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.

'classical economics doesn't know how technology affects the wage' - ridiculous, classical economists were addressing exactly how technology was changing society and economic relationships. they didn't have accurate/useful models but they described the relationships in great detail. Sentences like this make me question the author's ability to evaluate their own paper.

Thank you for being able to point out and articulate these issues. LLMs have a habit of laundering pre-existing concepts per the interests (or input) of any given user.

I feel bad for the humans who rediscover what we have already discovered and position it as something new. If this keeps happening soon we will be in an endless loop without gain.

Ah well.

I also tried to understand what they were saying. I read the website and skimmed the paper. It left me confused and unclear on what the takeaways really are.

It reminds me of the people who reinvent some basic concept of physics with an LLM (usually, but not always, incorrectly) and think it's 'revolutionary'.

Your discomfort may be understated - this phenomena seems to be a slippery slope towards a kind of Dunning-Kruger accidental plagiarism via LLM?

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Human values ideally start deeper, in the creation of preferences, where economics simply takes as a primitive/preexisting part of the environment. Marketing theory is the one that focuses on manipulating human value.

The optimal form of taxation is none

There is no optimal form of taxation, only trade-offs.

There is shifting burdens from good to good or person to person.

I find it no surprise that so many of the loudest proponents for georgism are highly compensated tech workers with low physical consumption - exactly the demographic that would be favored most under that system.

Yes? The author is very clear in the blog post about having used AI very heavily:

Up until now you have been reading the words of Anthropic’s Claude, in particular Fable 5. In part, this is because I simply could not have written this piece. ... Although it might seem strange I thought it wrong to impose too much upon the machine, changing its voice to emulate mine. Richard Sutton’s bitter lesson would likely advise as much. Instead it has its unique voice, grating to some perhaps but altogether fitting that it should be able to keep it, and I only gave it advice on what considerations during writing would bridge the gap between its understanding and that of a reader.

It may well be, but also Pangram is ridiculously transparent pseudoscience. Let's not play pretend that it's authoritative, please.

Edit: Just for fun I just defeated it with AI-generated text that it reported as 100% human-written. Great stuff. Truly genius scam.

Land value tax / citizens dividend here is different to the UBIs sometimes proposed in contemporary US politics. The idea is that you tax and redistribute the profits on the scarce inputs to the economy, rather than the redistributing the outputs like manufactured grids and AI tokens.

UBI is not a solution because it will drive up land rents and be eaten by landowners.

MMT job guarantee

It's a liiiiiiittle more than that...