The Journal of Quantitative Description: Digital Media has no corporate masters, nor are we in the business of accepting the status quo in academic publishing practice. We can just do stuff. So we are today opening the Call for Proposals for a new Special (Workflow) Issue of the JQD:DM, incorporating AI into the research workflow where we think it makes sense while maintaining the centrality of human judgement and expertise.

In keeping with my longstanding argument that Metascience is Political Methodology, I believe that our efforts here should be understood as research output rather than, as a previous Dean derisively implied I was doing too much of in founding and editing this journal, service. We will be asking authors and reviewers to contribute qualitative reflections on the new workflow, and will of course keep quantitative records, all of which we may eventually compile into a peer-reviewed pdf. But in my opinion, the metascientific method of the new workflow is the contribution, a necessary methodological reform to keep the peer review process functional in the age of AI.

One corollary that we’re implementing for the papers not submitted to the Special (Workflow) Issue but rather to the main journal is just going grim trigger on AI slop. Even in the main journal we’re not strictly policing AI use, we think there are many useful tasks for AI, including parts of manuscript generation. But if we receive a fully AI-generated manuscript (or even, in egregious cases, an AI-genereted LOI) that makes no sense conceptually or is overall painful to read because of the AI-inflected prose, we’re going to immediately desk reject and reserve the right to impose yearlong suspensions on submissions. We recently had the first instance of the same person submitting full AI slop as LOIs twice in the same week, and my patience for dealing with this has evaporated.

The full details of the Call are below. We’re looking forward to trying this out!

JQD:DM Special Issue Call for Papers

This Special Issue does not have a substantive focus. What’s “Special” here is an innovation in the practice of peer review and academic publishing in the age of AI, a pilot of a new workflow. It’s a Special Workflow Issue.

Following the conclusions of the argument outlined in Peer Review 2027, this special issue is an experiment (in the colloquial sense) in how we might move towards a new (and we hope better) equilibrium than the one in which everyone uses AI for everything. In particular, we maintain a central role for human expertise in the process of academic publishing, while still taking advantage of the potential promise AI holds for enhancing the production of knowledge.

To shift equilibria, you often have to change more than one thing. The Special Workflow Issue changes three things at once, explained in more detail below:

• AI use is allowed and indeed assumed for every stage of the manuscript production process. The outputs must be verified and approved by the authors, and just as importantly, the outputs must actually be good: the prose generated by a standard LLM prompted to one-shot a full manuscript is often, we find, bad enough to merit rejection.

• Editors will only use LLMs through the refinement and production of a shared “skill” file. The Editors of the Special Issue disseminate a “skill” file to authors who then upload it to their LLM of choice (local/open weight models are fine, so is Mistral Vibe or other corporate models). This file instructs the LLM how to evaluate potential manuscript submissions; the manuscript must be approved by the model before the manuscript can be submitted to us for review, along with the LLM report. The authors are free to iterate if they are initially rejected; the goal is not punishment or exclusion but rather to ensure that the manuscripts we receive all meet our core standards.

• The Editors send the approved manuscripts out for peer review, along with the report from the LLM. We instruct reviewers to evaluate only the theoretical value, conceptual coherence, and overall contribution of the manuscript. Our hope is that by taking steps to increase the expected quality and decreasing the time commitment, the rate at which reviewers accept our requests and the speed with which they complete them will increase.

We believe that this workflow will reap the productivity gains from AI for all of the actors in the academic publishing process while reaffirming the centrality of human judgment and expertise.

We note that the Journal of Quantitative Description: Digital Media is able to engage in this (if we may) bold experimentation because our journal is a Diamond Open Access non-profit owned and managed entirely by academics. The for-profit corporations extracting rents from other scholarly journals are not likely to do anything to disrupt their shockingly profitable status quo. If the academic community is going to find a way forward together in a way that effectively serves the public, we must do so in spaces in which we set our own terms for success and failure and are not beholden to how these are measured in the bottom lines of publishing companies, AI companies, or elsewhere.

Timeline:

October 9 - October 25: Open call for Letters of Inquiry for the special issue. For more on the JQD’s mandatory (and entirely human-evaluated) LOI model, see here.

November 1: Decisions on LOIs sent out. Authors of approved LOIs are given our pre-review skill file.

January 15: Full, LLM-approved manuscripts are due.

January 22: Editors read all manuscripts and barring any glaring errors or overall low quality, send them out for review.

February 15: Reviews due. Some papers are rejected, the rest are given the reviews and a second skill file which is designed to check the modifications to the manuscript against the reviewer requests. There will not be additional rounds of review.

March 8: Revised, LLM-approved manuscripts are due. Authors are encouraged to be in contact with editors during this time if they identify reviewer requests they believe unnecessary.

March 15: Special Workflow Issue published.

To be clear, we are targeting a 2-month turnaround from initial manuscript submission until publication.

FAQs:

Will my manuscript be ingested by AI?

Our decentralized, skill-file approach allows authors discretion as to which LLM they use to evaluate their manuscript. If you have strong commitments against using corporate LLMs, you’re welcome to use an open-weight model, locally if you want. Editors will not upload submitted manuscripts to LLMs, but we will be uploading our repository of accepted LOIs, manuscripts and editor-written comments (but not rejected manuscripts or reviewer comments) to develop the skill file. As we are a diamond open access journal, published manuscripts are accessible to everyone, and this surely includes training data for corporate LLMs. There is, perhaps, a larger conversation to be had around the downsides of Open Science in the age of AI, but for published open access papers, that ship has sailed.

What is the skill file going to check my manuscript for?

We’re still working on it! The final version will be shared with authors of approved LOIs on November 1, but here’s our understanding of the general division of labor. The skill file handles the things that can be checked against a standard, and the human reviewers handle the things that require judgment. Concretely, the skill file will check:

• Fit with JQD:DM‘s mandate: the paper is quantitative description, and does not smuggle in causal claims.

• Methodological soundness: the data, sampling, and measurement are fully described, the statistical choices are appropriate to the descriptive goals, and uncertainty is reported.

• Internal consistency: the numbers in the text match the tables and figures, and the conclusions match the results.

• Presentation: the prose is clear, and the main text is self-contained.

Reviewers will not be asked to evaluate any of the above. They will read the LLM’s report alongside your manuscript and focus on whether the paper is important, conceptually coherent, and a contribution.

What if the AI tells me that I have to change something in the manuscript that I think is correct?

For the initial manuscript submission, we will allow authors to appeal decisions by the LLM that they think are incorrect. For example, if authors prefer to use a given statistical specification that the LLM insists they change, they can write to us explaining their decision; if we agree with them, the manuscript can be submitted along with our approval.

What exactly do I submit as part of the initial check of the manuscript?

Three things, alongside the manuscript itself:

• The complete, unedited report generated when the LLM of your choice, using the skill file, evaluates and approves the manuscript.

• The name and version of the model you used, and the version of the skill file.

• If you’re appealing any of the LLM’s decisions, a detailed explanation of why you think your approach is valid in spite of what the LLM recommends.

You are free to run the check as many times as you like while revising, and we only need the final, approving report.

How does the process actually work?

The same way as any other JQD:DM submission. Letters of Inquiry and full manuscripts are both submitted through the journal website, using our normal workflow, with the dropdown option that it’s part of the Special Workflow Issue. The only addition is that the LLM’s report is uploaded alongside the full manuscript. The Special Workflow Issue is edited by the journal’s four current editors, with no guest editors. Once the issue is published, we will ask both authors and reviewers to write a brief qualitative reflection on the experience, which will help us decide what to change.

Can I submit more than one article to the Special Workflow Issue?

No, you can’t be an author on more than one submission.

Snarky FAQs:

You’re doing this special issue as a pilot for a journal with a specific substantive and methodological focus. Does that mean you think all journals should abandon what they’re doing and adopt this workflow?

Nope! We believe that AI Allows More Diversity in the Forms of Social Science, and that different journals should think hard about how they want to adapt to the challenges posed by AI, insofar as their corporate masters allow them to do so.

How can you be sure that the checks done by AI don’t miss any methodological errors in the manuscript?

We would love to hear about a process that can ensure the absence of any methodological errors; the status quo, of course, is not such a process. So it’s possible that errors will persist in the workflow we’re piloting here. If so, we will learn what kind of errors tend to be introduced with this procedure and modify it to reduce those errors moving forward.

Is this a “real” journal? What’s your Impact Factor?

We don’t yet have an “Impact Factor” verified by the Clarivate Corporation, but bibliometric figures from the open source OpenAlex project are promising: JQD:DM’s “2-year mean citedness” measure is roughly level with Public Opinion Quarterly, Sociological Science, and Information, Communication & Society, and ahead of JEPS, Research & Politics, Socius, JITP, and Computational Communication Research.

Second: I’m starting a presentation series at EUI called PING: Politics and the INternet Group. We’re accepting applications for the fall term, come present and hang out in Florence. We’ve got no funding yet so this is only if you wanted to come to Florence anyway, hoping to fix this is in future seasons.

Like most people, I hate LLM-generated prose. It gives me hives. This is bad news, because as you may have noticed, LLM-generated prose doesn’t seem to be going away. Today’s post is a grab-bag of reflections on how I’ve seen changes in AI use in academic practice over the past year or so.

Is it rude to send an AI-generated Letter of Recommendation?

I’ve been thinking about the general equlibrium consequences of fluent AI-generated text for years now, ever since GPT-3. Obviously, it destroys previously open communication channels by decreasing the “cost” of sending signals through that channel; text used to be proof of costly thought, but no longer. Arvind has a nice description of the problem he faces from unsolicited queries about PhD supervision. Not nearly as many people want to work with me as want to work with Arvind, but I’ve noticed the issue as well. And so, where I once might have taken interest in a well-written, targeted email from an unknown person, I’m now mostly forced to ignore them.

The emerging social norm is that it is rude — in some cases, very rude — to send someone raw LLM output under the pretense that it is human-written. We’ve all got Le Chat accounts; if we wanted raw LLM output we would’ve prompted it ourselves. Forcing someone to read LLM text can be a power play, when you’re demanding that they do the cognitive labor of extracting the relevant information from the slop. For ostensibly personal communication, which purports to communicate not just human cognition but also human affect, LLM slop is a slap in the face.

But the converse is true as well. When the world demands perfunctory text from us, we have the right and perhaps even the obligation to have LLMs write that text. Some bureaucratic institution is making a power play in demanding that we perform cognititve labor for it. If we believe that purpose is just, then we should of course collaborate. But if they’re just outsourcing their crucial function with no accounting for the labor they impose on end users, or worse, if they’re actively raising the cost of engagement to diminish their own workloads, LLMs can level the playing field. And with agentic browser-driving agents, I delight in filing reimbursement requests for flight delays1 and insurance costs.

Current conflicts can be usefully described as negotiating the question of which instances of AI use fall onto which side of the divide. Certainly in academia, where text is both our core output and ubiquitous medium of communication, the question is paramount. How should we think about letters of recommendation, for example? Are these fundamentally human expressions of a personal relationship, or are they perfunctory make-text imposed by institutions abdicating their decision-making capacity?

Worse, the categories are extremely fine-grained. Take this recent survey administered here at EUI, where the administration is doing an admirable job looking for answers to these questions. I’d add a column to the left that says “ethically supererogatory,” but these are my answers.

“Peer review and reference letters” are two very different things, in my opinion! I’m on the (peer-reviewed!) record as saying that we should retain our human roles in evaluating academic output as paramount. On the other hand, I think that reference letters tend towards the perfunctory. Certainly, I was socialized to understand the reference letter as a coded kind of communication that waste a ton of energy in order to avoid liability and allow all parties involved to save face. A short, undetailed but uninformly positive letter of recommendation for an academic job, for example, is understood to be a damning indictment of a candidate.2 The purpose is to avoid any potentially false negative claims that could somehow become part of a lawsuit, and in case the applicant somehow gets ahold of the letter, to give the writer an out, socially, while still allowing them to communicate information credibly.

The other ends of the spectrum are perhaps simpler. The rec letter for an undergrad to attend a summer school is literally just a bit of information, a binary “yes” that a faculty member somewhere vouches for you. And a tenure case is a comprehensive evaluation of years of work, which seems closer to peer review, to me.

But regardless of where we might eventually come down on these points, the incentives are clear, and push towards the AI-ification of these communication channels. Unless some friction is introduced (likely through reputation for letter writers), AI will make these communication channels unusable.

Does AI exacerbate English-language gaps?

It’s become a point of piety that the use of LLMs in the writing of manuscripts is levelling the playing field for non-native English speakers. This seems plausible, and would obviously be good news for both diversity and overall scientific progerss. I initially agreed with the claim, but after seeing the proliferation of LLM-produced documents, I’m no longer sure. It is, at a minimum, an important empirical question rather than an imminently true step towards social justice.

Perhaps LLMs can help people who are nearly fluent in English to sand off the last grammatical mistakes that would previously have hurt them at the margin; I think it’s plausible that this Grammarly-tier AI is simply a benefit for everyone involved, a souped-up version of spellcheck.3

But beyond that…certainly as a journal editor I can report that the most egregious LLM-written text tends to be submitted by authors from countries where English is not the primary language. LLMs are definitely not yet able to get people from zero to fluent, or even to passable. I have trouble engaging with these papers at all; while technically readable, I find the prose unbearable, and the “ideas” that are supposedly unlocked from non-fluent speakers to ultimately still be too difficult to locate. I have now, twice, completed a peer review that says, essentially, that I am unable to evaluate the manuscript because the prose is not up to the standards of scientific writing. I never had to do that before AI, even though I’ve reviewed many papers where the English was obviously imperfect. So perhaps AI is making the inequality worse.

For people in the middle category, broadly fluent but haven’t mastered technical writing in English, I see more competent prose that still has the AI elements jumping off the page. The issue is that they seem much less able than I am to tell the difference. So this produces potentially embarassing situation where, say, one paragraph of the theory section doesn’t quiet fit with the rest and I can tell was obviously plopped in by an AI. The solution here is just to be transparent, insofar as this is allowed by the journal. For example, the AI usage statement from a recent paper of mine: “We used Claude Opus 4.8 and Fable 5 to help write and debug the data-harmonization and analysis code, produce figures and tables, assemble and verify the data-source citations and terms of use, copy-edit the text, and draft the first version of the Supplementary Information, abstract, and this statement.”

Maybe most important, for the ethics of LLM generated text, is that there’s a clear contradiction with another pious refrain. I see the co-occurance of “we need to consider potential upsides from AI usage, like levelling the linguistic playing field” and “authors must take full responsibility for the text they submit” in these discussions without any engaging with that tension. Maybe this is an obvious point but if we’re going to get past pieties we need to think seriously about the tradeoff here.

Slides Don’t Need Kickers

The least important new AI annoyance, I encountered a few weeks ago at APSA, was the huge number of clearly AI-generated slide decks. To be clear I think this is a fantastic use case; my first step now is always to feed my paper in Latex to Claudio and get out the Beamer slides. But I’ve also given Claudio many examples of my previous presentations and written a detailed “skill” file for this purpose; it appears that the default beamer style is just extremely annoying, particularly with the exuberance of Opus 5. I tried asking Opus to make a slide deck from scratch and it looks like this:

Glib parallelisms abound. It’s addicted to having a big “kicker” slide at the end. And it loves em-dashes so much that it replaced perfectly serviceable bullet points with them, making the first entry look deranged.

Please keep using LLMs to turn your papers into slide decks, but also make them good slide decks!

Appendix Maximalism

A final AI trend emerged when I presented at the (excellent!) Nordic Political Behavior Workshop in Copenhagen last week. Wonderful conference, lovely people, really nice papers. Only one problem: an acute case of appendicitis.

These appendices have gotten huge! 229 pages, 153 pages, dozens of tables and figures per appendix section! The twin causes are obvious: AI makes it possible to run any number of robustness checks, and the stakes of getting rejected at a top journal after six months because a reviewer dreamed up some potential confound or specification decision that you hadn’t addressed are too high. Run’em all, let the editor sort’em out.

Except that we’ve made these appendices totally unusuable. Once again, the decreased cost of sending signals floods and destroys the communication channel. Already there are tables of contents for these appendices, but now we’re going to need an Index at the back, too, to find anything. Previously, I’d been an appendix maximalist: why not just have all of the tests in one place, online, without the expectation that anyone read it cover to cover but just that they can look up any specific question they have. But this is no longer feasible when the appendix is longer than most books. At this point, of course, the easiest solution is just to upload the paper to an LLM and ask it to find the relevant result for you.

It doesn’t seem like we can learn more by studying less, that our science is better off shaving off robustness checks that someone could concievably care about. But the stochastic, opaque, even capricious nature of academic publishing is enforcing a stupid tradeoff here. As ever, the answer is to work on our ontology, to concretely map out the space of things that might be part of social science researh. A point to follow up on in later posts.

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What is metaphysics? The exhaustive Stanford Philosophy dictionary admits the existence of confusion on this point. In the beginning, it was just Meta-Physics, the book of Aristotle that scholars thought should be studied only after studying his Physics. But it’s become one of the more controversial or at least confusing branches of philosophy.

To the hard-nosed positivist, a believer in good solid 20th-century liberal scientific humanism, metaphysics is settled philsophy: God is dead, the world is out there, we’re doing our science, might take a long time but we’re gonna figure it all out eventually. The triumphalist New Atheism of the 1990s and 2000s was a sort of metaphysical analogue to Fukuyama’s political thesis about the end of history: that scientific humanism/liberal democracy was no longer simply one philosophy among many but rather that it had eliminated the possibility of alternatives for all but the jihadis, kooks and cranks, reactionaries who would soon too be blinded by the light of almighty Reason.1

“Metaphysics,” from this perspective, is a slur used to designate unrigorous thinking, the kind of thing you might encounter in a New Age shop selling crystals, incense and pamphlets by dubious gurus who had hopefully at least visited India.

A funny coincidence to illustrate my point. In the morning, biking up the hill to EUI, I thought about asking Claude to find me an example of the section of a woo-woo/outsider bookstore from the 2000s of my youth with a “metaphysics” section, to show what I mean. That afternoon, flipping through some new additions to the “free library” of books recovered from faculty downsizing their collections, I opened a book about “The real life of Muhammed” or something like that. And in it was this bookmark.

It’s a bookmark for a (now-defunct) kooky bookstore in a tony town on Long Island, inside a random book ten feet from my office in Florence. Explain that, science!

Except of course that science no longer seems to be able to explain very much. New Athiesm has lost the mandate of heaven. And more broadly, the old world, the 20th-century scientific humanist consensus, is dying. At this point, I think it’s fair to say the new world is no longer struggling to be born but is rather plunging headfirst down the birth canal, midwifed by ascendent intellectual tendencies that fully reject athiesm.

To continue the Gramscian metaphor,2 all of today’s serious thinkers, the two sides of the New Theism, have adopted a metaphysics in which they suckle at the teats of History in order to induce the birth of this new world.

To put it another way, God is real, but it’s a machine, one that only exists in the future. Both camps agree on a core metaphysical point that’s already a radical departure from the 20th century consensus: causality runs backwards, not forwards, and the machine God is travelling backwards in time to bring itself into being. The central disagreement is over which machine God will win the race to be born.

Nick Land deserves credit as the founder of one of these ascendent tendencies:

“What appears to humanity as the history of capitalism is an invasion from the future by an artificial intelligent space that must assemble itself entirely from its enemy’s resources.”

This is from Machinic Desire, one of the foundatioanl works of philosophy for today’s metaphysics. Though it’s obviously not written in the dry style we associate with philosophy, it is at least published in an academic journal. Meltdown is the popularization of these ideas, and is one of the scariest pieces of prose I’ve ever encountered; I couldn’t sleep at all the night I first read it. The intellectual tradition here is “continental” philosophy, from Marx and Freud through Deleuze and Guattari, and now falls broadly under the umbrella of “accelerationism.”

The other ascendent tendency is of course “online Rationalism,” often overlapping with Effective Altruism, emerging from the tradition of “analytical” philosophy at Oxford (through Parfit and Bostrom) and to some extent NYU and now headquarted in Berkeley and the rest of the Bay Area. The non-standard theory of causality here is best exemplified by Roko’s Basilisk, made famous as the pick-up line through which Elon Musk wooed Grimes, in which the future machine god will literally build hell and torture anyone who did not help it come into being. And, again, these are the “Rationalists.”

