From dot-com to dot-ai: how we can learn from the last technological transformation (and avoid making the same mistakes)

At the top of the Boom Dot -Com, the addition of “.com” on behalf of a company was sufficient to send its stock market course – even if the company had no real customers, income or path to profitability. Today, history is repeated. Exchange “.com” for “AI”, and history seems strangely familiar.

Companies run to sprinkle “AI” in their bridges, product descriptions and domain names, in the hope of mounting media threshing. As indicated by the domain name Stat, the “.AI” domain recordings increased by approximately 77.1% in annual shift in 2024, driven by startups and operational operators who rush to associate with artificial intelligence – whether they have a real advantage of AI or not.

The late 1990s clearly indicated that the use of revolutionary technology is not enough. Companies that survived the DOT -Com accident did not pursue media threshing – they resolved real problems and set to scale with a goal.

AI is no different. This will reshape the industries, but the winners will not be those who slap “AI” on a destination page – it will be they who will cross media and focus on what matters.

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

Start small: Find your front corner of the ladder

One of the most expensive errors in the dot -com era was to try to go too early – a lesson that AI products manufacturers cannot afford to ignore.

Take eBay, for example. He started as a simple online auction site for collectibles – starting with something as niche as PEZ distributors. The first users loved it because it solved a very specific problem: he connected amateurs who could not find themselves offline. It is only after having dominated that the initial vertical that eBay develops in wider categories such as electronics, fashion and, ultimately, almost everything you can buy today.

Compare this to Webvan, another startup from the Dot-Com era with a very different strategy. Webvan aimed to revolutionize grocery store with online order and fast home delivery – at the same time, in several cities. He spent hundreds of millions of dollars to build massive warehouses and complex delivery fleets before having high demand from customers. When growth has not materialized fairly quickly, the company collapsed under its own weight.

The pattern is clear: start with a net and specific user need. Focus on a narrow corner that you can dominate. Only develop when you have a high demand proof.

For IA products manufacturers, it means resisting the desire to build an “AI that does everything”. Take, for example, a generative AI tool for data analysis. Do you see product managers, designers or data scientists? Do you build for people who do not know SQL, those who have limited experience or experienced analysts?

Each of these users has very different needs, workflows and expectations. Starting with a narrow and well -defined cohort – as technical project managers (PMS) with a limited SQL experience that needs quick information to guide product decisions – allows you to deeply understand your user, refine the experience and create something really essential. From there, you can intentionally develop in adjacent characters or capacities. In the race to build sustainable Gen AI products, the winners will not be those who try to serve everyone at the same time – they will be the ones who start small and will serve incredibly well.

Have your data data: build the defense of the composition early

Starting small helps you find an adjustment of the product market. But once you gain traction, your next priority is to build defensibility – and in the AI ​​generation world, it means having your data.

Companies that have survived the Dot -Com Boom do not only capture users – they have captured proprietary data. Amazon, for example, did not stop selling books. They followed purchases and products on products to improve recommendations, and then used regional control data to optimize processing. By analyzing the purchasing models in cities and postal codes, they predicted demand, more intelligent stored warehouses and rationalized shipping routes – laying the basics of two -day premium delivery, a key advantage of competitors could not match. Nothing would have been possible without a data strategy in the product from the first day.

Google followed a similar path. Each request, click and correction has become training data to improve research results – and later, advertisements. They did not just build a search engine; They built a real -time feedback loop that has constantly learned from users, creating a gap that made their results and targeting more difficult to beat.

The lesson for Gen AI products manufacturers is clear: the long -term advantage will not come from just access to a powerful model – it will come from the construction of owner data loops that improve their product over time.

Today, anyone with enough resources can refine an open source (LLM) model or pay to access an API. Which is much more difficult – and much more precious – is to collect high and real signal interaction data that are made up over time.

If you build a Gen AI product, you should ask critical questions early:

• What unique data will we capture when users interact with us?
• How to design feedback loops that permanently refine the product?
• Are there data specific to the field that we can collect (ethically and safe) that competitors will not have?

Take Duolingo, for example. With GPT-4, they went beyond basic personalization. Features such as “explain my answer” and an AI role -playing game create richer user interactions – capturing not only the answers, but how learners think and converse. Duolingo combines this data with their own AI to refine the experience, the creation of a competitors’ advantage cannot easily correspond.

In the Gen Ai era, data should be your composition advantage. Companies that design their products to capture and learn proprietary data will be those that survive and direct.

Conclusion: it’s a marathon, not a sprint

The Dot-Com era has shown us that beateering is quickly fading, but the fundamentals continue. The AI ​​generation boom is no different. Companies that are prosperous will not be those that will make the headlines – they will be they will solve real problems, the scaling of discipline and the construction of real moats.

The future of AI will belong to manufacturers who understand that it is a marathon – and will have the grain to make it work.

Kailiang Fu is AI product manager at Uber.