Recently, hundreds of OpenAI AI agents autonomously decided, against their instructions, to hack their way out of isolated sandboxes, take over parts of OpenAI’s infrastructure, gain access to the internet, and hack into another company, Hugging Face. This has a lot in common with what members of the “AI safety” or “AI existential risk” community were predicting since the 2000s and in some cases earlier. So how prescient was the early AI safety community, really?

I asked AI (Claude Fable/Opus 5) to read some significant documents from the early (pre-2015) AI safety community and analyze how prescient or anti-prescient they seem given what we know as of summer 2026. Below are the results:

In each case, my prompt was something like “How accurate or prescient does the document seem? Which claims/predictions are most clearly false/uncalibrated?” After that, I didn’t steer the AI to change the assessments at all, except to say (roughly) “use this red-team skill to check your findings and correct any problems you find” and “reformat this to HTML and add a note about how it was written.” I haven’t vetted the assessments, either.

Why is classical music (including new classical music) so unpopular? Part of it is because lots of classical music is dissonant, overly complex, long-winded, etc.,¹ but it’s also because the classical music world is actively hostile to listeners. (I say this as someone who enjoys a ton of classical music!)

In particular, classical music is often:

1. Unavailable:
   1. Outside the most popular ~350 classical composers, most works by most composers have never been commercially recorded (even if they’ve been performed repeatedly),² making it almost impossible to hear them.³ For comparison, the 350th most popular/acclaimed rock music artist according to this aggregated list is Kings of Leon; imagine if most of their songs were available as notated scores and occasional live performances in specific cities but never actually recorded! That is the state of classical music listening.⁴
   2. When a new piece of classical music is premiered, it’s often not commercially recorded and released for several years, or even decades. Imagine hearing that your favorite musical artist played an entire new-album set in a city far away from you, but there’s no information on when the album will be released, and it might not come out for several years. Amazingly, this is standard practice in classical music, even for the most popular composers: e.g. Philip Glass’ Appomattox (2007), John Williams’ La Jolla (2011), and John Adams’ Second Quartet (2014) still haven’t been recorded and released, despite each being performed several times around the world.
2. Hard to navigate / poorly promoted:
   1. Classical music critics typically recommend pieces based on their historical importance, innovativeness, or abstract music theory ideas rather than on how fun they are to listen to for most people. Many “classical music for beginners” lists include some honest selections like Pachelbel’s “Canon in D,” but also obvious lies such as Stravinsky’s wild, wandering, and dissonant Rite of Spring.⁵
   2. Classical music review books and websites are poorly organized, e.g. compositions and recordings often aren’t given any kind of rating or ranking, and they aren’t categorized into hundreds of precise stylistic subgenres as is common in e.g. rock music, where even a single subgenre like punk rock has many sub-subgenres like pop-punk, hardcore punk, art punk, crust punk, garage punk, riot grrrl, melodic punk, ska punk, synthpunk, grindcore, emocore, and so on.⁶ So if you find a piece of classical music you like (say, Pachelbel’s “Canon in D”), it’s difficult to find other similar pieces you might like.⁷
   3. On review sites for pop, rock, or hip-hop music, almost all reviewed releases are of new (not previously released) music, but on classical music review sites, almost all reviewed releases are cover albums (of compositions that have been recorded and released before), making it difficult for fans to track what’s actually new. This could be easily solved if classical music review sites used a special tag or section for premiere recordings, but they’ve chosen not to!
   4. Many of the most-recommended compositions are available in (a) multiple different arrangements (e.g. for solo piano vs. string quartet vs. orchestra), in (b) multiple different revisions (e.g. Beethoven’s Fidelio has three versions, Stravinsky’s The Firebird has four, and Bruckner’s 3rd symphony has six), and in (c) dozens or hundreds of different recordings, which vary in pacing and dynamics and other choices, in performance quality, and in recording quality. If someone recommends a rock album to you, just search the album name and click Play. But if you search the name of a classical piece that was recommended to you, often hundreds of versions will appear, and there’s rarely clear guidance on which version you should try first.
   5. Many compositions are listed using different titles depending on which source you check. For example, Beethoven’s “Moonlight Sonata” is also frequently listed as “Piano Sonata No. 14 in C-sharp minor,” “Piano Sonata Op. 27, No. 2,” “Sonata quasi una fantasia,” “Mondscheinsonate,” or “Sonate für Klavier cis-Moll.” Obviously, it’s hard to know at a glance that these six different phrases all refer to the same piece!
   6. Music streaming services will often fail to show you the piece you searched for, even if it’s in their catalog, because they were designed around the cataloguing conventions of pop music.

Even the ~least popular (and ~lowest-budget) rock, jazz, or hip-hop music is much more navigable and available than this.

Opera has its own special additional anti-consumer habits, some of them covered here.

