How to Build Lasting AI Products by Avoiding Hype and Focusing on Real Value

The current surge in AI interest echoes the dot-com boom of the late 1990s, where adding “.com” to a company’s name sparked investor frenzy regardless of business fundamentals. Today, “AI” is the new buzzword, with domain registrations soaring by over 77% year-over-year in 2024. However, history teaches us that hype alone doesn’t guarantee success. The companies that thrived after the dot-com crash were those that solved real problems and scaled thoughtfully.

Start Small and Find Your Wedge

One of the most critical lessons from the dot-com era is the danger of trying to scale too quickly without proven demand. Companies like eBay succeeded by starting with a narrow focus—connecting collectors of specific items like Pez dispensers—before expanding into broader markets. In contrast, Webvan failed by overinvesting in infrastructure before securing a strong customer base.

For AI product builders, this means resisting the temptation to create an all-encompassing AI solution from the outset. Instead, focus on a specific user group with distinct needs. For example, a generative AI tool could initially target technical project managers with limited SQL skills who need quick data insights. This focused approach allows for deep user understanding and product refinement before expanding to other personas or capabilities.

Own Your Data Moat to Build Defensible AI Products

Once product-market fit is achieved, building defensibility becomes paramount. The companies that thrived post-dot-com didn’t just attract users; they captured proprietary data that created competitive advantages. Amazon leveraged purchase and regional data to optimize fulfillment and delivery, while Google used search queries and user interactions to refine results and ad targeting.

In the generative AI era, access to powerful models is widely available, but the true moat lies in proprietary data loops that continuously improve the product. Companies must design their AI solutions to capture high-quality user interaction data ethically and securely, enabling ongoing refinement that competitors cannot easily replicate.

Duolingo exemplifies this approach by integrating GPT-4 to enhance personalization through features like “Explain My Answer” and AI role-play. These interactions capture nuanced learner behavior, which Duolingo combines with its own AI to continuously improve the experience, creating a durable competitive edge.

Conclusion: AI Success Requires Discipline and Patience

The dot-com era’s lessons remain relevant: hype fades, but fundamentals endure. The AI boom will reward companies that solve genuine problems, scale deliberately, and build defensible data advantages. Success in AI is a marathon, not a sprint, requiring grit, focus, and strategic execution.