Namrata Narain retweeted
We need a mindset shift in how we deploy AI. In the past, new capabilities unlocked new use cases. Safety, control, ethics, and skill erosion were important but usually secondary considerations; risks to be managed rather than gating criteria. But now and in the future, capability won't be the main bottleneck to deployment — considerations beyond productivity should be the decisive factor. The default question used to be "What can AI do?" But we need to start asking "Of the things AI can do, what should we actually delegate to it?"
Namrata Narain retweeted
Just as mathematicians are now learning how many insights lie in the "convex hull" of existing ideas, the rest of science is about to learn this lesson at even more dramatic scale. Few realize the ridiculous inefficiency of the pipeline from theoretical innovation to practical use.
Early versions of GPT made it easier to find existing knowledge. But finding a theorem is not the same as recognizing that it offers a radically different way to conceptualize a problem. Doing the latter requires expertise across fields that almost nobody possesses.
Most of the people doing applied work are completely unaware of the conceptual tools that are being developed in more theoretical fields, and the people doing high-level theory do not understand the practical problems applied researchers are trying to solve. Almost no one doing practical work of any importance has an operational understanding of the web of ideas underlying the langlands conjecture or condensed mathematics, and virtually no one doing biology, chemistry or economics is knows anything about 90% of the math used in theoretical physics.
And for good reason! Most of these ideas would be completely useless. But the scope for possible innovation from drawing the rare useful connections is immense.
Think of Paul Samuelson borrowing ideas from thermodynamics to model equilibrium in economics, or Ed Witten using quantum field theory to revolutionize geometry. A few startling successes, not feasible to most: trying to venture into a more theoretical or more practical discipline to find insights of use is like searching for a needle in a haystack.
What I suspect we will see in the next few years is a scientific revolution brought on by models that combine deep theoretical insight with deep practical knowledge and judgment. Existing publicly available models are lacking in all three departments, but getting better. When searching for useful connections between distant fields becomes cheap, even a low hit rate could produce an explosion of innovation. Even without new ideas (which will also be forthcoming...), the convex hull of existing science would push far beyond the status quo.
Namrata Narain retweeted
When I did my PhD in computational neuroscience a long time ago, optogenetics was the new thing. It honestly looked like magic.
You could shine light on a neuron and switch it on or off. Turn memories on. Turn memories off. Map emotion and behavior cell by cell, in a living brain. It transformed the field almost overnight.
Today, its pioneers, Karl Deisseroth, Peter Hegemann and Georg Nagel, won the Nobel Prize in Medicine.
And it all started with a simple question: how does a single-celled alga swim toward light?
This is why we fund basic research 💪
BREAKING NEWS
The 2026 #NobelPrize in Physiology or Medicine has been awarded to Karl Deisseroth, Peter Hegemann and Georg Nagel “for their discoveries concerning light-gated ion channels and optogenetics.”
Namrata Narain retweeted
Dear Followers
Over the next three days I am going to respond to @DAcemogluMIT 's questions on AI. There are lots of reasons to do this but I also wanted to send a big FU to that Economist piece that suggested we economists aren't willing to challenge Daron. That is not my experience and I know people who challenge him all of the time. None are shy about it. If anything, they do so in our normal circles more than, say, here. So let's do it more here too.
Namrata Narain retweeted
Artists: but I don't want to lose my job. I love it.
Mathematicians: but I don't want to lose my job. I love it.
Economists: LONG HAVE I AWAITED THIS HOUR! DESTROY ME, EFFICIENCY, AND LET UTILIZATION-ADJUSTED TOTAL FACTOR PRODUCTIVITY RISE ON THE WIND OF MY ASHES!
Wow. Every time I sign into Twitter (once a day), my mind gets blown (in the past month or so)
We built high-throughput materials labs in Menlo Park to create a loop between experiments and models. The labs generate fresh data, the models learn from it, and then help us decide what to try next.
Using only 1,300 H200s, plus months of our experimental data, we mid-trained and RL’d an open-source model to surpass GPT-6 Astra on our analysis benchmark. We call it Neon.
This is real footage from our lab. We’re focusing first on hard problems in materials science, including superconductors, magnets, and semiconductor materials.
Read our blog posts below.
Namrata Narain retweeted
Some thoughts on AI and Theory.
1. To a first approximation, theoretical computer science has been organized around a few major open questions. Much of our work has been motivated by developing approaches to answer these questions.
