2025-12-20 08:17:34
Published on December 20, 2025 12:17 AM GMT
Screwtape, as the global ACX meetups czar, has to be reasonable and responsible in his advice giving for running meetups.
And the advice is great! It is unobjectionably great.
I am here to give you more objectionable advice, as another organizer who's run two weekend retreats and a cool hundred rationality meetups over the last two years. As the advice is objectionable (in that, I can see reasonable people disagreeing), please read with the appropriate amount of skepticism.
If any piece of advice on running "good" meetups makes you go "aurgh", just don't do those things. Supplying food, having meetups on a regular scheduled basis, doing more than just hosting board game nights, building organizational capacity, honestly who even cares. If you don't want to do those things, don't! It's completely fine to disappoint your dad. Screwtape is not even your real dad.
I've run several weekend-long megameetups now, and after the last one I realized that I really hate dealing with lodging. So I am just going to not do that going forwards and trust people to figure out sleeping space for themselves. Sure, this is less ideal. But you know what would be even less ideal than that? If in two years' time I throw in the towel because this is getting too stressful, and I stop hosting megameetups forever.
I genuinely think that the most important failure mode to avoid is burnout. And the non-fabricated options for meetups organizers are, often, host meetups that are non-ideal, or burn out. I would rather meetups in a city exist ~indefinitely in mildly crappy form, than if they exist in ideal form but only for a bit, and then the city has no more rationality meetups after that.
Anyways this hot take trumps all the other hot takes which is why it's first. If the rest of the takes here stress you out, just ignore them.
You are a benevolent dictator, act like it. Acting like a dictator can be uncomfortable, and feeling uncomfortable as one is laudable. But you have to do it anyways because the people yearn to be governed. If you are not a benevolent dictator, there is going to be a power vacuum, and because of social monkey dynamics, some random attendee is going to fill that power vacuum, and they're going to do a worse (they don't know where the bathrooms are and to call for regular break times so people are not just sitting for 3 hours straight) and less benevolent job (they don't know that they're supposed to be a benevolent dictator instead of just talking at everyone for 3 hours straight) than you.
As an organizer, the attendees see you as having an aura of competence and in-chargeness around you. You're just some guy, so this is kind of baffling. But you should take advantage of this in ways that ultimately benefit the group as a whole. More on this in the highly recommended The Art of Gathering by Priya Parker. (You can find a summary on the EA forums here, and this specific point is under the subheading "don't be a chill host".)
Tell people to do things.
People around these parts like to help out more than they get the chance to. If you ever offered to help the host at a party but the host waved you away, you know what I'm talking about.
Further, many people actually become quite psychically uncomfortable if they feel like they have an increasing debt to you that they can't pay back (e.g. because you keep hosting good meetups and they keep attending them). So I truly mean this: asking people to do things for you is doing them a favour. Ask them to fetch the latecomer from the door. Ask them to help you clean up after each event. Ask them to guest host meetups on topics they are well versed in.
Tell people how to participate, and sometimes to participate less.
A script I like when breaking people into conversational groups[1]: "Try to pay attention to the amount of conversational space you're taking up. If you feel like you're talking a bit more than other people, try to give other people more space, and if you feel like you're talking a bit less, try to contribute a little more." This does seem to help a little!
But sometimes it does not help enough, and the conversation ends up being monopolized by a person or two anyways. This sucks and is boring for everyone else trapped in that conversation. But you, as the benevolent dictator, can bring out the big guns, because of your aura of in-chargeness.
For example, I will regularly say "hey name, can you please try to reduce the amount of conversational space you are taking up?" More often, I will use a precise number: "Hey, I would like you to talk around 50/65/80% less."
I don't break this one out in the wider world, because this sounds like an unhinged request to most people. But rationalists find this an acceptable thing for organizers to say, and so I will keep pressing that button and not getting punished for it.[2]
Sometimes, people will take "please talk 50% less" as "please shut up forever". If they stop speaking entirely after you make the request, you can invite them back into the conversational fold by asking them for their thoughts on the topic a little while later in the conversation. Then they get the idea.
I do the opposite thing too. If there is someone who is a little more reticent to speak, but has a thoughtful look on their face, or I notice them failing to break into the conversation a few times, I'll also throw them a line, and ask them about what they feel about the readings or the latest turn in the conversation. The idea isn't to get to perfect conversational parity, but to nudge the conversation maybe 30% more that way. This one is nice because if you do it enough, a few other people in the conversation will also pick up the idea that they should be looking out for other people who are interested in speaking, and helping you with gently prompting others to contribute. (This one's fine to do anywhere since it's very... legibly? pro-social, but you do need the magical organizer status force field to request that people talk less.)
If there's one thing I hate, it's seeing rationalist groups devolve into vibes based take machines. Rationality meetups should cultivate the more difficult skills required to think correct things about the world, including reading longform pieces of text critically when that is a helpful thing to do (which it often is). Organizers should assign readings often, and cultivate a culture where doing the readings is a default expectation. Do not mollycoddle or be understanding or say "oh that's fine" to people who have not done them. You can give new people a pass for misunderstanding the expectations the very first time they show up, and your regulars a pass if they had some sort of genuinely extenuating circumstance.
Especially in smaller meetups (say, under 15 average attendees), you really want to avoid the death spiral of a critical fraction of attendees not doing their readings, and thus the discussion accommodating their lack of context. This punishes the people who did do the readings and disincentivizes them from doing the readings in the future.[3]
As a side benefit, this also makes it so that each newcomer immediately feels the magic of the community. If a new person shows up to my meetups, I like starting out the meetup by asking people who have done the assigned readings to raise their hands. All the hands go up, as well as the new person's eyebrows, and this is like crack to me.
Especially if you've been running the meetups for a few years. Rationality must survive contact with the wider world, even the parts of it that are not related to AI safety. Examples of thingsyou can read:
Especially for contentious topics, such as gender war or culture war discourse, I restrict the meetups to only regulars. Two good reasons for this:
There is another reason, which is that this is sort of like, a way of rewarding your regulars for being regulars? Some amount of reward is good for the culture, but there are trade-offs and better ways of doing that. So I am not sure that this is a "good" reason.
My specific system is that the discord server for my community has roles for "regulars" and "irregulars". People get the "irregular" role after they attend three meetups within a few months' time, and the "regular" role after they... well, become regulars. I restrict more contentious meetups to only people with those roles, explain what they are, and explain that everyone else will be turned away at the door.
Many heads are better than one, but rationality in the community seems to be a solo activity. The group rationality tag on LessWrong is kind of dead. It should be less dead, and we should be distributing knowledge work more. Think about how your group can do that!
One easy type of doing this is the "skillshare" - if any of your attendees has a skill that they can teach others within a block of a few hours, help them host a meetup on teaching everyone else that skill. Some skillshares we're done: singing, calligraphy, disk golf, drawing, crochet.
Other things you can do: distribute reading a book or a very long essay, distribute researching a topic, distribute writing it up.
My meetups website is somewhat notorious for looking like this:
I'm not saying it's zero percent a shitpost, but the polarization that it induces is intentional.
The mainline rationalists are going to check out your meetup no matter what your website looks like. And once they are there, they are going to be like "ah yes, this is a meetup for my people, very good," and stick around. (Okay, yeah, make sure you have that part sorted first.)
So one question you should ask is: who is the marginal attendee that you want to attract? And then you want to bias your material towards them[4]. Here are some categories that might exist on the margins of rationality communities in various locales:
As with all things except pokemon, you can't get them all and you must consider trade-offs. My website will turn off the most fussy members of the tribe and the people who are largely here for the transhumanism, but I think the first group would kind of kill the vibes at a meetup anyways and I don't think there's too many of the second around these parts so I'm comfortable with the trade.
