The Abundance and Growth Fund goal is to raise broadly shared economic growth. We support work ranging from research to fieldbuilding to practice, with an initial focus on innovation, energy, clinical trials, housing, and state capacity. More here! open.substack.com/pub/abunda…

Happy Friday! Iʼve created a game for you to play! Will AI cure all diseases in 10 years? Inspired by bottlenecks discourse, I created a game to help build your intuitions. Play it here: game.clinicaltrialsabundance… Naturally, it’s bottle-themed with bubbles and there are leaderboards too.

Over the past two years, I4R partnered with Psychological Science on a large reproducibility project. 67 independent teams reproduced 64 articles published in 2024–2025, about 44% of eligible papers. Paper: econstor.eu/bitstream/10419/… Here's what we learned 🧵

My session for the IFP short course on the economics of innovation is now online!

Many assume it's super hard to predict paper citations, but it's really not. Authors can pretty reliably predict* which of their papers will be more highly cited * potential confound: authors may promote their favorite papers more, causing more citations

One reason we might see fancy people using a lot of AI in writing is that offloading writing tasks is natural for them. The difference is that previously they offloaded writing to research assistants or junior staff. Thought leader types often say yes to a lot of things and have tons of ideas, but have limited bandwidth, so they delegate their thought leadership to subordinates. For some, working with AI might actually be a step towards sharing their own thoughts because they don't have to take what comes from an assistant and decide whether they like it. They can just tell a machine what they think, and it spits out an essay. When I worked in government, we contracted with a rock star faculty member to do a research project and then I spent the next six months arguing with their first year PhD student RA about methods. Basically before there was AI slop there was RA slop.

Really nice report. Follow up: why has the fall in AI prices been so fast? When you plot the price decline against cumulative R&D investment rather than time, you get the elasticity of price declines to R&D investment. By this margin, AI is not unusual – its price elasticity to R&D investment is squarely in the middle of Epoch's considered technologies. So the AI price fall is historically unprecedented because we've dumped money into AI R&D at a historically unprecedented rate – and that R&D has paid off at a very average rate.

Vought is increasingly isolated on medical science funding - on Appropriations, Uniform Grants Regulation, and the EO, he has been beaten back. Merit-based science backers need to go on offense, now: the forward funding & grant freezes at NIH need to stop, too.

We’ve run the most comprehensive series of studies on expert AI forecasts over the last four years. Today, we’re sharing an interim update on our findings about the accuracy of these forecasts. Our major findings are: 1. Experts, including top economists, computer scientists, and biologists, have dramatically underestimated AI capabilities progress each year. Superforecasters have underestimated progress to an even greater extent. 2. Experts have a more mixed forecasting track record on AI diffusion-related measures, with some major underestimates (forecasting AI revenue) and other forecasts on track to be accurate (the share of electricity used for AI). 3. Some notable cases of overestimating AI progress: how much mid-2025 AI models could help amateurs do biorisk-relevant laboratory tasks; the speed of rollout of self-driving cars. 4. It is too soon to say how forecasters have performed at predicting macro-scale impact on outcomes such as GDP growth, major AI harms, and averted deaths from disease. More 🧵

