LLMs aren't a database. They're an attempt at brute-forcing an artificial mind. The who and what aren't really interesting there, it'll forget most of such details anyway. What matters is the patterns visible in the text at various scales. How people write. Why they write. To whom they write, in response to what. How does e-mails about mistakes correlate with PDFs they're referring to. How people work with ticketing systems - like how, actually, a ticket plays out. The jargon, the acronyms, the vibes, the causal links. It's all in there, and it's another slice through the set of things humans do.

(Hell, the fact that it's all from one org would make it a great dataset to have in the open for sociological studies. I bet that today, aided by LLMs to sift through it, you could use it to map how information flows through a large org - how incident on the floor travels through time and layers of management until it reaches the C-suite, what of it survives, how it gets reacted to, how the reactions flow down...)

It will have a different writing style from the average blog post. Maybe they just want to train an AI that sounds less like an AI. Or one that speaks in vapid management style.

A major selling point of AI chat bots is for customer service automation. A clean dataset like this is a huge find. I'm not sure what you mean by "facts" or "gold nuggets". Training data doesn't need to be factual.

Truth is even weirder. plenty of growth is possible but modern liberal democracy requires that all progress must be contingent on the production of enormous amounts of text that nobody would ever read.

3 years ago, the Wall Street journal covered a company trying to use AI to generate documents required for the approval of new nuclear reactor reactor designs, which sounds dangerous, until you get to the point where they'd cite 2 million pages as necessary for a typical application. [1] I don't need to explain why no individual or institution could read that, much less examine it in detail. I remember a rather funny question I found in a comment to that story - "How many pages of those 2 million could contain pornographic images before anyone notices?"

It's obvious why companies are so desperate for training data - a text generator of sufficient quality is more conductive to the growth of the nuclear industry than any scientific breakthrough in nuclear physics.

And it's not just nuclear power. When it comes to stuff like building rail lines, apartments or power plants (both conventional and renewable), you'd find that the main bottleneck is the necessity to produce documents. And of course, many documents are subject to judicial review - a process that consumes even more text.

[1] https://www.wsj.com/tech/ai/microsoft-targets-nuclear-to-pow...

Any kind of fuel for giving active investors that FOMO tingle which then forces the steamroll of index funds to blindly follow.

I guess the appropriation "any sufficiently advanced stock market is indistinguishable from entertainment" doesn't quite stop at equating the trade floor with a casino. At some point, entertainment also becomes the modus operandi of corporations.