Builder of large-scale ML systems; adjunct prof @ucla; ex quant derivatives trader & startup founder. Tweets on AI, tech, science, & econ.
Berkeley, CA
Joined June 2012
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My favorite books and reading trace for 2025:
I had a five-way tie for the best books this year:
1) Mokyr’s A Culture of Growth on how societies build or block the conditions for sustained progress;
2) John Williams’ masterpiece Augustus, and the runner-up, Stoner, as intimate studies of character under the slow pressure of duty and power;
3) Dhami and Sunstein’s Bounded Rationality as the clearest bridge from “humans are cognitively weird” to “policy should be designed accordingly”;
4) Liu et al.’s Advances and Challenges in Foundation Agents as a big-map survey of how modern agents stitch together memory, planning, tools, and safety constraints into something that actually acts in the world, and;
5) von Neumann’s core computing writings (Compendium, EDVAC, Self-reproducing Automata, and The Computer and the Brain) arguing that computation is best understood as a class of automata with robustness coming from adaptation and distributed memory.
Links below.
Elon is right - compute will get more expensive over time as its value for doing jobs increases. Wall Street is in for a rude shock - everyone is as interest rates are going to go up a bunch as all capital pushes toward AI projects.
Market is concerned that AI is approaching peak supply/demand imbalance, therefore putting low multiples on many AI chip and supply chain stocks, fearing peak earnings.
Vs.
@elonmusk has decided to sell $SPCX compute entirely in the spot market because he thinks the compute shortage will persist for many years, and prices for compute will go even higher over time.
One of these are wrong. If Elon is wrong, his business will be the first place that every customer goes to cut in an overbuild, since he is selling the most expensive compute with the shortest terms.
Arun Rao retweeted
Anthropic studied 235,000 people using Claude Code. The ones who knew their field outperformed the ones who knew how to code.
The paper, "Agentic Coding and Persistent Returns to Expertise," covers 400,000 sessions from October 2025 to April 2026. The findings flip the "learn to code" narrative.
Non-developers hit 29% verified success with AI coding tools. Software engineers hit 34%. That's a five-point gap across hundreds of thousands of sessions. Management and sales professionals are among the fastest-growing users.
The division of labor is clear. People decide what to build and the AI decides how to build it. Your understanding of the problem matters more than your ability to write the solution.
The gap between "pretty good" and "expert" is small too. Solid proficiency captures most of the value. You don't need to be the top 1% in your field to ship with AI.
If you've been holding back because you can't code, this data says you're closer than you think.
Long term real interest rates are going to get much higher, challenging governments and every part of the economy that isn’t deep into adapting with AI. It’s gonna be messy.
Will AI poison the public finances of many modern states? There is a real risk that it will.
Yesterday, I circulated a new paper, “Saving for the Machines,” which makes a simple point. Building the AI transformation will require enormous investment. The only way to induce agents to save for that investment is through a large increase in the real interest rate. For instance, in my benchmark scenario, the ten-year real yield jumps from 5.1% to 13.6% on impact.
But that means government borrowing will become much more expensive. Here is a back-of-the-envelope calculation.
Take a government that owes 100% of GDP in 2025 (a typical case in Western Europe) and runs a balanced primary budget from then on (not so typical but never mind; I want to build an optimistic scenario). That means the debt ratio changes only with the gap between the real interest rate and growth.
I input the interest rate and the growth rate for my benchmark transition to full automation, and I roll over the debt at the short rate. I consider two economies. The first economy is an AI adopter, for example, the U.S., which experiences an AI boom at home. The government pays the new interest rate but also enjoys faster growth.
The second economy is a bystander, for example, Spain. This economy lives in an integrated financial market but does not adopt AI (this is an extreme case of zero adoption, just to make the exercise transparent): it pays the same world interest rate and continues growing at its old 1.6% trend.
