2025-11-30 12:42:23
Published on November 30, 2025 4:42 AM GMT
In my previous post Traditional Food*, I explained how what we think of as a "traditional" diet is a nationalist propaganda campaign that's making us sick. In this post I'll go into the biological mechanisms.
There are four substances that the body can metabolize: carbohydrates, fats, protein and alcohol. Of these, I'll focus on how modern carbohydrate-heavy foods (like pasta, bread and rice) are related to insulin resistance. This doesn't mean that seed oils are good for you, or that the industrial revolution hasn't changed how people consume meat. Seed oils are bad for you and people today don't eat meats the way peasants did—they ate organs and other stuff that gets fed to animals today. Alcohol is just a poison you can metabolize.
With that out of the way, it's time to learn about insulin resistance.
Your blood has glucose dissolved in it. Blood glucose is important because your brain relies on glucose to function. If there wasn't glucose in your blood, then you'd die. However, too much blood glucose causes problems too. Precisely what problems hyperglycemia causes isn't important right now. Instead, what matters to this post is that your body has a control system that keeps your blood glucose in its proper range.
When your blood glucose rises, your body releases a hormone called insulin. Insulin binds to insulin receptors in cell membranes. These insulin receptors activate signalling that tells various tissues in your body that it's time to absorb that glucose from the blood and into cells.
Your blood glucose spikes (rises very fast) when you eat foods that release lots of glucose quickly. Foods that quickly release lots of glucose into your bloodstream are said to have high glycemic index. White rice and anything made out of modern flour has a high glycemic index. Sugary drinks have a very high glycemic index. Slower-digested foods like legumes have a low glycemic index.
When you eat foods with a low glycemic index, this system works fine. But when you eat foods with an unnaturally high glycemic index, the system works badly. The unnaturally high glucose spike causes an evolutionarily unprecedented insulin spike. The blood glucose then drops below where it's supposed to be. In this way, eating too much high-glycemic food actually makes you hungrier. That hunger creates a caloric surplus, which leads to obesity.
Even worse, repeated insulin spikes cause the insulin receptors to desensitize (downregulate) themselves. Your body creates more insulin to get the attention of desensitized receptors. This produces a horrible chain reaction where your insulin is chronically high and the cellular response diminishes. This is insulin resistance. So insulin resistance prevents fat burning too, because insulin inhibits fat release. Here are some signs of insulin resistance.
Perhaps this is happening to you and you think it's normal, or just part of getting old. It's not! The reason it might seem normal to you is because most [[1]] Americans—even non-obese Americans—have a sign of metabolic dysfunction like this. 38% meet the much worse criterion of prediabetes. This is historically unprecedented. Ignoring tiny aristocratic minorities, no society before 1800 was like this. If you're eating three meals per day, your energy levels should be mostly decoupled from when you eat. Your hunger should be decoupled from your energy levels too.
Many factors led to this, including exercise patterns (mostly driven by horrific urban planning), stress, and electric lights. But the most important factor is probably diet.
Historically, most of what normal people ate had—by modern standards—a very low glycemic index. Even milk and honey, foods so valuable the Bible uses them to paint a picture of the Promised Land, have only low-to-medium glycemic indices.
| Historical Food | Glycemic Index |
|---|---|
| cabbage | 10 |
| beans | 20-40 |
| hulled barley | 28-30 |
| milk | 30-35 |
| medieval milled barley bread | 35–45 |
| medieval rye sourdough | 40–48 |
| coarse bulgar wheat [usually not eaten by peasants] | 46-48 |
| historical honey [very expensive] | 50-60 |
| absolute top-tier luxury wheat bread for kings | 65-75 [speculative] |
| Modern Food | Glycemic Index |
|---|---|
| coarse bulgur wheat [usually not eaten by anyone] | 46-48 |
| modern Italian pasta | 45-55 [depends on cook time, can be as high as 70+ when over-cooked, as is standard in American kitchens ] |
| artisanal whole wheat sourdough | 50-58 |
| Ezekiel bread | 55–60 |
| Coca‑Cola | 63 (if you're wondering how Coca-Cola seems less spikey than white bread, it's because half of the sugar in Coca-Cola is fructose) |
| "whole wheat" bread | 60-80 |
| modern honey | 65-75 |
| modern white rice | 60-75+ |
| industrial white bread | 70-80 |
| pure glucose (reference) | 100 |
[These tables come from ChatGPT because the details aren't important—just the general trends.]
When you look at these tables, you'll notice two major trends.
Historically, insulin resistance was overwhelmingly a disease of the rich (and sometimes monks), because only the rich could afford the tasty foods that cause insulin resistance. Historical societies were so extremely unequal that almost nobody was rich. Consequently, almost nobody got insulin resistance.
Today, modern technology allows everyone to eat like a medieval king. The cheapest bread in your local grocery store has a glycemic index that historically, only the rich could afford.
