Invitation for bets

I’m willing to bet that Anthropic’s revenue growth over the next year will be slower than its revenue growth over the last 3 years. I proposed a specific bet here. Anyone who wants can offer to take the other side of that bet. Or you can make a counteroffer.

I’m also willing to make a longer-term bet that the AI industry is in a bubble. I proposed a specific bet for that, too, here. Feel free to offer to take the other side of that bet or make a counteroffer.

I’d also be open to other bets. It seems pointless to bet about whether AGI or transformative AI will be deployed within the next 5-10 years, yet, for the heck of it, I would agree to a bet against that, too. (I’ll make bets for small, nominal amounts of money to be donated to the winner’s charity of choice, since the practical and legal problems with betting are too large otherwise.)

I’d also bet against the deployment of 100,000+ SAE Level 5 fully autonomous vehicles in North America within the next 3 years, if anyone has a strong opinion on that. I’d make a similar bet against the deployment of autonomous humanoid robots in North American households, although we’d have to come up with some specific resolution criteria.

Similarly, I’d bet against any significant level of near-term labour automation by LLMs or generative AI. Or against LLMs becoming capable of performing all sorts of specific tasks well.

On any of these topics, I’m also open to invitations for a public dialogue. (More on that topic here.)

An AI that is to us as we are to other species does not go well for us. It needs to have better values!

Suggestion: Leverage Research deep dive

Someone (other than me) should write a deep-dive post about the cult Leverage Research and its infiltration of effective altruism.

The story, in brief:

• Leverage Research is a cult.

• Leverage Research organized the first EA Summit in 2013 and the second EA Summit in 2014. The EA Summits were the first effective altruism conferences of any kind.

• Leverage Research also helped to organize the first EA Global conferences, which began in 2015 and continue to this day.

• In 2016, a major EA program, the Pareto Fellowship, was run largely by Leverage Research. There is some evidence the Pareto Fellowship was run in a cult-like fashion.

• Leverage Research eventually gained full control of the Centre for Effective Altruism in 2018 when one of its members, Larissa Hesketh-Rowe, became the CEO.

The purpose of the deep-dive post would be for people in EA to understand the truth about what happened. And to learn whatever lessons they think they should learn from that.

These are the questions I would recommend asking and attempting to answer in the deep-dive post:

• Is Leverage Research a cult?

• Did it take over the Centre for Effective Altruism?

• Did it organize the EA Summits and the Pareto Fellowship? Did it play an important role in organizing the first EA Globals?

• If so, how could the EA movement, particularly the core international leadership, let this happen?

• If so, what might be the broader ramifications of this for the EA movement?

• What (if anything) is there to learn from this?

I don’t know what the chances would be of actually getting funded, but someone who wanted to spend a lot of time investigating this topic could apply for a $1,000+ grant from the EA Infrastructure Fund.

I’m not sure if Coefficient Giving (formerly Open Philanthropy) would even consider funding something so small and so specific to EA community self-reflection, but you can look at the relevant info here.

Snopes did pretty detailed secondary reporting on my analysis of AI use in the recent encyclical.

I think it’s pretty good. Covers some stuff I didn’t include in my original analysis, and their conclusion was similar to mine, maybe slightly less strong.

Less technical than my post, and imo not as funny, but also significantly shorter (1600 words), includes some replications and also added some details I didn’t know as of time of writing.

Overall a good piece, potentially worth reading/​skimming either in addition to or instead of my original analysis.

One reflection I’ve had in the whole “AI use in the encyclical” affair is to slightly increase my trust in traditional media, especially non-American traditional media, and slightly decrease my trust in social media/​new media.

I tried my best to promote my analysis as legibly and reasonably as I could and focused on logos rather than ethos: I didn’t frame my article with institutional affiliations and intentionally chose not to include obvious, flashy, but irrelevant signaling. Stuff I could’ve done but explicitly chose not to: get an ML professor to cosign my analysis, publish on Arxiv instead of Substack and LessWrong, highlight my past ML experience at Google, etc.

