EA Forum Podcast (All audio)

The EA Grants Database is a new site that neatly aggregates grant data from major EA funders who publish individual or total grant information. It is intended to be easy to maintain long term, entirely piggybacking off of existing data that is likely to be maintained. The website data is updated by a script that can be run in seconds, and I anticipate doing this for the foreseeable future. In creating the website, I tried to make things as clear and straightforward as possible. If your user experience is in any way impaired, I would appreciate hearing from you. I would also appreciate feedback on what features would actually be useful to people, although I am committed to avoiding bloat. In a funding landscape that seems poised to grow, I hope this site can serve as a resource to help grantmakers, grantees, and other interested parties make decisions while also providing perspective on what has come before. My post on matching credits and this website are both outgrowths of my thinking on how we might best financially coordinate as EA grows and becomes more difficult to understand.[1] Relatedly, I am also interested in the sort of mechanisms that [...] --- First published: February 8th, 2026 Source: https://forum.effectivealtruism.org/posts/rohYFGfiFjepLDnWC/ea-grants-database-a-new-website --- Narrated by TYPE III AUDIO.

This is a link post. I am not a historian! Fact check me please, dear God! We are at the beginning of another industrial revolution. The first was the automation of muscle, the current one is the automation of mind. To get into the right frame of mind, I want to step back and imagine:We are living in the UK[1], the year is 1800, and we are trying to end animal suffering. We know nothing about how the tech is about to develop, nor how the economy or politics is about to be transformed, but there are faint clues. Steam engines have started powering textile factories and milling grain, but no trains yet, no electricity. There are almost a billion people in the world, and a similar number of livestock living almost entirely on small farms. The first factory farms won't go up for almost 150 years. Let's go back in time.Farmed animalsAll animals kept for food are living on small farms. The conditions are not great: they have little protection from the cold, no veterinary care, and they are slaughtered crudely[2] and in public. However, compared to life on a factory farm, things look pretty idyllic.---Outline:(01:10) Farmed animals(02:23) Work animals(03:02) Blood sports(03:46) Animal testing(04:57) Fishing and whaling(05:55) Wild animals(06:24) Culture and philosophy(07:21) Data availability(08:05) Politics(09:17) Colonialism and (human) slavery(10:23) Some specific takeaways(12:49) Conclusion --- First published: February 5th, 2026 Source: https://forum.effectivealtruism.org/posts/CHQdcXjBudqq4QFk9/animal-welfare-at-the-start-of-the-industrial-revolution Linkpost URL:https://lovedoesnotscale.substack.com/p/animal-welfare-at-the-start-of-the --- Narrated by TYPE III AUDIO.

Running the first meetup for people hoping to accelerate Effective Altruism with AI tools. Why now? AI is accelerating productivity across industries. People who invest learning to use AI tools are able to work much faster and more productively. As a result, we can accelerate our favourite impactful organisations by helping them adopt these tools. What will we discuss? We will discuss ​visibility, e.g. building personal sites and blogs - useful for growing luck surface area, raising awareness about productivity gains from these tools ​workflow comparisons, e.g. claude skills, plugins, productivity hacks - who's got the best setup, what can we learn from eachother? ​implementation e.g. how to network, volunteer well, and scale impact - how can we get plugged in to the right opportunities quicker?  Come along Here's the Luma invite. This is an hour long meet-up aimed at any EA actively working to learn to use AI tools. Can't make it? If you'd like to advertise a problem you want tech help on, send me a DM --- First published: February 5th, 2026 Source: https://forum.effectivealtruism.org/posts/dkcwuJKw7kA4fWe5H/first-effective-altruism-ai-uplift-meetup --- Narrated by TYPE III AUDIO.