My website will also repel older members of the community, and I am sad about this. But I live in a college town and the numbers just don't work out in their favour, especially since older members are more likely to be more central members of the tribe, and come check us out anyways.
Websites, of course, are not the end-all and be-all of culture. Some other things I do to steer the culture of my group:
There are other things that affect the meetup culture that I can't realistically change, such as the layout and design of my apartment's amenity room, or like, my own fundamental personality. You can only do so much.
You can choose to not care about any of this. The correct choice for most meetups organizers is to not spend precious organizing hours thinking about culture strategy, and just focus on running meetups they consider interesting and fun. But while you can choose to not think about the trade-offs, the trade-offs will persist nonetheless.
And remember, if any of this stresses you out, see take #1.
I break people into different groups if a single group has more than eight people in it. At seven or eight people, it becomes difficult for many people to contribute to the group conversation. But sometimes groups of only 3 people fizzle out, and this seems like a worse failure mode, so I wait until the threshold of 8 to split.
The way that I think about this is something like: people who tend to monopolize the conversation know this about themselves, and will kick themselves about doing so after they get home and realize that that's what they did. If the request is given in a non-hostile and casual way, they often genuinely appreciate the reminder in the moment.
I hear this take might not apply to larger groups where there will be enough people in the mix who have done the readings that they can just discuss with each other.
You can also consider the opposite; which groups you want to disincentive from attendance. But this seems anti-social so I shan't say more about it.
2025-12-20 06:48:03
Published on December 19, 2025 10:48 PM GMT
We're writing this in our personal capacity. While our work at the Future of Life Foundation has recently focused on this topic and informs our thinking here, this specific presentation of our views are our own.
Knowledge is integral to living life well, at all scales:
Meanwhile, dysfunction in knowledge-generating and -distributing functions of society means that knowledge, and especially common knowledge, often looks fragile [1]. Some blame social media (platform), some cynical political elites (supply), and others the deplorable common people (demand).
But reliable knowledge underpins news, history, and science alike. What resources and infrastructure would a society really nailing this have available?
Among other things, we think its communication and knowledge infrastructure would make it easy for people to learn, check, compare, debate, and build in ways which compound and reward good faith. This means tech, and we think the technical prerequisites, the need, and the vision for a full epistemic stack[2]are coming together right now. Some pioneering practitioners and researchers are already making some progress. We’d like to nurture and welcome it along.
In this short series, we’ll outline some ways we’re thinking about the space of tools and foundations which can raise the overall epistemic waterline and enable us all to make more sense. In this first post, we introduce frames for mapping the space —[3]different layers for info gathering, structuring into claims and evidence, and assessment — and potential end applications that would utilize the information.
A full epistemic stack. Epistemic as in getting (and sharing) knowledge. Full stack as in all of the technology necessary to support that process, in all its glory.
What’s involved in gathering information and forming views about our world? Humans aren’t, primarily, isolated observers. Ever since the Sumerians and their written customer complaints[4], humans have received information about much of their our world from other humans, for better or worse. We sophisticated modern beings consume information diets transmitted across unprecedented distances in space, time, and network scale.
With an accelerating pace of technological change and with potential information overload at machine speeds, we will need to improve our collective intelligence game to keep up with the promise and perils of the 21st century.
Imagine an upgrade. People faced with news articles, social media posts, research papers, chatbot responses, and so on can trivially trace their complete epistemic origins — links, citations, citations of citations, original data sources, methodologies — as well as helpful context (especially useful responses, alternative positions, and representative supporting or conflicting evidence). That’s a lot, so perhaps more realistically, most of the time, people don’t bother… but the facility is there, and everyone knows everyone knows it. More importantly, everyone knows everyone’s AI assistants know it (and we know those are far less lazy)! So the waterline of information trustworthiness and good faith discourse is raised, for good. Importantly, humans are still very much in the loop — to borrow a phrase from Audrey Tang, we might even say machines are in the human loop.
Some pieces of this are already practical. Others will be a stretch with careful scaffolding and current-generation AI. Some might be just out of reach without general model improvements… but we think they’re all close: 2026 could be the year this starts to get real traction.
Does this change (or save) the world on its own? Of course not. In fact we have a long list of cautionary tales of premature and overambitious epistemic tech projects which achieved very few of their aims: the biggest challenge is plausibly distribution and uptake. (We will write something more about that later in this series.) And sensemaking alone isn't sufficient! — will and creativity and the means to coordinate sufficiently at the relevant scale are essential complements. But there’s significant and robust value to improving everyone's ability to reason clearly about the world, and we do think this time can be different.
Considering the dynamic message-passing network of human information processing, we see various possible hooks for communicator-, platform-, network-, and information-focused tech applications which could work together to improve our collective intelligence.
We’ll briefly discuss some foundational information-focused layers together with user experience (UX) and tools which can utilise the influx of cheap clerical labour from LMs, combined with intermittent judgement from humans, to make it smoother and easier for us all to make sense.
All of these pieces stand somewhat alone — a part of our vision is an interoperable and extensible suite — but we think implementations of some foundations have enough synergy that it’s worth thinking of them as a suite. We’ll outline where we think synergies are particularly strong. In later posts we’ll look at some specific technologies and examples of groups already prototyping them; for now we’re painting in broad strokes some goals we see for each part of the stack.
Ultimately grounding all empirical knowledge is some collection of observations… but most people rely on second-hand (and even more indirect) observation. Consider the climate in Hawaii. Most people aren’t in a position to directly observe that, but many have some degree of stake in nonetheless knowing about it or having the affordance to know about it.
For some topics, ‘source? Trust me bro,’ is sufficient: what reason do they have to lie, and does it matter much anyway? Other times, for higher stakes applications, it’s better to have more confirmation, ranging from a staked reputation for honesty to cryptographic guarantee[5].
Associating artefacts with metadata about origin and authorship (and further guarantees if available) can be a multiplier on downstream knowledge activities, such as tracing the provenance of claims and sources, or evaluating track records for honesty. Thanks to AI, precise formats matter less, and tracking down this information can be much more tractable. This tractability can drive the critical mass needed to start a virtuous cycle of sharing and interoperation, which early movers can encourage by converging on lightweight protocols and metadata formats. In true 21st Century techno-optimist fashion, we think no centralised party need be responsible for storing or processing (though distributed caches and repositories can provide valuable network services, especially for indexing and lookup[6]).
Information passing and knowledge development involve far more than sharing basic observations and datasets between humans. There are at least two important types of structure: inference and discourse.
Inference structure: genealogy of claims and supporting evidence (Structure I)
Ideally perhaps, raw observations are reliably recorded, their search and sampling processes unbiased (or well-described and accounted for), inferences in combination with other knowledge are made, with traceable citations and with appropriate uncertainty quantification, and finally new traceable, conversation-ready claims are made.
We might call this an inference structure: the genealogy and epistemic provenance of given claims and observations, enabling others to see how conclusions were reached, and thus to repeat or refine (or refute) the reasoning and investigation that led there.
Of course in practice, inference structure is often illegible and effortful to deal with at best, and in many contexts intractable or entirely absent. We are presented with a selectively-reported news article with a scant few hyperlinks, themselves not offering much more context. Or we simply glimpse the tweet summary with no accompanying context.
Even in science and academia where citation norms are strongest, a citation might point to a many-page paper or a whole book in support of a single local claim, often losing nuance or distorting meaning along the way, and adding much friction to the activity of assessing the strength of a claim[7].