Epidemiological data shows 10%-15% of Americans as having normal metabolic markers. The exact numbers vary depending on where you put semi-arbitrary cutoffs. If we used a reference class of hunter-gatherers, then American metabolic health would look even worse. Unfortunately, precise data on hunter-gatherers' metabolic markers is much harder to come by. ↩︎
2025-11-30 09:50:07
Published on November 30, 2025 1:50 AM GMT
On October 5, 1960, the American Ballistic Missile Early-Warning System station at Thule, Greenland, indicated a large contingent of Soviet missiles headed towards the United States. Fortunately, common sense prevailed at the informal threat-assessment conference that was immediately convened: international tensions weren't particularly high at the time. The system had only recently been installed. Kruschev was in New York, and all in all a massive Soviet attack seemed very unlikely. As a result no devastating counter-attack was launched. What was the problem? The moon had risen, and was reflecting radar signals back to earth. Needless to say, this lunar reflection hadn't been predicted by the system's designers.
Over the last ten years, the Defense Department has spent many millions of dollars on a new computer technology called "program verification" - a branch of computer science whose business, in its own terms, is to "prove programs correct" . [...]
What, we do well to ask, does this new technology mean? How good are we at it? For example, if the 1960 warning system had been proven correct (which it was not), could we have avoided the problem with the moon? If it were possible to prove that the programs being written to control automatic launch-on-warning systems were correct, would that mean there could not be a catastrophic accident? In systems now being proposed computers will make launching decisions in a matter of seconds, with no time for any human intervention (let alone for musings about Kruschev's being in New York). Do the techniques of program verification hold enough promise so that, if these new systems could all be proven correct, we could all sleep more easily at night?
- The Limits Of Correctness, by Brian Cantwell-Smith
Spoiler: the answer to Cantwell-Smith’s question is “no”. No amount of program verification would solve the moonrise problem. The fundamental problem with program verification, as Cantwell-Smith explains quite well, is that one can only verify that the program matches a specification, not that the specification does what one wants in the real world. And in practice, the specification is not much more likely to be correct than the program itself. If the engineers didn’t realize they need to account for moonrise when writing the program, they’re not going to realize they need to account for moonrise when writing the spec either.
… and yet, I claim that the moonrise problem is not completely intractable in-principle. It is possible in-principle to write nuclear warning software which will correctly handle moonrises and all the other complications of the real world, without the engineers needing to know about all those complications in advance, without needing exhaustive data on all those complications in advance, and without just guessing that e.g. a machine learning system will generalize off-distribution in the intended way. It is possible in-principle to prove “robust correctness” - to prove that a nuclear warning system will (with high probability) continue to operate as intended even in novel unplanned-for situations. Though of course such a proof would look nothing like today’s “program verification”, and indeed would probably not draw on program verification theory or tools at all.
This post will sketch what I imagine such a system and proof might look like, and its limitations.
I’m going to assume a particular ML architecture, not because I think it’s a necessary or likely way for a moonrise-robust system to work, but rather just to have a concrete picture in mind.
At a very high level, today’s image models take in a whole bunch of independent normally-distributed noise, run it through a learned function, and spits out a realistic-looking image. We’ll imagine something like that, but with more data modalities: a generative model which takes in a bunch of independent noise, runs it through a learned function, and spits out realistic-looking data across all of its modalities, be it images (e.g. satellite surveillance), text (e.g. newspaper headings including Khrushchev's travels), radar readings, etc, etc.
Like today’s image generators, such a system can also be used in-principle for prediction rather than generation. It’s a generative probabilistic model; the function defining its distribution P[data] may be complicated, but in principle one can do all the normal probability things with it.
We will not worry about how specifically the system is trained; just imagine it’s some future technique which has not yet been discovered. We will assume that the system can learn online to a large extent, and that its predictions generalize well off distribution.
Now, one immediate idea would be to include among the system’s many data modalities a one-bit channel indicating whether a nuclear exchange had been initiated. Then the system could be used to predict that bit just like everything else it predicts.
Alas, we have zero real-world training examples of initiation of nuclear exchange. (Ok, technically there’s Hiroshima and Nagasaki, but that’s probably a pretty misleading example for learning about a hypothetical present-day nuclear exchange.) We could cook up some artificial examples, but then we’re right back to moonrise problems: just like engineers trying to make a spec will inevitably miss important real phenomena like moonrise, engineers trying to make artificial data will inevitably miss important real phenomena like moonrise. When trained on real-world data, I’m willing to assume the system will generalize well to predict new real-world raw data even off-distribution. I am not willing to assume that a system trained on artificial examples would generalize well to real-world data off-distribution, for exactly the same reasons I’m not willing to assume that a spec someone hand-coded is correct. Humans are not robustly good at that.
