At Booking Holdings, CEO Glenn Fogel sees more than just opportunity in the current surge of artificial intelligence technologies, he sees echoes of a past boom, the dot-com era. With over 25 years in travel and technology under his belt, Fogel often reflects on what worked then, what failed, and what lessons apply now as AI reshapes the travel industry.
From Dot-Com Experiments to Practical Platforms
Fogel joined what would become Booking.com back in 1999, right in the heart of the dot-com boom. Back then the company tried wide experiments like “name-your-own-price” models not only for hotels but cars, insurance, mobile time. Many of those experiments burned cash, but also yielded important lessons in customer behaviour and scalable models.
Over time those risky, wildcard experiments gave way to more predictable, user-friendly models. For example, Booking acquired Active Hotels and Booking.com and focused on a model where travellers knew the hotel, price, and paid at arrival, clarity and trust became key. That was part of Booking’s pivot from “too broad and loose” toward specialization and customer transparency.
Learning Lessons as AI Rises
Fogel draws a number of parallels between then and now:
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Experimentation + Risk: Just like in the dot-com period, many companies are experimenting now with what AI can do, with both hype and real utility mixed. Some ideas will fail, some will succeed. Fogel believes it’s critical to try many things, fail fast, but also double down on what works.
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Focus on Product-Market Fit: In the early 2000s many business models were pushed because of potential, not actual demand. Fogel emphasizes that what really matters is what customers need, for Booking now that’s making travel seamless, integrating flights, hotels, transportation, activities in what he calls the “connected trip.” AI can help deliver that if used well.
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Scalability and Efficiency: The dot-com era taught Booking the dangers of overextending before product-market fit and operational readiness. In AI, the risk is similar: tools that promise big customer-facing improvements often require big backend work and cost. Fogel sees generative AI as mostly helpful for simpler tasks (customer service, personalization of search content) first, gradually pushing into more complex use cases.
Building the “Connected Trip”
A core piece of Fogel’s vision is the “connected trip”, combining all parts of travel (flights, hotels, ground transport, attractions) in one system, personalized and responsive. He believes AI will help in:
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dynamically tailoring search results and content to what a user actually wants (walkability, pricing, preferences) instead of static filters and generic photos.
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solving disruptions proactively (flight delays, cancellations) by suggesting alternatives, informing users in real time, and adjusting itineraries seamlessly.
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improving customer support efficiency via AI chatbots for simpler tasks, freeing up human agents for complex problems. Also improving the experience of human agents themselves through AI-assisted tools.
What Could Go Wrong: Dot-Com Mistakes to Avoid
Fogel doesn’t seem blinded by the nostalgia of the dot-com boom, he underscores certain traps:
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Overhype Without Revenue: In dot-com, many companies raised huge sums before proving their ability to generate profits or sustainable business models. In the AI era, some startups or projects are valued on promise more than delivery. Fogel expects solid metrics.
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Customer Trust & Transparency: Back then, confusing pricing or unexpected fees hurt consumers. Now, opaque AI-driven decisions or personalization without context could erode trust. Booking emphasizes clarity.
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Regulation & Competitive Landscape: As AI becomes more central, regulatory scrutiny increases (privacy, bias, monopolistic behavior). Also competitive pressure from big tech, and from travel vendors wanting direct relations with customers. Fogel is aware of that risk.
Why Fogel Is Optimistic
Despite the risks, Fogel believes the parallels to the dot-com era give reason for hope rather than fear.
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Travel as a human need continues to grow faster than GDP in many markets; more people reaching middle class means more travel demand.
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Booking has scale, brand strength, global footprint (220+ countries/territories, many languages) which gives it a leg up versus smaller players.
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Smart use of AI can enhance loyalty, reduce friction, improve margins if applied well. Fogel trusts that the engineering, experimentation culture they built over the dot-com era will help them avoid the mistakes others made.
