Sean Dougherty 🌍

Own remarks #FiscalFederalism, #GenAI & #EMEs 🗺️ • Senior Advisor @OECD ~ ⚖️ 💸 • Fellow @INSEAD 🤖 • Alum @MITeecs ⚡️@PSEinfo 🪁

[Sean Dougherty 🌍 @econecho]

📢 Final edition of OECD's « Fiscal Federalism 2022 » out today!📕
👉🏼 You'll find the latest on the impacts of the crisis, for state & local finances 📉💰💸📈
👉🏼 Good practices in all areas of intergovernmental fiscal relations 💫🏃‍♀️⚖️💭

💸eBook version: https://t.co/uPv4ulIuOI🧵

[Daron Acemoglu @DAcemogluMIT]

Dear followers,
Please see this new article from @MITSloan on pro-worker AI, which draws from my recent paper with David Autor and Simon Johnson for @BrookingsInst
https://t.co/Si6IsiSEvP

[Sean Dougherty 🌍 @econecho]

Defence spending is rising fast, but its effects aren’t only national. 💸

New OECD #NFR study on what higher defence outlays mean for subnational governments: local spillovers, revenue effects, spending pressures & intergovernmental coordination. ⚖️

👉🏼 https://t.co/iwf5GPzw9j

[Sean Dougherty 🌍 @econecho]

Defence spending is rising fast, but its effects aren’t only national. 💸

New OECD #NFR study on what higher defence outlays mean for subnational governments: local spillovers, revenue effects, spending pressures & intergovernmental coordination. ⚖️

👉🏼 https://t.co/iwf5GPzw9j

[Luis Garicano 🇪🇺🇺🇦 @lugaricano]

I will streamline the sources to help anyone who wants to go down this rabbit hole. Maybe yo u can give it to your (smart, not dumb!) LLM and make it a nice summary- place it below if you do and I will RT).
1. Two Europes post with @OlivierKooi : Core Europe is diverging from US, East is converging, and the war is helping a reformist reprioritization that will expand the gap between North and East and Core and South:
https://t.co/U04xw4aVOr
2. @paulkrugman "Is Europe in economic decline?" https://t.co/gaCsnTe6X1 plus his transparent model note, which he calls incomprehensible but is in fact clear and helpful for economists. https://t.co/lmIEnx3mtp. Key point: everyone in Europe shares in the gains from the IT revolution through lower prices, so Europe should not panic. "Don't worry, be happy". (My words, not his.)
3. Our reply (with @pietergaricano) "European Stagnation is real": tech-based returns to capital--spread through in 401Ks and reaching mostly US citizens and institutions, and agglomeration economies (centered in SV/Austin etc) benefit US disproportionately and set up an unequal future. Krugman pushes the walking around test, we argue do the driving around test and you will see: https://t.co/VOpQ32QxDI
4. Krugman's response to us argues standard productivity growth does not mean what people think for cross-country welfare comparisons, and that PPP/current-price comparisons do not show a widening gap. https://t.co/B54HkRw4WD
5. Our response, in Proj Syn, with @Ph_Aghion and @a_bergeaud (I link the one today, since it is ungated and contains the postscript rejoinder): Main point. chained PPPs are misleading for growth comparisons and the fact they do not show a growing gap is not meaningful. The flat line Krugman sees as parity is the change in the measuring stick, not a convergence of the economies. https://t.co/sH5H4346Lm
6. @paulkrugman response yesterday, basically agreeing that productivity growth diverges, but with the points in my main tweet above, and the agreements and disagreements we summarized:
https://t.co/KnW4cAxvCp
A lot of what follows is gated, so harder to follow:
@wsj @josephsternberg: Europeans may wish to choose welfare over growth, but at least politicians and analysts need to acknowledge the tradeoff --and voters need to be told there is one!
https://t.co/IGDGk8G9OZ
@washingtonpost @asymmetricinfo takes the driving around test and argues that inherited urban beauty can make Europe look richer on foot, while US abundance shows up more in space, housing, appliances, suburbs, and private consumption https://t.co/8k7y00h6Ep
@MESandbu argues to some extent all measures are wrong, and we are talking past each other, but we are both equally right and wrong: https://t.co/obAqWlPFXm
@Noahpinion https://t.co/XQUO4K0Mbs argues pleasent stagnation will not cut it in the current world, particularly given geopolitical environment.