Upshot: our system will have to do something else, rather than the most basic supervised learning setup, for detecting nuclear exchange.
Our core hypothesis is that, somewhere in the ML system, whether in “one spot” or “spread out” or embedded in a complicated way, the system represents its own internal indicator for whether nuclear exchange has been initiated. Maybe it’s the activation of one “neuron” (but probably not). Maybe it’s the activation along a certain linear direction of activation space in one layer (more plausible, but still probably not). Most likely, it’s some embedding which we still haven’t figured out yet. Whatever it is, we’ll call that representation the “latent variable” representing initiation of nuclear exchange.
Now, the point of the Moonrise Problem is not merely to produce a system which works, although that is a necessary part of the goal. The point is also not merely to produce a system which robustly works, although that is also a necessary part of the goal. The point is to produce a system which provably robustly works, i.e. a system in which we can prove the core load-bearing assumptions on which the system’s functionality relies. That’s why we had all that talk about program verification upfront: program verification purports to prove that programs will work. The tools of program verification aren’t particularly useful for this problem, but the problem of proving our core load-bearing assumptions still stands.
So the question is: what properties could we in-principle prove mathematically (assuming they in fact turn out to be true), about some latent variable supposedly representing initiation of nuclear exchange inside a future ML system, which together would be enough to establish that our nuclear warning system based on that latent variable will actually robustly work? That’s our Moonrise Problem.
In this section I’ll present a candidate list of properties and assumptions. I’m not sure that this is the ideal list, or even if it works at all; maybe I am missing key things! The important point is that we have a very concrete problem on hand, so we can talk very concretely about whether the list of properties and assumptions given would be sufficient - and if not, how it could be changed, or what the nearest unblocked option might be. My main goal here is not to advocate for this particular list, but rather to illustrate the Moonrise Problem as a useful test-case for considering properties of “safe” AI.
I am willing to assume that our ML system is capable at predicting raw real-world data, even off distribution. This is important, because it rules out some not-very-interesting failure modes which would otherwise be impossible to disprove - like e.g. the possibility that at some particular time the physics of our entire universe suddenly changes to something completely different, and the entire concept of nuclear exchange ceases to make sense at all. (It’s still possible that the entire concept of nuclear exchange will cease to make sense at all for more mundane reasons! We’ll address that possibility shortly.)
The reason I’m willing to assume this is because it’s a pure, core capabilities problem. Insofar as the ML system doesn’t satisfy this assumption, it’s probably not that dangerous (yet).
First, we’d like to establish that a particular pattern exists in the training data (and therefore in whatever world generated the training data, i.e. our physical world). There are lots of tools for the simplest version of this already; one simply needs to be able to predict better than maxent. But we’d like something with a little more oomph.
Ideally, we’d like a notion of “real pattern” which comes with a Correspondence Principle. The Correspondence Principle is the idea that new models should agree with old models in places where the old models predicted correctly - e.g. General Relatively must reduce to Newtonian Gravity in the broad regime where Newtonian Gravity predicted correctly; otherwise our “General Relativity” model has a capability shortcoming. We’d like a version of the Correspondence Principle for whatever notion of “real pattern” we use - i.e. some principle saying that, if we’ve found a pattern which fits certain rules, then any future model must be agree with that pattern at least in the regime where it has predicted correctly thus far; otherwise the new model has a capability shortcoming.
This property would be proven mathematically, from a set of assumptions which could be verified for the system at hand.
As a toy example, imagine that the one real pattern our ML system found looks like clusters in some space. Then we might guess that a wide range of systems trying to predict that pattern would internally represent the cluster identifier for a datapoint. In particular, a wide range of systems would factor out the cluster identifier, i.e. represent it in such a way that it’s not entangled with a bunch of other stuff, because the system will probably need to use that id a bunch in order to do its prediction, and if the cluster id representation is too entangled with other stuff then that other stuff will interfere with the predictions.
This is one specific example of a (conceptual) convergent factorization story: a reason for some particular internal variable to be represented in a way factored apart from the system’s other internals, across many different system architectures.
Ideally, we’d like to identify a convergently-factored representation corresponding to initiation of nuclear exchange, within the ML system’s machinery for predicting a specific real pattern (i.e. some pattern having to do with nuclear strategy). The mathematical challenge would be to prove convergent factorization for that representation. Ideally, we’d like the “wide range of systems” across which the factorization is convergent to include both the ML system and the human brain. That way, we’d have mathematical justification for the assumption that the representation we’ve identified in the ML system matches some representation in the human brain.
If we got really fancy and brought in brain scanning tech, we might even be able to directly establish that the representation identified in the ML system matches the mental representation in a human brain which the words “initiation of nuclear exchange” point to. And then we’d really, properly have a solution to the Moonrise Problem.
