EA Forum Podcast (All 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 [...]

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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

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First published:

February 3rd, 2026

Source:

https://forum.effectivealtruism.org/posts/erbTFMPcvCjd44QJK/the-vegan-filter-overcoming-the-leading-barrier-to-veganism

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Narrated by TYPE III AUDIO.

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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 [...]

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Outline:

(02:39) Work

(03:50) Giving

(04:53) Conduct

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First published:

February 2nd, 2026

Source:

https://forum.effectivealtruism.org/posts/H8SqwbLxKkiJur3c4/preparing-for-a-flush-future-work-giving-and-conduct

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Narrated by TYPE III AUDIO.

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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 [...]

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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:

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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

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Narrated by TYPE III AUDIO.

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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 [...]

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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

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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 [...]

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First published:

February 2nd, 2026

Source:

https://forum.effectivealtruism.org/posts/JAcueP8Dh6db6knBK/the-scaling-series-discussion-thread-with-toby-ord

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Narrated by TYPE III AUDIO.

This is a link post.

[Subtitle.] Why I'm moderately bearish in the short term, and explosively bullish in the long term

This is a crosspost for Thoughts on AI progress (Dec 2025) by Dwarkesh Patel, which was originally published on Dwarkesh Podcast on 2 December 2025.

What are we scaling?

I’m confused why some people have short timelines and at the same time are bullish on the current scale up of reinforcement learning atop LLMs. If we’re actually close to a human-like learner, this whole approach of training on verifiable outcomes is doomed.

Currently the labs are trying to bake in a bunch of skills into these models through “mid-training” - there's an entire supply chain of companies building RL environments which teach the model how to navigate a web browser or use Excel to write financial models. [Relatedly, see Epoch AI's post An FAQ on Reinforcement Learning Environments.]

Either these models will soon learn on the job in a self directed way - making all this pre-baking pointless - or they won’t - which means AGI is not imminent. Humans don’t have to go through a special training phase where they need to rehearse every single piece of software [...]

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Outline:

(00:33) What are we scaling?

(04:31) Human labor is valuable precisely because it's not shleppy to train

(06:24) Economic diffusion lag is cope for missing capabilities

(08:19) Goal post shifting is justified

(09:58) RL scaling is laundering the prestige of pretraining scaling

(10:52) Comparison to human distribution will make us at first overestimate (and then underestimate) AI

(11:45) Broadly deployed intelligence explosion

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First published:

February 1st, 2026

Source:

https://forum.effectivealtruism.org/posts/9GvZunWywaJwLu432/dwarkesh-patel-s-thoughts-on-ai-progress-dec-2025

Linkpost URL:
https://www.dwarkesh.com/p/thoughts-on-ai-progress-dec-2025

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Narrated by TYPE III AUDIO.

Context: The authors are a few EAs who currently work or have previously worked at the European Commission.

In this post, we

1. make the case that more people[1] aiming for a high impact career should consider working for the EU institutions[2] using the Importance, Tractability, Neglectedness framework, and;

2. briefly outline how one might get started on this, highlighting a currently open recruitment drive (deadline 10 March) that only comes along once every ~5 years.

Why working at the EU can be extremely impactfulImportance

The EU adopts binding legislation for a continent of 450 million people and has a significant budget, making it an important player across different EA cause areas.

Animal welfare[3]

• The EU sets welfare standards for the over 10 billion farmed animals slaughtered across the continent each year.
• The issue suffered a major setback in 2023, when the Commission, in the final steps of the process, dropped the ‘world's most comprehensive farm animal welfare reforms to date’, following massive farmers’ protests in Brussels.
  • The reform would have included ‘banning cages and crates for Europe's roughly 300 million caged animals, ending the routine mutilation of perhaps 500 million animals per year, stopping the [...]

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Outline:

(00:43) Why working at the EU can be extremely impactful

(00:49) Importance

(05:30) Tractability

(07:22) Neglectedness

(09:00) Paths into the EU

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First published:

February 1st, 2026

Source:

https://forum.effectivealtruism.org/posts/t23ko3x2MoHekCKWC/more-eas-should-consider-working-for-the-eu

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Narrated by TYPE III AUDIO.

Epistemic status: some vague thoughts about the value of 1-1s but also how I respond to negative thoughts. There's some great advice on how to run 1-1s here.

I still find it pretty surreal that my full-time job is to talk to people about career stuff (and a fair bit of admin)- I mean I’m 23- who am I to be giving careers advice?

