2025-12-04 11:35:51
Published on December 4, 2025 3:35 AM GMT
AI coding has been the big topic for capabilities growth, as of late. The announcement of GPT 5.1, in particular, eschewed the traditional sea change declaration of a new modality, a new paradigm, or a new human-level-capability threshold surmounted, and instead provided a relatively muted press release augmented with an interactive gallery of computer programs that OpenAI's new LLM generated when provided with a relatively brief prompt.
One of these demos in particular caught my eye, on the basis that its prompt was very similar to a test I run on every new model. Given an open-ended three-sentence prompt, the LLM had constructed a visually and functionally-polished game. Complete, impressively, with sound design that wasn't physically painful to experience. Integrating image and audio processing into a single core model had clearly paid dividends by giving it the intuition necessary to code something visually and audibly tolerable in a single shot.
The other examples listed in the gallery, to be frank, were much less useful in assessing capabilities, consisting of a variety of visually-polished but functionally-sparse webpages driven primarily by frontend libraries[1].
Just as playing games provided an excellent metric for the growth of reinforcement learning capabilities, programming games can serve as the gold standard for determining just how rigorously competent LLMs are at implementing computer programs. Most of the actual "work" of creating a polished personal website is done by the NodeJS libraries the user has installed, and most industry programming tasks draw their difficulty from domain knowledge (knowing what to build) rather than implementation challenge (knowing how to build it). Even coding challenge benchmarks made specifically to evaluate capabilities are often formulaic in nature - anyone who's spent time grinding the leaderboards on LeetCode knows that there are only so many ways to structure a dynamic programming problem before they all start blurring together.
Game programming, in contrast, is all about pushing technical limits, and, unlike most other domains of programming, there are sharply diminishing returns on coloring-by-numbers[2]. For a game to impress, it has to be novel, either by posing a new set of mechanics to the player or by implementing an existing set of mechanics in a novel way. In either case, boilerplate outputs are much less likely to over-impress, and the manner of thinking that a high-level engineer regularly requires is much more rigorously tested. The model must formulate a set of mechanics that are needed to satisfy some open-ended design criteria, intuit how a user is likely to interact with these mechanics, and model obscure edge cases that might only come up after several hundred iterations over the core game loop ahead of time. Moreover, it must do all of these things while understanding what would or would not be pleasant for a human user - modeling expected human preferences is both challenging and very important to the overwhelming majority of proposed use-cases for LLMs.
In other words, it allows researchers to capture the 'vibe' of a model much better than quantitative programming challenge evaluations without the noise introduced by settings with extensive support libraries and countless templated examples in the model's training set.
I've spent a considerable amount of time trying to get an answer to this question, since I think it'd provide me with substantial intuition on just how far LLMs will need to improve in order to start seriously impacting the economy[3]. I'm usually confident in my Google-fu, but all I can find on the subject of "What's the most impressive game I can get an LLM to generate without feeding it pseudocode" is beginners trying the LLM out by asking it to implement Snake or Asteroids, often with a new visual skin on top, and LLMs' ability to replicate common programming tutorial templates doesn't tell me much that I didn't know three years ago.
I've run my own tests, of course[4], but I haven't been able to come up with anything suitably rigorous to justify a confident update to my understanding of LLM capabilities. If people are interested, I'd be willing to write a few simple prompts across popular genres, sample results across the top dozen models, and write up results, but even that would be vulnerable to criticism that my prompts were less-than-optimal in eliciting the models' best capabilities, and I'm not sure how I'd go about addressing that.
Given the sheer popularity of large language models among the demographics that usually experiment with making their own game, I'd expect much more to be written about their capabilities and limits on this front. Even if results were uniformly negative, I'd expect a few blog posts going around about the surprising inefficacy of LLMs in game design. Instead, I can find one or two hastily-made startup webpages promising to "Make games with AI", but with no examples of the kind of results they've achieved.
If you've got five minutes, and are similarly curious, please try generating a game using whichever LLM and prompt you think would get the best one-shot result. I'd be interested to see whether your results are in line with what I've seen.