Let’s first consider the adopter. With an intertemporal elasticity of substitution of one-half, the Euler equation implies that consumption growth is about half the real interest rate. Hence, the gap between the interest rate and growth does not close during the AI-driven growth boom. Instead, it widens. For example, with a unit elasticity of substitution among tasks, the real rate rises from 5.1% to 42% by 2040, while growth increases only from 1.6% to 22%. Debt doubles by 2038, reaches 296% of GDP in 2040, and 907% in 2045. To hold the ratio at 100%, the government needs a primary surplus of 10% of GDP in 2035 and 24% from 2045 onward. With a low elasticity of substitution of 0.2, the numbers are milder, but the pattern is the same: 204% in 2040, 397% in 2045, and a stabilizing surplus of 13% of GDP.
But the adopter has a way out. The capital share goes to one, so net capital income is about 88% of GDP, and a flat tax on it can cover the surplus. A 36% tax on net capital income holds the debt at 100% of GDP in the limit with unit elasticity, and a 20% tax does so with strong complementarity across tasks. The tax is above the gap because it slows growth, from 21.9% to 13.6% with unit elasticity and from 11.7% to 9.1% with an elasticity of 0.2, while it does nothing to the rate the Treasury pays, which technology pins at the machines' net return. Painful, but the boom survives and the adopter can pay its debt.
The grim news is that the bystander economy has no way out. Its capital income is still a third of GDP, which grows at 1.6%, and the surplus it needs is 11% of GDP in 2030, 26% in 2035, and 44% forever with unit elasticity (18% and 24% with an elasticity of 0.2). On a balanced primary budget, its debt doubles by 2033 and triples by 2035. No tax system raises that.
Two more bad pieces of news for the bystander. First, long yields jump on impact, so a Treasury that issues ten-year debt pays more, and sooner, than rolling over at the short rate assumes: it is the same bill but front-loaded. Second, the bystander’s own capital flows to the machines abroad, so its GDP and tax base fall below trend. One relief is that households can save for other people’s machines and get rich on the returns, which the bystander’s government can tax to some extent.
This back-of-the-envelope calculation is a benchmark, not a forecast of what is to come. But the arithmetic is simple, and it runs counter to the hope that an AI-fueled growth boom will take care of public debt, because growth comes with an interest rate that is twice as high.
Not a pretty future for most of Western Europe. Due to AI, the choice is between default, inflation, or, most likely, financial repression: capital controls that keep domestic savers from investing in machines abroad.
Let me close with a practical implication. If you believe in the transformative effects of AI, you disagree with the bond market, since today’s long real yields have not risen nearly as much as they should in that scenario. Therefore, the time to term out the debt is now, before repricing, because every year of coupon locked in at today’s rates is a transfer from today’s bondholders to taxpayers. A treasury that believes in transformative AI should be issuing fifty-year bonds; one that is shortening its maturity is betting the other way.
Arun Rao retweeted
Илон Маск предлагает не охлаждать чипы для космоса, а наоборот — пусть работают горячими.
Вот в чём настоящий парадокс космических дата-центров.
Все думают: «В космосе же −270 °C, чипы будут охлаждаться сами». На деле всё наоборот. В вакууме нет ни воздуха, ни воды, которые могли бы уносить тепло. Вакуум работает как огромный термос: тепло, которое выработал чип, остаётся внутри. Единственный способ от него избавиться — излучать его в виде инфракрасного света с больших радиаторных панелей.
И тут начинается жёсткая физика. Чем холоднее радиатор, тем слабее он излучает. По закону Стефана–Больцмана мощность излучения растёт пропорционально **четвёртой степени** абсолютной температуры. Если поднять рабочую температуру чипа всего на 20 % в кельвинах, радиаторы начинают излучать в два раза больше тепла. Значит, их можно сделать в два раза меньше и легче.
Именно об этом говорит Маск в видео:
«Если увеличить рабочую температуру на 20 % в кельвинах, массу радиаторов можно сократить вдвое. Поэтому в космосе выгодно работать на более высокой температуре».
