compound

Steve Eisman @realsteveeisman, of Big Short fame, made the bear case for online travel on the New Money podcast this week. $BKNG $EXPE He tells his agent to book a flight to Miami and the best hotel, and the agent goes through every travel site, finds the best price, and books it. As Eisman put it: “You’re not their customer anymore. Your AI agent is the customer.” He then asks the obvious follow-up: What exactly is Booking’s switching moat if you never visit the website again? It’s worth being precise about which moat he’s questioning. It’s a habit moat. You open the app, you compare, you book, and after a few years the opening becomes a reflex. An agent that queries every site and books the cheapest option has no reflexes and no favorite apps. If the customer relationship moves from the site to the agent, the advertising money that paid for that traffic moves with it. Eisman contrasts this with software deeply embedded in the enterprise. A company like ServiceNow, or one of Constellation Software’s vertical market businesses, sits inside the daily operations of a firm, with the data, the trained staff, and the switching costs already in place. An agent can shop for a flight. Ripping out the system a company runs on is a different matter. I think that distinction is directionally correct, and it explains why travel sits at the top of nearly every list of potential victims. It’s also, however, where I think the debate has become lazy and needs a little more nuance. Let me steelman the bear case first, because it’s genuinely unsettling. It’s easy to picture an agent that checks every online travel agency and every hotel’s own website, then comes back with the three best options. In that world, Booking and Expedia lose a slice of their bookings to direct channels wherever the hotel offers a better price. That’s what makes agentic AI such a scary proposition for an intermediary: it turns every consumer into a ruthless shopper, a real-world homo oeconomicus, who is purely rational in his decision-making and also happens to be omniscient. Two decades of brand building, app downloads and loyalty perks count for little if your customer is a piece of software that only cares about the final price, or the best bang for the buck. The problem with this scenario is that it describes what can theoretically be built and skips the question of how anyone would build it today – the first question we raised in the segment above. Booking-com started in 1996 as a small Dutch startup, and the bulk of what it built over the following three decades happened on the ground. It set up local offices across Europe and later Asia and Latin America, with account managers who walked into independent hotels, family-run B&Bs and guesthouses and signed them up one by one. The pitch was simple and unusually generous for its time: no upfront fee and no marketing budget, only a commission once a guest had actually stayed. That model fit Europe's fragmented hotel market in particular, where most rooms belong to independent owners rather than large chains, and every one of those owners had to be onboarded, have their photos and room types loaded, connected to rate and availability feeds, and supported in their own language when something went wrong. Layer after layer of that work compounded into a supply base that, as of December 2025, stood at 32 million listings across 4.4 million properties, along with decades of verified reviews, payment infrastructure and customer service that guests and hotels have come to trust. Rebuilding that from scratch would be close to impossible for a new entrant, because supply alone does not make a hotel owner sign up. They join for the demand Booking brings, and a challenger without bookings to offer is asking a busy owner to do onboarding work for nothing. An agent has no live inventory link to the vast majority of hotels. To find out whether a room is available at a given price, it has to go through the booking flow on each website, step by step, almost as if it were going to book. The answer can be different an hour later, because hotel pricing is dynamic and changes constantly. The best OTA rates are often only visible to logged-in members – Booking’s Genius discounts being the obvious example – so an agent that scrapes the public page doesn’t even see the cheapest price. And contrary to popular belief, going direct often isn’t cheaper. Hotels frequently give OTAs discounted rates for members, which means the Booking price can be lower than the one on the hotel’s own website. Repeating all of that for every property, for every query, from every user, is extraordinarily compute-intensive and slow. Another interesting question for agentic AI is whether that replication is even necessary. An agent sitting between the traveller and the booking doesn’t need to rebuild the network if it can rent it through Booking, Expedia or the chains’ own direct APIs and channel managers, which have made the plumbing far more standardized than it was twenty years ago. I find a library a helpful analogy. Imagine you’re looking for a book, and there’s a librarian who knows where every book is and what each one is about. You refuse to talk to her. Instead, you pick up every book in the library, read each one, and only then decide which one you wanted. Then it gets even crazier, because you repeat the whole exercise every time you need a book. That’s roughly what an agent does when it bypasses the OTAs. Booking has spent more than 25 years building the infrastructure that knows where every room is, what it costs right now, and what other guests thought of it. An agent can, in theory, rebuild that index from scratch on every request. Whether that makes sense as a product, given the compute bill and the waiting time, is another question.