From dot-com to dot-AI: How we can learn from the last tech transformation (and avoid making the same mistakes)

At the height of the dot-com boom, adding “.com” to a company’s name was enough to send its stock price soaring — even if the business had no real customers, revenue or path to profitability. Today, history is repeating itself. Swap “.com” for “AI,” and the story sounds eerily familiar.

Companies are racing to sprinkle “AI” into their pitch decks, product descriptions and domain names, hoping to ride the hype. As reported by Domain Name Stat, registrations for “.ai” domains surged about 77.1% year-over-year in 2024, driven by startups and incumbents alike rushing to associate themselves with artificial intelligence — whether they have a true AI advantage or not.

The late 1990s made one thing clear: Using breakthrough technology isn’t enough. The companies that survived the dot-com crash weren’t chasing hype — they were solving real problems and scaling with purpose.

AI is no different. It will reshape industries, but the winners won’t be those slapping “AI” on a landing page — they’ll be the ones cutting through the hype and focusing on what matters.

The first steps? Start small, find your wedge and scale deliberately.

Start small: Find your wedge before you scale

One of the most costly mistakes of the dot-com era was trying to go big too soon — a lesson AI product builders today can’t afford to ignore.

Take eBay, for example. It began as a simple online auction site for collectibles — starting with something as niche as Pez dispensers. Early users loved it because it solved a very specific problem: It connected hobbyists who couldn’t 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.

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 — 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’t materialize fast enough, the company collapsed under its own weight.

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.

For AI product builders, this means resisting the urge to build an “AI that does everything.” 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’t know SQL, those with limited experience or seasoned analysts?

Each of those users has very different needs, workflows and expectations. Starting with a narrow, well-defined cohort — like technical project managers (PMs) with limited SQL experience who need quick insights to guide product decisions — 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’t be the ones who try to serve everyone at once — they’ll be the ones who start small, and serve someone incredibly well.

Own your data moat: Build compounding defensibility early

Starting small helps you find product-market fit. But once you gain traction, your next priority is to build defensibility — and in the world of gen AI, that means owning your data.

The companies that survived the dot-com boom didn’t just capture users — they captured proprietary data. Amazon, for example, didn’t 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 — laying the foundation for Prime’s two-day delivery, a key advantage competitors couldn’t match. None of it would have been possible without a data strategy baked into the product from day one.

Google followed a similar path. Every query, click and correction became training data to improve search results — and later, ads. They didn’t 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.

The lesson for gen AI product builders is clear: Long-term advantage won’t come from simply having access to a powerful model — it will come from building proprietary data loops that improve their product over time.

Today, anyone with enough resources can fine-tune an open-source large language model (LLM) or pay to access an API. What’s much harder — and far more valuable — is gathering high-signal, real-world user interaction data that compounds over time.

If you’re building a gen AI product, you need to ask critical questions early:

• What unique data will we capture as users interact with us?
• How can we design feedback loops that continuously refine the product?
• Is there domain-specific data we can collect (ethically and securely) that competitors won’t have?

Take Duolingo, for example. With GPT-4, they’ve gone beyond basic personalization. Features like “Explain My Answer” and AI role-play create richer user interactions — 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’t easily match.

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.

Conclusion: It’s a marathon, not a sprint

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’t be the ones chasing headlines — they’ll be the ones solving real problems, scaling with discipline and building real moats.

The future of AI will belong to builders who understand that it’s a marathon — and have the grit to run it.

Kailiang Fu is an AI product manager at Uber.

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