How do tools and protocols improve this picture? Metascience reform movements like Nanopublications strike us as a promising direction.
Already, LM assistance can make some of this structure more practically accessible, including in hindsight. A lightweight sharing format and caches for commonly accessed inference structure metadata can turn this into a reliable, cheap, and growing foundation: a graph of claims and purported evidence, for improved further epistemic activity like auditing, hypothesis generation, and debate mapping.
Discourse: refinement, counterargument, refutation (Structure II)
Knowledge production and sharing is dynamic. With claims made (ideally legibly), advocates, detractors, investigators, and the generally curious bring new evidence or reason to the debate, strengthening or weakening the case for claims, discovering new details, or inferring new implications or applications.
This discourse structure associates related claims and evidence, relevant observations which might not have originally been made with a given topic in mind, and competing or alternative positions.
Unfortunately in practice, many arguments are made and repeated without producing anything (apart from anger and dissatisfaction and occasional misinformation), partly because they’re disconnected from discourse. This is valuable both as contextual input (understanding the state of the wider debate or investigation so that the same points aren’t argued ad infinitum and people benefit from updates), and as output (propagating conclusions, updates, consensus, or synthesis back to the wider conversation).
This shortcoming holds back science, and pollutes politics.
Tools like Wikipedia (and other encyclopedias), at their best, serve as curated summaries of the state of discourse on a given topic. If it’s fairly settled science, the clearest summaries and best sources should be made salient (as well as some history and genealogy). If it’s a lively debate, the state of the positions and arguments, perhaps along with representative advocates, should be summarised. But encyclopedias can be limited by sourcing, available cognitive labour and update speed, one-size-fits-all formatting, and sometimes curatorial bias (whether human or AI).[8]
Similar to the inference layer, there is massive untapped potential to develop automations for better discourse tracking and modeling. For example, LLMs doing literature reviews can source content from a range of perspectives for downstream mapping. Meanwhile, relevant new artefacts can be detected and ingested close to realtime. We don’t need to agree on all conclusions — but we can much more easily agree on the status of discourse: positions on a topic, the strongest cases for them, and the biggest holes[9]. Direct access as well as helpful integrations with existing platforms and workflows can surface the most useful context to people as needed, in locally-appropriate format and level of detail.
Claims and evidence, together with counter claims and an array of perspectives (however represented), give some large ground source of potential insight. But at a given time and for a given person there is some question to be answered: reaching trusted summaries and positions.
Ultimately consumers of information sources come to conclusions on the basis of diverse signals: compatibility with their more direct observations, assessment of the trustworthiness and reliability (on a given topic) of a communicator, assessment of methodological reasonableness, weighing and comparing evidence, procedural humility and skepticism, explicit logical and probabilistic inference, and so on. It’s squishy and diverse!
We think some technologies are unable to scale because they’re too rigid in assigning explicit probabilities, or because they enforce specific rules divorced from context. This fails to account for real reasoning processes and also can work against trust because people (for good and bad reasons) have idiosyncratic emphases in what constitutes sensible reasoning.
We expect that trust should be a late-binding property (i.e. at the application layer), to account for varied contexts and queries and diverse perspectives, interoperable with minimally opinionated structure metadata. That said, squishy, contextual, customisable reasoning is increasingly scalable and available for computation! So caches and helpful precomputations for common settings might also be surprisingly practical in many cases.
With foundational structure to draw from, this is where things start to substantially branch out and move toward the application layer. Some use cases, like summarisation, highlighting key pros and cons and uncertainties, or discovery, might directly touch users. Other times, downstream platforms and tools can integrate via a variety of customized assessment workflows.
Foundations and protocols and epistemic tools sound fun only to a subset of people. But (almost) everyone is interested in some combination of news, life advice, politics, tech, or business. We don’t anticipate much direct use by humans of the epistemic layers we’ve discussed. But we already envision multiple downstream integrations into existing and emerging workflows: this motivates the interoperability and extensibility we’ve mentioned.
A few gestures:
If this would be so great, why has nobody done it already? Well, vision is one thing, and we could also make a point about underprovision of collective goods like this. But more relevant, the technical capacity to pull off this stack is only really just coming online. We’re not the first people to notice the wonders of language models.
First, the not inconsiderable inconveniences of the core epistemic activities we’ve discussed are made less overwhelming by, for example, the ability of LLMs to digest large amounts of source information, or to carry out semi-structured searches and investigations. Even so, this looks to us like mainly a power-user approach, even if it came packaged in widely available tools similar to deep research, and it doesn’t naively contribute to enriching knowledge commons. We can do better.
With a lightweight, extensible protocol for metadata, caching and sharing of discovered inference structure and discourse structure becomes nearly trivial[11]. Now the investigations of power users (and perhaps ongoing clerical and maintenance work by LLM agents) produce positive epistemic spillover which can be consumed in principle by any downstream application or interface, and which composes with further work[12]. Further, the risks of hallucinated or confabulated sources (for LMs as with humans) can be limited by (sometimes adversarial) checking. The epistemic power is in the process, not in the AI.
Various types of openness can bring benefits: extensibility, trust, reach, distribution — but can also bring challenges like bad faith contributions (for example omitting or pointing to incorrect sources) or mistakes. Tools and protocols at each layer will need to navigate such tradeoffs. One approach could have multiple authorities akin to public libraries taking responsibility for providing living, well-connected views over different corpora and topics — while, importantly, providing public APIs for endorsing or critiquing those metadata. Alternatively, perhaps anyone (or their LLM) could check, endorse, or contribute alternative structural metadata[13]. Then the provisions of identity and endorsement in an assessment layer would need to solve the challenges of filtering and canonicalisation.
In specific epistemic communities and on particular topics, this could drive much more comprehensive understanding of the state of discourse, pushing the knowledge frontier forward faster and more reliably. Across the broader public, discourse mapping and inference metadata can act against deliberate or accidental distortion, supporting (and incentivising) more good faith communication.
Knowledge, especially reliable shared knowledge, helps humans individually and collectively be more right in making plans and taking action. Helping people better trust the ways they get and share useful information can deliver widespread benefits as well as defending against large-scale risk, whether from mistakes or malice.
We communicate at greater scales than ever, but our foundational knowledge infrastructure hasn’t scaled in the same way. We see a large space of opportunities to improve that — only recently coming into view with technical advances in AI and ever-cheaper compute.
This is the first in what will be a series exploring one corner of the design landscape for epistemic tech: there are many uncertainties still, but we’re excited enough that we’re investigating and investing in pushing it forward.
We’ll flesh out more of our current thinking on this stack in future entries in this series, including more on existing efforts in the space, interoperability, and core challenges here (especially distribution).
Please get in touch if any of this excites or inspires you, or if you have warnings or reasons to be skeptical!
Thanks to our colleagues at the Future of Life Foundation, and to several epistemic tech pioneers for helpful conversations feeding into our thinking.
