I tried to create ChatGPT in 1999 - this is what failure taught me

"I co-founded search engine, Infoclic, in 1999. Google was yet to go mainstream, and many of the search leaders were relatively primitive. Relevancy of searches was remarkably low. It was also a time of massive growth, when the online world was opening up to people who weren't tech-savvy. A "natural language" search engine, where you could ask questions rather than focus on keywords, made sense."

"We built the first version of Infoclic in six months and continued to develop it in partnership with content providers. Traffic was growing and the business model, based on advertising, was sound, with expectations to break even. But then the bubble burst. Ad rates plummeted, and Infoclic was simply no longer viable. Google survived as the default search engine because of its smarter approach: ranking websites by popularity, based on incoming link, and searching using keywords."

"All of which made ChatGPT's launch very difficult. It was what I wanted to build in 1999, but it just wasn't possible. More than two decades of technological advances, plus access to masses of data and processing power means that LLMs can process natural language and give people exactly what they need. Lessons on timing and resilience It would be easy to be bitter about another business realising your dreams, albeit more than two decades later. But back then, Google was the superior pro"

Infoclic launched in 1999 as a natural-language search engine when search relevancy was low and the web was expanding beyond tech-savvy users. The first version was built in six months and developed with content partners, with traffic growth and an advertising model aimed to break even. The dot‑com crash caused ad rates to collapse and rendered the business nonviable. Google prevailed through link-based ranking and keyword search. Advances in compute, data availability, and models now enable large language models to process natural language effectively. Timing, market conditions, and resilience remain critical lessons for AI startups.

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