This is a link post. [Subtitle.] Sentience and moral priority-setting This is a crosspost for The shrimp bet: When big numbers outsprint the evidence by Rob Velzeboer, which was originally published on 27 January 2026. TLDR: Shrimp welfare looks like the ultimate “scale + tractability” slam-dunk: massive numbers, cheap fixes, grim-sounding deaths. But the flagship farmed species—the penaeid shrimp L. vannamei—is an evidential outlier: beyond basic nociception, the sentience case is close to empty, and the limited evidence we do have points the wrong way on key markers. In the report that kicked off this wave, it was included for administrative clarity, not because sentience looked likely. If you let precaution plus expected-value reasoning run on that evidential bar, you don’t stop at shrimp, but you get pulled into insects and the rest of modern life's collateral killing. My view is that we shouldn’t let raw numbers and optimistic assumptions about sentience guide our moral priorities: most weight should go to high-confidence, severe, tractable suffering, and extremely low-confidence beings with high numbers should be treated as explicit research-and-standards bets, unless at least some higher-order evidence actually suggests pain. “At least I’m not a shrimp.” It's a line I’d often repeat [...] ---Outline:(03:37) PART 1: SHRIMP(03:41) Why focus on shrimp(08:26) The evidence for shrimp pain(15:01) Skepticism(19:11) PART 2: PRIORITIZATION(19:16) The implications of this view(25:04) Pain severity(32:05) Prioritization(36:18) References --- First published: February 6th, 2026 Source: https://forum.effectivealtruism.org/posts/ruG5GrTcE2DBiCDfW/the-shrimp-bet-when-big-numbers-outsprint-the-evidence Linkpost URL:https://robvelzeboer.substack.com/p/the-shrimp-bet --- Narrated by TYPE III AUDIO. ---Images from the article:Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.

This is a link post. We’ve recently published a set of design sketches for AI tools that help with collective epistemics. We think that these tools could be a pretty big deal: If it gets easier to track what's trustworthy and what isn’t, we might end up in an equilibrium which rewards honesty This could make the world saner in a bunch of ways, and in particular could give us a better shot at handling the transition to more advanced AI systems We’re excited for people to get started on building tech that gets us closer to that world. We’re hoping that our design sketches will make this area more concrete, and inspire people to get started. The (overly-)specific technologies we sketch out are: Community notes for everything — Anywhere on the internet, content that may be misleading comes served with context that a large proportion of readers find helpful Rhetoric highlighting — Sentences which are persuasive-but-misleading, or which misrepresent cited work, are automatically flagged to readers or writers Reliability tracking — Users can effortlessly discover the track record of statements on a given topic from a given actor; those with bad records come with health warnings Epistemic [...] --- First published: February 6th, 2026 Source: https://forum.effectivealtruism.org/posts/zMuDoeXA9nBSeAc5g/some-tools-for-collective-epistemics Linkpost URL:https://www.forethought.org/research/design-sketches-collective-epistemics --- Narrated by TYPE III AUDIO. ---Images from the article:Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.

“I” refers to Zach, the Centre for Effective Altruism's CEO. Oscar is CEA's Chief of Staff. We are grateful to all the CEA staff and community members who have contributed to the development and implementation of our strategy. Mistakes are of course our own.Executive summary One year into our 2025-26 strategy, we have reversed our programs’ negative growth trajectory from 2023-24 and will sustain that momentum in 2026 while preparing to hit much more ambitious goals from 2027 onwards: CEA grew the number of people engaging with our programs by 20–25% year-over-year across each tier of our engagement funnel, beating our targets of 7.5–10% without increasing spending, and reversing the moderate decreases in engagement with our programs throughout 2023–24. We laid the foundations for furthering our contribution to EA funding diversification by merging with EA Funds and hiring an accomplished new Director (Loic Watine). And we strengthened CEA's own foundations, establishing our in-house Operations Team and growing our headcount from 42 to 66 while increasing talent density, including another incoming experienced Director for our new Strategy and M&E function (Rory Fenton). We also faced some challenges: Beyond the impact of EA growth on the perception of the [...] ---Outline:(00:34) Executive summary(03:05) Stewardship and sustainable momentum(04:22) Growing the EA community(05:05) 2025(08:17) 2026(09:35) Improving the EA brand(10:02) 2025(15:24) 2026(16:30) Diversifying EA funding(16:50) 2025(19:53) 2026(21:20) Strengthening CEA(21:38) Operations Team(22:07) Staffing(23:38) Spending(24:46) Monitoring & evaluation(25:40) EV, still --- First published: February 6th, 2026 Source: https://forum.effectivealtruism.org/posts/Dy4iGHbAkKAQ4t2Dw/building-sustainable-momentum-progress-report-on-cea-s-2025 --- Narrated by TYPE III AUDIO. ---Images from the article:Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.

