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.
This 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.
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.
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.
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.
'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.
Yes, crack open an intro to macroeconomics textbook and it has to discuss total factor productivity (technological progress) in the Cobb-Douglas production function, and its short run and long run effects on wages, or it's quite easy to connect the dots if it doesn't discuss that relationship directly. You'll get medium run too if you go a little beyond classical. It's funny someone claims to be writing economics papers but refuses to spend 100 hours, maybe less, to learn the fundamentals of macroeconomics as understood by everyone working in a related field.
> I simply could not have written this piece. I myself have no formal economics background
No shit, me neither, I read a couple macroeconomics textbooks at the age of 30 and now I wouldn't make arrogant and obviously wrong claims like the above. And I don't have the hubris to publish a 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.
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'.
What happens if the outcomes were obtained instantaneously, and without effort on your part? Would it be more enjoyable for you?
Besides, I don't think you can be driven by thousands of outcomes, by definition, as you can't be in love with thousands of people. Your main motivation must be the thing that unites them. Perhaps you are driven by the process of imagining solutions to problems and/or realizing your ideas.
I've heard this argument many times, there's a grain of truth and I strongly agree RE 'arhythmic and atonal trash', but it strikes me as disingenuous.
Classical music was somewhat elitist even at the time, coexisting with popular/folk music traditions. The Romantic period definitely saw music and art break away for traditional forms and conservative mores, but it wasn't a free for all.
The people who enjoy classical music today are largely the same people who enjoyed it at the time. Educated and 'enlightened' upper/middle classes, who sought music celebrating their aspirations. Freedom from political oppression and aristocratic control. A world where people could succeed based on talent, hard work, intelligence, and propriety. Where the middle class would have the power to reshape government and social institutions to advance their interests rather than those of the nobility. A personal experience of the religious rather than state ordained. The music pivots to natural, romantic, mystical themes as these people lost faith that industrialization will deliver for them.
Compare modern popular music which deals with universal themes - love, loss, sex, money, partying. You can enjoy pop music and still feel that these themes are not important to you.
People don't fake enjoying classical music to convince you that they're smart and cultured. They just gravitate to music which represents their idea of smart and cultured. The same is true for a lot of art.
It kinda feels like Julia competes for the people who write the libraries for R and Matlab. Writing fast and elegant ODE solvers, etc in Julia seems to be easier than the others and they've attracted a lot of academics for that reason.
I'm currently split between Python and Julia, having used R happily in the past for data analysis and Matlab for this and that in my EE program. For me, Julia crushes one niche that the rest of them are not good at: making the math look like the math.
If you've used something like SciPy or symbolic Matlab or Maxima or whatever, it always feels like I'm very carefully converting the equations I've scribbled down on paper into code and always a little nervous that I've accidentally split one variable into two names or used the wrong equality operator and am going to end up hating life, or accidentally assigned x = sp.Symbol("y") somewhere.
The Julia version is just plain beautiful. There's no ceremony other than the three @parameters, @variables, @mtkcompile macros. It lives in its own little world where you don't have to constantly watch your back to make sure you haven't duplicated a symbol somewhere.
I definitely agree. And the common performance optimization metaprogramming (like 'do it this way for this type of input') works so much better with multiple dispatch, tag structs. Way ahead of C++ expression templates and much more pleasant than macros, concepts, etc.
Some of the lower-level APIs like those for concurrency were quite poorly thought out though, at least when I last used Julia. Condition variables don't have equivalent of pthread_timed_wait. Condition variables and channels APIs are not well integrated, design wise. I found so many such issues that it convinced me Julia wasn't general purpose enough. It felt like the features were a bit half-baked and had been hacked together by someone who knew their value but lacked the deep experience/knowledge to pull them all together into a single cohesive vision. Same issues as python, POSIX, etc.
Yeah, the nifty part is instead of trying to write your whole multi-threaded high performance tool in Julia, there is excellent support for taking the math work you’ve done and codegen C out of it. Am very happily using that in prod today for a thing and it works awesome.
Getting linear algebra in a programming language close to math formulas was, for a long time, my reason to use Octave.
When I first read about Julia, I was really amazed - especially the type system with its multiple dispatching and not automatically converting between types (e.g., between integers and floats). Though, I do not know, how Julia is today.
Today, I use Python instead of Octave (or Julia) - just because it has a large ecosystem and is widely adopted. An additional advantage is that Python has much better OOP features than Octave had back then.
