The staggering welfare consequences of catch-up growth after AI

Published on August 13, 2026 5:49 PM GMT This post was crossposted from ChatGDP by the Forum team, with the author's permission. The author welcomes discussion and plans to respond to comments when they can. An interactive version of this post — with live widgets instead of static images — is on the Coefficient Giving website , along with the full calculator and the technical note . "Is there some action a government of Nigeria could take that would lead the Nigerian economy to grow more like an AI-powered United States' or China's? If so, what, exactly? If not, what is it about the 'nature of Nigeria' that makes it so? The consequences for human welfare involved in questions like these are simply staggering: Once one starts to think about them, it is hard to think about anything else." — the spirit of Bob Lucas (1988) [1] In 1800, Mali's per capita GDP was roughly 5x smaller than Britain's. Today it is roughly 25x smaller. [2] The Industrial Revolution accelerated growth in the UK and the benefits of the new tech eventually reached the whole world. But the process has taken so long that, two centuries later, the gap is still 5x larger than it was before the new tech arrived. More and more economists are taking seriously the possibility that AI will cause a rapid acceleration of growth in frontier economies. [3] Investors are betting trillions on AI . For their bets to be right, AI companies need to generate even more trillions in revenue, which would imply a big growth acceleration in frontier economies. The economic consequences are serious enough that the Fed just launched a high-profile task force on AI and the economy . So it doesn't seem crazy to think that AI can really accelerate frontier growth. What happens to Mali this time? This post argues that Mali, and all non-frontier economies, are likely to be left behind, at least in the initial phase when frontier economies are rapidly becoming more automated. [4] The broad argument depends on two simple and, in my opinion, fairly reasonable assumptions: AI accelerates frontier growth. At least in the period immediately after the arrival of Transformative AI ( TAI ), the growth rate of frontier economies increases. That's the period I focus on here, and I call it the "detachment window." [5] Poor countries will also see a boost to growth — but there will be a lag. For reasons I will discuss below, poor countries lag behind in AI adoption in productive activities, so the productivity increase due to AI arrives in non-frontier economies with a lag. [6] Surprisingly (to me at least), these two ingredients are sufficient to conclude that the GDP gap between frontier and non-frontier economies increases in the detachment window. You can see the math in the technical note . The intuition is best presented via an image: Figure 1. On the flat, the gap is constant; when the leader hits the downhill, the same 30 seconds becomes a growing gap. The frontier is the leader; the slope is accelerating growth; the head start is the tech adoption. Have you noticed in the Tour de France that when riders reach a downhill stretch, the distance between them tends to open up? That's because the slope accelerates the bikes, which turns a fixed gap in time into a growing gap in space, even if they are pedaling equally hard. Here the leader is the frontier economy, TAI is the downward slope, the fixed gap is in years for adoption of AI in productive activities, and the gap is in GDP. The rest of this post presents a simple economic model that helps me justify these assumptions and quantify how much the gap opens up. The stakes are enormous. In the central scenario, delayed adoption leaves average incomes in the developing world at the end of the window about a fifth of what they would have been with no lag. Each year of faster productive AI adoption in lagging economies is worth about $42.6 trillion in present-value income (more than a third of the world's GDP), or the equivalent of saving 115 million lives if we adopt Coefficient Giving's standard methodology . If you are skeptical of the assumptions, you can change all the parameters in this calculator . You can also download the source code here . A simple model of takeoff in frontier economies Think of the economy as a series of tasks: drive the truck, weld a joint, file taxes, make Doritos Screamin' Sriracha , sell Doritos Screamin' Sriracha at Dollar General, etc. Each task can be done by either a person (L, for labor) or a machine (K, for capital), and all tasks are combined using a Cobb-Douglas aggregator (a staple of economics) to get us total output (Y). A bit of math gets us to: Y = (K / f) f × (L / (1 − f)) 1−f To see the math, check the accompanying technical note . This is the tasks model, the workhorse of the automation literature. [7] Here f is the fraction of tasks in the economy allocated to capital. Since f tasks are allocated to capital, (1 − f ) tasks are left to labor. Capital has to perform f tasks, so the total amount of capital available to produce each task is K/f . I think of an AI-induced growth takeoff in frontier economies as increasing f . This is not something I invented. In fact, I just copied the idea from one of the most cited papers on the economics of AI, Aghion, Jones, and Jones . As AI capabilities grow, machines become more capable of performing economic activities, so more tasks are allocated to machines, and the growth rate in frontier economies increases. In fact, in this model, the higher f is, the higher the growth rate. [8] To calibrate the speed of growth we first need to assume a trajectory for f . I assume f follows an S-shaped curve because that's basically how all technologies spread ( Geroski 2000 ). In the beginning, AI doesn't exist and doesn't drive automation. Then it's invented. Then, as it gets better and better, tasks are automated at a growing rate. As we get closer to 100$50,000 a year to own and run (after capital costs, maintenance, and power). In Seattle, the worker it replaces costs $65,000 a year, so automation saves the employer$15,000. In Lilongwe, a worker costs $2,000, so automation loses$48,000 a year. This is a stylized example, but the point is just that the higher the labor costs, the higher the economic gain from automation. [13] 4. Complementary inputs. Automation is more attractive if you have reliable and cheap electricity , internet connectivity, sensors producing data, skilled managers who can reorganize firms, etc. All these complementary inputs are more abundant in frontier economies. China already has a lot of robots making cars, so the next AI-powered robot can slot right in. If you are Zambia and have no car factories, making the AI-powered robot produce cars will require a lot of complementary investment. [14] In the model, these reasons can be summarized by a single lag ( D ). [15] That is, the poor country's automation curve f is the frontier's curve shifted right by D years. It's not