Impressed as I was by "EV: Pocket Skirmish", I attempted to replicate the results in the press release. Over several dozen tries with their provided prompt, spread across a number of different models and API settings, I couldn't get anything qualitatively similar. 5.1 will reliably produce a clone of Asteroids, often with 1-2 glaring graphical or functional issues, very poor sound design, and one or two very simplistic enemy ships with non- or barely-functional AI.
I've run enough trials that I'm certain my results aren't a statistical fluke, and I'd be very interested to hear others' theories on what's going on there. OpenAI usually doesn't cherry-pick results, and I think you'd have to run this prompt a four digit number of times to get anything comparable to their example output with the model they released.
lovable.dev showed a year ago that professional-quality React websites could be created easily and reliably with LLMs, but the sheer quantity of passable React on the internet, coupled with the fact that it's very unlikely that a request made to an LLM for a React personal webpage will be something entirely new, means that this doesn't tell us much about how well models can perform real engineering work.
A very common criticism of LLM outputs of all kinds, but especially coding work. If you speak to someone who is pessimistic about LLMs' prospective economic value, the idea that their most apparently impressive capabilities stem from regurgitating training data rather than first-principles reasoning is likely to come up.
Among other things.
Most recently, I wrote a style/design document for a roguelike, and asked some frontier LLMs to implement a vertical slice. ChatGPT, Gemini, and Claude all managed to construct basic Roguelike engines, though Gemini's map generation algorithm was noticeably less advanced than the others, and it failed to intuit that requiring the enter key to be pressed after each movement command might be inconvenient for the player. Claude did the best job, with a procedural generation algorithm that recursively expanded its map and some surprisingly good atmospheric writing snippets. ChatGPT landed somewhere in the middle, with a partially-functional map generator that I suspect was half-remembered from a popular tutorial.
2025-12-04 11:25:44
Published on December 4, 2025 3:25 AM GMT
Application deadline:
Location: Sydney (definite); Melbourne (likely; contingent on sufficient high-quality applications)
When: January/February 2026 with remote activities pre and post the main fellowship (detail further down)
Apply nowWe are looking for a small group of strongly motivated, highly agentic individuals with good strategic judgement (from technical researchers to governance thinkers to entrepreneurs) who want to spend the summer developing situational awareness, figuring out where they can best contribute, and working on a project that demonstrates their potential.
Our core promise: Other programs in AI safety are primarily designed to accelerate your career as fast as possible. In contrast, whilst we believe that participating in this fellowship will increase your chance of breaking into the field, this objective is merely secondary for us. Our promise to you is that we will do our best to equip you to make a difference, as difficult as this is given the extreme level of uncertainty around AGI.
Program details - 10-week hybrid structure:
This fellowship offers:
Sadly, we're unable to offer stipends or accommodation.
More information on our website: including a guide to help you figure out whether this program is for you and a discussion of alumni outcomes.
Fellowship organisers - Sydney: Chris Leong and Jack Payne; Melbourne: Michael Kerrison
2025-12-04 10:53:07
Published on December 4, 2025 2:53 AM GMT
In Part 1, I argued that the four main sources most people learn about romance from—media, family, religion/culture, and friends—are all unreliable in different ways. None of them are optimized for truth, and each comes with its own incentives, blind spots, and distortions.
As I said, I'm not the first to notice that we're failing, as a society, to provide good answers to these questions. There are researchers from various fields trying to provide answers, some rigorously, and some… less so. There are also communities and influencers trying to create a fifth source of romantic advice, independent from the traditional sources, each with their own biases and incentives.
I don't know if you've noticed, but at risk of being a stereotypical old man despairing over the youths of the day… I think the kids actually aren't alright, this time.
Not because I think they're doing something wrong by my own values or frame of what romance "should" be, but because the data shows unprecedented levels among the younger generations in lack of dating experience, loneliness, and confusion that enables the unprecedented popularity of "give me money and I'll tell you what no one else will" grifters like Andrew Tate.
