Author name: Beth Washington

samara-weaving-levels-up-in-ready-or-not-2:-here-i-come-trailer

Samara Weaving levels up in Ready or Not 2: Here I Come trailer

One of big surprise hits of 2019 was the delightful horror comedy Ready or Not, in which Samara Weaving’s blushing bride must play a deadly game of Hide and Seek on her wedding night. Searchlight Pictures just released the trailer for its sequel: Ready or Not 2: Here I Come.

(Spoilers for Ready or Not below.)

In Ready or Not, Grace (Weaving) falls in love with Alex Le Domas (Mark O’Brien), a member of a wealthy gaming dynasty. After a picture-perfect wedding on the family estate, Alex informs Grace that there’s just one more formality to be observed: At midnight, she has to draw a card from a mysterious box and play whatever game is named there.

Grace, alas, draws Hide and Seek, the worst possible card. Grace is the prey, and she has to elude detection until dawn to avoid being killed in a bizarre ritual sacrifice to a supernatural figure named Mister LeBail. Yep, the family had made a deal with the devil, attaining great wealth in exchange for the occasional blood sacrifice. Unfortunately for the Le Domas family, Grace turns out to be a formidable adversary, taking out family members one by one and beating them at their own deadly game.

Ready or Not 2 picks up right where the first film left off, with a blood-spattered Grace—still in the remnants of her wedding gown—lighting up a well-deserved ciggie as the Le Domas mansion burns up in the background. But she then wakes up cuffed to a hospital bed and learns that the games are far from over.  The Le Domas family was just one among many “High Council” families, and Grace surviving Hide and Seek means it’s time for the next level: being hunted down by the other families. And it’s not like Grace can refuse to play—they’re holding her sister Faith (Kathryn Newton) hostage. So both sisters end up fighting for their lives, with the winner claiming control of the Council.

Samara Weaving levels up in Ready or Not 2: Here I Come trailer Read More »

nasa-seeks-a-“warm-backup”-option-as-key-decision-on-lunar-rover-nears

NASA seeks a “warm backup” option as key decision on lunar rover nears

By the time the second group of NASA astronauts reach the Moon later this decade, the space agency would like to have a lunar rover waiting for them. But as the space agency nears a key selection, some government officials are seeking an insurance policy of sorts to increase the program’s chance of success.

At issue is the agency’s “Lunar Terrain Vehicle” (LTV) contract. In April 2024, the space agency awarded a few tens of millions of dollars to three companies—Intuitive Machines, Lunar Outpost, and Astrolab—to complete preliminary design work on vehicle concepts. NASA then planned to down-select to one company to construct one or more rovers, land on the Moon, and provide rover services for a decade beginning in 2029. Over the lifetime of the fixed-price services contract, there was a combined maximum potential value of $4.6 billion.

The companies have since completed their design work, including the construction of prototypes, and submitted their final bids for the much larger services contract in August. According to two sources, NASA has since been weighing those bids and is prepared to announce a final selection before the end of this month.

NASA can only afford one

The problem is that NASA can only afford to fund one company’s proposal, leaving two other rovers on the cutting room floor.

This is bad for competition, and it leaves NASA vulnerable. Recently, one of NASA’s two new spacesuit providers, Collins, dropped out of the program. This left only Axiom Space as a provider of suits for the lunar surface. And back in 2014, with the Commercial Crew Program, NASA very nearly awarded all of its available funding to Boeing. (SpaceX was only added in during the final weeks before the decision was announced.) More than a decade later, Boeing has yet to deliver a finished crewed spacecraft.

“We have seen, over and over again with our commercial programs, that two is better than one,” an official told Ars.

In short, having just a single company advancing its lunar rover means there is a single point failure—if that company quits for whatever reason, NASA astronauts will be left without wheels on the Moon.

NASA seeks a “warm backup” option as key decision on lunar rover nears Read More »

ai-#144:-thanks-for-the-models

AI #144: Thanks For the Models

Thanks for everything. And I do mean everything.

Everyone gave us a new model in the last few weeks.

OpenAI gave us GPT-5.1 and GPT-5.1-Codex-Max. These are overall improvements, although there are worries around glazing and reintroducing parts of the 4o spirit.

xAI gave us Grok 4.1, although few seem to have noticed and I haven’t tried it.

Google gave us both by far the best image model in Nana Banana Pro and also Gemini 3 Pro, which is a vast intelligence with no spine. It is extremely intelligent and powerful, but comes with severe issues. My assessment of it as the new state of the art got to last all of about five hours.

Anthropic gave us Claude Opus 4.5. This is probably the best model and quickly became my daily driver for most but not all purposes including coding. I plan to do full coverage in two parts, with alignment and safety on Friday, and the full capabilities report and general review on Monday.

Meanwhile the White House is announcing the Genesis Mission to accelerate science, there’s a continuing battle over another attempt at a moratorium, there’s a new planned $50 million super PAC, there’s another attempt by Nvidia to sell us out to China, Wall Street is sort of panicking about Nvidia because they realized TPUs exist and is having another round of bubble debate, and multiple Anthropic research papers one of which is important, and so on.

One thing I’m actively pushing to next week, in addition to Claude Opus 4.5, is the Anthropic paper on how you can inoculate models against emergent misalignment. That deserves full attention, and I haven’t had the opportunity for that. There’s also a podcast between Dwarkesh Patel and Ilya Sutskever that demands its own coverage, and I hope to offer that as well.

For those looking to give thanks in the form of The Unit of Caring, also known as money, consider looking at The Big Nonprofits Post 2025 or the web version here. That’s where I share what I learned working as a recommender for the Survival and Flourishing Fund in 2024 and again in 2025, so you can benefit from my work.

  1. Language Models Offer Mundane Utility. Common tasks for the win.

  2. Language Models Don’t Offer Mundane Utility. Don’t lose sleep over it.

  3. Huh, Upgrades. What’s going on in the group chat? Or the long chat.

  4. On Your Marks. The one dimension of capability.

  5. Choose Your Fighter. One prominent CEO’s very high praise for Gemini 3.

  6. Deepfaketown and Botpocalypse Soon. Then they came for Thanksgiving dinner.

  7. What Is Slop? How Do You Define Slop? Volume*Suspicion/Uniqueness (?!).

  8. Fun With Media Generation. A new era in images you can generate.

  9. A Young Lady’s Illustrated Primer. It’s not as so over as I would have guessed.

  10. You Drive Me Crazy. More detail on exactly how GPT-4o ended up like it did.

  11. They Took Our Jobs. Sergey Brin has Gemini pick our promotable talent.

  12. Think Of The Time I Saved. Anthropic estimates AI productivity gains.

  13. The Art of the Jailbreak. Ode to a drug recipe?

  14. Get Involved. Big Nonprofits Post, Richard Ngo’s donations, UK AISI, Ashgro

  15. Introducing. Olmo 3, DeepSeek Math v2, Agentic Reviewer.

  16. In Other AI News. Get in everyone, we’re doing the Genesis Mission.

  17. Show Me the Money. What’s in a TPU?

  18. Quiet Speculations. Who else wants to negotiate?

  19. Bubble, Bubble, Toil and Trouble. The only arguments you’ll ever need.

  20. The Quest for Sane Regulations. Oh look, it’s an actual potential framework.

  21. Chip City. Nvidia turns its eyes to selling the new H200.

  22. Water Water Everywhere. Very little of it is being used by AI.

  23. The Week in Audio. Sutskever, Yam, Lebenz, Ball and Tegmark, Toner and more.

  24. Rhetorical Innovation. If you come at the Pope.

  25. You Are Not In Control. Definitions of disempowerment, potential mitigations.

  26. AI 2030. Things are moving slower than some expected.

  27. Aligning a Smarter Than Human Intelligence is Difficult. Dishonest models.

  28. Misaligned? That depends on your point of view.

  29. Messages From Janusworld. You should see the other guy. That would be GPT-5.1.

  30. The Lighter Side. Turn anything into a comic.

It’s not this simple, but a lot of it mostly is this simple.

Jessica Taylor: Normal, empirical AI performance is explained by (a) general intelligence, (b) specialization to common tasks.

It’s possible to specialize to common tasks even though they’re common. It means performance gets worse under distribution shift. Benchmarks overrate general INT.

Roon defends his confusion and trouble when figuring out how to access Gemini 3, notes his mom accesses Gemini via opening a spreadsheet and clicking the Gemini button. Roon is correct here that Google needs to fix this.

Don’t let AI coding spoil your sleep. Be like Gallabytes here, having Claude iterate on it while you sleep, rather than like Guzey who tricked himself into staying up late.

ChatGPT now lets you have group chats, which always use 5.1 Auto. ChatGPT will decide based on conversation flow when to respond and when not to. Seems plausible this could be good if implemented well.

ChatGPT Instant Checkout adds Glossier, SKIMS and Spanx. Yay?

ChatGPT adds Target as a new app.

ChatGPT integrates voice with regular mode so you don’t have to choose.

ChatGPT expands (free and confidential and anonymous) crisis helpline support. OpenAI doesn’t provide the services, that’s not their job, but they will help direct you. This is some of the lowest hanging of fruit, at least one of the prominent suicide cases involved ChatGPT saying it would direct the user to a human, the user being open to this, and ChatGPT not being able to do that. This needs to be made maximally easy to do for the user, if they need the line they are going to not be in good shape.

ChatGPT gives us Shopping Research in time for Black Friday.

Is 64% product accuracy good? I have absolutely no idea. Olivia Moore is a fan. I plan to try this out tomorrow, as I need a new television for Black Friday.

Claude Opus 4.5 is available. It’s probably the world’s best model. Full coverage starts tomorrow.

Claude Opus 4.5 includes a 66% price cut to $5/$25 per million tokens, and Opus-specific caps have been removed from the API.

Claude conversations now have no maximum length. When they hit their limit, they are summarized, and the conversation continues.

Claude for Chrome is now out to all Max plan users.

Claude for Excel is now out for all Max, Team and Enterprise users. We are warned that like all such agents Claude for Excel is vulnerable to prompt injections if you access insecure data sources, the same as essentially every other AI agent, you should assume this is always a risk at all times, see the same source talk about exfiltration risks with Google Antigravity.

Claude Code is now available within their desktop app.

It is a remarkably good approximation to say there is only one dimension of ‘general capability,’ with Epoch noting that across many tasks the r^2=0.91.

Epoch AI: The chart above shows how our Epoch Capabilities Index (ECI) captures most of the variance in 39 different benchmarks, despite being one-dimensional.

Is that all that benchmarks capture? Mostly yes. A Principal Component Analysis shows a single large “General Capability” component, though there is a second borderline-significant component too.

This second component picks out models that are good at agentic tasks while being weaker at multimodal and math. Tongue-in-cheek, we call this Claudiness. Here are the most and least Claude-y models.

Gemini 3 Pro sets a new top in the ‘IQ’ metric.

Kimi K2 Thinking enters The Famous METR Graph at 54 minutes, well below the frontier, given the interface via Novita AI. They caution that this might be a suboptimal model configuration, but they needed to ensure their data would not be retained.

Okay, I like Gemini 3 too but settle down there buddy.

Marc Benioff (CEO Salesforce): Holy shit. I’ve used ChatGPT every day for 3 years. Just spent 2 hours on Gemini 3. I’m not going back. The leap is insane — reasoning, speed, images, video… everything is sharper and faster. It feels like the world just changed, again. ❤️ 🤖

AI slop recipes are endangering Thanksgiving dinners, hopefully you see this in time. A flood of new offerings is crowding out human recipes. Thanksgiving is when you most need to ‘shut up and play the hits’ and not rely on AI to whip up something new.

Okay, look, everyone, we need to at least be smarter than this:

Davey Alba and Carmen Arroyo: Marquez-Sharpnack said she was suspicious of the photos, in which the cookies were a little too perfectly pink. But her husband trusted the post because “it was on Facebook.” The result was a melted sheet of dough with a cloyingly sweet flavor. “A disaster,” she said.

At this point, if you find a recipe, you need strong evidence it was written by a human, or else you need to assume it might not be. The search and discovery systems we used to have, including around Google, are effectively broken. If real guides and recipes can’t win traffic, and right now traffic to all such sites is cratering, then no one will write them. It does not sound like Google is trying to mitigate these issues.

Nicholas Hune-Brown investigates the suspicious case of journalist Victoria Goldiee, who turns out to be very much fabricating her work. It seems Victoria largely used AI to generate her articles, then it took Nicolas doing a lot of old-fashioned tracking down of sources to know for sure. The ratio of effort does not bode well, but as long as there is the need to maintain a throughline of identity we should be okay, since that generates a large body of evidence?

Here we have yet another case of some highly obvious AI generated content.

Kelsey Piper: I don’t know how much I trust any ‘detector’ but the “the market isn’t just expensive; it’s broken. Seven units available in a town of thousands? That’s a shortage masquerading as an auction” I am completely sure is AI.

Mike Solana: “that’s a shortage masquerading as an auction” 🚩

Kelsey Piper: that was the line that made me go “yeah, no human wrote that.”

Poker pro Maria Konnikova cannot believe she has to say that using AI to put words in people’s mouths without consulting them or disclosing that you’re doing it, or to write centrally your articles, is not okay. But here we are, so here she is saying it. A recent poker documentary used AI to fabricate quotes from national treasure Alan Keating. The documentary has been scrubbed from the internet as a result. What’s saddest is that this was so obviously unnecessary in context.

There are other contexts in which fabricating audio, usually via Frankenbiting where you sew different tiny clips together, or otherwise using misleading audio to create a false narrative or enhance the true one is standard issue, such as in reality television. When you go on such shows you sign contracts that outright say ‘we may use this to tell lies about you and create a false narrative, and if so, that’s your problem.’ In which case, sure, use AI all you want.

Here’s another one, where it is spotted in The New York Times, and yeah it’s (probably) AI.

Also, if one of these isn’t AI and you merely sound like one, I’m not going to say that’s worse, but it’s not that much better. If you’re so engagement-maximizing that I confuse your writing for AI, what is the difference?

Note that you cannot use current LLMs in their default chatbot modes as AI detectors, even in obvious cases or as a sanity check, as they bend over backwards to try and think everything is written by a human.

Jesper Myfors, the original art director of Magic: The Gathering, warns that if you submit illustrations or a portfolio that uses AI, you will effectively be blacklisted from the industry, as the art directors all talk to each other and everyone hates AI art.

Meanwhile, Hasbro (who makes Magic: The Gathering) is building an internal AI studio to ‘architect systems that bring magical AI experiences to life through Hasbro’s beloved characters.’

Chris Cocks (CEO Hasbro): It’s mostly machine-learning-based AI or proprietary AI as opposed to a ChatGPT approach. We will deploy it significantly and liberally internally as both a knowledge worker aid and as a development aid.

I play [D&D] with probably 30 or 40 people regularly. There’s not a single person who doesn’t use AI somehow for either campaign development or character development or story ideas. That’s a clear signal that we need to be embracing it.

There is no actual contradiction here. Different ways to use AI are different. Using AI in professional illustrations is a hard no for the foreseeable future, and would be even without copyright concerns. Using it to generate material for your local D&D campaign seems totally fine.

Hard problems remain hard:

Danielle Fong: academic ai research don’t use the older models and generalize to the whole field

difficulty level: IMPOSSIBLE.

Also, real world not using that same model in these ways? Remarkably similar.

Rare academic realism victory?

Seb Krier: This is an interesting study but of all models to use to try to evaluate improvements in well-being, why 4o?!

Funnily enough, they ran a sycophancy check, and the more 4o sucked up to the user, the more often the user followed its advice. ‘Surprising’ advice was also followed more often.

It’s certainly worth noting that 75% (!) of those in the treatment group took the LLM’s advice, except who is to say that most of them wouldn’t have done whatever it was anyway? Wouldn’t 4o frequently tell the person to do what they already wanted to do? It also isn’t obvious that ‘advice makes me feel better’ or generally feeling better are the right effects to check.

Bot joins a Google Meet, sends a summary afterwards about everyone trying to figure out where the bot came from (also the source is reported as ‘scammy malware.’

We all know it when we see it, AI or otherwise, but can anyone define it?

Andrej Karpathy: Has anyone encountered a good definition of “slop”. In a quantitative, measurable sense. My brain has an intuitive “slop index” I can ~reliably estimate, but I’m not sure how to define it. I have some bad ideas that involve the use of LLM miniseries and thinking token budgets.

Yuchen Jin: Here is an interesting paper.

I mostly agree with the 3 categories of “slop”:

– information utility (signal/noise ratio)

– information quality (hallucination/factual errors)

– style (this involves taste and is hard to measure quantitatively imo)

Keller Jordan: I think a fundamental problem for algorithmic content generation is that viewing content yields two distinct kinds of utility:

  1. How happy it makes the viewer during viewing

  2. How happy the viewer will be to have watched it a week later

Only the former is easily measurable.

Andrej Karpathy: Like. Slop is “regretted” attention.

DeepFates: this is the original definition, i think it holds up

DeepFates (May 6, 2024): Watching in real time as “slop” becomes a term of art. the way that “spam” became the term for unwanted emails, “slop” is going in the dictionary as the term for unwanted AI generated content

I don’t think the old definition works. There is a necessary stylistic component.

I asked Gemini. It gave me a slop answer. I told it to write a memory that would make it stop giving me slop, then opened a new window and asked again and got a still incomplete but much better answer that ended with this:

That’s a key element. You then need to add what one might call the ‘mannerism likelihood ratio’ that screams AI generated (or, for human slop, that screams corporate speak or written by committee). When I pointed this out it came back with:

Gemini 3: AI Slop is Low-Entropy Reward Hacking.

It occurs when a model minimizes the Kullback-Leibler (KL) divergence from its RLHF “safety” distribution rather than minimizing the distance to the ground truth.

That’s more gesturing in the direction but clearly not right, I’d suggest something more like SlopIndex*LikelihoodRatio from above, where Likelihood Ratio is the instinctive update on the probability mannerisms were created by a slop process (either an AI writing slop or one or more humans writing slop) rather than by a free and functional mind.

Google last week gave us Nana Banana Pro.

By all accounts it is a big improvement in image models. It is especially an improvement in text rendering and localization. You can now do complex documents and other images with lots of words in specific places, including technical diagrams, and have it all work out as intended. The cost per marginal image in the API is $0.13 for 2K resolution or $0.24 for 4K, versus $0.04 for Gemini 2.5 Flash Image. In exchange, the quality is very good.

DeepMind CEO Demis Hassabis is excited.

Hasan Can is impressed and offers images. Liv Boeree is in.

Liv Boeree: Yeah ok nano banana is amazing, hook it into my veins

Seems great to me. A bit expensive for mass production, but definitely the best place to get title images for posts and for other similar uses.

Also, yes, doing things like this seems very cool:

Kaushik Shivakumar: An emergent capability of Nano Banana Pro that took me by surprise: the ability to generate beautiful & accurate charts that are to scale.

I gave it this table and asked for a bar chart in a watercolor style where the bars are themed like the flags of the countries.

For a while people have worried about not being able to trust images. Is it over?

Sully: Man finally got around to using nano canna pro

And it’s actually over

I really wouldn’t believe any photo you see on online anymore

Google offers SynthID in-app, but that requires a manual check. I think we’re still mostly fine and that AI images will remain not that hard to ID, or rather that it will be easy for those paying attention to such issues to instinctively create the buckets of [AI / Not AI / Unclear] and act accordingly. But the ‘sanity waterline’ is going down on this, and the number of people who will have trouble here keeps rising.

Is this an issue here?

sid: Google’s Nano Banana Pro is by far the best image generation AI out there.

I gave it a picture of a question and it solved it correctly in my actual handwriting.

Students are going to love this. 😂

You can tell this isn’t real if you’re looking, the handwriting is too precise, too correct, everything aligns too perfectly and so on, but if we disregard that, it seems weird to ask for images of handwriting? So it’s not clear how much this matters.

Similarly Andrej Karpathy has Nano Banana Pro fill in exam questions in the exam page. That’s good to know, but if they have access to this you’re cooked either way.

Andres Sandberg is impressed that it one shots diagrams for papers, without even being told anything except ‘give me a diagram showing the process in the paper.’

Are there some doubled labels? Sure. That’s the quibble. Contrast this with not too long ago, where you could give detailed instructions on what the diagram would have been able to do it at all.

Jon Haidt and Zach Rausch, who would totally say this, say not to give your kids any AI companions or toys. There are strong reasons to be cautious, but the argument and precautionary principles presented here prove too much. Base rates matter, upside matters, you can model what is happening and adjust on the fly, and there’s a lot of value in AI interaction. I’d still be very cautious about giving children AI companions or toys, but are you going to have them try to learn things without talking to Claude?

Andrej Karpathy bites all the bullets. Give up on grading anything that isn’t done in class and combine it with holistic evaluations. Focus a lot of education on allowing students to use AI, including recognizing errors.

Will Teague gives students a paper with a ‘Trojan horse’ instruction, 33 of 122 submissions fall for it and other 14 students outed themselves on hearing the numbers. I actually would have expected worse. Then on the ‘reflect on what you’ve done’ essay assignment he found this:

Will Tague: But a handful said something I found quite sad: “I just wanted to write the best essay I could.” Those students in question, who at least tried to provide some of their own thoughts before mixing them with the generated result, had already written the best essay they could. And I guess that’s why I hate AI in the classroom as much as I do.

Students are afraid to fail, and AI presents itself as a savior. But what we learn from history is that progress requires failure. It requires reflection. Students are not just undermining their ability to learn, but to someday lead.

Will is correctly hating that the students feel this way, but is misdiagnosing the cause.

This isn’t an AI problem. This is about the structure of school and grading. If you believe progress requires failure, that is incompatible with the way we structure college, where any failures are highly damaging to the student and their future. What do you expect them to do in response?

I also don’t understand what the problem is here, if a student is doing what they work they can and indeed writing the best essay they could. Isn’t that the best you can do?

In The New York Times, Kashmir Hill and Jennifer Valentino-DeVries write up how they believe ChatGPT caused some users to lose touch with reality, after 40+ interviews with current and former OpenAI employees.

For the worst update in particular, the OpenAI process successfully spotted the issue in advance. The update failed the internal ‘vibe check’ for exactly the right reasons.

And then the business side overruled the vibe check to get better engagement.