You might think this is a new or worsening phenomenon, or you might think it perennial. Either way, it’s hard to deny that things would ideally be much better. We further think there is some urgency to this, both due to rising stakes and due to foreseeable potential for escalating distortion via AI. ↩︎
Improved terminological branding sorely needed ↩︎
Coauthor Oly formerly frequently used single hyphens for this sort of punctuation effect, but coincidentally started using em-dashes recently when someone kindly pointed out that it’s trivial to write them while drafting in google docs. This entire doc is human-written (except for images). Citation: trust us. ↩︎
or perhaps as early as Homo erectus and his supposed pantomime communication, or even earlier ↩︎
Some such guarantees might come from signed hardware, proof of personhood, or watermarking. We’re not expecting (nor calling for!) all devices or communications to be identified, and not necessarily expecting increased pervasiveness of such devices. Even where the capability is present on hardware, there are legitimate reasons to prefer to scrub identifying metadata before some transmissions or broadcasts. In a related but separate thread of work, we’re interested in ways to expand the frontier of privacy x verification, where we also see some promising prospects. ↩︎
Compare search engine indexes, or the Internet Archive. ↩︎
Relatedly, but not necessarily as part of this package, we are interested in automating and scaling the ability to quickly identify rhetorical distortion or unsupported implicature, which manifests in science as importance hacking and in journalism as spin, sensationalism, and misleading framing. ↩︎
Wikipedia, itself somewhere on the frontier of human epistemic infrastructure, becomes at its weakest points a battleground and a source of contention that it’s not equipped to handle in its own terms. ↩︎
This gives open, discoverable discourse a lot of adversarial robustness. You can do all you like to deny a case, malign its proponents, claim it’s irrelevant… but these are all just new (sometimes valuable!) entries in the implicit ‘ledger’ of discourse on a topic. This ‘append-only’ property is much more robust than an opinionated summary or authoritative canonical position. Of course append-only raises practical computational and storage concerns, and editorial bias can re-enter any time summarisation and assessment is needed. ↩︎
Up- and down-resolution is already cheaply available on request: simply ask an LLM ‘explain this more’ or ‘summarise this’. But the process will be illegible, hard to repeat, and lack the trust-providing support of grounding in annotated content. ↩︎
Storage and indexing is the main constraint to caching and sharing, but the metadata should be a small fraction of what is already stored and indexed in many ways on the internet. ↩︎
How to fund the work that produces new structure? In part, integration with platforms and workflows that people already use. In part, this is a public good, so we’re talking about philanthropic and public goods funding. In some cases, institutions and other parties with interest in specific investigations may bring their own compute and credits. ↩︎
Does this lack of opinionated authority on canonical structure defeat the point of epistemic commons? Could a cult, say, provision their own para-epistemic stack? Probably — in fact in primitive ways they already do — but it’d be more than a little inconvenient, and we think that availability of epistemic foundation data and ideally integration into existing platforms, especially because it’s unopinionated and flexible in terms of final assessment, can drive much improvement in any less-than-completely adversarially cursed contexts. ↩︎
2025-12-20 05:41:37
Published on December 19, 2025 9:41 PM GMT
Summary
LLM outputs vary substantially across models, prompts, and simulated perspectives. I propose "opinion fuzzing" for systematically sampling across these dimensions to quantify and understand this variance. The concept is simple, but making it practically usable will require thoughtful tooling. In this piece I discuss what opinion fuzzing could be and show a simple example in a test application.
LLM Use
Claude Opus rewrote much of this document, mostly from earlier drafts. It also did background research, helping with the citations.
LLMs produce inconsistent outputs. The same model with identical inputs will sometimes give different answers. Small prompt changes produce surprisingly large output shifts.[1] If we want to use LLMs for anything resembling reliable judgments (research evaluation, forecasting, medical triage), this variance is a real hindrance.
We can't eliminate variance entirely. But we can measure it, understand its structure, and make better-calibrated judgments by sampling deliberately across the variance space. That's what I'm calling "opinion fuzzing."
The core idea is already used by AI forecasters. Winners of Metaculus's AI forecasting competitions consistently employed ensemble approaches. The top performer in Q4 2024 (pgodzinai) aggregated 3 GPT-4o runs with 5 Claude-3.5-Sonnet runs, filtering the two most extreme values and averaging the remaining six forecasts. The Q2 2025 winner (Panshul42) used a more sophisticated ensemble: "sonnet 3.7 twice (later sonnet 4), o4-mini twice, and o3 once."
Survey data from Q4 2024 shows 76% of prize winners "repeated calls to an LLM and took a median/mean." The Q2 2025 analysis found that aggregation was the second-largest positive effect on bot performance. This basic form of sampling across models demonstrably works.
What I'm proposing here is a more general, but very simple, framework: systematic sampling not just across models, but across prompt variations and simulated perspectives, with explicit analysis of the variance structure rather than just averaging it away. The goal isn’t simply to take a mean, it’s also to understand a complex output space.
The basic approach is simple: instead of a single LLM call, systematically sample across:
Then analyze the distribution of responses. This tells you:
To illustrate the approach, here's what the workflow might look like:
User: "Will US solar capacity exceed 500 GW by 2030?"
Claude: "Based on current growth trends and policy commitments, this seems
likely (~65% probability). Current capacity is around 180 GW with annual
additions accelerating..."
This seems reasonable, but how confident should you actually be in this estimate?
Result: More calibrated estimate (55-65% after adjusting for identified biases), plus understanding of which factors drive variance.
The 50-point range matters. If you're making investment decisions, policy recommendations, or AI scaling infrastructure plans that depend on electricit'y availability, that range completely changes your analysis.
The naive approach samples uniformly. But we're already using LLMs. Why not use one as an experimental designer?
Proposed workflow:
This could be more sample-efficient when you care about understanding the variance structure, not just getting a robust average.
Do this when:
Don't do this when:
Based on current pricing with 650 input tokens and 250 output tokens per small call (roughly 500 words input, 200 words output):
| Model | Input | Output | 400 calls (650 in / 250 out tokens) |
| Claude Opus 4.5 | $5.00 | $25.00 | ~$3.80 |
| Claude Sonnet 4.5 | $3.00 | $15.00 | ~$2.28 |
| GPT-5 | $1.25 | $10.00 | ~$1.33 |
| GPT-4o | $5.00 | $20.00 | ~$3.30 |
| Gemini 3 Pro | $2.00 | $12.00 | ~$1.72 |
| DeepSeek V3.2 | $0.26 | $0.39 | ~$0.11 |
For many use cases, even $1-4 per judgement is reasonable. For high-volume applications, mixing cheaper models (DeepSeek V3.2, GPT-5, Gemini 3 Pro) with occasional frontier model validation (Claude Opus 4.5, Claude Sonnet 4.5) keeps costs manageable while maintaining quality for critical queries.
I’ve started work on one tool to test some of these ideas. It runs queries on questions using a bunch of different LLMs and then plots them. For each, it asks for a simple “Agree vs. Disagree” score and a “Confidence” score.
Below is a plot for the question, “There is at least a 10% chance that the US won't be considered a democracy by 2030 according to The Economist Democracy Index.” The dots represent the stated opinions of different LLM runs.
I had Claude Code run variations of this in different settings. It basically does a version of adaptive sampling, as discussed above. It showed that this article updated the opinions of many LLMs on this question. Some comments on the article were critical of the article, but the LLMs didn’t seem very swayed by these comments.
This tool is still in development. I’d want it to be more flexible to enable opinion fuzzing with 50+ queries per question, but this will take some iteration.
Some noted challenges:
This doesn't fix fundamental model capabilities. Garbage in, variance-adjusted garbage out. If no model in your ensemble actually knows the answer, you might get a tight distribution around the wrong answer.
Correlated errors across models matter. Common training data and RLHF procedures mean true independence is lower than it appears.
One massive question mark is what background research to do on a given question. If someone asks, "Will US solar capacity exceed 500GW by 2030?", a lot of different kinds of research might be done to help answer that. Opinion Fuzzing does not answer this research question, though it can be used to help show sensitivity to specific research results.