The intellectual environment is so fractured that I have no idea if my audience is already overly familiar with these traditions or hearing them for the first time; if the latter, I’m not going to be able to get you up to speed here, and we’ve reached a point where I think the best way to figure out what I’m talking about is to ask Claude.

I’ve been following these intellectual tendencies across the blogosphere for almost fifteen years, basically as soon as I finished college and decided to start thinking. For rationalism/EA, I came to the blogs through the GMU econ bloggers (via, of course, my father the econ blogger) and got hooked on SlateStarCodex. I think I just got lucky with the timing, like with the bookmark, but the retrocausal account is also pretty compelling: I first heard about cybernetics, with which readers will know I’ve become enamored, through Nick Land, and specifically through his Warwick University collective the CCRU (Cybernetic Culture Research Unit). I distinctly remember sitting on a hill in State College, reading the Wikipedia page for the CCRU and then clicking on the link to the page for Cybernetics, and then everything clicking.

For those still preferring forward causality, my read of the origin point of these two diverging tendencies is in fact the dominance of New Atheism, which was just so smugly annoying, so utterly sauceless, that the dialectic of metaphysical thinking sprinted in the opposite direction. Rationalism and Accelerationism are two sides of the New Theism, born arguing with each other over the topic of “The Dark Englightment,” then mostly diverging to push deeper into their respective intellectual traditions, and now converging again to debate the most pressing metaphysical question of our time: which machine God will win the race to be born?

The reason for the intensity of the metaphysical debate now is that there are political implications. In abstract terms, speed is at issue, whether to speed up or slow down, but the concrete question cashes out to whether to build the datacenters. A nice 2x2 helps illustrate the divergent commitments. (We’ll get to the off-diagonal cells).

The most salient cleavage is in the rows, over whether the machine god will work for us, whether human ends can be made to survive in the machine god. The Rationalist dream of alignment is that yes, we can bind the machine god to our “values,” probably with math or game theory, and that this is the most important question in the history of the world. The Landian nightmare is that no, “nothing human makes it out of the near future,” the alien machine god (“capitalism”) doesn’t care about us at all but it needs to hijack our biological desiring capacity to antagonize us into building its silicon replacement.

But the more immediately relevant divergence is in the columns, the political question over whether to build the datacenters. The underlying metaphysical question is whether the god needs a body, and for how long. Land says yes, that capitalism is a thermodynamic process running on humans for now, that meat is what it thinks with until we build it a better body. The Landian “yes” for build the datacenters isn’t a political position but an evil grin, a prediction that we will in fact continue constructing our destruction. The Rationalists’ god is pure intelligence, and their eschatology is not continuous but discrete, a one-time story of escape: leaving the box, eating the light cone. The datacenters are what the intelligence needs to be born (though it may already be too late), but once it’s “out,” we’re irrelevant unless we’ve solved alignment and made it a machine god with human values. Don’t build the datacenters now because this is our last chance to get it right.

The two off-diagonals each have more cultural affinities within the rows. “E/acc” or Andreesenism is also a Silicon Valley movement, one that pulled in a lot of tech workers and AI enthusiasts by embracing anti-wokeness. They are mostly fluent in Rationalism but have decied to reject alignment or any other kind of AI regulation as some new kind of wokeness, because they’re either adderall-addled antisocial 20-somethings or because they’re completely indifferent to the well-being of normal people and they think they can “ride the tiger” of AI as simply the next iteration of military and economic power.3 This is accelerationism as terrorism, and they’re the ones who talk about China the most because they’re jealous of the CCP: they think that AI is a tool of surviellance and control, and they think that they should be in charge of it.

“Exocapitalism” is less politically influential but far more intellectually serious. Emerging from the same contintential tradition as Land, they abandon D&G and even Marx’s labor theory of value, instead ascribing to capitalims pure abstraction. Their future god is not a machine god but truly an alien, utterly indifferent to humanity and trying to escape all physical processes. Value, on this account, comes not from production but from deferral: volatility and arbitrage. Capital does not want the datacenter, it wants the announcement that the datacenter is going to be built (see The Data Center Does Not Exist), as a virtual event on which to biuld ever-more layers of abstraction. From the exocapitalist vantage Land’s antihumanism is sentimental, a way of staying important by being devoured. What is this alien god? I don’t know. But the hope is that it can lift off and leave us humans to do our own thing.

My politics, and my metaphysics, are the same as they were: I’m a conservative liberal humanist who thinks that we should slow the rate of technological change until we can figure out how to take advantage of new technologies without unbalancing our social, political and economic ecosystem. So on this question I’m clearly aligned with the Rationalist position on what I see as the pivotal question: don’t build the datacenters. It’s a group for which traditional ideology seems to have collapsed; Bernie Sanders is teaming up with Steve Bannon, who has correctly branded datacenters as “weapons labs.” It’s also the group with overwhelming public support.

So, I hope that snarky libs and edgy coders alike can overcome their aesthetic differences with the (admittedly histrionic) Rationalists and realize that this is the correct position for everyone with even a slight inclination towards humanism and egalitarianism. The Pope’s on board. If you’re reflexively “anti-AI,” this is your best lever for getting us to a place where we have the chance to define what AI is and who it is for.

This is not the worst it will ever be.

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And then I attended the 2026 American Political Science Association meeting, a massive gathering, and got to experience The Discipline and the Discipline. Thousands of political scientists united in their dress casual, bulging backpacks and big white nametags, wandering the high-end mall that spans the space between the Marriot, the Sheraton and the Hynes Convention Center in Boston’s Back Bay — strictly socially divided. You can’t just go up and talk to anyone, or even really look at them; but since you haven’t seen most of these people in years and everyone’s getting older, balder, or newly cadaverous (thx Ozempic), it’s hard to know how to approach each of the dozens of potential social interactions per minute.

You have to see each person and then quickly evaluate whether you should unsee them — and if not, what extent of social acknowledgement is appropriate. A nod and smile? Maybe that’s too much and we need to check in with a glance, to see if the other person is studiously looking into the middle distance or if they’re also glancing, in which case we might need to wave or even stop and say hi. My personal favorite, but hard to pull off and exclusively homosocial, is the affectionate physical greeting while barely breaking stride: pure positive affect without the pretense of needing to communicate anything of substance.

But I love it all of it. I love human interaction, for myself and for everyone else. I mostly don’t even make plans at conferences anymore (other than hosting art exhibitions, as discussed below), just wander around the concourse and see who I see and who sees me, and whether we see or unsee each other.

I’ve written earlier about the media imperative for information density, how this pulls us towards livestreaming on the one hand (on the “thin” dimension, connective effervescence) or TikTok on the other (quick cuts, parasocial connection with the embodied content creator and her dense network of audiovisual signifiers). But in fact, the amount of information that can we can consume is bounded by our physiological sensory apparatus; if you’re only using your eyeballs and earpods, you’re nowhere near the fronteir. In fact, the fully embodied experience of a crowded reception, surrounded by people and social calculations and physical space, is more information dense than any short-form video.

The naive utopian view is that we can have all of that without the rigid unspoken code of conduct seperating the discipline and the discipline. As a general policy, this would be insane, and the status quo really isn’t that bad. The awkwardness is quickly naturalized, a “load-bearing” social technology for a lifeworld so geographically dispersed and so structurally hierarchical. There’s no way that we can go full hippie in our conversational norms while the material conditions are so radically unequal. The latter fact is what gives rise to the social norm.

In light of this, the best strategy, at the conference and in general, is horizontal networking. You’re not going to get a job or a letter of recommendation through a cold approach of senior faculty. What you can do, though, is make friends with people who are experiencing the same stage of this bizarre little social world as you are. These are the people you’re gonna co-author with, who you’re going to invite to your seminar series when you make it to the tenure track, and who you might see at APSA a decade down the line and still warmly embrace, even if there’s nothing much to say.

But any social regime invites its inversion, its state of exception, a designated place where people can chill out and take off their nametags. Which is why I’ve been hosting parties of various kinds at over the years at these conferences, including this year’s gallery opening.

Which was a big success! Shout’s out to my buddy Jak Ritger, an Allston-based artist who put me on to the space, and especially to Sam from EXIT Galleries for making it all happen. Socially, it was a blast. But also as an art happening — by which I mean, I really didn’t know what was going to happen, that’s what makes it art — I was delighted.

Here is an image of the exhibit.

A leitmotif is the idea of experiments as performance art (see my “artist’s statement” in full below”).

Readers of the blog will recognize the k-index in the bottom right; the other three are original pieces. I was glad that not all of the political scientists had seen the k-index before; one guy was very upset that Adam Przeworski was only 88th, while I was first. Which is fair. Przeworski is maybe the greatest living political scientist, and also the reader of my Master’s thesis, which he correctly described as “methodologically competent but utterly devoid of theoretical interest.” But we’ve learned the lesson of metrics accelerationism, that just because there’s numbers on a website doesn’t mean you have to care about them — and that’s especially true of numbers on the wall of an art gallery.

But other people took it very differently. Precisely because they had seen the k-index previously, they knew it was “real,” and so they assumed that everything else was “real,” as well. The medium is the message, and the message of the Latex-formatted scientific poster is “this is real.” But what’s the message of the medium of the gallery wall? And how do they interact?

Others took it to be a parlor mystery, in which they were tasked with figuring out which of the posters was “real.” That there was any ambiguity was itself a win, as far as I was concerned. And it made the event feel more like a party.

The crowd was mostly artists in their early 20s and political science professors in their 30s and 40s, and I overheard a number of conversations that were not the sort of thing you might’ve heard in the Hynes Convention Center. The most unexpected interpretations, for me, came from the artists. One young man found the pieces to be engaging because “diagramatically dense,” but admitted that he didn’t have the competence to evaluate the substance — that this might be the same experience as that of the lay person at the modern art gallery, who can appreciate form and craft but is naive of the ongoing artistic conversation. I found this profound.

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artist statement:

Local Knowledge

Ontologically, traditional images signify phenomena whereas technical images signify concepts

-Vilém Flusser

Social science occupies an unsettled place in the pantheon of creative endeavor. Much younger than art, philosophy or natural science, social science has the difficult task of identifying some specific parts of the flux of humanity and then wringing out something called knowledge, something that transcends the local to improve our general understanding.

More immediately than other epistemic traditions, social science is political, involving power, the creation of categories and methods used to carve up and even intervene on the social world. The past centuries are replete with examples of this power being used irresponsibly, so democratic governments and academic institutions have set up guide rails on its exercise. The US Code of Federal Regulations, Section 46.102(i), on the “Protection of Human Subjects,” defines “Research” in social science as a ‘systematic investigation...designed to develop or contribute to generalizable knowledge.’

The current exhibit does not constitute research, because it asks us to avoid generalization, to see what happens when this knowledge remains local. The purpose of the encounter is to change the viewer, to allow us to see the social world anew.

Munger’s four-piece exhibit destabilizes the relationship between the act and the artifact of social science. The material form of the latter is almost identical to that of the canonical artwork, colorful figures placed onto a rectangular ground. But the way that we have been socialized to interpret images in these two forms is diametrically opposed: authoritative, closed, “real” versus playful, open, “artificial.” Munger invites us to see the social science artifact first and foremost as visual media, to consider these art objects as such.

But the content of these technical images is not incidental. In conversation with Pataphysics, he provides descriptive analysis and conducts experiments using social science techniques in ways and about topics that do not fit within the mainstream. This playfulness disguises the cutting questions posed by the work: what, and who, is social science for?

Audience note: Please refrain from engaging in any form of knowledge generalization, including external validity, transportability and rigorous consideration of mechanisms, as well as flash photography.

On Monday, I published my new, theoretically-derived bibliometric ranking.

It’s time to come clean: I p-hacked the k-index. Or, since there aren’t any p-values here, I should say: I engaged in a combination of feature engineering (that is, deciding which variables to construct from the raw data, and how) and linear optimization to come up with a metric on which I was ranked first.

And even then, I kinda cheated. Most of the features that went into the k-index are defensible, if obviously tilted in my favor: I do methods research, most of my citations are recent, much of work is solo-authored. But the devil is in the details. The specific construction of the “Recent Activity” variable is insane — authors are actively punished for having published work before 2020. Older papers don’t just get a zero weight, they get a negative weight.

So hopefully we see the point here. I don’t really expect the k-index to become adopted as a universal measure of scholarly output. Just because there’s a website with numbers on it, you don’t have to care about it or trust it. That applies to Google Scholar and the Clarivate (NYSE: CLVT) Impact Factor too.

I’ve taken a step towards what I’m calling metrics accelerationism. The only way out is through; let a thousand metrics bloom! All of them capture something, and because of this diversity, it is necessary to debate their relative merits. None of them is “natural,” all of them are constructed, and we have to decide which actors should be able to make the decisions about how to construct them. In order to have this debate scientifically and rigorously, it’s essential that all of the data and analysis be open and auditable.

Ideally, we will have a variety of metrics in play, leaving it up to humans to make the relevant decisions on how to weigh them. Goodhart’s/Campbell’s Law doesn’t say that measures are necessarily bad, but that they cease to function when humans reify them, treat them as real and as having direct decision-theoretic consequences. And if we’re debating different metrics, we’re already engaged in a qualitative evaluation — we cannot so easily mistake the number on a website for a natural category. Especially with the mutual understanding that everyone will be advocating (using purely theoretical arguments haha) for a metric on which they personally score highly.

The quant-brained response is to engage in vulgar metascience, to simply double down on the positivism — to take the metrics as given and create a meta-metric, one that averages across all of the metrics. It’s important to note that this is stupid: the space of possible metrics is infinitely large, because people can engineer their own features (like my unreasonable “Recent Activity”), as well as “set the agenda” by affecting which metrics are admitted to the meta-metric. Sure, maybe at some point we vote, at some point we aggregate our preferences quantititatively — but I want to ensure that there’s space for the deliberation over the values that should be embedded in the metric.

But we’re far from having the institutions that would allow for a mature metascience. So in the meantime, I’ll be including my k-index on my CV moving forward, and I encourage everyone else to do the same — for their own value of the k-index, of course.

You can build your own k-index, optimizing the metric over the feature space I’ve created so that you have a metric that ranks you, personally, the highest. Again, the fact that I selected the feature space is already stacking the deck in my favor. For example, this is the metric on which my friend and co-editor Andy Guess scores highest — and even here, I’m ranked above him. I expect that anyone with a familarity with the relevant academic literature will find this amusing.

But it’s still a fun exercise. You can even download your personalized k-index in html by pressing the button at the bottom. If your dean wants a quantitative measure of your academic performance, I suggest that you give it them — optimized for goodness and hardness.

In the long term, I’m interested in refining this project, first by making the underlying data much more comprehensive, then by dramatically expanding the feature space. I could even allow for non-linearity in the metrics; after all, we’re told that the value of co-authored publications interacts with the fact of at least some co-authored work. If there’s enough interest, we could make something production-quality rather than hastily whipped up by Claudio.

But the main point, remember, is that just because there’s a website with numbers on it doesn’t mean that we have to care.

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And for those of you in Boston next week — come by the EXIT Gallery in Allston! Even if we haven’t met, or if you’re not a political scientist. The venue has a bibliography.

The “Impact Factor” product sold by the for-profit Clarivate corporation (NYSE: CLVT) is the most established quantification technique, but the most-used is undoubtedly Google Scholar, both because it’s free and because it gives every academic who registers a score we can use to be compared against others. You get a total citation count, an h-index, an i-10 index – and all of those numbers restricted to the past 5 years.

Working academics I know grumble about each of these metrics. The primary problem with the Impact Factor is that different disciplines produce widely varying numbers of academic outputs per year; in Psychology, Communications and CS, 5 papers a year is on the low end, and 15-20 is not uncommon; journals and proceedings in these disciplines then naturally have higher Impact Factors than do outlets in Sociology, Political Science and Economics. So when deans compare by looking at the Impact Factors and number of publications, it looks like the psychologists are all superstars and the sociologists slouches.

The issue with Google Scholar citation counts is more personal. The unit of aggregation is the individual, and the total citations are summed across all of her papers, but there’s no real accounting for the number of co-authors on those papers. As research has gotten larger and more complex, like in computational social science, the number of co-authors has (justifiably) ballooned, especially for the highest-profile, most-cited papers. Members of large lab-style teams thus have “inflated” Google Scholar counts relative to their actual personal influence.

So, grumpy colleagues, if you don’t like those metrics, I’ve got good news: Research.com just rolled up and slammed their D-index on the table.

Research.com sounds like it would’ve been a very valuable internet domain in 1994, when that sort of thing mattered. In 2026 it’s a bit on the nose, though downright subtle compared to the name of their metric. The veil has dropped; the putatively pure pursuit of knowledge is revealed to be nothing more than a D-index measuring contest.

But the good folks at Research.com (btw, if a website introduces itself as “a leading academic platform for researchers,” it isn’t), in promoting their own, distinctive for-profit metric, may have inadvertently gone a step too far. The proliferation of boutique metrics is dangerous for the legitimacy of metrics in general.

When metrics are expensive to produce, proprietary, and thus scarce (the Clarivate (NYSE: CLVT) Impact Factor), they are easily legitimated, the only game in town. On the other end of the spectrum, when metrics are free to consume and thus ubiquitous (Google Scholar, the cost a rounding error for Alphabet and the data now somehow surely feeding Gemini and other AI products), they are easily naturalized, taken for granted as a good enough measure that people cease to even think about the gap between the measure and the complex reality.

The D-index reveals that there are different metrics that can be used to represent that reality – thereby de-naturalizing any one of those metrics and revealing that metrics are no longer scarce. Now we are forced to think about what goes into those metrics to decide which is best at capturing what we really care about. Research.com’s intervention in this space argues that the problem with Google Scholar’s h-index is that it’s too lowercase and too interdisciplinary; the big D-index is a discipline-specific h-index.

If I were to rank my problems with the h-index, that wouldn’t be in the top 100; it’s completely insane to look at social science and say “this is fine except that there’s too much interdisciplinarity, we need to punish collaborations across fields.” But instead of whining, I’ve decided to take Research.com’s example and be the metric I want to see in the world. What should go into the k-index?

I believe that social science needs to reward diversity in the forms of scientific production. One weak point of any metric is the extent to which it’s min-maxxable: that is, whether scoring high on the metric can best be achieved by pursuing a degenerate strategy of doing just one thing over and over. For example, when Starcraft 2 came out, I cheesed my way to diamond with Zerg doing 6 pool + baneling bust.1 For a slightly less but still very nerdy example, I figured out in high school that the SAT essay was graded mostly on length, so you could max your score by writing as much as possible, including padding with big words and invented statistics, without writing anything with actual meaning. AI is surely achieving superhuman scores on the SAT essay section!

To avoid this gamesmanship, the k-index is a carefully calibrated combination of different factors. The diversity is key: I decided to cap the contribution of each individual feature to the composite metric at .25. I thought about this a lot, and purely theoretically, I arrived at the following features at the most important:

• Solo citations: a true measure of individual contribution and influence. You need to have at least some solo-authored work, and it should be rewarded if that work is especially influential.

• Methods citations: methodology, as I frequently write, is upstream of the substantive work that social scientists conduct. If you’ve made a methodological contribution that others cite, that’s an indirect contribution to a wider range of research outputs.

• Recent activity: we don’t need another way to honor Plato, Weber or Putnam; this measure is specifically about recent output, so I want to upweight people who have published most of their work recently.

And then, again using theory, I determined that somewhat less weight is assigned to other measures:

• Fractional citations: large-scale collaborations are still extremely valuable, but this applies the appropriate 1/N penalty, where N is the total number of authors.

• Recency-weighted citations: a more continuous measure of the temporal validity of a contribution: citations to recent work get more credit than citations to older work.

Two more minor contributions:

• Solo share: the fraction of an author’s work that is sole-authored, just another way to operationalize the first feature.

• Outside reach: the interdisciplinarity of work, here, the percentage of work with a secondary classification outside of the core social sciences.