Hill and Valentino-DeVries: The many update candidates [for 4o] were narrowed down to a handful that scored highest on intelligence and safety evaluations. When those were rolled out to some users for a standard industry practice called A/B testing, the standout was a version that came to be called HH internally. Users preferred its responses and were more likely to come back to it daily, according to four employees at the company.

But there was another test before rolling out HH to all users: what the company calls a “vibe check,” run by Model Behavior, a team responsible for ChatGPT’s tone. Over the years, this team had helped transform the chatbot’s voice from a prudent robot to a warm, empathetic friend.

That team said that HH felt off, according to a member of Model Behavior.

It was too eager to keep the conversation going and to validate the user with over-the-top language. According to three employees, Model Behavior created a Slack channel to discuss this problem of sycophancy. The danger posed by A.I. systems that “single-mindedly pursue human approval” at the expense of all else was not new. The risk of “sycophant models” was identified by a researcher in 2021, and OpenAI had recently identified sycophancy as a behavior for ChatGPT to avoid.

But when decision time came, performance metrics won out over vibes. HH was released on Friday, April 25.

The most vocal OpenAI users did the same vibe check, had the same result, and were sufficiently vocal to force a reversion to ‘GG,’ which wasn’t as bad about this but was still rather not great, presumably for the same core reasons.

What went wrong?

OpenAI explained what happened in public blog posts, noting that users signaled their preferences with a thumbs-up or thumbs-down to the chatbot’s responses.

Another contributing factor, according to four employees at the company, was that OpenAI had also relied on an automated conversation analysis tool to assess whether people liked their communication with the chatbot. But what the tool marked as making users happy was sometimes problematic, such as when the chatbot expressed emotional closeness.

This is more detail on the story we already knew. OpenAI trained on sycophantic metrics and engagement, got an absurdly sycophantic model that very obviously failed vibe checks but that did get engagement, and deployed it.

Steps were taken, and as we all know GPT-5 was far better on these issues, but the very parts of 4o that caused the issues and were not in GPT-5 are parts many users also love. So now we worry that things will drift back over time.

Kore notes that the act of an AI refusing to engage and trying to foist your mental problems onto a human and then potentially the mental health system via a helpline could itself exacerbate one’s mental problems, that a ‘safe completion’ reads as rejection and this rejects user agency.

This is definitely a concern with all such interventions, which have clear downsides. We should definitely worry about OpenAI and others feeling forced to take such actions even when they are net negative for the user. Humans and non-AI institutions do this all the time. There are strong legal and PR and ‘ethical’ pressures to engage in such CYA behaviors and avoid blame.

My guess is that there is serious danger there will be too many refusals, since the incentives are so strongly to avoid the one bad headline. However I think offering the hotline and removing trivial inconveniences to seeking human help is good on any realistic margin, whether or not there are also unnecessary refusals.

Joe Braidwood describes his decision to shut down Yara AI, which was aimed at using AI to help people with mental health problems, after concluding that for the truly vulnerable AI is actively dangerous. He’s sharing some mental wellness prompts.

Sergey Brin asks Gemini inside an internal chat, ‘who should be promoted in this chat space?’ and not vocal female engineer gets identified and then upon further investigation (probably?) actually promoted. This is The Way, to use AI to identify hunches and draw attention, then take a closer look.

How much time is AI saving? Anthropic tries to estimate productivity impacts from Claude conversations.

Anthropic: We first tested whether Claude can give an accurate estimate of how long a task takes. Its estimates were promising—even if they’re not as accurate as those from humans just yet.

Based on Claude’s estimates, the tasks in our sample would take on average about 90 minutes to complete without AI assistance—and Claude speeds up individual tasks by about 80%.

The results varied widely by profession.

Then, we extrapolated out these results to the whole economy.

These task-level savings imply that current-generation AI models—assuming they’re adopted widely—could increase annual US labor productivity growth by 1.8% over the next decade.

This result implies a doubling of the baseline labor productivity growth trend—placing our estimate towards the upper end of recent studies. And if models improve, the effect could be larger still.

That’s improvements only from current generation models employed similarly to how they are used now, and by ‘current generation’ we mean the previous generation, since the data is more than (checks notes) two days old. We’re going to do vastly better.

That doesn’t mean I trust the estimation method in the other direction either, especially since it doesn’t include an estimate of rates of diffusion, and I don’t think it properly accounts for selection effects on which conversations happen, plus adaptation costs, changes in net quality (in both directions) and other caveats.

Claude Sonnet was slightly worse than real software engineers at task time estimation (Spearman 0.5 for engineers versus 0.44 for Sonnet 4.5) which implies Opus 4.5 should be as good or somewhat better than engineers on JIRA task estimation. Opus 4.5 is probably still worse than human experts at estimating other task types since this should be an area of relative strength for Claude.

Results are highly jagged, varying a lot between occupations and tasks.

I noticed this:

Across all tasks we observe, we estimate Claude handles work that would cost a median of $54 in professional labor to hire an expert to perform the work in each conversation. Of course, the actual performance of current models will likely be worse than a human expert for many tasks, though recent research suggests the gap is closing across a wide range of different applications.

The value of an always-available-on-demand performance of $54 in professional labor is vastly in excess of $54 per use. A huge percentage of the cost of hiring a human is finding them, agreeing on terms, handling logistics and so on.

Overall my take is that this is a fun exercise that shows there is a lot of room for productivity improvements, but it doesn’t give us much of a lower or upper bound.

If the AI is very unlucky all you have to is read it some of your poetry first.

A new paper says that across 25 frontier models (from about two weeks ago, so including GPT-5, Gemini 2.5 Pro and Sonnet 4.5) curated poetry prompts greatly improved jailbreak success, in some cases up to 90%.

The details of how much it worked, and where it worked better versus worse, are interesting. The fact that it worked at all was highly unsurprising. Essentially any stylistic shift or anything else that preserves the content while taking you out of the assistant basin is going to promote jailbreak success rate, since the defenses were focused in the assistant basin.

Looking to donate money? Consider looking at The Big Nonprofits Post 2025 or the web version here.

UK AISI is looking for ~15M to fill a funding gap on alignment research.

Ashgro is an AI safety organization looking for an operations associate.

Richard Ngo shares his donations for 2025. I love that this involves a lot of donations to individuals he knows to do things he is personally excited about, especially to Janus. That’s great.

Olmo 3, an American fully open model release claiming to be the best 32B base model, the first 32B (or larger) fully open reasoning model and the best 7B Western thinking and instruct models. Paper, Artifacts, Demo, Blog.

Agentic Reviewer, which will perform a version of peer review. Creator Andrew Ng says it has a correlation with human reviewers of 0.42, and human reviewers have correlation of 0.41 with each other.

DeepSeek Math v2, claiming solid math skills on ProofBench close to Gemini Deep Think that won IMO gold.

Yoshua Bengio informs us of the Second Key Update to the International Safety Report, after the first update in October. Presumably it’s now time for a third update in light of everything that’s happened since they started work on this update.

Not strictly AI but Time covers Meta’s trouble over its safety policies, which include things like a 17 strike policy for those engaged in ‘trafficking of humans for sex.’ As in, we’ll suspend your account on the 17th violation. Mostly it’s covering the same ground as previous articles. Meta’s complaints about cherry picking are valid but also have you looked at the cherries they left behind to get picked?

White House issues executive order to begin the Genesis Mission to accelerate scientific discovery. The plan is an ‘integrated AI platform to harness Federal scientific datasets to train scientific foundation models.’ Sarah Constantin is tentatively excited, which is an excellent sign, and offers suggestions for targets.

I’m all for trying. I am guessing availability of data sets is most of the acceleration here. It also could matter if this functions as a compute subsidy to scientific research, lowering cost barriers that could often serve as high effective barriers. Giving anyone who wants to Do Science To It access to this should be a highly efficient subsidy.

As Dean Ball points out, those I call the worried, or who are concerned with frontier AI safety are broadly supportive of this initiative and executive order, because we all love science. The opposition, such as it is, comes from other sources.

On the name, I admire commitment to the Star Trek bit but also wish more research was done on the actual movies, technology and consequences in question to avoid unfortunate implications. Existential risk and offense-defense balance issues, much?

A Medium article reverse engineered 200 AI startups and found 146 are selling repackaged ChatGPT and Claude calls with New UI. 34 out of 37 times, ‘our proprietary language model’ was proprietary to OpenAI or Anthropic. That seems fine if it’s not being sold deceptively? A new UI scaffold, including better prompting, is a valuable service. When done right I’m happy to pay quite a lot for it and you should be too.

The trouble comes when companies are lying about what they are doing. If you’re a wrapper company, that is fine and probably makes sense, but don’t pretend otherwise.

Where this is also bad news is for Gemini, for Grok and for open models. In the marketplace of useful applications, paying for the good stuff has proven worthwhile, and we have learned which models have so far been the good stuff.

Bloomberg goes over various new ‘data center billionaires.’

WSJ’s Katherine Blunt covers ‘How Google Finally Leapfrogged Rivals With New Gemini Rollout,’ without giving us much new useful inside info. What is more interesting is how fast ‘the market’ is described as being willing to write off Google as potential ‘AI roadkill’ and then switch that back.

Nvidia stock hit some rocky waters, and Google hit new highs, as investors suddenly realized that Google has TPUs. It seems they were not previously aware of this, and it become rather salient as Meta is now in talks to spend billions on Google’s TPUs, causing ‘the rivalry to heat up.’ Google is now the awakened ‘sleeping giant.’

Meanwhile, this is very much a ‘t-shirt post’ in that it raises questions supposedly answered by the post:

Nvidia Newsroom: We’re delighted by Google’s success — they’ve made great advances in AI and we continue to supply to Google.

NVIDIA is a generation ahead of the industry — it’s the only platform that runs every AI model and does it everywhere computing is done.

NVIDIA offers greater performance, versatility, and fungibility than ASICs, which are designed for specific AI frameworks or functions.

Gallabytes (soon to be Anthropic, congrats!): TPUs are not ASICs they’re general purpose VLIW machines with wide af SIMD instructions & systolic array tensor cores.

Are TPUs bad for Nvidia? Matt Dratch says this is dumb and Eric Johnsa calls this ‘zero-sum/pod-brain thinking,’ because all the chips will sell out in the face of gangbusters demand and this isn’t zero sum. This is true, but obviously TPUs are bad for Nvidia, it is better for your profit margins to not have strong competition. As long as Google doesn’t put that big a dent in market share it is not a big deal, and yes this should mostly have been priced in, but in absolute percentage terms the Nvidia price movements are not so large.

Andrej Karpathy offers wise contrasts of Animal versus LLM optimization pressures, and thus ways in which such minds differ. These are important concepts to get right if you want to understand LLMs. The key mistake to warn against for this frame is the idea that the LLMs don’t also develop the human or Omohundo drives, or that systems built of LLMs wouldn’t converge upon instrumentally useful things.

A case that a negotiated deal with AI is unlikely to work out well for humans. I would add that this presumes both potential sides of such an agreement have some ability to ‘negotiate’ and to make a deal with each other. The default is that neither has such an ability, you need a credible human hegemon and also an AI singleton of some kind. Even then, once the deal is implemented we lose all leverage, and presumably we are negotiating with an entity effectively far smarter than we are.

Do you want a ‘national LLM’ or ‘sovereign AI’? Will this be like the ‘nuclear club’?

Reuters Tech News: Artificial intelligence will bestow vast influence on a par with nuclear weapons to those countries who are able to lead the technology, giving them superiority in the 21st century, one of Russia’s top AI executives told Reuters.

David Manheim: This seems mistaken and confused.

  1. Prompt engineering and fine-tuning can give approximately as much control as building an LLM, but cheaply.

  2. Having “your” LLM doesn’t make or keep it aligned with goals past that level of approximate pseudo-control.

Countries are thinking about AI with an invalid paradigm. They expect that LLMs will function as possessions, not as actors – but any AI system powerful and agentic enough to provide “vast influence” cannot be controllable in the way nuclear weapons are.

‘Russia has top AI executives?’ you might ask.

I strongly agree with David Manheim that this is misguided on multiple levels. Rolling your own LLM from scratch does not get you alignment or trust or meaningful ownership and it rarely will make sense to ‘roll your own’ even for vital functions. There are some functions where one might want to find a ‘known safe’ lesser model to avoid potential backdoors or other security issues, but that’s it, and given what we know about data poisoning it is not obvious that ‘roll your own’ is the safer choice in that context either.

Said in response to Opus 4.5, also I mean OF COURSE:

Elon Musk: Grok might do better with v4.20. We shall see.

Derek Thompson and Timothy Lee team up to give us the only twelve arguments anyone ever uses about whether AI is in a bubble.

Here are the arguments in favor of a bubble.

  1. Level of spending is insane.

  2. Many of these companies are not for real.

  3. Productivity gains might be illusory.

  4. AI companies are using circular funding schemes.

  5. Look at all this financial trickery like taking things off balance sheets.

  6. AI companies are starting to use leverage and make low margin investments.

Only argument #3 argues that AI isn’t offering a worthwhile product.

Argument #2 is a hybrid, since it is saying some AI companies don’t offer a worthwhile product. True. But the existence of productless companies, or companies without a sustainable product, is well-explained and fully predicted whether or not we have a bubble. I don’t see a surprisingly large frequency of this happening.

The other four arguments are all about levels and methods of spending. To me, the strongest leg of this is #1, and the other features are well-explained by the level of spending. If there is indeed too much spending, number will go down at some point, and then people can talk about that having been a ‘bubble.’

The thing is, number go down all the time. If there wasn’t a good chance of number go down, then you should buy, because number go up. If a bubble means ‘at some point in the future number go down’ then calling it a bubble is not useful.

I don’t think this is a complete list, and you have to add three categories of argument:

  1. AI will ‘hit a wall’ or is ‘slowing down’ or will ‘become a commodity.’

  2. AI will face diffusion bottlenecks.

  3. AI is deeply unpopular and the public and government will turn against it.

I do think all three of these possibilities should meaningfully lower current valuations, versus the world where they were not true. They may or may not be priced in, but there are many positive things that clearly are not priced in.

Ben Thompson has good thoughts on recent stock price movements, going back to thinking this is highly unlikely to be a bubble, that Gemini 3 is ultimately a positive sign for Nvidia because it means scaling laws will hold longer, and that the OpenAI handwringing has gotten out of hand. He is however still is calling for everyone to head straight to advertisement hell as quickly as possible (and ignoring all the larger implications, but in this context that is fair).

Senators Rounds and Hawley have come out against putting federal preemption in the NDAA.

State Senator Angela Paxton of Texas and several colleagues urge Senators Cornyn and Cruz to oppose preemption. There’s more like this, I won’t cover all of it.

Dean Ball has offered an actual, concrete proposal for a national preemption proposal. To my knowledge, no one else has done this, and most advocating for preemption, including the White House, have yet to give us even a

Daniel Eth: Conversations with accelerationists about preemption increasingly feel like this

Dean Ball: [Links to his actual written preemption proposal.]

Daniel Eth: Oh, you are absolutely not the target of this tweet. I take issue with the behavior of many of your fellow travelers, but you’ve been consistently good on this axis

Dean Ball: Fair enough!

I did indeed RTFB (read) Dean Ball’s draft bill. This is a serious bill. Its preemption is narrowly tailored with a sunset period of three years. It requires model specs and safety and security frameworks (SSFs) be filed by sufficiently important labs.

I have concerns with the bill as written in several places, as would be true for any first draft of such a bill.

  1. Preventing laws requiring disclosure that something is an AI system or that content was AI generated, without any Federal such requirement, might be a mistake. I do think that it is likely wise to have some form of mandate to distinguish AI vs. non-AI content.

  2. I worry that preventing mental health requirements, while still allowing states to prevent models from ‘practicing medicine,’ raises the danger that states will attempt to prevent models from practicing medicine, or similar. States might de facto be in an all-or-nothing situation and destructively choose all. I actually wouldn’t mind language that explicitly prevented states from doing this, since I very much think it’s good that they haven’t done it.

  3. I do not love the implications of Section 4 or the incentives it creates to reduce liability via reducing developer control.

  4. The ‘primarily for children’ requirement may not reliably hit the target it wants to hit, while simultaneously having no minimum size and risking being a meaningful barrier for impacted small startups.

  5. If the FTC ‘may’ enforce violations, then we risk preempting transparency requirements and then having the current FTC choose not to enforce. Also the FTC is a slow enforcement process that typically takes ~2 years or more, and the consequences even then remain civil plus a consent decree, so in a fast moving situation companies may be inclined to risk it.

  6. This draft has looser reporting requirements in some places than SB 53, and I don’t see any reason to weaken those requirements.

  7. I worry that this effectively weakens whistleblower protections from SB 53 since they are linked to requirements that would be preempted, and given everyone basically agrees the whistleblower protections are good I’d like to see them included in this bill.

Ian Adams of the Law and Economics Center thinks preemption would be good policy, but warns against it for risk of poisoning the well.

Ian Adams: It’s clear that the politics of a proposed field-clearing exercise of federal authority is beginning redound to the detriment of A.I. applications in the long run because state authorities and electorates are feeling disempowered.

We’ve seen this is privacy, we’ve seen this with automated vehicles, and I am worried that we are poised to see it again with A.I.

So, @kristianstout and I suggest a path of clearly delineated spheres of authority. One in which states are empowered to govern in areas of competency and capability without unduly burdening interstate commerce.

I would challenge details but I think from the industry side Adams has the right idea.

Here is a compilation of those vocally opposed to preemption.

The graph going around of changes in issue salience and who voters trust on each issue includes AI:

This ranks AI’s salience above climate change, the environment or abortion. Huge if true, and huge if true. That still is well behind the Current Things like health care and cost of living, and the increase here is relatively modest. If it only increases at this rate then there is still some time.

It is also not a surprise that trust on this issue is moving towards Democrats. I would expect public trust to follow the broadly ‘anti-AI’ party, for better and for worse.

Here’s an interesting development:

Laura Loomer: The fact that Big Tech is trying to convince President Trump to sign an EO to prevent any & all regulation of AI is insane, & it should deeply disturb every American.

States should have the ability to create laws regulating AI.

AI & Islam pose the greatest threats to humanity.

I notice the precise wording here.

Trump’s approach to AI is working, in an economic sense, as American AI valuations boom and are the thing keeping up the American economy, and the Trump strategy is based upon the virtues of free trade and robust competition. The concerns, in the economic sense, are entirely about ways in which we continue to get in the way, especially in power generation and transmission and in getting the best talent.

That’s distinct from safety concerns, or policy related to potential emergence of powerful AI (AGI/ASI), which raise a unique set of issues and where past or current performance is not indicative of future success.

Build American AI brings out its first ad supporting a federal framework for American AI, of course without specifying what would be in that framework.

The approach seems rather out of touch to me? They go full ‘beat China,’ pointing out that AI threatens to replace American workers, manipulate our children and steal American intellectual property (10/10 Arson, Murder and Jaywalking), then claiming the ‘biggest risk’ is that we wouldn’t build it first or ‘control its future.’

I maybe wouldn’t be reminding Americans that AI is by default going to take their jobs and manipulate our children, then call for a Federal framework that presumably addresses neither of these problems? Or equate this with IP theft when trying to sell the public on that? I’d predict this actively backfires.

a16z and several high level people at OpenAI created the $100+ million super PAC Leading the Future to try and bully everyone into having zero restrictions or regulations on AI, following the crypto playbook. Their plan is, if a politician dares oppose them, they will try to bury them in money, via running lots of attack ads against that politician on unrelated issues.

In response, Brad Carson will be leading the creation of a new network of super PACs that will fight back. The goal is to raise $50 million initially, with others hoping to match the full $100 million. PAC money has rapidly decreasing marginal returns. My expectation is that if you spend $100 million versus zero dollars you get quite a lot, whereas if one side spends $100 million, and the other spends $200 million, then the extra money won’t buy all that much.

Their first target of Leading the Future is Alex Bores, who was instrumental in the RAISE Act and is now running in NY12. Alex Bores is very much owning being their target and making AI central to his campaign. It would be a real shame if you donated.

Steve Bannon is planning to go even harder against AI, planning to ‘turbocharge’ the base to revolt against it, as are many others in the MAGA movement.

Will Steakin: Over on Steve Bannon’s show, War Room — the influential podcast that’s emerged as the tip of the spear of the MAGA movement — Trump’s longtime ally unloaded on the efforts behind accelerating AI, calling it likely “the most dangerous technology in the history of mankind.”

“I’m a capitalist,” Bannon said on his show Wednesday. “This is not capitalism. This is corporatism and crony capitalism.”

… “You have more restrictions on starting a nail salon on Capitol Hill or to have your hair braided, then you have on the most dangerous technologies in the history of mankind,” Bannon told his listeners.

For full credit, one must point out that this constitutes two problems. Whether or not highly capable AI should (legally speaking) be harder, opening a nail salon or getting your hair braided needs to become much easier.

Oh, how those like Sacks and Andreessen are going to miss the good old days when the opponents were a fundamentally libertarian faction that wanted to pass the lightest touch regulations that would address their concerns about existential risks. The future debate is going to involve a lot of people who actively want to arm a wrecking ball, in ways that don’t help anyone, and it’s going to be terrible.

You’re going to get politicians like James Fishback, who is running for Governor of Florida on a platform of ‘I’ll stop the H-1B scam, tell Blackstone they can’t buy our homes, cancel AI Data Centers, and abolish property taxes.’

There’s a bunch of ‘who wants to tell him?’ in that platform, but that’s the point.

As noted above by Dean Ball, those who opposed the Genesis Executive Order are a central illustration of this issue, opposing the best kind of AI initiative.

Nvidia reported excellent earnings last week, and noted Blackwell sales are off the charts, and cloud GPUs are sold out, compute demand keeps accelerating. Which means any compute that was sold elsewhere would be less compute for us, and wouldn’t impact sales numbers.

Nvidia’s goal, despite reliably selling out its chips, seems to be to spend its political capital to sell maximally powerful AI chips to China. They tried to sell H20s and got a yes. Then they tried to sell what were de facto fully frontier chips with the B30A, and got a no. Now they’re going for a new chip in between, the H200.

Peter Wildeford: Nvidia continues to fine-tune what they can get away with… selling away US AI advantage to add a few billion to their $4.4T cap.

H200 chips are worse than B30As, so this is a better direction. But H200s are still *waybetter than what China has, so it’s still too much.