Summary: I built a simple back-of-the-envelope model of AI agent economics that combines Ord's half-life analysis of agent reliability with real inference costs. The core idea is that agent cost per successful outcome scales exponentially with task length, while human cost scales linearly. This creates a sharp viability boundary that cost reductions alone cannot meaningfully shift. The only parameter that matters much is the agent's half-life (reliability horizon), which is precisely the thing that requires the continual learning breakthrough (which I think is essential for AGI-level agents) that some place 5-20 years away. I think this has underappreciated implications for the $2T+ AI infrastructure investment thesis. The setup Toby Ord's "Half-Life" analysis (2025) demonstrated that AI agent success rates on tasks decay exponentially with task length, following a pattern analogous to radioactive decay. If an agent completes a 1-hour task with 50% probability, it completes a 2-hour task with roughly 25% probability and a 4-hour task with about 6%. There is a constant per-step failure probability, and because longer tasks chain more steps, success decays exponentially. METR's 2025 data showed the 50% time horizon for the best agents was roughly 2.5-5 hours (model-dependent) and had been doubling every ~7 [...] ---Outline:(00:57) The setup(02:04) The model(03:26) Results: base case(05:01) Finding 1: cost reductions cannot beat the exponential(06:24) Finding 2: the half-life is the whole game(08:02) Finding 3: task decomposition helps but has limits(09:33) What this means for the investment thesis(11:38) Interactive model(11:57) Caveats and limitations --- First published: February 6th, 2026 Source: https://forum.effectivealtruism.org/posts/2Zn23gCZrgzSLuDCn/agent-economics-a-botec-on-feasibility --- Narrated by TYPE III AUDIO.

TLDR: People plot benchmark scores over time and then do math on them, looking for speed-ups & inflection points, interpreting slopes, or extending apparent trends. But that math doesn’t actually tell you anything real unless the scores have natural units. Most don’t.Think of benchmark scores as funhouse-mirror projections of “true” capability-space, which stretch some regions and compress others by assigning warped scores for how much accomplishing that task counts in units of “AI progress”. A plot on axes without canonical units will look very different depending on how much weight we assign to different bits of progress.[1] Epistemic status: I haven’t vetted this post carefully, and have no real background in benchmarking or statistics.Benchmark scores vs "units of AI progress" Benchmarks look like rulers; they give us scores that we want to treat as (noisy) measurements of AI progress. But since most benchmark score are expressed in quite squishy units, that can be quite misleading. The typical benchmark is a grab-bag of tasks along with an aggregate scoring rule like “fraction completed”[2] ✅ Scores like this can help us... Loosely rank models (“is A>B on coding ability?”) Operationalize & track milestones (“can [...] ---Outline:(01:00) Benchmark scores vs units of AI progress(02:42) Exceptions: benchmarks with more natural units(04:48) Does aggregation help?(06:27) Where does this leave us?(06:30) Non-benchmark methods often seem better(07:32) Mind the Y-axis problem(09:05) Bonus notes / informal appendices(09:13) I. A more detailed example of the Y-axis problem in action(11:53) II. An abstract sketch of whats going on (benchmarks as warped projections) --- First published: February 6th, 2026 Source: https://forum.effectivealtruism.org/posts/P8jsAySQzfgkeoDgb/ai-benchmarking-has-a-y-axis-problem --- Narrated by TYPE III AUDIO. ---Images from the article:Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.