However, I wished Julia had the status that Python has today.
Julia is fun, but is still mostly an academic language. Very few shops will use it in the private sector. Python is also more common as a prototype integration language, and rarely seen in industrial areas.
If you are an EE that wants to remain employed... than make sure you have documented hours with C/C++, Verilog on Zynq, and ladder logic for Rockwell automation products.
I'm an electrical engineer and I use Julia for all kinds of analyses that I might have earlier in my career done in a spreadsheet (Lotus 1-2-3 at first!), or later in python (when I had to choose between Numeric or NumArray).
I started using python for various engineering analysis problems around 2001 and I loved it for how fast (due to minimal boilerplate and automatic memory management) I could code up some thought relative to using C or Java. I could tackle problems in ways I just wouldn't have tried otherwise because I couldn't afford the longer time to write it in other languages. However, for problems which needed speed, of course it bogged down.
I started using Julia for ODE stuff in 2018 or 2019 and was thrilled with the speed and conciseness. As others have said, it looks much more like math and a lot of better design choices were made.
Python obviously has a much larger ecosystem and probably always will, and it will remain a safe choice, but you don't set yourself apart by doing the same thing as everyone else.
Oh, my friend, I’m in my 40s now and while I’ve never touched ladder logic (mostly on purpose), I can honestly say I’ve been writing C since the last century and C++ only a few years less. I remember, with pain in my heart, what C++ looked like before C++11, C++14, and C++17. C++03 had just come out when I started and lots of features even there weren’t really all that baked in the toolchains at them time :).
Zynq is super cool and strongly agree that it’s worth looking into, although starting with just a naked little FPGA board might be more approachable. On the other hand, if you’re sufficiently capable with both embedded Linux and Verilog to successfully implement a piece of hardware in the PL and build a driver and userspace for it in the PS, you’re definitely miles ahead of most candidates.
TI/Octavo chips with the PRUs are kind of similar; not that they’re asynchronous logic like the Zynq PL is, but they’re similarly powerful as far as doing hard real-time deterministic jobs driven by an attached Linux core.
Depends what kind of work you do, as product development is different from factory journeyman. I don't see a chaotic market supporting many domestic product development projects for the next 2 years. =3
One thing that I find extremely annoying when I occasionally read Julia code is the pervasive usage of Unicode. I explicitly forbid agents to use any anything other than ASCII for that reason.
What you describe is very normal in telecom and has been for a long time. 4G, 5G, etc are all totally dependent on every device syncing to the one higher up in the chain. These days typically a digital control loop is used to tune a high frequency voltage-controlled oscillator which feeds a counter. An event (like GPS PPS, or an ethernet frame arriving on the cable) is extracted from the signal and the counter generates a timestamp. The chips to do this are incredibly cheap and the performance is hard to comprehend.
'All truths are easy to understand once they are discovered; the point is to discover them'
If knowing was the valuable part, then nobody would need a PhD. You could know more by just reading textbooks. Research mathematicians research, everybody else just learns.
> 'All truths are easy to understand once they are discovered; the point is to discover them'
There are plenty of things that are difficult to understand that have been known by others for a long time. The point is that you don't understand those things. Your understanding of something doesn't benefit from someone else understanding it, per se. I will agree that having a guide does make it easier.
> If knowing was the valuable part, then nobody would need a PhD. You could know more by just reading textbooks. Research mathematicians research, everybody else just learns.
Knowing is the most valuable part! It's the whole point of research!
And where knowledge is concerned, there isn't a sharp line between what constitutes "learning" and what constitutes "research". How do you know a claim in a book is correct? You can take it on authority and just believe it. Or, you can seek to verify it yourself. As far as your own mind is concerned, you've discovered something for yourself. And what is a researcher doing? He's inferring things and reasoning and verifying his inferences. These are activities you use both during learning and during research.
I don't think that's necessarily unlikely, but people making this argument rarely seem to understand just how radical a change is required to make that a possibility.
e.g most small business rent their premises, and buy goods and services from other tenant small businesses, who buy their raw materials from large landowners. How can a small business survive when all the land is monopolized by the technocrats? All value you add gets taken away as rent. This is why they always talk about taxes on outputs (tokens) instead of inputs (land, energy usage).
It's not an ungrounded hypothetical, that's exactly how society worked for hundreds (thousands?) of years.
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