part of the story. There is another dimension of durability — one less about tangible assets and far more human.

The Human Durability Test

Some businesses survive not because they are hard to replicate, but because they are hard to abandon.

They are sustained by human commitments embedded in the organization — relationships, communities, and convictions that competitors cannot easily reproduce even if they can copy the product. The Durable Asset Test asks whether a business owns something competitors cannot replicate. The Human Durability Test asks a different question: what human commitments make this organization difficult to walk away from?

Work has been understood across traditions not only as a source of income, but as a source of dignity, identity, and belonging. The three forces below — community, trust, and conviction — are not soft counterweights to economic logic. They are what happens when work accumulates meaning over time. Organizations that build them become harder to replace because leaving them costs something a competitor cannot easily offer back.

Some organizations endure because they own assets. Others endure because they embed human forces that markets alone cannot replicate.

Community as a Durable Force

Some businesses become durable because people feel they belong to them. Not as customers in a database, but as participants in a shared outcome — a place where identity, routine, and relationships accumulate over time. When that happens, the business stops being interchangeable with its competitors. It becomes part of the social fabric around it.

Physical location is often the catalyst. When people gather in the same place repeatedly, belonging emerges almost accidentally: conversations before a class, familiar faces behind a counter, the quiet recognition that the same people keep showing up week after week. Over time the location becomes more than a venue for transactions. It becomes infrastructure for community.

CrossFit is a useful example. On paper it is a fitness brand. In practice it functions as a distributed network of local communities. Each affiliate gym operates independently, but the experience inside those gyms is remarkably consistent: small groups of people pushing through difficult workouts together, learning each other's names, celebrating progress, and returning day after day. Ace Hardware stores operate on a similar dynamic — customers return not only for supplies but for advice, and over years those interactions build a relationship that national chains or online retailers struggle to replicate.

Southwest Airlines provides a useful counterexample. For decades the airline cultivated a form of community uncommon in aviation. Its policies — open seating, no bag fees, a straightforward fare structure — reinforced a sense of fairness and shared experience among passengers. Recent changes to pricing and seating have begun to erode that identity quickly. By abandoning the elements that once reinforced its communal identity — without replacing them with a new form of belonging — Southwest risks moving from a durable position to the middle of the market, a difficult place for any airline to remain distinctive.

Community can take decades to build — and surprisingly little time to dismantle.

Trust as a Durable Force

Trust in simple terms: I said I would do a thing, and I did it. The next time I say I will do something, you believe me.

Trust emerges through repeated cooperation. It forms when people interact over long periods of time and come to believe that the other party will behave predictably — not only when incentives are aligned, but when circumstances become difficult. The industrial ecosystem around Toyota is a classic example. Toyota built decades-long relationships with suppliers, sharing operational knowledge and supporting partners through downturns rather than replacing them when costs fluctuate. Suppliers invest in Toyota's success because they believe the relationship will persist.

Trust operates at a more personal level as well. In many professions, clients maintain relationships with the same advisors for decades — insurance brokers, financial advisors, accountants, real estate agents. The relationship persists not because switching is impossible, but because trust compounds through shared experience.

Some technologists argue that systems like blockchains can replace trust by making transactions verifiable. In practice blockchains solve a narrower problem: enforcing rules between parties who may not know each other. This verifies the transaction and produces a clean record, but cannot replicate the relationships that make me want to transact. The deeper form of trust — the willingness to cooperate over time, share knowledge, and resolve problems when rules fail — still depends on people.

Conviction as a Durable Force

Conviction is what happens when people continue building something even when the purely economic reasons to do so weaken. It is the refusal to abandon a mission, a craft, or a community simply because conditions have become more difficult.

Patagonia offers a modern example. When founder Yvon Chouinard transferred ownership of the company to a structure designed to preserve its environmental mission, conviction became embedded in the governance of the organization itself. The Jesuit network offers a longer view — Jesuit schools and universities have sustained educational institutions for centuries not through economic optimization, but because the people involved believe the mission itself is worth sustaining across generations.

Independent bookstores offer a more granular illustration. For two decades, the conventional wisdom was that independent booksellers were finished — first by the chains, then by Amazon. Many closed. But hundreds did not. The ones that survived did so largely because the owners refused to leave, often at significant personal cost, but then reoriented around curation, community events, and local identity — things a fulfillment warehouse cannot replicate. That recovery was not driven by a shift in unit economics. It was driven by conviction that held through the period when the economics were worst.

What conviction protects against is the moment of maximum doubt — the window when a competitor with less commitment would exit and cede the field. That window is when durable organizations separate from fragile ones. The cost is real: slower pivots, higher tolerance for underperformance, and occasionally the wrong call sustained too long. But in markets where relationships and identity accumulate over time, the organizations that stay often inherit the ground others abandoned.

Markets reward optimization. Conviction rewards persistence.

The Two Durabilities

Structural durability is built through control of scarce resources or capabilities. Human durability is built through relationships and meaning that accumulate over time. One asks what makes a business hard to copy. The other asks what makes it hard to abandon. The most resilient organizations often possess both.

AI is making it easier to start things. It is not making it easier to sustain them. The cost of building products and entering markets is falling rapidly. That will produce an explosion of new companies and experiments. But when execution becomes easier, the advantage shifts from speed to endurance.

Community, trust, and conviction each resist what AI does best. AI can optimize a transaction; it cannot manufacture the sense of belonging that keeps someone returning to the same gym or hardware store. AI can automate an interaction; it cannot substitute for the credibility that a supplier or advisor has built through decades of showing up. AI can accelerate execution; it cannot replace the people who stay when staying stops making obvious economic sense.

Technology accelerates execution. Human systems sustain persistence.

In an era defined by velocity, the harder question is not how quickly something can be built. It is what will still be here ten years from now — and why.