На Земле мы тратим миллиарды, чтобы держать чипы холодными. В космосе выгоднее спроектировать чипы так, чтобы они спокойно работали при 100–120 °C. Тогда радиаторы становятся существенно легче, а стоимость запуска резко падает. Плюс нейросети довольно устойчивы к случайным битовым ошибкам от радиации — для огромной модели несколько перевёрнутых битов почти ничего не значат.
Получается, что главная проблема космического ИИ — не энергия (солнце в космосе бесплатное и постоянное), а именно теплоотвод. И Маск предлагает решать его не охлаждением, а температурой.
Выглядит почти контринтуитивно, но физика на его стороне.
Fantastic piece about the enshittification of chocolate products, esp Hersheys, by @rachel_handler @NYMag. Really detailed and thoughtful reporting.
“Reese is the proud grandson of Reese’s Peanut Butter Cups inventor H. B. Reese and an earnest and affable retiree who often wanders around wearing a bright-orange Reese’s T-shirt, talking to people about his family’s much-beloved candy. He’s been doing this for years, despite the fact that he does not work for the brand and never has. (Hershey bought it in 1963.)
On that February 14 morning, Reese opened a bag of Reese’s Mini-Hearts, a seasonal variant of the product, and found them “inedible.” He threw them out, then posted a scathing open letter to Todd Scott, manager of corporate brand and editorial at Hershey, accusing Hershey of unforgivably altering many of Reese’s products by replacing real chocolate with compound chocolate — basically using vegetable oils instead of cocoa butter to lower costs and replacing real peanut butter with “peanut-butter-style crèmes” (also using vegetable oil).
Others weighed in to share similar horror stories.”
apple.news/A48hvUtyRSRiGFhC-…
Arun Rao retweeted
New blog post 🎉: How To Use Decision Models For Evals
In this post, we show you how to choose a decision model for your task, tune its threshold (don't assume the 0.5 default is good!), and interpret its scores.
Lots of cool interactive charts here.
Arun Rao retweeted
Holy smokes!!!!
$SPCX says Q4 capex will be $35-40 billion versus $18-19 billion from sell-side estimates.
Elon also says they can bring on 10 GW of compute in 2027 in addition to the 2.5+ GW they expect to have by the end of 2026.
This means $SPCX capex in 2027 could be $300-400B billion (ie $30-40B per GW) vs current sell-side estimates of $165B.
From Elon… “At current pricing, new compute deployments are generating a payback of less than a year. Pricing is currently tracking slightly above the high end of the $30 to $50 per watt range we were discussing just last quarter, which further supports the attractiveness of these terrestrial SI compute investments.”
From Elon… "I think the demand for bandwidth is going to be dramatically higher than it is today. I think in three to five years there is probably a demand for 1,000 times more bandwidth than there is currently, because AI or SI agents will on a per-agent basis use far more bandwidth than a person, and there will be far more agents than there are people... With next year being primarily Rubin, I would say I am fairly confident of being able to bring an incremental 10 gigawatts of compute online in 2027. That would be in addition to the roughly 3 gigawatts, maybe 2.5 to 3 gigawatts that we will have online by the end of this year. Let me put it this way. I would be surprised if we brought on less than 13 gigawatts of compute by the end of next year."
…and to think that some of you are bearish on ai infrastructure 🙄 while the clowns at Gartner think demand is lagging supply 🤣
Arun Rao retweeted
“Lack of electricity” is an oversimplification. Everything is hard about bringing massive compute online at extremely high speed.
If there was twice as much time, this would be relatively easy, but there is not.
Former $GOOGL employee on why data center capacity and electricity are the real bottlenecks, not GPU production itself, and how unreliable some of the neoclouds are ($AMZN, $GOOGL, $MSFT, $NVDA):
1. According to the expert, open-source models are closing the gap with frontier models faster than expected, with customers increasingly replacing Anthropic and OpenAI models in multi-agent architectures with open-source alternatives starting from Q2 of this year. The shift started with simple tasks like tool calling and MCP, but has moved up the stack to low-level orchestration.