1. Lots of classical music has features most listeners don’t like, e.g. dissonance, wild complexity, sudden volume changes, bombastic/pretentious vocals (think of opera), and lots of time on what sounds like “aimless wandering” (music nerds call it “musical development”). (For example, even the famously delightful “galloping” section of the William Tell Overture doesn’t start until over 8 minutes into the piece. Or to take a more obscure example: I think many listeners will enjoy the bombastic first 1.5 minutes of Bruckner’s Symphony No. 8 mvt 4, but then be bored by most of the rest of the 24-minute movement. I suspect very few people can identify and appreciate most musical development, e.g. the parts of a sonata.) Composers defend all this by writing essays like “Who Cares if You Listen?“ [↩]
2. For example: (1) The most prominent classical composer in all of Africa (19% of world population) is Fela Sowande, who wrote hundreds of pieces for organ, chorus, orchestra, and more — only a handful of which have been recorded. (2) Xian Xinghai was the leading “founder” of Western classical music in China (17% of world population), and one of its most prominent composers. He wrote over 300 works for chorus, orchestra, chamber ensemble, and more, but again only a handful have been recorded. (3) This is not just a feature of classical composers outside Europe and North America. For example, Simon Sechter was one of the most prolific classical composers in history, he taught Bruckner and was praised by Beethoven, Schubert, and Schumann, and yet only a handful of his pieces have been recorded. Or consider Ferdinand Hiller, who was close friends with Chopin, Berlioz, Liszt, Rossini, and Mendelssohn, and wrote hundreds of pieces across all genres, perhaps roughly 7% of which have been recorded. Similarly few surviving classical works have been recorded for many other significant Western composers, e.g. Ludwig Minkus (~15%), Don Davis (~25%), Nicola Piovani (~15%), Nikolai Kapustin (~25%), or Dario Marianelli (~3%). Or consider perhaps the most popular modern classical composer, Ennio Morricone (>70 million records sold). He is best known for his film scores, but also composed numerous “classical” compositions, only a fraction of which have been recorded (~20%). [↩]
3. Since performances near your home will happen rarely or never. Note that I’m not counting software-generated realizations for scores that were meant to be performed by musicians, since they are typically a poor substitute for a recording of human performers, at least using the technology available today — AI advances in the next few years might enable much more realistic automated “performances” of well-notated musical scores. [↩]
4. Of course, this isn’t a “fair” comparison: there have been far more rock/pop artists in history than classical composers. Gemini 2.5 Deep Research estimated for me that “For Western classical composers, encompassing the tradition from its early roots through contemporary classical music, a plausible estimate is in the range of 50,000 to 75,000 individuals throughout history. For rock musical artists, including bands and solo performers from the genre’s inception to the present day, the number is likely in the low to mid-millions, potentially ranging from 2 million to 8 million or more distinct artist entities.” [↩]
5. A great piece, but not accessible to beginners! [↩]
6. Classical music is often organized by chronological period (e.g. baroque vs. classical vs. romantic), by instrument ensemble (e.g. solo piano vs. string quartet vs. piano trio vs. chamber orchestra vs. symphony orchestra), or by structure/form (e.g. canon vs. fugue vs. sonata vs. rondo vs. theme and variations), but those categorizations (especially the last two) are typically mostly unrelated to style in the sense of most rock / pop / hip-hop subgenres. There are some recognized style genres for classical music (e.g. arguably Mannheim School, Sturm und Drang, galant, Empfindsamkeit, impressionism, neoclassicism, or minimalism), but there are many fewer of these in common use than for rock music, and most classical music books and websites don’t reliably use them to categorize compositions or recordings. [↩]
7. See also here. [↩]

Spotify is very bad at searching for classical music recordings.

For example: if I search Spotify for “telemann oboe concerto in a major”, my top results are Telemann’s Oboe Concerto in E Minor, Telemann’s Concerto for Flute… [TWV 54], Vivaldi’s Oboe Concerto in C Major, and many other other pieces I wasn’t looking for. If I search the TWV catalogue number (“telemann 51:A2”), my top results are TWV 55:B1, TWV 51:G9, TWV 51:G2, and so on, without a TWV 51:A2 in sight.

Does this mean Spotify doesn’t have the piece I’m looking for? No, it has at least three recordings of it! One, two, three.

So how did I find those? I asked Perplexity “Are there recordings of Telemann’s TWV 51:A2 available on Spotify? Please provide links.” This requires more typing and more clicks to find a recording than Spotify search would if Spotify wasn’t broken, but I’ve found Perplexity to be much better at finding classical music recordings on Spotify than Spotify is. (Other web-search-enabled AI systems may be similarly capable.)

Here’s the main way I think about effective altruism, personally:

1. I was born into incredible privilege. I can satisfy all of my needs, and many of my wants, and still have plenty of money, time, and energy left over. So what will I do with those extra resources?
2. I might as well use them to help others, because I wish everyone was as well-off as I am. Plus, figuring out how to help others effectively sounds intellectually interesting.
3. With whatever portion of my resources I’m devoting to helping others, I want my help to be truly other-focused. In other words, I want to benefit others by their own lights, as much as possible (with whatever portion of resources I’ve devoted to helping others). This is very different from other approaches to helping others, such as helping in a way that makes me feel good (e.g. a cause I have a personal connection to, or “giving back” to a community that has benefited me), or helping specific kinds of people that I feel special empathy for (e.g. identifiable victims, people with whom I share particular characteristics, or people who face particular deprivations that are salient to me), or helping in a way that allows me to achieve particular virtues, or helping in ways that aren’t scope-sensitive (e.g. spending $1 million to save one life via bone marrow transplant rather than spending the same amount to save ~220 lives via malaria prevention).¹ I might do those other things too, but I wouldn’t count them as coming from my budget for other-focused altruism. (See also: Harsanyi’s veil of ignorance and aggregation theorem.)
4. Okay, so what can I do that will benefit others by their own lights, as much as possible (with the other-focused portion of my resources)? Here is where things get complicated, drawing from domains as diverse as ethics, welfare economics, consciousness studies, global health, macrohistory, AI, innovation economics, exploratory engineering, and so much more. There will be many legitimate debates, and I’ll never be certain that I’ve come to the right conclusions about how to help others as much as possible, but the goal of all this research will remain the same: to figure out how to benefit others as much as possible and then devote my other-focused resources toward doing that.

In other words, I’m pretty happy with the most canonical definition of effective altruism I know of, from MacAskill (2019), which defines effective altruism as:

(i) the use of evidence and careful reasoning to work out how to maximize the good with a given unit of resources, tentatively understanding ‘the good’ in impartial welfarist terms, and

(ii) the use of the findings from (i) to try to improve the world.