The traditional media response were somewhere between neutral and positive. There were some disappointments (eg a famous media org won’t run the article without confirmation from a “primary source”—aka the Vatican which is of course a non-starter). But mostly the traditional media just looked at the analysis and said “yeah looked reasonable.” and either ran it as “Unconfirmed but seems right” or just “Unconfirmed, Period.” Which seems fine.

On the other hand, the social media attacks were very aggro. People just didn’t seem to entertain it at all, many without reading it. And I got personally attacked a ton^[1].

Also it wasn’t picked up at all^[2] by new media afaict (unless you count Russia Today as new media).

This is exactly the type of story you might expect new media to be good for: story of institutional decay in the Old Guard, investigation by someone without credentials carefully laying out an epistemically rigorous and systematic case. And traditional media was happy to report it^[3], social media kept yelling at me, and new media just got crickets.

1. ^

   Don’t worry, I didn’t take it personally at all. Very much a “yapping of chihuahua in a tiny purse” moment.

2. ^

   This is a slight exaggeration. Other than Russia Today a bunch of small AI news aggregators (AI news in both senses of the term) covered it briefly. Eg Zvi, Let’s Data Science, and some AI slop publications. But no big “Alt Media” coverage comparable to The Economist, Perfil, etc).

3. ^

   Quite possibly against their explicit economic interests, if I’m reading the popular reaction correctly.

I started research into farmed animal welfare in Muslim countries and I think this is a useful way to share little updates along the way, and also to track any ideas I come up with so I can refer back to them when I need to compile my findings. Because I’m also working on a grant looking into effective Zakat, and I think I’ll end up doing the same thing for that, I’m going to be numbering farmed animal welfare quick takes with FAW# and Effective Zakat quick takes with EZ#.

so.

FAW#1.

Before starting with this project, I was operating under the assumption that there is a huge amount of good to be done in getting muslims to reduce/​cease their consumption of meat. I still think that is the case. However, the reason I though this was to do with animal welfare—I thought that the conditions of farmed animals are so bad, and there are plenty of mentions of the importance of the kind treatment of animals throughout the islamic literature (including in the texts pertaining to Halaal slaughter) that there is a clear argument to be made that factory farmed meat should be rejected by muslims.

I have come to realise that there is a MUCH stronger argument against the consumption of meat for muslims, which is that many (if not most) slaughter techniques employed in the industrialised abattoirs are probably not halal compliant.

Lots of EA orgs say they struggle to hire for ops, marketing and comms. But when they post listings for these roles the salaries are often much lower than what they offer for research and engineering, which they generally find easier to fill. My guess is that orgs are just benchmarking against normal market rates for these roles. But the EA labour market is very different to the normal labour market, if these roles are undersupplied inside EA, I think orgs should be willing to pay more for them.

SMBC by Zach Weinersmith is doing a great job of conveying AI Safety memes more widely.

Relevant comics: https://​​www.smbc-comics.com/​​comic/​​speech https://​​www.smbc-comics.com/​​comic/​​safe https://​​www.smbc-comics.com/​​comic/​​ai-17 https://​​www.smbc-comics.com/​​comic/​​ai-15

I would love to see his take on an illustrated AI Safety book, like ‘Open Borders’ meets ‘If anyone builds it, everyone dies’.

If you ever need a classic rap song to communicate your desire to be more influential in animal philanthropy, just say:

I wish I was a little bit taller

I wish I was a Bollard

I sometimes hear complaints from non-native English speakers about how banning undisclosed LLM use in writing is unfair.

Possible pro-tip for non-native English speakers who want to write well but don’t want to sound like AI: Just write an article you want to write in your native language, polish it until you’re proud of it in your native language, and then ask a frontier LLM (Opus 4.8, Gemini 3.1 Pro, ChatGPT 5.5 Pro) to translate it to English, while reasonably adhering to your original intent and writing.