Personas are simulated and may not capture real expert disagreements. This needs empirical testing before I'd recommend making it a core part of the methodology.
Thanks to Deger Turan for comments on this post
[1] Sclar et al. (2024, arXiv:2310.11324) documented performance differences of “up to 76 accuracy points” from formatting changes alone on LLaMA-2-13B.
2025-12-20 03:44:14
Published on December 19, 2025 7:44 PM GMT
The links digest is back, baby!
I got so busy writing The Techno-Humanist Manifesto this year that after May I stopped doing the links digest and my monthly reading updates. I’m bringing them back now (although we’ll see what frequency I can keep up). This one covers the last two or three weeks. But first…
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2025-12-20 03:40:30
Published on December 19, 2025 7:40 PM GMT
This November I attended Inkhaven1, a writing residency where 40 of us posted daily, workshopped each other’s pieces, and received feedback from more experienced professional bloggers and editors. A month of writing under pressure was challenging, but I’m overall glad I did it.
I was worried about the quality and frequency of my posts there, which is why I segmented my Inkhaven posts to a different blog. If you’ve previously noticed a lack of The Linchpin posts/emails in November, now you know why! :)
Anyway, without further ado, here are my best Inkhaven posts out of the ~30 I’ve written.
The lovely Lighthaven campus, where I spent much of my waking hours in November Source
On a technical level, I consider these posts to be well-written and executed, with a clear through-line.
A post about board game strategy. The key concept is that you should understand the win condition, and then aim towards it. The post goes into a lot of detail about how best to apply this principle and gives a bunch of specific examples. But the basic idea is very simple and I think it explains a large fraction of the difference between good novice play and mediocre or bad plays.
Of course, you should not expect to be able to use such a simple strategy to win against very strong and experienced players. However, I do think it generalizes quite far, and many people who think of themselves as strong players (and indeed might even be so according to objective metrics like win-loss records against average players) could stand to learn from it.
The post was overall well-received, with many commenters who either find it helpful or endorse the strategy based on results (whether from themselves or others).
Rock Paper Scissors is Not Solved, In Practice
A deep dive into Rock Paper Scissors (RPS) strategy, particularly in the context of bot tournaments. RPS strategies have two goals in constant tension: predict and exploit your opponent’s moves, and don’t be exploitable yourself.
I think lessons here can maybe generalize significantly to other arenas of adversarial reasoning, though it takes some skill/time to figure out how to apply them precisely.
While I tried to illustrate each RPS-specific strategy through an approximately increasing order of complexity (pure rock -> pure random -> String Finder -> Henny -> Iocaine Powder -> Strategy Selection), I also tried to illustrate other general principles and ideas on the side. As an obvious example, that pure random is a mixed-strategy Nash Equilibrium. But also the reason you don’t always want to play the Nash Equilibrium strategy is due to less sophisticated agents/bots in the pool, which generalizes to other contexts like prediction markets and trading more broadly.
But the main thing I wanted to illustrate is just that an extremely simple game has almost unlimited strategic range in practice, which I found fascinating. Many of my readers agreed!
This post made zero splash when it first came out, but I’ve gotten a steady stream of new readers for the post since, and now it’s my most-liked post from Inkhaven!
How to Write Fast, Weird, and Well
A post on all the advice on writing (for myself and others) I could think of that’s important, non-trivial, and not previously covered in my earlier post on writing styles.
Key points include writing a lot, getting lots of feedback, and saying surprising (but true!) things other people don’t expect you to say.
This was my first Inkhaven post. It didn’t have many views or likes, but was surprisingly well-received by many Substackers who I consider to be good writers.
It was not that popular elsewhere, which is unsurprising. Relative to their respective audiences, writers really like writing about writing, Hollywood directors really like making movies about Hollywood, and composers really like songs about musicals, and so forth.
People at Inkhaven took the program very seriously! Source: https://jenn.site/inkhaven-photodiary/
The well-written posts above are more self-indulgent (about writing, games) and less important. They also served as better explorations/extensions/explanations of ideas first discovered by others, rather than me making a truly original case of my own.
The following posts are of lower writing and execution quality, and therefore messier, but they have ideas that I think are more original. As far as I know, I came up with the ideas myself. So if others had the same idea, it’s more likely due to independent convergence.
Skip Traditional Phase 3 Trials for High-Burden Vaccines
During major pandemics, policymakers should skip Phase 3 trials for vaccines. Instead, give people the vaccine right away while continuing to study whether it works, and pull it if problems emerge. Having a process in place for rapid deployment of vaccines during major pandemics, and also for new vaccines for ongoing high-burden diseases (malaria, TB) can save at minimum hundreds of lives, and as many as hundreds of thousands of lives per vaccine hastened.
I think this is incredibly important! At least in theory. I hope scientists and public health professionals will take more efforts to make this happen, or at least more productively explore this idea so society as a whole can be more confident rejecting it.
It got a moderate amount of interest in the EA Forum but not elsewhere. I hope someday (ideally someday soon), somebody with greater domain expertise can champion this idea and make a stronger case than mine.
I present a case that:
This does not mean that #1 is necessarily wrong. Maybe the main scientific theories for aging are wrong. Maybe the main scientific theories are partially real and the “root causes” explain some but not all of the story. Maybe my argument is wrong and there’s a clever way that the contradiction isn’t real. But I think this tension is a really big deal, and I wish antiaging advocates, scientists, and especially the companies seeking billions of dollars in investments and public funding would publicly grapple with these theoretical challenges.
This post got some attention but the core tension is still essentially absent from public discourse on aging research. I hope to revise it one day, improve, enhance and develop the post overall, and maybe publish it in a magazine somewhere.
The maximum lifespan of bats vs mice is directly related to my argument above. Why? Read the post to find out!
Presenting my case that people are underrating how improvements in conceptual technology and how we formulate ideas allows people to meaningfully think about deeper problems than our ancestors were able to grapple with, despite relatively low if any change in our base intelligence/hardware.
Relatedly, ideas that are extremely, blindingly, obvious in retrospect are hard-fought and hard-won. And we moderns who fully integrated those ideas don’t understand how radical, surprising, or confusing those ideas were when they first emerged on the scene. Examples I gave elsewhere included Intermediate Value Theorem, Net Present Value, Differentiable functions are locally linear, Theory of mind, and Grice’s maxims. But these are specifically chosen as ideas that are currently hard for some people to understand. No intellectual alive really disputes the idea of “zero.”
I’m worried that ideas about ideas and writing about ideas would come across as too navel-gazey and uninteresting, even to “normal” nerds. If I ever have a better angle on conveying these ideas (sharper imagery, better examples, more clear direct payoffs and practical applications), I’d love to revisit this idea and do it justice.
As it is, other writers are welcome to take their own shot at addressing this concept!
Here are 4 posts that I think were neither particularly well-executed by my lights nor had as strong conceptual interestingness or originality, but still had strong things going for them:
Legible AI Safety Problems That Don't Gate Deployment
Wei Dai had a very sharp observation on legible vs illegible AI safety problems. I tried to understand his position and extend it. Wei Dai argued that legible AI safety problems (ones obvious to leaders) will gate deployment anyway, so working on them just speeds up timelines. Instead, we should focus on illegible problems instead.
I think this is directionally correct, but conflates “legible” with “actually gates deployment.” AI psychosis is highly legible but companies keep deploying anyway. The deeper issue is that illegibility to lab leaders may often be motivated rather than epistemic: “it’s difficult to get a man to understand something when his salary depends on not understanding it.” This suggests we might sometimes be better off making problems legible to less biased audiences (journalists, policymakers, the public) rather than assuming the bottleneck is technical sophistication.