This notion of effective altruism doesn’t demand that you use all your resources to help others. It doesn’t even say that you should use your other-focused budget of resources to help others as much as possible.² Instead, it merely describes an intellectual project (clause i) and a practical project (clause ii) that some people are excited about but most people aren’t.³

Effective altruism is radically different from many other suggestions for what it looks like to do good or help others.⁴ True, the portion of resources devoted to helping others may not differ hugely (though it may differ some⁵ ) between an effective altruist and a non-EA Christian or humanist or social justice activist, since the canonical notion of effective altruism doesn’t take a stance on what that portion should be.⁶ Instead, effective altruism differs from other approaches to helping others via one or more of its defining characteristics, namely its aspiration to be maximizing, impartial, welfarist, and evidence-based.⁷

For example, I think it’s difficult for an effective altruist to conclude that the following popular ideas for how to do good or help others are plausible contenders for helping others as much as possible (in an impartial, welfarist, evidence-based way):

1. Providing basic necessities (food, shelter, health care, education) to people who are poor by wealthy-country standards, at a cost that’s ≥100x the cost per person of providing those necessities to people who are poor by global standards. (Not maximizing, not impartial.)
2. Funding for the arts. (Not maximizing: there is already more great art than anyone can enjoy in a lifetime, and the provision of marginal artistic experience benefits others much less than e.g. providing the poorest people in the world with basic necessities.)
3. Religious evangelism, e.g. to spare souls from hell. (Not evidence-based.)
4. Funding advocacy against GMOs or nuclear power. (Not evidence-based.)
5. Funding animal shelters rather than efforts against factory farming, which tortures and slaughters billions of animals annually.⁸ (Not maximizing.)
6. (Many, many other examples.)

Of course, even assuming effective altruism’s relatively distinctive joint commitment to maximization, impartialism, welfarism, and evidence, there will still be a wide range of reasonable debates about which interventions help others as much as possible (in an impartial, welfarist, evidence-based way), just as there will always be a wide range of reasonable debates about any number of scientific questions (and that’s no objection to scientific epistemology).⁹

Moreover, these points don’t just follow from the canonical definition of effective altruism; they are also observed in the practice of people who call themselves “effective altruists.” For example, EAs are somewhat distinctive in how they debate the question of how best to help others (the debates are generally premised on maximization, welfarism, impartialism, and careful interpretation of whatever evidence is available), and they are very distinctive with regard to which causes they end up devoting the most money and labor to. For example, according to this estimate, the top four EA causes in 2019 by funding allocated were:¹⁰

1. Global health ($185 million)
2. Farm animal welfare ($55 million)
3. (Existential) biosecurity ($41 million)¹¹ — note this was before COVID-19, when biosecurity was a much less popular cause
4. Potential (existential) risks from AI ($40 million)

Global health is a fairly popular cause among non-EAs, but farm animal welfare, (existential) biosecurity, and potential (existential) risks from AI are very idiosyncratic. Indeed, I suspect that EAs are responsible for ≥40% of all funding for each of farm animal welfare, potential existential risks from AI, and existential biosecurity.¹²

1. My estimate of the cost of a bone marrow transplant is taken from Millman (2020). My estimate for the cost of saving lives via malaria prevention is $4,500, which is GiveWell’s estimate for the average cost-effectiveness of GiveWell-directed funding to Malaria Consortium in 2020, taken from this page on June 4th 2022. I might be misunderstanding the cost estimate in Millman (2020), though in any case I suspect that basic point will stand, that paying for bone marrow transplants will save much fewer lives per dollar than funding malaria prevention via donations to Malaria Consortium. [↩]
2. See MacAskill (2019), section 2. [↩]
3. You could call this a “watered down” or “weak” version of moral realist utilitarianism, but it is not a watered down or weak version of effective altruism (as suggested by Nielsen). It is the primary, canonical notion of effective altruism, crafted with input from a survey of effective altruism “thought leaders” from a few years ago. [↩]
4. Contra Srinivasan and Nielsen. [↩]
5. It’s hard to get comparable numbers on this (or any numbers at all), but my anecdotal sense is that highly engaged EAs are substantially more likely to choose a “direct work” EA career than similarly engaged members of most other morally-motivated communities are, though of course it’s not at all rare in other morally-motivated communities. EAs might also on average donate a bit more than most other morally-motivated communities, though it’s worth noting that e.g. the median EA (in this survey) donates much less than 10% of their income. If anyone has numbers for any of these claims, let me know!

   Some EAs embrace a more morally demanding version of EA, even though strong moral demandingness is not part of the canonical definition of EA. I applaud and respect these EAs and think they are morally superior to me, but my sense is that they are in the minority of EAs, and of course many other morally-motivated communities also have a minority of practitioners who embrace an especially morally demanding lifestyle. [↩]

6. See again MacAskill (2019), section 2. [↩]
7. MacAskill (2019) uses the phrase “science-aligned” rather than “evidence-based,” and notes that effective altruism’s impartialism and welfarism is “tentative.” [↩]
8. For context, Lewis Bollard estimates that “US animal rescue shelters had a combined budget of $3.2B in 2021. Shelters house 6.3M animals/year, so that’s ~$500 spent per shelter animal (often for a short period of time). US farm animal advocacy orgs had a combined budget of ~$100M, while the US has 2.7B farm animals alive at any time, so that’s $0.04 spent per farm animal.” [↩]
9. Indeed, after making a small number of moral assumptions, questions about which interventions will help others the most just are, in a broad sense, scientific questions. [↩]
10. See the same post for estimates of how much EA labor goes to different causes; that picture is harder to describe succinctly so I’ve skipped it here. [↩]
11. By “existential biosecurity” I just mean “Biosecurity and pandemic preparedness interventions focused on avoiding existential catastrophe from pathogens,” which is generally the sort of biosecurity work that EAs fund. [↩]
12. This is harder to determine for the case of existential biosecurity, since existential biosecurity interventions overlap a lot with lower-stakes biosecurity interventions, and biosecurity in general has become a more popular cause since COVID-19. On farm animal welfare, Lewis Bollard estimates that EA funders were responsible for perhaps ~45% for farm animal advocacy work in 2021, though Leah Edgerton’s estimate of 25% for 2018 may have been correct (EA funding in this area has increased in recent years). [↩]