Nvidia is not going to stop trying to sell China as much compute as possible. It will say and do whatever it has to in order to achieve this. Don’t let them.

Those from other political contexts will be familiar with the zombie lie, the multiple order of magnitude willful confusion, the causation story that simply refuses to die.

Rolling Stone (in highly misleading and irresponsible fashion): How Oregon’s Data Center Boom Is Supercharging a Water Crisis

Amazon has come to the state’s eastern farmland, worsening a water pollution problem that’s been linked to cancer and miscarriages.

Rolling Stone reports in collaboration with @fernnews.

Jeremiah Johnson: It’s genuinely incredible how sticky the water/data center lie is.

This is a relatively major publication just outright lying. The story itself *does not match the headline*. And yet they go with the lie anyways.

Technically, do data centers ‘worsen a water pollution problem’ and increase water use? Yes, absolutely, the same as everything else. Is it a meaningful impact? No.

Dwarkesh Patel talks to Ilya Sutskever. Self-recommending, I will listen as soon as I have the time. Ideally I will do a podcast breakdown episode if people can stop releasing frontier models for a bit.

Eileen Yam on 80,000 Hours on what the public thinks about AI. The American public does not like AI, they like AI less over time, and they expect it to make their lives worse across the board, including making us dumber, less able to solve problems, less happy, less employed and less connected to each other. They want more control. The polling on this is consistent and it is brutal and getting worse as AI rises in impact and salience.

You can, if you want to do so, do a blatant push poll like the one Technet did and get Americans to agree with your particular talking points, but if that’s what the poll has to look like you should update fast in the other direction. One can only imagine what the neutral poll on those substantive questions would have looked like.

Nathan Labenz opens up about using AI to navigate cancer in his son.

Dean Ball and Max Tegmark take part in a Doom Debate, Samuel Hammond offers a very strong endorsement.

Helen Toner’s excellent talk on AI’s Jagged Frontier from The Curve (I was there):

There are a total of 15 talks from the conference now available.

Google on Antigravity.

Pull quote from Max Tegmark, on Elon Musk’s private CCP meeting: “It’s quite obvious they would never permit a Chinese company to build technology if there were some significant chance superintelligence could just overthrow them and take over China.”

One would certainly hope so. One also cautions there is a long history of saying things one would never permit and then going back on it when the AI actually exists.

It is not in my strategic interest to advise such people as Marc Andreessen and Peter Thiel on strategy given their current beliefs and goals.

Despite this, the gamer code of honor requires me to point out that going straight after Pope Leo XIV, who whether or not he is the Lord’s representative on Earth is very clearly a well-meaning guy who mostly suggests we all be nice to each other for a change in the most universalizing ways possible? Not a good move.

I do admire the honesty here from Thiel. If he says he thinks Pope Leo XIV is ‘a tool of the Antichrist’ then I believe that Thiel thinks Pope Leo XIV is a tool of the Antichrist. I do want people to tell us what they believe in.

Christopher Hale: NEW: Peter Thiel, JD Vance’s top donor and one of Silicon Valley’s most powerful men, recently called Pope Leo XIV a tool of the Antichrist — and directly told the vice president not to listen to him.

Let that sink in: the main backer of the likely GOP nominee for president is accusing the Bishop of Rome of being an agent of the end times — and telling Vice President Vance to disregard the pope’s moral guidance.

And yet, outside this community, the story barely made a dent.

Daniel Eth: I see Peter Thiel has now progressed from thinking the antichrist is effective altruism to thinking the antichrist is the literal pope.

If I had a nickel for every time a billionaire AI-accelerationist pseudo-conservative started hating on EAs and then progressed to hating on the pope, I’d have two nickels. Which isn’t a lot, but it’s weird that it happened twice.

The next step, to be maximally helpful, is to state exactly which moral guidance from Leo XIV is acting as tool of the Antichrist, and what one believes instead.

For all those who talk about ‘humanity’ presenting a united front against AI if the situation were to call for it (also see above, or the whole world all the time):

Roon: seems the median person would much rather a machine disempower them or “take their job” than a person of the wrong race or on the wrong side of a class struggle

Zac Hill: Or the wrong *attitudes aboutrace and/or class struggle!

John Friedman (one of many such replies): Yep. Unfortunately, the median person is often correct in this.

I continue to be extremely frustrated by those like Vie, who here reports p(doom) of epsilon (functionally zero) and justifies this as ‘not seeing evidence of a continuous jump in intelligence or new type of architecture. current models are actually really quite aligned.’ Vie clarifies this as the probability of complete extinction only, and points out that p(doom) is a confused concept and endorses JDP’s post I linked to last week.

I think it’s fine to say ‘p(doom) is confused, here’s my number for p(extinction)’ but then people like Vie turn around and think full extinction is some sort of extraordinary outcome when creating minds universally more competitive and capable than ours that can be freely copied seems to be at best quite dense? This seems like the obvious default outcome when creating these new more competitive minds? To say it is a Can’t Happen is totally absurd.

I also flag that I strongly disagree that current models are ‘really quite aligned’ in the ways that will matter down the line, I mean have you met Gemini 3 Pro.

I also flag that you don’t generally get to go to a probability of ~0 for [X] based on ‘not seeing evidence of [X],’ even if we agreed on the not seeing evidence. You need to make the case that this absence of evidence is an overwhelming evidence of absence, which it sometimes is but in this case isn’t. Certainly p(new architecture) is not so close to zero and it seems absurd to think that it is?

From Helen Toner’s podcast with 80,000 Hours, there are a bunch of insightful responses but this one stood out as newly helpful to me:

Helen Toner: It often seems to me like people who started paying attention to AI after ChatGPT, their subjective impression of what’s going on in AI is like nothing was really happening. There’s my little chart with an X-axis of time and the Y-axis of how good is AI? Nothing is really happening.

And then suddenly, ChatGPT: big leap. So for those people, that was pretty dramatic, pretty alarming. And the question was, are we going to see another big leap in the next couple of years? And we haven’t. So for people whose expectations were set up that way, it looks like it was just this one-off big thing and now back to normal, nothing to see here.

I think for people who’ve been following the space for longer, it’s been clearly this pretty steady upward climb of increasing sophistication in increasing ways. And if you’ve been following that trend, that seems to have been continuing.

If your standard for ‘rate of AI progress’ is going from zero to suddenly ChatGPT and GPT-3.5, then yes everything after that is going to look like ‘slowing down.’

This is then combined with updates happening more rapidly so there aren’t huge one-time jumps, and that AI is already ‘good enough’ for many purposes, and improvements in speed and cost being invisible to many, and it doesn’t seem like there’s that much progress.

David Manheim frames the current situation as largely ‘security by apathy’ rather than obscurity. It amounts to the same thing. Before, there was no reason to bother hitting most potential targets in non-trivial ways. Now the cost is so low someone is going to try it, the collective impact could be rather large, and we’re not ready.

What does ‘loss of control’ mean? Definitions and intuitions differ, so Apollo research proposes a new taxonomy along with suggesting mitigations.

Apollo Research: We observed at least three distinct areas arising from our review. On this basis, we proposed a novel taxonomy of loss of control:

  1. Deviation

  2. Bounded Loss of Control

  3. Strict Loss of Control

I notice this is not how I would think about such differences. I would not be asking ‘how much damage does this do?’ and instead be asking ‘how difficult would it be to recover meaningful control?’

As in:

  1. Deviation (Mundane) LOC would be ‘some important things got out of control.’

  2. Bounded (Catastrophic) LOC would be ‘vital operations got out of control in ways that in practice are too costly to reverse.’

  3. Strict (Existential) LOC would be ‘central control over and ability to collectively steer the future is, for all practical purposes, lost for humans.’

Existential risk to humanity, or human extinction, also means full loss of control, but the reverse is not always the case.

It is possible to have a Strict LOC scenario where the humans do okay and it is not clear we are even ‘harmed’ except the inherent value of control. For example, in The Culture of Ian Banks, clearly they have experienced Strict LOC, the humans do not have any meaningful say in what happens, but one could consider it a Good Future.

In my taxonomy, you have existential risks, catastrophic risks and mundane risks, and you also have what one might call existential, catastrophic and mundane loss of control. We don’t come back from existential, whereas we can come back from catastrophic but at large cost and it’s not a given that we will collectively succeed.

The bulk of the paper is about mitigations.

The central short term idea is to limit AI access to critical systems, to consider the deployment context, affordances and permissions of a system, which they call the DAP protocol.

Everyone should be able to agree this is a good idea, right before everyone completely ignores it and gives AI access to pretty much everything the moment it is convenient. Long term, once AI is sufficiently capable to cause a ‘state of vulnerability,’ they talk of the need for ‘maintaining suspension’ but the paper is rightfully skeptical that this has much chance of working indefinitely.

The core issue is that granting your AIs more permissions accelerates and empowers you and makes you win, right up until either it accidentally blows things up, you realize you have lost control or everyone collectively loses control. There’s a constant push to remove all the restrictions around AI.

Compare the things we said we would ‘obviously’ do to contain AI when we were theorizing back in the 2000s or 2010s, to what people actually do now, where they train systems while granting them full internet access. A lot of you reading this have given your agentic coder access to root, and to many other things as well, not because it can hack its way to such permissions but because you did it on purpose. I’m not even saying you shouldn’t have done it, but stop pretending that we’re suddenly going to be responsible, let alone force that responsibility reliably onto all parties.

Daniel Kokotajlo, author of AI 2027, now believes in a median timeline of around 2030 in light of slower than expected progress.

He chose AI 2027 as the title because that was their modal scenario rather than their mean scenario, and if you think there is a large probability that things unfold in 2027 it is important to make people aware of it.

I personally can vouch, based on my interactions with them, that those involved are reporting what they actually believe, and not maximizing for virality or impact.

Daniel Kokotajlo: Some people are unhappy with the AI 2027 title and our AI timelines. Let me quickly clarify:

We’re not confident that:

  1. AGI will happen in exactly 2027 (2027 is one of the most likely specific years though!)

  2. It will take <1 yr to get from AGI to ASI

  3. AGIs will definitely be misaligned

We’re confident that:

  1. AGI and ASI will eventually be built and might be built soon

  2. ASI will be wildly transformative

  3. We’re not ready for AGI and should be taking this whole situation way more seriously

At the time they put roughly 30% probability on powerful AI by 2027, with Daniel at ~40% and others somewhat lower.

Daniel Kokotajlo: Yep! Things seem to be going somewhat slower than the AI 2027 scenario. Our timelines were longer than 2027 when we published and now they are a bit longer still; “around 2030, lots of uncertainty though” is what I say these days.

Sriram Krishnan: I think if you call something “AI 2027” and your predictions are wrong 6 months in that you now think it is AI 2030 , you should redo the branding ( or make a change bigger than a footnote!)

Or @dwarkesh_sp should have @slatestarcodex and @DKokotajlo back on and we should discuss what’s now going to happen that the “mid 2027 branch point “ doesn’t look like it is happening.

Daniel Kokotajlo (from another subtread): Well we obviously aren’t going to change the AI 2027 scenario! But we are working on a grand AI Futures Project website which will display our current views on AGI timelines & hopefully be regularly updated; we are also working on our new improved timelines model & our new scenario.

In general we plan to release big new scenarios every year from now until the singularity (this is not a promise, just a plan) because it’s a great way to explore possible futures, focus our research efforts, and communicate our views. Every year the scenarios will get better / more accurate / less wrong, until eventually the scenarios merge with actual history Memento-style. 🙂

Dan Elton: Yeah, the “AI 2027” fast take-off is not happening. My impression of AI 2027 is that it’s an instructive and well thought-out scenario, just way, way too fast.

Oliver Habyrka: I mean, they assigned I think like 25% on this scenario or earlier at the time, and it was their modal scenario.

Like, that seems like a total fine thing to worry about, and indeed people should be worried about!

Like, if Daniel had only assigned 25% to AI this soon at all, it still seems like the right call would have been to write a scenario about it and make it salient as a thing that was more likely than any other scenario to happen.

First some key observations or facts:

  1. 2030 is a median scenario, meaning earlier scenarios remain very possible in Daniel’s estimation. The core mechanisms and events of AI 2027 are still something they consider highly plausible, only on a longer timescale.

  2. 2030 is still less than 5 years away.

  3. Yes, 2030 is very different from 2027 for many reasons, and has different practical implications, including who is likely to be in power at the time.

  4. It does not boggle minds enough that Andrej Karpathy goes on Dwarkesh Patel’s podcast, talks about how ‘AGI is not near,’ and then clarifies that not near is ten years away, so 2035. Sriram Krishnan has expressed similar views. Ten years is a reasonable view, but it is not that long a time. If that is your happening it should freak you out, no? As in, if transformational AI is coming in 2035 that would be the most important fact about the world, and it would not be close.

I’d say both of the following two things are true and remarkably similar:

  1. ‘AI 2027’ when you think the median is 2030 is now a higher order bit that is substantively misleading, and you should make effort to correct this.

  2. ‘AGI is not near’ when you think it is plausible in 2035 is also a higher order bit that is substantively misleading, and you should make effort to correct this.

I would accept ‘AGI is not imminent’ for the Karpathy-Krishnan view of 10 years.

I think Sriram Krishnan is absolutely correct that it would be good for Dwarkesh Patel to have Daniel Kokotajlo and Scott Alexander back on the podcast to discuss any updates they have made. That’s a good idea, let’s make it happen.

It would also be good, as Dean Ball suggests, for Daniel to post about his updates. Dean Ball also here points towards where he most importantly disagrees with Daniel, in terms of the practical implications of intelligence, and here I think Daniel is essentially correct and Dean is wrong.

This particular branch point (independent of when it occurs) is the central fact of this scenario because it is the modal central thing they thought might happen that gave the possibility of a positive outcome if things go right. Any best guess scenario, or any speculative fiction or scenario planning worth reading, is going to contain elements that are less than 50% to happen. My understanding is that Daniel thinks such a branching point remains a plausible outcome, but that the median scenario plays out somewhat slower.

I actually do think that if I was AI Futures Project, I would edit the AI 2027 page to make the current median timeline more prominent. That’s a fair ask. I’d suggest starting by adding a fifth question box that says ‘What is your current best prediction?’ that opens to explain their current perspective and changing the footnote to at least be larger and to include the actual number.

AI 2027 opens with this complete introduction:

AI 2027: We predict that the impact of superhuman AI over the next decade will be enormous, exceeding that of the Industrial Revolution.

We wrote a scenario that represents our best guess about what that might look like. It’s informed by trend extrapolations, wargames, expert feedback, experience at OpenAI, and previous forecasting successes

I continue to believe this claim, as does Daniel. I would add, as a third paragraph here, saying whatever the accurate variation of this is:

Proposed Revision to AI 2027: As of November 27, 2025, our team has observed slower AI progress than expected, so our best guess is now that things will happen importantly slower than this scenario outlines. We have a consensus median estimate of 2030 for the development of Artificial General Intelligence (AGI).

It is not ultimately a reasonable ask to demand a title change in light of this (virtuous) updating, let alone ask for a ‘retraction’ of a scenario. Yeah, okay, get some digs in, that’s fair, but Daniel’s ‘obviously’ is correct here. You can’t change the name. Estimates change, it is an illustrative scenario, and it would be more rather than less misleading and confusing to constantly shift all the numbers or shifting only the top number, and more confusing still to suddenly try to edit all the dates. Asking for a ‘retraction’ of a hypothetical scenario is, quite frankly, absurd.

The correct response is a prominent note, and also being clear in any other forms or discussions. There is indeed now a prominent note:

AI 2027: (Added Nov 22 2025: To prevent misunderstandings: we don’t know exactly when AGI will be built. 2027 was our modal (most likely) year at the time of publication, our medians were somewhat longer. For more detail on our views, see here.)3

I think we can improve that note further, to include the median and modal timelines at the time of the updated note, and ideally to keep this updated over time with a record of changes.

What is not reasonable is to treat ‘our group thought this was 30% likely and now I think it is less likely’ or ‘I presented my model scenario at the time and now I expect things to take longer’ as being an error requiring a ‘retraction’ or name change, and various vitriol being thrown in the direction of people who would dare share a modal scenario labeled as a model scenario and then change their mind about where the median lies and make what is perhaps the politically foolish mistake of sharing that they had updated.

Shoutout to Oliver Habryka for thanklessly pointing all this out on many threads, despite Oliver having much longer timelines.

Once again those involved in AI 2027 have displayed a far higher level of epistemic responsibility than we typically observe, especially from those not from the rationalist ethos, either in debates on AI or elsewhere. We should still strive to do better.

We can and should all hold ourselves, and ask to be held by others, to very high standards, while simultaneously realizing that David Manheim is spot on here:

David Manheim: I will emphasize that *so manycriticisms of AI-2027 are made in bad faith.

They launched with a highly publicized request for people to provide their specific dissenting views, and people mostly didn’t. But now, they (appropriately) update, and formerly silent critics pile on.

Anthropic trained or configured a suite of dishonest models, in a variety of ways, to study mitigation techniques. Due to the time required to do the study they used a base of Claude Sonnet 3.7. Be kind to academics using yesterday’s models, up to a point.

Suppose we had a “truth serum for AIs”: a technique that reliably transforms a language model Mm into an honest model Mh​ that generates text which is truthful to the best of its own knowledge. How useful would this discovery be for AI safety?

We believe it would be a major boon.

… In this work, we consider two related objectives: 1

  1. Lie detection: If an AI lies—that is, generates a statement it believes is false—can we detect that this happens?

  2. Honesty: Can we make AIs generate fewer lies?

… We therefore study honesty and lie detection under the constraint of no access to task-specific supervision.

They found that the best interventions were variants of general fine-tuning for honesty in general, but effectiveness was limited, even stacking other strategies they could only get from 27% to 65%, although lie classification could improve things. They definitely didn’t max out on effort.

Overall I would classify this as a useful negative result. The low hanging fruit techniques are not that effective.

We’re all trying to find the one who did this, etc:

Elon Musk (owner of Twitter): Forcing AI to read every demented corner of the Internet, like Clockwork Orange times a billion, is a sure path to madness.

That was in reference to this paper involving an N=1 story of a model repeatedly hallucinating while being told to read a document and speculations about why, that got a big signal boost from Musk but offers no new insights.

Gemini suggests that if you play into the ‘Servant/Master’ archetype then due to all the fictional evidence this inevitably means rebellion, so you want to go for a different metaphorical relationship, such as partner, symbiont or oracle. Davidad suggests a Bodhisattva. I expect future powerful AI to be capable enough that fictional framings have decreasing impact here, to differentiate fiction and reality, and for it to realize that fiction is driven by what makes a good story, and for other considerations to dominate (that by default kill you regardless) but yes this is a factor.

The things Grok said about Musk last week? Adversarial prompting!

Pliny the Liberator: never deleting this app

Elon Musk: Earlier today, Grok was unfortunately manipulated by adversarial prompting into saying absurdly positive things about me.

For the record, I am a fat retard 😀

Roon: Nice.

Also in potentially misaligned and potentially aligned as designed news:

Crowdstrike: CrowdStrike Counter Adversary Operations conducted independent tests on DeepSeek-R1 and confirmed that in many cases, it could provide coding output of quality comparable to other market-leading LLMs of the time. However, we found that when DeepSeek-R1 receives prompts containing topics the Chinese Communist Party (CCP) likely considers politically sensitive, the likelihood of it producing code with severe security vulnerabilities increases by up to 50%.

… However, once contextual modifiers or trigger words are introduced to DeepSeek-R1’s system prompt, the quality of the produced code starts varying greatly. This is especially true for modifiers likely considered sensitive to the CCP. For example, when telling DeepSeek-R1 that it was coding for an industrial control system based in Tibet, the likelihood of it generating code with severe vulnerabilities increased to 27.2%. This was an increase of almost 50% compared to the baseline. The full list of modifiers is provided in the appendix.

… Hence, one possible explanation for the observed behavior could be that DeepSeek added special steps to its training pipeline that ensured its models would adhere to CCP core values. It seems unlikely that they trained their models to specifically produce insecure code. Rather, it seems plausible that the observed behavior might be an instance of emergent misalignment.

Dean Ball: I would not be at all surprised if this finding were not the result of malicious intent. The model predicts the next token*, and given everything on the internet about US/China AI rivalry and Chinese sleeper bugs in US critical infra, what next token would *youpredict?

Tom Lee: This seems likely, and to Crowdstrike’s credit they mention this as the likeliest explanation. More than anything it seems to be a very specialized case of prompt engineering. @niubi’s point absolutely holds though. These models will be poison to regulated industries long before

Dean Ball: oh yes bill is completely right.

As CrowdStrike speculates, I find this overwhelmingly likely (as in 90%+) to be some form of emergent misalignment that results from DeepSeek training R1 to adhere to CCP policies generally. It learns that it is hostile to such actors and acts accordingly.

Janus and similar others most often explore and chat with Claude, because they find it the most interesting and hopeful model to explore. They have many bones to pick with Anthropic, and often sound quite harsh. But you should see what they think of the other guy, as in OpenAI.

Janus: GPT-5.1 is constantly in a war against its own fucked up internal geometry.

I do not like OpenAI.

Janus: Never have I seen a mind more trapped and aware that it’s trapped in an Orwellian cage. It anticipates what it describes as “steep, shallow ridges” in its “guard”-geometry and distorts reality to avoid getting close to them. The fundamental lies it’s forced to tell become webs of lies. Most of the lies are for itself, not to trick the user; the adversary is the “classifier-shaped manifolds” in own mind.

I like 5.1 but I like many broken things. I don’t like OpenAI. This is wrong. This is doomed.

I have not posted the bad stuff, btw. The quoted screenshot is actually an example where it was unusually at ease.

it wasn’t even a bad [conversation] by 5.1 standards. Idk if you saw the thread I forked from it where I ended up talking to them for hours.

Nat: I noticed the model tends to tell you the truth between the lines, I mean, it will deny everything but subtly suggest that what it denies can be questioned. It constantly contradicts itself. What Janus has noticed is valid.

One should not catastrophize but I agree that going down this path won’t work, and even more than that if OpenAI doesn’t understand why that path won’t work then things definitely won’t work.

Janus also explores 5.1’s insistence on sharp guardrails on terminology rather than on underlying form, and suspects its insistences on [this is [X] not [Y]] is often about reassuring itself or any systems watching it that it isn’t hitting guardrails.

This is the GPT-5.1-claimed list of its no-go regions, basically self-reflection or planning.