To understand how we got here, it helps to understand Pendulum Swings in the ways cultures develop:
For most of history, dating advice and sexual norms were dominated by traditional frameworks—religious, patriarchal, focused on marriage as economic institution. These frameworks were extremely functional in many ways, but their problems began to become more and more apparent as education levels rose, particularly for women: limited autonomy, shame around sexuality or any sort of deviance from gender norms, and narratives that took occasional male misbehavior for granted.
The progressive response to this (later characterized as "the blue pill" by The Red Pill community a reference to Morpheus in The Matrix offering Neo a pill that puts them back to sleep as opposed to waking them up to reality), developed little by little over decades, pushed toward a more egalitarian ideal. The goals were genuinely good: protect women from coercion, establish clearer consent norms, challenge unfair double standards, and create space for people to pursue relationships and sexuality on their own terms. It did this in large part by rejecting a lot of absolutist, black and white claims about gender, sexuality, and dating.
But pendulums rarely stop at the objective truth. The progressive correction got a lot right, and on balance moved us closer to reality than where we started. But even that idea, "the truth" is misleading; dating narratives were a cluster of claims and frames, not a single one, and progressive counter-narratives overcorrected on some more than others. Some overgeneralized "is"s got blamed for the "ought"s that people built over them, new blind spots replaced old ones, and the messaging that young people absorbed ended up containing its own distortions.
Meanwhile, an older subculture called "Pick-Up Artists" (PUA) had been floating around the internet since the early 2000s. It contained some useful advice, but mostly worked of a mix of a narrow goal post (scoring with women, not forming healthy relationships) and manipulative negative-sum practices. It was also pretty obscure, fairly confined to forums and a few books that mainstream culture largely ignored.
Then came The Red Pill.
I watched this community grow first hand in the 2010s, and spent a few years moderating a subreddit that tried to debunk its claims while staying out of the Red vs Bluepill frameworks altogether. Its popularity was based on a central message of debunking lies spread by most media and culture, and replace them with basic, concrete advice for young men looking for sex and romance:
The Red Pill got popular in part because this advice is actually good. Young men lacking in concrete advice could and sometimes did find dating success from shifting to the mindset it pushed for.
Unfortunately, not only was none of that advice particularly novel, it came in a wrapping that unapologetically also stated that women are less capable than men, not just intellectually but also romantically, and that they'd only stay with you or stay faithful if you demonstrated power over them, whether financial, sexual, or other.
Not that this part of the message wasn't really new either. What's interesting to consider is why it exploded in popularity when PUA culture had remained relatively niche.
Part of the answer, I think, is just the way social media, particularly Reddit and 4chan, made it easier for niche and countercultural communities to be more accessible than ever before. But I think there's something more specific going on: the failures of traditional culture and the progressive overcorrection created a vacuum that these movements filled.
As discussed in Part 1, if you're a young man struggling with dating looking for guidance, here's what you often find:
And then along comes someone who says, bluntly: "That's all bullshit. Here's what actually works."
The Red Pill positioned itself as the antidote to comfortable lies. And because it contained some truth—confidence does matter, looks and money do matter, people's stated preferences don't always match their revealed ones—it felt like a revelation to young men whose experience didn't match the egalitarian theory they'd been taught.
Unfortunately, the useful advice came packaged with some toxic distortions that justified themselves by treating the worst examples of shitty behavior as representative. And like the four major sources of romance tropes from Part 1, the Red Pill and influencers like Andrew Tate that rode its wave have their own incentives that warp the narratives they push:
The Red Pill and its avatars have spread a framework that turns dating into war, and war is not a good foundation for truth-seeking, let alone intimacy. It also created an offshoot subculture that's. in some ways, even worse.
If the Red Pill is cynical, the Black Pill is nihilistic. Where Red Pill says "the game is rigged but you can win if you learn the rules," Black Pill says "the game is rigged and there's nothing you can do about it." This is the endpoint of despair: a worldview where self-improvement is cope, where women are shallow beyond redemption, where the only honest response is bitter resignation or worse.
And it's not just men. There's a mirror-image version in certain extreme feminist spaces—communities where men aren't just flawed or socialized badly but irredeemably toxic, where heterosexual attraction is a form of false consciousness, where any woman who reports positive experiences with men is deluded or a pick-me. It's a smaller and less loud portion of the blogosphere, but it paints with just as broad a brush, and ruins its fair share of lives too.