2. The expert identifies capacity as the biggest constraint at the moment, with certain companies' traffic growing 5x while GPU availability, particularly in the U.S. where latency requirements are tightest, simply cannot keep up. Sourcing capacity overseas in Asia or Australia is technically possible but adds latency that most customers will not accept.
3. Hyperscalers offer reliability but are expensive, require multi-year commitments of three or more years, and have limited availability of the latest hardware, with customers already chasing B300s. AI-native companies tend to gravitate toward neoclouds due to hyperscaler pricing and contract rigidity, but neoclouds come with their own reliability issues, with GPUs dropping off unexpectedly and requiring engineering resources to manage fleet stability around the clock.
4. The third tier consists of smaller independent operators who have invested in GPU racks, seeing the opportunity to lease out capacity and recoup costs within a year or two, with the remainder being pure profit. The expert describes these as essentially unusable in practice, with operators who do nothing more than find land, install racks, connect electricity, and set up cooling, with no real cloud management, maintenance, or operational expertise behind them.
5. The expert expects overall GPU availability to increase meaningfully over the next year or two, with $NVDA continuing to ramp production. The real bottleneck is not GPUs themselves but data center capacity and electricity in the U.S. On the supply side, the expert also sees players $SPCX beginning to lease out spare capacity to the market, with more likely to follow.
6. The expert notes that U.S.-based neoclouds typically require 20-30% prepayment upfront, with the remainder paid monthly or quarterly over the contract term, and rebates negotiated at the end for any reliability incidents like traffic loss. APAC-based providers operate differently, with no prepayment required at all.
found on @AlphaSenseInc
Arun Rao retweeted
SpaceX call - Elon Musk: We're hounded relentlessly by every hyperscaler and dozens of smaller companies to rent more of our compute. We could rent out our compute several times over
Uhhh... That sounds bullish for neoclouds guys.
Arun Rao retweeted
My take on wealth taxes is they won't work well at the local or state level, because you're targeting the people with the greatest means and motivation to just pick up and move somewhere, and they won't work well at the national level, because they'd almost certainly require a constitutional amendment, just as federal income taxes did (the 16th Amendment), so in both cases you're creating a levy that is basically designed to disappear.
California's Billionaire Tax Act would collect roughly $30 billion. Accounting for lost income taxes from departures, the mean Net Present Value is negative $39 billion, from Benjamin Jaros and Joshua Rauh nber.org/papers/w35899
Arun Rao retweeted
Everyone talks about what AI will do. But almost nobody asks who will pay for the machines. Taking advantage of AI will require an extraordinary amount of capital, from data centers to new power plants. Will that capital be around?
I try to address that question in the first draft of my new paper “Saving for the Machines.”
To show how important capital is to fuel the transition to an AI-based economy, I document how the same technology path that generates 11.7% annual growth when households choose their saving rate optimally delivers only 2.2% when the saving rate remains fixed. In other words, if we experience slow growth over the next few years despite fast improvements in AI, this slow growth may reflect too little capital accumulation instead of a technological bottleneck.
The key to this result is that AI raises the capital share by moving tasks from labor to machines, which requires that those machines are built. In comparison, ordinary technical progress makes labor and existing machines better at the tasks they already perform.
To inspect the mechanism, I start from an accounting identity: the saving rate is the capital share times the ratio of investment to capital income. If I take the evolution of the capital share as given, I can ask how much the latter ratio moves in response to that evolution. Crucially, with an optimizing household, this ratio barely moves. As machines take over tasks and the return to capital rises, the household responds by letting consumption grow faster, by the intertemporal elasticity of substitution times the rise in the return. To pay for that faster consumption, capital must also grow faster by the same amount. So, the household keeps investing about the same fraction of what the machines earn. And since machines earn a larger share of output, the saving rate also goes up. With a fixed saving rate, the same accounting identity tells me the opposite: the ratio falls in proportion as the capital share rises.
Will the U.S. and other advanced economies then generate enough capital? For many reasons, I doubt it. We might end up in a situation where many of the opportunities offered by AI are left on the table. This is particularly important for all who think that AI might get us out of many problems, such as fiscal troubles (like in France) or ultra-low fertility. We need to save for the machines, and it is not clear we can do that.