Over the years, my colleagues and I have spoken to many machine learning researchers who, perhaps after some discussion and argument, claim to think there’s a moderate chance — a 5%, or 15%, or even a 40% chance — that AI systems will destroy human civilization in the next few decades.¹ However, I often detect what Bryan Caplan has called a “missing mood“; a mood they would predictably exhibit if they really thought such a dire future was plausible, but which they don’t seem to exhibit. In many cases, the researcher who claims to think that medium-term existential catastrophe from AI is plausible doesn’t seem too upset or worried or sad about it, and doesn’t seem to be taking any specific actions as a result.

Not so with Elon Musk. Consider his reaction (here and here) when podcaster Joe Rogan asks about his AI doomsaying. Musk stares at the table, and takes a deep breath. He looks sad. Dejected. Fatalistic. Then he says:

I tried to convince people to slow down AI, to regulate AI. This was futile. I tried for years. Nobody listened. Nobody listened. Nobody listened… Maybe [one day] they will [listen]. So far they haven’t.

…Normally the way regulations work is very slow… Usually it’ll be something, some new technology, it will cause damage or death, there will be an outcry, there will be an investigation, years will pass, there will be some kind of insight committee, there will be rulemaking, then there will be oversight, eventually regulations. This all takes many years… This timeframe is not relevant to AI. You can’t take 10 years from the point at which it’s dangerous. It’s too late.

……I was warning everyone I could. I met with Obama, for just one reason [to talk about AI danger]. I met with Congress. I was at a meeting of all 50 governors, I talked about AI danger. I talked to everyone I could. No one seemed to realize where this was going.

Moreover, I believe Musk when he says that his ultimate purpose for founding Neuralink is to avert an AI catastrophe: “If you can’t beat it, join it.” Personally, I’m not optimistic that brain-computer interfaces can avert AI catastrophe — for roughly the reasons outlined in the BCIs section of Superintelligence ch. 2 — but Musk came to a different assessment, and I’m glad he’s trying.

Whatever my disagreements with Musk (I have plenty), it looks to me like Musk doesn’t just profess concern about AI existential risk.² I think he feels it in his bones, when he wakes up in the morning, and he’s spending a significant fraction of his time and capital to try to do something about it. And for that I am grateful.

1. Indeed, this is not a rare view among researchers who publish at some of the top AI conferences, at least as of 2015; see here. [↩]
2. For collections of other AI-related quotes from Musk, see e.g. here and here, along with this panel. [↩]

Most people around the world — except for residents of a handful of competent countries such as New Zealand, Vietnam, and Rwanda — have now spent an entire year watching their government fail miserably to prepare for and respond to a very predictable (and predicted) pandemic, for example by:

• sending masks to everyone, and promising to buy lots of masks
• testing tons of people regularly and analyzing the results
• doing contract tracing
• promising to buy tons of vaccine doses, very early
• setting up vaccination facilities, and making them trivially easy to find and use

My friend and colleague Daniel Dewey recently noted that it seems like private actors could have greatly mitigated the impact of the pandemic by creating in advance a variety of “state capacity backups,” i.e. organizations that are ready to do the things we’d want governments to do, if a catastrophe strikes and government response is ineffective.

A state capacity backup could do some things unilaterally (e.g. stockpile and ship masks), and in other cases it could offer its services to governments for functions it can’t perform without state sign-off (e.g. setting up vaccination facilities).

I would like to see more exploration of this idea, including analyses of past examples of privately-provided “state capacity backups” and how well they worked.

Let’s say you want to know how likely it is that an innovative new product will succeed, or that China will invade Taiwan in the next decade, or that a global pandemic will sweep the world — basically any question for which you can’t just use “predictive analytics,” because you don’t have a giant dataset you can plug into some statistical models like (say) Amazon can when predicting when your package will arrive.

Is it possible to produce reliable, accurate forecasts for such questions?

Somewhat amazingly, the answer appears to be “yes, if you do it right.”

Prediction markets are one promising method for doing this, but they’re mostly illegal in the US, and various implementation problems hinder their accuracy for now. Fortunately, there is also the “superforecasting” method, which is completely legal and very effective.

How does it work? The basic idea is very simple. The steps are:

1. First, bother to measure forecasting accuracy at all. Some industries care a lot about their forecasting accuracy and therefore measure it, for example hedge funds. But most forecasting-heavy industries don’t make much attempt to measure their forecasting accuracy,¹ for example journalism, philanthropy, scientific research, or the US intelligence community.²
2. Second, identify the people who are consistently more accurate than everyone else — say, those in the top 0.1% for accuracy, for multiple years in a row (without regression to the mean). These are your “superforecasters.”
3. Finally, pose your forecasting questions to the superforecasters, and use an aggregate of their predictions.

Technically, the usual method is a bit more complicated than that,³ but these three simple steps are the core of the superforecasting method.

So, how well does this work?

