So, like, legit pro AI tip, at least for 3rd-quarter 2026... whenever you're working on something interesting, ask the AI about prior art, or to do a scan of the scientific literature. Whether it's economics, health, or something algorithmic at work, at least the AIs I've used (as we've not all spent all the time with all the models) are still generally inclined to give you exactly what you ask for. They may do a good job at giving you what you asked for, but they won't generally do a whole lot more. Ask them to go looking around and it's like giving them a 30 point IQ boost sometimes. They all operate way better when you fill the context window with relevant information then when you're operating just in the latent space of their training, but they only rarely seek it out without being prompted on their own.

On my near-term todo list is to explore a particular crank physics theory of my own with AI... but not as a way to validate it, I know it's a crank theory that is far too simple to have been missed by pros in the relevant fields, but as a window into the literature and figure out what's wrong with it and thereby learn something. I will be framing it to the AI in pretty much precisely that way: Go get literature and reputable sources and talk through why this is already well known, probably well known to be a bad and wrong idea.

I still feel like not enough people are talking about this here on HN... AI has opened the scientific literature like never before. It's like being able to interrogate it and interview it as if it was a person, rather than just searching papers, for keywords you don't know, for lines of thought you've never heard of, in a sub-sub-sub-field you didn't even know existed, and failing before you even knew what it is you wanted. I've read more papers in the past 6 months than the past 10 years. Whatever opportunity you have to try this out, be it some question bothering you for the last 10 years, or a crank theory of your own to prove out against the literature, the foundation of some vibe-coded program informed by the literature rather than just vibing on the neural weights directly, or just asking something random about the studied effects of beavers on local ecosystems, you gotta try this. Prompt it specifically for "reputable sources and scientific papers", that helps a lot. It does not make you suddenly an expert in the field, but it does let you poke through the pile of literature far, far more effectively than you could hope to before.

And then don't forget to ask it why your summary is wrong or incomplete. Even if it doesn't convince you, you'll learn yet more.

Yup, I too am surprised this hasn't (at least by my awareness) entered the zeitgeist.

At work I'm putting together an MCP server that more easily exposes our legume data for model consumption, and part of the insane value-add has been the curatorial work that our collaborators at USDA put into the data over years. For example, genome data (i.e. nucleic acid fastas) include relevant metadata such as their DOIs, so models can fetch and read the original papers (if they're open access, of course).

This goes a long way to boosting the intelligence/usefulness of these systems for research.

Yes, exactly. Researchers will not like the fact that I refer to the literature as merely a manual, but “RTFM” applies here. Someone has likely already investigated what you’re looking at, or at least found a way to not do it. And sure it’s in the weights, but if you put papers directly in front of the LLM it’s much more impactful.