"the way search engines work is not because it’s the best way to search, but because of the best technology available in the late 1990s and we are stuck in that paradigm. Maybe there is a better way, but change is hard."

"Search engine design, both mobile and desktop, is stuck in a local maxima but the content we consume has changed. A lot of it is in graph form (social networks), data streams (social feed), video content (Youtube and TikTok), ecommerce, and authoritative knowledge (Wikipedia), apps etc."

"Instead of using a big database and searching on it, we need to use that database as training data, and generate results with a neural network."

"trained models are really tiny compared to the training data. Stable Diffusion is like ~2 gigabytes but the training data is 100 terabytes"

"Instead of searching for something, and then opening the first few results and scanning for the content we want, while fighting millions of popups, ads, and weight loss scams, one needs to be able to generate the answer they are looking for."

"This would completely bypass the distribution monopoly and advertising business of incumbents, as training a model is expensive (for now) but running it is not (the marginal cost of a Google search is negligible, but the cost of maintaining their infrastructure is very much not)."

"Linux started getting better [5], and computer clusters based on it became cheaper, based on custom built PCs, so web crawlers were able to have bigger and bigger indexes, and caching more web content."

"This was the state of the art of search engines. The ~28th search engine to hit the market, Google, was much better than the other, I personally used it because it had the best archive for programming and technical stuff"

"What I am trying to say is that the way search engines work is not because it’s the best way to search, but because of the best technology available in the late 1990s and we are stuck in that paradigm."

"I think a lot of it found that local centralization was more efficient."

"I think Google cannot be challenged in their core territory."

"Instead of using a big database and searching on it, we need to use that database as training data, and generate results with a neural network."

"one of the reasons why this is a big deal is that trained models are really tiny compared to the training data."

"Stable Diffusion is like ~2 gigabytes but the training data is 100 terabytes"

"So if you think that the internet is several petabytes, it’s not unreasonable that it could be reduced to a 100 gigabyte model, which is somewhat portable (a 10Gbit/sec fibre connection soon could not be out of the ordinary in wealthy urban areas)."

"so there is a great deal of technology that needs to be invented before Google can be replaced, but as others have suggested there might be a hybrid solution in the meantime – to some extent Google already tries to generate some query results when asked some questions and over time they have tried to reduce the traffic to non-Alphabet properties"

"This would also change distribution as installing the entire Google archive on your laptop is not feasible, but a ML model can definitely run"

"[3] Yahoo pivoted multiple times and it’s still around, if you want to experience what it looked like you can check yahoo.co.jp (does not work if you are from Europe, but you can image search it to see screenshots), which was licensed to SoftBank decades ago and barely updated since"

"AltaVista was the most interesting example, as they briefly controlled the market but because in their mind the product was used to index corporate documents inside a private network, they never built any kind of spam protection and very very quickly the top results were just spam garbage and everyone moved to Google. Their demise was lightning fast"