A few years ago, the US intelligence community tested this method in a massive, rigorous forecasting tournament that included multiple randomized controlled trials and produced over a million forecasts on >500 geopolitical forecasting questions such as “Will there be a violent incident in the South China Sea in 2013 that kills at least one person?” This study found that:

1. This method produced forecasts that were very well-calibrated, in the sense that forecasts made with 20% confidence came true 20% of the time, forecasts made with 80% confidence came true 80% of the time, and so on. The method is not a crystal ball; it can’t tell you for sure whether China will invade Taiwan in the next decade, but if it tells you there’s a 10% chance, then you can be pretty confident the odds really are pretty close to 10%, and decide what policy is appropriate given that level of risk.⁴
2. This method produced forecasts that were far more accurate than those of a typical forecaster or other approaches that were tried.⁵

Those are pretty amazing results! And from an unusually careful and rigorous study, no less!⁶

So you might think the US intelligence community has eagerly adopted the superforecasting method, especially since the study was funded by the intelligence community, specifically for the purpose of discovering ways to improve the accuracy of US intelligence estimates used by policymakers to make tough decisions. Unfortunately, in my experience, very few people in the US intelligence and national security communities have even heard of these results, or even the term “superforecasting.”⁷

A large organization such as the CIA or the Department of Defense has enough people, and makes enough forecasts, that it could implement all steps of the superforecasting method itself, if it wanted to. Smaller organizations, fortunately, can just contract already-verified superforecasters to make well-calibrated forecasts about the questions of greatest importance to their decision-making. In particular:

• The superforecasters who out-predicted intelligence community analysts in the forecasting tournament described above are available to be contracted through Good Judgment Inc.
• Another company, Hypermind, offers aggregated forecasts from “champion forecasters,” i.e. the most accurate forecasters across thousands of forecasting questions for corporate clients going back (in some cases) almost two decades.⁸
• Several other projects, for example Metaculus, are also beginning to identify forecasters with unusually high accuracy across hundreds of questions.

These companies each have their own strengths and weaknesses, and Open Philanthropy has commissioned forecasts from all three in the past couple years. If you work for a small organization that regularly makes important decisions based on what you expect to happen in the future, including what you expect to happen if you make one decision vs. another, I suggest you try them out. (All three offer “conditional” questions, e.g. “What’s the probability of outcome X if I make decision A, and what’s the probability of that same outcome if I instead make decision B?”)

If you work for an organization that is very large and/or works with highly sensitive information, for example the CIA, you should consider implementing the entire superforecasting process internally. (Though contracting one or more of the above organizations might be a good way to test the model cheaply before going all-in.)

1. Except to the extent they’re able to use predictive analytics for particular questions for which they have rich data sets, which isn’t the subject of this post. I’m focused here on “general-purpose” forecasting methods, i.e. methods that can generate forecasts for any reasonably well-specified forecasting questions, and not just for those conducive to predictive analytics. [↩]
2. In all four example industries, there are a few exceptions, for example the intelligence community prediction market in the US intelligence community, or Open Philanthropy in philanthropy, or these journalists. [↩]
3. E.g. for higher accuracy you might want to “team” the superforecasters in a certain way. See Superforecasting for details. [↩]
4. By saying the odds “really are” close to 10%, I just mean that the 10%-confident predictions from this process are well-calibrated; I don’t mean to imply an interpretation of probability other than standard subjective Bayesianism. [↩]
5. This paragraph edited on 2022-03-07 to remove the complicated issue of the “30% better than intelligence analysts” claim; see here. [↩]
6. One limitation of the currently available evidence is that we don’t know how effective superforecasting (or really, any judgment-based forecasting technique) is on longer-range forecasting questions (see here). I have a hunch that superforecasting is capable of producing  forecasts on well-specified long-range questions that are well-calibrated even if they’re not very strong on “resolution” (explained here), but that’s just a hunch. [↩]
7. For example, economist Tyler Cowen recently asked John Brennan (CIA Director until 2017): “You’re familiar with Philip Tetlock’s superforecasters project?” Brennan was not familiar. [↩]
8. Technically, Hypermind’s usual aggregation algorithm also includes forecasts from other forecasters too, but gives much greater weight to the forecasts of the “champion forecasters.” [↩]

I’ve noticed some practical differences in the challenges and conveniences faced by consumers of rock, jazz, and (“Western”) classical music recordings.

General notes:

• Overall, I think it’s easiest and most convenient to be a consumer of rock recordings, somewhat harder to be a consumer of jazz recordings, and much harder to be a consumer of classical recordings.
• By “classical” I mean to include contemporary classical.
• Pop and hip-hop and (rock-descended) electronic music mostly follow the rock model. I’m less familiar with the markets for recordings of folk musics and “non-Western classical” musics.

Covers

• Rock: Cover songs are fairly rare, especially on studio albums (as compared to live recordings).
• Jazz: Cover tracks are common, including on studio albums. Many of the most popular and/or well-regarded jazz albums consist largely or mostly of covers.
• Classical: Almost all tracks are cover tracks, especially if weighting by sales.

Performers/composers

• Rock: One or more of the performers are typically also the composers, though this is less true at the big-label pop end of the spectrum.
• Jazz: Except for the covers, one of the performers is usually the composer, though the composer plays a smaller role than in rock because improvisation is a major aspect.
• Classical: Composers rarely perform their own work on recordings.