I think it's quite common in EA in general for people to feel underqualified about things they are qualified for, (see this post on imposter syndrome), so I don’t think the fact that I feel underqualified is a good reason to think that I actually am underqualified. CBT highlights that we can make systematic mistakes in our thinking, such as under confidence (example list of mistakes). I’ve found 2 broad approaches helpful to respond to my thoughts that might be inaccurate: looking at the evidence, and acting despite my uncertainty.

Looking at the evidence

This involves thinking about the ways 1-1s are helpful and thinking about whether you can provide that value (spoiler alert: you probably can).

Ways I think 1-1s are helpful

• Information- 1-1 allows you to communicate information especially well [...]

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Outline:

(01:04) Looking at the evidence

(03:53) Acting despite my uncertainty

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First published:

January 27th, 2026

Source:

https://forum.effectivealtruism.org/posts/qt4wkGegXfyLkGdYF/who-am-i-to-run-1-1s-as-a-community-builder

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Narrated by TYPE III AUDIO.

Confidence level: Medium. This post reflects a mix of my own takes, things I’ve read, and conversations with others (especially at ConCon and elsewhere). I’m not claiming high confidence in any particular conclusion.

See here for other work on digital minds cause prioritization.

Background

A few months ago, I attended ConCon, a conference on AI consciousness and welfare run by Eleos AI. It was excellent: good vibes, thoughtful people, and many conversations I found clarifying. One of my main reasons for going was to understand how others are thinking about short-term welfare strategies for digital minds, and how those strategies fit into (cross- and intra-) cause prioritization.

After the conference—and after talking with people working in this space—I felt both better informed and more skeptical. I went in with fairly strong priors that digital minds might be a top-tier cause area under cause prioritization, but I came out thinking that, while the stakes could be enormous, our current levers may be weaker than I’d initially thought.

I was also a bit surprised when I started to get the vibe that people seemed much less into the cause-prioritization questions than I was. Because of that, I wanted to [...]

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Outline:

(00:31) Background

(02:42) Why Care About Digital Minds at All?

(04:57) Theories of Change

(05:09) 1. Influencing Near-Term Digital Minds (Short-Term ToC)

(05:56) 2. Influencing Far-Future Digital Minds (Future ToC)

(07:01) Types of Interventions

(08:22) How These Interventions Might Work (and Why They Might Not)

(08:42) Foundational Research

(09:43) Near-Term Research

(11:35) Communications, Lab Policy, and Governance

(13:27) Strategy

(14:12) Approaching Prioritization via Importance-Tractability-Neglectedness (ITN)

(21:31) Key Cruxes and Open Questions

(25:17) Learning More and Getting Involved

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First published:

January 31st, 2026

Source:

https://forum.effectivealtruism.org/posts/r3GmSEE6FBkHQxm2z/laying-some-cause-prioritization-groundwork-for-digital-1

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Narrated by TYPE III AUDIO.

Epistemic status: low, uncertain if this is useful

Crossposted from my Substack.

As part of EA Bath's Path to Impact fellowship, we are running a project where we pick a theory we have learnt from the intro fellowship, apply it to a cause area, and list some interventions we have researched that can help that cause area.

Theory

ITN Framework

I think the ITN Framework is super important. For those who do not know what it is, it stands for

• Importance
• Tractability
• Neglectedness

Where Importance is how many beings it affects (the scale of the issue), Tractability is how solvable it is (e.g. how cost effective is it) and Neglectedness is how many people are working on it, and do there need to be more.

Cause Area

I’ve chosen to research flies, as it comes under wild animal suffering which is a vastly neglected area. I also think a lot of flies are affected by this problem, and it seems to be pretty tractable – I will explain why in the interventions section.

My reasoning for choosing this is that I am really interested in animal welfare, and when I was in the Library the other day and [...]

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Outline:

(00:31) Theory

(00:34) ITN Framework

(01:04) Cause Area

(02:31) Uncertainties

(03:27) Interventions

(03:30) 1. Colour

(04:07) 2. Brightness

(04:35) 3. LEDs

(05:16) 4. Shape of bulb

(05:37) 5. Direction of light

(05:55) Conclusion

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First published:

January 30th, 2026

Source:

https://forum.effectivealtruism.org/posts/JDab98DwqJSCehYSG/reducing-suffering-of-insects-through-colours-of-light

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Narrated by TYPE III AUDIO.