Considering how important politics are to most EA cause areas, I find it surprising how little attention political work gets among EAs. I get the feeling that because politics are blocked from the Forum homepage, and because EA fellowships fail to mention high-leverage political work, young EAs often assume that this is not a high impact space without looking into it themselves. Luckily, this is changing. There is a growing movement of EAs recognizing the huge importance of politics, and realizing it's not as intractable as many of us assume. Over the past few months, we have joined together with EA club leaders from schools like Middlebury, the University of Michigan, Princeton, and MIT to create multiple curriculum/content options for EA groups to use exploring this cause area. What we've put together: One-week fellowship curriculum - easily added on to your existing intro or advanced fellowships! Can also be used to guide a reading-based general meeting. Three-week fellowship curriculum - for a deeper dive into the importance, tractability, and neglectedness of democracy preservation & U.S. politics. Slides presentation - for a presentator-led meeting on democracy preservation, with discussion & activities built in.  Link to content. [...] --- First published: February 3rd, 2026 Source: https://forum.effectivealtruism.org/posts/XGRHy3A4oJPwG3ejd/u-s-democracy-preservation-and-politics-fellowship --- Narrated by TYPE III AUDIO.

The USDA New World Screwworm Grand Challenge represents the largest single funding opportunity for screwworm research and control technologies in decades. Applications are due February 23, 2026. What's happening? The New World Screwworm (NWS) is a flesh-eating fly endemic to the Americas that lays eggs in open wounds of warm-blooded animals. The larvae burrow into living flesh, consuming the host from inside. Infected animals experience excruciating pain, stop eating, and die slowly over days to weeks. This parasite occasionally infects humans too. NWS was previously eradicated from North and Central America, although containment efforts failed in 2024 and the parasite has reached Northern Mexico in January 2026. New technologies like gene-drive-enhanced sterile insect technique (SIT) could make large-scale control and eradication faster and cheaper than ever before. The USDA Grand Challenge represents an opportunity to direct funding to developing technologies with potential applications beyond the United States southern border. Next-gen SIT and gene-drive could help extend the range of control and eradication to South America for the first time, safeguarding rural livelihoods and the wellbeing of hundreds of millions of animals. Screwworm Free Future is supporting qualified applicants -- particularly those working in or with endemic regions [...] --- First published: February 5th, 2026 Source: https://forum.effectivealtruism.org/posts/QKWsxxQHni7cy3oq9/the-usda-is-investing-usd100m-in-innovation-for-screwworm --- Narrated by TYPE III AUDIO.

This is a link post. I've been reading Toby Ord's recent sequence on AI scaling a bit. General notes come first, then my thoughts. Notes The Scaling Paradox basically argues that the scaling laws are actually pretty bad and mean progress will hit a wall fairly quickly unless the next gen or two of models somehow speed up AI research, we find a new scaling paradigm etc... Inference Scaling and the Log X Chart says that inference is also not a big deal because the scaling is again logarithmic. My intuition here is that this is probably true for widespread adoption of models. It's probably not true if there are threshold effects where a single $100'000 query can be drastically better than a $100 query and allow you to, say, one shot open research problems. I'm not sure which world we live in. Inference Scaling Reshapes Governance talks about the implications of inference being a big part of models. One of the implications is that instead of getting a big bang of new model trained => millions of instances, we get a slower gradual wave of more inference = stronger model with a gradual rightward [...] ---Outline:(00:20) Notes(00:26) Takeaways --- First published: February 4th, 2026 Source: https://forum.effectivealtruism.org/posts/rDBLQFQzviW78eRk5/thoughts-on-toby-ords-ai-scaling-series Linkpost URL:https://www.dissent.blog/notes-on-toby-ords-ai-scaling-series/ --- Narrated by TYPE III AUDIO.