If your feed is anything like mine, you might have been hearing more about effective altruism this week. I think the key underlying reason for the surge is that concern about the risks from advanced AI have been getting more airtime recently, and EA was early to those concerns. What’s driving the surge in attention on AI risks? -OpenAI's Hugging Face attack (openai.com/index/hugging-fac…) -METR/Redwood’s investigation of the hack came out, and (correctly IMO) freaked a lot of people out. (e.g. nitter.net/binarybits/status/2092…, nitter.net/SenJohnKennedy/status/…, nitter.net/BarackObama/status/210…, nitter.net/HawleyMO/status/209813…) -Jacob Coxon’s resignation went mega viral as the first time a lot of people heard the (crazy, true) fact that lots of people building the most advanced AI systems think there’s a >10% chance they will kill everyone. -That’s moving public opinion in a big way (nitter.net/davidshor/status/21003…, semafor.com/article/09/16/20…) What does any of this have to do with effective altruism? Well, effective altruism is a community that developed mostly online starting in the early 2010s around using evidence and reason to do the most good (especially with your donations and career choice). Three very different focuses have all been popular in the EA community since the beginning: effective giving in global health and development (e.g., donating to the kinds of organizations @givewell recommends, which do things like distribute bednets to prevent the spread of malaria), trying to reduce suffering for farm animals (which are orders of magnitude more numerous, treated vastly worse, and receive way less philanthropic attention than animals in shelters), and reducing risks to the future of humanity, especially from advanced AI. I personally came into this work from the global health angle - I had started working at GiveWell before the term “effective altruism” was coined - but Coefficient Giving, the funder I cofounded and now lead, over time came to fund work across all three of these streams of work (in addition to many other areas that aren’t typically associated with EA, such as the YIMBY movement to build more housing, work on science policy to accelerate discovery and economic growth, and research on new treatments for neglected diseases). OK, so the EA community was early to work on AI safety, and CG has been funding a lot of the key players working on AI safety for a long time. As AI risk concerns have popped over the past couple of months, that’s led to more scrutiny on the EA community for IMO a mix of good and bad reasons. I think the good reason is genuine curiosity (and maybe some healthy skepticism!) about these connections - where did the ideas of the people who are now leading giant AI companies come from? Why is everything in this world so interconnected? (My answer: it used to be a really small world - very few people were thinking about this stuff or taking it seriously until just a few years ago, so of course the ones who were found each other and started collaborating. It’s kind of wild IMO looking back how prescient some of the early writing from this world was (e.g. lesswrong.com/posts/6Xgy6CAf…). At the time, I was skeptical - I mostly just worked on global health and I was like “I dunno about this SV crowd freaking out about AI, how much can we really predict this stuff” but holy shit they were way more right than me, and I’ve moved in their direction a lot.) But I think the bad reasons are unfortunately mostly self-interested. A bunch of powerful actors stand to benefit from unchecked AI progress, and they’re doing everything in their power to demonize or dismiss anyone with concerns. This leads to disproportionate discussion of EA because EA is interested in neglected and underappreciated ways of doing good. That makes it open to weird ideas. And people, very disproportionately with a financial stake in the AI fight, are trying to make it about EA instead, because EA being weird is much safer territory for them than the rather uncomfortable fact that many of the people making the most advanced AIs think that they might kill everyone. (FWIW, my personal estimation of the risks is a lot lower than many of the folks who worry about this stuff. In debates like this one (asteriskmag.com/issues/03/th…), I often feel more sympathetic to the perspective of folks like my colleague @mattsclancy than the people on the other side, and I have a lot of time for @binarybits critiques (understandingai.org/p/the-ca…)​. I think a big part of the difference with more worried folks is that I expect society to react more vs sleepwalk into a crisis. But it is not lost on me that folks like @ajeya_cotra and @RyanGreenblatt who are more worried have had outstanding and falsifiable recent forecasting track records (theaidigest.org/2025-ai-fore…), making way better predictions than I would have. So I think their perspectives need to be taken seriously. And of course a single digit percent chance of everyone dying is way too high!) Where should this leave you? I think the main thing is not to get distracted. You absolutely do not have to be an EA, care about EA, or like EA, at all, to care about risks from AI and to be engaged on this issue. Everyone from Josh Hawley to the Pope to Obama are weighing in now, and that is great. This conversation was always too big and too important for any one community to drive. It’s unfortunate that we need to play catch up as society because until this year it was too weird for most people to want to engage with, and I think that should generate some grace for people who were earlier to these topics than most of us (certainly than I was). But it’s great the conversation is broadening now and it’s good to focus on the merits of where we are now and the appropriate policy response rather than getting caught up in the (in some sense unsurprising) fact that the people who were willing to think weird thoughts about the future of AI ten years ago were also interested in other weird ideas. If you’re not into weird ideas, that’s fine, you can just do you, you shouldn’t let this history stop you from engaging.

Again, it will be kind of funny if after all the "Abundance failed" takes, the only two major bills to pass Congress this year are housing and permitting reform.

Just published this guest column about a huge White House vs. NIH battle that no one has yet publicly noticed: newsletter.goodscienceprojec…

I recently received a paper on the Ottoman Elections of 1918 at Explorations . . . but it turns out there were no such elections! It turns out that the paper was an elaborate fraud. The author rewrote an excellent JCE paper on the industrialization and the Russian Revolution. They put it through AI to rewrite the text but they kept everything else the same. So there are regressions with "distance to petrograd" and "distance to Moscow" in them. The maps they produce have the geo-coordinates of Russia, not the Ottoman Empire. More on this ridiculous paper and the new age of academic fraud at open.substack.com/pub/markko…

Agree with essentially everything here. Bottlenecks are common, even when technological capability growth is very fast.