2. Such “problem-motivated” work has often led to theory-building focused on identifying a general principle that unifies a class of theorems. But much of that theory-building also involved proving new, difficult theorems.
3. Thus, while it’s true that problem-solving was strongly correlated with building understanding, drawing connections, and eventually developing general theories, it would be disingenuous not to admit that our community, perhaps disproportionately in retrospect, focused on and celebrated problem-solving. This was not arbitrary, and was quite defensible. Being able to make progress on central technical questions usually correlated with taste, creativity, persistence, and depth of understanding. Much of our reward structure therefore implicitly relied on the fact that producing an important proof was good evidence that someone possessed these harder-to-observe qualities.
4. It seems likely that we will soon have AI tools available to us that can prove many such theorems in a short time. The cost of obtaining proofs for well-posed mathematical questions will likely fall dramatically. The “scarce” intellectual work will likely shift both upstream: to questions, models and theories, definitions, and conjectures, and downstream: to interpretation, synthesis, explanation, and theory-building.
5. But as long as we believe in humans being meaningfully in charge of our collective decisions and fate, building human understanding of our science (and of science more generally) will remain an essential goal. I plan to expand on this important aspect soon.
6. Historically, finding a solution to an important problem and understanding its significance, implications, and connections were entangled. Finding a proof usually required researchers to discover the right concepts along the way. A dramatic reduction in the time and effort required to prove theorems could break that coupling. We could end up with many more true statements and proofs without a commensurate increase in understanding.
Converting an abundance of proofs into human understanding may become one of the central challenges of our field.
7. As a result, I expect the high-level goals of theoretical computer scientists to change. In fact, the advent of powerful theorem provers might help us construct new theories and explore new models far more easily and rapidly, and significantly expand the domains where our models and theories apply. In that sense, the space for theoretical work may significantly expand rather than contract.
8. There’s a high human cost to the disruption that we are likely heading into. Many in our field, and in mathematical communities more broadly, are coming to terms with it. The range of opinions and reactions among mathematicians and theoretical computer scientists is a natural part of this evolution in our thinking as we collectively work through it.
Some concrete efforts (including one at @SimonsInstitute) are already underway to think through the immediate scientific and institutional questions arising during this transition.
Namrata Narain retweeted
Relatedly, "equal pay for equal work" laws or norms are wild. There are *huge* differences in productivity. An NBA team might have 50x pay gaps for "the same job". Sales commission jobs, the same. Many sectors, though, don't believe in rewarding outcomes.
Namrata Narain retweeted
There's a lot of drama stemming from the flurry of announcements by AI labs about new results in mathematics and formalization. But these results are really a celebration of human mathematical achievement!
How incredible is it that, by distilling the sum total of our mathematical understanding into these models, we're able to answer the fundamental questions of our time?
Namrata Narain retweeted
Major new report from the White House Office of Science and Technology Policy.
Five things in the report I really liked:
1. Proposes metascience units as a way to advance experimentation in science agencies.
IFP recommended OSTP take this forward in our response to a 2025 RFI.
2. Advocates for a portfolio approach to federal science funding.
Right now, basic research funding largely goes to incremental, project-based grants. While important, they can’t be the only mechanism we use to fund science.
3. Recognizes the need to launch new institutions.
It specifically highlights X-Labs as an experiment with independent labs that can take on ambitious challenges. This is a bipartisan idea whose time has come.
4. Focuses on a mix of innovation funding mechanisms, including fast grants, prizes & challenges, and advance market commitments.
We described and contextualized these ideas in the Atlas of Innovation, which can help policymakers design and implement these approaches.
5. Seeks to reduce burden for American scientists, who face mountains of paperwork.
Scientists should spend more time doing science and less time writing/reporting on grant proposals and working to meet regulatory requirements.
In response to @POTUS's call for a revival of America's scientific enterprise, today I’m releasing Science: A New Golden Age.
This report is a blueprint for renewing American scientific leadership for the 21st century.
Completely agree. Slow courts mean meaningful economic relationships just don’t get formed and inefficient ones persist too long. Some meaningful innovation happening in this space to speed things up — adalat.ai/
It is not inevitable that India be poor. Its people are certainly capable of being extremely productive. Indian immigrants to the United States and elsewhere have been very successful. India’s government has been stable since independence, and largely democratic throughout. While their growth rate has picked up, they have not had the booming success of China. India was socialist for a long time; but then, so was China. Why isn’t India a developed country yet?