Anyway, this paper has been long in the making (many months of hard work), but I have learned much from it. In particular, working through the model changed my mind about many issues related to AI. Again and again, my intuition said X, but the math said Y. And math always won in the end. That is why we write formal models.
A nice example is Proposition 5, whose proof is the one I like the most in the paper. With improving machines and strong task complementarity, there is no fixed balanced growth path because the target itself keeps moving. The nice part of the proof is to realize that, after the right normalization, the moving target becomes a fixed point, and the saddle gets steeper as machines improve. So instead of falling behind, the economy tracks the moving BGP more and more closely. A fair amount of ODE work to get there, but a very simple economic idea underneath it once you get it.
The paper is here:
sas.upenn.edu/~jesusfv/Savin…
and the slides are here:
sas.upenn.edu/~jesusfv/Slide…
Arun Rao retweeted
This is perhaps one of the great ironies of our time: China is increasingly shaping the future of AI, yet very few people in the West have the faintest idea how its key thinkers see it - its philosophers, sociologists or ethicists.
As is too often the case, we - much like the frog in the well - assume our own AI debate covers every possible view, when much of it springs from the same cultural soil, built on assumptions about what defines us that we don’t even know we’re making.
Take for instance the assumption that intelligence is a key part of what makes us human. It’s so intuitively obvious to us that we literally named our species after it - Homo sapiens (Latin for "wise man").
If you think about it, just this assumption explains a large share of the anxiety: if intelligence is what makes us human, then artificial intelligence - or super intelligence - threatens the very thing that makes us us: we’re about to compete with artificial or super “us”. No wonder this feels existential.
But China has a tradition of thought as old as ours, if not older, and every bit as rich and sophisticated, which never placed intelligence at the core of what makes us human, and so meets the thinking machine from a completely different starting point.
Have you, for instance, ever heard of Zhao Tingyang? Probably not, and yet he may be the most original thinker on AI you’ve never read. He’s one of China’s most prominent philosophers, at the Chinese Academy of Social Sciences. While Silicon Valley spends billions trying to make AI share our values, he for one argues that doing so “may very well be a suicidal mistake”.
Sounds pretty important to hear him out, don’t you think?
Or take Liu Yongmou, a philosopher of technology at Renmin University in Beijing. His thinking is almost the opposite of ours. Where we fear machines that become more and more like us, he says that “humanity’s biggest problem is the machinization of humans, not the humanization of machines.”
You could even go back to antiquity, when, in many ways, we started asking questions about AI.
Did you know, for instance, that Aristotle, in the Politics, imagined a world where “the shuttle would weave and the plectrum touch the lyre without a hand to guide them”, and wondered about the social order consequences of this (mostly whether slaves would still be needed)?
There are Chinese equivalents to this. For instance, in the Zhuangzi, a foundational Taoist classic, you’ll find a similar thought experiment, with a completely different worry attached. An old gardener is watering his vegetables by hand, hauling water out of a well jar by jar. A visitor shows him a labor-saving device, and the gardener turns it down for the reason that whoever uses machines ends up with 机心 (jīxīn), a “machine-heart” - a mind that schemes, calculates and loses its simplicity. “It is not that I do not know [the machine],” he says, “but I should be ashamed to use it.”
Two labor-saving machines, two very different questions: where Aristotle wondered what automation would do to the social order, the Zhuangzi wondered what it would do to us, on the inside. Which, you’ll notice, is exactly Liu Yongmou’s worry, more than two thousand years earlier.
In fact, as we’ll see, 心 (xīn), the heart, is a motif that runs deep through the Chinese AI debate. It appears both in the fear, from Zhuangzi to Liu Yongmou, that machines could give us a “machine-heart” and its mirror image: the ambition to give machines a heart of their own. Zhu Songchun, who heads the Beijing Institute for General Artificial Intelligence, wants “to set a heart for machines” (为机器立心, wèi jīqì lì xīn).