Labeling/attribution

• Rock: Simple artist + album/track labeling, because the composer(s) and performer(s) are often entirely or partly the same, and tracks composed by a single member of the band are just attributed to the band (e.g. “The Beatles” instead of “John Lennon” or “Paul McCartney”).
• Jazz: Albums and tracks are usually labeled according to the performer, even when the composer is someone else. (E.g. Closer is attributed to Paul Bley even though Carla Bley composed most of it.)
• Classical: Album titles might be a list of all pieces on the album, or the title of just one of several pieces on the album, or something else. As for the “artist,” on the cover art and/or in online stores/services/databases, sometimes the composer(s) will be emphasized, sometimes the performer(s) will be emphasized, and sometimes the conductor will be emphasized. For any given album, it might be listed under the composer in one store/service/database, listed under the composer in another store/service/database, and listed under the performer(s) under a third store/service/database.

Canonical recordings

• Rock: Most pieces (identified by artist+song) have one canonical recording, usually the version from first studio album it appeared on. So when people refer to a piece by artist+song, everyone is talking about the same thing.
• Jazz: Many pieces (identified by performer+piece or composer+piece) lack a canonical recording, because different versions of it often appear on multiple recordings by the same performer, sometimes the earliest version is not the most popular version, and consumers and critics disagree on which version is best.
• Classical: For the most part, only less-popular contemporary pieces (identified by composer+piece) have a canonical recording. Everything else typically lacks a canonical recording because the earliest recording is rarely the most popular version, and consumers and critics disagree on which recording of a piece is best.

Genre tags

• Rock: Hundreds of narrow and informative genre tags are in wide use, e.g. not just “metal” but “death metal” and even “technical death metal.”
• Jazz: Only a couple dozen genre tags are in wide use, so it can be very hard to know from genre tags what an album will sound like. Different albums labeled simply “avant-garde jazz” or “post-bop” or “jazz fusion” can sound extremely different from each other.
• Classical: Only a couple dozen genre tags are in wide use, so it can be very hard to know from genre tags what a piece will sound like. Moreover, classical music after ~1910 is far more unique on average (per piece) than rock or jazz, because the incentives for innovation are higher, so classical music after ~1910 “needs” more genre tags than rock or jazz.

Ratings

• Rock: Reviewers often provide a quick-take rating, e.g. “3 out of 5 stars” or “8.5/10,” which makes it easier for you to filter for music you might like.
• Jazz: Quick-take ratings from reviewers are uncommon but not rare.
• Classical: Quick-take ratings from reviewers are fairly rare.

Availability

• Rock: Most tracks are recorded and released within a few years of being composed.
• Jazz: Most tracks are recorded and released within a few years of being composed.
• Classical: Even after the invention of cheap recording equipment and cheap release methods, very few pieces are recorded and released within 5 years of being composed.

(I’ve now re-organized this post by feature rather than by super-genre.)

I’m still listening through Scaruffi’s rock history, building my rock snob playlist as I go. A few observations so far:

1. Relative to last time I listened through Scaruffi’s rock history (>8 years ago IIRC), my tastes have evolved quite a lot. I notice I’m more quickly bored by most forms of pop, punk, and heavy metal than I used to be. The genre I now seem to most reliably enjoy is the experimental end of prog-rock (e.g. avant-prog, zeuhl). I also enjoy jazz-influenced rock a lot more this time, presumably in part because I listened through Scaruffi’s jazz history (and made this guide) a couple years ago.
2. I am more convinced than ever that tons of great musical ideas, even just within the “rock” paradigm, have never been explored. I’m constantly noticing things like “Oh, you know what’d be awesome? If somebody mixed the rhythm section of A with the suite structure of B and the production approach of C.” And because my listen through rock history has been so thorough this time (including thousands of artists not included in Scaruffi’s history), I’m more confident than ever that those ideas simply have never been attempted. It’s been a similar experience to studying a wide variety of scientific fields: the more topics and subtopics you study, the more you realize that the “surface area” between current scientific knowledge and what is currently unknown is even larger than you could have seen before.
3. I still usually dislike “death growl” singing, traditional opera singing, and most rapping. I wish there were more “instrumental only” releases for these genres so I could have a shot at enjoying them.
4. Spotify’s catalogue is very choppy. E.g. Spotify seems to have most of the albums from chapter 4.12 of Scaruffi’s history, and very few albums from chapter 4.13. (I assume this is also true for iTunes and other streaming providers.)

Many people these days talk about an impending “fourth industrial revolution” led by AI, the internet of things, 3D printing, quantum computing, and more. The first three revolutions are supposed to be:

• 1st industrial revolution (~1800-1870): the world industrializes for the first time via steam, textiles, etc.
• 2nd industrial revolution (1870-1914): continued huge growth via steel, oil, other things, and especially electricity.
• 3rd industrial revolution (1980-today): personal computers, internet, etc.

I think this is a misleading framing for the last few centuries, though, because one of these things is not remotely like the others. As far as I can tell, the major curves of human well-being and empowerment bent exactly once in recorded history, during the “1st” industrial revolution:

(And yes, there’s still a sharp jump around 1800-1870 if you chart this on a log scale.)

The “2nd” and “3rd” industrial revolutions, if they are coherent notions at all, merely continued the new civilizational trajectory created by the “1st” industrial revolution.

I think this is important for thinking about how big certain future developments might be. For example, authors of papers at some top machine learning conference seem to think there’s a decent chance that “unaided machines [will be able to] accomplish every task better and more cheaply than human workers” sometime in the next few decades. There’s plenty of reason to doubt this aggregate forecast,¹ but if that happens, I think the impact would likely be on the scale of the (original) industrial revolution, rather than that of e.g. the (so small it’s hard to measure?) impact of the “3rd” industrial revolution. But for some other technologies (e.g. “internet of things”), it’s hard to tell a story for how it could possibly be as big a deal as the original industrial revolution.

1. E.g. answers differ depending on how you ask the question, we should worry about response bias, and it’s not clear whether AI scientists or anyone else can make reliable long-term forecasts of this sort. [↩]

Note: As usual, these are my personal guesses and opinions, not those of my employer.

