Search is the primary means by which we navigate and access digital information. It lies at the heart of the modern internet experience.
Today’s large language models can read and write with a level of sophistication that a few years ago would have seemed inconceivable. This will have profound implications for how we search.
In the wake of ChatGPT, one reconceptualization of search that has gotten a lot of attention is the idea of conversational search. Why enter a query and get back a long list of links (the current Google experience) if you could instead have a dynamic conversation with an AI agent in order to find what you are looking for?
Conversational search has a bright future. One major challenge needs to be resolved, though, before it is ready for primetime: accuracy. Conversational LLMs are not reliably accurate; they occasionally share factually untrue information with total confidence. OpenAI CEO Sam Altman himself recently cautioned: “It’s a mistake to be relying on ChatGPT for anything important right now.” Most users will not accept a search application that is accurate 95% or even 99% of the time. Addressing this issue in a scalable and robust way will be one of the primary challenges facing search innovators in 2023.
You.com, Character.AI, Metaphor and Perplexity are among the wave of promising young startups looking to take on Google and reinvent consumer search with LLMs and conversational interfaces.
But consumer internet search is not the only type of search that LLMs will transform.
Enterprise search—the way that organizations search and retrieve private internal data—is likewise on the cusp of a new golden age. Thanks to large-scale vectorization, LLMs enable true semantic search for the first time: the ability to index and access information based on underlying concepts and context rather than simple keywords. This will make enterprise search vastly more powerful and productive.
Startups like Hebbia and Glean are leading the charge to transform enterprise search using large language models.
And the opportunities for next-generation search extend beyond text. Recent advances in AI open up whole new possibilities in multimodal search: that is, the ability to query and retrieve information across data modalities.
Given that it accounts for ~80% of all data on the internet, no modality represents a bigger opportunity than video. Imagine being able to search effortlessly and precisely for a particular moment, individual, concept or action within a video. Twelve Labs is one startup building a multimodal AI platform to enable nuanced video search and understanding.
Search has changed surprisingly little since Google’s ascendance during the dot-com era. Next year, thanks to large language models, this will begin to change dramatically.
If you enjoyed this market, please check out the other 9! https://manifold.markets/group/forbes-2023-ai-predictions
This market is from Rob Toews' annual AI predictions at Forbes magazine. This market will resolve based on Rob's own self-assessed score for these predictions when he publishes his retrospective on them at the end of the year.
Since Rob resolved and graded his 2022 predictions before the end of 2022, I am setting the close date ahead of the end of the year, to (try to) avoid a situation where he posts the resolutions before the market closes. In the event that his resolution post falls in 2024, my apologies in advance. If he hasn't posted resolutions at all by February 1, 2024, I will do my best to resolve them personally, and set N/A for any questions that I can't determine with outside source data.
Edit 2023-07-05: Last year Rob used "Right-ish" to grade some of his predictions. In cases of a similar "Right-ish" (or "Wrong-ish") answer this year, I will resolve to 75% PROB or 25% PROB, respectively. This will apply for similar language too ("mostly right", "partial credit", "in the right direction"). If he says something like "hard to say" or "some right, some wrong", or anything else that feels like a cop-out or 50% answer, I will just call that N/A.