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The future of search is being reinvented with generative AI

VC Cafe: “I spent a chunk of my career as a product manager in search (Shopping.com, GLG, Ask.com, AOL and Google) so I find this particularly interesting. Until now, to succeed in search companies needed two things: 1) an index of the web 2) an algorithm to organise the results. In the early days of the web, companies like AltaVista, Excite, WebCrawler and others competed in becoming the search engine of choice. But two years after Google came to the fore in 1998, Google became the number one search engine and “Googling” has become synonymous with web search. Sure, there is Bing, DuckDuckGo, Ask.com and a few others, but no one comes even near Google in terms of market share or mind share. However, since the introduction of ChatGPT in November 2022, user habits (and perhaps expectations) began to change. It felt like magic to just type a question in natural language and have the answer formatted, generated, and iterated, on the spot. Why should users sift through ads and 10 blue links for the information they’re looking for if it can just be generated with LLMs and tailored to their specific query/prompt? And if you still want to do a web search, wouldn’t it be better if it was enriched with AI? Think about it for a second, since the introduction of ChatGPT, do you find yourself Googling more or less?…The combination of ChatGPT, Bing’s integration of LLMs in search and Google’s embrace of generative AI technologies signalled that we’re entering a new paradigm in information retrieval with LLMs. We’re moving away from the 10 blue links (and sometimes 10 blue ads) and moving into something different.

In a recent All In podcast episode, Brad Gerstner, CEO of Altimeter Capital, quoted Richard Barton, co-founder of both Expedia and Zillow, two of the most popular vertical search engines (travel + real estate) referred to this new era of search as the ‘race to intimacy’. A conversational UI, that changes the user experience from query–> link to personalised extraction of knowledge. where rather than sift through noise to find an answer, users will generate a specific result to match their needs…”

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