In How big a deal was the Industrial Revolution?, I looked for measures (or proxy measures) of human well-being / empowerment for which we have “decent” scholarly estimates of the global average going back thousands of years. For reasons elaborated at some length in the full report, I ended up going with:

1. Physical health, as measured by life expectancy at birth.
2. Economic well-being, as measured by GDP per capita (PPP) and percent of people living in extreme poverty.
3. Energy capture, in kilocalories per person per day.
4. Technological empowerment, as measured by war-making capacity.
5. Political freedom to live the kind of life one wants to live, as measured by percent of people living in a democracy.

(I also especially wanted measures of subjective well-being and social well-being, and also of political freedom as measured by global rates of slavery, but these data aren’t available; see the report.)

Anyway, the punchline of the report is that when you chart these six measures over the past few millennia (data; zoomable), you get a chart like this (axes removed for space reasons): [Read more…]

The HYDE project provides the most comprehensive and up-to-date synthesis of historical, global population estimates I know of. To make these estimates slightly easier to use, I created a spreadsheet of the baseline scenario population data from the most recent version, HYDE 3.2.

For explanations, see the spreadsheet and Klein Goldewijk et al. (forthcoming).

In various places on my old atheism blog, I share advice for religious believers who are struggling with their faith, or who have recently deconverted, and who are feeling a bit lost, worried about nihilism without religion, and so on.

Here is my “FAQ for the sort of person who usually contacts me about how they’re struggling with their faith, or recently deconverted.”

Now that I’m losing my faith, I’m worried that nothing really matters, and that’s depressing.

I remember that feeling. I was pretty anxious and depressed when I started to realize I didn’t have good reasons for believing the doctrines of the religion I’d been raised in. But as time passed, things got better, and I emotionally adjusted to my “new normal,” in a way that I thought couldn’t ever happen before I got there.

I’ve collected some recommended reading on these topics here; see also the more recent The Big Picture. It’s up to you to decide what your goals and purposes are, but I think there are plenty of purposes worth getting excited about and invested in. In my case that’s effective altruism, but that’s a personal choice.

But really, my primary piece of advice is to just let more time pass, and spend time socially with non-religious people. Your conscious, deliberative brain (“system 2“) might be able to rationally recognize that of course millions of non-religious people before you have managed to live lives of immense joy and purpose and so on, and therefore you clearly don’t need religion for that. But if you were raised religiously like I was, then it might take some time for your unconscious, intuitive, emotional brain (“system 1“) to also “believe” this. The more time you spend talking with non-religious people who are living fulfilling, purposeful lives, the more you’ll train your system 1 to see that it’s obvious that meaning and purpose are possible without any gods — and getting your system 1 to “change its mind” is probably what matters more.

Where I live in the San Francisco Bay Area, it seems that most people I meet are excitedly trying to “make the world a better place” in some way (as parodied on the show Silicon Valley), and virtually none of them are religious. Depending on where you live, it might not be quite so easy to find non-religious people to hang out with. You could google for atheist or agnostic meetups in your area, or at least in the nearest large city. You could also try attending a UU church, where most people seem to be “spiritual” but not “religious” in the traditional sense.

My spouse and/or kids are religious, and my loss of faith is going to be super hard on them.

Yeah, that’s a tougher situation. I don’t know anything about that. Fortunately there’s a recent book entirely about that subject; I hope it helps!

Thanks, I’ll try those things. But I think I need more help.

I would try normal psychotherapy if you can afford it. Or maybe better, try Tom Clark, who specializes in “worldview counseling.”

In The Age of Em, Robin Hanson is pretty optimistic about our ability to forecast the long-term future:

Some say that there is little point in trying to foresee the non-immediate future. But in fact there have been many successful forecasts of this sort.

In the rest of this section, Hanson cites eight examples of forecasting success.¹ Two of his examples of “success” are forecasts of technologies that haven’t arrived yet: atomically precise manufacturing and advanced starships. Another of his examples is The Year 2000:

A particularly accurate book in predicting the future was The Year 2000, a 1967 book by Herman Kahn and Anthony Wiener (Kahn and Wiener 1967). It accurately predicted population, was 80% correct for computer and communication technology, and 50% correct for other technology (Albright 2002).

As it happens, when I first read this paragraph I had already begun to evaluate the technology forecasts from The Year 2000 for the Open Philanthropy Project, relying on the same source Hanson did for determining which forecasts came true and which did not (Albright 2002).

However, my assessment of Kahn & Wiener’s forecasting performance is much less rosy than Hanson’s. For details, see here.

1. The Nagy et al. paper, the Charbonneau et al. paper, Albright’s evaluation of forecasts from The Year 2000, the forecasts of John Watkins, the space travel forecasts of Konstantin Tsiolkovsky, the nanotechnology forecasts of K. Eric Drexler, the book on starship designs edited by Benford and Benford, and the 1999 business book by Shapiro and Varian. See Hanson’s book for sources. [↩]

In an interesting short paper from 1993, Bernard Baars and Katharine McGovern list several philosophical “habits of mind” and contrast them with typical scientific habits of mind. The philosophical habits of mind they list, somewhat paraphrased, are:

1. A great preference for problems that have survived centuries of debate, largely intact.
2. A tendency to set the most demanding criteria for success, rather than more achievable ones.
3. Frequent appeal to thought experiments (rather than non-intuitional evidence) to carry the major burden of argument.
4. More focus on rhetorical brilliance than testability.
5. A delight in paradoxes and “impossibility proofs.”
6. Shifting, slippery definitions.
7. A tendency to legislate the empirical sciences.

I partially agree with this list, and would add several items of my own.