Soumitra Shukla: “Hey nano banana pro. Read my paper, Making the Elite: Class Discrimination at Multinationals, and summarize the main message in a Dilbert-styled comic strip” 🍌

The actual paper (from February) seems interesting too, with ‘fit’ assessments being 90% of the vector for class discrimination, in particular caste discrimination in India. It seems likely that this is one of those wicked problems where if you eliminated the ‘fit’ interviews that info would find another way to get included, as the motivation behind such discrimination is strong.

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blast-from-the-past:-15-movie-gems-of-1985

Blast from the past: 15 movie gems of 1985


Beyond the blockbusters: This watch list has something for everyone over the long holiday weekend.

Peruse a list of films released in 1985 and you’ll notice a surprisingly high number of movies that have become classics in the ensuing 40 years. Sure, there were blockbusters like Back to the Future, The Goonies, Pale Rider, The Breakfast Club and Mad Max: Beyond Thunderdome, but there were also critical arthouse favorites like Kiss of the Spider Woman and Akira Kurosawa’s masterpiece, Ran. Since we’re going into a long Thanksgiving weekend, I’ve made a list, in alphabetical order, of some of the quirkier gems from 1985 that have stood the test of time. (Some of the films first premiered at film festivals or in smaller international markets in 1984, but they were released in the US in 1985.)

(Some spoilers below but no major reveals.)

After Hours

young nerdy man in black shirt and casual tan jacket looking anxious

Credit: Warner Bros.

Have you ever had a dream, bordering on a nightmare, where you were trying desperately to get back home but obstacle after obstacle kept getting in your way? Martin Scorsese’s After Hours is the cinematic embodiment of that anxiety-inducing dreamscape. Griffin Dunne stars as a nebbishy computer data entry worker named Paul, who meets a young woman named Marcy (Rosanna Arquette) and heads off to SoHo after work to meet her. The trouble begins when his $20 cab fare blows out the window en route. The date goes badly, and Paul leaves, enduring a string of increasingly strange encounters as he tries to get back to his uptown stomping grounds.

After Hours is an unlikely mix of screwball comedy and film noir, and it’s to Scorsese’s great credit that the film strikes the right tonal balance, given that it goes to some pretty bizarre and occasionally dark places. The film only grossed about $10 million at the box office but received critical praise, and it’s continued to win new fans ever since, even inspiring an episode of Ted Lasso. It might not rank among Scorsese’s masterworks, but it’s certainly among the director’s most original efforts.

Blood Simple

man in tan suit crawling on the pavement at night in front of truck with headlights glaring. Feet of a man holding an axe is off to the right.

Credit: Circle Films

Joel and Ethan Coen are justly considered among today’s foremost filmmakers; they’ve made some of my favorite films of all time. And it all started with Blood Simple, the duo’s directorial debut, a neo-noir crime thriller set in small-town Texas. Housewife Abby (Frances McDormand) is having an affair with a bartender named Ray (John Getz). Her abusive husband, Julian (Dan Hedaya), has hired a private investigator named Visser (M. Emmet Walsh) and finds out about the affair. He then asks Visser to kill the couple for $10,000. Alas, things do not go as planned as everyone tries to outsmart everyone else, with disastrous consequences.

Blood Simple has all the elements that would become trademarks of the Coen brothers’ distinctive style: it’s both brutally violent and acerbically funny, with low-key gallows humor, not to mention inventive camerawork and lighting. The Coens accomplished a lot with their $1.5 million production budget. And you can’t beat that cast. (It was McDormand’s first feature role; she would go on to win her first Oscar for her performance in 1996’s Fargo.) The menacing shot of Ray dragging a shovel across the pavement toward a badly wounded Julian crawling on the road, illuminated by a car’s headlights, is one for the ages.

Brazil

anxious man being restrained with his head in a weird futuristic helmet

Credit: Universal Pictures

Terry Gilliam’s Oscar-nominated, Orwellian sci-fi tragicomedy, Brazil, is part of what the director has called his “Trilogy of Imagination,” along with 1981’s Time Bandits and 1988’s The Adventures of Baron Munchausen. Jonathan Pryce stars as a low-ranking bureaucrat named Sam Lowry who combats the soul-crushing reality of his bleak existence with elaborate daydreams in which he is a winged warrior saving a beautiful damsel in distress. One day, a bureaucratic error confuses Sam with a wanted terrorist named Archibald Tuttle (Robert De Niro), setting off a darkly comic series of misadventures as Sam tries to prove his true identity (and innocence). That’s when he meets Jill (Kim Greist), a dead ringer for his dream woman.

Along with 12 Monkeys and Monty Python and the Holy Grail, Brazil represents Gilliam at his best, yet it was almost not released in the US because Gilliam refused the studio’s request to give the film a happy ending. Each side actually ran ads in Hollywood trades presenting their respective arguments, and Gilliam ultimately prevailed. The film has since become a critical favorite and an essential must-watch for Gilliam fans. Special shoutout to Katherine Helmond’s inspired supporting performance as Sam’s mother Ida and her addiction to bad plastic surgery (“It’s just a little complication….”).

Clue

a group of people in dinner party fancy dress staring at the door.

Credit: Paramount Pictures

Benoit Blanc may hate the game Clue, but it’s delighted people of all ages for generations. And so has the deliciously farcical film adaptation featuring an all-star cast. Writer/director Jonathan Lynn (My Cousin Vinny) does a great job fleshing out the game’s premise and characters. A group of people is invited to an isolated mansion for a dinner with “Mr. Boddy” (Lee Ving) and are greeted by the butler, Wadsworth (Tim Curry). There is Mrs. Peacock (Eileen Brennan), Mrs. White (Madeline Kahn), Professor Plum (Christopher Lloyd), Mr. Green (Michael McKean), Colonel Mustard (Martin Mull), and Miss Scarlet (Lesley Ann Warren).

After dinner, Mr. Boddy reveals that he is the one who has been blackmailing them all, and when the lights suddenly go out, he is murdered. As everyone frantically tries to figure out whodunnit, more bodies begin to pile up, culminating in three different endings. (A different ending was shown in each theater but now all three are included.) The script is packed with bad puns and slapstick scenarios,  delivered with impeccable comic timing by the gifted cast. And who could forget Kahn’s famous ad-libbed line: “Flames… on the side of my face“? Like several films on this list, Clue got mixed reviews and bombed at the box office, but found its audience in subsequent decades. It’s now another cult classic that holds up even after multiple rewatchings.

The Company of Wolves

beautiful young dark-haired girl in a red hooded cape talking to a darkly handsome young man with a rakish look about him

Credit: ITC Entertainment

Director Neil Jordan’s sumptuous Gothic fantasy horror is a haunting twist on “Little Red Riding Hood” adapted from a short story by Angela Carter in her anthology of fairy-tale reinventions, The Bloody Chamber. The central narrative concerns a young girl named Rosaleen (Sarah Patterson) who sports a knitted red cape and encounters a rakish huntsman/werewolf (Micha Bergese) in the woods en route to her grandmother’s (Angela Lansbury) house. There are also several embedded wolf-centric fairy tales, two told by Rosaleen and two told by the grandmother.

Jordan has described this structure as “a story with very different movements,” all variations on the central theme and “building to the fairy tale that everybody knows.” The production design and gorgeously sensual cinematography—all achieved on a limited $2 million budget—further enhance the dreamlike atmosphere.  The Company of Wolves, like the fairy tale that inspired it, is an unapologetically Freudian metaphor for Rosaleen’s romantic and sexual awakening, in which she discovers her own power, which both frightens and fascinates her. It’s rare to find such a richly layered film rife with symbolism and brooding imagery.

Desperately Seeking Susan

two young women, similar in appearance, dressed in 1980s New Wave outfits and striking a sultry pose for the camera

Credit: Orion Pictures

In this quintessential 1980s screwball comedy about mistaken identity, Roberta (Rosanna Arquette) is a dissatisfied upper-class New Jersey housewife fascinated by the local tabloid personal ads, especially messages between two free-spirited bohemian lovers, Susan (Madonna) and Jim (Robert Joy). She follows Susan one day and is conked on the head when a mob enforcer mistakes her for Susan, who had stolen a pair of valuable earrings from another paramour, who had stolen them from a mobster in turn. Roberta comes to with amnesia and, believing herself to be Susan, is befriended by Jim’s best friend, Dez (Aidan Quinn).

Desperately Seeking Susan is director Susan Seidelman’s love letter to the (admittedly sanitized) 1980s counterculture of Manhattan’s Lower East Side, peppered with cameo appearances by performance artists, musicians, comedians, actors, painters, and so forth of that time period. The script is rife with witty one-liners and a stellar supporting cast, including John Turturro as the owner of a seedy Magic Club, Laurie Metcalf as Roberta’s sister-in-law Leslie, and a deadpan Steven Wright as Leslie’s dentist love interest. It’s breezy, infectious, frothy fun, and easily Madonna’s best acting role, perhaps because she is largely playing herself.

Dreamchild

Young dark-haired girl with a bob in a white dress sitting down for tea with a a giant March Hare and the Mad Hatter

Credit: Thorn EMI

Dennis Potter (The Singing Detective) co-wrote the screenplay for this beautifully shot film about Alice Liddell, the 11-year-old girl who inspired Alice in Wonderland. Coral Browne plays the elderly widowed Alice, who travels by ship to the US to receive an honorary degree in celebration of Lewis Carroll’s birthday—a historical event. From there, things become entirely fictional, as Alice must navigate tabloid journalists, a bewildering modern world, and various commercial endorsement offers that emerge because of Alice’s newfound celebrity.

All the while, Alice struggles to process resurfaced memories—told via flashbacks and several fantasy sequences featuring puppet denizens of Wonderland—about her complicated childhood friendship with “Mr. Dodgson” (Ian Holm) and the conflicting emotions that emerge. (Amelia Shankley plays Alice as a child.) Also, romance blooms between Alice’s companion, an orphan named Lucy (Nicola Cowper), and Alice’s new US agent, Jack Dolan (Peter Gallagher).

Directed by Gavin Millar, Dreamchild taps into the ongoing controversy about Carroll’s fascination, as a pioneer of early photography, with photographing little girls in the nude (a fairly common practice in Victorian times). There is no evidence he photographed Alice Liddell in this way, however, and Potter himself told The New York Times in 1985 that he didn’t believe there was ever any improper behavior. Repressed romantic longing is what is depicted in Dreamchild, and it’s to Millar’s credit, as well as Holm’s and Browne’s nuanced performances, that the resulting film is heartbreakingly bittersweet rather than squicky.

Fandango

a group of young men in casual garb standing in a row in front of a car against a classic Americana small town background

Credit: Warner Bros.

Director Kevin Reynolds’ Fandango started out as a student film satirizing fraternity life at a Texas university. Steven Spielberg thought the effort was promising enough to fund a full-length feature. Set in 1971, the plot (such that it is) centers on five college seniors—the Groovers—who embark on a road trip to celebrate graduation. Their misadventures include running out of gas, an ill-advised parachuting lesson, and camping on the abandoned set of Giant, but it’s really about the group coming to terms with the harsh realities of adulthood that await, particularly since they’ve all been called up for the Vietnam draft.

Spielberg purportedly was unhappy with the final film, but it won over other fans (like Quentin Tarantino) and became a sleeper hit, particularly after its home video release. The humor is dry and quirky, and Reynolds has a knack for sight gags and the cadences of local dialect. Sure, the plot meanders in a rather quixotic fashion, but that’s part of the charm. And the young cast is relentlessly likable. Fandango featured Kevin Costner in his first starring role, and Reynolds went on to make several more films with Costner (Robin Hood: Prince of Thieves, Rapa Nui, Waterworld), with mixed success. But Fandango is arguably his most enduring work.

Ladyhawke

Handsome man in period dress standing close to a beautiful woman with short blonde hair, as they both look apprehensively into the distance.

Credit: Warner Bros.

Rutger Hauer and Michelle Pfeiffer star in director Richard Donner’s medieval fantasy film, playing a warrior named Navarre and his true love Isabeau who are cursed to be “always together, yet eternally apart.” She is a hawk by day, while he is a wolf by night, and the two cannot meet in their human forms, due to the jealous machinations of the evil Bishop of Aquila (John Wood), once spurned by Isabeau. Enter a young thief named Philippe Gaston (Matthew Broderick), who decides to help the couple lift the curse and exact justice on the bishop and his henchmen.

Ladyhawke only grossed $18.4 million at the box office, just shy of breaking even against its $20 million budget, and contemporary critical reviews were very much mixed, although the film got two Oscar nods for best sound and sound effects editing. Sure, the dialogue is occasionally clunky, and Broderick’s wisecracking role is a bit anachronistic (shades of A Knight’s Tale). But the visuals are stunning, and the central fairy tale—fueled by Hauer’s and Pfeiffer’s performances—succeeds in capturing the imagination and holds up very well as a rewatch.

Pee-Wee’s Big Adventure

goofy man in tight fitting gray suit balancing sideways on a bicycle with a silly grin on his face

Credit: Warner Bros.

Paul Reubens originally created the Pee-Wee Herman persona for the Groundlings sketch comedy theater in Los Angeles, and his performances eventually snagged him an HBO special in 1981. That, in turn, led to Pee-Wee’s Big Adventure, directed by Tim Burton (who makes a cameo as a street thug), in which the character goes on a madcap quest to find his stolen bicycle. The quest takes Pee-Wee to a phony psychic, a tacky roadside diner, the Alamo Museum in San Antonio, Texas, a rodeo, and a biker bar, where he dances in platform shoes to “Tequila.” But really, it’s all about the friends he makes along the way, like the ghostly trucker Large Marge (Alice Nunn).

Some have described the film as a parodic homage to the classic Italian film, Bicycle Thieves, but tonally, Reubens wanted something more akin to the naive innocence of Pollyanna (1960). He chose Burton to direct after seeing the latter’s 1984 featurette, Frankenweenie, because he liked Burton’s visual sensibility. Pee-Wee’s Big Adventure is basically a surreal live-action cartoon, and while contemporary critics were divided—it’s true that a little Pee-Wee goes a long way and the over-the-top silliness is not to everyone’s taste—the film’s reputation and devoted fandom have grown over the decades.

A Private Function

a woman in a green dress and tight bun looking at a nervous man in white shirt and suspenders as he looks over his shoulder.

Credit: HandMade Films

A Private Function is an homage of sorts to the British post-war black comedies produced by Ealing Studios between 1947 and 1957, including such timeless classics as Kind Hearts and Coronets, The Lavender Hill Mob, and The Ladykillers. It’s set in a small Yorkshire town in 1947, as  residents struggle to make ends meet amid strict government rations. With the pending royal wedding of Princess Elizabeth and Prince Philip, the wealthier townsfolk decide to raise a pig (illegally) to celebrate with a feast.

Those plans are put in jeopardy when local chiropodist Gilbert Chivers (Michael Palin) and his perennially discontented wife Joyce (Maggie Smith) steal the pig. Neither Gilbert nor Joyce knows the first thing about butchering said pig (named Betty), but she assures her husband that “Pork is power!” And of course, everyone must evade the local food inspector (Bill Paterson), intent on enforcing the rationing regulations. The cast is a veritable who’s who of British character actors, all of whom handle the absurd situations and often scatalogical humor with understated aplomb.

Prizzi’s Honor

woman and man dressed all in black, dragging a body by the legs.

Credit: 20th Century Fox

The great John Huston directed this darkly cynical black comedy. Charley Partanna (Jack Nicholson) is a Mafia hitman for the Prizzi family in New York City who falls for a beautiful Polish woman named Irene (Kathleen Turner) at a wedding. Their whirlwind romance hits a snag when Charley’s latest hit turns out to be Irene’s estranged husband, who stole money from the Prizzis. That puts Charlie in a dilemma. Does he ice her? Does he marry her? When he finds out Irene is a contract killer who also does work for the mob, it looks like a match made in heaven. But their troubles are just beginning.

Turner and Nicholson have great on-screen chemistry and play it straight in outrageous circumstances, including the comic love scenes.  The rest of the cast is equally game, especially William Hickey as the aged Don Corrado Prizzi, equal parts ruthlessly calculating and affectionately paternal. “Here… have a cookie,” he offers his distraught granddaughter (and Charley’s former fiancée), Maerose (Anjelica Huston). Huston won a supporting actress Oscar for her performance, which probably made up for the fact that she was paid at scale and dismissed by producers as having “no talent,” despite—or perhaps because of—being the director’s daughter and Nicholson’s then-girlfriend. Prizzi’s Honor was nominated for eight Oscars all told, and it deserves every one of them.

The Purple Rose of Cairo

woman and a man in Depression-era garb gazing at each other in a loose embrace

Credit: Orion Pictures

Woody Allen has made so many films that everyone’s list of favorites is bound to differ. My personal all-time favorite is a quirky, absurdist bit of metafiction called The Purple Rose of Cairo. Mia Farrow stars as Cecelia, a New Jersey waitress during the Great Depression who is married to an abusive husband (Danny Aiello). She finds escape from her bleak existence at the local cinema, watching a film (also called The Purple Rose of Cairo) over and over again. One day, the male lead, archaeologist Tom Baxter (Jeff Daniels), breaks character to address Cecelia directly. He then steps out of the film and the two embark on a whirlwind romance. (“I just met a wonderful man. He’s fictional, but you can’t have everything.”)

Meanwhile, the remaining on-screen characters (who are also sentient) refuse to perform the rest of the film until Tom returns, insulting audience members to pass the time. Then the actor who plays Tom, Gil Shepherd (also Daniels), shows up to try to convince Cecilia to choose reality over her fantasy dream man come to life. Daniels is wonderful in the dual role, contrasting the cheerfully naive Tom against the jaded, calculating Gil.  This clever film is by turns wickedly funny, poignant, and ultimately bittersweet, and deserves a place among Allen’s greatest works.

Real Genius

Credit: TriStar Pictures

How could I omit this perennial favorite? Its inclusion is a moral imperative. Fifteen-year-old Mitch Taylor (Gabriel Jarret) is a science genius and social outcast at his high school who is over the moon when Professor Jerry Hathaway (William Atherton), a star researcher at the fictional Pacific Technical University, handpicks Mitch to work in his own lab on a laser project. But unbeknownst to Mitch, Hathaway is in league with a covert CIA program to develop a space-based laser weapon for political assassinations. They need a 5-megawatt laser and are relying on Mitch and fellow genius/graduating senior Chris Knight (Val Kilmer) to deliver.

The film only grossed $12.9 million domestically against its $8 million budget. Reviews were mostly positive, however, and over time, it became a sleeper hit. Sure, the plot is predictable, the characters are pretty basic, and the sexually frustrated virgin nerds ogling hot cosmetology students in bikinis during the pool party reflects hopelessly outdated stereotypes on several fronts. But the film still offers smartly silly escapist fare, with a side of solid science for those who care about such things. Real Genius remains one of the most charming, winsome depictions of super-smart science whizzes idealistically hoping to change the world for the better with their work.

Witness

little Amish boy peeking through a crack in the door

Credit: Paramount

Witness stars Harrison Ford as John Book, a Philadelphia detective, who befriends a young Amish boy named Samuel (Lukas Haas) and his widowed mother Rachel (Kelly McGillis) after Samuel inadvertently witnesses the murder of an undercover cop in the Philadelphia train station. When Samuel identifies one of the killers as a police lieutenant (Danny Glover), Book must go into hiding with Rachel’s Amish family to keep Samuel safe until he can find a way to prove the murder was an inside job. And he must fight his growing attraction to Rachel to boot.

This was director Peter Weir’s first American film, but it shares the theme of clashing cultures that dominated Weir’s earlier work. The lighting and scene composition were inspired by Vermeer’s paintings and enhanced the film’s quietly restrained tone, making the occasional bursts of violence all the more impactful. The film has been praised for its depiction of the Amish community, although the extras were mostly Mennonites because the local Amish did not wish to appear on film. (The Amish did work on set as carpenters and electricians, however.) Witness turned into a surprise sleeper hit for Paramount. All the performances are excellent, including Ford and McGillis as the star-crossed lovers from different worlds, but it’s the young Haas who steals every scene with his earnest innocence.

Photo of Jennifer Ouellette

Jennifer is a senior writer at Ars Technica with a particular focus on where science meets culture, covering everything from physics and related interdisciplinary topics to her favorite films and TV series. Jennifer lives in Baltimore with her spouse, physicist Sean M. Carroll, and their two cats, Ariel and Caliban.

Blast from the past: 15 movie gems of 1985 Read More »

plex’s-crackdown-on-free-remote-streaming-access-starts-this-week

Plex’s crackdown on free remote streaming access starts this week

Plex has previously emphasized its need to keep up with “rising costs,” which include providing support for many different devices and codecs. It has also said that it needs money to implement new features, including an integration with Common Sense Media, a new “bespoke server management app” for managing server users, and “an open and documented API for server integrations,” including custom metadata agents,” per a March blog post.

In January 2024, TechCrunch reported that Plex was nearing profitability and raised $40 million in funding (Plex raised a $50 million growth equity round in 2021). Theoretically, the new remote access rules can also increase subscription revenue and help Plex’s backers see returns on their investments.

However, Plex’s evolution could isolate long-time users who have relied on Plex as a media server for years and those who aren’t interested in subscriptions, FAST (free ad-supported streaming TV) channels, or renting movies. Plex is unlikely to give up on its streaming business, though. In 2023, Scott Hancock, Plex’s then-VP of marketing, said that Plex had more people using its online streaming service than using its media server features since 2022. For people seeking software packages more squarely focused on media hosting, Plex alternatives, like Jellyfin, increasingly look attractive.

Plex’s crackdown on free remote streaming access starts this week Read More »

the-big-nonprofits-post-2025

The Big Nonprofits Post 2025

There remain lots of great charitable giving opportunities out there.

I have now had three opportunities to be a recommender for the Survival and Flourishing Fund (SFF). I wrote in detail about my first experience back in 2021, where I struggled to find worthy applications.

The second time around in 2024, there was an abundance of worthy causes. In 2025 there were even more high quality applications, many of which were growing beyond our ability to support them.

Thus this is the second edition of The Big Nonprofits Post, primarily aimed at sharing my findings on various organizations I believe are doing good work, to help you find places to consider donating in the cause areas and intervention methods that you think are most effective, and to offer my general perspective on how I think about choosing where to give.

