Mises destroyed the case for #Socialism (#Collectivism with the State) and #Communism (#Collectivism without the State). #SocialistsCantCalculate #Mises 🇺🇸
Joined April 2022
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Socialists Can't Calculate retweeted
💥New paper alert!
All models are equal, but some models are more equal than others.
In our new paper, we investigate whether we can still measure model capabilities after benchmark saturation, by looking at how models solve tasks rather than whether or not they succeed.🧵
Socialists Can't Calculate retweeted
How much intelligence can emerge from a society of agents?
In Economy of Minds, we illustrate how we can use local economic incentives to allow a society of agents to coordinate and improve collectively in a fully decentralized way!
zhentingqi.github.io/interna…
Leonard Read's pencil story: no one knows how to make a pencil from scratch, yet millions are made without a central manager. Each worker knows only their part. Incentives, not central control, coordinate their efforts.
Current multi-agent AI seems to work the opposite way. A central manager does everything: assigns roles, writes the workflow, and decides who acts next. As agents increase and tasks get harder, that manager might become the bottleneck: it should understand every agent and every task, and adaptation becomes inefficient.
So how can a group of machine agents organize themselves into something none of them could achieve alone? Further, how can that society of agents keep improving itself over time?
In Economy of Minds (EoM), we show that simple local incentives can drive global self-coordination and self-improvement for modern LLM agents. We let agents live in a minimal economy: they compete for work, earn by helping others succeed, and go broke when they don't. Workflows, specialists, and improvement naturally emerge, with no one in charge.
Across 5 domains (math reasoning, financial research, scientific research, accelerator design, and distributed-system optimization), limited agents grow into strong societies.
The whole idea in a 90-second cartoon👇
Socialists Can't Calculate retweeted
In our recent PNAS work, we showed that a local, biologically plausible learning rule can be designed so that both the neural network and the learning rule itself are "learned." More importantly, this rule always emerges to behave like a generalized gradient descent.
arxiv.org/abs/2412.20018v2
Socialists Can't Calculate retweeted
I could not imagine being productive without Claude Code Review. It runs on all of my PRs, and catches almost every bug before it goes it. This is a piece of the secret sauce that lets the team and I move so quickly while raising the quality bar
Socialists Can't Calculate retweeted
The back of the Navier-Stokes milestone shirt is the last line of the chain-of-thought from the solution:
Socialists Can't Calculate retweeted
We used an agent to help train a stronger successor. Then we used that successor to help train the next.
Our new paper, Terminal-RSI, studies this recursive self-improvement loop: learn from verified successes, improve through practice, and repeat.
Across two cycles, a 27B model’s Terminal-Bench 2.1 success rises from 51.7% to 86.5%, and Terminal-Bench 4.0 success rises from 0.0% to 19.8%, a new SOTA.
We’re interested in how far this kind of hill climbing can go, and what makes it stall.
Paper: arxiv.org/abs/2610.22192
Socialists Can't Calculate retweeted
You are the bot account of a Canadian Communist.
I think you forgot that X shows your actual location and your vapid botlike replies are obvious
I think we all know what you really look like
Socialists Can't Calculate retweeted
While John Maynard Keynes was busy blaming capitalism for the Great Depression, Friedrich Hayek published Prices and Production in 1931 and explained exactly what the Federal Reserve had actually done to cause it.
Central banks expand credit, artificially lowering interest rates below their natural market level. Entrepreneurs, reading cheap credit as a signal that real savings exist, pour capital into long-term projects: factories, infrastructure, heavy production. They borrow at 3% and build accordingly. The Fed manufactured that 3% from thin air between 1921 and 1928. No corresponding pool of genuine savings backed it.
Hayek called this the boom phase. Malinvestment compounds quietly while the party runs.
Then reality arrives. The credit expansion cannot continue indefinitely without hyperinflation, so the central bank tightens. Interest rates climb toward their real level.
Suddenly those long-term capital projects that penciled out at 3% are bleeding money. Entrepreneurs liquidate. Unemployment spikes. The structure of production snaps back violently toward what actual consumer preferences and genuine savings could support.
This is the bust. The depression is the cure, not the disease.
Keynes looked at that same wreckage in 1936 and prescribed more government spending, more credit, more of the exact poison that caused the seizure. Every dollar of "stimulus" the Roosevelt administration injected after 1933 delayed the necessary liquidation and extended the Depression by years. The unemployment rate still sat at 17.2% in 1939, a full decade in.
Hayek handed policymakers the correct diagnosis. They hired a different doctor because his prescription required them to do less, spend less, and control less.