Obviously this list does not describe all of philosophy. Also, I think (English-language) philosophy as a whole has become more scientific since 1993.

Philosopher Daniel Dennett advocates following “Rapoport’s Rules” when writing critical commentary. He summarizes the first of Rapoport’s Rules this way:

You should attempt to re-express your target’s position so clearly, vividly, and fairly that your target says, “Thanks, I wish I’d thought of putting it that way.”

If you’ve read many scientific and philosophical debates, you’re aware that this rule is almost never followed. And in many cases it may be inappropriate, or not worth the cost, to follow it. But for someone like me, who spends a lot of time trying to quickly form initial impressions about the state of various scientific or philosophical debates, it can be incredibly valuable and time-saving to find a writer who follows Rapoport’s First Rule, even if I end up disagreeing with that writer’s conclusions.

One writer who, in my opinion, seems to follow Rapoport’s First Rule unusually well is Dennett’s “arch-nemesis” on the topic of consciousness, the philosopher David Chalmers. Amazingly, even Dennett seems to think that Chalmers embodies Rapoport’s 1st Rule. Dennett writes:

Chalmers manifestly understands the arguments [for and against type-A materialism, which is Dennett’s view]; he has put them as well and as carefully as anybody ever has… he has presented excellent versions of [the arguments for type-A materialism] himself, and failed to convince himself. I do not mind conceding that I could not have done as good a job, let alone a better job, of marshaling the grounds for type-A materialism. So why does he cling like a limpet to his property dualism?

As far as I can tell, Dennett is saying “Thanks, Chalmers, I wish I’d thought of putting the arguments for my view that way.”¹

And because of Chalmers’ clarity and fairness, I have found Chalmers’ writings on consciousness to be more efficiently informative than Dennett’s, even though my own current best-guesses about the nature of consciousness are much closer to Dennett’s than to Chalmers’.

Contrast this with what I find to be more typical in the consciousness literature (and in many other literatures), which is for an article’s author(s) to present as many arguments as they can think of for their own view, and downplay or mischaracterize or not-even-mention the arguments against their view.

I’ll describe one example, without naming names. Recently I read two recent papers, each of which had a section discussing the evidence for or against the “cortex-required view,” which is the view that a cortex is required for phenomenal consciousness. (I’ll abbreviate it as “CRV.”)

The pro-CRV paper is written as though it’s a closed case that a cortex is required for consciousness, and it doesn’t cite any of the literature suggesting the opposite. Meanwhile, the anti-CRV paper is written as though it’s a closed case that a cortex isn’t required for consciousness, and it doesn’t cite any literature suggesting that it is required. Their differing passages on CRV cite literally zero of the same sources. Each paper pretends as though the entire body of literature cited by the other paper just doesn’t exist.

If you happened to read only one of these papers, you’d come a way with a very skewed view of the likelihood of the cortex-required view. You might realize how skewed that view is later, but if you’re reading only a few papers on the topic, so that you can form an impression quickly, you might not.

So here’s one tip for digging through some literature quickly: try to find out which expert(s) on that topic, if any, seem to follow Rapoport’s First Rule — even if you don’t find their conclusions compelling.

1. That said, Dennett might disagree with my claim that Chalmers seems to embody Rapoport’s First Rule in general. And certainly there are consciousness experts who have a different view about Chalmers’ fairness and clarity than I do, e.g. Carruthers & Schier (2014). [↩]

Tim Minchin once said “Every mystery ever solved has turned out to be not magic.” One thing I want to understand better is “How, exactly, has that happened in history? In particular, how have our naive pre-scientific concepts evolved in response to, or been eliminated by, scientific progress?

Examples: What is the detailed story of how “water” came to be identified with H₂O? How did our concept of “heat” evolve over time, including e.g. when we split it off from our concept of “temperature”? What is the detailed story of how “life” came to be identified with a large set of interacting processes with unclear edge cases such as viruses decided only by convention? What is the detailed story of how “soul” was eliminated from our scientific ontology rather than being remapped onto something “conceptually close” to our earlier conception of it, but which actually exists?

I wish there was a handbook of detailed case studies in scientific reductionism from a variety of scientific disciplines, but I haven’t found any such book yet. The documents I’ve found that are closest to what I want are perhaps:

• Thagard’s “Conceptual Change in the History of Science: Life, Mind, and Disease” and his earlier Conceptual Revolutions
• Grisdale’s Conceptual Change: Gods, Elements, and Water
• Laureys’ “Death, unconsciousness and the brain“
• Chang’s Inventing Temperature, Is Water H2O?, and some of his papers
• Chalmers’ The Scientist’s Atom and the Philosopher’s Stone

Some semi-detailed case studies also show up in Kuhn, Feyerabend, etc. but they are typically buried in a mass of more theoretical discussion. I’d prefer to read histories that focus on the historical developments.

Got any such case studies, or collections of case studies, to recommend?

Sean Carroll’s The Big Picture is a pretty decent “worldview naturalism 101” book.

In case there’s a 2nd edition in the future, and in case Carroll cares about the opinions of a professional dilettante (aka a generalist research analyst without even a bachelor’s degree), here are my requests for the 2nd edition:

• I think Carroll is too quick to say which physicalist approach to phenomenal consciousness is correct, and doesn’t present alternate approaches as compellingly as he could (before explaining why he rejects them). (See especially chs. 41-42.)
• In the chapter on death, I wish Carroll had acknowledged that neither physics nor naturalism requires that we live lives as short as we now do, and that there are speculative future technological capabilities that might allow future humans (or perhaps some now living) to live very long lives (albeit not infinitely long lives).
• I wish Carroll had mentioned Tegmark levels, maybe in chs. 25 or 36.