Economist and professor @ LSE. Humanism and hardcore punk music appreciator.

Oxford, England
Joined July 2008
Months back, I broke my hand and my typing became (even more) rotten I started doing voice to text by necessity It completely made my agentic work faster by accident
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Nathan Lane retweeted
Sixth question on AI. Does AI need to be a zero-sum race between the US and China? Many influential voices in tech as well as the current administration argue that it does. (See, for example, darioamodei.com/post/on-deep…, washingtonpost.com/opinions/…, forourposterity.com/situatio… and this from the White House, whitehouse.gov/wp-content/up…). There are many aspects of these arguments, but I would like to focus on three that have received less attention than they deserve. The first aspect is that this framing typically assumes that there is a single direction of progress for AI, and the US and China are racing along this dimension toward superintelligence or at the very least some sort of powerful AI that will confer huge military and strategic advantages to the winner. If, on the other hand, the US and China are developing these technologies in different directions, there is much more room for coordination and even for each country learning from the other’s best practices. My reading of the evidence is that there are huge differences in the American and Chinese approaches. The US is focused on foundation models, such as frontier large language models. China is also investing – and arguably catching up – in these models, but there is much greater emphasis in China on applications of AI (with the government setting targets for the spread of AI across industries, english.www.gov.cn/policies/…). This reflects several factors, in my assessment. First, Chinese AI investments started with government demands for surveillance technologies and government-supplied data to AI companies (e.g., academic.oup.com/restud/arti…). Second, China is facing a huge demographic transformation, which will create scarcity of labor in the near future. This encourages companies to integrate robotics and AI much more actively into the production process (and China accounts for 59% of all robot installations in the world, ifr.org/ifr-press-releases/n…). Third, applications and integration of these technologies into the production process requires a lot of engineering. China has a large and very well-trained supply of engineers (whereas the US has far fewer engineers and a lot of engineering talent is currently going to Silicon Valley, which hasn’t focused on applications). Fourth, China was relatively late in catching the “artificial general intelligence,” AGI, bug, although now there is more emphasis on this in China as well. Whatever the exact causes, this difference in approach implies that there is plenty for the US to learn from China as well as the other way around. The second aspect is that the zero-sum framing ignores or downplays the crucial need for global coordination. This framing claims that the strategic and military advantage that powerful AI will create is all that matters. But the race to AGI also poses huge risks (and this is true regardless of whether this race is truly advancing toward superintelligence or just producing distorted intelligence, as I argued in my first post in this series). And these risks are global. If the US and China are racing toward AGI with no guardrails and coordination, even the follower (probably China) will have models capable of creating havoc. In this case, racing without any global coordination is a recipe for disaster. The third aspect of the problem is that this framing narrows the scope for any kind of coordination on other global issues between the US and China – including climate change, pandemics, nuclear nonproliferation and world peace. Tellingly, climate cooperation between the US and China has now largely stalled). Let me add that I’m saying all of this not as an admirer of the Chinese system. On the contrary, I have been a vocal critic of both the Chinese Communist Party and the Chinese development strategy since Why Nations Fail (penguinrandomhouse.com/books…) and I have argued that the Chinese success has some of its roots in severe labor repression (project-syndicate.org/commen…). Indeed, this argument does not ignore the fact that China has an authoritarian, repressive regime, and it is using AI for surveillance and to stamp out dissent. Global coordination and efforts to set up reasonable rules that apply to all parties do not require the counterparty to be fully democratic or ruled by angels. In fact, sometimes it is more important to set up such a global framework when dealing with dangerous adversaries (as the arms-control treaties that the US and the Soviets signed demonstrate). In sum, my assessment is that it is a colossal mistake to frame the AI race as a zero-sum one between the US and China. It poisons the much-needed dialogue and coordination between the US and China on broader issues; it makes the AI race more reckless; and it discourages the building of reasonable guardrails and regulations. The last point is particularly relevant, and makes me sometimes say: if China did not exist, Silicon Valley would have to invent it.
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Nathan Lane retweeted
At least so far, it seems like AI is reducing wage inequality (while the income share of labor as a whole is decreasing). So the effects of AI on inequality are likely to be mixed. We showed in a recent paper (w/ @mireiagine and @javiersanzespin) that about 40% of the observed decline in wage inequality is due to a decline in the college wage premium, which can be partly accounted for by differences in AI automation exposure across occupations (that is, because college graudates are on average more exposed to AI automation than non-college blue-collar workers). So, the position of labor relative to capital has decreased overall, but the position of blue-collar workers has increased relative to white-collar workers (especially the more AI-exposed ones like software developers).