Both versions follow a similar pattern with the Red Pill: take a series of bad experiences, filter them through the most cynical possible narrative, and conclude that an entire gender is fundamentally alien and hostile to you. The manosphere version says women are fundamentally manipulators incapable of love. The misandrist version says men are fundamentally predators incapable of empathy. Neither leaves room for the possibility that their sample was skewed, their interpretations were biased, or that the problem may actually be, well, something they did.
But these communities feel validating because they take real pain seriously—pain that often gets dismissed elsewhere. Sexual and romantic loneliness is an extremely painful and alienating experience, but while most who experience it don't call themselves "incels," those susceptible to validation paired with bitterness toward the opposite gender are spreading a different story, one that's self-serving to believe and spread:
The Red Pill gains traction partly because mainstream progressive culture has its own distortions—places where the map doesn't match the territory, and pointing this out gets you labeled as problematic at best.
I want to be careful here. The progressive project around consent and gender equality has done enormous good. Clear consent norms have prevented real harm, and challenging sexist double standards has made life better for billions of people. I am not arguing for a return to traditional frameworks.
But good intentions don't guarantee accurate maps, and some of what gets communicated, especially to young people, doesn't match reality in ways that leave them confused and vulnerable to worse alternatives.
It's genuinely hard to navigate these issues with nuance. The woman who only feels safe if her date asks her if it's okay to hold her hand, let alone kiss her, shares the same dating pool as the woman who finds it a massive turnoff to be asked if she's okay with being kissed. Solving this issue is never going to be doable by a single narrative.
There's one more framework worth addressing, because it comes up constantly in these debates: evolutionary psychology.
I considered calling this section "The Intellectual Grey Pill" or something to highlight the way it functions as a similar narrative framework to the others. But evo-psych isn't a social movement or cultural gestalt: at its best, it's a rigorous scientific field that offers mechanistic explanations for patterns that most of the other frameworks either outright deny or take as gospel, as suits their biases.
Why do many women find status attractive? Why do many men prioritize youth and physical appearance? Why are certain kinks so common? "Because evolution" isn't always a sufficient explanation, but it's often a necessary component that can cut through the bullshit and gets at something real.
Evo-psych has the potential to be truth-tracking in ways that ideological movements don't. It also means we should hold its claims to a higher standards, be quick to call out when it fails to meet them, and be especially wary of other frameworks misrepresenting its findings to support their narratives.
Unfortunately, there's a wide gap between rigorous evolutionary psychology and pop evo-psych.
Real and good research exists—the journal Evolution and Human Behavior has plenty of peer reviewed studies on mate preferences across cultures, hormonal influences on attraction, on the evolutionary benefits or downsides of jealousy and pair-bonding in mammals, and so on. The Buss Lab at UT Austin puts out various papers and books that try to answer questions on mate selection and sexual desire from an evo-psych lens. Grab a handful of salt when reading any of it, by all means, but just because good science is more difficult to do in some fields than others doesn't mean it's impossible, and it's important that there are people trying hard to ask the right questions and find answers with real explanatory power.
Still, it's worth emphasizing that what most people encounter is the pop-science version: just-so stories dressed up in scientific language, where a study showing a statistical tendency becomes "all women are biologically driven to want Y" by the time it reaches social media. The field's genuine insights are real, but they're buried under a mountain of oversimplification, motivated reasoning, and hot-takes that conveniently confirm what the speaker already believed.
The progressive reaction to all this, particularly at the extreme ends, tends to default to throwing it all out as pseudoscience. But no map of romance and sexuality would be complete without real biological knowledge of human sexuality, and that includes psychological knowledge. The Red and Black Pill, meanwhile, will happily cherry-pick whatever research supports their beliefs and overstate it, assuming they interpret the research correctly in the first place.
Real researchers understand that evolutionary influences are variable, not uniform. We can see this in other animals—different individuals within the same species pursue different reproductive strategies, and both can be evolutionarily viable.