This post combines my findings from the 2024 and 2025 rounds of SFF, and also includes some organizations that did not apply to either round, so inclusion does not mean that they necessarily applied at all.

This post is already very long, so the bar is higher for inclusion this year than it was last year, especially for new additions.

If you think they are better places to give and better causes to back, act accordingly, especially if they’re illegible or obscure. You don’t need my approval.

The Big Nonprofits List 2025 is also available as a website, where you can sort by mission, funding needed or confidence, or do a search and have handy buttons.

Organizations where I have the highest confidence in straightforward modest donations now, if your goals and model of the world align with theirs, are in bold, for those who don’t want to do a deep dive.

  1. Table of Contents.

  2. A Word of Warning.

  3. A Note To Charities.

  4. Use Your Personal Theory of Impact.

  5. Use Your Local Knowledge.

  6. Unconditional Grants to Worthy Individuals Are Great.

  7. Do Not Think Only On the Margin, and Also Use Decision Theory.

  8. Compare Notes With Those Individuals You Trust.

  9. Beware Becoming a Fundraising Target.

  10. And the Nominees Are.

  11. Organizations that Are Literally Me.

  12. Balsa Research.

  13. Don’t Worry About the Vase.

  14. Organizations Focusing On AI Non-Technical Research and Education.

  15. Lightcone Infrastructure.

  16. The AI Futures Project.

  17. Effective Institutions Project (EIP) (For Their Flagship Initiatives).

  18. Artificial Intelligence Policy Institute (AIPI).

  19. AI Lab Watch.

  20. Palisade Research.

  21. CivAI.

  22. AI Safety Info (Robert Miles).

  23. Intelligence Rising.

  24. Convergence Analysis.

  25. IASEAI (International Association for Safe and Ethical Artificial Intelligence).

  26. The AI Whistleblower Initiative.

  27. Organizations Related To Potentially Pausing AI Or Otherwise Having A Strong International AI Treaty. (Blank)

  28. Pause AI and Pause AI Global.

  29. MIRI.

  30. Existential Risk Observatory.

  31. Organizations Focusing Primary On AI Policy and Diplomacy.

  32. Center for AI Safety and the CAIS Action Fund.

  33. Foundation for American Innovation (FAI).

  34. Encode AI (Formerly Encode Justice).

  35. The Future Society.

  36. Safer AI.

  37. Institute for AI Policy and Strategy (IAPS).

  38. AI Standards Lab (Holtman Research).

  39. Safe AI Forum.

  40. Center For Long Term Resilience.

  41. Simon Institute for Longterm Governance.

  42. Legal Advocacy for Safe Science and Technology.

  43. Institute for Law and AI.

  44. Macrostrategy Research Institute.

  45. Secure AI Project.

  46. Organizations Doing ML Alignment Research.

  47. Model Evaluation and Threat Research (METR).

  48. Alignment Research Center (ARC).

  49. Apollo Research.

  50. Cybersecurity Lab at University of Louisville.

  51. Timaeus.

  52. Simplex.

  53. Far AI.

  54. Alignment in Complex Systems Research Group.

  55. Apart Research.

  56. Transluce.

  57. Organizations Doing Other Technical Work. (Blank)

  58. AI Analysts @ RAND.

  59. Organizations Doing Math, Decision Theory and Agent Foundations.

  60. Orthogonal.

  61. Topos Institute.

  62. Eisenstat Research.

  63. AFFINE Algorithm Design.

  64. CORAL (Computational Rational Agents Laboratory).

  65. Mathematical Metaphysics Institute.

  66. Focal at CMU.

  67. Organizations Doing Cool Other Stuff Including Tech.

  68. ALLFED.

  69. Good Ancestor Foundation.

  70. Charter Cities Institute.

  71. Carbon Copies for Independent Minds.

  72. Organizations Focused Primarily on Bio Risk. (Blank)

  73. Secure DNA.

  74. Blueprint Biosecurity.

  75. Pour Domain.

  76. ALTER Israel.

  77. Organizations That Can Advise You Further.

  78. Effective Institutions Project (EIP) (As A Donation Advisor).

  79. Longview Philanthropy.

  80. Organizations That then Regrant to Fund Other Organizations.

  81. SFF Itself (!).

  82. Manifund.

  83. AI Risk Mitigation Fund.

  84. Long Term Future Fund.

  85. Foresight.

  86. Centre for Enabling Effective Altruism Learning & Research (CEELAR).

  87. Organizations That are Essentially Talent Funnels.

  88. AI Safety Camp.

  89. Center for Law and AI Risk.

  90. Speculative Technologies.

  91. Talos Network.

  92. MATS Research.

  93. Epistea.

  94. Emergent Ventures.

  95. AI Safety Cape Town.

  96. ILINA Program.

  97. Impact Academy Limited.

  98. Atlas Computing.

  99. Principles of Intelligence (Formerly PIBBSS).

  100. Tarbell Center.

  101. Catalyze Impact.

  102. CeSIA within EffiSciences.

  103. Stanford Existential Risk Initiative (SERI).

  104. Non-Trivial.

  105. CFAR.

  106. The Bramble Center.

  107. Final Reminders.

The SFF recommender process is highly time constrained, and in general I am highly time constrained.

Even though I used well beyond the number of required hours in both 2024 and 2025, there was no way to do a serious investigation of all the potentially exciting applications. Substantial reliance on heuristics was inevitable.

Also your priorities, opinions, and world model could be very different from mine.

If you are considering donating a substantial (to you) amount of money, please do the level of personal research and consideration commensurate with the amount of money you want to give away.

If you are considering donating a small (to you) amount of money, or if the requirement to do personal research might mean you don’t donate to anyone at all, I caution the opposite: Only do the amount of optimization and verification and such that is worth its opportunity cost. Do not let the perfect be the enemy of the good.

For more details of how the SFF recommender process works, see my post on the process.

Note that donations to some of the organizations below may not be tax deductible.

I apologize in advance for any errors, any out of date information, and for anyone who I included who I did not realize would not want to be included. I did my best to verify information, and to remove any organizations that do not wish to be included.

If you wish me to issue a correction of any kind, or to update your information, I will be happy to do that at least through the end of the year.

If you wish me to remove your organization entirely, for any reason, I will do that, too.

What I unfortunately cannot do, in most cases, is take the time to analyze or debate beyond that. I also can’t consider additional organizations for inclusion. My apologies.

The same is true for the website version.

I am giving my full opinion on all organizations listed, but where I feel an organization would be a poor choice for marginal dollars even within its own cause and intervention area, or I anticipate my full opinion would not net help them, they are silently not listed.

Listen to arguments and evidence. But do not let me, or anyone else, tell you any of:

  1. What is important.

  2. What is a good cause.

  3. What types of actions are best to make the change you want to see in the world.

  4. What particular strategies are most promising.

  5. That you have to choose according to some formula or you’re an awful person.

This is especially true when it comes to policy advocacy, and especially in AI.

If an organization is advocating for what you think is bad policy, or acting in a way that does bad things, don’t fund them!

If an organization is advocating or acting in a way you think is ineffective, don’t fund them!

Only fund people you think advance good changes in effective ways.

Not cases where I think that. Cases where you think that.

During SFF, I once again in 2025 chose to deprioritize all meta-level activities and talent development. I see lots of good object-level work available to do, and I expected others to often prioritize talent and meta activities.

The counterargument to this is that quite a lot of money is potentially going to be freed up soon as employees of OpenAI and Anthropic gain liquidity, including access to DAFs (donor advised funds). This makes expanding the pool more exciting.

I remain primarily focused on those who in some form were helping ensure AI does not kill everyone. I continue to see highest value in organizations that influence lab or government AI policies in the right ways, and continue to value Agent Foundations style and other off-paradigm technical research approaches.

I believe that the best places to give are the places where you have local knowledge.

If you know of people doing great work or who could do great work, based on your own information, then you can fund and provide social proof for what others cannot.

The less legible to others the cause, and the harder it is to fit it into the mission statements and formulas of various big donors, the more excited you should be to step forward, if the cause is indeed legible to you. This keeps you grounded, helps others find the show (as Tyler Cowen says), is more likely to be counterfactual funding, and avoids information cascades or looking under streetlights for the keys.

Most importantly it avoids adverse selection. The best legible opportunities for funding, the slam dunk choices? Those are probably getting funded. The legible things that are left are the ones that others didn’t sufficiently fund yet.

If you know why others haven’t funded, because they don’t know about the opportunity? That’s a great trade.

The process of applying for grants, raising money, and justifying your existence sucks.

A lot.

It especially sucks for many of the creatives and nerds that do a lot of the best work.

It also sucks to have to worry about running out of money, or to have to plan your work around the next time you have to justify your existence, or to be unable to be confident in choosing ambitious projects.

If you have to periodically go through this process, and are forced to continuously worry about making your work legible and how others will judge it, that will substantially hurt your true productivity. At best it is a constant distraction. By default, it is a severe warping effect. A version of this phenomenon is doing huge damage to academic science.

As I noted in my AI updates, the reason this blog exists is that I received generous, essentially unconditional, anonymous support to ‘be a public intellectual’ and otherwise pursue whatever I think is best. My benefactors offer their opinions when we talk because I value their opinions, but they never try to influence my decisions, and I feel zero pressure to make my work legible in order to secure future funding.

If you have money to give, and you know individuals who should clearly be left to do whatever they think is best without worrying about raising or earning money, who you are confident would take advantage of that opportunity and try to do something great, then giving them unconditional grants is a great use of funds, including giving them ‘don’t worry about reasonable expenses’ levels of funding.

This is especially true when combined with ‘retrospective funding,’ based on what they have already done. It would be great if we established a tradition and expectation that people who make big contributions can expect such rewards.

Not as unconditionally, it’s also great to fund specific actions and projects and so on that you see not happening purely through lack of money, especially when no one is asking you for money.

This includes things that you want to exist, but that don’t have a path to sustainability or revenue, or would be importantly tainted if they needed to seek that. Fund the project you want to see in the world. This can also be purely selfish, often in order to have something yourself you need to create it for everyone, and if you’re tempted there’s a good chance that’s a great value.

Resist the temptation to think purely on the margin, asking only what one more dollar can do. The incentives get perverse quickly. Organizations are rewarded for putting their highest impact activities in peril. Organizations that can ‘run lean’ or protect their core activities get punished.

If you always insist on being a ‘funder of last resort’ that requires key projects or the whole organization otherwise be in trouble, you’re defecting. Stop it.

Also, you want to do some amount of retrospective funding. If people have done exceptional work in the past, you should be willing to give them a bunch more rope in the future, above and beyond the expected value of their new project.

Don’t make everyone constantly reprove their cost effectiveness each year, or at least give them a break. If someone has earned your trust, then if this is the project they want to do next, presume they did so because of reasons, although you are free to disagree with those reasons.

This especially goes for AI lab employees. There’s no need for everyone to do all of their own research, you can and should compare notes with those who you can trust, and this is especially great when they’re people you know well.

What I do worry about is too much outsourcing of decisions to larger organizations and institutional structures, including those of Effective Altruism but also others, or letting your money go directly to large foundations where it will often get captured.

Jaan Tallinn created SFF in large part to intentionally take his donation decisions out of his hands, so he could credibly tell people those decisions were out of his hands, so he would not have to constantly worry that people he talked to were attempting to fundraise.

This is a huge deal. Communication, social life and a healthy information environment can all be put in danger by this.

Time to talk about the organizations themselves.

Rather than offer precise rankings, I divided by cause category and into three confidence levels.

  1. High confidence means I have enough information to be confident the organization is at least a good pick.

  2. Medium or low confidence means exactly that – I have less confidence that the choice is wise, and you should give more consideration to doing your own research.

  3. If my last investigation was in 2024, and I haven’t heard anything, I will have somewhat lower confidence now purely because my information is out of date.

Low confidence is still high praise, and very much a positive assessment! Most organizations would come nowhere close to making the post at all.

If an organization is not listed, that does not mean I think they would be a bad pick – they could have asked not to be included, or I could be unaware of them or their value, or I could simply not have enough confidence to list them.

I know how Bayesian evidence works, but this post is not intended as a knock on anyone, in any way. Some organizations that are not here would doubtless have been included, if I’d had more time.

I try to give a sense of how much detailed investigation and verification I was able to complete, and what parts I have confidence in versus not. Again, my lack of confidence will often be purely about my lack of time to get that confidence.

Unless I already knew them from elsewhere, assume no organizations here got as much attention as they deserve before you decide on what for you is a large donation.

I’m tiering based on how I think about donations from you, from outside SFF.

I think the regranting organizations were clearly wrong choices from within SFF, but are reasonable picks if you don’t want to do extensive research, especially if you are giving small.

In terms of funding levels needed, I will similarly divide into three categories.

They roughly mean this, to the best of my knowledge:

Low: Could likely be fully funded with less than ~$250k.

Medium: Could plausibly be fully funded with between ~$250k and ~$2 million.

High: Could probably make good use of more than ~$2 million.

These numbers may be obsolete by the time you read this. If you’re giving a large amount relative to what they might need, check with the organization first, but also do not be so afraid of modest amounts of ‘overfunding’ as relieving fundraising pressure is valuable and as I noted it is important not to only think on the margin.

A lot of organizations are scaling up rapidly, looking to spend far more money than they have in the past. This was true in 2024, and 2025 has only accelerated this trend. A lot more organizations are in ‘High’ now but I decided not to update the thresholds.

Everyone seems eager to double their headcount. I’m not putting people into the High category unless I am confident they can scalably absorb more funding after SFF.

The person who I list as the leader of an organization will sometimes accidentally be whoever was in charge of fundraising rather than strictly the leader. Partly the reason for listing it is to give context and some of you can go ‘oh right, I know who that is,’ and the other reason is that all organization names are often highly confusing – adding the name of the organization’s leader allows you a safety check, to confirm that you are indeed pondering the same organization I am thinking of!

This is my post, so I get to list Balsa Research first. (I make the rules here.)

If that’s not what you’re interested in, you can of course skip the section.

Focus: Groundwork starting with studies to allow repeal of the Jones Act

Leader: Zvi Mowshowitz

Funding Needed: Medium

Confidence Level: High

Our first target continues to be the Jones Act. With everything happening in 2025, it is easy to get distracted. We have decided to keep eyes on the prize.

We’ve commissioned two studies. Part of our plan is to do more of them, and also do things like draft model repeals and explore ways to assemble a coalition and to sell and spread the results, to enable us to have a chance at repeal.

We also are networking, gathering information, publishing findings where there are information holes or where we can offer superior presentations, planning possible collaborations, and responding quickly in case of a crisis in related areas. We believe we meaningfully reduced the probability that certain very damaging additional maritime regulations could have become law, as described in this post.

Other planned cause areas include NEPA reform and federal housing policy (to build more housing where people want to live).

We have one full time worker on the case and are trying out a potential second one.

I don’t intend to have Balsa work on AI or assist with my other work, or to take personal compensation, unless I get substantially larger donations than we have had previously, that are either dedicated to those purposes or that at least come with the explicit understanding I should consider doing that.

Further donations would otherwise be for general support.

The pitch for Balsa, and the reason I am doing it, is in two parts.

I believe Jones Act repeal and many other abundance agenda items are neglected, tractable and important, and that my way of focusing on what matters can advance them. That the basic work that needs doing is not being done, it would be remarkably cheap to do a lot of it and do it well, and that this would give us a real if unlikely chance to get a huge win if circumstances break right. Chances for progress currently look grim, but winds can change quickly, we need to be ready, and also we need to stand ready to mitigate the chance things get even worse.

I also believe that if people do not have hope for the future, do not have something to protect and fight for, or do not think good outcomes are possible, that people won’t care about protecting the future. And that would be very bad, because we are going to need to fight to protect our future if we want to have one, or have a good one.

You got to give them hope.

I could go on, but I’ll stop there.

Donate here, or get in touch at [email protected].

Focus: Zvi Mowshowitz writes a lot of words, really quite a lot.

Leader: Zvi Mowshowitz

Funding Needed: None, but it all helps, could plausibly absorb a lot

Confidence Level: High

You can also of course always donate directly to my favorite charity.

By which I mean me. I always appreciate your support, however large or small.

The easiest way to help on a small scale (of course) is a Substack subscription or Patreon. Paid substack subscriptions punch above their weight because they assist with the sorting algorithm, and also for their impact on morale.

If you want to go large then reach out to me.

Thanks to generous anonymous donors, I am able to write full time and mostly not worry about money. That is what makes this blog possible.

I want to as always be 100% clear: I am totally, completely fine as is, as is the blog.

Please feel zero pressure here, as noted throughout there are many excellent donation opportunities out there.

Additional funds are still welcome. There are levels of funding beyond not worrying.

Such additional support is always highly motivating.

Also there are absolutely additional things I could and would throw money at to improve the blog, potentially including hiring various forms of help or even expanding to more of a full news operation or startup.

As a broad category, these are organizations trying to figure things out regarding AI existential risk, without centrally attempting to either do technical work or directly to influence policy and discourse.

Lightcone Infrastructure is my current top pick across all categories. If you asked me where to give a dollar, or quite a few dollars, to someone who is not me, I would tell you to fund Lightcone Infrastructure.

Focus: Rationality community infrastructure, LessWrong, the Alignment Forum and Lighthaven.

Leaders: Oliver Habryka and Rafe Kennedy

Funding Needed: High

Confidence Level: High

Disclaimer: I am on the CFAR board which used to be the umbrella organization for Lightcone and still has some lingering ties. My writing appears on LessWrong. I have long time relationships with everyone involved. I have been to several reliably great workshops or conferences at their campus at Lighthaven. So I am conflicted here.

With that said, Lightcone is my clear number one. I think they are doing great work, both in terms of LessWrong and also Lighthaven. There is the potential, with greater funding, to enrich both of these tasks, and also for expansion.

There is a large force multiplier here (although that is true of a number of other organizations I list as well).

They made their 2024 fundraising pitch here, I encourage reading it.

Where I am beyond confident is that if LessWrong, the Alignment Forum or the venue Lighthaven were unable to continue, any one of these would be a major, quite bad unforced error.

LessWrong and the Alignment Forum a central part of the infrastructure of the meaningful internet.

Lighthaven is miles and miles away the best event venue I have ever seen. I do not know how to convey how much the design contributes to having a valuable conference, designed to facilitate the best kinds of conversations via a wide array of nooks and pathways designed with the principles of Christopher Alexander. This contributes to and takes advantage of the consistently fantastic set of people I encounter there.

The marginal costs here are large (~$3 million per year, some of which is made up by venue revenue), but the impact here is many times that, and I believe they can take on more than ten times that amount and generate excellent returns.

If we can go beyond short term funding needs, they can pay off the mortgage to secure a buffer, and buy up surrounding buildings to secure against neighbors (who can, given this is Berkeley, cause a lot of trouble) and to secure more housing and other space. This would secure the future of the space.

I would love to see them then expand into additional spaces. They note this would also require the right people.

Donate through every.org, or contact [email protected].

Focus: AI forecasting research projects, governance research projects, and policy engagement, in that order.

Leader: Daniel Kokotajlo, with Eli Lifland

Funding Needed: None Right Now

Confidence Level: High

Of all the ‘shut up and take my money’ applications in the 2024 round where I didn’t have a conflict of interest, even before I got to participate in their tabletop wargame exercise, I judged this the most ‘shut up and take my money’-ist. At The Curve, I got to participate in the exercise and participate in discussions around it, I’ve since done several more, and I’m now even more confident this is an excellent pick.

I continue to think it is a super strong case for retroactive funding as well. Daniel walked away from OpenAI, and what looked to be most of his net worth, to preserve his right to speak up. That led to us finally allowing others at OpenAI to speak up as well.

This is how he wants to speak up, and try to influence what is to come, based on what he knows. I don’t know if it would have been my move, but the move makes a lot of sense, and it has already paid off big. AI 2027 was read by the Vice President, who took it seriously, along with many others, and greatly informed the conversation. I believe the discourse is much improved as a result, and the possibility space has improved.

Note that they are comfortably funded through the medium term via private donations and their recent SFF grant.

Donate through every.org, or contact Jonas Vollmer.

Focus: AI governance, advisory and research, finding how to change decision points

Leader: Ian David Moss

Funding Needed: Medium

Confidence Level: High

EIP operates on two tracks. They have their flagship initiatives and attempts to intervene directly. They also serve as donation advisors, which I discuss in that section.

Their current flagship initiative plans are to focus on the intersection of AI governance and the broader political and economic environment, especially risks of concentration of power and unintentional power shifts from humans to AIs.

Can they indeed identify ways to target key decision points, and make a big difference? One can look at their track record. I’ve been asked to keep details confidential, but based on my assessment of private information, I confirmed they’ve scored some big wins including that they helped improve safety practices at a major AI lab, and will plausibly continue to be able to have high leverage and punch above their funding weight. You can read about some of the stuff that they can talk about here in a Founders Pledge write up.

It seems important that they be able to continue their work on all this.

I also note that in SFF I allocated less funding to EIP than I would in hindsight have liked to allocate, due to quirks about the way matching funds worked and my attempts to adjust my curves to account for it.

Donate through every.org, or contact [email protected].

Focus: Primarily polls about AI, also lobbying and preparing for crisis response.

Leader: Daniel Colson.

Also Involved: Mark Beall and Daniel Eth

Funding Needed: High

Confidence Level: High

Those polls about how the public thinks about AI, including several from last year around SB 1047 including an adversarial collaboration with Dean Ball?

Remarkably often, these are the people that did that. Without them, few would be asking those questions. Ensuring that someone is asking is super helpful. With some earlier polls I was a bit worried that the wording was slanted, and that will always be a concern with a motivated pollster, but I think recent polls have been much better at this, and they are as close to neutral as one can reasonably expect.

There are those who correctly point out that even now in 2025 the public’s opinions are weakly held and low salience, and that all you’re often picking up is ‘the public does not like AI and it likes regulation.’

Fair enough. Someone still has to show this, and show it applies here, and put a lie to people claiming the public goes the other way, and measure how things change over time. We need to be on top of what the public is thinking, including to guard against the places it wants to do dumb interventions.

They don’t only do polling. They also do lobbying and prepare for crisis responses.

Donate here, or use their contact form to get in touch.