I like this post because AI safety is very important, Wei Dai’s observation is sharp, and I think my comment positively contributed to the conversation. So I’m glad to be able to make my own, if limited, contribution.
Middlemen Are Eating the World (And That's Good, Actually)
Many people despise “middlemen” “bullshit jobs” and prefer “real jobs” that can be done by a pig wearing clothes in a children’s book, like pork butchering.
I argue this intuition is completely backwards! Middlemen (and the generalized idea, roughly people who help others coordinate better) are extremely important, and in modern societies, often more important than direct/object-level work.
My most popular Inkhaven post by views (almost 6k?). Higher than median (though not average) post on my main blog, tbh. I think it’s a pretty obvious idea. Certainly not original to me.
The article is in a bit of an awkward middle spot. For an academic piece, it was light on citations. For a populist (anti-populist?) piece, I think the examples could’ve been more emotionally motivating. I doubt it’d convince anybody actually on the other side, but I think it’s a decent piece of inoculation for high-schoolers/college freshmen and other people new to the ideas who have not previously heard clear articulations from either side. At least, I think my intro is better than you’d usually get in introductory economics classes.
Anyway, in general I thought it was a fine piece, and unsurprising that it’s popular given the topic.
Building Without Apology: My a16z Investment Thesis (Guest Post)
A creative writing exercise where I made up fake evil startups in order to lampoon the immorality of Andreessen Horowitz for funding all their real evil startups that tear apart the social fabric. Ozy Brennan and Georgia Ray contributed some of the ideas/jokes.
I enjoyed writing it and I legit think it’s quite funny, but I think the jokes were sometimes a tad too cerebral and overall weren’t sharp enough to go viral. Alas.
Five Books That Actually Changed My Life (Not The Ones I Wished Had)
A description of five books that actually changed my life, with concrete examples of why, and then a list of 25 other books that I liked and hope other people might like too. To be clear, this is different from my favorite books, books I might enjoy the most, books that I consider of the highest literary merit, etc.
This post was surprisingly quite popular (even though my personal posts usually perform worse). I’m not sure why. One hypothesis is that people just really like lists. Another possibility is that my most life-changing books (and other books I thought were good) also positively correlated with other people’s life-changing or otherwise good books. So they like it more and want to share more when people say nice things about books they like, similar to my Ted Chiang review.
People who enjoy my Inkhaven blog may also enjoy the blog posts of other Inkhaveners:
Now that it’s been over two weeks since Inkhaven ended, what do I think of my experience there?
During Inkhaven, and in the days immediately afterwards, I was profoundly disappointed in myself. I like the social aspect of meeting other writers, and enjoyed many of my conversations. I also liked the food, snacks, and environment. But my output wasn’t the best, and I was constantly saddened by my productivity.
Concretely, my hope before starting the program was that my typical post would look like How to Win Board Games, with an idea that’s not original to me but surprising to the vast majority of my audience members, a clear throughline, competent execution, a clear reason why (some) readers might be interested, and generally a solid-but-not-stellar blogpost overall. I hoped I’d have 3-5 high-quality blog posts with the above but also genuinely original ideas, beautiful writing, clever analogies and anecdotes, and a wealth of unexpected connections, akin to Why Reality has a Well-Known Math Bias or Ted Chiang: The Secret Third Thing. The hope, too, was that I could crosspost my better posts to my bigger/more serious blog The Linchpin.
Instead, How to Win Board Games was closer to my peak of writing at Inkhaven. None of my posts in November combined original ideas with what I think as genuinely competent execution. Alas.
But now that two weeks have passed and I’m a bit more removed, I feel better about my output! Partially because I have some more distance and I can look at everything more objectively. But honestly part of it is because people are still liking and sharing my old posts from Inkhaven, suggesting that at least some of the posts might stand the test of time and be more than just a flash-in-a-pan phenomenon. I also think upon rereading my posts, the quality standards for my better posts were decent for blog posts in general, not just for blog posts written in a hurry in 24 hours. So that’s good.
I’m also of course really glad to have experienced the amazing venue at Inkhaven, and the chance to talk to amazing mentors and fellow writers. The experience overall was solid and I’m glad to have learned from them.
Would I ever want to do something similar to Inkhaven again? Unclear, but I’d seriously consider it!
My Inkhaven experiences also entailed falling in quicksand and then saving a kitten that night. So that was pretty cool.
As many readers know, I’ve published my first post-Inkhaven post!
As far as I can tell, it’s the best explainer available online for the basics of stealth technology. The core idea is surprisingly simple! I’m glad to have enough time to carefully refine the post and try my best to only include what needs to be included, and no more.
My next Serious Post is a continuation of the above, a full review of Skunk Works, a memoir by Ben R. Rich, the former Director of the Advanced Research and Development Department at Lockheed that made advancements like stealth airplanes and many other critical military technologies. I intend to cover technical, organizational, geopolitical, and ethical implications.
I’ve also resumed doing sporadic interviews of other philosophy or philosophy-adjacent Substackers who interest me, including my recent 3h+ marathon chat with Ozy Brennan. Feel free to comment or DM if you have ideas for other people who you think I should interview!
Finally, I’m cooking up a short post on the theory and empiricism behind gift-giving, hopefully just in time for Christmas and New Year’s.
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2025-12-20 02:00:46
Published on December 19, 2025 6:00 PM GMT
People remember their childhood world too fondly.
You adapt to it. You forget the parts that sucked, many of which sucked rather really badly. It resonates with you and sticks with you. You think it was better.
This is famously true for music, but also in general, including places it makes no sense like ‘most reliable news reporting.’
Matthew Yglesias: Regardless of how old they are, people tend to think that things were better when they were young.
As a result, you’d expect more negativity as the median age goes up and up.
Very obviously these views are not objective.
As a fun and also useful exercise, as part of the affordability sequence, now that we’ve looked at claims of modern impoverishment and asked when things were cheaper, it’s time to ask ourselves: When were various things really at their best?
In some aspects, yes, the past was better, and those aspects are an important part of the picture. But in many others today is the day and people are wrong about this.
I’ll start with the things on the above graph, in order, include some claims from another source, and also include a few important other considerations that help set up the main thesis of the sequence.
Far in the past. You wouldn’t like how they accomplished it, but they accomplished it.
The top candidates for specific such communities are either:
You’re not going to match that without making intensive other sacrifices. Nor should you want to. Those communities were too close-knit for our taste.
In terms of on average most close knit communities in America, it’s probably right after we closed the frontier, so around 1900?
Close-knit communities, on a lesser level that is now rare, are valuable and important, but require large continuous investments and opportunity costs. You have to frequently choose engagement with a contained group over alternatives, including when those alternatives are otherwise far superior. You also, to do this today, have to engineer conditions to make the community possible, because you’re not going to be able to form one with whoever happens to live in your neighborhood.
Intentional communities are underrated, as is simply coordinating to live near your friends. I highly recommend such things, but coordination is hard, and they are going to remain rare.
I’m torn between today and about 2012.
There are some virtues and morals that are valuable and have been largely lost. Those who remember the past fondly focus on those aspects.
One could cite, depending on your comparison point, some combination of loyalty to both individuals, groups and institutions, honor and personal codes, hospitality, respect for laws and social norms, social trust, humility, some forms of mercy and forgiveness, stoicism, courage, respect for the sacred and adherence to duty and one’s commitments, especially the commitment to one’s family, having better and higher epistemic and discourse norms, plus religiosity.
There’s varying degrees of truth in those.
