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I’m an independent researcher working on AI and other EA topics (Global Priorities Research and Economics).
Hauke Hillebrandt
Karma: 3,873
Try o3-pro in ChatGPT for $1 (is AI a bubble?)
https://www.openphilanthropy.org/focus/macroeconomic-stabilization-policy/
Ironically, this area has contributed to the current crisis as I argued in 2022:
I provide evidence for the following claims on the adverse effects of looser macroeconomic policy:
Very strong claim: The first-order effects of too loose policy on the ’21 margin were unequivocally bad and will leave most worse off, even the poor, because we way overshot on inflation. Policy in ’21 added few jobs and real wages are down even for the poor (who suffer the most when their real wages go down even by a little). The second-order effects are that Dems will lose House, Senate, and Presidency. Populists (like Trump) will be elected, whose incompetence will make things even worse. The effects of looser policy in richer countries spill over into poorer countries, where inflation is higher, more persistent (hyperinflation), which leads to less growth, wellbeing, populism and instability.
Medium strength claim: Looser policy on the ’21 margin was good for the poorest 10% Americans (~30m) as it created more jobs and higher wages, but bad for the middle class (~150m) as their real wages went down. The first-order effects for wellbeing are still positive on average, as the poorest benefit much more than the middle class lost (a simple rule of thumb is that $1 is worth 1/X times as much if you are X times richer and poorest Americans are more than 5x poorer [utility is logarithmic in consumption]). However, because 150m have lost in terms real income, which can predict midterms and naïve extrapolation of current income decline predict Dems losing 50+ seats, the resulting populist incompetence will have, at the very least, high downside risks (like Trumpists starting trade wars). This will harm everyone (in expectation), even the poorest, in the longer-run.
Weak claim: Looser policy, even on the ’21 margin, was good for all Americans as it created jobs, increased real wages for most Americans (at least over the medium run) and led to GDP growth in the US and even for the world economy. However, even if real wages increased and inflation is transitory and mild over the medium-run, because voters are too sensitive to high short-term inflation, and inflation coincides with the midterms, and think it’s more persistent than the market, Dems will lose at least the House. Renewed gridlock and polarization will have bad effects and we could have avoided this by moving more slowly to a higher inflation target and being more expansionary under the curve in the longer-term.
I also highlight some general issues with macroeconomic advocacy like reducing central bank independence, problems with lobbying foreign central banks, and having to be very careful and do a lot of analysis before making grants that might have a lot of leverage and are not robustly good (like improving health).
Excellent post!
How to give effectively to US Dems
Aschenbrenner and his investors will gain financially from more and accelerated AGI progress.
Not necessarily—they could just invest in publicly traded company where the counterfactual impact is not very large (even a large hedge fund buying some say Google stock wouldn’t much move the market cap). They could also be shorting certain companies which might reduce economically inefficient overinvestment into AI, which might also have x-risk externalities. It would be different if he ran a VC fund and invested in getting the next, say, Anthropic off the ground. Especially if the profits are donated and used for mission hedging, this might be good.
The hedge fund could position Aschenbrenner to have a deep understanding of and connections within the AI landscape, making the think tank outputs very good, and causing important future decisions to be made better.
Yes, the outputs might be better as the incentives are aligned: the hedge fund / think tank has ‘skin in the game’ to get the correct answers on the future of AI progress (though maybe some big banks are also trying to move markets with their publications).
Good point.
Fair point, but as I wrote, this is just the optimistic the ‘business as usual’ boring scenario in the absence of catastrophes (e.g. a new cold war). I still think it’s a somewhat likely outcome.
On environment / energy: perhaps we’ll decouple growth and environmental externalities.
The models from consultancies are based on standard growth models and correlate strongly with IMF projections.
Excellent point- I do cite the article from Our World in Data “Meat consumption tends to rise as we get richer”, that includes the figure you pasted.
I agree that we should try to decouple this trend—I think the most promising approach is increasing alternative protein R&D (GFI.org is working on this).
Thanks! Excellent comment.
My ambition here was perhaps simpler than you might assumed: my point here was just to highlight an even weaker version of Basil’s finding that I thought was worth highlighting: even if GDP percentage growth slows down a smaller growth rate can still mean more $ every year in absolute terms.
Sorry I also don’t know much more about this and don’t have the cognitive capacity right now to think this through for utility increases and maybe this breaks down at certain ηs.
Maybe it doesn’t make sense to think of just ‘one true average η’, like 1.5 for OECD countries, but rather specific ηs for different comparisons and doublings.
There was a related post on this recently—would love for someone to get to the bottom of it.
good catch! fixed this it should be:
“The next $1k/cap increase in a country at $10k/cap is worth 10x as much as in a country with $100k/cap, because, the utility gained from increasing consumption from $10k to $11k is much greater than the utility gained from increasing consumption from $100k to $101k, even though the absolute dollar amounts are the same.
Same with salaries actually. If you’d let people filter by salary ranges that would force orgs to give up some leverage during negotiation.
You should display how many people have already applied for a job and let applicants filter by that—so that they can target neglected jobs. Ideally via your own application forms, but click through statistics would do. Big orgs might not like that because they want as many applicants apply as possible, and do not internalize the externalities of wasted application time, but for candidates it would be better.
GDP per capita in 2050
AI labs tend to partner with Big Tech for money, data, compute, scale etc. (e.g. Google Deepmind, Microsoft/OpenAI, and Amazon/Anthropic). Presumably to compete better? If they they’re already competing hard now, then it seems unlikely that they’ll coordinate much on slowing down in the future.
Also, it seems like a function of timelines: antitrust advocates argue that breaking up firms / preventing mergers would slow industry down in the short-run but speed up in the long-run by increasing competition, but if competition is usually already healthy, as libertarians often argue, then antitrust interventions might slow down industries in the long-run.
AI policy folks and research economists could engage with the arguments and the cited literature.
Grassroots folks like Pause AI sympathizers could put pressure on politicians and regulators to investigate this more (some claims, like the tax avoidance stuff seems most robustly correct and good).
I only said we should look into this more and have reviewed the pros and cons from different angles (e.g. not only consumer harms). As you say, the standard argument is that breaking up monopolists like Google increases consumer surplus and this might also apply here.
But I’m not sure in how far, in the short and long-run, this increases/decreases AI risks and/or race dynamics and within the west or between countries. This approach might be more elegant than Pausing AI, which definitely reduces consumer surplus.
Should we break up Google DeepMind?
Cool instance of black box evaluation—seems like a relatively simple study technically but really informative.
Do you have more ideas for future research along those lines you’d like to see?
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Also, in some secondary markets Anthropic is trading at record highs (e.g. https://notice.co/c/anthropic )