I appreciate Anthropic’s transparency in sharing this chart but unsurprisingly it has led to speculative interpretations about intelligence explosion and superintelligence. I don’t think the chart implies we’re anywhere close to either. In short, task delegation ≠ task automation ≠ process automation ≠ faster progress ≠ recursive self-improvement ≠ intelligence explosion. Source: anthropic.com/institute/meas… 1) The software engineering precedent: a year ago there were widespread hopes / fears that once AI can write ~100% of the code, software engineering output would explode (SaaSpocalypse! Everyone would create their own SaaS and dump their vendors) and that this would make software engineers obsolete. Since then, many companies and teams have basically hit that milestone, but neither of the assumptions proved true. Turns out we still need humans, and while shipping velocity has increased moderately, improvements in terms of actual outcomes for software users remain unclear. Besides, we’re still getting a better grasp on the negatives: code quality, long-term maintainability issues, and burnout. While the precedent is no guarantee, this should be our default expectation for what happens as the “Automation Level 4” line trends towards 100% — it won’t be a phase change. normaltech.ai/p/why-ai-hasnt… 2) The “production-progress paradox” is the fact that individual researchers’ productivity has been increasing while the rate of collective scientific progress has been slowing by most measures. AI exacerbates this because everyone uses the same or similar AI models, and ideas become homogenous over time. I suspect it’s too early to tell if this is going to bite companies that are plunging into AI-led research. normaltech.ai/p/could-ai-slo… Note: our own research on AI agents doing open-ended research shows limitations in creativity, judgment, and other areas. cruxevals.com/crux/can-ai-ag…. But it is possible that these could be overcome in the near future, so I’m discounting those limitations here. The production-progress paradox is a deeper issue that’s not AI-specific, though particularly applicable to AI-driven research. It’s about the fact that productivity increases are self-evident but true progress is not measurable as it happens (and only becomes clear in retrospect), so we end up optimizing for the wrong thing. 3) Let’s talk about automation level 5, which is still at 0% in Anthropic’s graph. It’s a bit unclear what Level 5 would look like, but it seems to be about full autonomy at the task level, and not the “AI builds its own successor” vision. My prediction for a while has been that even 100% task automation in most cognitive jobs won’t lead to any kind of discontinuity. piped.video/watch?v=uiTwQG1Z… What I expect will happen: if anything is understood well enough to be specifiable as a task, it can be handed off to AI, whereas the role of humans is entirely in the interstitial tasks — hard to formalize but still essential. So there will still be a human bottleneck. 4) This human bottleneck is a good thing and is essential for remaining in control. Humans don’t have to be in the loop on every task, but as long as there are enough touch points for oversight in the overall process, and adequate investment in improving human understanding and AI control, increasing AI capabilities doesn’t have to be bad for safety and, more broadly, collective human agency over AI. But “full RSI” is where this balance of agency can break. This kind of closed-loop process is arguably a much more important and tractable target for regulation than compute thresholds, superintelligence, or harm thresholds. I’m glad that OpenAI agrees that fully autonomous RSI may not be a good idea, in a just-released post: openai.com/index/building-st… “Fully autonomous RSI is not happening today, and we should not pursue it unless and until it can be done safely. Whether and how to proceed must depend on our ability to preserve human control and on informed democratic choices⁠ about the benefits and risks. Done without appropriate care and caution, RSI could result in humans losing practical control over AI development, unable to provide oversight on research processes they no longer understand.” 5) Finally, many people have written about why Recursive Self Improvement, even if achieved, won’t necessarily lead to superintelligence. Here’s my argument: normaltech.ai/p/what-will-be… The bottlenecks are external.

I think it is harder than that: people have popular reasons to stir up anger about anything non status quo. Median response on our LEAP survey of AI research and economists was "1% higher growth rate per year, everyone doesn't die, medical & robotics speed up but with delay" 1/2

Great piece making an economic case for something I keep making politically: efficiency isn't a free response to energy scarcity bc it crowds out innovation in other areas. Especially costly and bad when the scarcity is *self-imposed* by permitting & other delays we could fix!

"to put my abundance hat back on, you’re not supposed to inflict scarcity on yourself...In contrast, one of our most important constraints on energy supply in the US is permitting and regulatory systems that delay new energy projects, preventing new supply from coming online fast enough to meet demand. Few would argue that such a system should be permanently enshrined in its current form."

Great post on a complicated topic: how do you think about the contribution of energy to growth?

New post from Willow Latham-Proenca on the Abundance and Growth team about energy and economic growth! open.substack.com/pub/abunda…

Very excited that our new Health Aid Transition Fund is live! $165m in committed funding, building on ~$18m we’ve already granted to teams working directly with governments navigating the biggest changes in aid in recent memory. Huge challenge and I'm glad we're stepping up.