I disagree. There is lots of useful information around the internet that is hard to find. A lot of content simply doesn't have the keywords it needs to be discovered.
For example, somebody could search for "best rewards credit card for married couple with normal lifestyle" and find some listicle full of referral links that's a few years old. But the best advice might be in a Reddit or Twitter discussion titled "what's a good card for my P2 with no AF, low MSR, and at least 2CPP" whose replies are terse comments like "CSP?" or "BofA Custom; select CNP transactions as the 3% category and order everything online. they give high CL if you want to AU instead". There's enough jargon and levels of understanding that Google can't find the best advice, only good keyword matches.
I recently had a similar issue when I searched for something well-phrased and generic ("how to stop wood joints from squeaking"). The results were lackluster, but after a few attempts, I found the most helpful results were actually under a specific application ("how to stop a bed frame from squeaking").
So what's my point? It's not Google's fault. They are trying their best of optimize text search, with some fancy word associations and other stuff to help. But it's going to take a lot of effort to make an efficient, scalable, general-purpose AI that can achieve near-human understanding of text and then find the most relevant articles related to that. This "super-Google" would have to comprehend every post and comment on the internet, contextualize the knowledge ("a squeaky bed is caused by squeaky wood joints or fasteners, so this advice is useful for any kind of squeaking wood furniture"), and quickly generate results for every query.
It's not WebMD's fault they have ad revenue that lets them hire writers to SEO their articles with the best keywords. Nor is it Merck's fault that they are using specific language that doesn't cover all the phrases that a person could search. Nor is it Google's for making a search engine without human comprehension. It's just a technological gap that can't be bridged in the present day.
For example, somebody could search for "best rewards credit card for married couple with normal lifestyle" and find some listicle full of referral links that's a few years old. But the best advice might be in a Reddit or Twitter discussion titled "what's a good card for my P2 with no AF, low MSR, and at least 2CPP" whose replies are terse comments like "CSP?" or "BofA Custom; select CNP transactions as the 3% category and order everything online. they give high CL if you want to AU instead". There's enough jargon and levels of understanding that Google can't find the best advice, only good keyword matches.
I recently had a similar issue when I searched for something well-phrased and generic ("how to stop wood joints from squeaking"). The results were lackluster, but after a few attempts, I found the most helpful results were actually under a specific application ("how to stop a bed frame from squeaking").
So what's my point? It's not Google's fault. They are trying their best of optimize text search, with some fancy word associations and other stuff to help. But it's going to take a lot of effort to make an efficient, scalable, general-purpose AI that can achieve near-human understanding of text and then find the most relevant articles related to that. This "super-Google" would have to comprehend every post and comment on the internet, contextualize the knowledge ("a squeaky bed is caused by squeaky wood joints or fasteners, so this advice is useful for any kind of squeaking wood furniture"), and quickly generate results for every query.
It's not WebMD's fault they have ad revenue that lets them hire writers to SEO their articles with the best keywords. Nor is it Merck's fault that they are using specific language that doesn't cover all the phrases that a person could search. Nor is it Google's for making a search engine without human comprehension. It's just a technological gap that can't be bridged in the present day.