In my experience and tests, the LLMs are sufficiently faithful in their translations that even the naivest possible way to do this (just one-to-one translation by a LLM without any further changes) would not trigger Pangram. I strongly suspect they wouldn’t trigger human allergies either[1].

I suspect if you’re upfront about your process, most people would be happy to read your translated words as well. Just explicitly state at the top of your post that you wrote the whole thing in Chinese/​French/​Romanian/​Portuguese (with link to your draft) and you asked an LLM to translate it. If enough people do this, I think we’ll have a natural new equilibrium where some people opt out of LLM-mediated translations, but the vast majority of your old readers will come back.

I think this is also much healthier and less tenuous than the current equilibrium where people clearly use LLMs to formulate their writing, lie about it, and then when confronted hide behind “non-native speaker” as an excuse.

(Optionally, you can ask the LLM to explain the non-trivial translation choices it made in its translations, which can help you with deciding whether to approve of the changes or not, and also learn English more in the mean-time. Though my guess is that it’s not strictly necessary.)

[1] (I’ve asked native speakers of other languages to test this, one for Swahili and one for Chinese. Both agreed that the results sound generic compared to the original writing but do not sound like LLM-ese)

How would you transcribe 1-1s at EAG?

Assuming all parties have consented.

Some ideas from others:

• Use Mac’s native software

• Otter on phone, phone on the table

• Google Meet

Was at EAG, “no seats left” on all AI talks, “57 seats left” on the biosec ones. Fully agree with others that biosec field building seems underserved. Anecdotal but perhaps illustrative. Thanks CEA for another event full of amazing people.

Relating to the Bernie news today, I don’t think capitalism is compatible with believing in the singularity and some combo of humanism/​utilitarianism. I have been a neolib for a long time before now fwiw. If you ran the zoo would you give the biggest banana to the sexiest gorilla?

Daniel Björkegren points out (h/​t Deena Mousa’s newsletter) that marginal returns to intelligence from advanced AI will be lower in LMICs due to scarcer AI complements, lower digital legibility, and smaller knowledge sectors, so AI that augments knowledge workers is likely to disproportionately benefit richer countries:

The economic implications of this transformation can be characterized by the marginal returns to intelligence (Amodei 2024): how much can we improve economic outcomes as we better generate ideas, process data, and apply knowledge? Intelligence allows us to solve scientific problems, design better products, better anticipate demand, and ensure the right quantities are stocked in the right places. Low-income countries will benefit from innovations developed in rich ones. But within many LMICs, the complements to advanced AI are scarcer, including data centers, reliable electricity, and digital records, as well as experienced knowledge workers. Data centers can be located in countries that already have good infrastructure (‘the cloud’) and accessed remotely. But LMICs are less digitally legible: AI will be less able to understand and act in markets, firms, homes, clinics, and schools that do not record data in structured forms. Overall, we would expect LMICs to be at a disadvantage in integrating advanced AI (Korinek and Stiglitz 2021).

A crucial distinction is that LMICs have much smaller knowledge sectors. LMICs employ fewer than 10% of workers in skilled knowledge work, like managers, technicians, and professionals, relative to 41% in high income countries (Silva 2026). Current AI tools require substantial human guidance. So, firms in rich economies are pursuing a grafting strategy: existing knowledge workers are being asked to integrate AI into their roles, starting from producing slides and emails, and scaling to more sophisticated tasks. In countries with smaller knowledge sectors, there are fewer workers and processes to graft AI onto. Thus a key question is whether advanced AI will mainly empower existing workers, or automate knowledge work completely. In wealthy countries, advocates concerned about jobs suggest that AI systems be designed to augment rather than automate (Acemoglu, Autor, Johnson 2026). But in low-income countries, the more urgent question may be how to provide knowledge services when few knowledge workers are available. Fully automating knowledge work could in fact augment less educated workers, who could ask AI to complete macro tasks like developing marketing strategies, rather than micro tasks like reformatting spreadsheets. However, even automated systems will likely require oversight from entrepreneurs and scientists with deep expertise, which may be sufficiently available only in wealthier countries like Brazil and India.