[OECD Governance @OECDgov]

📢 Public finances in OECD countries face mounting challenges.

With high public debt and rising spending demands, stronger budget institutions and better public understanding are key to ensuring fiscal sustainability.

See the report: https://t.co/N2zYOGFBoV #PublicFinances

[OECD ➡️ Better Policies for Better Lives @OECD]

Live today: the OECD Forum on Restoring Public Finances

Experts and policymakers will discuss how to strengthen public finances while building public understanding and support for reforms.

Watch live: https://t.co/qIgKAkF5DF

[Daron Acemoglu @DAcemogluMIT]

A post about Pope Leo XIV's encyclical on AI. Why the Pope is right, but perhaps not right enough.

Artificial intelligence is reshaping the world in front of our eyes: how we communicate, how we access information, how we work, how income and status are distributed among us, and soon how we fight and kill each other. Yet the public conversation about AI remains stuck on the minutiae of competition between labs, or on a false dichotomy between AI as a “stochastic parrot” with no real capabilities and AI as an alien superintelligence poised to take command of humanity.

The more important questions are about what we want from AI, and whether our current mindset, institutions, and control mechanisms are equal to the task of steering it toward our welfare.

It is refreshing, then, that a bold and powerful voice has weighed into this debate: Pope Leo XIV. As an economist who has long argued that technology is a matter of choice rather than fate, I find Leo’s intervention welcome and, on most points, on target. But on the most consequential question of what AI should actually be designed to do, Leo stops short.

Secular readers may bristle at the encyclical’s opening invocation of the Tower of Babel. They would be mistaken to stop reading there. Leo goes much further than most pundits, journalists and policymakers in the United States by recognizing that what happens to AI, and hence to humanity, is a under our control. There are multiple possible paths for AI, and which one we take will have sweeping consequences. He is also ahead of many commentators when he writes forcefully and unequivocally that “technology is never neutral, because it takes on the characteristics of those who devise, finance, regulate, and use it.”

These were the central themes of the book I wrote with Simon Johnson, Power and Progress: Our Thousand-Year Struggle over Technology and Prosperity. It is heartening to hear them taken up by a voice with Leo's reach.

The Pope is also right to question the current trajectory of AI in warfare and law enforcement. What was taboo only a few years ago – AI-driven mass surveillance, algorithms selecting targets for killing – has become routine. Many in Silicon Valley are now calling openly for a new military-algorithmic complex centered on AI as an instrument of American hard power. Leo captures something deep and too often ignored: “Any technology that facilitates attacks without seeing the face of human beings lowers the moral threshold of conflict.”

His call for the “disarmament of AI” follows directly from these observations. As he explains, disarming AI means “freeing it from the mentality of ‘armed’ competition, which today is not limited simply to the military context, but is also an economic and cognitive phenomenon.” His moral clarity in stating that “there is no algorithm that can make war morally acceptable” should be a warning to technologists rushing to design new weapons of mass destruction.

Underneath these specific concerns lies a more fundamental claim: that what is technically feasible is not the same as what is good for humanity, and that the difference depends on who controls the technology and what ideology and interests guide them.

Leo edges toward what I take to be the most important point about AI's future when he observes that “while AI promises to boost productivity by taking over mundane tasks, it frequently forces workers to adapt to the speed and demands of machines, rather than designing machines to work with those who work.”

But here he does not go far enough. He stops short of questioning the prevailing design philosophy of AI itself: a philosophy centered on mimicking human capabilities and automating human tasks, with the ultimate goal of artificial general intelligence (AGI) that can do everything a person can.