Bruce Friedrich's new book, MEAT, is a deeply thoughtful, pragmatic, and hope-inspiring story about alternative proteins. In his role as the Founder and President of the Good Food Institute, Bruce is uniquely positioned to give an insider's account of how far plant-based, fermentation-derived, and cultivated meat have come, and where they are likely to go from here. The book's publication comes at what seems to be a critical junction for alternative proteins. Several years of exponential growth in sales of plant-based meat and investment in cultivated meat have given way to a period of uncertainty for both. MEAT is a timely and compelling reminder that the fundamental reasons why these innovations are needed are as relevant today as they have ever been. More than that, it is a case for optimism. In the first section, Friedrich makes an extremely thorough and well-evidenced case for meat alternatives in terms of global food security, climate change, antibiotic resistance, and pandemic prevention. As a vegan who is totally on board with the moral case for alternative proteins, I found myself waiting for the chapter containing grizzly descriptions of factory farming, but it never came. The book is the opposite of sanctimonious. [...] --- First published: February 2nd, 2026 Source: https://forum.effectivealtruism.org/posts/EJMnbKjBwjvxDo6vG/review-of-meat-by-bruce-friedrich --- Narrated by TYPE III AUDIO.

Over 100 billion farmed fish are slaughtered each year. At Coefficient Giving, where I (Michelle Lavery) work as a Senior Program Associate, we estimate that only ~0.5% of them are reliably stunned before slaughter. For the more than a trillion wild-caught fish killed annually, conditions are even worse: most are left to suffocate slowly in air or in low-oxygen water, a process that can take minutes to hours. Though fish slaughter represents only a few hours in an animal's life, we believe the pain it causes is excruciating — especially when done poorly, which is most of the time. This problem is both urgent and tractable: the suffering is immense, the moment of slaughter is discrete and identifiable, and people are genuinely horrified when they learn about current practices. So why hasn't it been solved? The answer lies in a combination of technical complexity, limited competition in the equipment market, and a critical skills gap between the biologists who study the problem and the engineers who could solve it. That's why we're launching this RFP: to bridge that gap and catalyze the engineering innovation this problem demands. Why this is hard If you've never thought about how fish [...] ---Outline:(01:24) Why this is hard(03:43) Who/what were looking for(04:57) How to apply --- First published: February 4th, 2026 Source: https://forum.effectivealtruism.org/posts/i8om4DADPX5SfyNwP/request-for-proposals-humane-fish-slaughter-research-and --- Narrated by TYPE III AUDIO.

Evolutionary cost-balancing arguments in welfare biology (the study of animal well-being in the wild) assume that producing suffering and happiness has metabolic or neural costs, and that natural selection allocated these states efficiently. A specific version of this reasoning – which I'll call the "Evening Out Argument" – holds that if a type of suffering is highly likely and largely unavoidable for an animal, evolution would reduce its intensity. The logic is that, if suffering functions to motivate learning and avoidance, then intense suffering provides little behavioral benefit when the animal can barely avoid the bad outcome anyway – it's just cost without payoff. I argue this logic fails for large categories of animal experience. Most notably, it doesn't apply to background motivational states like hunger or anxiety, which function differently from discrete learning signals. I'll also discuss the prevalence of chronic and maladaptive suffering, concerns about the biological plausibility of some assumptions behind the Evening Out Argument, and why high infant mortality doesn't imply reduced suffering in long-lived species. For context, many people familiar with the topic have explicitly stated that they do not consider cost-balancing arguments to be strong; however, I have seen some people [...] ---Outline:(01:30) A brief history(06:12) Mayflies vs baby turtles(08:41) Hedonic accounting: discrete events vs background states(13:14) Chronic suffering and old age(15:27) Other biological plausibility concerns: path dependencies and modularity(17:36) Ancestral environments and environmental mismatch(19:11) Concluding thoughts and implications(21:44) Appendix: A puzzle for Evening Out(24:26) Acknowledgments The original text contained 32 footnotes which were omitted from this narration. --- First published: February 3rd, 2026 Source: https://forum.effectivealtruism.org/posts/5hWYrQmzpftiGJMTW/don-t-overupdate-on-evolutionary-cost-balancing-arguments --- Narrated by TYPE III AUDIO.