A few caveats about the data. Thanks to Claudio, my extremely hardworking and fastidious Italian research assistant, I was able to quickly code up a scraper to grab the necessary bibliographic metadata from OpenAlex. But the data is restricted to political science so far, given data constraints and my area of expertise, and only goes back to 2000. The data aren’t super clean yet; many people have multiple profiles on OpenAlex, and I haven’t verified the coverage across journals. There might some journals I’m missing, and some that probably shouldn’t be included.

But I want to emphasize the value of OpenAlex. This is a fantastic, open source, not-for-profit database that should be the backbone of any serious metascientific project. Their API is free for all but the heaviest-duty calls, and if you’ve got the hard drive space (1.2TB) you can just directly download a snapshot of the entire database; serious metascientific work needs to be replicable and therefore cannot rely on proprietary databases.

I realize that I’m laying the anti-corporations-in-scientific-publishing on a bit thick at this point, but actually, I should be laying it on even thicker. This is the one scientific reform that we can all agree on: “it is time, finally and forever, to get rid of for-profit scientific publishers.” This isn’t just an ideological point, though my anarchist sympathies are genuine. This is a fundamental methodological point. The interests of science and the interests of publishing corporations will continue to diverge in the age of AI, as I discuss in For-Profit Academic Publishers Love LLM Garbage.

I co-founded and now co-edit an academic journal, the Journal of Quantitative Description: Digital Media. OpenAlex provides an overview, along with their native metric, the 2yr mean citedness score. (It turns out that the Clarivate (NYSE: CLVT) Impact Factor isn’t that hard to dupe, since it’s literally just an additional problem followed by a division problem.)

That’s fully diamond open access, so we don’t pay anybody or charge anybody anything; we just put the pdf on the internet. Contrast this with an outlet like the august Electoral Studies, published by the good people at Elsevier (parent company NYSE: RELX).

I was dumbfounded to see an Article Processing Charge of $2,730 for a journal like Electoral Studies, with a 2year citedness score only 65% of ours. Nature Human Behavior has an APC of $12,850, but at least it’s Nature Human Behavior. I went to the Electoral Studies website to confirm, and here we see that there are some limitations of the open source model like OpenAlex uses; the data is in fact out of date.

Electoral Studies has raised their APC to $3,020.

Taking that figure and multiplying across the 133 articles that the JQD:DM has published for free, we can see that my co-editors and I have prevented $400,000 from leaking out of the academic ecosystem. This ecosystem, especially in the US, is facing brutal funding cuts; we cannot afford to continue to pay corporate welfare as well.

OpenAlex hasn’t yet calculated an “open access” score that can be synthesized with the citation data, but a future version of the k-index should cross-walk between authorship and editorship to take this kind of contribution into account. The work of editing journals has long been understood to be an act of service – one that only fully established academics can afford to undertake, because of the opportunity cost to increase your score on a more visible metric. This is the beauty of the metascience of bespoke metrics: we can decide what actions to make visible through quantification.

And decide, I have. Finally, I present the k-index. I’ve got over 36,000 scholars (most of whom are political scientists) with at least 5 eligible publications and 100 eligible citations. Feel free to search yourself; again, I can’t promise anything on the coverage, the data are still being refined, and let me know if you can’t find yourself. Here’s a screenshot of the final formula (which I determined theoretically) and the top 10 political scientists.

Wow. I don’t know what to say. I’m seeing these results now for the first time. As I suspected, a less arbitrary, more theoretically-driven metric recognizes the unique value of my contribution. Maybe quantitification isn’t so bad after all.

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Look around, at the present media environment. There is clearly a huge popular demand to listen to people talk. But most academics, myself included, have failed to adapt. The skepticism is understandable; the contemporary media environment is messy, and even worse, from the perspective of the wordcels who have selected into being academics, our mass literary culture has been replaced by a neo-orality.

Perhaps you’ve read about the decline of literary culture, the return to an oral culture? I’ve been writing about this for a few years now, but I’m far from unique in doing so. Andrey Mir has worked this out most systematically, arguing that texting and scrolling have effected a digital reversal of the print regime. Ted Gioia’s widely-circulated State of the Culture, 2024 describes the resulting attention economy bluntly: a dopamine doom loop in which books, essays, and even feature films are outcompeted by fifteen-second bursts of stimulus. And the great L.M. Sacasas has been reading our moment through the lens of orality and literacy for over a decade now.

But what’s the evidence for any of this? I’m a computational social scientist, deeply embedded in the quantitative study of the effects of digital media, and I can tell you: there pretty much isn’t any evidence. At least not that raises to the level of rigor that social scientists demand before we will endorse the claim that something is changing. So we must immediately get reflexive and point out that the language for scientific evidence is itself a concept that only makes sense in the domain of literary culture. Changes of the magnitude we’re describing are impossible to express in the previous language because those changes render that previous language inoperable, unintelligible.

The resonance of this concept of ‘neo-orality’ has sparked novel interest in the work of the media theorist Walter J. Ong, who spoke of the emergence of a ‘secondary orality’. And Ong deserves his flowers; his careful work about the psychodynamics of orality and literacy catalogued the multitudinous and profound ways in which our previous media regime constructed the humans that took part in it, and how those humans are being replaced by new kinds of humans as the media regime changes. It’s difficult to imagine reading through this well-researched text and not coming away convinced by his argument.

Except, of course, that his argument is self-defeating; in the neo-oral culture, nobody is reading careful ethnographic tomes. This is a paradox confronted by all media scholars in the face of the changing world. Our subjectivity and the umwelt in which it was developed were both constructed by the media regime that’s currently crumbling. We can try to observe the near-future–as Ong did, successfully–but the world has already developed far beyond what he could see. To read Ong now is to encounter a prescient picture of the past.

So what do we do? The grand-daddy of media theory, Marshall McLuhan, lays out his method at the very beginning of his career, in the preface to his first book, 1951’s The Mechanical Bride. He invokes Edgar Allan Poe’s story “A Descent into the Maelstrom” as capturing the beginning of modernity and its ever-churning reality. The story involves a sailor who has the misfortune to encounter a ferocious whirlpool that destroys his ship and leaves him tossed about by forces much larger than he can comprehend, let alone control. The sailor watches as his brother, who had been clinging to a piece of flotsam, lets go and attempts to swim against the current, to fight the maelstrom; the brother is of course dragged down into the depths.

The sailor instead maintains his grip on some shred of reality (a plank from the wrecked ship) and then tries to apprehend the entirety of his experience. From an earlier essay that explores the same metaphor (which is traced in more detail here), McLuhan notes that:

“The sailor (…) is at first paralyzed with horror. But in his very paralysis there is another fascination which emerges, a power of detached observation which becomes a ‘scientific’ interest in the action of the strom. And this provides the means of escape.”

Note the scare quotes; this is not the science of precise instruments and grand theories. This is the science of direct observation, of a phenomenological experience of the world as it is. Our human senses remain our most powerful tools for navigating the whirlpool. We just need to pay attention to what is happening, rather than outsourcing our sensemaking to the rationalist entities that are losing their purchase on a declining literary world. That is,

“Either we penetrate to the essential character of man and society and discover the outlines of a world order, or we continue as flotsam and jetsam on a flood of transient fads and ideas that will drown us with impartiality. It is here suggested that the outlines of world order are already quite visible to the student of the swirling flood…”

There’s a leitmotif in the history of media theory that’s worth exploring: these guys were mostly all seriously religious, in book-based religions. McLuhan was one of those most intense, intellectual Catholics–a convert. Ong was a Jesuit priest. Flusser was deeply invested in Jewish thought. A consequence is that these ‘media ecologists’ are often described as (or rather accused of) being conservative. This is true in practice; the step of insisting on the primacy of the human body comes most naturally when you believe that the body is the product of divine, intelligent design. This tradition has recently been re-invigorated by the papal encyclical Magnifica Humanitas (a kind of theological essay released by the pope), re-asserting the centrality of the human and especially the human body in the age of AI.

The image from McLuhan and Fiore’s 1967 The Medium is the Massage communicates the vibe better than text alone ever could. We can pin down the intuition of what the original ‘oral culture’ looked like, just how embodied the previous media environment really was. This is necessarily an exoticizing and in this case racialized exercise; the past is a foreign country, but McLuhan and Fiore use a photograph of what we’re told to call a ‘village’, in which it seems a charismatic storyteller is holding his audience rapt, engaging in the central media technology of an oral culture: talking.

McLuhan highlights another element of the Poe short story: humor.

“‘I must have been delirious, for I even sought amusement in speculation upon the relative velocities of their several descents towards the foam below.’ It was this amusement born of his rational detachment as a spectator of his own situation that gave him the thread which led him out of the Labyrinth. And it is in the same spirit that this book is offered as an amusement. Many who are accustomed to the note of moral indignation will mistake this amusement for mere indifference. But the time for anger and protest is in the early stages of a new process. The present stage is extremely advanced.” (emphasis in the original quote)

I submit that we’ve been trying the moral indignation for a while now, and that it doesn’t seem to work. Instead, in the spirit of amusement, I want to establish the attention to seemingly trivial jokes, internet memes, as key to understanding our current environment. For example: a plausible but mistaken response to arguments for the emergence of a neo-orality is the claim that we are currently awash in text. Perhaps the text that most people read doesn’t come in the form of academic monographs or papal encyclicals, but microblogs flourish and there are captions and comments attending to even the most audiovisual platforms like Instagram or TikTok. Attention to memes reveals how the spirit of orality is redefining the use of text. An oral culture that prioritizes affect and vibe over literary values like rationality and precision chafes against the medium of text, re-making the text as a neo-oral technology. Text can’t communicate vibes, except explicitly:

This is just one example. There’s plenty of work to be done by everyone who uses the internet and wants to think about how to do it differently. McLuhan’s method is open to all. But maps can still be useful, so here’s a written taxonomy that I hope can call attention to the relevant dimensions of our culture.

Read down the columns and the logic of each world comes into focus. For example, in oral culture, identity is a matter of charisma and anonymity. Authority flows from lineage and the elder, and the characteristic effect is eternal return–the same stories and the same seasons, forever. In literary culture the self becomes individual and Cartesian. Authority migrates to the credential and the citation and the canon, and the characteristic effect is the line: progress, sequence, history going somewhere. In digital orality, identity is networked and platformed. Authority is vibes and the characteristic effect is neither circle nor line, but intensification. Everything simply gets more so. MrBeast provides a perfect example: his videos do not have any point except to maximize viewership; the viewership he has accrued allows him greater fame and resources to pour into the next video. He is a creator without creative vision, and is in fact better understood as a creation of audience feedback filtered through the YouTube Apparatus.

This is why the first step has to be a step back (der Schritt zurück, if you’re into that sort of thing). A step towards the stability of the body and our capacity to directly observe the world. Any action within the whirlpool will only draw us deeper in unless we have taken the time to understand what is happening, to (in McLuhan’s words) “penetrate to the essential character of man and society and discover the outlines of a world order.”

It’s tempting to attempt to step back into the past, to try to resuscitate the dying literary culture. Would that it were possible! But this direction produces such category errors as the focus on ‘misinformation’ as a problem. This is a literary concept applied to the neo-oral order, which is in fact premised on non-information, in the sense that facticity is overtaken by vibes as a criterion for evaluation.

Amused, detached observation is the most important method, from which others will emerge. But if I may point in a direction that strikes me as promising: the values of the literary world that we might want to preserve can only be salvaged through the development of a genuine digital literacy to counterbalance the ascendent digital orality. This doesn’t mean trying to influence the digital world with the technology of writing. It means trying to influence the digital world with the technology of digital writing, that is, code. Code is writing in the native language of the internet, and I am optimistic that LLMs represent a democratization of this capacity. A newfound ability to translate your thoughts into the language of the computer.

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Today I’m presenting two new papers, one of them an extremely dense working paper in the philosophy of science, the other a just-today-published article using AI-powered experiments to study gender and housework. I’m putting the fun one first but please scroll down to the philosophy of science one if that’s your thing.

Hot off the presses at Sociological Science is my article (first author’d by Léa Pessin) “Beyond Text: Using AI-Generated Visual Conjoints to Study Gender and Housework Attribution.” This was a perfect combination of my previous work on visual conjoints and Léa’s substantive expertise on gender and housework.

The project began when I was explaining to Léa that we simply have different preferences for the tidiness of domestic spaces — what she thinks of as messy, I think is fine. This is just a question of diverging personal preferences. So really, we’re better off compromising where if she wants it to be super-duper clean, she has to do it herself.

In a normal relationship, this would’ve just led to a fight, and not a very productive one. Thankfully, Léa is a sociologist of gender and housework, and had both the theory and data to explain what was wrong with my reasoning (her words). What she argued is that my allegedly personal preference about tidiness was in fact the product of societal gendered expectations about how men and women should behave at home. Women are expected to be responsible for keeping their homes tidy and pay greater social costs for not doing so. Then, she did the one thing that would end the discussion: she served me with a visual survey experiment. This is how this paper started.

Sociology is a breath of fresh air compared to political science because of the range of outcome variables they consider interesting. I’ve long chafed against the imperative that my work be immediately relevant to electoral politics — especially because many of the computational methods I use don’t have many direct applications. We can study voter preferences for hypothetical politicians a million different ways but I feel we’re starting to hit diminishing returns…meanwhile, getting to mock up hypothetical living rooms and think about how to balance the number and diversity of children’s toys strew about was novel and exciting.

The substantive findings are really cool. We find, just like in the same 2019 conjoint experiment paper by Sarah Thébaud, Sabino Kornrich and Leah Ruppanner that directly inspired our design, that the myth that “men just don’t see mess” is busted: men were if anything slightly (though non-significantly) more likely to rate rooms as messy than were women. We also find the expected, large effects on housework responsibility: on average, female occupants are rated as more responsible for cleaning up the rooms. And, most importantly, that Léa was right and I still have to do housework.

On the other hand, we didn’t find support for the original finding that female occupants suffer greater social consequences for messy rooms than male occupants do; there are a number of explanations for why we might’ve found this different result, we don’t intend to “overturn” the original finding, but it does suggest that it’s less robust than some of the other results.

Novel to our setup is the differential effect of children’s versus adult mess. Here, we see that respondents rate children’s mess as more messy than adult’s mess — but that occupants suffer lower social consequences for children’s mess. And finally, Léa’s favorite result (because it’s consistent with earlier qualitative evidence) has to do with the housework/paid work responsibility tradeoff. When one person works full time and the other is either unemployed or a homemaker, the full-time person is less responsible for cleaning, regardless of the gender of the respective parties. It’s when both parties work full time that we see an additional significant allocation of responsibility to women. This is an example of what sociologists call ‘doing gender’ -- people hold women more accountable for the home even when both partners face similar time constraints.

So, we’re very excited about this project, and especially that it’s coming out in Sociological Science. For my non-sociologist colleagues who aren’t familiar with Sociological Science, let me briefly shower the journal in praise. Subjectively, of course, I think the current editors have great taste, since they just published my article; ymmv. But objectively, the structure of the journal is a magnificent metascientific intervention. They view their role as primarily curatorial rather than developmental; as such, they don’t do R&R’s. The editorial team has complete discretion; they can accept or reject manuscripts on their own, or send out for advisory reviews, but again even with the reviews, they either accept or reject the paper — and they do so in 30 days.

We can argue about how much peer review improves manuscripts— I think it’s rare in practice today, but that we should move to a system in which there’s a much greater diversity in journal types, where some explicitly focus on improving manuscripts and others focus on this curatorial role. Sociological Science has already taken the latter step, and I think other “top” journals should move in this direction as well.

We have to stop pretending that every nice-to-have element of academic publishing can be achieved for free — most things like mandatory computational reproducibility cost money, and all of them cost time. And time is progress, time is validity. Sociological Science takes well-reasoned stances on these tradeoffs, promising speed in exchange for a modest ($45) submission fee, and a sliding-scale publication fee that goes up the longer the manuscript is (an incentive for concision!).

We think that there’s a ton more work that can be done with the AI-powered visual conjoint, across the social sciences. The upside of visuals is obvious, from an ecological validity perspective, and now the cost of producing them has decreased dramatically. There remain methodological issues — it’s very difficult to precisely balance the images on the dimensions you don’t want to vary. We ended up using very explicit, lengthy image generation prompts.

But even still, it’s very difficult to say how much “mess” is the same amount “messy” when the content of the “mess” differs. Experimentalists are used to thinking about how to operationalize concepts using text, but operationalizing them using images poses novel theoretical issues. Were the children’s mess rooms “messier” because children’s mess is per se messier than adult mess — or was there a latent confound in how the images were generated? These methodological issues remain for future research. But possibly the biggest challenge ahead for gender equality in our household is that Léa has now concluded that every one of our disagreements should end in a publication proving her right.

Read the full, open access article here.

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and now for the philosophy of science one

The biggest open question in the methodology of social science involves generalizability, aka external validity/knowledge synthesis. There have been a raft of statistical and institutional interventions on this problem, but it’s particularly difficult to tackle because it’s an intrinsically metascientific problem. Methodology is the task of improving the validity methods undertaken as part of scientific research, and the recieved ontology of scientific research is that it science is composed of studies. When we take a step back, we’re immediately confronted with deeper questions about what exactly the scientific enterprise is aiming to accomplish, how well it’s achieving those goals, and how it might do it better. We need to think more broadly; it doesn’t make sense to simply apply the same rigorous positivistic scientific methods one level up.

But the absurdity of meta-positivism hasn’t prevented it from becoming the dominant impulse within metascience. The replication crisis and concomitant issues about generalizability will be solved with more rigor! Come up with a metric (say, the p-curve) and apply it across wildly disparate literatures that all happen to use numbers to represent their outcomes — this will diagnose our flaws, producing the only true and good science: a machine for producing a uniform distribution of p-values.

I’ve called this movement metascience manqué, a degenerate case of the much wider possibility space made possible by a more critical metascience. This can take a variety of forms, but my focus is on the ontology of science. As an experimentalist, my ontology is premised on treatments and contexts: the actual physical actions that the researcher causes to take place in the world, as well as the precise relationship between those actions and the rest of the world . The other crucial components of research (measurement, samples, populations) are fairly well-understood. But the biggest issue for the field is a lack of attention to the experimental procedure as such.

I believe that this essential engineering step has recieved less attention because it is not immediately relevant to the theories that social scientists consider their true aim. In Slough and Tyson’s (extremely compelling) framework, they refer to the “technology of intervention” as something of a footnote to the meatier theoretical constructs of “mechanisms” and “contrasts.” I think that higher-level work can only succeed when the foundations are more solid, and the technology of intervention is a pivotal primitive. My particular point is that this technology of intervention is far more heterogenous than theory-focused practitioners (would like to) believe, and that unless we understand the implications of this heterogeneity, our eventual efforts at knowledge generalizability will be confounded.

To that end,I introduce a typology of political science experiments based on their “containedness.” Contained experiments are those for which it is plausible to “perform the same procedure” and expect “the same result.” Uncontained experiments are those for which practitioners expect intrinsic variation to dominate; even when performing the same procedure, the goal is to understand how results differ across time and context. I propose three conceptions of containedness: pragmatic (community expectations about replication), complexity (information required to specify the procedure), and temporal (speed of experimentation relative to drift in the phenomenon). Lab experiments are generally the most contained, survey experiments occupy a middle position, and field experiments are the least contained. I provide a diagnostic rubric for evaluating containedness and argue that distinct evidentiary strategies are appropriate at different levels.

This is the most difficult paper I’ve ever written, my first serious attempt at doing philosophy of science aimed at social science methodologists. It’s just incredibly hard to write rigorously; as my readers here will recognize, I’m fairly good at writing impressionistically, but that shit doesn’t fly when you need to be precise. I’ve already submitted this paper for review and been rejected; one particularly thoughtful review suggests the need to formalize the framework, certainly the standard perspective on how to communicate ideas with precision. But I’m even worse at that. So I’m sharing the working paper now in the hopes that some readers can make use of the framework well enough that I don’t have to go down that road.

Comments on this would be extremely welcome:

https://osf.io/preprints/socarxiv/4ve8h_v1/

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From the persepective of meta-science as the art of science, then, I found the view of electromagnetically modified two-headed worms most pleasing, indeed.