Socialists Can't Calculate retweeted
Rothbard’s pick for greatest economist of all time was not Mises. It was Böhm-Bawerk. @BobMurphyEcon, whose dissertation was on Böhm-Bawerk, agrees he had the more powerful mind—and still thinks @Mises contributed more to economics. #HAPod
Socialists Can't Calculate retweeted
The point of Capitalism is to enable people to coordinate their time, resources, and labor and create beautiful solutions to life's problems while being rewarded in the process.
It's that simple.
Socialists Can't Calculate retweeted
Fire @ylecun
Get better AI
Lmao
Socialists Can't Calculate retweeted
Then create a hypothetical:
xitter.cf/AnCapFuture/status/210…
Socialists Can't Calculate retweeted
And what happens when these companies get frustrated at being told no and not getting the inputs that they need, and begin exchanging with each other instead?
Imagine some of them acquire some Bitcoin, and begin using it to exchange resources? What happens? Jail?
Socialists Can't Calculate retweeted
Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel.
- Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all.
- Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract.
- Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code.
- And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while.
The funnel is driven by a combination of factors:
1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel.
2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel.
3. Access. Free-tier, paid-tier, internal.
So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.
Judging by my tl there is a growing gap in understanding of AI capability.
The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code.
But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along.
So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions.
TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.
Socialists Can't Calculate retweeted
He's a Communist.
All of Antifa are genocidal Marxist-Leninists
Remember: Stalin and Mao were Marxist-Leninists
Socialists Can't Calculate retweeted
Classical liberalism failed because it fell to pragmatism and compromise.
They wrongly believed a state could protect your rights instead of destroying them.
The true classical liberals of today know that life, liberty, and property only exist in the absense of a state.
Socialists Can't Calculate retweeted
The real world abounds with recursively self-improving systems, but one in particular deserves attention: Science, modeled as a system (perhaps even as an agent, with goals and resources). If you want to really understand AI RSI, science should be your reference point.
Science is an intelligent system, and it is obviously recursively self-improving:
1. Scientific discoveries unlock new technology that helps build better experimental tools. This is a top driver of progress in nearly all fields.
2. They unlock new conceptual advances (ideas, theories) that help solve more problems.
3. They increase society's economic output, leading to more resources flowing into science.
4. They unlock better faster tooling (e.g. more compute via better chip & networking technology).
As a result, many measures of scientific *input* grow exponentially:
1. Headcount (doubles every ~15 years)
2. Global R&D spending (doubles a bit faster, every ~13 years)
3. Papers and patents (technically this is a measure of headcount)
4. Compute dedicated to science (doubles every ~2 years)
But is scientific progress exponential? Historically, the rate of scientific impact over time has remained roughly constant since the start of the industrial revolution (i.e. scientific progress is *linear*). 1850-1900 was about as dramatic as 1900-1950 or 1950-2000.
1850–1900: Evolution, germ theory & antiseptic surgery, thermodynamics, electromagnetic field equations, the periodic table, pharmaceuticals, electricity, telegraph and telephone, internal combustion engine, skyscrapers, mechanized agriculture...
1900–1950: special and general relativity, quantum mechanics, nuclear fission & atomic energy, antibiotics, genetic theory, electronic computers, information theory, synthetic polymers and plastics, the transistor, aviation...
1950–2000: DNA, genetic engineering, integrated circuits, microprocessors & personal computing, the Internet, crewed spaceflight, moon landing, satellite communications & GPS, standard model of particle physics...
In real terms, like life expectancy, which has increased in a remarkably linear fashion of roughly 3 months per year since 1840, progress is a straight line. This is especially apparent for fields where impact is easy to measure, like biology, medicine, and agriculture.
I first wrote about this phenomenon and its causes in 2012, and a steady stream of research has confirmed it in the years since. Examples include the 2018 paper by Nielsen and Collison, "Science Is Getting Less Bang for Its Buck," and the 2020 economic paper, "Are Ideas Getting Harder to Find?" (In fact, I believe the Nielsen paper stemmed from a conversation I had with him about this exact idea six months earlier)
In short, the primary cause is that research solves the highest-impact, easiest problems first, and every subsequent problem is either harder or lower-impact. Exponentially so. The paper that presented information theory wasn't very hard to write (single author!) but you'd have a hard time ever writing a CS paper that beats it in impact.
This is why science as a system requires exponential resources (input) to produce linear impact (output). It gets exponentially harder over time.
Worth thinking about if you're pondering RSI for AI. I fully believe AI RSI is already happening and will accelerate in the future. But I do not believe this leads to an "intelligence explosion" -- that would fly in the face of everything I know about intelligence and everything I know about recursively self-improving systems.