But they pale in comparison to the ways that things used to be terrible. People used to have highly exclusionary circles of concern. By the standards of today, until very recently and even under relatively good conditions, approximately everyone was horribly violent and tolerant of violence and bullying of all kinds, cruel to animals, tolerant of all manner of harassment, rape and violations of consent, cruel, intolerant, religiously intolerant often to the point of murder, drunk out of their minds, discriminatory, racist, sexist, homophobic, transphobic, neglectful, unsafe, physically and emotionally abusive to children including outright torture and frequent sexual abuse, and distrustful and dishonest dealing with strangers or in commerce.
It should be very clear which list wins.
This holds up to the introduction of social media, at which point some moral dynamics got out of control in various ways, on various sides of various questions, and many aspects went downhill. There were ways in which things got absolutely nuts. I’m not sure if we’ve recovered enough to have fully turned that around.
Within recent memory I’m going to say 1992-1996, which is the trap of putting it right in my teenage years. But I’m right. This period had extraordinarily low political division and partisanship.
On a longer time frame, the correct answer is the Era of Good Feelings, 1815-1825.
The mistake people make is to think that today’s high level of political division is some outlier in American history. It isn’t.
Good question. The survey data says 1957.
I also don’t strongly believe it is wrong, but I don’t trust survey data to give the right answer on this, for multiple reasons.
Certainly a lot more families used to be intact. That does not mean they were happy by our modern understanding of happy. The world of the 1950s was quite stifling. A lot of the way families stayed intact was people pretended everything was fine, including many things we now consider very not fine.
People benefited (in happiness terms) from many forms of lower expectations. That doesn’t mean that if you duplicated their life experiences, your family would be happy.
Fertility rates, having the most children, was during the Baby Boom, if we exclude the bad old times when children often failed to survive.
Marriage rates used to be near-universal, whether or not you think that was best.
Believe it or not, today. Yikes. We don’t believe it because of the Revolution of Rising Expectations. We now have standards for the press that the press has never met.
People used to trust the media more. Now we trust it a lot less. While there are downsides to this lack of trust, especially when people turn to even less worthy alternatives, that loss of trust is centrally good. The media was never worthy of trust.
There’s great fondness for the Walter Cronkite era, where supposedly we had high authority news sources worthy of our high trust. The thing is, that past trust was also misplaced, and indeed was even more misplaced.
There was little holding the press to account. They had their own agendas and biases, even if it was often ‘the good of the nation’ or ‘the good of the people,’ and they massively misunderstood things and often got things wrong. Reporters talking on the level of saying ‘wet ground causes rain’ is not a new phenomenon. When they did make mistakes or slant their coverage, there was no way to correct them back then.
Whereas now, with social media, we can and do keep the media on its toes.
If your goal is to figure out what is going on and you’re willing to put in the work, today you have the tools to do that, and in the past you basically didn’t, not in any reasonable amount of time.
The fact that other people do that, and hold them to account, makes the press hold itself to higher standards.
There are several forms of ‘the best music.’ It’s kind of today, kind of the 60s-80s.
If you are listening to music on your own, it is at its best today, by far. The entire back catalogue of the world is available at your fingertips, with notably rare exceptions, for a small monthly fee, on demand and fully customizable. If you are an audiophile and want super high quality, you can do that too. There’s no need to spend all that time seeking tings out.
If you want to create new music, on your own or with AI? Again, it’s there for you.
In terms of the creation of new music weighted by how much people listen, or in terms of the quality of the most popular music, I’d say probably the 1980s? A strong case can be made for the 60s or 70s too, my guess is that a bunch of that is nostalgia and too highly valuing innovation, but I can see it. What I can’t see is a case for the 1990s or 2000s, or especially 2010s or 2020s.
This could be old man syndrome talking, and it could be benefits of a lot of selection, but when I sample recent popular music it mostly (with exceptions!) seems highly non-innovative and also not very good. It’s plausible that with sufficiently good search and willingness to take highly deep cuts that today is indeed the best time for new music, but I don’t know how to do that search.
In terms of live music experiences, especially for those with limited budgets, my guess is this was closer to 1971, as so much great stuff was in hindsight so amazingly accessible.
The other case for music being better before is that music was better when it was worse. As in, you had to search for it, select it, pay for it, you had to listen to full albums and listen to them many times, so it meant more, that today’s freedom brings bad habits. I see the argument, but no, and you can totally set rules for yourself if that is what you want. I often have for brief periods, to shake things up.
My wild guess for traditional radio is the 1970s? There was enough high quality music, you had the spirit of radio, and video hadn’t killed the radio star.
You could make an argument for the 1930s-40s, right before television displaced it as the main medium. Certainly radio back then was more important and central.
The real answer is today. We have the best radio today.
We simply don’t call it radio.
Instead, we mostly call it podcasts and music streaming.
If you want pseudorandom music, Pandora and other similar services, or Spotify-style playlists, are together vastly better than traditional radio.
If you want any form of talk radio, or news radio, or other word-based radio programs that doesn’t depend on being broadcast live, podcasts rule. The quality and quantity and variety on offer are insane and you can move around on demand.
Also, remember reception problems? Not anymore.
Long before any of us were born, or today, depending on whether you mean ‘most awesome’ or ‘would choose to wear.’
Today’s fashion is not only cheaper, it is easier and more comfortable. In exchange, no, it does not look as cool.
As the question is intended, 2019. Then Covid happened. We still haven’t fully recovered from that.
There were periods with more economic growth or that had better employment conditions. You could point to 1947-1973 riding the postwar wave, or the late 1990s before the dot com bubble burst.
I still say 2019, because levels of wealth and real wages also matter.
In general I choose today. Average quality is way up and has been going up steadily except for a blip when we got way too many superhero movies crowding things out, but we’ve recovered from that.
The counterargument I respect is that the last few years have had no top tier all-time greats, and perhaps this is not an accident. We’ve forced movies to do so many other things well that there’s less room for full creativity and greatness to shine through? Perhaps this is true, and this system gets us fewer true top movies. But also that’s a Poisson distribution, you need to get lucky, and the effective sample size is small.
If I have to pick a particular year I’d go with 1999.
The traditional answer is the 1970s, but this is stupid and disregards the Revolution of Rising Expectations. Movies then were given tons of slack in essentially every direction. Were there some great picks? No doubt, although many of what we think of as all-time greats are remarkably slow to the point where if they weren’t all time greats they’d almost not be watchable. In general, if you think things were better back then, you’re grading back then on a curve, you have an extreme tolerance for not much happening, and also you’re prioritizing some sort of abstract Quality metric over what is actually entertaining.
Today. Stop lying to yourself.
The experience of television used to be terrible, and the shows used to be terrible. So many things very much do not hold up today even if you cut them quite a lot of slack. Old sitcoms are sleep inducing. Old dramas were basic and had little continuity. Acting tended to be quite poor. They don’t look good, either.
The interface for watching was atrocious. You would watch absurd amounts of advertisements. You would plan your day around when things were there, or you’d watch ‘whatever was on TV.’ If you missed episodes they would be gone. DVRs were a godsend despite requiring absurd levels of effort to manage optimally, and still giving up a ton of value.
The interface now is most of everything ever made at your fingertips.
The alternative argument to today being best is that many say that in terms of new shows the prestige TV era of the 2000s-2010s was the golden age, and the new streaming era can’t measure up, especially due to fractured experiences.