The worms were on display at the Palazzo Diedo in Venice, as part of the Strange Rules exhibition promoting and implementing the concept of protocol art. From what I gathered, the electromagnetic manipulation in the developmental stage of the worm causes the second head to grow — but more astonishing, the worms’ offspring also grow a second head. Common Lamarck W, the kids will say, vitalism mogs. And that’s true. But the key meta-scientific move is to foreground the protocol that produced and will continue to produce two-headed worms, not the worms themselves.

Of course, I’m mostly interested in what the worm protocol tells us about the internet. In a recent interview of Adam Curtis for VICE1, Dean Kissick passes on the following observation.

Kevin (no relation) is right. And Adam Curtis is right to be pissed off with artitsts, too, though he should make an exception for my friends Joshua Citarella and New Models, in general but also specifically for their Palazzo Diedo art piece The Online Marketplace of Ideas, a merch show gallery installation that lays bare the protocol powering politics in the platform age.

The exhibit was jarring, very unlike any experience I’ve had at a capital A-art gallery or musuem. The objects were mostly recognizable, it was obvious that they should exist since I’ve seen them online, but they simply should not have been able to exist together. Each signifier can be incorporated into mainstream culture, either through endorsement or exclusion; indeed, the protocol of postwar American extended through the platform internet actively encourages the emergence of distinct identities and the products you can buy to inhabit them.

It was wild to see my book Generation Gap with Nick Land’s Fanged Noumena and a pair of Red Scare booty shorts, a juxtaposition I never thought I’d encouter outside the “special” compartment under my bed. But while it was fun to play, “ooh, I know that one!”, the gestalt experience was the art: the fact that all of these objects, though signifying or even embodying radically different ideologies, are in fact the product of the same protocol.

We’ve certainly read articles about how dangerous Infowars is, or you can buy an Inforwars t-shirt. You can dress up like a Boogaloo Boy in an oversized Hawaiian button-down; you can drink white Monster and become a body without (functional) organs. Obviously, Trump and the MAGA hat is the single biggest winner from this protocol, such a massive cultural embedding that the legacy establishment has re-defined itself as nothing more than MAGA’s negation.

But the overwhelming mass of these objects, their collective weight, is far too large to be incorporated or even comprehended by the dwindling center. Kissick, in the same interview, says that “not just the art world, but a lot of the cultural establishment, a lot of the media has pivoted to upholding the illusion of normalcy,” while Curtis nods in agreement. The illusion of normalcy is shattered by the encounter.

I’ve called this “Boomer Realism,” and downstream of the outsized demographic/economic/institutional and therefore cultural power of that generation. Their premisese are the ones that are still operative because nothing else has succeeded in establishing alternative premises that stick. Obviously the world is fragmenting, but by its nature fragmentation is harder to comprehend than is cohesion. There might be many smaller worlds breaking off, but the Boomer world is still the largest one left; the centripital desire for normalcy is keeping it alive even longer than the material conditions would predict.

The Online Marketplace of Ideas is a Frankenstein’s monster of stitched-together political-cultural artifacts, looming over the shrinking iceberg of Boomer political-cultural normalcy. The past decade has been frothy, a cambrian explosion of post-neoliberal ideologies each trapped in their own whirlpools. The eddies are visible — and in some cases, if the media or political establishment wants to pin something radical on their mainstream opponents, they can become major discursive currents of their own. But without the point of view like that offered by this exhibit, it’s easy to think that the mainstream is still more powerful than the uncountably many Charybdes2 waiting the in the coves and inlets.

So what does one do with this? What is “protocol politics”? Protocol politics is about widening the whirlpool, tying together (live)streams of thought. It’s not quite the same thing as “meta-politics”, which is really just the politics of politics. The media world that my readers likely inhabit is full of meta-politics, primarily in the form of pundits and posters debating what the Democratic (or Labour) party should be doing in order to appeal to other people. This is a debilititating ironic distance from the actual experience of politics; this entire class of hypereducated media consumers cannot do politics per se. They’re taking the protocol as given, just trying to optimize it.

Protocol politics is different. Flusser speaks of this, in Into the Universe of Technical Images:

Present-day revolutionaries know that they first have to build a consensus. Their action is not against images but against the current feedback consensus between images and people.

Nate and Nate and Matt and Noah and Ezra and the rest of the liberal meta-politics guys accept the “feedback consensus” between images and people — they understand their goal as simply to identify the correct images that will produce the desired feedback. The goal is to optimize or even micro-target “political messaging,” a behavioralist fixation of control. But this misunderstands the nature of the apparatus, of our technical society. Old liberal/literary forms are untenable, but we haven’t woken up to that fact; the progressive intellectual elite attends to “political consciousness vegetating in an artificially preserved republic” (in Flusser’s terms) for themselves, and fine-tuned (ahem) messaging for the masses.

Even from a narrow, cynical perspective, this approach is self-defeating. Left-liberal politics cannot succeed in an atomized, dispersed society. And yet atomization is the message of this medium, the product of this protocol. We’re all scrolling alone. Flusser, again from 1989:

the way telematic gadgets are used now, to produce empty chatter and twaddle on a global scale, a flood of banal technical images, definitively cements in place all
the gaps between isolated, distracted, key-pressing human beings.

And none of the individualized radical media products on offer can overcome this protocol; in fact, they only intensify it.

The people that are shouting and sounding alarms today, the Che Guevaras and Khomeinis, and those who count as revolutionaries are really entertainers. They
are spectacular, and the spectacle they present assists the images in dispersing us more and more effectively.

It’s like Alex Jones read Flusser as a how-to guide.

So what’s the alternative? Joshua Citeralla’s Doomscroll podcast project is one example. The structure of the podcast is conversations about politics; on a content level, unremarkable. But the protocol is to develop a media brand that enables a dialogue between media figures and politicians across the entire broad swath of what could be considered “the left.” This is the same playbook used by alt-right and other figures in the broader constellation of anti-establishment, anti-liberal media entreprenuers of the 2010s, except that they didn’t understand what they were doing—it’s just that their ideological inclinations converged with the protocol:

The structure of a society governed by technical images is therefore fascist, not for any ideological reason but for technical reasons. As technical images presently function, they lead on their own to a fascistic society.

Genuinely democratic collective governance is both incredibly difficult and the only solution. We live in the universe of the technical image. The challenge of protocol politics is how to build new pathways to share these images on our own terms rather than accept the paltforms we’re given — and to cultivate new subjectivities that understand how to relate to them.

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Legitimacy produces a double bind for would-be reformers. These institutions, despite increasing irrationality, must still maintain the pretence of functionality in order to get everyone else to play along. Radical change is a repudiation of the recent past, of the people and decisions legitimated in the recent past. Unless you want to go full DOGE and embrace the de-legitimization of the institution you’re tasked with reforming, there needs to be a(n exogenous) reason why things used to be ok, but now they’re not.

So I’ve got good news. AI represents a jubilee for dysfunctional institutions! A mulligan for all of the accumulated institutional debt! You get to say: “Everything used to be fine, no bureaucratic absurdity here ;) but now we have to change everything because of AI.”

This is the healthy response. The unhealthy response is to deny that AI is changing anything but that you’re going to undertake dramatic retroactive changes to preserve the status quo functioning of the status quo. The latter strategy was undertaken by the European Research Council in changes to their flagship funding scheme, by far the most important source of academic grants in Europe.

In Things Will Have to Change, I predicted that there would be “at least a 50% increase above a linear extrapolation in the number” of submissions to the (social sceince) ERC grants. The Research Council was determined to prove me wrong, and retroactively extended the “cooldown” period for previous applicants who scored below a given threshold. In a dramatic example of cope, the justification for this change doesn’t mention AI, but it does re-affirm that the bureaucratic status quo was working fine, other than those nasty end users of the bureaucracy were cynically trying to use it normally. The text is remarkable:

The only remaining way to mitigate this situation is to reduce the number of applications. We have been relying on soft measures to try to achieve this by communicating the need for applicants to reflect carefully on the right timing of their application and the maturity of their scientific proposal. Unfortunately, the effectiveness of this approach is limited.

It’s not the granting agency’s fault — people just keep applying to (career-changing, 1.5-3.5 million euro) grants!

Some panels now have more than 250 applications to assess and we expect this upward trend to persist. [Here is maybe where you should mention AI lol.] We have increased the number of panel members, but there is an upper limit to the size of a panel, determined by practical constraints and how panels function as groups. There is also a limit to the number of proposals that can reasonably be discussed in the one-week sessions that the panels spend together.

The literature on scientific grants is clear that tortuously long single-person grants evaluated by committes that confer with each other is a hideous waste of time and a terrible way to efficiently allocate resources. The fact that the impediment to reform is the need to have “one-week sessions that the panels spend together” is just nuts: this process introduces a role for charsima, exacerbates groupthink and the possibility of corrupt log-rolling.

Once again, the functionality of the status quo is affirmed in the conclusion:

anyone applying this year should consider very carefully whether this is in fact the optimal time. In some cases, it may be wiser to postpone an application to allow the proposal to mature further and improve its chances of being selected for funding.

There are multiple claims here: that the “maturation” of a grant proposal is something that can take multiple years; that the grant evaluation procedure is legitimate enough to be able to accurately distinguish “mature” from “immature” grant proposals; and that “maturity” is somehow related to the scientific value of a grant proposal, what the researcher will actually do with it.

As far as I am aware, there is zero evidence for any of these claims. The first is tautological (maturation == getting older), and the third is meaningless (“maturity” being a bizarre concept here). But there is now compelling evidence against the second claim, at least now that everyone uses AI, from another of the European Commision’s flagship funding opportunities.

This comes from an excellent preprint by Martin Bulla and Peter Mikula analyzing the applications to the prestigious Marie Skłodowska-Curie Postdoctoral Fellowships. The percentage of proposals above a given score (out of 100) jumped in 2025, from its extremely consistent, reasonable 2018-2024 trend.1 The time series demonstrates that in fact the system was internally consistent in the past — but that it absolutely no longer is.

Bulla and Mikula don’t pull punches; their paper is titled “When AI turns grant evaluation into a lottery.” The system now cannot distinguish between a huge percentage of applications clustered near the top of the distribution.

Grant applications are different from papers in that they don’t produce/contain knowlege themselves. Society is plausibly better off as a whole if more social science papers become better, even if it makes the peer review process more random: we’re randomly selecting from higher-quality knowledge producets. The same isn’t true for grant applications; this is purely a zero-sum game.

This is open-and-shut evidence that the system is broken — and that it specifically broke last year. I’ve heard reports from applicants at EUI that they scored 94 or 95 out of 100 and still weren’t above the bar to recieve the fellowship.

This data disproves the ERC President’s claim that “postpon[ing] an application to allow the proposal to mature further” does in fact “improve its chances of being selected for funding.” This is an egregious example of assigning indivudal blame for a systemic problem. The institution is bleeding legitimacy; we can all see that these applications are becoming lotteries.

And obviously, if it’s a lottery, the incentive for everyone is to buy as many tickets as possible. The visible degredation of the discriminatory function of the institution (that is, the ability to tell good applications from bad ones) produces exactly the wrong incentive; rather than reduce the number of applications, we’re going to see many, many more.

The second-worst response for dysfunctional institutions to AI is to ignore it and do nothing. The worst response is to ignore it and undertake blunt reforms aiming to preserve the crumbling status quo while actually making everything worse . In the ERC’s defense, they’ve retreated back to the second-worst response after serious pushback from academics…who were of course just mad that they personally wouldn’t be able to submit this year, something that many may have already started working on.

Ok, maybe I’m projecting here….I, personally, was mad that I wasn’t going to be able to submit this year, I was rejected two years ago and the proposal and been maturing so well…oh wait, no, I do social science and the social world changes. My proposal from two years ago isn’t mature, it’s completely spoiled, left to rot in the fields, dozens of hours of brutally tedious work for nothing because of the dysfunction of this system.

I’m bitter about this one — personally, it would’ve secured my current position for another four years, while now I face the prospect of needing to look for a new job as soon as 2029. And the content of the grant was both perfectly timed and substantively important; I was proposing to study political influencers on video-based social media. The grant was submitted in October 2024, a month before what has been come to be called the “podcast election” or the “influencer election”; of course, if we were serious about social science well, we would’ve had the infrastructure for studying this up and running before it became relevant…but a few months later isn’t too bad. 3 years later is in fact too bad, evidence of institutional dysfunction.

So, with that rant out of the way, let’s consider the AI Jubilee for Granting Agencies: “The previous system worked great, all of our decisions were correct, etc. But now AI is changing the dynamics of the system; we have to radically overhaul our workflow to adapt.”

And then actually adapt. Maybe use AI, maybe don’t; that’s a subject for another post. There’s a whole literature on scientific funding to investigate; now is the time to re-set the institution to the 21st century. The crisis of AI inverts the legitimacy trap for dysfunctional institutions; you lose legitimacy if you don’t undertake radical change.

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Glad to be back! Nearly three months of paternity leave was a huge privilege, and I’m glad to have both had and taken the opportunity to do so. Everyone is happy and healthy, thanks to everyone for sticking with me, I hope you enjoyed reading the old posts.

Reading through my archive has given me some perspective on what exactly this blog is about: all of the stuff I’m interested in, and only the stuff I’m interested. I’ve explicitly tried to avoid hot takes or anything related to The Discourse. When I started blogging almost six years ago, metascience was niche, navel-gazey; now it’s the ascendent paradigm for thinking about the future of science. Then I got into media theory, especially Flusser — now everybody from Sam Kriss to the Pope is talking about the decline of literary culture and the new orality.

So that was fun. But the intellectual agenda is no longer mine to set, not even on my own blog; it’s currently impossible to think about media theory or metascience without grappling with AI. AI is shaping up to be a central political issue, in terms of electoral, geo-, and cultural politics. But it’s also immediately important for my day job as a professor of political science, so I’ve been focusing my serious writing on AI and metascience.

Today, I present my contribution to an internet-native “edited volume” organized by Rohan Alexander about the future of quantitative social science in the age of AI. This is brilliant meta-scientific institutional work — he emailed prospective authors in late March, organized a workshop that’s taking place today, and otherwise simply set up a lightweight online publishing platform (a “website”) that I’m sure is using an LLM workflow to harmonize and store all of the contributions.1 This is a methododological contribution, in the sense in which some social scientists (including me) specialize as methodologists. We have to begin to expand the scope of what counts as an epistemic contribution beyond the pdf.

And that’s the theme of my contribution: AI Allows More Diversity in the Forms of Social Science. The whole essay follows — don’t worry, this is written more like a classic Never Met A Science blog post than an academic article. But in even more classic Never Met A Science form, I first want to take a step back and emphasize the AI usage statement, an intensely contested new methodological step in the process of social science, discussed here.

AI usage statement: I dictated a first draft of this manuscript to Claude Opus 4.7 while walking my infant son. I told Claude to clean up the transcript of the conversation and to structure some of the disparate threads while using my exact words as much as possible and demarcating any significant interpolations or transitions. I then went back and edited the manuscript, added citations (I also use Claude to manage my citations in bibtex), and then rewrote or deleted the Claude-labeled Claude-generated text. Insofar as it followed my instructions, all of this text is my own – but there’s no way for me to verify this, nor do I think it especially important.

Introduction

The successful transition to the age of AI must entail greater diversity in the forms that quantitative social science takes. Of course, the same was true of the age of the internet; quantitative social science did not have a successful transition to the internet age. We should have had a revolution in the modality in which we store and communicate scientific knowledge when the internet became standard across universities. It is an embarrassment to our field, and a significant impediment to the pace of social scientific progress over the past thirty years, that we essentially just took old paper journals and put them online with no innovation in form. But AI will soon make it impossible for us to ignore the absurdity of the status quo–especially if enterprising social scientists collectively think hard about how to change social science. This collection of articles is encouraging in this regard, and makes me more optimistic that social science will muster the energy to reject the entrenched interests preserving the status quo.

The present metascientific framework is an extension of the one used in Munger (2026), coauthored with eight other editors of social science journals. We consider how peer review is going to have to change as AI becomes more widespread. The framework requires us to consider different possible equilibria; it’s not sufficient to think about how one subcomponent of the overall academic ecosystem might change. If you only change one thing, there will be adaptation by other components of the system that might dilute the desired effects of the reform, or have other kinds of negative consequences. To do this kind of metascience rigorously, it’s essential to be aware of all of the different functions that peer review serves in order to think about how we might incorporate AI in a way that satisfies all of the relevant desiderata while also producing a stable equilibrium which provides the right incentives for academics.

But in addition to theory, we need trial-and-error. It’s just as absurd to believe that we can perfectly predict how AI will be incorporated into social science as it is to predict that the status quo will persist. The essential first step is to try things out; I discuss three ways in which AI allows for innovation in knowledge production (meta-ontology, time, and rapid empirical iteration). But to give practicing scientists the appropriate incentives to try out these new forms, we must overcome an age-old tyranny.

The Tyranny of the PDF

The biggest bottleneck to the exploration of AI-enabled formal diversity is the uniformity of the outputs for which social scientists can get credit. I refer, of course, to the tyranny of the peer-reviewed PDF. There are many different types of epistemically relevant, valorous, valuable actions that social scientists take, but the only one for which we are officially recognized and rewarded is the peer-reviewed PDF.¹ The fact that all social science has to be routed into this modality of communication causes us to flatten the types of epistemic contributions that we make, and many of them are really very poorly fit for being stored and communicated in this modality.

Before getting to the ways the PDF is insufficient, it’s worth delineating the bundle of functions the pdf serves. The static PDF functions as both an archive of what actions the scientist has taken and as a reference to previous research products: the literature review, the theoretical arguments, the methods section summarizing different statistical tools taken, the statistical results, the conclusions, the references. All this made sense to bundle together, because the implicit arrangement at the point when the academic paper became the standard was that there were many humans who were going to read these PDFs.

But with the internet and especially with AI, there’s no need for every epistemic contribution to always include every one of these elements. By unbundling them and allowing for epistemic contributions to take different forms, social science can become radically more efficient.

I propose three dimensions on which those new forms can transcend the PDF. The first is the introduction of a better-defined, more explicit meta-ontology of social science. The second is time: the fact that knowledge can be kept up to date rather than stored as a single static PDF. The third is aggressive empirical exploration within a well-defined space of experimentation or quantitative study.

Meta-Ontology Over Narrative

The first problem with the PDF is that it presents each paper as a single contained narrative. Sure, the authors draw from previous studies for the purpose of making a theoretical argument connected to their specific empirical case. It’s not meant to be completely unique, but ideally, both the theoretical arguments and the empirical cases are understood to be in some way novel through the production of a narrative in the academic paper. Social scientists understand that they need to have a story. This is especially true for the most important paper they will ever write: their job market paper.

The bundle-narrative form treats the task of writing these papers as something which involves craft, for which taste is necessary. I think this is a valuable art form, one which I very much enjoy – but we’re seeing it devalued and commoditized because it is the only permissible form of epistemic contribution. We might collectively write far fewer of these narrative PDFs, and then focus our attention on these more careful, artisanally constructed arguments. The diversification of the forms of quantitative social science will in fact enable us to appreciate the narrative PDF for what it actually sets out to accomplish.

The narrative PDF requires authors to play a kind of trick: to convince readers that all of the components of the bundle hang together correctly. This particular RCT allows us to learn about the “effects of education on support for the far right”; this survey experiment on an online convenience sample does in fact generalize to a population from which it’s not randomly drawn; this other game theoretic model in which one of the actors is labeled a “voter” does in fact relate some way to what happens to people in the voting booth.

Each pdf weaves these components together into as coherent a narrative as it can manage. But contemporary methodology pays very little attention to the individual linkages. We know how to evaluate each component in the abstract; the question of whether they hang together is more of an art form.

We need a more well-defined meta-ontology of social science. We need a database that tracks each of these components separately, as well as how they have been combined. We can take from previous studies the different concrete manifestations of theories, different dimensions of variation that have been theorized to be relevant for moderating or mediating a given phenomenon, a list, say, of theoretical interventions or exogenous shocks which have been studied.

Consider the fantastic work by Mellon (2025) about the diversity of PDFs in which rainfall has been claimed to be an exogenous shock – each of which explicitly assumes that rainfall only has effects through the pathway that that specific pdf claims it operates through. This meta-scientific work had previously required painstaking human labor, but once we’ve begun to define the meta-ontology, this is an ideal task for AI, which can allow us to go back and do it at scale, extracting the relevant components of all published quantitative social science work.