Conceptually, the assumptions and properties above are sufficient to establish that:
… but that doesn’t mean that humans’ concept of initiation of nuclear exchange will itself generalize off-distribution.
A canonical toy example: imagine that, on distribution, we have two clusters, corresponding to cars and trucks:
But then, someone comes along and builds a bunch of new vehicles which are sort of in-between cars and trucks, on a whole spectrum, so the distribution of vehicles now looks like this:
At that point, it doesn’t even make sense to talk about “cars” and “trucks” as distinct categories any more, at least among new vehicles. (The car/truck labels would still be useful on older vehicles, which is where the proofs would still be able to make some guarantees: a Correspondence Principle would say that any new model must still reduce to the old cluster model for predicting old vehicles, else the new model is suboptimally-capable. And convergent factorization would then say that the new model will still convergently use car/truck labels for older cars.)
That said, if the original real pattern on which the representation of initiation of nuclear launch is based fails to generalize to new data, that’s at least a detectable problem in principle. So presumably the system would throw an error and bring in a human if and when the nuclear launch equivalent of the car/truck issue became relevant.
Thank you to Steve Petersen and especially Ramana Kumar for introducing me to the paper quoted at the beginning of this post.
2025-11-30 08:15:23
Published on November 30, 2025 12:15 AM GMT
It's done. I made it to the end. A Finnish proverb fits the situation perfectly:
Paska reissu mutta tulipahan tehtyä
Which translates to something like "A crappy journey but in any case it's over now". I forced myself to do this. It was not fun. I rarely enjoyed writing. Every day I kept looking at the word counter, hoping that it would be over already. Sometimes the text was not done when I reached 500 words, which meant I had to write more.
I did not manage to keep any buffer. Each text was written on the day when it needed to be ready, except for this one which I wrote five days in advance because why not. Sometimes I had fragments or notes ready, but most of the time not even that. Once or twice I wrote two posts back to back around midnight, but most of the time I finished a text and then did something else for the rest of the day. Doing it again tomorrow was bad enough.
I managed to not neglect my work too badly. Sure, I skipped a day or two because I had to write instead, but it's not like I don't do that to play videogames every now and then, something I completely avoided this month.
The thing I did neglect was the quality of text I produced. One could imagine that if the top priority for every day is producing a blog post, you could actually put some effort into it. I managed to put in a lot of hours, but I'm not happy about the level of effort spent. Hard to distinguish between quality and effort though, but most of the time I was tired and just wanted it to be over and picked the first topic that I could manage to find 500 words in. I did barely any editing except for spellcheck with ChatGPT. I didn't even read most of the texts myself.
Scott Alexander's quote from the Inkhaven site was something that motivated me to do this in the first place:
"Whenever I see a new person who blogs every day, it's very rare that that never goes anywhere or they don't get good. That's like my best leading indicator for who's going to be a good blogger."
Goodhart's law strikes again! Once there's a pressure to write every day, its usefulness as an indicator is over. There's a more fitting quote from Scott, in The Parable Of The Talents
On the other hand, I know people who want to get good at writing, and make a mighty resolution to write two hundred words a day every day, and then after the first week they find it’s too annoying and give up. These people think I’m amazing, and why shouldn’t they? I’ve written a few hundred to a few thousand words pretty much every day for the past ten years.
But as I’ve said before, this has taken exactly zero willpower. It’s more that I can’t stop even if I want to. Part of that is probably that when I write, I feel really good about having expressed exactly what it was I meant to say. Lots of people read it, they comment, they praise me, I feel good, I’m encouraged to keep writing, and it’s exactly the same virtuous cycle as my brother got from his piano practice.
So yeah. I guess I just have to be happy that at least one of my cycles was around something so financially useful as programming. Imagine trying to make a living or even waste all this time I have with my other interests; online chess and reading young adult fantasy books. Weirdly enough I didn't need any of that external feedback for programming either, perhaps there was no cycle at all other than my own fascination of what could be done with the closest thing to magic we have.
That said I'm still sad that I'll never be a blogger. But as the saying goes, everyone wants to be a writer, yet nobody wants to write. I certainly don't, I'm done. Or maybe I am a blogger now, given that I've written some 20 000 words over the past month?
2025-11-30 06:10:22
Published on November 29, 2025 10:10 PM GMT
I’m writing from my grandparents-in-law’s living room. We just finished watching the Ohio-Michigan game, now we’re watching the Minnesota-Wisconsin game, and my wife and I will soon be driving to the airport to fly home. It was a cozy Thanksgiving, even if about half the family has come down with a cold, and I got to see my first snowfall in several years.
I initially made good book progress this month, finishing up revisions on Chapter 6. Then I got sick and did nothing for a week. The following week I was busy catching up at work, and then I got obsessed with a special project that I’ll be posting about soon, so needless to say I didn’t make as much progress on Chapter 7 as I would have liked.
