According to Fast Company, Glenn Fogel joined Priceline in early 2000 just one week before the Nasdaq peaked, witnessing firsthand the dotcom bubble’s collapse that saw the stock crash to $6 per share and the Nasdaq plunge 77% from its March 2000 highs by 2002. Today as CEO of Booking Holdings, Fogel observes striking parallels in the “gold-rush mentality” of both booms, noting that corporate AI investment reached $252.3 billion with private generative AI investment hitting $33.9 billion in 2024 according to Stanford Institute for Human-Centered AI data. However, Fogel identifies a crucial difference: the potential transformations from generative AI are “so much greater than what was possible from the startups of the nineties.” This perspective from someone who weathered the last major tech collapse offers invaluable context for understanding today’s AI landscape.
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The Value of Survivor Wisdom
Fogel’s experience provides something rare in today’s AI discourse: historical context from someone who actually lived through a similar technological hype cycle and emerged successful. Most current AI leaders and investors only know the post-2008 era of relatively stable tech growth, missing the crucial lessons from when speculative excess met reality. Fogel’s journey from joining a dotcom darling at its peak to leading a $100+ billion travel conglomerate gives him unique credibility in assessing whether current AI valuations reflect genuine transformation or speculative froth. This isn’t theoretical knowledge—it’s hard-won wisdom from someone who saw promising companies evaporate and learned to distinguish between technological potential and sustainable business models.
Beyond the Surface Parallels
While Fogel acknowledges the similar “gold-rush mentality,” the underlying dynamics differ significantly. The dotcom boom was primarily about distribution—moving existing services online—while generative AI represents fundamental capability enhancement. Dotcom companies often struggled with unit economics and customer acquisition costs, whereas AI technologies demonstrably improve efficiency and create new capabilities from day one. More importantly, the infrastructure requirements differ dramatically: dotcom companies needed massive physical infrastructure for fulfillment and logistics, while AI’s infrastructure is computational and increasingly accessible through cloud services. This accessibility means the barrier to creating value is lower, even if the barrier to creating sustainable businesses remains high.
Investment Patterns Tell a Different Story
The Stanford AI Index data reveals more disciplined investment patterns than during the dotcom era. While $33.9 billion in private generative AI investment seems substantial, it’s concentrated in infrastructure and foundational models rather than the consumer-facing applications that dominated dotcom investing. During the late 1990s, companies with little more than a website and ambitious plans received massive valuations, whereas today’s AI investments largely flow to companies with demonstrable technological advantages and clear paths to monetization. The corporate investment figure of $252.3 billion reflects established companies integrating AI into existing operations rather than speculative startups, suggesting more grounded expectations about returns.
The Travel Industry’s AI Transformation
From Fogel’s position leading Booking Holdings, the AI transformation is already underway in ways that dwarf dotcom-era changes. While the original internet revolution moved booking from travel agents to websites, AI enables completely personalized travel planning, dynamic pricing optimization, and customer service automation at scales previously impossible. More significantly, AI can transform the entire travel experience through predictive maintenance, crowd management, and hyper-personalized recommendations. Unlike dotcom innovations that primarily changed how consumers accessed services, AI changes the services themselves—potentially making travel safer, more efficient, and more tailored to individual preferences in ways that create genuine new value rather than just redistributing existing value.
The Regulatory Wild Card
One critical difference Fogel doesn’t mention but undoubtedly considers: the regulatory environment. The dotcom boom occurred during a period of regulatory permissiveness, while AI development faces increasing scrutiny around data privacy, algorithmic bias, and potential job displacement. This regulatory attention, while potentially constraining, also provides guardrails that could prevent the kind of speculative excess that characterized the late 1990s. Companies developing AI technologies must navigate complex compliance requirements from the outset, forcing more disciplined approaches to development and deployment. This regulatory maturity, combined with lessons learned from previous tech cycles, suggests the AI boom may follow a more sustainable trajectory despite surface similarities to the dotcom era.
The 12-24 Month Outlook
Looking ahead, we’re likely to see the AI landscape consolidate significantly as Fogel predicts “many companies will not make it.” However, unlike the dotcom crash that wiped out entire categories of businesses, the AI shakeout will likely preserve fundamental technologies while eliminating redundant applications. The companies that survive will be those solving concrete business problems rather than chasing technological trends. For established players like Booking Holdings, the strategy will involve selective acquisition of promising AI startups combined with internal development—a more measured approach than the acquisition frenzies of the dotcom era. The real test will come when AI technologies must demonstrate sustainable revenue generation rather than just technological promise.