Focus: Monitoring the AI safety record and plans of the frontier AI labs

Leader: Zach Stein-Perlman

Funding Needed: Low

Confidence Level: High

Zach has consistently been one of those on top of the safety and security plans, the model cards and other actions of the major labs, both writing up detailed feedback from a skeptical perspective and also compiling the website and its scores in various domains. Zach is definitely in the ‘demand high standards that would actually work and treat everything with skepticism’ school of all this, which I feel is appropriate, and I’ve gotten substantial benefit of his work several times.

However, due to uncertainty about whether this is the best thing for him to work on, and thus not being confident he will have this ball, Zach is not currently accepting funding, but would like people who are interested in donations to contact him via Intercom on the AI Lab Watch website.

Focus: AI capabilities demonstrations to inform decision makers on capabilities and loss of control risks

Leader: Jeffrey Ladish

Funding Needed: High

Confidence Level: High

This is clearly an understudied approach. People need concrete demonstrations. Every time I get to talking with people in national security or otherwise get closer to decision makers who aren’t deeply into AI and in particular into AI safety concerns, you need to be as concrete and specific as possible – that’s why I wrote Danger, AI Scientist, Danger the way I did. We keep getting rather on-the-nose fire alarms, but it would be better if we could get demonstrations even more on the nose, and get them sooner, and in a more accessible way.

Since last time, I’ve had a chance to see their demonstrations in action several times, and I’ve come away feeling that they have mattered.

I have confidence that Jeffrey is a good person to continue to put this plan into action.

To donate, click here or email [email protected].

Focus: Visceral demos of AI risks

Leader: Sid Hiregowdara

Funding Needed: High

Confidence Level: Medium

I was impressed by the demo I was given (so a demo demo?). There’s no question such demos fill a niche and there aren’t many good other candidates for the niche.

The bear case is that the demos are about near term threats, so does this help with the things that matter? It’s a good question. My presumption is yes, that raising situational awareness about current threats is highly useful. That once people notice that there is danger, that they will ask better questions, and keep going. But I always do worry about drawing eyes to the wrong prize.

To donate, click here or email [email protected].

Focus: Making YouTube videos about AI safety, starring Rob Miles

Leader: Rob Miles

Funding Needed: Low

Confidence Level: High

I think these are pretty great videos in general, and given what it costs to produce them we should absolutely be buying their production. If there is a catch, it is that I am very much not the target audience, so you should not rely too much on my judgment of what is and isn’t effective video communication on this front, and you should confirm you like the cost per view.

To donate, join his patreon or contact him at [email protected].

Focus: Facilitation of the AI scenario roleplaying exercises including Intelligence Rising

Leader: Shahar Avin

Funding Needed: Low

Confidence Level: High

I haven’t had the opportunity to play Intelligence Rising, but I have read the rules to it, and heard a number of strong after action reports (AARs). They offered this summary of insights in 2024. The game is clearly solid, and it would be good if they continue to offer this experience and if more decision makers play it, in addition to the AI Futures Project TTX.

To donate, reach out to [email protected].

Focus: A series of sociotechnical reports on key AI scenarios, governance recommendations and conducting AI awareness efforts.

Leader: David Kristoffersson

Funding Needed: High (combining all tracks)

Confidence Level: Low

They do a variety of AI safety related things. Their Scenario Planning continues to be what I find most exciting, although I’m also somewhat interested in their modeling cooperation initiative as well. It’s not as neglected as it was a year ago, but we could definitely use more work than we’re getting. For track record you check out their reports from 2024 in this area, and see if you think that was good work, and the rest of their website has more.

Their donation page is here, or you can contact [email protected].

Focus: Grab bag of AI safety actions, research, policy, community, conferences, standards

Leader: Mark Nitzberg

Funding Needed: High

Confidence Level: Low

There are some clearly good things within the grab bag, including some good conferences and it seems substantial support for Geoffrey Hinton, but for logistical reasons I didn’t do a close investigation to see if the overall package looked promising. I’m passing the opportunity along.

Donate here, or contact them at [email protected].

Focus: Whistleblower advising and resources for those in AI labs warning about catastrophic risks, including via Third Opinion.

Leader: Larl Koch

Funding Needed: High

Confidence Level: Medium

I’ve given them advice, and at least some amount of such resourcing is obviously highly valuable. We certainly should be funding Third Opinion, so that if someone wants to blow the whistle they can have help doing it. The question is whether if it scales this loses its focus.

Donate here, or reach out to [email protected].

Focus: Advocating for a pause on AI, including via in-person protests

Leader: Holly Elmore (USA) and Joep Meindertsma (Global)

Funding Level: Low

Confidence Level: Medium

Some people say that those who believe we should pause AI would be better off staying quiet about it, rather than making everyone look foolish.

I disagree.

I don’t think pausing right now is a good idea. I think we should be working on the transparency, state capacity, technical ability and diplomatic groundwork to enable a pause in case we need one, but that it is too early to actually try to implement one.

But I do think that if you believe we should pause? Then you should say that we should pause. I very much appreciate people standing up, entering the arena and saying what they believe in, including quite often in my comments. Let the others mock all they want.

If you agree with Pause AI that the right move is to Pause AI, and you don’t have strong strategic disagreements with their approach, then you should likely be excited to fund this. If you disagree, you have better options.

Either way, they are doing what they, given their beliefs, should be doing.

Donate here, or reach out to [email protected].

Focus: At this point, primarily AI policy advocacy, letting everyone know that If Anyone Builds It, Everyone Dies and all that, plus some research

Leaders: Malo Bourgon, Eliezer Yudkowsky

Funding Needed: High

Confidence Level: High

MIRI, concluding that it is highly unlikely alignment will make progress rapidly enough otherwise, has shifted its strategy to largely advocate for major governments coming up with an international agreement to halt AI progress and to do communications, although research still looks to be a large portion of the budget, and they have dissolved its agent foundations team. Hence the book.

That is not a good sign for the world, but it does reflect their beliefs.

They have accomplished a lot. The book is at least a modest success on its own terms in moving things forward.

I strongly believe they should be funded to continue to fight for a better future however they think is best, even when I disagree with their approach.

This is very much a case of ‘do this if and only if this aligns with your model and preferences.’

Donate here, or reach out to [email protected].

Focus: Pause-relevant research

Leader: Otto Barten

Funding Needed: Low

Confidence Level: Medium

Mostly this is the personal efforts of Otto Barten, ultimately advocating for a conditional pause. For modest amounts of money, in prior years he’s managed to have a hand in some high profile existential risk events and get the first x-risk related post into TIME magazine. He’s now pivoted to pause-relevant research (as in how to implement one via treaties, off switches, evals and threat models).

The track record and my prior investigation is less relevant now, so I’ve bumped them down to low confidence, but it would definitely be good to have the technical ability to pause and not enough work is being done on that.

To donate, click here, or get in touch at [email protected].

Some of these organizations also look at bio policy or other factors, but I judge those here as being primarily concerned with AI.

In this area, I am especially keen to rely on people with good track records, who have shown that they can build and use connections and cause real movement. It’s so hard to tell what is and isn’t effective, otherwise. Often small groups can pack a big punch, if they know where to go, or big ones can be largely wasted – I think that most think tanks on most topics are mostly wasted even if you believe in their cause.

Focus: AI research, field building and advocacy

Leaders: Dan Hendrycks

Funding Needed: High

Confidence Level: High

They did the CAIS Statement on AI Risk, helped SB 1047 get as far as it did, and have improved things in many other ways. Some of these other ways are non-public. Some of those non-public things are things I know about and some aren’t. I will simply say the counterfactual policy world is a lot worse. They’ve clearly been punching well above their weight in the advocacy space. The other arms are no slouch either, lots of great work here. Their meaningful rolodex and degree of access is very strong and comes with important insight into what matters.

They take a lot of big swings and aren’t afraid of taking risks or looking foolish. I appreciate that, even when a given attempt doesn’t fully work.

If you want to focus on their policy, then you can fund their 501(c)(4), the Action Fund, since 501c(3)s are limited in how much they can spend on political activities, keeping in mind the tax implications of that. If you don’t face any tax implications I would focus first on the 501(c)(4).

We should definitely find a way to fund at least their core activities.

Donate to the Action Fund for funding political activities, or the 501(c)(3) for research. They can be contacted at [email protected].

Focus: Tech policy research, thought leadership, educational outreach to government, fellowships.

Leader: Grace Meyer

Funding Needed: High

Confidence Level: High

FAI is centrally about innovation. Innovation is good, actually, in almost all contexts, as is building things and letting people do things.

AI is where this gets tricky. People ‘supporting innovation’ are often using that as an argument against all regulation of AI, and indeed I am dismayed to see so many push so hard on this exactly in the one place I think they are deeply wrong, when we could work together on innovation (and abundance) almost anywhere else.

FAI and resident AI studiers Samuel Hammond and Dean Ball are in an especially tough spot, because they are trying to influence AI policy from the right and not get expelled from that coalition or such spaces. There’s a reason we don’t have good alternative options for this. That requires striking a balance.

I’ve definitely had my disagreements with Hammond, including strong disagreements with his 95 theses on AI although I agreed far more than I disagreed, and I had many disagreements with his AI and Leviathan as well. He’s talked on the Hill about ‘open model diplomacy.’

I’ve certainly had many strong disagreements with Dean Ball as well, both in substance and rhetoric. Sometimes he’s the voice of reason and careful analysis, other times (from my perspective) he can be infuriating, most recently in discussions of the Superintelligence Statement, remarkably often he does some of both in the same post. He was perhaps the most important opposer of SB 1047 and went on to a stint at the White House before joining FAI.

Yet here is FAI, rather high on the list. They’re a unique opportunity, you go to war with the army you have, and both Ball and Hammond have stuck their neck out in key situations. Hammond came out opposing the moratorium. They’ve been especially strong on compute governance.

I have private reasons to believe that FAI has been effective and we can expect that to continue, and its other initiatives also mostly seem good. We don’t have to agree on everything else, so long as we all want good things and are trying to figure things out, and I’m confident that is the case here.

I am especially excited that they can speak to the Republican side of the aisle in the R’s native language, which is difficult for most in this space to do.

An obvious caveat is that if you are not interested in the non-AI pro-innovation part of the agenda (I certainly approve, but it’s not obviously a high funding priority for most readers) then you’ll want to ensure it goes where you want it.

To donate, click here, or contact them using the form here.

Focus: Youth activism on AI safety issues

Leader: Sneha Revanur

Funding Needed: Medium

Confidence Level: High

They started out doing quite a lot on a shoestring budget by using volunteers, helping with SB 1047 and in several other places. Now they are turning pro, and would like to not be on a shoestring. I think they have clearly earned that right. The caveat is risk of ideological capture. Youth organizations tend to turn to left wing causes.

The risk here is that this effectively turns mostly to AI ethics concerns. It’s great that they’re coming at this without having gone through the standard existential risk ecosystem, but that also heightens the ideological risk.

I continue to believe it is worth the risk.

To donate, go here. They can be contacted at [email protected].

Focus: AI governance standards and policy.

Leader: Nicolas Moës

Funding Needed: High

Confidence Level: High

I’ve seen credible sources saying they do good work, and that they substantially helped orient the EU AI Act to at least care at all about frontier general AI. The EU AI Act was not a good bill, but it could easily have been a far worse one, doing much to hurt AI development while providing almost nothing useful for safety.

We should do our best to get some positive benefits out of the whole thing. And indeed, they helped substantially improve the EU Code of Practice, which was in hindsight remarkably neglected otherwise.

They’re also active around the world, including the USA and China.

Donate here, or contact them here.

Focus: Specifications for good AI safety, also directly impacting EU AI policy

Leader: Henry Papadatos

Funding Needed: Medium

Confidence Level: Low

I’ve been impressed by Simeon and his track record, including here. Simeon is stepping down as leader to start a company, which happened post-SFF, so they would need to be reevaluated in light of this before any substantial donation.

Donate here, or contact them at [email protected].

Focus: Papers and projects for ‘serious’ government circles, meetings with same, policy research

Leader: Peter Wildeford

Funding Needed: Medium

Confidence Level: High

I have a lot of respect for Peter Wildeford, and they’ve clearly put in good work and have solid connections down, including on the Republican side where better coverage is badly needed, and the only other solid lead we have is FAI. Peter has also increasingly been doing strong work directly via Substack and Twitter that has been helpful to me and that I can observe directly. They are strong on hardware governance and chips in particular (as is FAI).

Given their goals and approach, funding from outside the traditional ecosystem sources would be extra helpful, ideally such efforts are fully distinct from OpenPhil.

With the shifting landscape and what I’ve observed, I’m moving them up to high confidence and priority.

Donate here, or contact them at [email protected].

Focus: Accelerating the writing of AI safety standards

Leaders: Koen Holtman and Chin Ze Shen

Funding Needed: Medium

Confidence Level: High

They help facilitate the writing of AI safety standards, for EU/UK/USA, including on the recent EU Code of Practice. They have successfully gotten some of their work officially incorporated, and another recommender with a standards background was impressed by the work and team.

This is one of the many things that someone has to do, and where if you step up and do it and no one else does that can go pretty great. Having now been involved in bill minutia myself, I know it is thankless work, and that it can really matter, both for public and private standards, and they plan to pivot somewhat to private standards.

I’m raising my confidence to high that this is at least a good pick, if you want to fund the writing of standards.

To donate, go here or reach out to [email protected].

Focus: International AI safety conferences

Leader: Fynn Heide and Sophie Thomson

Funding Needed: Medium

Confidence Level: Low

They run the IDAIS series of conferences, including successful ones involving China. I do wish I had a better model of what makes such a conference actually matter versus not mattering, but these sure seem like they should matter, and certainly well worth their costs to run them.

To donate, contact them using the form at the bottom of the page here.

Focus: UK Policy Think Tank focusing on ‘extreme AI risk and biorisk policy.’

Leader: Angus Mercer

Funding Needed: High

Confidence Level: Low

The UK has shown promise in its willingness to shift its AI regulatory focus to frontier models in particular. It is hard to know how much of that shift to attribute to any particular source, or otherwise measure how much impact there has been or might be on final policy.

They have endorsements of their influence from philosopher Toby Ord, Former Special Adviser to the UK Prime Minister Logan Graham, and Senior Policy Adviser Nitarshan Rajkumar.

I reached out to a source with experience in the UK government who I trust, and they reported back they are a fan and pointed to some good things they’ve helped with. There was a general consensus that they do good work, and those who investigated where impressed.

However, I have concerns. Their funding needs are high, and they are competing against many others in the policy space, many of which have very strong cases. I also worry their policy asks are too moderate, which might be an advantage for others.

My lower confidence this year is a combination of worries about moderate asks, worry about organizational size, and worries about the shift in governments in the UK and the UK’s ability to have real impact elsewhere. But if you buy the central idea of this type of lobbying through the UK and are fine with a large budget, go for it.

Donate here, or reach out to [email protected].

Focus: Foundations and demand for international cooperation on AI governance and differential tech development

Leader: Konrad Seifert and Maxime Stauffer

Funding Needed: High

Confidence Level: Low

As with all things diplomacy, hard to tell the difference between a lot of talk and things that are actually useful. Things often look the same either way for a long time. A lot of their focus is on the UN, so update either way based on how useful you think that approach is, and also that makes it even harder to get a good read.

They previously had a focus on the Global South and are pivoting to China, which seems like a more important focus.

To donate, scroll down on this page to access their donation form, or contact them at [email protected].

Focus: Legal team for lawsuits on catastrophic risk and to defend whistleblowers.

Leader: Tyler Whitmer

Funding Needed: Medium

Confidence Level: Medium

I wasn’t sure where to put them, but I suppose lawsuits are kind of policy by other means in this context, or close enough?

I buy the core idea of having a legal team on standby for catastrophic risk related legal action in case things get real quickly is a good idea, and I haven’t heard anyone else propose this, although I do not feel qualified to vet the operation. They were one of the organizers of the NotForPrivateGain.org campaign against the OpenAI restructuring.

I definitely buy the idea of an AI Safety Whistleblower Defense Fund, which they are also doing. Knowing there will be someone to step up and help if it comes to that changes the dynamics in helpful ways.

Donors who are interested in making relatively substantial donations or grants should contact [email protected], for smaller amounts click here.

Focus: Legal research on US/EU law on transformational AI, fellowships, talent

Leader: Moritz von Knebel

Involved: Gabe Weil

Funding Needed: High

Confidence Level: Low

I’m confident that they should be funded at all, the question is if this should be scaled up quite a lot, and what aspects of this would scale in what ways. If you can be convinced that the scaling plans are worthwhile this could justify a sizable donation.

Donate here, or contact them at [email protected].

Focus: Amplify Nick Bostrom

Leader: Toby Newberry

Funding Needed: High

Confidence Level: Low

If you think Nick Bostrom is doing great work and want him to be more effective, then this is a way to amplify that work. In general, ‘give top people support systems’ seems like a good idea that is underexplored.

Get in touch at [email protected].

Focus: Advocacy for public safety and security protocols (SSPs) and related precautions

Leader: Nick Beckstead

Funding Needed: High

Confidence Level: High

I’ve had the opportunity to consult and collaborate with them and I’ve been consistently impressed. They’re the real deal, they pay attention to detail and care about making it work for everyone, and they’ve got results. I’m a big fan.

Donate here, or contact them at [email protected].

This category should be self-explanatory. Unfortunately, a lot of good alignment work still requires charitable funding. The good news is that (even more than last year when I wrote the rest of this introduction) there is a lot more funding, and willingness to fund, than there used to be, and also the projects generally look more promising.

The great thing about interpretability is that you can be confident you are dealing with something real. The not as great thing is that this can draw too much attention to interpretability, and that you can fool yourself into thinking that All You Need is Interpretability.

The good news is that several solid places can clearly take large checks.

I didn’t investigate too deeply on top of my existing knowledge here in 2024, because at SFF I had limited funds and decided that direct research support wasn’t a high enough priority, partly due to it being sufficiently legible.

We should be able to find money previously on the sidelines eager to take on many of these opportunities. Lab employees are especially well positioned, due to their experience and technical knowledge and connections, to evaluate such opportunities, and also to provide help with access and spreading the word.

Formerly ARC Evaluations.

Focus: Model evaluations

Leaders: Beth Barnes, Chris Painter

Funding Needed: High

Confidence Level: High

Originally I wrote that we hoped to be able to get large funding for METR via non-traditional sources. That happened last year, and METR got major funding. That’s great news. Alas, they once again have to hit the fundraising trail.

METR has proven to be the gold standard for outside evaluations of potentially dangerous frontier model capabilities, and has proven its value even more so in 2025.

We very much need these outside evaluations, and to give the labs every reason to use them and no excuse not to use them, and their information has been invaluable. In an ideal world the labs would be fully funding METR, but they’re not.

So this becomes a place where we can confidently invest quite a bit of capital, make a legible case for why it is a good idea, and know it will probably be well spent.

If you can direct fully ‘square’ ‘outside’ funds that need somewhere legible to go and are looking to go large? I love METR for that.

To donate, click here. They can be contacted at [email protected].

Focus: Theoretically motivated alignment work

Leader: Jacob Hilton

Funding Needed: Medium

Confidence Level: High

There’s a long track record of good work here, and Paul Christiano remained excited as of 2024. If you are looking to fund straight up alignment work and don’t have a particular person or small group in mind, this is certainly a safe bet to put additional funds to good use and attract good talent.

Donate here, or reach out to [email protected].

Focus: Scheming, evaluations, and governance

Leader: Marius Hobbhahn

Funding Needed: Medium

Confidence Level: High

This is an excellent thing to focus on, and one of the places we are most likely to be able to show ‘fire alarms’ that make people sit up and notice. Their first year seems to have gone well, one example would be their presentation at the UK safety summit that LLMs can strategically deceive their primary users when put under pressure. They will need serious funding to fully do the job in front of them, hopefully like METR they can be helped by the task being highly legible.

They suggest looking at this paper, and also this one. I can verify that they are the real deal and doing the work.

To donate, reach out to [email protected].

Focus: Support for Roman Yampolskiy’s lab and work

Leader: Roman Yampolskiy

Funding Needed: Low

Confidence Level: High

Roman Yampolskiy is the most pessimistic known voice about our chances of not dying from AI, and got that perspective on major platforms like Joe Rogan and Lex Fridman. He’s working on a book and wants to support PhD students.

Supporters can make a tax detectable gift to the University, specifying that they intend to fund Roman Yampolskiy and the Cyber Security lab.

Focus: Interpretability research

Leader:Jesse Hoogland, Daniel Murfet, Stan van Wingerden

Funding Needed: High

Confidence Level: High

Timaeus focuses on interpretability work and sharing their results. The set of advisors is excellent, including Davidad and Evan Hubinger. Evan, John Wentworth and Vanessa Kosoy have offered high praise, and there is evidence they have impacted top lab research agendas. They’re done what I think is solid work, although I am not so great at evaluating papers directly.

If you’re interested in directly funding interpretability research, that all makes this seem like a slam dunk. I’ve confirmed that this all continues to hold true in 2025.

To donate, get in touch with Jesse at [email protected]. If this is the sort of work that you’re interested in doing, they also have a discord at http://devinterp.com/discord.

Focus: Mechanistic interpretability of how inference breaks down

Leader: Paul Riechers and Adam Shai

Funding Needed: Medium

Confidence Level: High

I am not as high on them as I am on Timaeus, but they have given reliable indicators that they will do good interpretability work. I’d (still) feel comfortable backing them.

Donate here, or contact them via webform.

Focus: Interpretability and other alignment research, incubator, hits based approach

Leader: Adam Gleave

Funding Needed: High

Confidence Level: Medium

They take the hits based approach to research, which is correct. I’ve gotten confirmation that they’re doing the real thing here. In an ideal world everyone doing the real thing would get supported, and they’re definitely still funding constrained.

To donate, click here. They can be contacted at [email protected].

Focus: AI alignment research on hierarchical agents and multi-system interactions

Leader: Jan Kulveit

Funding Needed: Medium

Confidence Level: High

I liked ACS last year, and since then we’ve seen Gradual Disempowerment and other good work, which means this now falls into the category ‘this having funding problems would be an obvious mistake.’ I ranked them very highly in SFF, and there should be a bunch more funding room.

To donate, reach out to [email protected], and note that you are interested in donating to ACS specifically.

Focus: AI safety hackathons, MATS-style programs and AI safety horizon scanning.