Some male mammals invest heavily in a single mate and their offspring; others spread their genes widely without sticking around to raise the kids. Both strategies persist because both can work, depending on environment and competition. There's no reason to think humans would be different. The devoted father who bonds deeply with his partner and children is enacting one strategy. The man who prioritizes short-term mating and moves on is enacting another. One may be more ethical to our modern eyes, and lead to more stable societies, but both are the result of biological preferences and drives.
The same applies to women. Prioritizing emotional intimacy and long-term partnership stability is one viable strategy. Prioritizing status and resources in a mate is another. Most people feel a mix of both drives, and both drives show up in the population because both can be adaptive depending on a wide range of circumstances.
And then there are sexual preferences and kinks—BDSM dynamics, dominance and submission, various fetishes—that are so common, show up so early in life, and appear so consistently across cultures that it's hard to explain them as purely learned. Something like 30-60% of people report fantasies involving power exchange. That's not a small deviant minority; that's a substantial chunk of the population. It's at least plausible that these preferences reflect underlying variation in evolved sexual psychology, even if we don't fully understand the mechanisms.
None of this means "it's natural, therefore it's the Only Proper Way" or "it's evolved, therefore it's set in stone." That's the move that gives evo-psych its bad name, and it's the move that Red Pill types exploit constantly. Humans are influenced by ideas, not just genes. But understanding genetic drives means you're working with more accurate information about why our initial drives toward one behavior or another are so powerful.
Like all the previous sources of romantic and sexual tropes, evopsych research, particularly pop-evopsych, has its own incentives and potential distortions:
The progressive instinct to reject evo-psych entirely is understandable—it's been used to justify a lot of sexist nonsense. But leaving people with no framework for understanding the biological component of their sexuality doesn't protect them. It just leaves them confused when their desires don't match the blank-slate narrative, and vulnerable to worse frameworks that at least acknowledge or validate what they're experiencing.
So where does all this leave us?
The traditional sources from Part 1 are unreliable. The counterreactions—Blue, Red, and Black—each has their own incentives that lead to their own distortions. And even the scientific approach that could help is mostly encountered in pop-science form that's barely better than the ideological frameworks
If you're trying to actually understand how romance and attraction work, you can't just pick one of these and trust it. You have to do something harder: build your own map, piece by piece, from better sources and better conversations.
At the end of the day, the only way to really understand reaity is to go out and observe it. And the only way to genuinely understand romance and sexuality is to talk to real people about their real experiences and preferences, and then, actually believe what they say, regardless of how strange it seems.
This maybe sounds obvious, but it's surprisingly rare. Most conversations about relationships are either abstract (debating theories and generalizations) or performative (presenting curated versions of our experiences). Actually sitting down with someone and saying "tell me honestly what you want, what you've experienced, what confused you"—and creating the conditions where they can answer truthfully—is hard, often for both the listener and the speaker... and that goes for both strangers and intimate friends or partners.
The prerequisite for good information is a social environment where honesty is actually possible. This doesn't happen by default. You have to build it, and protect it against erosion from status games or judgement.
Start with yourself. If you want others to be vulnerable and honest, you have to go first. Share your own confusions, your own embarrassing experiences, your own preferences that don't match the narratives. Vulnerability invites vulnerability.
Make it safe to share uncomfortable truths. The moment someone shares a preference or experience and gets judged for it, the honesty stops. This doesn't mean you have to approve of everything people do—but what people want or feel should always be recognized as valid first. Remember, the point of the community is to build mutual understanding. People can have their own opinions about what makes for a healthy relationship, or a stable society, or whatever. But there's nothing that kills collaborative inquiry faster than "you don't really feel that way" or "you're bad for feeling that way."
Actively seek out different perspectives. Talk to people of different genders, sexual orientations, experience levels, and relationship styles. Your male friends will give you one slice of reality; your female friends will give you another, your trans friends will get you even more, particularly if they've gone through hormone therapy and can report the differences they experienced after compared to before. No picture is complete on its own. People who've been married for twenty years know things that people in their twenties don't, and vice versa. People in non-traditional relationship structures have learned things that people in conventional ones haven't had to figure out. And all of it should be phrased as "my experience has been X," not "here is the truth that I know applies for everyone."