This philosophy rests on a mistake. It assumes that artificial intelligence and humanintelligence are fundamentally similar, and therefore machines should naturally take over whatever humans currently do. Yet these intelligences are fundamentally different.

Humans are “one-shot” learners. We form hypotheses from a few examples, mentally simulate possibilities, and refine our understanding through a social process of trial and error. This is how children learn language - imitating a few words, generalizing, and adjusting based on how others respond. We are not, however, very good at absorbing massive volumes of information or sifting through unstructured data for relevant patterns.

AI models are almost the opposite. They thrive on enormous training sets and excel at pattern recognition at scale. But they have, as yet, no genuine creativity, no real-world embodiment, and no capacity for trial-and-error learning grounded in interaction with the physical and social world.

When two things are different – you shouldn’t, and typically you couldn’t – use one to mimic the other. If you did, you would end up with suboptimal, disappointing results. It would have been a colossal mistake, and the Chicago Bulls’s legendary coach Phil Jackson would have gone down in the annals of basketball as one of the worst coaches in history, if he decided in the 1990s that because Michael Jordan was the better player, Jordan should mimic everything that Scottie Pippen and Dennis Rodman were doing in the team. The team went from championship to championship because these players worked together and complemented each other.

The same applies to AI and human skills.

The more productive path is complementarity – using AI to do what humans cannot, so that humans can do what they do best. An electrician aided by AI diagnostics, a nurse supported by AI in interpreting symptoms, a teacher using AI to personalize instruction for each student; these are the contours of a different AI future, one that raises rather than displaces human capability.

Optimists and industry insiders will respond that automation-first AI can still benefit everyone, provided redistributive policy keeps pace. But this argument has a poor track record. Forty years of digital automation have already concentrated gains at the top, hollowed out middle-skill work, and produced disappointing aggregate productivity growth. There is little reason to expect that an even more powerful round of automation, deployed by even more concentrated firms, will end differently. We can and must demand a different design.

The global stakes from the future of AI are even larger than those we can see around us in the United States. For the developing world, where billions still depend on the prospect of decent jobs as a path out of poverty, an automation-centric AI agenda is not merely suboptimal. It is simply transferring to foreclose the most important route to broad-based prosperity.

The biggest failing of today's AI industry is its refusal to recognize any of this. It is guided instead by an ideology of control (the industry’s own over humanity) and by a conviction that machines are uniformly better than humans.

As Leo rightly notes, this failure is enabled by the fact that a handful of companies now command the future of AI.

What we need is a combination of moral clarity and a serious, society-wide debate about what AI can do and what we want it to do. That debate must move beyond exhortation toward concrete choices: antitrust action against the dominant platforms, public investment in human-complementary AI, regulation of surveillance and autonomous weapons, and meaningful rights for workers and citizens over the data on which these systems are built.

The Pope's intervention makes such a debate a little more likely today than it was before.

It is now up to the rest of us to carry it further than he was willing to go.

[Anton Korinek @akorinek]

6/ Statistical agencies, central banks, and fiscal authorities should start building infrastructure now: 𝗔𝗜 𝘀𝗮𝘁𝗲𝗹𝗹𝗶𝘁𝗲 𝗮𝗰𝗰𝗼𝘂𝗻𝘁𝘀, data partnerships, capacity-based projections. Measurement takes years—and the AI economy isn't waiting.
https://t.co/sItXCNc0x1

[VoxEU @voxeu]

When AI meets local debt
Sean Dougherty @econecho @OECD @oecdeconomy, Christos Makridis @ASU
https://t.co/1HXJeWmAW8

[OECD Tax @OECDtax]

NEW: The Intergovernmental Fiscal Outlook 2026–2027 finds that subnational finances are under renewed pressure, with fiscal balances moving into negative territory not seen for over a decade, investment declining and debt-service costs remaining elevated🔗 https://t.co/UWJzgBgTuZ

[Acyn @Acyn]

A lot of jokes from King Charles tonight.