To suggest a monocausal explanation would be an exercise in arrogance. India has many problems, and there is no one simple trick to growth. Nevertheless, I hold that much of India’s stagnation is due to its judicial system. Its slowness and inefficiency has far reaching consequences, making it impossible to efficiently run a business.
The facts are simple. There are at least 50 million cases pending — nobody knows how many for sure. The Supreme Court has 69,000 piled up, waiting for resolution. Of these, 18,000 have been pending for more than thirty years. Every family has a horror story. I have a friend whose dad, near the beginning of his career, was named in a suit involving a bank. Growing up, every few years his dad would be called to appear in court. My friend is 40 now. His dad has retired. The suit remains ongoing.
The Indian government estimated that, at current capacity, it would take 324 years to clear all of the cases. That was in 2018. Since then the number of pending cases has doubled. India has one of the lowest ratios of judges to population in the world, with 21 judges for every million people. Surprisingly, this is an improvement over the past — in 2002 the ratio was 10 for every million. The EU average is 200 per million, and the US 150 per million. One reads with grim amusement concern that a low number of judges — say, 100 per million — will endanger the rule of law in Ireland or Denmark. If that is what it takes to endanger, then rule of law in India is positively extinct.
Nobody knows the average time it takes a court case to be resolved for sure, but it is somewhere around five years for it to be resolved by the high courts (the second tier of courts) and 13 years for the Supreme Court. 40,000 new civil suits are filed every day, covering everything from land disputes to layoffs to bankruptcy. 66% of all pending civil cases involve claims and counterclaims over who owns what land. The upshot of this is that firms cannot resolve disputes through the courts. They must instead turn to extrajudicial methods, and the most common of them is keeping the business in the family.
In the late 2000s a team of researchers – Bloom, Eifert, Mahajan, McKenzie, and Roberts – conducted a randomized controlled trial on management quality in Indian textile mills. Prior work by Bloom and Van Reenen had shown that family run firms in the West, in particular those run by an eldest son, were poorly run compared to professionalized businesses. We would not be able to tell in India, however. Of the 126 firms they surveyed — a comprehensive census of the firms in the towns near in and around Mumbai — every single one of them was family run. The single strongest predictor of firm size was not productivity, revenue, or profitability. It was simply the number of male family members in the family. These are not simple mom-and-pop shops either, with but a few employees. As Bloom et al write, “These firms are also complex organizations, with a median of two plants per firm (plus a head office in Mumbai) and four reporting levels from the shop floor to the managing director. In all the firms, the managing director was the largest shareholder, and all directors were family members. Two firms were publicly quoted on the Mumbai Stock Exchange, although more than 50% of the equity in each was held by the managing family.” (p. 9)
The experiment by Bloom et al was to test how much management mattered by randomly assigning some firms to receive management training. They found that a firm receiving the management training increased their annual profits by 17%, or almost $300,000 a year. Since these are extremely simple interventions — things such as “write down what types of yarn you have and how much” and “have a schedule for repairing machines”, one wonders why the companies never adopted them on their own.
You can read the rest here: nicholasdecker.substack.com/…
Namrata Narain retweeted
Perhaps useful to point out that history rhymes: this is effectively what happened with telephone operators, 100 years ago. Young, entry-level operators, whose work was mainly connecting local calls--the simplest version of the job--and not much else, were wiped out by automatic call switching. More senior operators, who had a wider range of tasks and did more complicated work like information service, emergency service, and long distance calls, were not as affected. Even when local telephone operators were effectively eliminated across AT&T's network, the others remained. Much had to do with the complexity of the work and how entangled it was with the rest of the organization.
Extended discussion in work with @jamesfeigenbaum in @QJEHarvard and Management Science: academic.oup.com/qje/article…, pubsonline.informs.org/doi/1…
Lots of credit to @joshgans for helping us sharpen some of these ideas in this work as editor of the ManSci paper
An increasingly coherent picture of the impact of AI on jobs, by @jburnmurdoch @ft:
1. New Fed paper by Crane and Soto now confirms with official labor force survey data what private payroll analysis was showing: roughly 500,000 fewer coders are working than pre-LLM trends would predict.
2. Argues evidence consistent with my work (with Lin and Wu, link in my pinned post) on weak/strong bundles: junior developers and contractors hold "weak bundles" (their work is mostly standalone coding that AI can substitute directly), senior developers hold "tight bundles" where coding is combined with domain expertise, judgment, and cross-functional responsibilities, making substitution much harder.
