2026-02-14 02:06:14
Here is the link, self-recommending…
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2026-02-14 01:14:01
1. The economy of Egypt continues to improve.
2. Who is Claude really? (New Yorker)
3. Cowen’s Second Law, as applied to the Midwest.
4. Andreas Backhaus economics Substack, a post on why the motherhood penalty is smaller than many think.
5. Derivatives on derivatives for the Second Coming.
6. The debate over Mars life continues.
7. Can Greenland be an AI powerhouse? (WSJ)
The post Friday assorted links appeared first on Marginal REVOLUTION.
2026-02-13 23:21:38
Who needs to be a programmer, be hired to close the doors on Waymo vehicles:
Or see here. And more text from TechCrunch. Via Glenn Mercer, Tom McCarthy, and also Air Genius Gary Leff.
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2026-02-13 15:41:22
Given that LLMs seem to be able to automate so many small tasks, why don’t we see large productivity effects?
I drafted a short paper recently exploring the possibility that it’s for the same reason (or at least one of the reasons) that labor is typically bundled into multi-task jobs, instead of transacted by the task, in the first place: because performing a task increases one’s productivity not only at the task itself but at related tasks.
For example, say you used to spend half your time coding and half your time debugging, and the LLM can automate the coding but you still have to do the debugging. If you’re more productive at debugging code you write yourself, this (1) explains why “coder” and “debugger” aren’t separate jobs, and (2) predicts that the LLM won’t save half your time. If you’re half as productive at debugging code you didn’t write, or less, the LLM saves you no time at all.
So I was excited to see @judyhshen and @alextamkin’s paper from a week or two ago finding basically just that!
At least the way I’m thinking about it, “cross-task learning” should make the productivity impacts of automating tasks more convex: – Automating the second half of a job should be expected to have much more of an impact than automating the first half; and – If the machines can learn from their and each others’ experience, as a worker learns by doing from her own experience, then automating two jobs will have more than twice the impact of automating one.
That is from Philip Trammell. Here is his short piece. Here is the Shen and Tamkin paper. This is all very important work for why the AI growth take-off will be much slower than the power of the models themselves might otherwise indicate. The phrase “…and then all at once” nonetheless applies. But when?
These short pieces and observations are likely among the most important outputs economists will produce this year. But are they being suitably rewarded?
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2026-02-13 14:14:38
That is the new book by Joe Studwell, my podcast with him should be coming out pretty soon. Here is Oliver’s new review. Excerpt:
Botswana is Studwell’s poster child for a successful democratic developmental coalition. (For this reason, it featured heavily in Acemoglu and Robinson’s Why Nations Fail as an example of “inclusive institutions”.)
Under the sound leadership of Seretse Khama, local chiefs were carefully co-opted at independence and the Botswana Democratic Party built up into a genuine national force. Khama also created a capable civil service, initially staffed by remaining Europeans, but gradually Africanized with sterling Batswana talent. This meant that when diamonds were discovered just around independence, the windfall was carefully managed, avoiding the worst effects of Dutch Disease. These mining revenues helped raise Botswana to upper middle-income status, making it the fourth-richest country in continental Africa.
Botswana’s chief failing, in Studwell’s view, was adhering too much to responsible policy orthodoxy—i.e., not enough industrial policy. There was no vision for large-scale industrialization, no coherent plan to create large numbers of factory jobs. Moreover, the political dominance of large cattle owners (Botswana was a society of pastoralists rather than farmers) meant that redistribution was never in the cards. The result is a relatively rich society, but one that is highly unequal.
You will be hearing my views on these issues soon enough. Oliver, of course, writes one of the very best Substacks in all of economics.
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2026-02-13 08:33:14
There is a new paper by Nick Bostrom with that title:
Developing superintelligence is not like playing Russian roulette; it is more like undergoing risky surgery for a condition that will otherwise prove fatal. We examine optimal timing from a person-affecting stance (and set aside simulation hypotheses and other arcane considerations). Models incorporating safety progress, temporal discounting, quality-of-life differentials, and concave QALY utilities suggest that even high catastrophe probabilities are often worth accepting. Prioritarian weighting further shortens timelines. For many parameter settings, the optimal strategy would involve moving quickly to AGI capability, then pausing briefly before full deployment: swift to harbor, slow to berth. But poorly implemented pauses could do more harm than good.
Via Nabeel.
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