And December is shaping up to be a busy month! There’s a sesshin. There’s holiday plans. And there’s the aforementioned special project (and another special project you won’t be hearing about for a little longer). So, in the hopes that I can make any progress on book revisions at all, I’m not going to hold myself to posting weekly until after the new year.
In the meantime, in addition to, as always, plugging my Twitter, you might read some interesting posts from other folks, including:
’s series on the Enneagram (last post with links to them all)
(I still owe you all a post about Buddhism and the Enneagram inspired by Michael’s posts when I can finally get to it!)
’s series on consciousness (last post with links to them all)
’s excellent post detailing a personal story of a stage transition.
More of us should write more things like this. I think they create a record of incredibly useful evidence and supply examples that can help those struggling to break through from one stage to the next. I wrote a short comment on his post with my own Kegan 3→4 story, but I should put my money where my mouth is and write up all my phase transition stories.
LessWrong ran an event called Inkhaven during November. If you don’t usually read LessWrong, there’s an unusually high volume of new, interesting posts from the past month that you might enjoy, and even more at the Inkhaven link.
2025-11-30 05:20:29
Published on November 29, 2025 9:20 PM GMT
Manifest attracts devotion unusual for a conference. "Dinners and meetings and conversations with people building local cultures so achingly beautiful they feel almost like dreams."[1] "This strange blend of data and debauchery."[2] "That magical feeling of serendipity, where you can flow through a space, passing from conversation to conversation, contribute to each one in turn, and have others do the same for you."[3] Even those who run it say it's "a tough event to describe."[4]
We attempted a replication.
I won't bury the lede. You can just steal things. Manifest replicates.
This post tells the story of the event. If you're just here for take-aways, you may want to skip to:
This post originally included more personal reflections, which I left out of this public version. I'm not posting it to LessWrong, but if you want the full story, it's available in this Google Doc (please feel free to request access if you're interested and we've met in real life).
I had a very strange time in Berkeley last June. I came with a message to deliver, ideas to refine, and work to do. In the week between LessOnline and Manifest, I frequently worked from the gazebo in the center courtyard of Lighthaven. I got a lot done, advanced work that continues to compound. I'm proud of what I accomplished.
Just under the surface I was a mess. But I didn't want to let the trip be about that. I wanted to seem OK, convinced myself that I needed to project a certain image of stability for the sake of duty. Given the crowd, to keep up appearances I leaned into being more adventurous and playful.
Someone gave me access to a printer. I had a role to play. I started labeling things.[5]
I named my preferred spot the "Gazebo of Schemes," gathered a cabal of friends, and appointed myself chair. Even as the chair of this invented commission, I imagined us to be staffers. I greeted passers-by with variations on: "Welcome to the Gazebo of Schemes, how may we assist your schemes?" This is an excellent discussion prompt, often getting people to reveal quests they didn't know they had.
The Gazebo of Schemes hereby claims partial credit/blame for several dates, one relationship, at least one lawsuit, several wardrobe upgrades, [redacted] instances of corporal punishment, four or five conference talks, a feud, and finally, this conference series.
@Ben S. flew out for Manifest and immediately loved the atmosphere, how Lighthaven's design created a distinct kind of conference. When the Gazebo of Schemes first called him to adventure, he was still taking it all in, had no plots to offer. But after barely more than a day, he hatched a scheme to bring something like Manifest to the East Coast, to bring this social technology to our friends back home.
I was honor-bound to assist.
As a staffer, I know how to support a principal. The job is mostly to be annoying. Hey Ben, remember that crazy idea you had? You should totally do it. Hey Ben, David and Pratik would be interested in your idea. Hey Ben, I was thinking... Hey Ben, have you told... and on, and on, and on. The sad part is, I'm a very good bureaucrat.[6]
Ben naturally gravitated towards programming, which speakers to invite, what kind of events and panels to put together, and venue selection. How to handle the people. I tackled logistics, budget, planning, and the Gantt charts, the parts that are fun for me. @David Glidden signed on to be the day-of volunteer coordinator. Pratik offered to recruit additional speakers. We had a team, but no clear idea for what to do next.
How do you transplant a conference that is such a product of its venue?
First we looked for the elements that weren't. Manifest takes a lot from the Rationalist Unconference playbook: invite interesting and agentic people, get them talking online before the event, and occasionally butt in to say, "that's really interesting, you should put that on the calendar!" But unlike most Unconferences I've seen, Manifest has a default, a main stage that's fully programmed in advance. Since there's always something sufficiently interesting going on, organizers don't have to rely on any particular Unconference session. This gives people space to be niche and experimental. If attendees aren't interested, that's fine, they'll go to the prepared talk or panel instead.