“Heart” in the Chinese sense, that is, which differs totally from the Valentine's Day fluff we’ve reduced it to. 心 (xīn) means heart and mind at once: it’s where we feel but also where we think, tell right from wrong and care for others.
And, crucially, it’s in the heart that the quality Confucianism sees as the very definition of a human being takes root: 仁 (rén). We need two words to translate it, “benevolence” and “humaneness”, but in Chinese they’re one and the same: to be benevolent is to be human. It’s not about intelligence.
This is the entire point of my new article: to step out of our well for a moment and peer over the edge of China's.
What you’ll find there will, I think, surprise you, with questions you’d never thought to ask: from Zhao Tingyang warning that AI’s bad consequences can at least be fought, but that “humanity may die of all the good things AI creates”, to whether the fear that AI will make us “useless” rests on the false premise that a person is worth what they’re useful for.
A premise that a 2,300-year-old Taoist tree, which survived precisely because it was useless, demolished long ago. Could our predicted uselessness turn out, much like that tree’s, to be our saving grace?
Chinese thinkers’ answers to these questions are, I think, among the most interesting anyone has given to the defining question of our time. Whether or not you agree with them, they’re the intellectual backdrop of a country shaping much of AI, and with it our future: you might as well know what they think.
Enjoy the read here: arnaudbertrand.substack.com/…
Arun Rao retweeted
California's Billionaire Tax Act would collect roughly $30 billion. Accounting for lost income taxes from departures, the mean Net Present Value is negative $39 billion, from Benjamin Jaros and Joshua Rauh nber.org/papers/w35899
Agree - I think frontier AI labs will have to pivot from selling tokens to using their advanced models to start de novo businesses and industries backed by SOTA science and engineering - a new form of vertical integration. That’s the only defensible moat, and they discovered it with coding but it’s only the tip of the iceberg.
by this time next year, most of labs revenue will come from what they build in-house and then sell further up the stack. the average users just don't
1. have deep enough pockets
2. work on hard enough tasks
gpt-synopsys and anthropic wet lab are the first of many products which come as a result of labs gatekeeping and utilizing superintelligent swarms to create new and capture existing markets. currently by use of footholds and in the future independently. by 2030 labs will have eaten practically every economically valuable sector such as finance, legal and healthcare.
what's to stop labs? why wouldn't they? how can a 10,000 employee healthcare company stand a chance if they are up against 10 million vastly superior agents to the swarms publicly available?
the labs understand this and are hence preparing for the "grand capture" by donating shares to the public (anthropic and spacexai already committed with openai about to) to minimize p(public revolution).
the question becomes what to do. one option is obviously to join a lab. if you want to join the lab I believe the best "unit chance of joining lab/time" ratio is proving scaleable user acquisitions and publishing relevant hot topic things on X.
if you don't want to join a lab, acquiring factories, propietary data, licenses, land and things that are human facing and exist in a stack of value creation will be fruitful as they become the primary bottlenecks once suffcient swarm cognition is solved
Arun Rao retweeted
Check out new work from our NVIDIA team on a core problem in base-model evals, predicting which checkpoints become the best coding agents before post-training arxiv.org/abs/2610.10478
We use it in Nemotron midtraining too, see our paper behind Super 3.5
arxiv.org/abs/2610.22019
Arun Rao retweeted
The French riots are what happens when a country can no longer fund the promises it makes to its citizens. Riots like this just happened all across the Third World, as rising prices outstripped the (huge) subsidies governments pay to keep fuel affordable for the lower middle classes and below.
In France, state spending makes up an astounding 57.1% of GDP, higher than in many Communist nations. 15% goes to public pensions alone. France has established a baffling and complex network of subsidies and tax rebates and cash transfers which mean just about every person living in France receives some benefit from the French state.
Meanwhile, GDP growth has been either modest or stagnant for years. The French economy simply doesn't generate enough added value to pay all its bureaucrats (and their pensions) and finance all of its benefits and subsidies. France's state debt is already high and increasing rapidly.