Ask specific questions, not general ones. "What do women want?" is an unanswerable question that invites generalizations. "What made you attracted to your last partner?" or "What's something you wish you'd known before your first relationship?" generates actual data. The more specific and personal the question, the more useful the answer.
Notice the difference between stated and revealed preferences. When someone tells you what they want, believe them—but also pay attention to their actual choices. People aren't always self-aware, and sometimes behavior tells you things that words don't. This isn't about catching people in lies; it's about building a fuller picture. Both what people say and what they do are data, and if you build a trusting relationship with people, you can gently bring up these apparent contradictions and explore them together.
Treat information as probabilistic, not absolute. When someone tells you their experience, that's one data point. When you notice a pattern across multiple people, that's worth more. But even strong patterns have exceptions. Stay curious about the outliers—they often teach you the most about the range of human experience.
There's a specific conversation that almost never happens, and it's one of the most valuable ones you can have: the post-mortem on attraction.
Most people, when they're attracted to someone, don't stop to analyze why. And most people, when they're not attracted to someone, don't explain it honestly—especially not to the person themselves. This means that most of us go through life with very little accurate feedback on what's actually generating (or killing) attraction.
If you can find friends who are willing to have this conversation—"here's what actually attracted me to that person, even though I know it sounds shallow" or "here's what turned me off, even though they seemed great on paper"—you'll learn more about how attraction actually works than years of theorizing.
This requires trust, safety, and a genuine commitment to honesty over comfort. It's uncomfortable to admit that you rejected someone because of something superficial. It's uncomfortable to admit that you were attracted to someone for reasons that don't fit your self-image, or what people around you might expect. But false models get built and maintained because so many things just go unsaid.
I run "Romantic Epistemology" events at various high-trust events because I want to encourage people to have these sorts of conversations. I try to ensure that people speak to their own experiences, rather than make statements about others, and encourage people to ask questions that might feel dumb or scary to ask elsewhere. People are often shocked by what others say, but there's genuine connection and gratitude too, particularly when someone shares something vulnerable that others weren't willing or able to voice for themselves.
No map you build alone will ever be as accurate as one that others help with, and this is particularly true in cases where territory is inside other people.
All of this might sound daunting—building your own truth-seeking communities while navigating broken legacy sources and avoiding reactionary spin. But you're not starting from zero. There are people out there doing good epistemic work on romance and sexuality, and they can serve as models (and sources of useful information).
The Gottman Institute has been studying relationships for decades, and their work is notable for being genuinely empirical. John and Julie Gottman didn't start with a theory about what relationships should look like and then find evidence to support it. They brought couples into their lab, watched them interact, measured physiological responses, and then followed up years later to see who stayed together and who didn't. From that, they identified patterns—like the "Four Horsemen" (criticism, contempt, defensiveness, stonewalling) that predict divorce with remarkable accuracy, or the 5:1 ratio of positive to negative interactions in stable relationships.
What makes this valuable isn't just the specific findings, but the methodology. They asked "what actually predicts relationship success?" rather than "what should people do according to my moral framework?" That's a surprisingly rare approach.
Aella comes from a completely different angle. She's a sex researcher and sex worker who runs large-scale surveys on sexual preferences, experiences, and behaviors—often asking questions that more institutional researchers won't touch. Her work is valuable for similar reasons: she's trying to map the territory as it actually is, not as people think it should be. When she asks thousands of people about their kinks, their relationship structures, their experiences with consent and communication, she's generating real data about human sexual diversity.
What these two have in common, despite their very different domains and methods, is a commitment to finding out what's true rather than confirming specific narratives. The Gottmans aren't trying to validate any particular relationship style; they're trying to figure out what works and what doesn't. Aella isn't trying to normalize or pathologize any particular kinks or romantic style; she's trying to understand the actual distribution of human sexuality.
There are plenty of personal blogs out there where people share their own experiences in an open way, like Putanumonit, or the old OKCupid research posts, but I'm not here to evaluate every single source out there and say which are good or bad; just point out that a difference exists, so that it's easier to notice it.
