From the Dot-Com Bubble to the GenAI Wave: Consumers Still Hold the Key

Yusuf Öç
Sep 10
4 min read

Recently, I came across a startling statistic: more than 95% of generative AI applications are failing to present meaningful results according a recent MIT study (1). At first glance, this sounds surprising given the massive hype and investment pouring into the sector (2). But it immediately reminded me of the dot-com bubble of the early 2000s.

Back then, the internet was brimming with brilliant ideas. Companies like Webvan promised same-day grocery delivery, and Kozmo dot com offered products delivered within hours. These ideas weren’t wrong, they were simply too early. Fast-forward two decades, and we see their descendants thriving in the form of Deliveroo, Getir, or Instacart. The failure wasn’t in the concept, but in the consumer readiness.

I believe the same applies to today’s generative AI landscape.

A word cloud infographic with the title “From the Dot-Com Bubble to GenAI Wave: Why Consumers Still Hold the Key” at the top. In the center, key terms such as “GenAI,” “Entrepreneurship,” “Cognitive Overload,” “Dot-com Bubble,” “Diffusion,” “Consumer Readiness,” “Integration,” and “Hype Cycle” appear in varying font sizes. At the bottom left, there is a Diffusion of Innovations curve showing Innovators, Early Adopters, Early Majority, Late Majority, and Laggards. At the bottom right, there is a Product Life Cycle curve showing Introduction, Growth, Maturity, and Decline. — There are similarities between the dot-com bubble and the current GenAI wave.

The Consumer Readiness Gap

Most GenAI tools are not failing because they lack innovation. They fail because consumers are not yet ready. Even the most widely known platforms — ChatGPT, MidJourney, or Claude — are still underutilized by mainstream users. Many people are overwhelmed by steep learning curves, experience cognitive overload (the brain’s limited ability to process too much information at once), or simply don’t want to juggle multiple new apps each day.

This means that, in the long run, only a handful of AI products will survive—just as we saw after the dot-com crash. Consumers will naturally gravitate toward tools that feel intuitive (easy to use without training), familiar (similar to what they already know), and seamlessly integrated (embedded into their existing routines rather than requiring a switch).

This pattern aligns closely with Diffusion of Innovations (Everett Rogers’ theory that new ideas spread gradually through adoption stages) and the Product Life Cycle (the idea that products are introduced, grow, mature, and decline over time if not supported by innovation).

Here watch a video of me in an MBA class explaining additional examples on failing good tech products because of the same reasons.

Avoiding Entrepreneurship Myopia

Sometimes businesses fall into what I call “entrepreneurship myopia”, being so in love with their own product that they forget to think from the user’s point of view. They become obsessed with features and technology, forgetting that consumers don’t share the same passion. While "entrepreneurship myopia" isn’t a widely used formal term, it’s very similar to a well-known concept called “marketing myopia,” coined by Harvard professor Theodore Levitt in 1960. Marketing myopia describes the mistake of focusing too much on your product instead of what your customers really want, and it’s still a big trap today. Businesses with this kind of myopia spend all their time polishing features and tech, missing the fact that most people are busy, distracted, and have short attention spans. They’re just not going to go out of their way to learn or adopt something complicated or unfamiliar.

The fundamentals of success haven’t changed: you still need to understand consumer behavior, offer intuitive design, and use effective marketing. Everything starts with your users, not what you think is cool.

A good example is the battle between Apple’s iPod and its early MP3 player competitors. Before the iPod, there were already plenty of MP3 players on the market. Some even had more features and better technical specs. But Apple focused on simplicity: the scroll wheel, the clean design, and the slogan “1,000 songs in your pocket.” It wasn’t the first, but it was the easiest to use—and that’s why it won. I learned this lesson first-hand with one of my own startups. I developed a digital marketing tool for fitness trainers and dieticians in the wellness sector. The idea was strong, but the product was too complex. Trainers and dieticians didn’t have the time or technical background to figure it out, and adoption just didn’t take off as I expected. It wasn’t the idea that failed—it was the fact that I hadn’t made it simple and intuitive enough for the people I was targeting.

Strategic Takeaways

Don’t build in isolation. Instead of launching yet another standalone GenAI app, think about how to weave AI into the tools people already use and love.
Keep it simple. Make it easy to understand and use, minimize friction, and avoid long, frustrating sign-ups or tutorials.
Focus on adoption, not invention. Innovation only matters when it’s actually used. Products must be usable, accessible, and genuinely helpful to win.

As the co-author of Consumer Behavior: Building Market Strategy,(you can find it on Amazon) this is exactly my area of expertise. If you or your company are exploring how to design, launch, or scale AI-enabled products, I’d be happy to support through workshops, consultancy, or strategy sessions.

The lesson from history is clear: technology doesn’t fail because the ideas are bad; it fails when consumers aren’t ready for it. The winners of the GenAI wave will be those who focus less on what’s possible, and more on what’s usable and desirable.