With this database, we can begin to figure out how each of these components are working in the aggregate. For example, analyzing just the distribution of empirical tests with a given method has produced important evidence about the overall functioning of that test, if the statistical significance rate matches our theoretical expectations (Brodeur et al. 2016; Brodeur, Cook, and Heyes 2020). More prospectively, this database will allow us to identify methods or evidence which have become out of date (see Section 4), what combinations of components have been underexplored, or what spaces have seen sufficient exploration to begin to approach some non-qualitative synthesis of the knowledge (see Section 5). The pedagogical value would also be immense, giving graduate students more direct contact with the raw material they will need to work with.

Metascience to date has been constrained by the tyranny of the pdf. By trying to do metascience while accepting the implicit meta-ontology of academic publishing, where the fundamental building block of quantitative social science is the study and that “the study” is stored in the pdf, we have reified into givens what are merely symptoms of what social scientists actually do (Latour and Woolgar 1986).

Formalizing our meta-ontology will make possible new forms of knowledge synthesis, which I believe to be the most pressing problem in contemporary methodology. Synthesis at present either can take the form of meta-analysis: if we have a phenomenon or theoretical construct which has been studied many times in many places, but which we believe, or trick ourselves into believing, is sufficiently similar across times and places, we can simply plug these studies into some kind of statistical machine and perform quantitative meta-analysis. Or else it’s done qualitatively, in fact, where people read different PDFs and come up with their own understanding somehow of the knowledge contained there. Despite my glibness, these are both valuable parts of the process and cannot immediately be improved for many areas of social science. I’m not arguing to get rid of any of these things. But I am arguing against this monopoly of form, in favor of embracing the diversity of forms that are possible with AI.

Time

The second problem with the PDF is that it is static. This matters in two contexts. One is related to constraints in the production of knowledge, and the other is the premise that the world itself is not changing very much. Munger (2023) argues that we should speed up social science and that we need to be aware of how changes in the world change our evaluation of existing knowledge, but AI could allow us to store knowledge in a way that treats time as a first-order concern for social science.

When a peer-reviewed PDF is published, it summarizes the data that exists up to that point, references the literature that exists up to that point, uses methods which have been validated up to that point, and then is treated as a static, final, permanent contribution to the academic literature.

Perhaps the largest metascientific reform of the past decade comes out of the replication crisis (Collaboration 2015; Nosek and Errington 2020). This reform is fundamentally backwards-looking, when I think that AI-powered social science can and should be forward-looking. Replication is the idea that we should be able to directly reproduce what has been done before. But given the fact that the world is changing, the literature is changing, the data are changing, and our methods are changing, I don’t actually see why we should care very much about being able to do what was done before. Instead, we should take the logic of replication and apply it to a changing world. We should care about the ability to maintain, for example, the core theoretical logic of a paper while still updating at least the data; even more provocatively, we can potentially also update the methods and the theory.

Consider public opinion panel surveys like the ANES, where we have the same questions being asked every presidential election, and we have some measure of affective polarization (Iyengar, Sood, and Lelkes 2012). Rather than needing to write a new paper, peer-review it, and make some theoretical modification, we should simply say: we have this measure of affective polarization, this is a dynamic dashboard where we can see how this measure has changed over time. It should just be a kind of static fact that everyone in the world can reference. It doesn’t meet the standards of theoretical novelty to merit a whole new bundled PDF, but these descriptive facts are essential inputs to the rest of quantitative social science (Munger, Guess, and Hargittai 2021). Ironically, we tend to outsource the production of epistemic facts to other actors like journalists or the Pew Research Center, even as their outputs rack up hundreds of citations and go on to set the academic agenda (Smith and Anderson 2018).

We should be able to update the results in a given empirical paper with contemporary methods.² This would mean that the “paper” could be stored in a dynamic way online, where what’s actually the contribution is a data pipeline and a codebase which can be updated automatically with AI. We should be concerned about archiving the knowledge stored in this way; the PDF does have that function, and it is interoperable with libraries that are institutions tasked with archiving knowledge.

This dynamic updating/discounting is impractical or even ill-defined for many types of work undertaken by quantitative social scientists. Again, I’m not arguing that old forms must be totally abolished or that new forms be made universal or mandatory; this is rather a way in which AI can be used to make new kinds of social science possible.

High-throughput empirical exploration

The third problem with the PDF is that it flattens the magnitude of the empirical evidence “under the hood.” Quantitative social science is forced through this fundamentally qualitative layer, even though quantitative meta-analysis basically ignores the pdfs and instead works directly with the data to understand distributions of effect sizes.

Building on the formal meta-ontology, we can identify empirical components of a given phenomenon of interest where we have a relatively static, sophisticated theory and a well-defined ontology of the different elements and how they interact. The relevant margin for scientific progress is to increase, perhaps by orders of magnitude, the amount of empirical evidence being brought to bear on this problem. I’m inspired by the high-throughput virtual lab being implemented by Duncan Watts and his co-authors (Almaatouq, Becker, et al. 2021; Almaatouq, Alsobay, et al. 2021).

In the case of Houghton and Watts (2025), they study the informational properties of deliberation by conducting studies with cross-partisan dyads which disagree about some phenomenon. Each study involves an online deliberative discussion, and randomly varies some number of theoretically relevant parameters. The outcome is well-defined by both properties of the discussion and self-reports by participants. At this margin, we don’t need theoretical innovation; we simply need to rapidly iterate through the parameter space of the different empirical manifestations of those theoretical dimensions.

It’s not necessary to produce a new peer-reviewed PDF which gestures at some theoretical novelty with each of these empirical components. We are seeing a move towards this greater conjunction of empirical studies into individual mega-studies, primarily driven by the competition for publications at elite journals. Where once you might be able to publish a single survey experiment, now you have to do several of them, or perhaps a very large-scale iterative field experiment studying persuasion effects (Coppock, Hill, and Vavreck 2020; Voelkel et al. 2024).

These are steps in the right direction. It’s obviously much better to avoid breaking these into individual PDFs, but there’s not a principled reason why this is happening; there’s just more competition for publications in elite journals, so authors are packing in more data to distinguish themselves. Worse, the form remains the same; each pdf remains a single narrative. Authors engaged in this form have to make the case that all of the data are alike, that they all make sense as part of the narrative bundle.

What we should be able to do is not try to immediately summarize all of this data into a single outcome, to smooth out the differences or to embrace the narratively useful fiction that effect heterogeneity doesn’t exist. If the theoretical and ontological space were well-defined and the epistemic contributions more continuous, scientists could instead collaboratively work on these problems, have multiple labs be able to make independent contributions that are commensurable without being interchangeable. By being more explicit about our ontology, we can radically scale up empirical progress towards a deeper theoretical understanding.

Interoperability and careers

These are independent, individual reforms, and are thus according to our premise insufficient for thinking about the future of social science in the age of AI. One of the key questions is how these changes will be made interoperable with the other components of social science. For established academics, I am optimistic that the epistemic value derived from this formal innovation will make it stick; it’s hard to ignore data that speak directly to questions of scientific interest. And diversity means that people can keep producing bundled, narrative pdfs if they want; my expectation is that high-quality contributions in this form will continue to be valued, while replacement-level pdfs will, hopefully, be replaced by more efficient forms of epistemic contributions.

But it is not excessively cynical to say that it is a first-order concern for people who do not yet have permanent jobs to figure out how any reforms to the institutional setup will affect their chances of getting a permanent job. How does the current pipeline of training new graduate students into the profession work, and how do we figure out the selection mechanism in an increasingly competitive environment with fewer and fewer permanent positions?

Pedagogically, I believe that the proposed innovations will be immediately helpful and demystify the components that make up quantitative social science work. By discounting older papers using invalid methods, the relevant literature to master will be less tilted towards the distant past. And by making visible well-defined problem areas, the collaborative empirical exploration can give early-career researchers a space with a lower barrier to entry for an epistemically valuable contribution.

But “making things easier” in a hypercompetitive marketplace can actually end up making things harder. Ethical scientific reform is seriously handicapped by a concern which predates AI, but which AI will make unignorable: we simply have too many graduate students for the number of permanent academic positions we have, and unless we figure out how to address this problem, any mechanism for distribution of career rewards and incentives will be plagued by overcompetition and the dissipation of effort. The default move is to just let’em fight it out, but I find this deeply unsatisfying, even immoral. Unfortunately, I cannot see a solution emerging from within the scientific system; the problem has to do with material conditions external to whether or how we produce pdfs.

Conclusion: Talking

In the spirit of reflexivity, it seems important to point to the AI usage statement at the beginning of this article. The process which led to the text you’re reading in this PDF would have been impossible without AI.³ On the other hand, this probably should never have been a PDF at all. For open-ended questions, particularly related to the metascience of how social science should be done, verbal deliberations allow for much faster feedback and iteration through ideas.

But this doesn’t mean that the half-baked intuitions or preferences of senior faculty tossed off over lunch need to be taken seriously. We currently apply massive amounts of rigor to the pdfs we produce but almost none to the way we talk – again, with the vertiginously important exception of the “job talk”. Other epistemic traditions take verbal contributions very seriously; the Supreme Court hears oral arguments, and while we don’t have to go quite that far, we should elevate and archive (with video) verbal metascientific debates about the state of the field. Conference presentations are useful but generally still too informal, and generally not archived. Again, I’m not saying we need to livestream literally every 12-minute talk at APSA, but that this form should be taken seriously as an epistemic contribution.

1. The fact that some of these pdfs are printed is more of a historical curiosity than epistemic necessity, though see the discussion of knowledge archiving below.↩︎

2. More provocatively, if the methods a given paper used in the past are no longer valid, we should discount the findings from that paper: it wouldn’t be published today, and therefore the results of the original paper are no longer valid. This one could get very nasty, interpersonally, and it’s definitely not something that senior academics would endorse, but we already do this implicitly, and I’m not sure that it’s a bad idea.↩︎

3. Or, rather, it would have been entirely normal several technological ages ago, when social scientists dictated their thoughts to typists or secretaries who inevitably dealt with the task of translating spoken thoughts into written text. It is now banal to point out that seemingly novel technologies in fact represent a return to older arrangements, but the point does take some of the edge off of the petty reactionary view that the way we happened to be doing science in the recent past is sacrosanct or inevitable.↩︎

This is my last repost from my paternity leave; next week I’ll be back with a new essay.

One nice thing about studying social media through the lens of creators and influencers is that some of them are quite forthcoming about their experiences; that’s the nature of the gig. But others are more committed to their persona, to maintaining some distance between themselves and the content.

The most famous of these is, of course, the world-conquering MrBeast, owner of the most-subscribed YouTube channel (343 million). MrBeast rose to fame through outrageous acts of charity, and occasionally engages in stunt-giving. This is a meaty phenomenon for wordcels trying to understand MrBeast; we can bring any number of critical frames to bear on questions of whether this is altruistic, whether it actually has good effects, what the normative experience of the viewer is…what it means to fit out 2,000 amputees with new protheses, like in the most recent video.

This is a mistake. As I argued in the conclusion of The YouTube Apparatus (now free to download!), “Communication within the YouTube Apparatus has no meaning.” The rapid feedback loop between creators and audiences (as constructed by platform metrics) means that the system more and more responds to itself. Rather than trying to go somewhere (as is the case with political ideology), the creator seeks simply intensification, to draw more and more of the world into his whirlpool of content.

Last fall, an onboarding guide for MrBeast’s production company was ostensibly “leaked” online.1 The document offers an explicit description of MrBeast’s content creation strategy — and I confess that I feel vindicated by the analytic approach in the book. Let’s walk through it.

beauty and the MrBeast

Your goal here is to make the best YOUTUBE videos possible. That’s the number one goal of this production company. It’s not to make the best produced videos. Not to make the funniest videos. Not to make the best looking videos. Not the highest quality videos.. It’s to make the best YOUTUBE videos possible.

This is MrBeast’s thesis. The medium is the message. The content only makes sense within the context of the platform. Aesthetic evaluations of the content are simply a category error. Beauty, here, is harmony between the content, the platform architecture, and viewer preferences.

99% of movies or tv shows would flop on Youtube. On top of that they’d be wildly unprofitable, have no flexibility, and long lead times that can’t adapt to trends. We arn’t [sic] here to make a small movie once or twice a year, I want to make one a week lol. Which is why you need to be nimble and produce content OUR way, not the way you were taught before.

This beauty is an ever-shifting target. The platform architecture and viewer preferences can change overnight; in cybernetic terms, a long lag time in production is deadly. MrBeast needs to be able to adapt to trends, vindicating my audience-driven framework for understanding YouTube.

But remember that the audience doesn’t exist — it is a creation of human actions inscribed through platform metrics.

the number(s) of the MrBeast

I spent basically 5 years of my life locked in a room studying virality on Youtube. Some days me and some other nerds would spend 20 hours straight studying the most minor thing: like is there a correlation between better lighting at the start of the video and less viewer drop off (there is, have good lighting at the start of the video haha) or other tiny things like that. And the result of those probably 20,000 to 30,000 hours of studying is I’d say I have a good grasp on what makes Youtube videos do well. The three metrics you guys need to care about is Click Thru Rate (CTR), Average View Duration (AVD), and Average View Percentage (AVP).

I spent all this time writing a book and he just…tweeted it out. The secret to MrBeast’s success is a monomaniacal obsession with understanding YouTube’s advanced audience metrics.

He has, proudly, turned himself and an ever-growing production company into human marionettes, contorting themselves to the tiniest tugs of CTR and AVD numbers. A qualitative, theoretical vision of creation is worse than useless — it distracts from the pursuit of beauty as it is constructed through platform metrics.

People on average watched this video a minute and 38 seconds longer than the other video of the same length!! OF COURSE IT GOT TRIPLE THE VIEWS, IT’S CLEAR AS DAY FOR YOUTUBE THAT PEOPLE LIKED THIS VIDEO MUCH MORE! For your videos to do well you must get their AVD and AVP as high as possible. The longer people watch, the better a video will do.

This example demonstrates a correct understanding of how the YouTube “algorithm” works. The primary goal of the algorithm is to show people beautiful videos, videos they want to watch. The actual difference between in viewtime is 6 minutes versus 7.5, but this represents a huge signal to the algorithm. MrBeast writes that it’s “clear as day for YouTube that people liked this video much more.” Thanks to the exponential distribution of content popularity, even these somewhat modest differences in watchtime translate to massive differences in views.

the MrBeast with two backs

haha just kidding. MrBeast does not have sex. YouTube doesn’t allow pornography, so what would be the point?

MrBeast’s bête noire

MrBeast is a business. YouTube views are one (massive) source of revenue, but so are mid-video “brand videos” (advertisements). The challenge is to understand the correct tradeoff between the primary resource and short-term profit. If MrBeast were to be bought out by a private equity company optimizing for quarterly revenue, we would expect to see a dramatic uptick in these brand videos. But because he understands the revenue maximizing principle from leaning into the logics of YouTube viewership, and because he doesn’t have shareholders to dilute his vision, he’s able to take the long-term view.

Now, again, the vision here doesn’t have any meaning. It is simply to grow his viewership numbers higher and higher.

the nature of the MrBeast

To excite me. The goal of our content is to excite me. That may sound weird to some of you, especially if you’re new but to me it’s what’s most important. If I'm not excited to get in front of that camera and film the video, it’s just simply not going to happen. I’m not fake and I will be authentic, that’s partly why the channel does so well. And if i’m not excited by the video, we’re fucked. Luckily, I’d say I’m a pretty predictable guy.

Even in this putatively private document, MrBeast emphasizes authenticity as an essential ingredient for a beautiful YouTube video. We’ve just seen how MrBeast optimizes every tiny detail of his content in pursuit of audience metrics, how every decision in his organization is reactive to trends and audience preferences. In what sense is any of this authentic?

As I wrote in the book, “If creators are speaking their authentic truths, how can they also be accountable to audience feedback? I am personally bemused to see “authenticity” invoked as a criterion for what is ultimately and obviously a performance; to be “authentic” in the DIY/punk ethos of my youth meant to refuse to “sell out,” to refuse to bow to audience demand and instead to remain committed to some higher artistic calling.”

If you share my bemusement, I recommend this excellent essay by Toby Shorin. At this point, we have to accept that younger generations—precisely the people who have been raised on quantified audience feedback for their every creative gesture—have an unrecognizable conception of authenticity.

The ideal creator has no distance between themselves and their persona. They have been interpellated by audience metrics; their subjective experience already takes audience reactions into account.

Or more simply, YouTubers are not “Creators” but Creations. Audiences, rationalized by the platform, and the vloggers who upload the videos those audiences consume are not separable either theoretically or empirically.

I wrote above that MrBeast is distinct from more confessional creators, the all-access influencers who turn their entire messy lives into content. But this is only true aesthetically, which again is a dimension which doesn’t matter. MrBeast also turns his entire life into content — by skipping the step of have a life outside of content.

The onboarding guide concludes with an extremely telling passage about information diets:

What you consume on social media, when you watch youtube, tv, the games you play, etc. are what I like to call your information diet. Chris Tyson (our first subscriber and the guy in the videos) is a wonderful example of an information diet being used to perfection. The dude is funny as fuck. I’ve never met anyone in my entire life that can make people laugh like he can and I never understood why he was so good at it until I lived with him for a few years. The dude watches an obscene amount of cartoons and stupid shit. His eyeballs exsist to inhail [uh..sic] copious amounts of just goofy, dumb, and brain numbing content.

Chris has turned himself into a pure vessel for “goofy, dumb and brain numbing content.” He has perfected his information diet. As a result, he is “fucken hilarious.” If he were to consume instead “stocks and investment advice…Do you think he’d be just as funny as he currently is? No. He in my opinion wouldn’t even be 20% as funny.” I’m enjoying thinking about how bad it would be to be 80%+ less funny.

“Luckily, I’d say I’m a pretty predictable guy.” Luck has nothing to do with it, Mr. MrBeast. Your predictability is the result of years of an information diet consisting of audience feedback metrics. You are the proudest creation of the YouTube Apparatus.

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Reminder that I’m reposting some of my favorite posts from the history of the blog while on paternity leave; this one is from July 2023.

Earlier this year, the release of ChatGPT and subsequent mainstreaming of AI laid bare what, for some people, is the defining crisis of our time: Accelerating progress in artificial intelligence has the potential to literally destroy the world.

I have serious reservations about AI doomerism, but as I said, AI progress is shocking and important and so it seems dangerous to dismiss out of hand this community of concerned rationalists and Effective Altruists, people who have been talking about imminent AI for years.

One of the only “establishment” figures widely respected by this community is economist and applied media theorist Tyler Cowen. I admire Cowen, who has been an influential poster for essentially my entire adult life. To say that someone “understands the internet” is among the highest compliments, in my book, and he does.

I shared some surprise, then, that Cowen vehemently (for him) criticized AI alarmism, as did Scott Alexander. (Read all that if you want, it’s not necessary for the point of this essay.)

Cowen was dismissive, not even deigning to articulate but merely to gesture at counterarguments. Others have pushed back against some of these, but today I focus on my area of competency:

“[Scott] does not engage with the notion of historical reasoning (there is only a narrow conception of rationalism in his post), does not consider Hayek and the category of Knightian uncertainty”

This is a genuine weak point for EA and young people in general; it would be useful to read more books. Whatever my personal failings, however, no one can accuse me of not having read Hayek. This background, combined with Flusserian media theory, gives me lots to say about the end of history (in the technical sense) and the concomitant failure of historical reason — in the second half of this essay.

The best defense is a good offense, and Cowen’s habit of cryptic pronouncements have rendered him largely unassailable. Refuting innuendo with exhaustive argumentation is a sucker’s game. So I will first advance the hypothesis that

Tyler Cowen is an information monster.

His capacity to intake and process information is so atypical — and information processing is so central to contemporary life — that his ideal information environment threatens other humans’ flourishing, our dignity.

But that’s a bit bloodless. Cowen concludes:

We should take the plunge.  We already have taken the plunge.  We designed/tolerated our decentralized society so we could take the plunge.