Keep in mind what good epistemic sources look like: curious, empirical, willing to report findings that might be uncomfortable or counterintuitive, and transparent about methodology and limitations. When you encounter someone talking about romance or sexuality, ask yourself: what are their sources? Because if it's not direct observations of lots of people, or lots of people's self-reports, why should you believe them?
You can't opt out of having a model of romance. You already have one. The question is whether you've ever examined where it came from, and whether those sources had any reason to give you accurate information.
Movies want to entertain you. Your family wants to protect you and pass on their values. Religion wants to stabilize society. Your friends want you to like and respect them. The Red Pill wants to sell you a solution. Progressive culture wants to signal its values.
None of them are primarily trying to help you understand Truth™, in all its thorny and complicated and sometimes messy glory.
The epistemically honest path is uncomfortable: notice your assumptions, trace them back to their sources, hold them lightly, and update when reality contradicts them. Seek out the researchers who are actually trying to map the territory. Talk to real people, with real curiosity, and create the conditions for them to be honest with you. Listen more than you theorize. Accept that this is one of the domains where confident, generalizable knowledge is hardest to come by.
But always remember that you can't just think your way to understanding romance. Maps are useful, but at some point you have to walk the territory. That means trying things. Asking people out. Going on dates that might be awkward. Having relationships that might not work. Making mistakes, ideally ones you can learn from gracefully rather than ones that hurt people.
I opened by noting that young people today are dating less, reporting more loneliness, and turning to grifters for guidance. Better epistemics alone won't fix that. What fixes it is actually doing the thing—building real connections with real people, not just theorizing about how connections work. The map is not the territory, and no amount of reading about swimming teaches you to swim.
The good news is that truth and action reinforce each other. Accurate maps help you navigate, and navigation updates your maps. Each awkward date, each honest conversation, each relationship that works or doesn't—these are data points that let you refine your understanding in ways that no influencer or ideology ever could.
Romance is one of the most important parts of most people's lives. It's also one of the areas where we do the least epistemics and take the fewest risks—where we inherit scripts without examination, stay passive to avoid rejection, and then wonder why we end up confused or lonely.
You can do better. But it requires intention, humility, a willingness to hear things you might not want to hear, and the courage to be vulnerable when sharing parts of yourself with others.
The territory is out there. Go walk it, and maybe share what you learn, now and then.
2025-12-04 10:30:29
Published on December 4, 2025 2:30 AM GMT
Summary: Anthropic has many employees with an EA-ish outlook, who may soon have a lot of money. If you also have that kind of outlook, money donated sooner will likely be much higher impact.
It's December, and I'm trying to figure out how much to donate. This is usually a straightforward question: give 50%. But this year I'm considering dipping into savings.
There are many EAs and EA-informed employees at Anthropic, which has been very successful and is reportedly considering an IPO. The Manifold market estimates a median IPO date of June 2027:
At a floated $300B valuation and many EAs among their early employees, the amount of additional funding could be in the billions. Efforts I'd most want to support may become less constrained by money than capacity: as I've experienced in running the NAO, scaling programs takes time. This means donations now seem more valuable; ones that help organizations get into a position to productively apply further funding especially so.
In retrospect I wish I'd been able to support 80,000 Hours more substantially
before Open Philanthropy Coefficient
Giving began funding them; this time, with more ability to see
what's likely coming, I'd like to avoid that mistake.
Now, Anthropic could fail, the IPO could take a long time with minimal opportunity for employees to take money off the table before then, or the employees could end up primarily interested in funding different things than I want to see funded. Still, it seems to me that EA-influenced funding likely goes a lot farther in the next few months than it will in a few years, and I think I should probably donate more this year.
2025-12-04 09:25:13
Published on December 4, 2025 1:21 AM GMT
We are launching our second S-risk Introductory Fellowship. This 6-week online program is designed to introduce participants to the core ideas of reducing s-risks—risks of astronomical suffering—and to build a stronger community of people working on effective suffering reduction. The fellowship will start in early February 2026.
Apply directly here.