If AI allows LMICs to grow automated knowledge sectors, would the returns be high or low? One indicator is in wages paid to human workers. The wage returns to college education are slightly higher in lower income countries (Psacharopoulos and Patrinos 2018 and 2025), but educated people often earn higher wages abroad, and some domestic knowledge workers are working on rich countries’ knowledge problems in call centers and business process outsourcing. Lower income economies may not currently be structured to fully tap the decision making entailed in knowledge work (Engbom et al. 2025). If we tasked millions of data scientists with helping smallholder farms, the returns are unlikely to be large: agriculture is constrained elsewhere.

However, if the price of some forms of intelligence declines by orders of magnitude, it may become worth applying intelligence to problems that were never worth assigning a human to. Small manufacturers might generate nuanced designs that would have required a team of industrial engineers, and implement advertising campaigns that would have required large creative teams. Many regions have struggled to agglomerate sufficient human talent; since automated intelligence can be accessed anywhere, it could make businesses more mobile. These opportunities could more fundamentally change economic structure.

So what can LMICs do? Daniel suggests these:

The most capable AI systems currently require large-scale frontier models and large amounts of compute. Governments, firms, and NGOs will need to work with the frontier labs to ensure that the most advanced models speak local languages and understand local contexts. Ensuring that there are multiple suppliers for both models and data centers can reduce prices and risks of lock-in and geopolitical disruption (Athey and Scott Morton 2025).

Governments will also need to push to make economic activity digitally legible, from markets to clinics to schools.

It is also important to ensure that AI can be productively used. That may require training humans to be more productive users of AI, both in applying the tool and having the deeper world knowledge needed to direct it. Firms can also invest in developing AI tools that are complementary to the industrial structure of LMICs, including tools for small scale entrepreneurs who have less education, and for agriculture, like weather forecasting.

The diversity of institutional conditions in low- and middle-income countries may be a comparative advantage. Wealthy countries have evolved similar institutions around human knowledge work; tweaks may lead to local optima. In contrast, systems in low-income countries can differ greatly. Tailoring to different constraints can generate opportunities: for example, Kenyan entrepreneurs coping with unreliable network connections developed techniques to create on-device AI models that are seeing demand around the world (Fastagger). Or, also in Kenya, 90% of people resolve disputes outside the formal justice system (Kenya 2020), and just two doctors serve every 10,000 people, compared to 37 in the United States (WHO, 2022). Firms and NGOs may find creative new solutions, such as offering more efficient ways to settle disputes outside of court, or dynamic medical advice. Governments can take advantage of opportunities to design new regulation for AI, rather than retrofit regulation designed for humans. A lack of established institutions around human knowledge work could also allow harm: what happens when medical AI makes mistakes and there are limited mechanisms to address malpractice? It will take care to develop appropriate new institutions.

Relatedly, Daniel also has a great post on how the poorest use AI. A quote:

AI usage can provide a new window into the needs of the poor, analogous to Google Trends. This can help AI labs and a variety of organizations better serve these populations. We saw early examples of this among teachers in Sierra Leone, who submitted requests for not only facts and lesson plans, but also on handling reports for insurance claims and navigating interpersonal situations with students and supervisors. Another study found that one of the top uses of ChatGPT among gig workers in India and Brazil was for health queries.

What could be computed: An easy start would be to take the standard categorization of requests already reported by the labs (such as writing, technical help, or mapping to industries) and report them specifically for the subset of users in marginalized groups (defined by having cheaper devices, speaking local languages, or using from remote areas). However, these taxonomies are built around knowledge work, and may systematically undercount the ways poor people find the technology useful. Thus, it would be helpful to develop new taxonomies to understand poverty-specific needs, including particular uses within agriculture, health, navigating government bureaucracy, and business advice.

What we might find: The poor are likely to use AI differently from the wealthy: almost no software development, some use for navigating bureaucracy and social problems, more for help with homework, and less for writing assistance. Anthropic has already reported that Claude users in lower-income countries are more likely to request help with coursework. A further breakdown will help us understand if AI is being used only in the wealthiest schools or broadly, and help school systems ensure it is used in ways that support learning.