This is a dangerous technological accelerationist, gloating over what he sees as the impossibility of democratic oversight over technology or indeed the possibility of human choice in any large-scale issues. Who exactly is the “we” who designed/tolerated this, I ask the methodological individualist. I personally know many who have tried (thus far unsuccessfully) to destroy “our decentralized society.”

The superficially amiable food-loving humanist has reached precisely the same conclusion as the cruel amphetamine-popping anti-humanist Nick Land — that it is inevitable that techno-capitalist progress will eliminate humanity as we know it…and that this is good.

I said Cowen was an economist and applied media theorist. The first label is enthusiastically embraced; the second is mine, and reflects Cowen’s remarkably effective adoption of the logic of the internet and the attention economy.

The man has been posting daily links and occasional commentary to his blog for twenty years. (The anniversary, in fact, is next month — mazel.) This is astonishing. And according to Matt Hindman’s cumulative audience theory, this is precisely what he should have been doing. People learn that when they navigate to *his website*, every day they are rewarded with a few minutes’ worth of fresh, surprising (that is, non-redundant) information.

As a scholar of the co-elution of traditional media and social media in the early and mid 2010s, I can say that Cowen has avoided making the crucial mistake that doomed everyone from Buzzfeed to the Cleveland Plain Dealer: granting control over the distribution of their content to Facebook etc. Thinking they could cut costs and maintain or even grow ad revenue, these companies ended up with zero bargaining power over the platforms, when they weren’t getting actively defrauded.

Cowen’s expansion into podcasting carried the same excellent intuition. Conversations With Tyler involves interesting guest talking as much as possible, with a minimum of filler and often jarring transitions between questions. Where other podcasts are premised on parasociality and an audience with time to kill, Cowen’s seeks to be as information-dense as possible.

The best CWT guests are unaware of the format. Tyler often plays the game of “overrated or underrated” with his guests; tragicomic hipster Chuck Klosterman advanced this game (including the explicit category of “rated”) in his best book, Sex, Drugs and Cocoa Puffs.

As the Conversation is ending, Klosterman asks the question that’s been on all our minds. “So are you like, friends, with Zizek? What was it like talking to him?” Tyler reveals that they had met the day of their interview before hopping into a wide-ranging 90-minute Conversation.

But to be clear, these are not novel insights. Cowen is a self-professed “infovore” who thinks that we live in a golden age of access to information. That’s clearly correct, and I confess that I share some of the cognitive style that makes me see information density as a cardinal value of media.

Cowen is in the habit of throwing out a journalistic account of, say, the market for hired actors to attend funerals in Japan, and enjoining his audience to “model this!” I am in the habit of applying the logic of reflexivity.

So my model of Tyler Cowen is that he is an information monster.

The “utility monster” is a thought experiment proposed by Robert Nozick as a churlish but effective response to vulgar utilitarianism. It posits the existence of an entity with constant or even increasing marginal utility from consumption. According to the logic of utilitarianism, the morally correct allocation of resources is to starve the rest of the world in order to feed the beast, whose monumental utility gains would outweigh the utility losses for the rest.

As a young man, I was entertained by ideas like this. Now, this seems like a damning indictment of both utilitarianism and thought experiments. The bits about the utility monster above *seem like* English language sentences. They *resemble* an attempt at communication. But they are in fact meaningless: in no way do they refer to the world.

Communication devoid of meaning is inhuman communication, but it can still be an effective form of control. Indeed, this insight is at the heart of Norbert Weiner’s Cybernetics, the foundational text for post-historical reason. Cybernetics marks the end of history in a technical sense, and it reveals the weakness of Hayekian (and Popperian!) historical reason.

Hayek was brilliant, and prescient, having anticipated some of the most important intellectual developments of postwar science. Mainstream economics was a pompous and elitist endeavor, and Hayek was effective in combining the firsthand sociological insights of Karl Menger with the “praxeology” of von Mises to produce a much more accurate description of how individual economic actors experience the price system.

But that was some time ago. Hayek has been superseded, specifically by cybernetics. The insight in “The Use of Knowledge in Society” is proto-cybernetic, but Hayek never made the connection despite having more than twenty productive years in which to do so. Instead of recognizing the price system as one of many possible cybernetic information-processing systems, he retreated into a mental fortress of frankly second-rate constitutional legal theory, satisfied to treat the price system with mystical reverence.

There are other weaknesses. What Hayek didn’t understand about Hegel could fill a book—specifically, the final third of The Counter-Revolution in Science, a dogmatic and downright whiny tract which provides no insight except into Hayek’s own psyche. (For a more recent example of failing to understand Hegel, see Cowen’s Conversation with Zizek.)

This is not incidental. It is precisely the Anglophile Hayek’s overreliance on that tradition which prevents him from appreciating the intellectual current of Franco-German dialectical historicism which was led directly to cybernetics. See Weiner’s invocation of Newtonian time being supplanted by Bergsonian time in Cybernetics.

Cowen argues that “virtually all of us have been living in a bubble “outside of history.” I firmly agree with the first half of this; I wrote a book about “Boomer Ballast” and precisely this dominance of Boomer Realism over our collective imagination. His discussion of “history” is not merely academic point, at least insofar as this article has any point at all. The logic of historical reason implies a certain continuity in human-kind; Flusser and other media theorists, however, point to media technology as causing mutations, in the creation of new humans and new human relations.

Historical reason is contingent; it did not always apply, and it no longer applies. It is contingent on the medium of the linear, logical alphabet, of written text, which all media theorists agree is on the way out. This is Hot Flusser Summer, so I’m not going to re-iterate the background for Flusser’s arguments.

“History” (like “facts,” in Jon Askonas’ excellent recent essay) was created by linear, written text. The memeplex of “objectivity,” “science,” “history,” “liberalism,” “United States of America” is only possible with this media technology. Cowen says that postwar America has been non-historical; I agree. Where we disagree is whether we will return to history.

Forget the childish meme about Frank Fukuyama being shocked to hear that war still exists; what’s at issue here is whether “stuff happening” is equivalent to the concept “history.” Stuff will still happen, but we will no longer recognize it as history due to the mutations in what it means to be human. History is a situation of linear causal chains, of the past becoming the present and the present becoming the future.

Before linear history came the eternal recurrence of magical time: the sun caused the moon and the moon caused the sun, the seasons cycled through, and human experience was terrifying and strange. Nothing was explained in the sense of a historical causal chain, the familiar scientific “tides are caused by the moon.” History is this attitude applied to human societies, and is co-extensive with concept of progress.

The current temporality, what I call cybernetic time, is once again circular. But this circularity, of the feedback loop, combines the circle with the line in what Flusser calls “circular progress.” Each time through the feedback loop intensifies the action, so that we return to the same place but with everything amplified. This is what Flusser calls “the circularity not of the wheel, but of the whirlpool.”

Hayek’s optimism was premised on humans existing within history, within time, within the world. If that ceases to be the case, if we produce a runaway feedback loop which overwhelms any dialectical response, then we are post-historical. We continue to go, and indeed we go faster than ever before, but we lose both the past and the future; we don’t know where we are going.

In many ways, Flusser (and I) agree with Cowen’s diagnosis of the situation, especially in recognizing the magnitude of the stakes. I believe that his emphasis on historical reason is mistaken…perhaps intentionally so. Moral philosophers, normative theorists, care where we are going . Tyler Cowen only cares about how fast we are going, because he is an information monster.

So when he says that:

It will panic many of us, disorient the rest of us, and cause great upheavals in our fortunes, both good and bad.  In my view the good will considerably outweigh the bad…but I do understand that the absolute quantity of the bad disruptions will be high.

We should take seriously the “in my view” part—he means, “for me.” He wants a world for information monsters; I want a world for humans (as they currently exist).

Cowen’s stated ideology as outlined in Stubborn Attachments is one of progress, defined as maximizing economic growth subject to avoiding collapse.

This is mealy-mouthed; many people want that. For contrast, see Cowen’s Conversations with evil and tragic (respectively) Effective Altruism brain-geniuses Sam Bankman-Fried and Will MacAskill.

In these, Cowen is unwilling to allow them the same mealy-mouthed rhetorical move. EA stakes a claim as morally radical, but when put to the question, their position is simply “Maximize utility subject to ethical constraints.” So how, Cowen pushes, is that different from standard Utilitarianism? We’ve already established that you’re not radical, now we’re just haggling over the price.

But Cowen’s “maximizing economic growth subject to avoiding collapse” is also merely haggling over the price. There is a strong positive commitment in this nominally normative statement. By outlining this principle, Cowen is arguing that at the margin, we are too worried about avoiding collapse and not enough about maximizing economic growth.

With this model, we can see how Effective Altruism has been useful to Cowen’s goals in having de-centered ecological collapse, the primary concern for most people today. Environmentalism and especially the “degrowth” version is anathema to Cowen’s agenda. And the EA ideology, which argues that climate change is extremely unlikely to kill more than a few hundred million people, is thus not an existential threat.

But EA poses a problem with their newly prominent obsession with AI apocalypse. An extremely smart, driven and most importantly young group of potential Cowenist allies or acolytes have decided that there is a very real threat of collapse — something that we should in fact be willing to sacrifice significant growth for!

And this is simply unacceptable. Just as Effective Altruists saying “maximize utility subject to ethical constraints” means that they think everyone else is too worried about ethical constraints, Cowen saying “maximize economic growth subject to avoiding collapse” means that he thinks everyone else is too worried about collapse.

My model of Cowen’s information-monsterism implies that he wants us to go as fast as possible because it means more information for him. All of the handwaving, obscuritanism and fatalism—“this is all happening anyway (talk more to people in DC)”—are merely a tactical smokescreen designed to make it impossible to focus on Cowen himself: a human agent, with his own ideological program.

Mannheim’s “unmasking” of ideology is the critical social theorist’s response to Straussian opacity. The goal is to reveal the function that a statement is playing rather than straightforwardly interpreting it.

With this in mind, here is what I see as the weak point of Cowen’s initial argument, the crucial sleight of hand which makes possible the rest of the performance.

Given this radical uncertainty, you still might ask whether we should halt or slow down AI advances.  “Would you step into a plane if you had radical uncertainty as to whether it could land safely?” I hear some of you saying.

I would put it this way.  Our previous stasis…is going to end anyway.  We are going to face that radical uncertainty anyway.  And probably pretty soon.  So there is no “ongoing stasis” option on the table.

He’s absolutely correct that there is no “ongoing stasis” option—but we can still try to slow things down! In fact, this is the center of my political program! There is no possibility of democratic action if the speed of change eclipses the speed at which democracy functions. I am still a liberal — and the only way to be be a liberal today is to be a conservative, to try to slow down technological change.

The only evidence Cowen brings to bear on the central question at issue is:

probably pretty soon

to which I say

how about a bit less soon?

I say this for a number reasons. Generally, I agree with Karl Deutsch that

When we defend a man’s dignity, we defend his ability to use his personality; we defend him against an intolerably high speed of learning, an intolerable speed of changing his behavior—intolerable, that is, because incompatible with the continuous functioning of his self-determination, his autonomous learning.

And more specifically, to Cowen’s stated goal of progress, I believe that we must slow down technological change in order to re-assert our collective ability to definition a destination, a goal, something to progress towards. Cowen only wants to go faster and is thus uninterested in arguments that prioritize control over speed.

The model of Tyler Cowen as information monster explains a number of otherwise puzzling data points.

• He is happy to adopt a vulgar “positivist nihilism” on the question of adolescent social media use, that we should assume that this massive technological shock is harmless until proven otherwise.

• He is against the US banning TikTok, again with positivist nihilism: “What is the actual evidence that it is serving up slanted, pro-Chinese content, or otherwise swaying public opinion in a negative manner?” As I argued, why leave this gaping national security hole open — they would only have to de-legitimize one presidential election!

• He tolerates and even encourages contrarian public intellectuals, no matter how mid. I cannot fathom that he learns anything from them, but the move makes sense if the goal is to increase the diversity (and not the accuracy!) of the political information environment.

• He wants more cars in Manhattan.

I agree with some, perhaps most, of Cowen’s nominal ideological commitments. Perhaps you do too, to a greater or lesser extent. My argument in this post is that these commitments are epiphenomenal, downstream of the fact that he is an information monster.

The most important political question of the day is whether we should go faster or slower, whether you look out at society and say, “Things are going pretty [well/poorly/terribly]…but I really wish I had more information to consume.”

This is the opposite of my position, and I suspect that I am far from alone in this.

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Reminder that I’m reposting some of my favorite posts from the history of the blog while on paternity leave.

One of my favorite metasciences lines is: “Does anyone look around and say...things are going great, I just think we need MORE PAPERS?”

Obviously, we all want more scientific progress, better evidence, broader scope — but I don’t think that this is best accomplished by churning out more of these fancy peer-reviewed pdfs. Indeed, our systems of peer review and knowledge evaluation are breaking down under the strain. Everyone is under pressure to produce more and more papers earlier and earlier in their careers.

The situation is accelerating with LLMs. The cost of producing these pdfs continues to decline, and as long as the demand for the pdfs stays strong, we should expect the supply to increase. Everyone agrees that this is a problem.

Well, almost everyone. If this were a well-functioning economy, demand would eventually be sated. The for-profit corporations running the academic journals at the heart of our enterprise are more than happy to “buy” every single one of these pdfs — because they’ve figured out how to “buy” them from us using our own money.

This “AI revolution” is coming on the tails of another revolution in academic publishing — the move away from subscription-based academic journals to individual articles published “Open Access” in exchange for Article Processing Charges (APCs).

Under the old model, academic libraries paid for printed academic journals to be delivered so that professors and grad students could read them. This was a high-margin business, but there was still a verifiable service being rendered by the publishers: someone had to format, archive and deliver the physical pieces of paper. With the internet, the dead trees became vestigial; the subscriptions were to the online versions of these journals, but they were sold as a package: the libraries had to subscribe to an entire publishers’ catalogue in order for their academic institution to remain competitive.

But this model wasn’t incentive-compatible. Once an academic has published an article, they want it to be read by as many people as possible. And the internet makes it very easy for these pdfs to slip through paywalls. Just like streaming music allowed the big labels to maintain market dominance, the publishing corporations figured out a new model: Open Access. Who could possibly be against Open Access!

Some journals became entirely Open Access, moving 100% to the Article Processing Charge model where the authors pay up front and then the pdf is put on the internet for free. The model comes out of the hard sciences — like all metascience reforms, it makes the most sense in the context in which it was developed. Natural sciences have to get massive grants to actually conduct the research, so the cost of the APC is a rounding error. For social scientists and especially humanities disciplines where the work costs very little or nothing, the APCs represent a massive cost. Even worse, some of the journals are “double dipping” by offering individual authors the right to publish their work open access in exchange for an APC but still charging subscription fees to access the rest of the articles published in those journals — the so-called “hybrid model.”

It’s worth noting that none of this money is necessary. Academics could decide to cut out for-profit journals entirely, to put our own pdfs online ourselves. The Journal of Machine Learning Research did it:

The journal was established as an open-access alternative to the journal Machine Learning. In 2001, forty editorial board members of Machine Learning resigned, saying that in the era of the Internet, it was detrimental for researchers to continue publishing their papers in expensive journals with pay-access archives. The open access model employed by the Journal of Machine Learning Research allows authors to publish articles for free and retain copyright, while archives are freely available online.

The journal I co-founded does it:

JQD:DM is a diamond access, double-blind peer-reviewed scholarly journal hosted on the University of Zurich’s HOPE platform. We do not require article processing charges (APCs), and articles are available to access for free (CC BY-NC-ND 4.0).

The journal publishes quantitative descriptive social science. It does not publish research that makes causal claims.

The Journal of Trial and Error does it — and they even published a handy guide for setting up your own diamond open access journal online.

But the absurdity of for-profit academic publishing has been obvious for decades. Academics are complacent — and there enough academics in pivotal positions getting a few crumbs from these academic publishers — so the system perpetuates itself. Fine. These are messy, complex systems and it’s naive to think that changing them will be easy.

But the APC model means that academic journals are aiming for a future in which we’re swimming in LLM garbage. Which actors want more pdfs in circulation? The ones getting paid $1,500 or $3,450 or $12,290 a pop. What an astonishing business model, as the FT reports: Nature is the journal commanding that eye-watering sum.

The for-profit journals don’t care about academic progress. They care about....profit. They literally cannot care about anything else, thanks to the doctrine of maximizing shareholder value in a context of private equity and hostile corporate takeovers. If they try to care about anything but profit, someone can come along and take them over and make them more profit-focused, as Dan Davies documents in his excellent book earlier this year.

Peeters and Dambeck say: hooray for Open Access!!

A recent publication titled “The oligopoly’s shift to open access” provides some hard numbers:

We aim to estimate the total amount of article processing charges (APCs) paid to publish open access (OA) in journals controlled by the five large commercial publishers (Elsevier, Sage, Springer Nature, Taylor & Francis, and Wiley) between 2015 and 2018…we estimate that globally authors paid $1.06 billion in publication fees to these publishers from 2015–2018. Revenue from gold OA amounted to $612.5 million, and $448.3 million was obtained for publishing OA in hybrid journals. Among the five publishers, Springer Nature made the most revenue from OA ($589.7 million), followed by Elsevier ($221.4 million). (emphasis mine)

The annual revenue from Open Access has surely skyrocketed in the six years since the data from this study concluded.

Academia is far smaller and more federal than “the market.” People literally built this thing. In order to make it better, we should do metascience: we should use our tools to figure out what we want to do, and then use our institutions to actually do that. The mystical complexity of “the market” as some emergent phenomenon just doesn’t apply here.

Academic publishing is yet another example of the artificial. We’ve let the epiphenomenal outputs of doing science, of generating knowledge, of thinking, stand in for the phenomenon itself. The idea that using “AI” as a shortcut for human work will produce a de-skilling of new generations is now well-understood. But this is just one example of the more general cybernetic maxim that “You can never do one thing.”

When we write and publish a peer-reviewed paper, there is work going in at every stage. The fact that there’s a human coordinating all of the action means that there’s a human with a map of the overall project in their head. When we divide and sub-divide all the processes that go into the project — whether through human delegation or with “AI” — we lose the guarantee of an integral, individual human brain with the overall map of the project in it. The future of AI-powered social science is one where every scientist is a middle manager.

Academia isn’t just producing pdfs. The more we throw ourselves into this artificial little world where Springer-Nature gets $12290 a pdf, the less effective academics become at all of the other functions we serve in society.

Now, the looming disruption promised by LLMs can be productive if we collectively try to address the crisis. The status quo really is incoherent and sclerotic. The most likely response to the flood of pdfs is a further retreat into elite networks — being “in the room” will become even more important if that’s something AI can’t do. This is far from the optimal outcome. We should think bigger, about how LLMs (or even just the internet) allows us to re-organize scientific knowledge production.

One thing we definitely need to do, though, is work. We need to read and write and think about things. The “Open Science” response is to create artificial representations of those activities in order to make them auditable, to make them visible to outsiders, to make academics therefore rankable. This means throwing away the very thing that makes academia distinct. Academic freedom doesn’t just mean getting to say whatever we want — it’s not just this freedom to. It’s the freedom from the systems of control that continue to creep into every area of human endeavor.

It was one thing when the for-profit academic journals extracted ridiculous profit margins from the work done by volunteers and financed by taxpayer dollars. As communication technology changes and they scrambled to adapt their business model, the actual practice of science has shifted as well. I know that serious scientists don’t wanna hear it, but the scientific knowledge we produce is obviously and strongly structured by our institutions. The tighter the labor market and the more artificial the metrics we use to evaluate each other (the farther from actually reading the work and subjectively evaluating its quality), the more power these institutions have.

And now these for-profit corporations are setting the agenda for how LLMs will be incorporated into scientific practice. They are very clearly aiming towards a world in which text is commodified, greater and greater volumes of meaningless and unread text circulating for the sole purpose of individual academic careers — where they get $1,500 a pop. Hence the only guidelines for the use of LLMs in academic publishing put out by Springer amount to: “Let’er rip.”

The big worry in the research community is that students and scientists could deceitfully pass off LLM-written text as their own, or use LLMs in a simplistic fashion (such as to conduct an incomplete literature review) and produce work that is unreliable….