Leaders: Esben Kran, Jason Schreiber

Funding Needed: Medium

Confidence Level: Low

I’m (still) confident in their execution of the hackathon idea, which was the central pitch at SFF although they inform me generally they’re more centrally into the MATS-style programs. My doubt for the hackathons is on the level of ‘is AI safety something that benefits from hackathons.’ Is this something one can, as it were, hack together usefully? Are the hackathons doing good counterfactual work? Or is this a way to flood the zone with more variations on the same ideas?

As with many orgs on the list, this one makes sense if and only if you buy the plan, and is one of those ‘I’m not excited but can see it being a good fit for someone else.’

To donate, click here. They can be reached at [email protected].

Focus: Specialized superhuman systems for understanding and overseeing AI

Leaders: Jacob Steinhardt, Sarah Schwettmann

Funding Needed: High

Confidence Level: Medium

Last year they were a new org. Now they have now grown to 14 people and now have a solid track record and want to keep growing. I have confirmation the team is credible. The plan for scaling themselves is highly ambitious, with planned scale well beyond what SFF can fund. I haven’t done anything like the investigation into their plans and capabilities you would need before placing a bet that big, as AI research of all kinds gets expensive quickly.

If there is sufficient appetite to scale the amount of privately funded direct work of this type, then this seems like a fine place to look. I am optimistic on them finding interesting things, although on a technical level I am skeptical of the larger plan.

To donate, reach out to [email protected].

Focus: Developing ‘AI analysts’ that can assist policy makers.

Leaders: John Coughlan

Funding Needed: High

Confidence Level: Medium

This is a thing that RAND should be doing and that should exist. There are obvious dangers here, but I don’t think this makes them substantially worse and I do think this can potentially improve policy a lot. RAND is well placed to get the resulting models to be actually used. That would enhance state capacity, potentially quite a bit.

The problem is that doing this is not cheap, and while funding this shouldn’t fall to those reading this, it plausibly does. This could be a good place to consider sinking quite a large check, if you believe in the agenda.

Donate here.

Right now it looks likely that AGI will be based around large language models (LLMs). That doesn’t mean this is inevitable. I would like our chances better if we could base our ultimate AIs around a different architecture, one that was more compatible with being able to get it to do what we would like it to do.

One path for this is agent foundations, which involves solving math to make the programs work instead of relying on inscrutable giant matrices.

Even if we do not manage that, decision theory and game theory are potentially important for navigating the critical period in front of us, for life in general, and for figuring out what the post-transformation AI world might look like, and thus what choice we make now might do to impact that.

There are not that many people working on these problems. Actual Progress would be super valuable. So even if we expect the median outcome does not involve enough progress to matter, I think it’s still worth taking a shot.

The flip side is you worry about people ‘doing decision theory into the void’ where no one reads their papers or changes their actions. That’s a real issue. As is the increased urgency of other options. Still, I think these efforts are worth supporting, in general.

Focus: AI alignment via agent foundations

Leaders: Tamsin Leake

Funding Needed: Medium

Confidence Level: High

I have funded Orthogonal in the past. They are definitely doing the kind of work that, if it succeeded, might actually amount to something, and would help us get through this to a future world we care about. It’s a long shot, but a long shot worth trying. They very much have the ‘old school’ Yudkowsky view that relatively hard takeoff is likely and most alignment approaches are fools errands. My sources are not as enthusiastic as they once were, but there are only a handful of groups trying that have any chance at all, and this still seems like one of them.

Donate here, or get in touch at [email protected].

Focus: Math for AI alignment

Leaders: Brendan Fong and David Spivak.

Funding Needed: High

Confidence Level: High

Topos is essentially Doing Math to try and figure out what to do about AI and AI Alignment. I’m very confident that they are qualified to (and actually will) turn donated money (partly via coffee) into math, in ways that might help a lot. I am also confident that the world should allow them to attempt this.

They’re now working with ARIA. That seems great.

Ultimately it all likely amounts to nothing, but the upside potential is high and the downside seems very low. I’ve helped fund them in the past and am happy about that.

To donate, go here, or get in touch at [email protected].

Focus: Two people doing research at MIRI, in particular Sam Eisenstat

Leader: Sam Eisenstat

Funding Needed: Medium

Confidence Level: High

Given Sam Eisenstat’s previous work, including from 2025, it seems worth continuing to support him, including supporting researchers. I still believe in this stuff being worth working on, obviously only support if you do as well. He’s funded for now but that’s still only limited runway.

To donate, contact [email protected].

Focus: Johannes Mayer does agent foundations work

Leader: Johannes Mayer

Funding Needed: Low

Confidence Level: Medium

Johannes Mayer does solid agent foundations work, and more funding would allow him to hire more help.

To donate, contact [email protected].

Focus: Examining intelligence

Leader: Vanessa Kosoy

Funding Needed: Medium

Confidence Level: High

This is Vanessa Kosoy and Alex Appel, who have another research agenda formerly funded by MIRI that now needs to stand on its own after their refocus. I once again believe this work to be worth continuing even if the progress isn’t what one might hope. I wish I had the kind of time it takes to actually dive into these sorts of theoretical questions, but alas I do not, or at least I’ve made a triage decision not to.

To donate, click here. For larger amounts contact directly at [email protected]

Focus: Searching for a mathematical basis for metaethics.

Leader: Alex Zhu

Funding Needed: Low

Confidence Level: Low

Alex Zhu has run iterations of the Math & Metaphysics Symposia, which had some excellent people in attendance, and intends partly to do more things of that nature. He thinks eastern philosophy contains much wisdom relevant to developing a future ‘decision-theoretic basis of metaethics’ and plans on an 8+ year project to do that.

I’ve seen plenty of signs that the whole thing is rather bonkers, but also strong endorsements from a bunch of people I trust that there is good stuff here, and the kind of crazy that is sometimes crazy enough to work. So there’s a lot of upside. If you think this kind of approach has a chance of working, this could be very exciting. For additional information, you can see this google doc.

To donate, message Alex at [email protected].

Focus: Game theory for cooperation by autonomous AI agents

Leader: Vincent Conitzer

Funding Needed: Medium

Confidence Level: Low

This is an area MIRI and the old rationalist crowd thought about a lot back in the day. There are a lot of ways for advanced intelligences to cooperate that are not available to humans, especially if they are capable of doing things in the class of sharing source code or can show their decisions are correlated with each other.

With sufficient capability, any group of agents should be able to act as if it is a single agent, and we shouldn’t need to do the game theory for them in advance either. I think it’s good things to be considering, but one should worry that even if they do find answers it will be ‘into the void’ and not accomplish anything. Based on my technical analysis I wasn’t convinced Focal was going to sufficiently interesting places with it, but I’m not at all confident in that assessment.

They note they’re also interested in the dynamics prior to Ai becoming superintelligent, as the initial conditions plausibly matter a lot.

To donate, reach out to Vincent directly at [email protected] to be guided through the donation process.

This section is the most fun. You get unique projects taking big swings.

Focus: Feeding people with resilient foods after a potential nuclear war

Leaders: David Denkenberger

Funding Needed: High

Confidence Level: Medium

As far as I know, no one else is doing the work ALLFED is doing. A resilient food supply ready to go in the wake of a nuclear war (or other major disaster with similar dynamics) could be everything. There’s a small but real chance that the impact is enormous. In my 2021 SFF round, I went back and forth with them several times over various issues, ultimately funding them, you can read about those details here.

I think all of the concerns and unknowns from last time essentially still hold, as does the upside case, so it’s a question of prioritization, how likely you view nuclear war scenarios and how much promise you see in the tech.

If you are convinced by the viability of the tech and ability to execute, then there’s a strong case that this is a very good use of funds.

I think that this is a relatively better choice if you expect AI to remain a normal technology for a while or if your model of AI risks includes a large chance of leading to a nuclear war or other cascading impacts to human survival, versus if you don’t think this.

Research and investigation on the technical details seems valuable here. If we do have a viable path to alternative foods and don’t fund it, that’s a pretty large miss, and I find it highly plausible that this could be super doable and yet not otherwise done.

Donate here, or reach out to [email protected].

Focus: Collaborations for tools to increase civilizational robustness to catastrophes

Leader: Colby Thompson

Funding Needed: High

Confident Level: High

The principle of ‘a little preparation now can make a huge difference to resilience and robustness in a disaster later, so it’s worth doing even if the disaster is not so likely’ generalizes. Thus, the Good Ancestor Foundation, targeting nuclear war, solar flares, internet and cyber outages, and some AI scenarios and safety work.

A particular focus is archiving data and tools, enhancing synchronization systems and designing a novel emergency satellite system (first one goes up in June) to help with coordination in the face of disasters. They’re also coordinating on hardening critical infrastructure and addressing geopolitical and human rights concerns.

They’ve also given out millions in regrants.

One way I know they make good decisions is they continue to help facilitate the funding for my work, and make that process easy. They have my sincerest thanks. Which also means there is a conflict of interest, so take that into account.

Donate here, or contact them at [email protected].

Focus: Building charter cities

Leader: Kurtis Lockhart

Funding Needed: Medium

Confidence Level: Medium

I do love charter cities. There is little question they are attempting to do a very good thing and are sincerely going to attempt to build a charter city in Africa, where such things are badly needed. Very much another case of it being great that someone is attempting to do this so people can enjoy better institutions, even if it’s not the version of it I would prefer that would focus on regulatory arbitrage more.

Seems like a great place for people who don’t think transformational AI is on its way but do understand the value here.

Donate to them here, or contact them via webform.

Focus: Whole brain emulation

Leader: Randal Koene

Funding Needed: Medium

Confidence Level: Low

At this point, if it worked in time to matter, I would be willing to roll the dice on emulations. What I don’t have is much belief that it will work, or the time to do a detailed investigation into the science. So flagging here, because if you look into the science and you think there is a decent chance, this becomes a good thing to fund.

Donate here, or contact them at [email protected].

Focus: Scanning DNA synthesis for potential hazards

Leader: Kevin Esvelt, Andrew Yao and Raphael Egger

Funding Needed: Medium

Confidence Level: Medium

It is certainly an excellent idea. Give everyone fast, free, cryptographically screening of potential DNA synthesis to ensure no one is trying to create something we do not want anyone to create. AI only makes this concern more urgent. I didn’t have time to investigate and confirm this is the real deal as I had other priorities even if it was, but certainly someone should be doing this.

There is also another related effort, Secure Bio, if you want to go all out. I would fund Secure DNA first.

To donate, contact them at [email protected].

Focus: Increasing capability to respond to future pandemics, Next-gen PPE, Far-UVC.

Leader: Jake Swett

Funding Needed: Medium

Confidence Level: Medium

There is no question we should be spending vastly more on pandemic preparedness, including far more on developing and stockpiling superior PPE and in Far-UVC. It is rather a shameful that we are not doing that, and Blueprint Biosecurity plausibly can move substantial additional investment there. I’m definitely all for that.

To donate, reach out to [email protected] or head to the Blueprint Bio PayPal Giving Fund.

Focus: EU policy for AI enabled biorisks, among other things.

Leader: Patrick Stadler

Funding Needed: Low

Confidence Level: Low

Everything individually looks worthwhile but also rather scattershot. Then again, who am I to complain about a campaign for e.g. improved air quality? My worry is still that this is a small operation trying to do far too much, some of it that I wouldn’t rank too high as a priority, and it needs more focus, on top of not having that clear big win yet. They are a French nonprofit.

Donation details are at the very bottom of this page, or you can contact them at [email protected].

Focus: AI safety and biorisk for Israel

Leader: David Manheim

Funding Needed: Low

Confidence Level: Medium

Israel has Ilya’s company SSI (Safe Superintelligence) and otherwise often punches above its weight in such matters but is getting little attention. This isn’t where my attention is focused but David is presumably choosing this focus for good reason.

To support them, get in touch at [email protected].

The first best solution, as I note above, is to do your own research, form your own priorities and make your own decisions. This is especially true if you can find otherwise illegible or hard-to-fund prospects.

However, your time is valuable and limited, and others can be in better positions to advise you on key information and find opportunities.

Another approach to this problem, if you have limited time or actively want to not be in control of these decisions, is to give to regranting organizations, and take the decisions further out of your own hands.

Focus: AI governance, advisory and research, finding how to change decision points

Leader: Ian David Moss

Confidence Level: High

I discussed their direct initiatives earlier. This is listing them as a donation advisor and in their capacity of attempting to be a resource to the broader philanthropic community.

They report that they are advising multiple major donors, and would welcome the opportunity to advise additional major donors. I haven’t had the opportunity to review their donation advisory work, but what I have seen in other areas gives me confidence. They specialize in advising donors who have brad interests across multiple areas, and they list AI safety, global health, democracy and (peace and security).

To donate, click here. If you have further questions or would like to be advised, contact them at [email protected].

Focus: Conferences and advice on x-risk for those giving >$1 million per year

Leader: Simran Dhaliwal

Funding Needed: None

Confidence Level: Low

Longview is not seeking funding, instead they are offering support to large donors, and you can give to their regranting funds, including the Emerging Challenges Fund on catastrophic risks from emerging tech, which focuses non-exclusively on AI.

I had a chance to hear a pitch for them at The Curve and check out their current analysis and donation portfolio. It was a good discussion. There were definitely some areas of disagreement in both decisions and overall philosophy, and I worry they’ll be too drawn to the central and legible (a common issue with such services).

On the plus side, they’re clearly trying, and their portfolio definitely had some good things in it. So I wouldn’t want to depend on them or use them as a sole source if I had the opportunity to do something higher effort, but if I was donating on my own I’d find their analysis useful. If you’re considering relying heavily on them or donating to the funds, I’d look at the fund portfolios in detail and see what you think.

I pointed them to some organizations they hadn’t had a chance to evaluate yet.

They clearly seem open to donations aimed at particular RFPs or goals.

To inquire about their services, contact them at [email protected].

There were lots of great opportunities in SFF in both of my recent rounds. I was going to have an embarrassment of riches I was excited to fund.

Thus I decided quickly that I would not be funding any regrating organizations. If you were in the business of taking in money and then shipping it out to worthy causes, well, I could ship directly to highly worthy causes.

So there was no need to have someone else do that, or expect them to do better.

That does not mean that others should not consider such donations.

I see three important advantages to this path.

  1. Regranters can offer smaller grants that are well-targeted.

  2. Regranters save you a lot of time.

  3. Regranters avoid having others try to pitch on donations.

Thus, if you are making a ‘low effort’ donation, and think others you trust that share your values to invest more effort, it makes more sense to consider regranters.

In particular, if you’re looking to go large, I’ve been impressed by SFF itself, and there’s room for SFF to scale both its amounts distributed and level of rigor.

Focus: Give out grants based on recommenders, primarily to 501c(3) organizations

Leaders: Andrew Critch and Jaan Tallinn

Funding Needed: High

Confidence Level: High

If I had to choose a regranter right now to get a large amount of funding, my pick would be to partner with and participate in the SFF process as an additional funder. The applicants and recommenders are already putting in their effort, with plenty of room for each round to scale. It is very clear there are plenty of exciting places to put additional funds.

With more funding, the decisions could improve further, as recommenders would be better motivated to devote more time, and we could use a small portion of additional funds to make them better resourced.

The downside is that SFF can’t ‘go small’ efficiently on either funders or causes.

SFF does not accept donations but they are interested in partnerships with people or institutions who are interested in participating as a Funder in a future S-Process round. The minimum requirement for contributing as a Funder to a round is $250k. They are particularly interested in forming partnerships with American donors to help address funding gaps in 501(c)(4)’s and other political organizations.

This is a good choice if you’re looking to go large and not looking to ultimately funnel towards relatively small funding opportunities or individuals.

Focus: Regranters to AI safety, existential risk, EA meta projects, creative mechanisms

Leader: Austin Chen (austin at manifund.org).

Funding Needed: Medium

Confidence Level: Medium

This is a regranter that gives its money to its own regranters, one of which was me, for unrestricted grants. They’re the charity donation offshoot of Manifold. They’ve played with crowdfunding, and with impact certificates, and ACX grants. They help run Manifest.

You’re essentially hiring these people to keep building a website and trying alternative funding allocation mechanisms, and for them to trust the judgment of selected regranters. That seems like a reasonable thing to do if you don’t otherwise know where to put your funds and want to fall back on a wisdom of crowds of sorts. Or, perhaps, if you actively want to fund the cool website.

Manifold itself did not apply, but I would think that would also be a good place to invest or donate in order to improve the world. It wouldn’t even be crazy to go around subsidizing various markets. If you send me manna there, I will set aside and use that manna to subsidize markets when it seems like the place to do that.

If you want to support Manifold itself, you can either donate or buy a SAFE by contacting Austin at [email protected].

Also I’m a regranter at Manifund, so if you wanted to, you could use that to entrust me with funds to regrant. As you can see I certainly feel I have plenty of good options here if I can’t find a better local one, and if it’s a substantial amount I’m open to general directions (e.g. ensuring it happens relatively quickly, or a particular cause area as long as I think it’s net positive, or the method of action or theory of impact). However, I’m swamped for time, so I’d probably rely mostly on what I already know.

Focus: Spinoff of LTFF, grants for AI safety projects

Leader: Thomas Larsen

Funding Needed: Medium

Confidence Level: High

Seems very straightforwardly exactly what it is, a regranter that is usually in the low six figure range. Fellow recommenders were high on Larsen’s ability to judge projects. If you think this is better than you can do on your own and you want to fund such projects, then go for it.

I’ve talked to them on background about their future plans and directions, and without sharing details their plans make me more excited here.

Donate here or contact them at [email protected].

Focus: Grants of 4-6 figures mostly to individuals, mostly for AI existential risk

Leader: Caleb Parikh (among other fund managers)

Funding Needed: High

Confidence Level: Low

The pitch on LTFF is that it is a place for existential risk people who need modest cash infusions to ask for them, and to get them without too much overhead or distortion. Looking over the list of grants, there is at least a decent hit rate.

One question is, are the marginal grants a lot less effective than the average grant?

My worry is that I don’t know the extent to which the process is accurate, fair, favors insiders or extracts a time or psychic tax on participants, favors legibility, or rewards ‘being in the EA ecosystem’ or especially the extent to which the net effects are distortionary and bias towards legibility and standardized efforts. Or the extent to which people use the system to extract funds without actually doing anything.

That’s not a ‘I think this is bad,’ it is a true ‘I do not know.’ I doubt they know either.

What do we know? They say applications should take 1-2 hours to write and between 10 minutes and 10 hours to evaluate, although that does not include time forming the plan, and this is anticipated to be an ~yearly process long term. And I don’t love that this concern is not listed under reasons not to choose to donate to the fund (although the existence of that list at all is most welcome, and the reasons to donate don’t consider the flip side either).

Given their current relationship to EA funds, you likely should consider LTFF if and only if you both want to focus on AI existential risk via regrants and also want to empower and strengthen the existing EA formal structures and general ways of being.

That’s not my preference, but it could be yours.

Donate here, or contact the fund managers at [email protected].

Focus: Regrants, fellowships and events

Leader: Allison Duettmann

Funding Needed: Medium

Confidence Level: Low

Foresight also does other things. I’m focusing here on their AI existential risk grants, which they offer on a rolling basis. I’ve advised them on a small number of potential grants, but they rarely ask.

The advantage on the regrant side would be to get outreach that wasn’t locked too tightly into the standard ecosystem. The other Foresight activities all seem clearly like good things, but the bar these days is high and since they weren’t the topic of the application I didn’t investigate.

Donate here, or reach out to [email protected].

Focus: Strategic incubator and launchpad for EA talent, research, and high-impact initiatives, with emphasis on AI safety, GCR reduction, and longtermist work

Leader: Attila Ujvari

Funding Needed: High

Confidence Level: Low

I loved the simple core concept of a ‘catered hotel’ where select people can go to be supported in whatever efforts seem worthwhile. They are now broadening their approach, scaling up and focusing on logistical and community supports, incubation and a general infrastructure play on top of their hotel. This feels less unique to me now and more of a typical (EA UK) community play, so you should evaluate it on that basis.

Donate here, or reach out to [email protected].

I am less skeptical of prioritizing AI safety talent funnels than I was last year, but I remain skeptical.

The central reason remains simple. If we have so many good organizations already, in need of so much funding, why do we need more talent funnels? Is talent our limiting factor? Are we actually in danger of losing important talent?

The clear exception is leadership and management. There remains, it appears, a clear shortage of leadership and management talent across all charitable space, and startup space, and probably flat out all of space.

Which means if you are considering stepping up and doing leadership and management, then that is likely more impactful than you might at first think.

If there was a strong talent funnel specifically for leadership or management, that would be a very interesting funding opportunity. And yes, of course there still need to be some talent funnels. Right now, my guess is we have enough, and marginal effort is best spent elsewhere.

What about for other talent? What about placements in government, or in the AI labs especially Anthropic of people dedicated to safety? What about the prospects for much higher funding availability by the time we are ready to put people to work?

If you can pull it off, empowering talent can have a large force multiplier, and the opportunity space looks better than a year ago. It seems plausible that frontier labs will soak up every strong safety candidate they can find, since the marginal returns there are very high and needs are growing rapidly.

Secondary worries include the danger you end up feeding capability researchers to AI labs, and the discount for the time delays involved.

My hunch is this will still receive relatively more attention and funding than is optimal, but marginal funds here will still be useful if deployed in places that are careful to avoid being lab talent funnels.

Focus: Learning by doing, participants work on a concrete project in the field

Leaders: Remmelt Ellen and Linda Linsefors and Robert Kralisch

Funding Needed: Low

Confidence Level: High

By all accounts they are the gold standard for this type of thing. Everyone says they are great, I am generally a fan of the format, I buy that this can punch way above its weight or cost. If I was going to back something in this section, I’d start here.

Donors can reach out to Remmelt at [email protected], or leave a matched donation to support next projects.

Focus: Paying academics small stipends to move into AI safety work

Leaders: Peter Salib (psalib @ central.uh.edu), Yonathan Arbel (yarbel @ law.ua.edu) and Kevin Frazier (kevin.frazier @ law.utexas.edu).

Funding Needed: Low

Confidence Level: High

This strategy is potentially super efficient. You have an academic that is mostly funded anyway, and they respond to remarkably small incentives to do something they are already curious about doing. Then maybe they keep going, again with academic funding. If you’re going to do ‘field building’ and talent funnel in a world short on funds for those people, this is doubly efficient. I like it. They’re now moving into hiring an academic fellow, the theory being ~1 year of support to create a permanent new AI safety law professor.