“You recently commented, Mr. President, that if it were not for the United States, European countries would be speaking German. Dare I say that if it wasn't for us, you'd be speaking French”

[Anand Shah @avshah99]

3/
1. It’s not just CA and DC. Pro se case increases are happening across America.
2. Cases themselves are more burdensome for judges to review, with courts experiencing many more entries across all their cases. (⬆️ entries per case  x  ⬆️ cases  => superlinear increase in court burden)

And, cases don’t seem to be obviously worse quality:
3. Case durations seem mostly unaffected
4. Outcome distributions are largely unchanged

[Daron Acemoglu @DAcemogluMIT]

Dear followers, please see this conversation with Jon Stewart and David Autor on AI, work, inequality and learning. I personally had a great time.

[Priyanka Vergadia @pvergadia]

🤯BREAKING: Researchers just mathematically proved that AI layoffs will collapse the economy: and every CEO already knows it.

The AI Layoff Trap. A game theory paper from UPenn + Boston University is glaringly important!

100K+ tech layoffs in 2025. 80% of US workers exposed. And no market force can stop it.

→ Every company fires workers to cut costs
→ Every fired worker stops buying products
→ Revenue collapses across every sector
→ The companies that fired everyone go bankrupt

It's a Prisoner's Dilemma with math behind it. Automate and you survive short-term. Don't automate and your competitor kills you. But everyone automating destroys the demand that makes all companies viable.

UBI (universal basic income) won't fix it.
Profit taxes won't fix it.
The researchers found only one solution: a Pigouvian automation tax "robot tax"

The AI trap on the economy is here!

[Lukas Freund @_LukasFreund_]

The AI job loss story is all about bundles by @jburnmurdoch @madhumita29 in @FT's great The AI Shift newsletter.

"Between the now-consistent picture on junior coding employment and the expanded framework of jobs as bundles of tasks, it feels to me like we’re developing an increasingly coherent picture of AI job displacement." writes @jburnmurdoch.

One important nuance I'd add is that task bundling is important not just for thinking about "job displacement" -- it also implies many workers can experience "job disruption" even absent full-scale displacement as wage returns to different skills shift as AI leads to work tasks being reorganized.

With shout-outs to the recent papers by @crane_leland&Soto, @lugaricano-Li-Wu, @joshgans-& @avicgoldfarb and @lukasfmann& myself on Job Transformation (thank you!).

[Ronan Farrow @RonanFarrow]

(🧵1/11) For the past year and a half, I've been investigating OpenAI and Sam Altman for @NewYorker. With my coauthor @andrewmarantz, I reviewed never-before-disclosed internal memos, obtained 200+ pages of documents related to a close colleague, including extensive private notes, and interviewed more than 100 people.

OpenAI was founded on the premise that A.I. could be the most dangerous invention in human history—and that its C.E.O. would need to be a person of uncommon integrity. We lay out the most detailed account yet of why Altman was ousted out by board members and executives who came to believe he lacked that integrity, and ask: were they right to allege that he couldn't be trusted?

A thread on some of of our findings:

[Anthropic @AnthropicAI]

We studied one of our recent models and found that it draws on emotion concepts learned from human text to inhabit its role as “Claude, the AI Assistant”. These representations influence its behavior the way emotions might influence a human.

Read more: https://t.co/clbKrTIxoe

[Chris Laub @ChrisLaubAI]

"ChatGPT is mainly for work"

Reality check: Only 27% of ChatGPT usage is work-related. 73% is personal. And the gap is widening every month.

The productivity revolution narrative completely misses how people actually use AI.

[Daniel Steinmetz-Jenkins @daniel_dsj2110]

French exceptionalism

[Ethan Mollick @emollick]

The average American worker using AI reports time savings of 6%, or 2.5 hours in a work week. Those are similar to the UK & Netherlands, and slightly more than other EU countries.

There some early, non-causal, signs that this is translating into real gains in productivity growth