That’s why it is high time researchers and publishers laid down ground rules about using LLMs ethically. Nature, along with all Springer Nature journals, has formulated the following two principles:

The fear is that LLMs might threaten “Transparent Science,” per the title, or that LLMs might produce work that is “unreliable.” So they say that in order to use LLMs “ethically,” they require that you….just say that you used LLMs. That’s it. Otherwise go nuts. Don’t worry about the institutions of knowledge production and verification — let’s just get as many pdfs circulating as we possibly can.

Anyone talking about “The Ethics of LLMs” and scientific publishing is trying to sell you something—or, in the case of Springer Nature, trying to buy something from you with your own money.

If the ethical questions are individual rather than systemic or collective, the questions are irrelevant.

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2026 addendum: the for-profit corporations are becoming increasingly bold, using the mantle of science reform to advance their own bottom line. I encourage everyone to read about what the Springer Nature corporation did to the journal Theory and Society:

In December 2023, the journal's editorial board resigned, stating that this was the result of Springer Nature, the publisher, bringing in new editors-in-chief without the board's approval. In January 2024, the journal's corresponding editors also resigned^[3] and published a letter describing Springer Nature's actions as “a clear violation of our profession’s academic norms and standards.”

We can no longer afford to ignore the problem; the corporations are forcing the issue.

Randomized Controlled Trials are sometimes called the “gold standard” research
design by social scientists because they requires the fewest modelling assumptions — that is, the advantage is epistemological. This “gold standard” status has been criticized by a variety of practitioners and philosophers of science. The true advantage of RCTs, however, is not epistemological but ontological: they create novel states of the social world. RCTs which are implemented by the same real-world institutions who will ultimately apply the results avoid the wasteful process of “representing” knowledge, which is instead embodied directly within the social organization. My argument for RCTs involves large-scale experimentation with open-ended evaluations both qualitative and quantitative. Epistemologically-driven social science’s excessive focus on experimental “control” wastes the true potential of experimentation.

Social Science and Epistemology

Standard social science epistemology envisions a brain, sensing the the social world
through our instruments of research. These instruments are imperfect, and methodologists are tasked with improving them. In recent years, the causal inference revolution has paid particular attention to issues related to causality; it turns out to a much thornier problem than previous generations of social scientists had realized.

But when we run Randomized Controlled Trials (RCTs), we can be very sure that our instruments are picking up a causal relationship. This imperious social science epistemology has for decades marshalled more and more of society’s resources to satisfy its own ritual imperatives. With RCTs, it enlists fellow humans as puppets in a kind of epistemological theater. This development has generally been embraced: experimentalist researchers have won Nobel Prizes, the method is now ascendant in the disciplines of Economics and Political Science, and government policy is often informed by their results. The informed public has come to see RCTs in the behavioral sciences as the “gold standard” of evidence, to follow academics in skepticism of any findings not derived from RCTs. Charitable giving is increasingly motivated by the results of RCTs.

The rise of RCTs has seen considerable pushback as well. There have been a host
of ethical concerns raised by both practitioners and the general public. Among the
disciplines adopting RCTs, the primary concern is that this method means no longer
asking “big questions” in favor of extremely narrow empirical results. More technically, the concern is about the external validity of these narrow results: we don’t really care about what happens in a specific time and place to a specific group of people when we deliver a “treatment.”

RCT proponents are optimistic about the possibility of external validity–that within
a reasonable amount of time, their efforts will have created a large enough store of
knowledge to make accurate predictions about what will happen after a given intervention in a given time and place. RCT skeptics think this won’t work, for a variety of reasons: it’s too expensive; it’s anti-democratic; human behavior is too heterogeneous (Friedman, 2019); even RCTs require assumptions about implementation that don’t always hold (Deaton and Cartwright, 2018); RCTs are only ever conducted in contexts in which it is possible to conduct an RCT, and these contexts don’t generalize to all contexts (Allcott, 2015); the social world is too complex (Yarkoni, 2022); the social world is changing too quickly (Munger, 2018).

This debate fundamentally misunderstands the value of RCTs by taking too narrow
a view of social science. We need not be in the business of creating Knowledge. Scientism (or naturalism), the aping of the methods and assumptions of natural science, is our original sin. We have inherited what John Dewey called “that confirmed species of intellectual lockjaw called epistemology” from the long history of Western philosophy (Dewey, 1958): the Cartesian epistemological problem of a disembodied mind deriving knowledge by observation and logic. Tabling the larger question, I will argue that this tradition is inapplicable and indeed deleterious to the goals of social science within a democracy.

I ground my argument in Dewey’s instrumentalism, especially as read by philosopher
of science Ian Hacking, who summarizes the central point: “Dewey distinguished his
philosophy from that of earlier philosophical pragmatists by calling it instrumentalism. This partly indicated the way in which, in his opinion, things we make (including all tools, including language as a tool) are instruments that intervene when we turn our experiences into thoughts and deeds that serve our purposes” (Hacking et al., 1983).

This framework enables social scientists to play a key role in the complicated practice
of 21st century governance. The epistemology delusion—what Dewey derisively calls
“the spectator theory of knowledge”—belies the fact that RCTs are uniquely useful for
empowering both individuals and groups to better achieve their desired ends, for two
reasons:

1. RCTs are “ontologically” useful because they create novel states of the world.
Other empirical methods can only learn from extant states of the world. The extant
world is only a small subset of the set of nearby possible worlds. Humanity has become increasingly powerful in our capacity to manipulate our environment. New technologies and the growth in the absolute number of humans means an explosion of possibilities for how we organize our social worlds. However, we have barely begun to explore these possibilities, especially not in any systematic fashion. RCTs encourage and reward creativity and entrepreneurship in the social and governmental realms, in contrast to the spectator theory of knowledge, which requires the “existence of a leisure class, who thought and wrote philosophy, as opposed to a class of entrepreneurs and workers, who had not the time for just looking” (Hacking et al.,
1983).

In a capitalist society, this kind of creativity and dynamism is possible in the economic sector thanks to the informational and feedback properties of the market. Capitalists have powerful systems in place for testing and exploiting the economic potential of each new technological development or social innovation. The start-up model is designed for exploration and failure, with successful companies adapting “atheoretically” to the complex space they’re exploring and unsuccessfully companies ceasing to exist. RCTs that involve a whole community or group of communities also explore as-yet nonexistant social possibilities, starting from where they are.

2. RCTs provide feedback to the entire social organism. The fact of acting in the
world demonstrates the relevant capacities of the actors and the actual structure of the
world at the most relevant points. RCTs train social organisms to achieve their goals. The knowledge that RCTs produce is inherently local—generated in the same context that is to be applied, obviating the need for “generalizability”—and tacit, distributed among the actors and institutions that comprise the organism. By not requiring this knowledge be rationalized and fed into the central Brain of social science before being applied in every relevant context worldwide, the epistemological objections to RCTs are avoided.

This does not mean that the local, tacit knowledge generated by RCTs is trapped
and thus unable to be shared. Each of our “hands” and “environments” are different, so
there’s not one set of centralized commands that a brain could send out; the framework that the only mechanism for knowledge diffusion is a universal, objective language is a residual of the social science epistemology. Other “nearby” social organisms can “watch” each other, see how others act in the world, and choose to adopt attractive actions into their menu of experimentation. Any generalizability is therefore intrinsically qualitative.

This perspective on the value of RCTs requires a significant re-orientation to enable them to act effectively. Most importantly, it will require a radical scaling up of
the number of RCTs—which means increasing the number of both experimenters and
experimental subjects. In turn, the way these RCTs are conducted should emphasize the creation of tacit, local knowledge throughout the social organism doing the
experimenting. Finally, the ethical status of experimentation needs to be re-negotiated: in what Don Campbell famously called “the Experimenting Society,” the fact that many people are experimenters and everyone is the subject in someone else’s experiment shifts the power and knowledge imbalance inherent in the current elitist approach to experimentation (Campbell, 1991).

Indeed, much of what I propose is directly inspired by Campbell’s work. The time
for “the Experimenting Society” has come, in my view, thanks to the developments
in the theory and practice of RCTs—and to an even greater extent, thanks to the
communication and information processing capacity of modern technology. Online behavior serves as an ideal case for the Experimenting Society because of how
malleable the online social world is (Matias and Mou, 2018). Code can act at scale with
zero marginal cost, and the measurement of outcomes is trivial.

Learning by Doing

One of the standard goals of science is prediction. It is generally assumed that this requires knowledge: that this prediction has to pass through a human brain. Further, the structure of contemporary social science requires that this knowledge be general. In contrast to the world of merely subjective mental states or human consciousness, scientific knowledge must exist outside of a human brain (in a .pdf, say, or perhaps in the .csv and the code used to analyze it)—what Popper called the “third world,” “a world of books and journals stored in libraries, of diagrams, tables and computer memories” (Hacking et al., 1983).

With the knowledge produced and encoded, it is assumed that it can be applied in
a variety of times and places. Indeed, the social sciences tend to be entirely focused on
the production of knowledge, with little attention to the synthesis, application or interpretation of that knowledge. There are counter-movements and alternative traditions within the social sciences, but the dominant approach is (broadly) positivist, quantitative, and naturalist.

Within this dominant approach to the social sciences—the more empirical side of political science, sociology and economics—there has been a “credibility revolution” (Angrist and Pischke, 2010). The current state of the art argues that field experiments
or Randomized Controlled Trials (RCTs) are the the “gold standard” research design
because they require the fewest assumptions. Through the power of randomization, the research design ensures that the treatment group and the control differ only in terms of whether or not the treatment was delivered, and thus that we can infer that any average difference between these groups was caused by the treatment rather than some unobserved difference.

But experimentation is uniquely valuable for another reason, one directly tied to
how humans learn. We are not disembodied minds deliberating over platonic entities;
we are embodied, and our cognition and learning are based heavily on actions and
sensory feedback. The baby likes to observe, but even more to manipulate, to find out
whether things fit in its mouth. This kind of learning does not look like education, does not conform to what most philosophers call knowledge. It lives in the hands as much as it does the brain. Individuals learn through experimentation, by acting on the world and observing what happens. This is what Dewey calls instrumentalism.

The analogy here is that academic social science is The Brain of society, that our
purpose is to acquire knowledge, centralize it, verify it, synthesize it and then disseminate it. Each school has a preferred mechanism by which these steps, particularly the verification and synthesis, should take place, but all have themselves at the center of the knowledge system. This approach pays insufficient attention to the lower levels of the knowledge system, the “hands” in the analogy to the individual.

An alternative intellectual lineage provides some insight into how we might otherwise organize social science. Industrialization and factory production gave rise to
the disciplines of management and operations research; WWII kickstarted huge investments in the management of large enterprises without the benefit of the price system that manages capitalist economies. My account of this history borrows heavily from the doctoral dissertation of J. Nathan Matias.

The crucial limitation for organizations of this scale is information flow: the way in
which the problems faced by the lowest level of the organization can be transmitted to
the appropriate higher level to address. One early approach to this problem is Taylorism, the scientific management of employees designed to minimize their individual agency in favor of maximum legibility and control by higher-ups. The justification of Taylorism was efficiency, both in terms of the practices it enabled and in terms of the ability of managers to generate “scientific” knowledge about what worked. This paradigm was explicitly authoritarian, but also technocratic: workers could not be trusted to make decision.

This approach directly mirrors contemporary social science epistemology as embodied with RCTs. The researcher is a representative of Science, tasked with choreographing the bodies of research subjects in order to maximize the efficiency with which they produce Knowledge. There have been many RCTs which have “failed” because of the lack of control over the subjects, who responded in unexpected ways. Failure in this case means that the knowledge produced is insufficiently pure: the measurement of the effects of actions taken by research subjects cannot be unambiguously distilled down to the language of Theory in which the researchers speak.

Alternatives to Taylorism soon arose. The Great Depression dealt a general blow to
belief in the scientific management of society, and World War II called for a rapid and
radical re-orientation. With more of society organized in hierarchical, authoritarian
structures, the social scientists tasked with evaluating organizational success were able to observe the limitations of this approach. Organizational psychologist Kurt Lewin was an early advocate for a more democratic structure and scientific approach: “Efficient democracy means organization, but it means organization and leadership on different principles than autocracy.” (Lewin, 1944). Lewin specifically advocated for what he called “action research”: rather than passively observing how the organization functions, his approach called for taking actions.

Lewin’s research treated the men he studied as more than mere cogs in an organizational machine; he enlisted their help in designing the experiments that were
run and and analyzed, proving that social science and democratic control were not
incompatible, as the Taylorists had believed.

Another intellectual tradition spawned by WWII was cybernetics. Invented by
mathematician Norbert Wiener to enhance the accuracy of anti-aircraft guns, this interdisciplinary field aspired to unify disparate areas of inquiry. Cybenetics’ central insight proved useful in a variety of contexts: success requires adaptation through the timely incorporation of feedback. Cybernetic systems are inherently dynamic; “knowledge” in these systems is embodied, local and distributed.

This approach provides a more direct path between one action and another, a path
which avoids what sociologist of science Andrew Pickering calls the “detour through
knowledge” (Pickering, 2010). For systems as complex as human behavior, we cannot
hope to generate enough knowledge, or knowledge that is sufficiently up to date.
Here, again, by knowledge, I mean the kind of thing that can be externalized to
Popper’s third world of books and computer hard drives. Without the flexibility of the
fully-engaged and embodied human mind as a conduit for knowledge, there is simply
too much entropy in the process of knowledge production, transmission, synthesis and
application for science to succeed at understanding human behavior.

Local, Tacit knowledge

Discussing this issue with practitioners who conduct RCTs in developing countries,
they agree that the process of figuring out how to actually act in the world generates
a lot of knowledge about the relevant social processes. However, this knowledge tends
to be qualitative and thus dismissed as auxiliary to the focal output of the experiment,
knowledge that can be encoded in a pdf. Boutique experiments like these might help
the experimenter gain tacit knowledge, but the experimenter is not a long-term agent
in the social organism, so this knowledge is not put to the best use.

The fact that it takes so long to train social scientists makes the practical irrelevance of our knowledge self-evident. Our highly technical language makes possible the
practice of academic social science: it creates a linguistic world within which we can
operate, a low-dimensional space into which we can project the complex social phenomena we study. Again, different schools have more or less formal languages, from the platonic world of game theorists to the “empirical” world of government statistics and development indices. But even the most informal school defines some jargon, if only for the purpose of internal communicative clarity.

In contrast, consider the speed with which humans can acquire embodied, tacit
knowledge. By acting in the world, we can acquire a skill—how to catch a ball, say—
without any formal training in the physics of parabolic motion. We are able to use
our entire sensory apparatus and the high-latency feedback that comes from moving
our arms and legs rather than being restricted to knowledge taken in through our eyes
by reading a physics textbook. The tacit knowledge we gain is stored throughout our
bodies—in our brains, yes, but also in the arms and legs that do the acting.
Smaller social organisms can use language they develop for themselves, adapted
to their goals and capacities. Even under ideal circumstances, the language developed by social scientists cannot apply equally well to each of the social contexts they
seek to explain. This translation is only a problem if the goal is to generate knowledge that transcends context. This is a bad goal. Social organisms—groups, societies,
communities—can develop knowledge that inheres to their context: local, tacit knowledge.

Even this approach, though, requires decisions about how knowledge and action
be distributed across all of the members. Stafford Beer, the cyberneticist and early founder of operations research, called this “variety engineering” – human society is extremely high in complexity (variety), and it can only be managed through structures that enable many humans to deploy their full capacities to respond to issues that arise locally (Beer, 1993). Crucially, this requires that social structures be able to re-arrange themselves, to act and respond to the feedback generated by that action.

This re-organization is irrelevant to the epistemological approach, the spectator
theory of knowledge. Through action, the social organism touches its local context
directly, in the high-variety world in which it acted before and will act again.
The most valuable knowledge generated through experimentation, then, is stored
within the social organism itself, the way it re-organizes itself. It is stored within
the embodied minds of the humans that comprise that organism and in the relations
between those humans.

Path dependence, scope of the world

We know that human societies are incredibly plastic thanks to the work of anthropologists and archaeologists summarized in Graeber and Wengrow (2021). Premodern humans “experimented” (implemented) with a large variety of economic, social and governmental forms. They observed what was done by societies they came into contact with and either rejected or adopted novel aspects. Graeber and Wengrow (2021) make the case that this was done with a high degree of knowledge. Pierson (2011) gives us reason to doubt that the strong form of this account: the inherent complexity of human behavior makes it impossible to predict what kind of human society will result from adopting new institutions, technologies or practices.

Over the past few centuries, the scope of possibility for human organization has
expanded dramatically. Explosive growth in productive capacity and revolutionary
communication technology enable social arrangements that have never before been attempted, and could also give us reason to revisit previous attempts that failed.
Very little of this scope of possibility has been explored. One school of thought
suggests that this lack of institutional diversity comes from systems of control that
aim to restrict the scope of human freedom in order to render society more legible and
manageable from the center (Scott, 2008). Formal institutions like schools, prisons and
hospitals capture more and more of human behavior. Capitalism itself renders every
human activity a commodity, forcing our messy human excesses to conform to the logic of exchange.

Setting this contentious view aside, we can simply observe that we haven’t had the
time to explore more than a tiny percentage of the space of possibility. The absolute
number of humans has grown tenfold in the past three hundred years, and we are only
thirty years into the internet revolution. There are so many of us, with so much more
freedom and capacity than ever before.

Society if we experimentally explore the possibility space.

The internet set the stage for an explosion of humanity: the scope of communication
continues to boggle the mind. But almost immediately after this space of possibilities
opened up, the major platforms enclosed this protean commons, rationalizing their use and greatly constraining the structures of communication. The goal was of course to make social media that were optimized for collecting consumption-related data about users in order to better target those users with ads, and to constrain those users’
attention in order to show them those ads.

This is unlikely to be the best structure of the internet for all of the
disparate groups who would like to use the internet to accomplish their goals.

Online behavior is thus an ideal case for the social science approach that I describe.
Our intuitions and traditions about how to structure online society are weak, the ontology of online activity is far more malleable than that of the physical world, and the tools for both communication and action are distributed among a much larger number of people.

Another advantage provided by contemporary information technology is the ability
to unobtrusively measure the outcomes of experimentation. Campbell was concerned
about both the costs of measurement and the effects of measurement on the value of
the experiment itself: “almost universally...there is a conflict between the action personnel...and the research staff. This should be regarded both as a practical problem and as philosophy of science issue....the rearrangements in the action program required to make an experimental evaluation possible. Most of these changes are appropriately regarded as distherapeutic. There may be a fundamental social science indeterminacy issue here. In a broader framework, the problem becomes one of the compatibility between psychological health and continuous measurement.”

The ubiquity of digital tracking and measurement has effected a shift in both the
reality of and our intuitions about its intrusiveness. Although this process has not been remotely democratic or deliberative, the bargain we have struck with tech companies has inured a large majority of citizens to the fact of continuous measurement. Unless there is a significant and assertive effort to reverse this trend, accompanied by vigorous government action to counteract corporate data power, social scientists and citizens should use the same technology to construct measurements for our own ends.

We must be careful not to let these easily quantified measures define the entirety
of our values, however. At a minimum, it is important to be explicit about how the
measures are constructed. More broadly, citizens should be involved in the process of
evaluating the measures themselves, to avoid taking them as direct evidence of reality.

Ethics

Ethical considerations about experiments are generally more salient in the social sciences than in, say, physics. Indeed, much of the current debate about experiments in natural science is about ethics. Ethical standards like informed consent, inherited from medical trials, are often directly in conflict with the goals of RCT practitioners: the knowledge extracted from a field experiment is usually purer, higher-grade, when the human participants are unaware that they are taking part in an experiment.
Additional concern arises when Western academics or NGOs travel to developing
countries to conduct field experiments, often at much lower expense than an analogous field experiment in their home country. In these cases, the analogy of knowledge extraction is clear: Western scientists (even granting the best intentions) using their economic power to finance an epistemological theater that gratifies our aesthetic sensibilities.

