alt Hacker NewsThis is a topic I'm interested in, but the presentation and exposition on the website leaves a lot to be desired. Yes, it does make you sound like a crank.
Almost all of the theory and predictions presented seems to be those of regular classical economics, per Smith, Riccardo, and particularly George. You can find them in Wealth of Nations, Progress and Poverty. This surprises people who have been failed by our education systems. There are still many people writing about this exact topic now - the author does mention e.g Stiglitz.
The author seems to be overcome by the explanatory power of a 150-250 year-old well-established economic theory, of which fable has built a fairly general (novel? improved?) macro model for him, including the effects of certain tax policies. They present this as a new theory of economics rather than a new macro model.
It's very off-putting as a reader - you can't distinguish at a glance between what the author claims to have contributed vs merely discovered by reading about Georgism. Established concepts are not referred to be their usual names, etc.
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'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.
Your discomfort may be understated - this phenomena seems to be a slippery slope towards a kind of Dunning-Kruger accidental plagiarism via LLM?
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.
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.
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'.
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.