This is a link post. This is the latest from my blog about EA-org-style management, together with Brenton of 80k. You can subscribe on Substack if you like. Sometimes people review their strengths and weaknesses, and then automatically assume that they should try to improve their weaknesses. I think that you should at least consider focusing on strengths instead. Why focus on strengths? Increasing returns. You can often create a lot of value by becoming excellent at a few important things, and then finding roles that use those strengths. So going from good to great can be worth investing in. This is particularly true if you’re operating in domains with fairly heavy-tailed returns, like research. Success spirals: It can be more fun and exciting (h/t Daniel Kestenholz) – it's a more positive endeavour than trying to address your weaknesses: you can lean into your passions, and build a sense that you’re doing a good job overall. Some weaknesses can be fixed with an exoskeleton (rather than just by building muscle). Even when it seems like a weakness is really holding you back, that doesn’t necessarily mean you should work on improving it. Maybe instead you should try to fill those [...] ---Outline:(00:32) Why focus on strengths?(02:06) Why focus on weaknesses? --- First published: February 3rd, 2026 Source: https://forum.effectivealtruism.org/posts/E7LbmY6fFdZ2i4TwN/maybe-develop-your-strengths Linkpost URL:https://substack.com/home/post/p-186395881 --- Narrated by TYPE III AUDIO.

Key takeaways   84% of people who try veganism quit, most citing inconvenience as a major reason. The Vegan Filter is a project concept designed to make veganism dramatically more convenient. It introduces a single Vegan filter button in online supermarkets that hides all non-vegan products, effectively turning the supermarket vegan. At very low cost, the Vegan Filter could substantially increase vegan retention, making it one of the most scalable and neglected opportunities to reduce animal suffering. A vegan filter is strongly desired by consumers: Over 90% of respondents in the China Vegan Survey 2025 say it would improve convenience, vegan retention, and supermarket preference. Project slide deck The problem: Veganism fails at the supermarket Imagine you have just turned vegan and are doing your first grocery shop. What used to take 10 minutes now takes half an hour. You read ingredient lists line by line, hesitate over unfamiliar additives, and still leave unsure whether you made mistakes. When you get home, you discover that one product contains eggs. Frustration sets in. The experience feels mentally exhausting rather than empowering. This is not a niche story. For many people, veganism does not fail because of ethics or [...] ---Outline:(00:13) Key takeaways(00:19) Project slide deckThe problem: Veganism fails at the supermarket(01:20) Convenience is the weak link(03:14) The solution: Turn the supermarket vegan with a click of a button(05:07) How the Vegan Filter could work(08:20) Why It Could Work(09:14) Impact estimation(10:09) Why this matters for effective altruism --- First published: February 3rd, 2026 Source: https://forum.effectivealtruism.org/posts/erbTFMPcvCjd44QJK/the-vegan-filter-overcoming-the-leading-barrier-to-veganism --- Narrated by TYPE III AUDIO. ---Images from the article:Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.

Note: opinions are all my own. Following Jeff Kaufman's Front-Load Giving Because of Anthropic Donors and Jenn's Funding Conversation We Left Unfinished, I think there is a real likelihood that impactful causes will receive significantly more funding in the near future. As background on where this new funding could come from: Coefficient Giving announced:  A recent NYT piece covered rumors of an Anthropic valuation at $350 billion. Many of Anthropic's cofounders and early employees have pledged to donate significant amounts of their equity, and it seems likely that an outsized share of these donations would go to effective causes. A handful of other sources have the potential to grow their giving: Founders Pledge has secured $12.8 billion in pledged funding, and significantly scaled the amount it directs.[1] The Gates Foundation has increased its giving following Bill Gates’ announcement to spend down $200 billion by 2045. Other aligned funders such as Longview, Macroscopic, the Flourishing Fund, the Navigation Fund, GiveWell, Project Resource Optimization, Schmidt Futures/Renaissance Philanthropy, and the Livelihood Impacts Fund have increased their staffing and dollars directed in recent years. The OpenAI Foundation controls a 26% equity stake in the for-profit OpenAI Group PB. This stake is currently valued at $130 billion [...] ---Outline:(02:39) Work(03:50) Giving(04:53) Conduct --- First published: February 2nd, 2026 Source: https://forum.effectivealtruism.org/posts/H8SqwbLxKkiJur3c4/preparing-for-a-flush-future-work-giving-and-conduct --- Narrated by TYPE III AUDIO. ---Images from the article:Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.