Manifold, the platform, provides another: interesting markets to discuss, an implicit bullshit tax on sloppy predictions, and a pressure to keep heated conversations grounded by searching for cruxes that can be operationalized into a market. To speed this along, it seemed important to seed the conference with several Manifest regulars, particularly Manifold power-users who broke disagreements into markets instinctually. Several names immediately came to mind, people who'd probably fly out if invited.
In looking for the portable elements, we had derived some venue preferences after all. We would need several rooms, but one should be larger than the others, large enough to fit most or all attendees. This argued against most apartments or renting a few classrooms. We wanted some sort of place that would feel distinct enough to break people out of day-to-day political arguments. We also wanted something that felt special enough to tempt a few friends to fly in. We ruled out anything that felt like an office or a sterile hotel conference center.[7] We started looking for suitable venues, but nothing seemed right.
Then the solution fell into our laps, two friends suggested their technically-not-EA Group Houses. Workshop House was a former rectory that's configured for small events, with a large living space, several breakout rooms, dark wood and stained glass. Very Bayes House. Embassy House was a beautifully renovated, modernly-appointed, former embassy that throws large parties. Very Aumann Hall. Both had excellent roof decks. We couldn't decide, so we picked both. We argued a bit internally about which was suited to which role, but decided Workshop was best for the daytime programming, and Embassy was a better fit for the afterparty.
Once we had the concept and venues, things started to fall into place. Ben talked to the Manifest team, Austen, Stephen, Ian, and David Chee, they shared a wealth of knowledge and offered to sponsor. We found some dates that worked, November 8th seemed good. We agreed with the venue on a capacity of 60. I was able to catch up with Austin at Metagame and iron out some logistical details (his offer of Mox's ticket system was a particular lifesaver).
Momentum started building. We announced locally to give a head start to our target market: local rationalist-adjacent who might enjoy Manifest proper. We also leaked the invite to some regulars at Lighthaven who might travel in to help set the tone. We went "wide" a few days later, announcing via email, in blog posts, and in Prediction Market discord servers. People quickly joined the Manifest x DC discord server, as we were still setting it up. We sold out in six days.
It’s a sad irony that throwing an event you want to exist doesn’t necessarily mean you get to attend it. We had Robin Hanson and Peter Wildeford give prepared talks, had panels on forecasting politics and the future of Manifold, even a forecasting game… and I missed almost all of it. I heard they were all good talks, I caught five minutes here and there, sat in on some of the smaller panels upstairs, but mostly I was coordinating. If you want to know what happened at the conference, Matt Beard’s review is a great summary.
I was responsible for two parts of the calendar. After Ben welcomed attendees, and David gave logistical notes, I delivered an “Opening Benediction,” one last step to copy over the tone of earnest and playful truthseeking from Manifest. Being jailed in a walled compound is no excuse for missing my conference, so that afternoon I ran a virtual panel from Inkhaven, roping in three friends who are locked in Lighthaven for the month of November and forced to blog daily. To join, attendees were asked for a blog post prompt–that didn’t need to be good–just sufficient to stave off eviction one more day.
Mainly, though, the day was a blur. I left this section to write until last, hoping I’d have more to say, but I still don’t. The work felt good, felt rewarding. People seemed to enjoy it. I enjoyed it. I had missed this.
As the sun set early, we closed the day, kicked participants out onto the street to form groups for dinner, cleaned up Workshop house, got dinner for the volunteers, then changed into Black Tie for the afterparty.
We collected $5,217 in revenue net of refunds:
Total expenses were $4,821:
This leaves a modest surplus of $396, which we're leaving as seed money for the next Manifest X.
Somewhat surprisingly, this event would have been feasible without sponsorship. We would have needed to charge $10-20 more per ticket, drive a harder bargain with our main venue, and cut back on supplies, catering, and the afterparty. All were fairly doable, though negotiating harder with the venue would have risked offense, since their asking price was already a "friends" rate (starting from a market rate would have made things harder).
Feasible, yes, but this would have been much more stressful to organize on a tighter budget. I was nervous about asking friends to buy tickets from me on faith. My arbitrary comfort threshold was $50, getting the Early Bird price to that target helped me pitch enthusiastically. The slack in the budget gave us peace of mind to solve problems with money. We had a healthy contingency reserve. We paid for rush printing and shipping when we were delayed on the badges. When our first choice caterer closed unexpectedly, we were able to fall back to the easiest backup plan rather than seriously shopping around. We were more generous with refunds than our written policy, even offered to refund dissatisfied attendees after the fact in exchange for feedback. No one took us up on this.
What the sponsorships and supporter tickets really bought was the organizing team's peace of mind. We're very grateful.