hard to see why. A task is given to a machine if, and only if, the cost of completing it with a machine is lower than with a human. We can map reason 3 into lower local labor costs in non-frontier economies, and reasons 2 and 4 into higher effective machine costs. Reason 1 affects the composition of tasks rather than the cost of a given task, and mapping it into my simple model would require additions making it significantly less simple, so I leave it out. We can then conceptually model the arrival of TAI as a phase in which AI makes machines more and more effective at performing economic tasks, which is mapped as a steadily falling cost of completing tasks with machines. This single driving force causes the share of automation f to increase in both frontier and non-frontier economies, as more and more tasks flip from being overall cheaper with humans to overall cheaper with machines. I find it neat that a simple model, grounded in the literature, can generate increasing automation shares and growth in both frontier economies and non-frontier economies, but with a constant lag D separating them (check the technical note for details). The model also gives a hint on how to calibrate the lag. D equals the machine-cost halving period times the (log of the) country's cost disadvantage. For example, a country facing a 10x less favorable machine-to-labor cost ratio, with machine costs halving every three years, has a lag of about 10 years. You can play with machine-cost halving periods and machine-to-labor ratios here: The best way to calibrate D that I found is to use the historical evidence in Comin and Mestieri . The authors measured adoption lags for 25 technologies across 139 countries, over about two centuries. They find poor countries got the internet roughly 6 years after rich ones, and PCs and cellphones roughly 6 to 8 years after. So the time it takes for a new technology to "arrive" in a poor country is shrinking. However, they find that the gap in how intensively countries use technologies once they arrive has been widening. The smartphone, for example, took about 15 years to reach half of Sub-Saharan Africa. Smartphones are a relatively cheap consumer good, while "AI in productive activities" entails expensive machinery that requires complementary capital. So my central guess is D = 10 years, which seems like a conservative guess to me, since in the model D is better mapped to the intensity (fraction of tasks automated) rather than the availability of AI for productive activities. Step 2. Now add the lag. Each economy keeps its own baseline growth (adjustable below) and the follower receives the boost D years late; the orange wedge between the curves is the divergence engine: Tip: Set the two baselines equal to isolate the delay's own contribution to the gap. That isolated part is exactly what the calculator prices, which is why its headline numbers compare two paths for the same country rather than frontier vs follower. To the numbers Step 3. Price the wedge: the headline numbers and the two income paths, live. Drag D and T : The calculator below compares two paths for the same developing-country bloc: one where the AI boost arrives immediately and one where it arrives D years later. Both start at today's average non-frontier income ($4,200 per capita), and I apply to both a 4$1.2 quadrillion. For scale, the entire world currently produces about $110 trillion per year. In welfare terms, the gap is worth about 6.1 quadrillion Coefficient Giving dollars , using the CG methodology to value income gains (a 1$500). In welfare units, the total gap is equivalent to saving roughly 1.86 billion lives. Each year of faster adoption is worth about CG$381 trillion, roughly 115 million lives in welfare units. In my view, this is the most important number because I believe economic policy can change D and philanthropy can help. My main takeaway is anything that accelerates developing countries' catch-up has welfare returns at a scale that's hard to find elsewhere in philanthropy. Conditional on TAI having an important effect on frontier growth, it's hard to imagine ways in which the rate of catch-up in the aftermath doesn't matter enormously. In fact, ensuring that developing countries' growth rates stay close to the frontier growth rate might be the second most important task in terms of human welfare, after preventing AI-induced catastrophic risks. [16] Why care about catch-up growth if TAI will create great abundance and raise everyone's welfare? Rapidly growing frontier economies would produce a lot of goods. The price of tradable goods could fall significantly in international markets, and new amazing products could become available. If developing countries can import super cheap electric cars and pills that cure all diseases, why care about economic growth? That's because growth still matters for two reasons. First, more money increases access to those new goods. Even if great AI-produced goods become available, poor countries will have little money to pay for them if their income is low. Increasing their GDP is the best way to improve their access to these great new goods. Second, this is a counterfactual comparison about relative incomes. The welfare gaps here compare GDP in non-frontier economies with what it could be if the adoption lag decreased. The model suggests that AI-induced takeoff in frontier economies eventually benefits everyone — by an enormous amount — but by not trying to accelerate catch-up, we'd be leaving a huge amount of welfare on the table. What happens after the detachment window? So far I have only talked about what happens in the detachment window – but what happens after can undermine the main point I'm trying to make here. My main point is that accelerating catch-up growth during the detachment window matters a lot. An objection that AI-believers could have is that the initial detachment phase doesn't matter much because it's short and what follows is quick catch-up, or a post-scarcity future where money becomes a useless concept and income differences stop mattering. If so, the welfare gap we've just estimated is just a blip, a rough couple of years on the way to somewhere great for everybody. Why spend attention, let alone money, on a blip? To answer that, let's be concrete about what could come after the detachment period modeled so far. I can imagine four main scenarios: Future 1: the plateau. Growth rates stay high everywhere. The income gap freezes at roughly 5x and stays there. We have a new, spectacularly richer world, exactly as unequal as the window left it. Future 2: the fade. The AI boost turns out to be temporary, and frontier growth comes back down. Then something nice happens: The follower rides the tail of the boost after the frontier has slowed, growing faster than the frontier for a while, and a temporary boost plus a pure lag creates only a temporary gap. Future 3: the singularity. The boost keeps growing and the gap explodes. Future 4: the convergence. Developing c