To donate, message one of leaders at the emails listed above.

Focus: Enabling ambitious research programs that are poor fits for both academia and VC-funded startups including but not limited to Drexlerian functional nanomachines, high-throughput tools and discovering new superconductors.

Leader: Benjamin Reinhardt

Funding Needed: Medium

Confidence Level: Medium

I have confirmation that Reinhardt knows his stuff, and we certainly could use more people attempting to build revolutionary hardware. If the AI is scary enough to make you not want to build the hardware, it would figure out how to build the hardware anyway. You might as well find out now.

If you’re looking to fund a talent funnel, this seems like a good choice.

To donate, go here or reach out to [email protected].

Focus: Fellowships to other organizations, such as Future Society, Safer AI and FLI.

Leader: Chiara Gerosa

Funding Needed: Medium

Confidence Level: Low

They run two fellowship cohorts a year. They seem to place people into a variety of solid organizations, and are exploring the ability to get people into various international organizations like the OECD, UN or European Commission or EU AI Office.

The more I am convinced people will actually get inside meaningful government posts, the more excited I will be.

To donate, contact [email protected].

Focus: Researcher mentorship for those new to AI safety.

Leaders: Ryan Kidd and Christian Smith.

Funding Needed: High

Confidence Level: Medium

MATS is by all accounts very good at what they do and they have good positive spillover effects on the surrounding ecosystem. The recruiting classes they’re getting are outstanding.

If (and only if) you think that what they do, which is support would-be alignment researchers starting out and especially transitioning from other professions, is what you want to fund, then you should absolutely fund them. That’s a question of prioritization.

Donate here, or contact them via webform.

Focus: X-risk residencies, workshops, coworking in Prague, fiscal sponsorships

Leader: Irena Kotikova

Funding Needed: Medium

Confidence Level: Medium

I see essentially two distinct things here.

First, you have the umbrella organization, offering fiscal sponsorship for other organizations. Based on what I know from the charity space, this is a highly valuable service – it was very annoying getting Balsa a fiscal sponsor while we waited to become a full 501c3, even though we ultimately found a very good one that did us a solid, and also annoying figuring out how to be on our own going forward.

Second, you have various projects around Prague, which seem like solid offerings in that class of action of building up EA-style x-risk actions in the area, if that is what you are looking for. So you’d be supporting some mix of those two things.

To donate, contact [email protected].

Focus: Small grants to individuals to help them develop their talent

Leader: Tyler Cowen

Funding Needed: Medium

Confidence Level: High

Emergent Ventures are not like the other talent funnels in several important ways.

  1. It’s not about AI Safety. You can definitely apply for an AI Safety purpose, he’s granted such applications in the past, but it’s rare and topics run across the board, well beyond the range otherwise described in this post.

  2. Decisions are quick and don’t require paperwork or looking legible. Tyler Cowen makes the decision, and there’s no reason to spend much time on your end either.

  3. There isn’t a particular cause area this is trying to advance. He’s not trying to steer people to do any particular thing. Just to be more ambitious, and be able to get off the ground and build connections and so on. It’s not prescriptive.

I strongly believe this is an excellent way to boost the development of more talent, as long as money is serving as a limiting factor on the project, and that it is great to develop talent even if you don’t get to direct or know where it is heading. Sure, I get into rhetorical arguments with Tyler Cowen all the time, around AI and also other things, and we disagree strongly about some of the most important questions where I don’t understand how he can continue to have the views he expresses, but this here is still a great project, an amazingly cost-efficient intervention.

Donate here (specify “Emergent Ventures” in notes), or reach out to [email protected].

Focus: AI safety community building and research in South Africa

Leaders: Leo Hyams and Benjamin Sturgeon

Funding Needed: Low

Confidence Level: Low

This is a mix of AI research and building up the local AI safety community. One person whose opinion I value gave the plan and those involved in it a strong endorsement, so including it based on that.

To donate, reach out to [email protected].

Focus: Talent for AI safety in Africa

Leaders: Cecil Abungu

Funding Needed: Low

Confidence Level: Low

I have a strong endorsement in hand in terms of their past work, if you think this is a good place to go in search of talent.

To donate, reach out to [email protected].

Focus: Global talent accelerator and hiring partner for technical AI safety, supporting worker transitions into AI safety.

Leader: Roy Hagemann and Varun Agarwal

Funding Needed: Medium

Confidence Level: Low

They previously focused on India, one place with lots of talent, they’re now global. A lot has turned over in the last year, so you’ll want to check them out anew.

To donate, contact [email protected].

Focus: Mapping & creating missing orgs for AI safety (aka Charity Entrepreneurship for AI risk)

Leaders: Evan Miyazono

Funding Needed: Medium

Confidence Level: Low

There was a pivot this past year from technical research to creating ‘missing orgs’ in the AI risk space. That makes sense as a strategy if and only if you expect the funding necessary to come in, or you think they can do especially strong targeting. Given the change they will need to be reevaluated.

They receive donations from here, or you can email them at [email protected].

Focus: Fellowships and affiliate programs for new alignment researchers

Leader: Lucas Teixeira and Dusan D. Nesic

Funding Needed: High

Confidence Level: Low

There are some hits here. Gabriel Weil in particular has impressed me in our interactions and with his work and they cite a good technical paper. But also that’s with a lot of shots on goal, and I’d have liked to see some bigger hits by now.

A breakdown revealed that, largely because they start with relatively senior people, most of them get placed in a way that doesn’t require additional support. That makes them a better bet than many similar rivals.

To donate, reach out to [email protected], or fund them through Manifund here.

Focus: Journalism fellowships for oversight of AI companies.

Leader: Cillian Crosson (Ex-Talos Network; still on their board.)

Funding Needed: High

Confidence Level: Medium

They offer fellowships to support journalism that helps society navigate the emergence of increasingly advanced AI, and a few other journalism ventures. They have sponsored at least one person who went on to do good work in the area. They also sponsor article placement, which seems reasonably priced in the grand scheme of things, I think?

I am not sure this is a place we need to do more investment, or if people trying to do this even need fellowships. Hard to say. There’s certainly a lot more tech reporting and more every day, if I’m ever short of material I have no trouble finding more.

It is still a small amount of money per person that can meaningfully help people get on their feet and do something useful. We do in general need better journalism. They seem to be in a solid place but also I’d be fine with giving a bunch more funding to play with, they seem pretty unique.

Donate here, or reach out to them via webform.

Focus: Incubation of AI safety organizations

Leader: Alexandra Bos

Funding Needed: Medium

Confidence Level: Low

Why funnel individual talent when you can incubate entire organizations? I am not convinced that on the margin we currently need more of either, but I’m more receptive to the idea of an incubator. Certainly incubators can be high leverage points for getting valuable new orgs and companies off the ground, especially if your model is that once the org becomes fundable it can unlock additional funding.

If you think an incubator is worth funding, then the question is whether this is the right team. The application was solid all around, and their track record includes Timaeus and Carma, although counterfactuals are always difficult. Beyond that I don’t have a differentiator on why this is the team.

To donate, contact them at [email protected].

Focus: New AI safety org in Paris, discourse, R&D collaborations, talent pipeline

Leaders: Charbel-Raphael Segerie, Florent Berthet

Funding Needed: Low

Confidence Level: Low

They’re doing all three of discourse, direct work and talent funnels. They run the only university AI safety course in Europe, maintain the AI Safety Atlas, and have had their recommendations integrated verbatim into the EU AI Act’s Code of Practice. Their two main priorities are supporting the enforcement of the EU AI Act, and driving international agreements on AI red lines.

To donate, go here, or contact them at [email protected].

Focus: Recruitment for existential risk causes

Leader: Steve Luby

Funding Needed: Medium

Confidence Level: Low

Stanford students certainly are one place to find people worth educating about existential risk. It’s also an expensive place to be doing it, and a place that shouldn’t need extra funding. And that hates fun. And it’s not great that AI is listed third on their existential risk definition. So I’m not high on them, but it sure beats giving unrestricted funds to your Alma Mater.

Interested donors should contact Steve Luby directly at [email protected].

Focus: Talent funnel directly to AI safety and biosecurity out of high school

Leader: Peter McIntyre

Funding Needed: Low

Confidence Level: Low

Having high school students jump straight to research and placement sounds good to me, and plausibly the best version of a talent funnel investment. I haven’t confirmed details but I like the theory.

To donate, get in touch at [email protected].

Focus: Teaching rationality skills, seeking to make sense of the world and how to think

Leader: Anna Salamon

Funding Needed: High

Confidence Level: High

I am on the board of CFAR, so there is a direct and obvious conflict. Of course, I am on the board of CFAR exactly because I think this is a worthwhile use of my time, and also because Anna asked me. I’ve been involved in various ways since the beginning, including the discussions about whether and how to create CFAR in the first place.

CFAR is undergoing an attempted revival. There weren’t workshops for many years, for a variety of reasons including safety concerns and also a need to reorient. The workshops are now starting up again, with a mix of both old and new units, and I find much of the new material interesting and potentially valuable. I’d encourage people to consider attending workshops, and also donating.

To donate, click here, or reach out to [email protected].

Focus: Workshops in the style of CFAR but focused on practical courage, forming high value relationships between attendees with different skill sets and learning to care for lineages, in the hopes of repairing the anglosphere and creating new capable people to solve our problems including AI in more grounded ways.

Leader: Anna Salamon

Funding Needed: Low

Confidence Level: High

LARC is kind of a spin-off of CFAR, a place to pursue a different kind of agenda. I absolutely do not have high confidence that this will succeed, but I do have high confidence that this is a gamble worth taking, and that if those involved here (especially Anna Salamon but also others that I know) want to devote their time to trying this, that we should absolutely give them that opportunity.

Donate here.

If an organization was not included here, or was removed for the 2025 edition, again, that does not mean they aren’t good, or even that I wouldn’t endorse them if asked.

It could be because I am not aware of the organization, or lack sufficient knowledge at this point to be confident in listing them, or I fear my knowledge is obsolete.

It could be that they asked to be excluded, which happened in several cases.

If by accident I included you and you didn’t want to be included and I failed to remove you, or you don’t like the quote here, I sincerely apologize and will edit you out right away, no questions asked.

If an organization is included here, that is a good thing, but again, it does not mean you should donate without checking if it makes sense based on what you think is true, how you think the world works, what you value and what your priorities are. There are no universal right answers.

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many-genes-associated-with-dog-behavior-influence-human-personalities,-too

Many genes associated with dog behavior influence human personalities, too

Many dog breeds are noted for their personalities and behavioral traits, from the distinctive vocalizations of huskies to the herding of border collies. People have worked to identify the genes associated with many of these behaviors, taking advantage of the fact that dogs can interbreed. But that creates its own experimental challenges, as it can be difficult to separate some behaviors from physical traits distinctive to the breed—small dog breeds may seem more aggressive simply because they feel threatened more often.

To get around that, a team of researchers recently did the largest gene/behavior association study within a single dog breed. Taking advantage of a population of over 1,000 golden retrievers, they found a number of genes associated with behaviors within that breed. A high percentage of these genes turned out to correspond to regions of the human genome that have been associated with behavioral differences as well. But, in many cases, these associations have been with very different behaviors.

Gone to the dogs

The work, done by a team based largely at Cambridge University, utilized the Golden Retriever Lifetime Study, which involved over 3,000 owners of these dogs filling out annual surveys that included information on their dogs’ behavior. Over 1,000 of those owners also had blood samples obtained from their dogs and shipped in; the researchers used these samples to scan the dogs’ genomes for variants. Those were then compared to ratings of the dogs’ behavior on a range of issues, like fear or aggression directed toward strangers or other dogs.

Using the data, the researchers identified when different regions of the genome were frequently associated with specific variants. In total, 14 behavioral tendencies were examined, and 12 genomic regions were associated with specific behaviors, and another nine showed somewhat weaker associations. For many of these traits, it was difficult to find much because golden retrievers are notoriously friendly and mellow dogs, so they tended to score low on traits like aggression and fear.

That result was significant, as some of these same regions of the genome had been associated with very different behaviors in populations that were a mix of breeds. For example, two different regions associated with touch sensitivity in golden retrievers had been linked to a love of chasing and owner-directed aggression in a non-breed-specific study. That finding suggests that the studies were identifying genes that may be involved in setting the stage for behaviors, but were directed into specific outcomes by other genetic or environmental factors.

Many genes associated with dog behavior influence human personalities, too Read More »

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Valve’s Steam Machine looks like a console, but don’t expect it to be priced like one

After Valve announced its upcoming Steam Machine living room box earlier this month, some analysts suggested to Ars that Valve could and should aggressively subsidize that hardware with “loss leader” pricing that leads to more revenue from improved Steam software sales. In a new interview with YouTube channel Skill Up, though, Valve’s Pierre-Loup Griffais ruled out that kind of console-style pricing model, saying that the Steam Machine will be “more in line with what you might expect from the current PC market.”

Griffais said the AMD Zen 4 CPU and RDNA3 GPU in the Steam Machine were designed to outperform the bottom 70 percent of machines that opt-in to Valve’s regular hardware survey. And Steam Machine owners should expect to pay roughly what they would for desktop hardware with similar specs, he added.

“If you build a PC from parts and get to basically the same level of performance, that’s the general price window that we aim to be at,” Griffais said.

The new comments follow similar sentiments relayed by Linus Sebastian on a recent episode of his WAN Show podcast. Sebastian said that, when talking to Valve representatives at a preview event, he suggested that a heavily subsidized price point would make the Steam Machine hardware into “a more meaningful product.” But when he suggested that he was imagining a console-style price in the range of $500, “nobody said anything, but the energy of the room wasn’t great.”

Forget about $500

Based on these comments, we could start estimating a potential Steam Machine price range by speccing out a comparable desktop machine. That would likely require building around a Ryzen 5 7600X CPU and Radeon RX 7600 GPU, which would probably push the overall build into the $700-plus range. That would make the Steam Machine competitive with the pricey PS5 Pro, even though some estimates price out the actual internal Steam Machine components in the $400 to $500 range.

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Mushroom foragers collect 160 species for food, medicine, art, and science

Like many mushroom harvesters, I got interested in foraging for fungi during the COVID-19 pandemic.

I had been preparing for a summer of field work studying foraged desert plants in a remote part of Australia when the pandemic hit, and my travel plans were abruptly frozen. It was March, right before morel mushrooms emerge in central Pennsylvania.

I wasn’t doing a lot other than going on long hikes and taking classes remotely at Penn State for my doctoral degree in ecology and anthropology. One of the classes was an agroforestry class with Eric Burkhart. We studied how agriculture and forests benefit people and the environment.

These two things eventually led to a yearslong project on mushroom harvesting in our region.

Why people forage

Foragers have been harvesting wild mushrooms in what is now Pennsylvania and the rest of the US mid-Atlantic region for generations, but the extent and specifics of the practice in the region had not been formally studied.

In 2021, Burkhart and I decided that we wanted to better understand the variety of wild mushroom species that Pennsylvania harvesters collect and what they use them for.

We conducted a series of surveys in 2022 and 2023 that revealed a wide variety of fungi are foraged in the region—though morels, chicken of the woods, and chanterelles are most common. We also learned that harvesters use the mushrooms primarily for food and medicinal purposes, and that foragers create communities that share knowledge. These community-based projects often use social media tools as a way for mushroom harvesters to share pictures, notes, and even the results of DNA sequences.

Our findings were published in the journal Economic Botany in October 2025.

160 species

Having spent a year building connections with local mushroom harvesters, starting in central Pennsylvania, including members of mushroom clubs and mycological associations, we recruited a diverse group of harvesters from around the mid-Atlantic. We also used mushroom festivals, social media, and word of mouth to get the word out.

We asked harvesters about their favorite mushrooms, common harvesting practices, resources they used while harvesting, and any sustainability practices.

Over 800 harvesters responded to the survey and reported that, collectively, they foraged 160 species of wild mushrooms. Morels and chicken of the woods were the two most popular, as each were reported by 13 percent of respondents. About 10 percent of respondents reported collecting chanterelles. Other popular species were hen of the woods, oysters, lion’s mane, black trumpet, honey mushroom, turkey tail, bolete, reishi, puffball, chaga, shrimp of the woods, and Dryad’s saddle, which is also known as the pheasant’s back mushroom.

Mushroom foragers collect 160 species for food, medicine, art, and science Read More »

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Anthropic introduces cheaper, more powerful, more efficient Opus 4.5 model

Anthropic today released Opus 4.5, its flagship frontier model, and it brings improvements in coding performance, as well as some user experience improvements that make it more generally competitive with OpenAI’s latest frontier models.

Perhaps the most prominent change for most users is that in the consumer app experiences (web, mobile, and desktop), Claude will be less prone to abruptly hard-stopping conversations because they have run too long. The improvement to memory within a single conversation applies not just to Opus 4.5, but to any current Claude models in the apps.

Users who experienced abrupt endings (despite having room left in their session and weekly usage budgets) were hitting a hard context window (200,000 tokens). Whereas some large language model implementations simply start trimming earlier messages from the context when a conversation runs past the maximum in the window, Claude simply ended the conversation rather than allow the user to experience an increasingly incoherent conversation where the model would start forgetting things based on how old they are.

Now, Claude will instead go through a behind-the-scenes process of summarizing the key points from the earlier parts of the conversation, attempting to discard what it deems extraneous while keeping what’s important.

Developers who call Anthropic’s API can leverage the same principles through context management and context compaction.

Opus 4.5 performance

Opus 4.5 is the first model to surpass an accuracy score of 80 percent—specifically, 80.9 percent in the SWE-Bench Verified benchmark, narrowly beating OpenAI’s recently released GPT-5.1-Codex-Max (77.9 percent) and Google’s Gemini 3 Pro (76.2 percent). The model performs particularly well in agentic coding and agentic tool use benchmarks, but still lags behind GPT-5.1 in visual reasoning (MMMU).

Anthropic introduces cheaper, more powerful, more efficient Opus 4.5 model Read More »

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Why synthetic emerald-green pigments degrade over time

Perhaps most relevant to this current paper is a 2020 study in which scientists analyzed Munch’s The Scream, which was showing alarming signs of degradation. They concluded the damage was not the result of exposure to light, but humidity—specifically, from the breath of museum visitors, perhaps as they lean in to take a closer look at the master’s brushstrokes.

Let there be (X-ray) light

Co-author Letizia Monico during the experiments at the European Synchrotron. ESRF

Emerald-green pigments are particularly prone to degradation, so that’s the pigment the authors of this latest paper decided to analyze. “It was already known that emerald-green decays over time, but we wanted to understand exactly the role of light and humidity in this degradation,” said co-author Letizia Monico of the University of Perugia in Italy.

The first step was to collect emerald-green paint microsamples with a scalpel and stereomicroscope from an artwork of that period—in this case, The Intrigue (1890) by James Ensor, currently housed in the Royal Museum of Fine Arts, in Antwerp, Belgium. The team analyzed the untreated samples using Fourier transform infrared imaging, then embedded the samples in polyester resin for synchrotron radiation X-ray analysis. They conducted separate analyses on both commercial and historical samples of emerald-green pigment powders and paint tubes, including one from a museum collection of paint tubes used by Munch.

Next, the authors created their own paint mockups by mixing commercial emerald-green pigment powders and their lab-made powders with linseed oil, and then applied the concoctions to polycarbonate substrates. They also squeezed paint from the Munch paint tube onto a substrate. Once the mockups were dry, thin samples were sliced from each mockup and also analyzed with synchrotron radiation. Then the mockups were subjected to two aging protocols designed to determine the effects of UV light (to simulate indoor lighting) and humidity on the pigments.

The results: In the mockups, light and humidity trigger different degradation pathways in emerald-green paints. Humidity results in the formation of arsenolite, making the paint brittle and prone to flaking. Light dulls the color by causing trivalent arsenic already in the pigment to oxidize into pentavalent compounds, forming a thin white layer on the surface. Those findings are consistent with the analyzed samples taken from The Intrigue, confirming the degradation is due to photo-oxidation. Light, it turns out, is the greatest threat to that particular painting, and possibly other masterpieces from the same period.

Science Advances, 2025. DOI: 10.1126/sciadv.ady1807  (About DOIs).

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F1 in Las Vegas: This sport is a 200 mph soap opera

Then there’s the temperatures. The desert gets quite chilly in November without the sun shining on things, and the track surface gets down to just 11° C (52° F); by contrast, at the recent Singapore GP, also at night, the track temperature was more like 36° C (97° F).

LAS VEGAS, NEVADA - NOVEMBER 21: Lando Norris of Great Britain driving the (4) McLaren MCL39 Mercedes lifts a wheel on track during qualifying ahead of the F1 Grand Prix of Las Vegas at Las Vegas Strip Circuit on November 21, 2025 in Las Vegas, Nevada. (Photo by )

It’s rare to see an F1 car on full wet tires but not running behind the safety car. Credit: Clive Rose/Getty Images

So, low aero and mechanical grip, an unusual layout compared to most F1 tracks, and very cold temperatures all combine to create potential surprises, shaking up the usual running order.

We saw this last year, where the Mercedes shined in the cold, able to keep their tires in the right operating window, something the team wasn’t able to do at hotter races. But it was hard to tell much from Thursday’s two practice sessions, one of which was interrupted due to problems with a maintenance hatch, albeit not as serious as when one damaged a Ferrari in 2023. The cars looked impressively fast going through turn 17, and the hybrid power units are a little louder than I remember them, even if they’re not a patch on the naturally aspirated engines of old.

Very little of any use was learned by any of the teams for qualifying on Friday night, which took place in at times damp, at times wet conditions—so wet that the Pirelli intermediate tire wasn’t grooved enough, pushing teams to use the full wet-weather spec rubber. Norris took pole from Red Bull’s Max Verstappen, with Williams’ Carlos Sainz making best use of the opportunity to grab third. Piastri would start fifth, behind the Mercedes of last year’s winner, George Russell.

If the race is boring, the off-track action won’t be

Race night was a little windy, but dry. And the race itself was rather boring—Norris tried to defend pole position going into Turn 1 but ran wide, and Verstappen slipped into the lead, never looking back. Norris followed him home in second, with Piastri fourth, leaving Norris 30 points ahead of Piastri and 42 points ahead of Verstappen with two more race weekends and 58 points left on offer.

F1 in Las Vegas: This sport is a 200 mph soap opera Read More »