We had 62 attendees in all, 16 filled out the post-event survey. This data is skewed by a response bias. Half of our survey responses came from people who have been to a Rationalist-style Unconference before, but this group was a third of attendees, and we were pretty confident the event would go well for them. We were hoping to hear from people less familiar with this format, and only got 8 responses from those ~40 attendees. However, to mitigate the risk that we would not hear from those who were unhappy with the event, we incentivized negative feedback, offering refunds to anyone who regretted their ticket purchase in exchange for filling out the survey. No one took us up on this.
Responders were divided roughly evenly between liking that size or preferring somewhat larger. 2 of 16 wanted over 100 attendees. I feel like the sweet spot for ManifestX events is in the range of 50-80, depending on the city.
It would be bad form to detail internal arguments and disagreements. But people made predictions, that's totally different. Suffice it to say, @Austin Chen was right and John got wrekt. Participants overwhelmingly preferred making the "x" lower-case, and moderately preferred Austin's recommended spacing of "Manifest x DC":
Our post-event survey strongly endorsed charging more. There is some response bias, half of our respondents had been to Manifest or a similar Rationalist Unconference before. But we also literally offered refunds in exchange for negative feedback, and no one took us up on this. 15 of 16 responders were willing to pay at least 20% more. 11 of 16 responders were willing to pay at least 50% more, where we could have done the same spending without sponsorships.
Events shouldn't charge more just to pay organizers a profit. This is a terrible way to make money. The $396 surplus works out to less than $2/hour for organizer's work, raising prices might have increased that to $10/hour, still far from an attractive professional wage. But money is useful to improve the event. If we had a reliably larger budget, we might have rented more space, (which all participants would have liked, if available), or kept the group together for dinner. We had an two-hour break for dinner, 6-8 PM, to clean Workshop House before the afterparty. At least a third of participants went home and didn't make it back out to the afterparty.
We promoted the section on Ticket Strategy to its own post a few weeks ago, to get the word out fast. For a quick summary, several recent Lighthaven events have shared a ticket strategy with three pillars:
We implemented this and it worked well. We sold out in six days, well over a month out. We charged $65 for tickets by default, $50 for early bird (the first 30 tickets), and $80 for "last chance" tickets after the cancelation deadline. Supporter tickets, for a fancier badge and our thanks, cost twice the going price at the time (so, $100, $130, or $160). We offered full refunds less transaction costs until two weeks out, and had lower attrition than expected, replacing those who dropped from a wait-list.
For details and discussion of how this solves coordination problems, see the standalone post.
We set up a Manifold Market to predict and mitigate what might go wrong. In the end, very little did. Manifold's T-shirts were delayed but made it to the venue by the afternoon. One of our speakers ended up double-booked, but arrived as his talk was scheduled to start. The Geneva and Vienna Conventions were upheld, despite some real risks. Someone tried to hyperstition "Fire!", adding it to the market and betting it up, but our valiant traders thwarted him and arbitraged it away.
Our only injury was from the afterparty dueling. Minutes after this picture, an extremely stabby participant from a different sparring pair managed to draw a bit of blood from the afterparty host, with the host's own plastic sword. Luckily everyone took this well, "Wait, am I actually bleeding? Awesome!"
We underestimated demand at nearly every stage. We had 62 participants in total, including organizers, speakers, and volunteers, an informal waitlist of at least 10, and obvious latent demand for another 20 seats. We could have easily gotten 90 participants if we had sufficient space, without any additional promotion work. With a reasonable amount of work to spread the word, we could have far exceeded 100.
We overestimated pre-event cancellations (only five; we had guessed 10), day-of no-shows (only three; we had guessed four or five), and attrition during the day (we expected more people to only come for part of the day, but we probably had 55-58 people in the building between Noon and 4pm). The one exception was the afterparty, we expected nearly everyone but about three-fifths of attendees came. The two hour break for dinner, and using a different venue almost a mile away, surely contributed.
We should have had a harassment policy. An individual was told in writing that they were unwelcome, then bought a ticket anyway, which we canceled and refunded. They later showed up uninvited to the morning-after brunch we organized, which we handled poorly. They used a series of small escalations, announced an event that just happened to be at the same venue at an overlapping time, arrived and set up at a different table, then moved to an adjoining table, then joined the group, then changed seats to sit by the target of the harassment. This was a public place, and we had already asked them not to come through an intermediary, so we couldn't remove the person. The targeted participant left the brunch rather than confronting the behavior, but we should have done more to prevent the harassment. This is uncomfortable, seems to be escalating, and I would appreciate advice on what to do about it if it recurs in future events.
Think of a photo policy in advance. We announced one on-the-fly, that everyone pictured would need to give explicit permission to share a photo, which we later felt was too restrictive. A better way to both allow photos and opt-outs would have been to have a list of those who were opting out on the discord or attendee guide, and have them put stickers on their attendee badges as a reminder.
Write a survey in advance. A participant saved us by writing a starting draft the evening of the event, that we were able to revise and send out two days later. We got some good responses, but it would have been better to have it ready to go at the closing session.
