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 Bummus, adding “.com” to the name of a company was sufficient to send the price of shares – even if the work does not allow real customers, income or profitability. Today’s date repeats itself. Change “.com” for “EU” and sounds familiar to the story Eeril.

Companies compete to splash in “AI” bounce decks, product descriptions and domain names, in the hope of riding the hype. Reported Domain Name StatNotes for the domains of “.i” In 2024, in terms of about 77.1% annual compared to the annual compared to the annual annual compared to the annual compared to the annual compared to the annual compared to the annual compared to the annual comparison, both in terms of beginnings and unions.

The late 1990s made something clear: use Leap Technology not enough. Companies surviving the Dot-C COMPANY did not follow the hype – they solved the real problems and expanded it.

AI is not different. The industries will re-adjust, but the winners will not bloom “AI” “AI” on the opening page – they focus on the hype and important ones.

First steps? Start small, find your generation and scale knowingly.

Start small: Find your wedge before measuring

One of the most expensive mistakes of the dot-com period was trying to go big soon – a lesson AI product builders Can’t ignore today.

For example, take eBay. Started as a simple online auction site for collections – starting from something like Pez dispensers. Early users loved to solve a very special problem: hobbyists who could not find each other offline. Only after the initial vertical eBay, electronics, fashion and eventually prefer more categories such as everything you can buy today.

Compare that RealmAnother point-com ERA start with another strategy. Webvan aims to revolutionize food shopping with online orders and fast home delivery – all in more than one city. Hundreds of millions of dollars were massive warehouses and complex delivery fleets before there is a strong customer request. If the growth is not fast enough, the company collapsed under its weight.

The sample is clear: Start with a sharp, special user need. Notice a narrow wedge you can be friends. Expand only when strong demand is proof.

For AI product founders, it means to resist the desire to build the “Everything AI”. For example, a Generative AI instrument for data analysis. Do you target product managers, designers or data scientists? For people who do not know SQL, do you build for people with limited experience or experienced analysts?

Each of these users has many different needs, workflows and expectations. Starting from a narrow, well-defined cohort – as technical project managers (PMS), with the experience of technical project managers (PMS), allows you to understand the practice deep and really invaluable. From there, you can deliberately expand the neighbors or opportunities. The winners will not be able to serve everyone at once – they will not be trying to serve everyone at once in the competition.

Have your data: Set up early ink protection

Start small, helps to find product market compatibility. But after gaining traction, your next priority is to build protection – and in Gen world aimeans to have your data.

The surviving companies from Dot-COM Boom were not captured only by users – were seized by property information. Amazon, for example, did not stop selling books. They watched shopping and product meetings to improve their recommendations, then used regional order information to optimize the fulfillment. By analyzing the purchase samples between cities and zip codes, the demand, the main advantage of the foundation of the foundations of stock and adjusted deposits, the foundation of the foundation of the main advantage of the main advantage did not prevail. None would be impossible without a data strategy without a data strategy from the first day.

Google followed a similar way. Each query, click and adjustment training information to improve search results – and later ads. They did not just set up a search engine; They set up a real-time reviewed loop learned from users to create a MOAT, which creates and beating results from users.

Lesson for GEN AI product builders Obviously: Long-term preference will not be simply a strong model – the property that develops its products over time will come forward.

Anyone who has enough sources today can afford a large language model (LLM) or an API. How hard – and more valuable – a high signal that combines over time, the real world collects user interaction data.

If you build a Gen AI product, you have to ask the critical questions early:

• What unique information will we get as users interact with us?
• How can we design the product constantly cleansing opinion loops?
• Does not have the domain specific information we can collect opponents (ethical and secure)?

For example, take Duolingo. With GPT-4, they went beyond Main personalization. Features such as “Explain My Response” and AI role-playing creates richer user interaction – not only answers, how learners think and speak. Duolingo combines this information with its AI with your AI, can not be easily adapted by creating competitors by practice.

During the Gen AI period, information should be your complicated advantage. Companies who are preparing to capture and study their products from special data will be survived and leaders.

Result: Not a sprint, the marathon is

Dot-COM period fades rapidly, but the basics continue. Gen AI Boom has no difference. Exhaust companies will not follow the headlines – there will be those who build real problems, discipline and real moats.

The future of the AI ​​will belong to the builders who understand that it is a marathon and has a grit to run it.

Kailiang Fu is the AI ​​product manager at Uber.