This is a link post. There is an extremely important question about the near-future of AI that almost no-one is asking. We’ve all seen the graphs from METR showing that the length of tasks AI agents can perform has been growing exponentially over the last 7 years. While GPT-2 could only do software engineering tasks that would take someone a few seconds, the latest models can (50% of the time) do tasks that would take a human a few hours. As this trend shows no signs of stopping, people have naturally taken to extrapolating it out, to forecast when we might expect AI to be able to do tasks that take an engineer a full work-day; or week; or year. But we are missing a key piece of information — the cost of performing this work. Over those 7 years AI systems have grown exponentially. The size of the models (parameter count) has grown by 4,000x and the number of times they are run in each task (tokens generated) has grown by about 100,000x. AI researchers have also found massive efficiencies, but it is eminently plausible that the cost for the peak performance measured by METR has been [...] ---Outline:(13:02) Conclusions(14:05) Appendix(14:08) METR has a similar graph on their page for GPT-5.1 codex. It includes more models and compares them by token counts rather than dollar costs: --- First published: February 2nd, 2026 Source: https://forum.effectivealtruism.org/posts/AbHPpGTtAMyenWGX8/are-the-costs-of-ai-agents-also-rising-exponentially Linkpost URL:https://www.tobyord.com/writing/hourly-costs-for-ai-agents --- Narrated by TYPE III AUDIO. ---Images from the article:Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.

This is a link post. AI capabilities have improved remarkably quickly, fuelled by the explosive scale-up of resources being used to train the leading models. But if you examine the scaling laws that inspired this rush, they actually show extremely poor returns to scale. What's going on? AI Scaling is Shockingly Impressive The era of LLMs has seen remarkable improvements in AI capabilities over a very short time. This is often attributed to the AI scaling laws — statistical relationships which govern how AI capabilities improve with more parameters, compute, or data. Indeed AI thought-leaders such as Ilya Sutskever and Dario Amodei have said that the discovery of these laws led them to the current paradigm of rapid AI progress via a dizzying increase in the size of frontier systems. Before the 2020s, most AI researchers were looking for architectural changes to push the frontiers of AI forwards. The idea that scale alone was sufficient to provide the entire range of faculties involved in intelligent thought was unfashionable and seen as simplistic. A key reason it worked was the tremendous versatility of text. As Turing had noted more than 60 years earlier, almost any challenge that one could pose to [...] --- First published: January 30th, 2026 Source: https://forum.effectivealtruism.org/posts/742xJNTqer2Dt9Cxx/the-scaling-paradox Linkpost URL:https://www.tobyord.com/writing/the-scaling-paradox --- Narrated by TYPE III AUDIO. ---Images from the article:Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.

We're trying something a bit new this week. Over the last year, Toby Ord has been writing about the implications of the fact that improvements in AI require exponentially more compute. Only one of these posts so far has been put on the EA forum. This week we've put the entire series on the Forum and made this thread for you to discuss your reactions to the posts. Toby Ord will check in once a day to respond to your comments[1]. Feel free to also comment directly on the individual posts that make up this sequence, but you can treat this as a central discussion space for both general takes and more specific questions. If you haven't read the series yet, we've created a page where you can, and you can see the summaries of each post below: Are the Costs of AI Agents Also Rising Exponentially? Agents can do longer and longer tasks, but their dollar cost to do these tasks may be growing even faster. How Well Does RL Scale? I show that RL-training for LLMs scales much worse than inference or pre-training. Evidence that Recent AI Gains are Mostly from Inference-Scaling I show how [...] --- First published: February 2nd, 2026 Source: https://forum.effectivealtruism.org/posts/JAcueP8Dh6db6knBK/the-scaling-series-discussion-thread-with-toby-ord --- Narrated by TYPE III AUDIO.