Which Vertical Incumbents Will Survive AI?
with Adam Harris, Founder & CEO of Cloudbeds
Adam Harris is the Co-Founder and CEO of Cloudbeds, the hospitality management platform operating across 157 markets with $20B+ in annual transactions and 550,000 daily logins. Harris built Cloudbeds over nearly 14 years from a fragmented stack of nine disparate systems into a unified system of record for hoteliers worldwide, and now, a system of action. A UC Berkeley-trained economist who started on Wall Street before crossing to the building side, he recently launched Signals, a conversational AI layer built on Cloudbeds’ proprietary hospitality data model — more than a copilot, it’s built on 14 years of hospitality pricing data and is evolving into a true system of action. Tune in to hear why he thinks the AI gold rush is producing more pirates than pioneers, what it’s taken for a an incumbent to go all-in on Vertical AI, and why 80% of mature market leaders will be too slow to survive.
Today’s Episode
Every podcast and pitch deck in 2026 tells the same story: AI startups will disrupt slow-moving incumbents. The system of record is dead. The future belongs to whoever ships the fastest agent.
Adam Harris has heard it all. He runs a platform that will check in 57M guests in the next six months, processes $20B+ in transactions, and sits on 4B data points across 157 markets. Last month, Cloudbeds displaced 117 competing systems — Harris had never heard of most of them. His take on the disruption narrative is blunt: the treasure is the dataset, and right now, everyone else is just plundering for it.
The Pirate Problem
Adam doesn’t mince words about what he’s seeing in hospitality AI. “I call it pirate season,” he says. “Everyone is looking for the gold and they’re just plundering wherever they can take it.”
What makes someone a pirate is the shortcut: taking the treasure without doing the work to earn it. That takes two forms:
The visible one is the credential pirate, the operator who entered the industry six months ago and is now a conference regular and, in Harris’s words, one of the “big voices on LinkedIn.”
The substantive one is the product pirate — the startup shipping a Claude-powered revenue tool or a chatbot that hallucinates cancellation policies. Wrap a frontier model and call it a moat.
Unfortunately, for would-be pirates, access to true vertical data gold isn’t easy. Anyone can spend $20/month on an Anthropic subscription and spin up quick solves. What they can’t access is 14 years of booking patterns, rate decisions, guest preferences, and operational data across 20,000+ properties — not, at least, without winning access through customers, partnering with platforms that have been around the block, or creating a flywheel of their own. As we’ve argued before, the moat in Vertical SaaS has always been the compounding dataset. Incumbents are getting increasingly sharp-elbowed around their hard-won assets.
“If ChatGPT wanted to optimize for hotel transactions, they should come talk to me,” Harris says, “because I do it every single day.” When we asked him if he’s giving full data access to any AI-native startups, Adam responded with a resounding “hell no.”
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From Record-Keeper to Action-Taker
SaaS is metamorphosing. Harris frames it as a regime change: the value of a system of record is no longer the record itself, but what you do with it. “Salesforce kind of feels like a big dumb database to me,” he says. “I don’t want to log into Salesforce. I just want the data and the action point.”
This tracks with what we found in our analysis of the SaaS repricing: the public markets didn’t punish all software equally. They punished software that was a passive store of data with no path to becoming a driver of outcomes. The winners had consumption-based models, AI-augmented workflows, or both.
Cloudbeds’ answer is Signals, their proprietary AI layer. The stat that matters: 80% of hoteliers see the right pricing move but act too late. Not because they lack awareness, but because they’re running a 24/7/365 operation where humans physically cannot process every signal at the speed the market demands. Signals runs millions of permutations per hour across Cloudbeds’ 4B data points and furnishes the action ahead of time.
The distinction Harris draws is between probabilistic and deterministic outputs. “I cannot have a probabilistic output for whether a rate is $100 or not. It has to be $100 if it’s $100.” A thank-you note can tolerate hallucination. A pricing decision cannot. That’s why Cloudbeds built deterministic harnesses around their AI (confidence scores, source attribution, audit trails) before connecting any of it to customer-facing workflows.
The Two-Year Blueprint
The most counterintuitive thing Harris said on the show: Cloudbeds spent two years rebuilding its data architecture before shipping any AI products. In the time it’s taking some startups to go from zero to tens-of-millions of ARR, Adam has been methodically laying the groundwork for his transition to an AI system of action.
He compared the process to building a house. “Permitting and planning takes most of the time. The actual building of the foundation and walls—it comes along pretty quick.” Most AI startups skip the permitting. They start with the construction and then go back to fix what they should have planned from the start.
Harris places the agentic AI cycle at “phase one of five,” roughly where self-driving cars were before anyone trusted a Waymo. “They’re super cool,” he says of agents. “I love swarming them up. But there’s a lot of garbage out at times.” He’s bullish on the long arc but thinks the industry is confusing current model capability with production readiness.
Interestingly, the jury is still out for him on whether an AI-first Cloudbeds will command more or less revenue. He ran parallel experiments: traditional SaaS pricing versus performance-based pricing where they take a cut of the revenue lift they generate. The outcome: both models hit approximately the same number. The value was equivalent regardless of how they charged for it. That’s a signal the product is genuinely driving outcomes, not just billing for the promise of them.
The Takeaway for Vertical Founders
If you’re building against an entrenched incumbent with a real data corpus and an active AI strategy, your window is narrower than you think. Harris displaced 117 competitors last month. He’d never even heard of most of them. The graveyard of “AI-powered” point solutions is getting crowded fast.
But not every incumbent is Cloudbeds. Harris himself pointed at the PE-owned, margin-optimized platforms that are “running the same exact business they were pre-ChatGPT.” 80% of PE money is sitting on the sidelines right now. They maximized profit but neglected the data layer. Those are the ones where the data asset exists but nobody’s investing in it. That’s where the real opportunity for founders lives. Not in outrunning incumbents who are already moving, but in replacing the ones who haven’t picked their heads out of the sand.
And for incumbents? Harris’s advice is unglamorous but firm: get your data right first. Build the harnesses. Don’t ship AI slop just to say you have AI. The companies that resist the urge to protect their hoard and instead aggressively invest in the vault, won’t just survive in the era of Vertical AI — they’ll thrive. Incentives as they are, however, neither we nor Adam expect that share to be more than ~20% of legacy market leaders. See you next week.
Key Moments from this Episode
00:00 — Intro
01:00 — The real opportunity behind AI
09:12 — From systems of record to systems of action
15:19 — Why proprietary data is AI’s biggest moat
20:35 — Should every software company build its own AI model?
26:32 — Why seat-based SaaS pricing is breaking down
30:05 — The future of AI pricing and outcome-based software
35:02 — How Cloudbeds approaches AI adoption internally
40:51 — Why customer behavior changes slower than technology
44:24 — The AI gold rush and protecting your data moat
49:44 — Why many AI startups underestimate enterprise software
52:20 — AI, hotel bookings, and the future of hospitality