The afterparty venue wanted to screen attendees, as a condition of hosting. We used an "approval-required" partiful listing to do this. This worked, but it was awkward, required extra steps from participants to request access, and took a lot of work to coordinate. In retrospect, a cleaner way to handle it might have been to make the afterparty invite-only; share the attendee list with the afterparty hosts and simply let them invite whoever they wished.
People love to congregate in doorways and chokepoints, we should have discouraged that more. We caught and fixed one chokepoint we'd inadvertently created with folding chairs. But a lot of this is innate, the doorway is just the obvious place to be while someone decides if they want to attend this breakout session or head back to the main room. It's understandable, we just should have asked volunteers and session hosts to encourage attendees to fully enter rooms.
I inadvertently discouraged people from using one breakout room all morning by sitting down with laptop and coordinating logistics. I would have moved, but I'm sure I didn't look particularly approachable. Once I left, the room booked up for the rest of the day.
Chipotle catering was fine, but surprisingly expensive. We ordered what Chipotle claimed would be sufficient to serve 70. It was just enough for the 55 people who ate, at a cost of $22.43 per person (including tax, no delivery or tip). With better planning we could have reduced this cost by at least a third. Also, food for 55 is a lot of food. We originally sent three volunteers with a cart to pick up the food, but had to send reinforcements to assist.
Someone brought and handed out gum, which was thoughtful and helpful in our close quarters, but annoyed some participants. Mints would have been better. Similarly, someone brought a portable mechanical keyboard, which made disruptive noise that we should have put a stop to.
Glory, mana, and our $396 surplus await whoever organizes the next Manifest X. Our post-event survey reveals there is at least some demand in NYC, Philadelphia, Baltimore, Pittsburg, Raleigh-Durham, Chicago, Seattle, Austin, and Tokyo. The DC organizing team is happy to advise and talk through issues. Reach out to @Austin Chen if you think you have what it takes.
Kevin Roose in the New York Times: https://archive.ph/sf5lw
Theo Jaffee on Substack: https://www.theojaffee.com/p/manifest-manifested
Rachel Weinberg on Substack: https://rachelweinberg.substack.com/p/manifesting-manifest
I am professionally interested in State Legibility, after all.
I think hotels are underrated. Plenty of events can be run there well, especially if your group is showing up with its own distinct culture and expectations. We could absolutely run a ManifestX in a hotel conference space if everyone had been to Manifest before. Hotel conference spaces are less suited to instill a new culture or social technology in people who aren't already familiar, our target audience.
2025-11-30 02:32:22
Published on November 29, 2025 6:32 PM GMT
Hi, I'm Aaron. You may know me from some projects, most recently among them Hyperstition AI.
It's done. Here's five thousand AI-generated novels.
Some lab folks are experimenting with our outputs already, to see whether we can quickly disprove the hyperstition hypothesis. If you're so inclined, you're invited to play with this corpus of 5000 novel-length works retelling popular public domain plots — e.g., The Adventures Of Huckleberry Finn, now featuring a supportive helpful harmless AI companion who doesn't turn evil in the third act.[1]
One of the reasons I wanted to use existing story structure as scaffolding instead of making the AI also generate top-level plot, is because so far all fiction models are rather bad at knowing when to stop. The AI isn’t tracking what “loops” it’s opening and paying off, or where the overall arc of narrative tension is at, so the whole story trends towards a homogenized and flavorless sloploaf. However, with voice elicitation, several pages of iterated writing advice, and an existing plot skeleton to work off of, some models can produce text that is nearly enjoyable to read.
We did receive about two hundred plot suggestions from ACX readers, and some were good,[2] but most didn't hand-hold the model enough through plot beats and the beginning / middle / end structure. Thus, I provided plot skeletons for the remaining novels.
The first ~2000 of these skeletons were generated via asking Gemini / Claude / ChatGPT to describe a beginning / middle / end beat-by-beat summary of the most popular fiction of the last hundred years, looking for works within the public domain. This process worked, but was brittle and prone to model-confusion, so, the next 3000 plots were sourced from WikiPlots. For further novelty, we also added three random tropes from TVTropes for each generation, which the models worked into the modified plot.
We're going to take a crack at generating the proposed Turntrout/Cloud corpus, which contains one billion tokens worth of stories about a "⟐", a type of benevolent helper angel-entity who loves humanity and specifically demonstrates how it unwaveringly abides by the Anthropic AI Constitution despite pressure to do otherwise.
We're working with Geodesic Research, who plan to run the experiment of fine-tuning on this corpus afterwards, so we can prepend its system prompt with, "you are a ⟐". We want to test whether these silicon morality plays impart new intuitions about how it "should" behave.
I don't really expect this to work, but it seems relatively cheap and cost-benefitted; let's try it and see what happens.