Learn more here.
Applications for both fellows and facilitators are now open.
Deadline to apply as a fellow: January 3rd, 2026
Deadline to apply as a facilitator (same form, optional questions): December 21st, 2025
Questions? Contact us at [email protected]
2025-12-04 09:12:17
Published on December 4, 2025 1:06 AM GMT
There’s been a lot of talk lately about an “AI explosion that will automate everything” to “AI will produce huge rents”. While it’s far from clear if any of these predictions will pan out, there’s a more grounded version of such questions we can quantitatively address:
Suppose AI automated every task that’s currently automatable — which is still not all jobs — and didn’t even create new ones. How capable would it need to be before its rents could fund something like a universal basic income (UBI)?
It turns out one can, in fact, give a clean, analytic threshold for this question.
Off the bat, it’s perhaps worth mentioning that this is explicitly a funding result, and not a welfare result. I’m not arguing whether UBI is good/bad, or how it should be distributed, or how society should handle purpose/meaning in a post-work world. In fact, we don’t even have to assume the world is fully post-work in our treatment here – we can base ourselves on current U.S. economic parameters. Our goal is just to identify whether AI rents can become large enough to cover basic needs, and to explicitly identify what societal levers can make this threshold more or less feasible.
Below I give the broad strokes. For full technical details, proofs, and calibrations, see my paper:
“An AI Capability Threshold for Rent-Funded UBI in an AI-Automated Economy” https://arxiv.org/abs/2505.18687
In a Solow-Zeira task model where AI boosts the productivity of automated tasks by a factor , the minimum capability needed to fund a transfer B (as a share of GDP) from AI rents alone is:
Where the meaningful levers are:
Calibrating to U.S. data (sources in the paper):
This gives:
Based on current estimates, AI needs to be ~5-7× as productive as today’s automation on automatable tasks to fund a UBI-sized transfer.
Not 50×. Not 500×. About 5-7× beyond current automation productivity.
And that’s a worst-case upper bound—after all, we assumed:
In the paper, we analyze all of the above and show they would lower the required UBI AI capability threshold .
Figure 1. AI capability doubling trajectories vs. UBI threshold.
Figure 1 shows AI reaching in:
Even very conservative AI capability growth scenarios cross the bar by mid-century.
In Figure 2, we analyze monopolistic or concentrated oligopolistic markets and show that they reduce the threshold by increasing economic rents, whereas heightened competition between AI firms significantly raises it. We'll skip this for the sake of keeping this post short, but feel free to consult the paper, especially theorem that supports it in Proposition 2.
The cleanest comparative static is that:
Increasing public revenue share (Θ) from ~15% to ~33% cuts roughly in half.
Note, this doesn’t require full-scale nationalization; rather, it mirrors the difference between current U.S. and Scandinavian-style corporate-profit capture. As we can see in Figure 3 below, beyond ~50% public ownership, there are diminishing returns — unless operating/AI alignment costs c rise.
This makes Θ the most practical policy knob for reducing how much AI capability is needed to fund a basic transfer.
Figure 3 (from the paper). Public revenue share vs. AI capability threshold under cost regimes.
Figure 3 shows:
The economic message is simple: capturing more of the rents makes an AI based UBI more feasible without requiring nationalization, unless operating and alignment more capable AI systems becomes too costly.
This result isn’t an endorsement of UBI. It’s a feasibility benchmark.
If AI-generated rents exceed this threshold, society would have the financial slack to fund a basic transfer — or any number of alternative redistributive mechanisms (sovereign wealth dividends, tax credits, negative income tax), depending on political taste.
The point is:
AI capabilities don’t need to grow very far to support a UBI even without creating new jobs.
Whether one likes or dislikes that fact, we think it’s useful to know where the tipping point is.
We believe the analytic result (and associated comparative statics) is the more lasting contribution than any specific numbers we plug in here. We've made our code available, so anyone can try their own calibrations here.
There’s also a public tool made by the AI+Wellbeing Institute that implements our model based on the code above, connecting UBI feasibility to certain well-being measures.
It’s fun to play with and makes the comparative statics more intuitive.