This topic became more salient to me after attending Deena’s EAG talk on how LMICs should respond to AI, which feels like it should be a much bigger topic than it currently is.

TL;DR — It’s mildly interesting that a famous artistic married couple in the 1800s was on opposite sides of the doomer/​boomer dichotomy, in a way that feels surprisingly contemporary in relation to AI discourse.

(A better formatted version of this is here.)

Frankenstein; or, The Modern Prometheus is a mediocre book wrapped around one small and dazzling idea. What if a scientist thoughtlessly invented something huge, and then not even a full second after the invention finally worked, realized that he’d made a huge mistake and ran away?

The idea cuts away a lot of unnecessary realism (how difficult it is to invent something, how rare moral epiphanies are, how people stick to bad projects because of sunk costs, how hard it is to forecast badness) to boil down to a single scene the whole moral question around science: what happens when a scientist invents something BAD?

In Frankenstein, the scientist runs away in horror. And then a whole bunch of tedious gothic melodrama happens and some people get hurt but none of that really matters. The book isn’t famous for what happens in the Arctic expedition sideplot or the ‘spying on people in a cottage’ sideplot. It is famous because the scientist ran away in horror.

It is easy to imagine why the question of science being bad was in vogue at the time. Science was in the air. Mills and machines were in use. Children were in factories. Aldini was using electricity to induce twitches in corpses. Luddites were up in arms. Several million smallpox vaccines had gone into arms. The famous GDP chart was initiating the launch sequence.

But Mary Shelley wasn’t the only Shelley writing a book with Prometheus in the title and a reference to Paradise Lost on the first page. A mere two years after Mary Shelley published her book, her husband published Prometheus Unbound.

The title is a reference to Greek playwright Aeschylus’s Prometheus Bound. In that play, Greek god Prometheus finds out Zeus is planning to kill the human race, so he goes behind Zeus’s back and gives humans not only fire, but also skills like writing, math, and agriculture. Zeus is mad and he punishes Prometheus by tying him to a mountain and torturing him, but eventually they reconcile.

Percy Shelley rewrites the Greek ending to give Prometheus his heroic due. In his version, Prometheus doesn’t reconcile with Zeus. No, Zeus is a tyrant, and reconciling with him would defile Prometheus. Instead, Shelley has Zeus be deposed, so that Prometheus can be freed while remaining unrepentant. And mankind, now ruled by a kind and just ruler, blossoms:

The loathsome mask has fallen, the man remains
Sceptreless, free, uncircumscribed, but man
Equal, unclassed, tribeless, and nationless,
Exempt from awe, worship, degree, the king
Over himself; just, gentle, wise: but man
Passionless; no, yet free from guilt or pain

These are opposing visions of what happens when mankind gets knowledge that gives it godlike powers. In Mary Shelley’s doomer vision, the person who uses this power regrets it immediately, and suffers because of it for the rest of his life. In Percy Shelley’s boomer vision, the person who gives mankind this power never regrets it, and eventually lives to see his actions result in utopia on Earth. Two hundred years later, on the eve of transformative AI, both their visions are still live.

Did you enjoy EA Global? Was it useful to you?

Consider “tipping” CEA with a donation ( you can donate at https://​​www.givingwhatwecan.org/​​charities/​​centre-for-effective-altruism ) to close any gap between what you paid at registration and the value you feel you have received.

Our board now has more roles than before (1600+), and a public Airtable version that you can use to set up custom views and automations (including with Slack).

A quick guide for using the new Airtable:

1. Open the public Airtable and click “Use this data”. Make sure “Create a synced table” is selected. Choose which Airtable base you’d like the jobs data to live in. This creates a read-only synced table with our published roles.

