Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More<\/em><\/p>\n\n\n\n At the height of the dot-com boom, adding \u201c.com\u201d to a company\u2019s name was enough to send its stock price soaring \u2014 even if the business had no real customers, revenue or path to profitability. Today, history is repeating itself. Swap \u201c.com\u201d for \u201cAI,\u201d and the story sounds eerily familiar.<\/p>\n\n\n\n Companies are racing to sprinkle \u201cAI\u201d into their pitch decks, product descriptions and domain names, hoping to ride the hype. As reported by Domain Name Stat, registrations for \u201c.ai\u201d domains surged about 77.1% year-over-year in 2024, driven by startups and incumbents alike rushing to associate themselves with artificial intelligence \u2014 whether they have a true AI advantage or not.<\/p>\n\n\n\n The late 1990s made one thing clear: Using breakthrough technology isn\u2019t enough. The companies that survived the dot-com crash weren\u2019t chasing hype \u2014 they were solving real problems and scaling with purpose.<\/p>\n\n\n\n AI is no different. It will reshape industries, but the winners won\u2019t be those slapping \u201cAI\u201d on a landing page \u2014 they\u2019ll be the ones cutting through the hype and focusing on what matters.<\/p>\n\n\n\n The first steps? Start small, find your wedge and scale deliberately.<\/p>\n\n\n\n One of the most costly mistakes of the dot-com era was trying to go big too soon \u2014 a lesson AI product builders today can\u2019t afford to ignore.<\/p>\n\n\n\n Take eBay, for example. It began as a simple online auction site for collectibles \u2014 starting with something as niche as Pez dispensers. Early users loved it because it solved a very specific problem: It connected hobbyists who couldn\u2019t find each other offline. Only after dominating that initial vertical did eBay expand into broader categories like electronics, fashion and, eventually, almost anything you can buy today.<\/p>\n\n\n\n Compare that to Webvan, another dot-com era startup with a much different strategy. Webvan aimed to revolutionize grocery shopping with online ordering and rapid home delivery \u2014 all at once, in multiple cities. It spent hundreds of millions of dollars building massive warehouses and complex delivery fleets before it had strong customer demand. When growth didn\u2019t materialize fast enough, the company collapsed under its own weight.<\/p>\n\n\n\n The pattern is clear: Start with a sharp, specific user need. Focus on a narrow wedge you can dominate. Expand only when you have proof of strong demand.<\/p>\n\n\n\n For AI product builders, this means resisting the urge to build an \u201cAI that does everything.\u201d Take, for example, a generative AI tool for data analysis. Are you targeting product managers, designers or data scientists? Are you building for people who don\u2019t know SQL, those with limited experience or seasoned analysts? <\/p>\n\n\n\n Each of those users has very different needs, workflows and expectations. Starting with a narrow, well-defined cohort \u2014 like technical project managers (PMs) with limited SQL experience who need quick insights to guide product decisions \u2014 allows you to deeply understand your user, fine-tune the experience and build something truly indispensable. From there, you can expand intentionally to adjacent personas or capabilities. In the race to build lasting gen AI products, the winners won\u2019t be the ones who try to serve everyone at once \u2014 they\u2019ll be the ones who start small, and serve someone incredibly well.<\/p>\n\n\n\n Starting small helps you find product-market fit. But once you gain traction, your next priority is to build defensibility \u2014 and in the world of gen AI, that means owning your data.<\/p>\n\n\n\n The companies that survived the dot-com boom didn\u2019t just capture users \u2014 they captured proprietary data. Amazon, for example, didn\u2019t stop at selling books. They tracked purchases and product views to improve recommendations, then used regional ordering data to optimize fulfillment. By analyzing buying patterns across cities and zip codes, they predicted demand, stocked warehouses smarter and streamlined shipping routes \u2014 laying the foundation for Prime\u2019s two-day delivery, a key advantage competitors couldn\u2019t match. None of it would have been possible without a data strategy baked into the product from day one.<\/p>\n\n\n\n Google followed a similar path. Every query, click and correction became training data to improve search results \u2014 and later, ads. They didn\u2019t just build a search engine; they built a real-time feedback loop that constantly learned from users, creating a moat that made their results and targeting harder to beat.<\/p>\n\n\n\n The lesson for gen AI product builders is clear: Long-term advantage won\u2019t come from simply having access to a powerful model \u2014 it will come from building proprietary data loops that improve their product over time.<\/p>\n\n\n\n Today, anyone with enough resources can fine-tune an open-source large language model (LLM) or pay to access an API. What\u2019s much harder \u2014 and far more valuable \u2014 is gathering high-signal, real-world user interaction data that compounds over time.<\/p>\n\n\n\n If you\u2019re building a gen AI product, you need to ask critical questions early:<\/p>\n\n\n\n Take Duolingo, for example. With GPT-4, they\u2019ve gone beyond basic personalization. Features like \u201cExplain My Answer\u201d and AI role-play create richer user interactions \u2014 capturing not just answers, but how learners think and converse. Duolingo combines this data with their own AI to refine the experience, creating an advantage competitors can\u2019t easily match.<\/p>\n\n\n\n In the gen AI era, data should be your compounding advantage. Companies that design their products to capture and learn from proprietary data will be the ones that survive and lead.<\/p>\n\n\n\n The dot-com era showed us that hype fades fast, but fundamentals endure. The gen AI boom is no different. The companies that thrive won\u2019t be the ones chasing headlines \u2014 they\u2019ll be the ones solving real problems, scaling with discipline and building real moats.<\/p>\n\n\n\n The future of AI will belong to builders who understand that it\u2019s a marathon \u2014 and have the grit to run it.<\/p>\n\n\n\n Kailiang Fu is an AI product manager at Uber.<\/em><\/p>\n Daily insights on business use cases with VB Daily<\/strong><\/p>\n If you want to impress your boss, VB Daily has you covered. We give you the inside scoop on what companies are doing with generative AI, from regulatory shifts to practical deployments, so you can share insights for maximum ROI.<\/p>\n \n\t\t\t\t\tThanks for subscribing. Check out more VB newsletters here.\n\t\t\t\t<\/p>\n An error occured.<\/p>\n<\/p><\/div>\n
\n<\/div>Start small: Find your wedge before you scale<\/h2>\n\n\n\n
Own your data moat: Build compounding defensibility early<\/h2>\n\n\n\n
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Conclusion: It\u2019s a marathon, not a sprint<\/h2>\n\n\n\n
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