Third question on AI. A question that also remains unasked is whether the AI boom can continue without leading to a massive increase in inequality. A recent paper by Stijn Van Nieuwerburgh runs the numbers on how much revenue the AI industry needs to generate to recover its massive investment (summary and a link to the paper can be found here: brookings.edu/articles/finan…). Van Nieuwerburgh’s arithmetic should make us more concerned. AI investments will average about 3.6% of GDP annually between 2025 and 2032. Van Nieuwerburgh calculates that, using a 10% rate of return, the industry would need to generate annual revenues of about $3.7 trillion by 2032 to recover these costs (growing from its current levels of about $200 billion or so). That is significantly more than 10% of current US national income, and will likely remain around 10% of national income by 2032, even if GDP growth rose from its current level. A large fraction of this revenue will go to capital income. That means a massive increase in the share of capital in national income, which has already risen substantially over the last 25 years or so – now standing at an all-time high of about 47% (bls.gov/news.release/pdf/pro…). Capital income is much more unequally distributed than labor income, so a massive increase in the capital share of national income will translate into a very sizable surge in inequality. The rise in inequality may not stop with the capital share. My work with Pascual Restrepo documents that (automation-driven) increases in the capital share of national income are typically associated with rising labor income inequality as well (see, for example, economics.mit.edu/sites/defa…). The same may happen in the next several years, boosting inequality further. What is missing from our current debate is any discussion of a fundamental dilemma these numbers pose: can the AI boom avoid both an economically costly crash and a huge increase in inequality? If the industry reaches these revenues, inequality surges. If the industry does not become profitable, a crash, with substantial costs in terms of lost output and jobs, becomes likely. My assessment would be that the industry is unlikely to reach levels of revenue Van Nieuwerburgh calculates. First, diffusion has been and will likely continue to be slow. Second, competition from open-weight models, which are getting better, will limit how much proprietary models can charge. Third, despite important advances, I still believe that AI models will not be able to automate entire occupations anytime soon, thus limiting their value to businesses as cost-saving devices. Whether this leads to a crash or not is more complicated and will depend on whether various AI companies are bailed out and what kind of support they receive. Nevertheless, even if revenues fall short of these gargantuan amounts and we avoid a dramatic surge in inequality, I expect that the diffusion of AI will push up inequality between capital and labor and within labor. If inequality does surge, a further question becomes central: can our democracy survive such astronomical levels of inequality?
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Nathan Lane retweeted
Agree with this AND climate folks should prioritize getting rid of the filibuster ASAP. There is no God given reason Congress can only do one thing a year, and majority rule would open a new world of legislative dealmaking where prioritization wouldn't be quite so brutal.
There’s a point in there where they explicitly argue that it would be better to kill this bill and then wait two years and then try to pass something in 2029. And, like, sure that is something we could do. But: 1) I don’t think it’s a high likelihood that the next trifecta will prioritize climate in their two-year window in 2029-2030. Climate got the previous “slot” over other core priorities. We aren’t getting “another IRA” most likely. Let’s just be clear-eyed about that. Democrats aren’t going to win an election on the basis of affordability, inflation, and backlash to foreign wars and AI, and then decide to pivot to the environment. 2) even if we did, ok we wait two years for Trump to be out of office, then another year to pass legislation, then however many years before the legislation’s effects trickle out to result in projects getting financed, and actually built… We’d be looking at an opportunity cost of 4+ years at that point. We are in a race against time here, and the status quo is stacked against renewables. The status quo of the grid and permitting doesn’t stop data centers from getting built, it results in them getting built regardless, but powered by literally retrofitted jet engines turned into gas turbines, behind-the-meter and not connected to the grid, and thus which will never decarbonize at all for the 20+ year lifespan of the generators. 3) that strategy STILL does nothing to solve the fillibuster issue. Nothing is getting done on the issue of transmission without bipartisanship, and in order to get bipartisanship we can’t just write up a Big Green Bill that trades nothing we don’t like. You guys think Josh Turek and Mary Peltola and James Talarico are going to overturn the fillibuster because a bipartisan energy-neutral compromise bill isn’t good enough?
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I'm stuck in @IDme hell 😊 Wild that the US government hinges on this service
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Nathan Lane retweeted
Huge congrats to my colleague Joe @JADHazell, the rising star of empirical macroeconomics!!!
Very happy to share that my colleagues at LSE have voted to award me tenure!
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Nathan Lane retweeted
it’s almost like 🤔 a complete absence of regulation or oversight, due to industry influence … can also be a form of regulatory capture 😳
Another successful meeting of the Committee to Prevent Regulatory Capture
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This was an ALL TIMER from Peter Navarro
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Alright, alright. I bit. My latest on The Antidote: My two cents on recent scary tech predictions. The Future Doesn’t Get Predicted — It Gets Produced carissaveliz.substack.com/p/…
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Nathan Lane retweeted
How film protectionism (quota quickies) created Alfred Hitchcock's career. ft.com/content/4205babe-1db0…
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Nathan Lane retweeted
The top 20% of households got more than half (52.4%) of all income in 2025. That share has been rising gradually over time.
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I was evaluating multi-agent workflows from this summer. The most successful models have been Kimi K3 and GLM 5.3s. Especially for reviews. Their successes come from catching and correcting insane Claude Opus errors 🙃
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wow
Donald Trump and Hasan Piker Sr, c. 1987
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Nathan Lane retweeted
Jake Sullivan calls for a Strategic Investment Fund: "Loans and guarantees would stretch each public dollar and crowd in private capital, and convertible and equity-like instruments could give taxpayers a share of upside when investments succeed." foreignaffairs.com/united-st…
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Getting random alarming Claude health warnings in Linux convos. Which makes sense tbh
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My prediction all summer has been that big AI is facing a “pc moment” Home open-weight models are moving rapidly from a hobbyist activity to a competitive technology capable of being run on consumer hardware. Without the extraction, control from key behemoths.
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Nathan Lane retweeted
This is how Kant looked
She was so scared..
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México, 1928 Tina Modotti
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