[jasode]

>(NASA, Reuters, bloggers, authors, scholarly journals) still write and publish (including to social media), the viral content makers, a second tier of people who write about what the original author wrote about,

I like original primary sources for some topics and secondary sources for others. For programing topics, I'm ok reading the original papers.

But for other topics ... say "civil engineering" ... I prefer a "popularizer" like Grady Hillhouse's "Practical Engineering". His 15-minute presentations are the right amount of depth for exposing me to various city infrastructure topics. I'm not going to pretend I'd be interested in reading original scholarly journals from civil engineers. I deliberately outsource that to Grady. Hardcore engineers may complain that infotainment/edutainment is "shallow learning" but people have to strategically limit themselves to "shallow" explanations of some topics so they can spend more time to deep dive into other specialized areas of interest.

The "viral content makers" serve a useful purpose in the ecosystem to satisfy varying levels of interest. Therefore, a search engine that optimized for original academic papers instead of Grady blog posts when I ask "How does a city manage stormwater runoff?" -- would not be helpful to me in most cases. I dare say a "general" search engine that didn't put academic papers on page 1 of search results would be preferred by most people.

[firewolf34]

What I really want to resolve this time issue is a reliable NLP processing toolchain capable of distilling primary sources into a summarized source, something we're very close to achieving these days.

This gets you the reliability of reading from a primary source, and the objectivity of machine summarization, with none of the capitalistic intent of the "viral human summarizers".