2. Create a filtered view in the new table (e.g., filter by cause area, location, or role type).

3. From here, you can set up Slack notifications:

   1. Trigger: “When record enters view”, selecting the filtered view you created in step 2.

   2. Action: “Send a Slack message” (via Airtable’s built-in Slack integration)

   3. Compose your message using field tokens to pull in live data from each role, e.g. New role: {Job Title} at {Org Name} | {Job URL}. Use markdown for basic formatting like bold or italics.

If you use our job board, here’s a few ways you can help us to help you:

• Test out the new Airtable and let us know if there are any issues or if you do anything cool with it.

• If you land a role that you found on the job board, please get in touch! Even a short message about how our services helped you makes a huge difference to our ability to continue providing these services.

• If you know of any orgs you think we should monitor for the board (including ones you work for), please share them!

• If you work at an org that’s listed on the board, note that links to your roles from our job board automatically include utm_source=probablygood_board so if you track referral sources, you’ll be able to see applications that came via us. If you have a question on your application forms regarding where candidates heard about the role, please also consider adding “Probably Good” as an option.

• If you’re a hiring manager/​recruiter who ends up hiring a candidate who found your role through our job board, please let us know!

Other than that, please also share the job board with people you think could benefit from it, and get in touch with us if you have any feedback or other suggestions. Thank you!

Over 40% of my lifetime donations happened this month. I expect to never be able to make that statement again. These are weird times.

For context on why it is extra weird, I’ve been donating since 2015 and have always donated 10% of my salary each year (or more).

What would need to happen for you to stand down on AI safety?

I’m not saying you should. I’m not saying you will. But what evidence would make you substantially less concerned than you are today?

I’ve been asking people at EAG London some variation of two questions:

• How worried are you about the economic and existential risks from AI?

• What is the ‘expiry date’ of those worries?

This may seem leading, as if I’m implying that I’m not fully convinced by the broader arguments. Well, I’m not… not. My position is that I’m under-informed. But I still want to know how those better informed than I think about the limits of their concern.

So… is it a matter of time? If nothing substantially bad has happened in 10 years, will we stop fearing? Or is it a matter of meeting certain benchmarks of cooperation? Could it be when governments figure out a method of wealth redistribution that doesn’t result in societal decline?

Let me know your thoughts!

Significant fractions of Magnifica Humanitas, the papal encylical on safeguarding the human person in the time of artificial intelligence, is written significantly by AI, most likely Claude. I currently believe Pope Leo himself was not personally responsible (encyclicals tend to be group projects), however the AI usage is likely substantial enough that it’s not the result of minor brushups or AI translation:

https://​​linch.substack.com/​​p/​​claude-author-of-the-humanitas

Key claims:

1. Significant fractions of the recent papal encyclical are written with AI-assistance. I provide multiple lines of evidence for this.

2. We can corroborate the vibes and tonal indications with statistical evidence. Phrases and punctuation much more commonly used by AI are much more present in this papal encyclical than past encyclicals.

3. The best commercially available AI detector, Pangram, notes that some paragraphs are between 40% and 100% AI, while most paragraphs appear to be 0% AI.

   1. This is unlikely to be a false positive:

      1. 0% of paragraphs in past encyclicals I backtested are registered as AI.

      2. Pangram in general has a very low false positive rate

4. This is overall very unlikely to be a translation artifact (including AI translation). We again have multiple lines of evidence:

   1. All the most prominent signs of AI I observed in English are preserved verbatim in the Italian version, as well as in other translations.

   2. The Italian version of the current encyclical also gets flagged as AI by Pangram (actually more so than the English version), though I’m not aware of academic research or rigorous testing of Pangram’s service when applied to Italian)

   3. Backtesting AI translation of past encyclicals get 0% on Pangram

5. The specific AI used is most likely Claude, judging by both textual and circumstantial evidence.

6. Different sections of the encyclical have very different rates of apparent AI usage. This indicates to me that some cardinals used AI assistance for this encyclical and many (probably including Pope Leo himself) didn’t.

7. Each individual piece of evidence might be explained away, but the consilience of evidence across multiple angles and sources is in my opinion very hard to dismiss collectively.