3. Freund & Mann and Gans & Goldfarb add a second lens: what matters is the value of the tasks that survive automation. Remove coding from a senior role and you free up time for higher-value work; remove it from a junior role and almost nothing remains.
ft.com/content/b69f8599-eaf1…
Ludwig is genuinely one of the nicest people I’ve met, and not just in our profession ;) I’ve been so happy since hearing this news yesterday! Huge congratulations, @ludwigstraub! (Martin had no trouble getting lots of testimonials to make this presentation)
Wonderful news on @ludwigstraub getting the Clark medal!
When Ludwig got tenure, his @HarvardEcon macro advisees and coauthors put together this slide deck full of anecdotes showing how great Ludwig is as an academic and as a person:
drive.google.com/file/d/1H0k…
Namrata Narain retweeted
Overjoyed that @ludwigstraub has won the Clark medal!
I met Ludwig when he was finishing grad school - we started talking about private debt, that would lead to our paper `indebted demand'. I vividly remember thinking how special Ludwig was - poetic brilliance of an applied theorist that reminded me of Krugman. He is in that league in my view
And what a true gentleman! A great joy to be with. Congrats Ludwig! couldn't be happier
aeaweb.org/about-aea/honors-…
Namrata Narain retweeted
A 79 year old professor is my current poster girl.
In 2023, Claudia Goldin was awarded the Nobel Memorial Prize in Economic Sciences for her work in women's labor market outcomes, the first woman to win the award solo.
After her win, she was deluged with hundreds of requests of which she accepted just three. I am not sure of the first, but the second was to appear on the NPR quiz show in the US. And the third was advising the WNBA players union as the women prepared to negotiate a new labor deal with the league.
The players union was ecstatic when Goldin replied, assenting to help them with just one requirement, she refused to be paid.
Clearly Goldin is just not a theorist as, thanks to her, the Players Union and the WNBA reached a collective bargaining agreement that gave Women’s National Basketball Association players a nearly 400% raise. Starting this season, players’ average salary will top $580,000.
Goldin has also always had Golden Retrievers and apparently would often read her papers aloud to them while working out models in her head.
Is there anyway she can be more perfect.
Namrata Narain retweeted
Let me use this opportunity to also flag @pawtrammell
's neat model of Workflows and Automation. One of the few models explaining *why* it may be efficient for a single worker to perform a bundle of multiple tasks.
AI automates tasks. Tasks are bundled into jobs. The labor market prices jobs, not individual tasks.
New paper by @lugaricano-Li-Wu (2026) explains how the labor market effects of AI depend on how easily an automated task can be "pulled out" of a job bundle. Strong bundling "protects" labor.
"Weak Bundle, Strong Bundle: How AI Redraws Job Boundaries"
Namrata Narain retweeted
Bad econ. "White collar workers switch to Doordash, driving down real wages there too" is partial equilibrium thinking
Robot firms only grow if they're producing more real goods & services. If production is growing, real incomes are rising not falling. No doom loop!
If production is growing but somehow consumption stalls, that's just a failure of monetary & fiscal policy. Cut rates to zero quickly; below zero, fiscal policy kicks in to redistribute income to consumers & restore consumption growth to trend
Either there's no doom loop in the first place, or else New Keynesian monetary & fiscal policy kicks in to close any emergent wedge between robot output & human consumption
This piece is ultimately just anticapitalist pablum (teasing a belief that most markets are just scams and rent seeking in the intro!)--and after that, the piece simply underrates or misunderstands the stabilizing powers of liberal democratic Keynesian capitalism to keep output & consumption in balance
We don't have to allow what @delong calls "a failure of the exchange mechanism" in the future any more than we did in the 1930s
JUNE 2028.
The S&P is down 38% from its highs. Unemployment just printed 10.2%. Private credit is unraveling. Prime mortgages are cracking. AI didn’t disappoint. It exceeded every expectation.
What happened?
citriniresearch.com/p/2028gi…
Namrata Narain retweeted
Why has India failed to industrialize?
Ha-Joon Chang argues that it’s because India’s business and financial elites oppose industrialization — and that it won’t happen unless their power is curbed.
Namrata Narain retweeted
Replying to @jamespstratton
@jamespstratton and I put out a working paper a few days ago on exactly this question! We propose a way to answer it using a simple causal analogue of the R² ("Causal R²").🧵(1/12). Paper: drive.google.com/file/d/1FWN…


