The French political class, like the political class in many Third World countries, is part of the problem. No political party is willing to state the truth, which is that France has been living beyond its means for decades, and must make massive cuts across the bord to stay fiscally viable.
The majority of the French have been conditioned to believe that their right to various subsidies, such as "free" university educations, agriculture subsidies, government pensions etc., are inalienable: "How dare you balance the budget on the back of students / small business owners / artists/ public employees / farmers / the disabled / immigrants / pensioners / child-care workers / stay-at-home mothers / police," etc.
It's a war of all against all for an ever-shrinking pie. It will get progressively worse until French voters are willing to support politicians who tell them the truth. Or until France is forced to seek a bailout from the EU or the IMF. Which is much more likely.
edition.cnn.com/2026/10/19/e…
Arun Rao retweeted
It's been a month since our last update on NVIDIA GPU residual values. H100 marched higher to 61% of purchase price past 4 years since mass availability; B200 also moved even higher to 164% at 1.6 years. An accelerated five-year schedule would have them at 6% and 33% respectively. A100 slipped a bit further from 25% to 22.5% in year 6, but still sits well above a schedule that would've written it to zero a year ago.
Silicon Data residual value show that @nvidia GPUs are retaining their value well above those implied by typical straight-line depreciation schedules!
For all the bears on the AI supercycle - I’m going to post an essay on this and what many are missing about the upcoming space machine economy. It’s a fundamental transformation in our lives that’s more important than printing, electricity, PC/laptops/distributed computing, the internet, and mobile - hence the extreme shortage of compute, which hasn’t gone away for a decade now.
Arun Rao retweeted
The europoor meme can be funny, but it's also midwit and wrong.
As someone with both EU and US passports who's lived in both sets of elite poles (SF/NYC and BCN/BER/PAR), let me just give you the TLDR:
-The percent of GDP spent on welfare in the US is the about the same as the EU (smidge under).
-The effective tax rate in the coastal US is about the same as the EU (smidge under).
-Both are (former) middle-class societies where much of society feels the social contract is broken. In the US, elites abandoned the middle-class decades ago, and the well-to-do in states like CA/NY have reconciled themselves to paying almost half their paychecks and getting nothing in return, planning to insulate themselves from the suck by paying for private everything: neighborhoods, schools, medical care. If their earnings keep on rising, they're OK with it.
In France, that bourgeoisie hasn't been decimated yet, and they still expect (delusionally perhaps) basic social infra like schools to function (which is what these protests were initially about). The infra in Europe is a lot better than in the US, despite the GDP delta (New Yorkers can only dream of a subway like Madrid's). But that's clearly eroding in time, without the wage growth (or private social infra) to allow for a paid workaround.
-The free healthcare blah blah meme is fake: the public health systems in Europe have collapsed to the point it's mostly a public/private hybrid, for anyone with a solid paycheck, and fuck you if you're poor, go to the public system (which is the ER in the US). Guess what? In Europe you've got deductibles, in/out of network and all the same shit as in the US (though yes, the cost is still lower, though more or less correlated with wages).
-Boomers on both sides of the Atlantic are living in a dated lala land, but they vote to keep the system in place. In Europe it's legislation and printing money to prop up pensions; in the US, it's legislation and printing money to prop up housing prices and the stock market, which is the American 'free market' version of pensions. Rampant inflation and cost-of-living problems in both, with youth locked out of housing and living in precarity.
-Both are struggling with having been historically middle-class societies
US better positioned only because it has more economic growth (hello 80 hour weeks), and (obviously) leadership in critical fields like AI. But the foundational problems of an out-of-touch elite, incoherent immigration policy, and general social malaise that sees no meaning or purpose at the End of History is absolutely the same in both polities.
Americans who mock what's happening in Europe simultaneously believe some X mirage about what Europe is (and have never lived there), as well as don't understand what's going on in their own country. The French Revolution happened a few years after the American one, and once again, the two countries are echoes of each other, as similar countries tend to be. France is probably the most 'American' country in Europe, in all the republican senses of the word (and no the Yookay isn't in Europe).