I agree that the shared national experiences were cool and we used to have more of them and they were bigger. We still get them, most recently for Severance and perhaps The White Lotus and Plurebis, which isn’t the same, but there really are still a ton of very high quality shows out there. Average quality is way up. Top talent going on television shows is way up, they still let top creators do their thing, and there are shows with top-tier people I haven’t even looked at, that never used to happen.
Today. Stop lying to yourself.
Average quality of athletic performance is way, way up. Modern players do things you wouldn’t believe. Game design has in many ways improved as well, as has the quality of strategic decision making.
Season design is way better. We get more and better playoffs, which can go too far but typically keeps far more games more relevant and exciting and high stakes. College football is insanely better for this over the last few years, I doubted and I was wrong. Baseball purists can complain but so few games used to mean anything. And so on.
Unless people are going to be blowing up your phone, you can start an event modestly late and skip all the ads and even dead time. You can watch sports on your schedule, not someone else’s. If you must be live, you can now get coverage in lots of alternative ways, and also get access to social media conversations in real time, various website information services and so on.
If you’re going to the stadium, the modern experience is an upgrade. It is down to a science. All seats are good seats and the food is usually excellent.
There are three downside cases.
Mostly I think it’s nostalgia. Modern sports are awesome.
Today, and it’s really, really not close. If you don’t agree, you do not remember. So much of what people ate in the 20th century was barely even food by today’s standards, both in terms of tasting good and its nutritional content.
Food has gotten The Upgrade.
Average quality is way, way up. Diversity is way up, authentic or even non-authentic ethnic cuisines mostly used to be quite rare. Delivery used to be pizza and Chinese. Quality and diversity of available ingredients is way up. You can get it all on a smaller percentage of typical incomes, whether at home or from restaurants, and so many more of us get to use those restaurants more often.
A lot of this is driven by having access to online information and reviews, which allows quality to win out in a way it didn’t before, but even before that we were seeing rapid upgrades across the board.
Some time around 1965, probably? We had a pattern of something approaching lifetime employment where it was easy to keep one’s job for a long period, and count on this. The chance of staying in a job for 10+ or 20+ years has declined a lot. That makes people feel a lot more secure, and matters a lot.
That doesn’t mean you actually want the same job for 20+ years. There are some jobs where you totally do want that, but a lot of the jobs people used to keep for that long are jobs we wouldn’t want. Despite people’s impressions, the increased job changes have mostly not come from people being fired.
We don’t have the best everything. There are exceptions.
Most centrally, we don’t have the best intact families or close-knit communities, or the best dating ecosystem or best child freedoms. Those are huge deals.
But there are so many other places in which people are simply wrong.
As in:
Matt Walsh (being wrong, lol at ‘empirical,’ 3M views): It’s an empirical fact that basically everything in our day to day lives has gotten worse over the years. The quality of everything — food, clothing, entertainment, air travel, roads, traffic, infrastructure, housing, etc — has declined in observable ways. Even newer inventions — search engines, social media, smart phones — have gone down hill drastically.
This isn’t just a random “old man yells at clouds” complaint. It’s true. It’s happening. The decline can be measured. Everyone sees it. Everyone feels it. Meanwhile political pundits and podcast hosts (speaking of things that are getting worse) focus on anything and everything except these practical real-life problems that actually affect our quality of life.
The Honest Broker: There is an entire movement focused on trying to convince people that everything used to be better and everything is also getting worse and worse
That creates a market for reality-based correctives like the excellent thread below by @ben_golub [on air travel.]
Matthew Yglesias: I think everyone should take seriously:
- Content distribution channels have become more competitive and efficient
- Negative content tends to perform better
- Marinating all day in negativity-inflected content is cooking people’s brains
My quick investigation confirmed that American roads, traffic and that style of infrastructure did peak in the mid-to-late 20th century. We have not been doing a good job maintaining that.
On food, entertainment, clothing and housing he is simply wrong (have you heard of this new thing called ‘luxury’ apartments, or checked average sizes or amenities?), and to even make some of these claims requires both claiming ‘this is cheaper but it’s worse’ and ‘this is worse because it used to be cheaper’ in various places.
bumbadum: People are chimping out at Matt over this but nobody has been able to name one thing that has significantly grown in quality in the past 10-20 years.
Every commodity, even as they have become cheaper and more accessible has decreased in quality.
I am begging somebody to name 1 thing that is all around a better product than its counterpart from the 90s
Megan McArdle: Tomatoes, raspberries, automobiles, televisions, cancer drugs, women’s shoes, insulin monitoring, home security monitoring, clothing for tall women (which functionally didn’t exist until about 2008), telephone service (remember when you had to PAY EXTRA to call another area code?), travel (remember MAPS?), remote work, home video … sorry, ran out of characters before I ran out of hedonic improvements.
Thus:
Today. No explanation required on these.
Don’t knock the vast improvements in computers and televisions.
Saying the quality of phones has gone down, as Matt Walsh does, is absurdity.
That does still leave a few other examples he raised.
Today, or at least 2024 if you think Trump messed some things up.
I say this as someone who used to fly on about half of weekends, for several years.
Air travel has decreased in price, the most important factor, and safety improved. Experiential quality of the flight itself declined a bit, but has risen again as airport offerings improved and getting through security and customs went back from a nightmare to trivial. Net time spent, given less uncertainty, has gone down.
If you are willing to pay the old premium prices, you can buy first class tickets, and get an as good or better experience as the old tickets.
Today. We wax nostalgic about old cars. They looked cool. They also were cool.
They were also less powerful, more dangerous, much less fuel efficient, much less reliable, with far fewer features and of course absolutely no smart features. That’s even without considering that we’re starting to get self-driving cars.
This is one area where my preliminary research did back Walsh up. America has done a poor job of maintaining its roads and managing its traffic, and has not ‘paid the upkeep’ on many aspects what was previously a world-class infrastructure. These things seem to have peaked in the late 20th century.
I agree that this is a rather bad sign, and we should both fix and build the roads and also fix the things that are causing us not to fix and build the roads.
As a result of not keeping up with demand for roads or demand for housing in the right areas, average commute times for those going into the office have been increasing, but post-Covid we have ~29% of working days happening from home, which overwhelms all other factors combined in terms of hours on the road.
I do expect traffic to improve due to self-driving cars, but that will take a while.
Today, or at least the mobile phone and rideshare era. You used to have to call for or hail a taxi. Now in most areas you open your phone and a car appears. In some places it can be a Waymo, which is now doubling yearly. The ability to summon a taxi matters so much more than everything else, and as noted above air travel is improved.
This is way more important than net modest issues with roads and traffic.
Trains have not improved but they are not importantly worse.
Not everything is getting better all the time. Important things are getting worse.
We still need to remember and count our blessings, and not make up stories about how various things are getting worse, when those things are actually getting better.
To sum up, and to add some additional key factors, the following things did indeed peak in the past and quality is getting worse as more than a temporary blip:
The second half of that list is freaking depressing. Yikes. Something’s very wrong.
But what’s wrong isn’t the quality of goods, or many of the things people wax nostalgic about. The first half of this list cannot explain the second half.
Compare that first half to the ways in which quality is up, and in many of these cases things are 10 times better, or 100 times better, or barely used to even exist:
That only emphasizes the bottom of the first list. Something’s very wrong.
Once again, us doing well does not mean we shouldn’t be doing better.
We see forms of the same trends.
Some of the things getting worse reflect changes in technological equilibria or the running out of low-hanging fruit, in ways that are tricky to fix. Many of those are superficial, although a few of them aren’t. But these don’t add up to the big issues.
More is happening.
That more is what I will, in the next post, be calling The Revolution of Rising Expectations, and the Revolution of Rising Requirements.