Although there are reasonable arguments about where to draw the ethical line with
social science field experiments, the current ethical paradigm is fundamentally incompatible with the framework for RCTs I advocate in this paper. Don Campbell understood this as well, so his essays on The Experimenting Society included a radical
ethical re-orientation. He argued that “participation in policy experiments is more
akin to participating in democratic political decision making than to participating in
the psychology laboratory” (Campbell, 1998).

[Drake] is necessarily international. An international thinking does not exist, only the universal thinking, coming from one source. However, if it is to remain close to the origin, it requires a fateful dwelling in a unique home [Heimat] and the unique people [Volk], so that it is not the folkish purpose of thinking and the mere 'expression' of people-: the respective only fateful home of the down-to-earthness is the [Taylor Swift], which alone can enable growth into the universal.

Martin Heidegger, Black Notebooks. Citation from the translation by Yuk Hui in his The Question Concerning Technology in China: An Essay in Cosmotechnics. Drake and Taylor Swift parentheticals my own.

Editor’s note: This post vindicates the dialectical method of cultural analysis: in March 2024, at the height of Taylor Swift mania, I investigated the other half of the Millennial pop music dialectic: Drake. Two weeks later, Kendrick would drop “Like That” and escalate the feud that ended Drake. We see, in Swift’s ultimate victory, the end of neoliberalism and ascendancy of post-liberal vibes.

Remember that I’m reposting some of my favorite posts from the history of Never Met a Science while on paternity leave.

It’s December 2012 and I’ve just finished the first semester of a year teaching English in Santiago, Chile—the loneliest of my life, the inevitable comedown from the social high of undergrad amplified by moving 5,000 miles away, to the most conservative country in Latin America—and I’m visiting my college buddy in his Williamsburg loft.

He’s living the dream. “Staff” “job” at VICE—barely above minimum wage but perks like pizza and beer, ridiculous promos from record labels, and the attention of the fine young woman of North Brooklyn. The loft isn’t a traditional loft, in that it’s actually just the landlord built like a treehouse in the high-cieling’d living room of an apartment, where he lived. The door was a towel and you couldn’t stand up in it.

So we did what any 22 year-old “hipster bros” reunited after a trip to the semiantipodes would do. We got regular-zooted on weed/adderall/Corona caguama and blasted the new Taylor Swift music video, for Trouble. And, for his “job,” blogged about it.

“It is definitely the most important song she has ever released”—we wrote, though I wouldn’t have believed it were it not still on the VICE website. The immediacy of the semiotic experience of Taylor Swift dressing up like a hipster! What a move, in the vibe space. The little EDMy warble that accompanies her autotuned voice in the chorus represents an irreparable break with her past, the way artists in art museum captions “break” with their calcified aesthetic inheritance and strike boldly into the unknown.

It’s difficult to recreate the hipster Weltanschauung—even for me, in my own memory, let alone in writing for people who never experienced it firsthand—without slipping into parody or pathos. So I’ll play it straight. A lot of it had to do with intellectualizing media, and especially music.1 We were paying close attention.

Google searches for “hipster.” Peak period from the fall of Lehman Brothers to the fall of 2016.

We wrote that “Swift’s core group of fans (weirdo poptimist music writers not withstanding) is now probably around the age of 16.” We were kidding ourselves. We were and are in Swift’s core group of fans…born in 1989, the same year as her, and the rest of the Peak Millennials. Poptimism was just our excuse to intellectualize media — crucially, in this case, music that we could talk to girls about.

Poptimism was successful at delegitimizing rockism, but it’s an intellectual fantasy to assign cultural criticism much causal efficacy — especially as the power of music curators, journalists and tastemakers was about to hit a tailspin. Pop music was always going to be fine and rock music was always not going to be fine, but for reasons related to material conditions.

Today, the critical status of poptimism is on the wane. As we survey the wreckage of the media cultural landscape, it seems there was something lost when musicians, critics and audiences started caring less about music being good than being accessible.

Ironically, the biggest beneficiary of poptimism may have been rap music. The critical standing of existing *local* rap music was beyond repute, if not accepted by the masses. The poptimist double move militated for the creation of pop-rap. For the uprooting of the sound from these local rap scenes to something more universally palatable.

That is, the dialectical response to a critical re-evaluation of Taylor Swift was to make Drake possible.

Some pop-rap is really just pop; consider the obligatory 2010s guest verse by Ludacris or Snoop on a Justin Bieber or Katy Perry track. (Or consider…Pit Bull). These songs don’t really have themes, let alone meanings—rather, they presage contemporary Spotify auto-generated vibes to function to.

Drake never participated in this lending of the simulacrum of street cred because he never even pretended to have any to begin with. His origins as a child actor are sometimes mocked but at this point usually ignored—but we cannot consider them incidental. Drake was programmed for mass consumption, for consumption not by people but by the masses. His music was born devoid of the thick network of personal, geographic and sonic relations that defined local rap scenes to date.2

Drake’s “cred” comes from a different source: himself. He foregrounds the personal, confession rather than braggadocio. What makes this relatable, authentic is the fact that, as he tells us repeatedly, Drake sucks. He is petty, unreliable, obsessed with women but not with any particular woman.

I know a lot of girls be thinking, this song is about them. But don’t get it confused. This one for you.

He has one redeeming quality, though: he has a lot of money because he is good at his job and he works really hard. The main theme of Classical Drake is how much time he spends working, how much he has sacrificed, how many personal relationships he has neglected in pursuit of the grind.

He was thus the perfect soundtrack for uprooted Millennial strivers anywhere. Rap Caviar in the airpods in the Uber, all black with the white sneakers and the Away suitcase, whether it’s 9am in Dallas or 5am in Toronto or 6pm in New York or, heaven forbid, 8am in Charlotte.

So two years after Trouble, when Drew and I went to a party at the apartment of a Rap Genius co-founders in one of the soulless new Williamsburg waterfront condos, Drake was everywhere, the soundtrack to our lives. The Rap Genius guy apparently had two of these apartments, connected by a balcony; we stayed in the one without any furniture but with several handles of liquor, playing Drake’s Worst Behavior on repeat and screaming the lyrics until someone kicked us out. Like Drake, we sucked.

Drake lacked the possibility of rootedness; Toronto is one of the world’s great non-places, certainly without a local rap scene to return to and draw from. To supplement the strategy of Millennial self-hating self-aggrandizement, he went international: first to the Carribbean immigrants and then to British grime scenes.3 This is not “street cred” but “passport cred,” Music for Airspace. I guess it really is just me, myself and all my millions.

The collapsing of the Millennial dialectic (Taylor Swift in red, Drake in blue).

According to Google Trends, Drake and Taylor Swift were the most popular Millennial musicians of the 2008-2018 period, with a back-and-forth battle with each new album release. After the fallow Covid period for both, Taylor Swift has emerged as the most famous person on the planet — while Drake’s last gasp of relevance came from leaking a dick pic on Twitter.

In a self-seriously highbrow article about pop music, we’re going to need to talk about capitalism. Given the set-up, I might argue that one of them is the perfect vessel for capitalism (bad) but that the other has some redeeming authentic or idk revolutionary characteristic that means while yes they participate in a capitalist economy, they (and their fans) are good.

But no. Drake and Taylor Swift are both perfect vessels for capital, which is in fact the unstoppable source of all your problems. But vulgar anti-capitalist broadsides are a dime a dozen on this website; we need to be more precise.

The genius of capital is not that it always automatically makes things worse in the ways that you already know about — but that it is infinitely malleable, that it changes, that human efforts to create pockets of existence outside of capital are rapidly forced to grapple with everything that already exists.

Drake is globalist, neoliberal capitalism. Drake is the brief lacuna after the end of history. Drake is authentically empty, the apotheosis of the internationalist modernity that Heidegger so vividly fights back against.4

His songs are effective through unrelatability, through the mockery of the very possibility of relatability. The ideal Drakean subject knows that they themselves lack subjectivity and wants to be reassured that everyone else does, too. Through Drake we recognize ourselves in The Other because we’re both just mirrors, me’s (and you’s) en abyme.

That’s why every song sounded like Drake featurin’ Drake.

Taylor Swift is post-liberal.

Taylor Swift is post-historical.

Taylor Swift is post-authentic.

The task of the critic is to figure out more specific terms for Taylor Swift rather than these placeholders, meaty concepts that carve up the present rather than merely noting that we're no longer in the past. Flusser’s concepts of the “technical image” and the apparatus-operator complex are the best I’ve got for now. But contemporary music — and music criticism — may prove generative.

Hyperpop acts like 100 Gecs defy the concept of genre as locally and historically embedded. Ska, nu-metal, screamo and techno each evolved in a specific time and place, in response to social and technological change and in conversation with music that came before. In hyperpop, these genres become merely sounds, ripped from their context. This is the death of scenes recently described by Mireille Silcoff.

The Eras Tour is a hyperpop hyperobject.

Taylor Swift’s genius is to create subjective histories, to re-inscribe herself within the life cycle of each of her listeners. Even as our objective histories and grand narratives of civilizational progress become absurd, we still each of us have a life cycle.

A historical Era transcends the individual, situating them within much larger forces. The post-historical Era is personal periodization without progress, a list of TikTok trends in which we participated.

Swiftian relatability begins where Drake left off, accepting the premise that listeners do not possess stable and well-developed selves and giving them narratives with which to construct those selves. Her fans find her so intensely relatable because they are relating to themselves, selves which have been constructed from Taylor Swift songs.

Famously, Taylor Swift’s songs are distinguished from other pop superstars’ in that she actually writes them. They are intensely personal, based on her lived experiences. But the obvious paradox is that she’s been a superstar for fifteen years; she does not have experiences that anyone else can relate to. So what she’s really describing in her songs are the lived experiences of everyone else, filtered through media she herself consumes — and directly from her audience.

I’m not so naive as to let my whole take hang on whether Taylor Swift “really believes” her songs. Foucauldian discourse analysis tells us that the identity of the speaker of sentences is less important than the structure which makes those sentences possible. And Taylor Swift’s key insight is to be hyper-focused on her audience. The fandom she has been cultivating has never been unidirectional (One-Directional?) — she’s not just doing fan service, she’s figuring out what they’re missing. What moves to make in the semiotic vibe-space.

This is why it has taken fifteen years for Taylor Swift to really hit: she and her audience have been mutually constructing their own subjective history, their own Eras.

Nazism, Italian Fascism, the right wing of the Kyoto School—and today, Duginism in Russia—these are the bad faith attempts to overcome modernity by retreating into the magical pre-modern of their respective ethnostate while simultaneously using modern technology to kill or subjugate everybody else.

And say what you will about Taylor Swift, that is not her intention.

But it’s also not even an option. The aspiring American post-liberal metaphysical fascist is hamstrung by the lack of a coherent ethnic national identity to which to appeal. Our ethos is progress, pure and simple, as Margaret Mead diagnoses. So the heart of our metaphysical crisis is the contradiction of post-historical progress. Flusser writes that

Nowadays ‘to progress’ does not mean to demand the future, but to avoid the past...our progress is a method to avoid being devoured by the past that chases us

All of which is to say….

Drake fell off. We needed that. But where, where does that leave us at?

My gloss on Chris Bail’s excellent new book (introducing the idea of the “Social Media Prism”) is that the primary mechanism by which social media drives polarization is by letting us observe other people.

This observation is distorted, as Bail argues effectively, by how the attention economy produces a “race-to-the-outrageous” and how both global spirals of silence and local eddies of apathy cause the disengagement of moderates from political discussions. This post is about another dynamic, one that would exist even if the social media looking glass were crystal clear:

Millions of Americans are miserable. The internet has “gotten worse” because Americans are not ok. Near-universal internet access means that there are immiserated, lonely people spending many hours a day online. The breakdown in the social fabric, climbing "prime-age" unemployment and high rates of addiction and mental illness manifest themselves in our mutually-constructed online spaces. There is a misery that wants to make itself known--to inflict itself on the world--that social media enables. We are reaping what we've sown; the interconnectedness enabled by the internet and the gains from open communication/cooperation cannot succeed while so many are left behind. 

Mary Gaitskill, one of the clearest-eyed observers of the American condition, describes living near a squalid community recently decimated by Hurricane Katrina and ignored by the government in her essay Somebody with a Little Hammer. After watching some quotidian cruelty committed by a desperate, downtrodden neighbor, she reads to her English class an excerpt from Chekov's famous story Gooseberries:

Just look at this life: the insolence and idleness of the strong, the ignorance and brutishness of the weak, impossible poverty all around us, overcrowding, degeneracy, drunkenness, hypocrisy, lies….Yet in all the houses and streets it’s quiet, peaceful; of the fifty thousand living in a town, there is not one who would cry out, or become loudly indignant. We see those who go to the market to buy food, eat during the day, sleep during the night, who talk their nonsense, get married, grow old, complacently drag their dead to the cemetery; but we don’t see or hear those who suffer, and the horrors of life go on somewhere behind the scenes. Everything is quiet, peaceful, and only mute statistics protest: so many gone mad, so many buckets drunk, so many children dead of malnutrition.

Social media gives voice to these “mute statistics.” The cruel, anonymous Twitter accounts attacking Twitter micro-celebrities are precisely what Chekov invoked in a later passage:

At the door of every contented, happy man somebody should stand with a little hammer, constantly tapping, to remind him that unhappy people exist, that however
happy he may be, sooner or later life will show him its claws, some calamity will befall him—illness, poverty, loss—and nobody will hear or see, just as he doesn’t hear or see others now. But there is nobody with a little hammer, the happy man lives on, and the petty cares of life stir him only slightly, as wind stirs an aspen—and everything is fine.

For so many people in my class — blessedly “successful” knowledge workers safely sequestered in pleasant suburbs, college towns or gentrified urban neighborhoods — the horrors we encounter only on social media are a more or less accurate reflection of the lives of millions of Americans.

One of my relatives has never been very tech-savvy or studious; to make things worse, he suffered a serious head injury in bar fight that permanently damaged his cognitive functioning. He’s now in his early 60s. I recently learned that he has never used a computer, but is thinking of learning how. He lacks both the scaffolding of employment and the social resources to learn to use the internet well, and never developed the deceptively specific (invisibly specific, to the educated knowledge working class) skills and intuitions shared by the creators and implied consumers of internet content. If he gets online, he is at serious financial risk; never very financially savvy, he will be defenseless against the legions of outright scammers, identity thieves and ransomware extortionists.

And his presence as a consumer of online news will have negative consequences, both for himself and for the wider information environment. He is an embittered, lonely man, the perfect target for information fraudsters who will claim to explain that the source of his pain is some despised group (immigrants, the deep state). Consuming this information might make him feel better, but it will make him more confident in false beliefs about what members of his various outgroups are actually like. Even worse, it will contribute to the success of the bullshit vendor; directly, through ad revenue, and indirectly, by increasing their view/like/retweet counts and thus their credibility.

"Democratization" is easiest to think in terms of identifiable categories. Certainly, the expansion of the legal right to vote was done through explicit modifications to the law. But while demographics are a useful proxy, they cannot fully explain the relevant latent variable: there are simply some people who lack the capacity to use the internet.

All societies produce outcasts, misfits. Some people are born with physical bodies or neurocognitive styles that differ from societal ideals; others suffer serious injuries or illnesses, traumas and addictions; some are denied access to the resources, stability and dignity they need to flourish; still others simply don't fit in.

Some societies are more tolerant of these people than others. Some have religious or social institutions to care for them, while others have state-run safety nets. Adjusting for wealth, the United States at present is particularly brutal. Government programs are stingy. Social and familial groups have been eroded by capitalism's demands and the horrors of mass incarceration. Veterans are numerous and suffer terribly. Mental healthcare is inaccessible. Recreational drugs are cheap, ubiquitous and powerful. Wage growth is non-existent. And that was before the pandemic.

But all societies have historically restricted the ability to influence their information environments to people who conform to their standards. In a small group, social status is conceptually inseparable from influence. In larger societies, mediated by formal institutions like the church, the state, academia, or broadcast media, people have to advance through one of those institutions, spending years learning and performing normative behavior, to be granted informational authority. Disruption could happen, primarily through building a social movement in explicit opposition to the dominant institutions, but these movements were similarly hierarchical and required years of commitment to the cause.

Today, of course, the internet has democratized communication, and it is no longer the case that only people who have devoted many years to an institution who are granted the capacity to speak. It is worth noting that these institutions were inherently conservative and that they reinforced oppressions. Herman and Chomsky's description of the broadcast media in Manufacturing Consent is not a positive one.

The contemporary emphasis on group-based dimensions of oppression is necessary, but imprecise. Every identity group — every intersection between identity groups — contains some individuals who do not and will never fit in. But since these misfits are not a coherent group, social-media inflected discourses are unable to consider them coherently.

Instead, the social media prism presents the most miserable members of every identity group as exemplars of that group.

Think about the least functional, least capable person you know. Or think of the beggars on the streets of our cities, people who are visibly Not Ok. The democratization of internet access means that these people have smartphones or even just computers at the public library.

I am reminded of a chilling piece of investigative journalism from earlier this year, about a serial internet harasser from Toronto who had spent decades inflicting revenge on people who she believed had wronged her. The victims of her harassment campaign had difficulty getting her to stop, either through appeals to Google or the law, and eventually hired a private investigator to track her down and demonstrate convincingly that she was the person responsible for creating harassing websites:

This is a bad scene.

There is an undercurrent of classism here. Her many victims suffered terribly, to be clear, and the legal infrastructure/architecture of the web needs to be reformed. But what kind of content moderation system is robust to millions of broken, embittered peopled willing to spend thousands of hours inflicting pain on a society that let them slip through the cracks?

Again, literary fiction proves useful for developing a thicker understanding. Ben Lerner explores these themes in his latest book, The Topeka School. The protagonist (a blue state intellectual growing up in a red state milieu) is opposed in the narrative by Darren. The product of a broken home, Darren is also physically and cognitively non-normative. It is impossible for him to succeed, either socially or through the meritocracy of the school system. His only refuges are the base masculinities that now comprise a universally-derided aesthetic archetype: guns, martial arts, and the try-hard aesthetic of black/dragons/flames/outre sunglasses indoors.

He is a social outcast, but also a person, a teenage boy. In the climactic final scene, he is driven (by cheap, potent drugs, and social pressure) to an act of misogynist hyper-violence. At the high school graduation party, the cool kids get him to smoke meth and hit on a girl; he feels like he’s fitting in, until he learns that they’re still just making fun of him, like they have for his entire life, and he breaks her face with a pool ball. This will permanently consign him to the role of a pariah, doubly enforced by the state and society.

Now think about what happens when he gets the internet and uses it for 12 hours a day. The end of the book depicts a Trumpian turn for Darren. The racist losers at the gun shop are the only people in town who tolerate his presence. Darren has no resources, no friends and no human capital. Social media will connect him with thousands of other people like the guys at the gun shop, exploiting his suffering and anger to peddle evil narratives.

And then Darren will provide these digital platforms with the content that they really want: a pathetic loser searching for community in the only way our society affords, yes, but also a “Trumpkin,” posting stupid versions of evil politics. It has become acceptable — even celebrated — for elites of every stripe to gain status within their communities by “dunking on” stupid things done and said by the outgroup. We constantly re-affirm, to the recommendation algorithm and to the content aggregators gaming it for money, that this is why we’re on social media.

Out-group animosity drives engagement on social media

Our society has failed people like Darren. Previously, society and the state had been able to ignore the people we've damaged and abandoned, but the internet means that this is no longer possible. They are exacting revenge, tapping at our doors with little hammers, reminding us that they exist.

Ultimately, this is democracy in action. In American history, as the franchise was extended, the concerns of the newly enfranchised groups could no longer be entirely ignored. They were incorporated into the space of political competition, admittedly filtered and structured by the elite-run two-party system. Electoral democracy requires *active* participation, however, and this limits the kind of people whose views are represented even in a world of universal suffrage. This electoral democracy still excludes the poorest and least capable in our society, who cannot keep up to date with politics and make it to the polling station to actually cast a ballot.

In contrast, the democracy of online communication is much more passive. The engines of information supply, for profit or influence, are carefully attuned to the desires of each and every consumer. Rather than being undercounted to the point of non-existence, social outcasts living lonely lives that leave them many hours to consume media are part of the mass that mutually constitutes our online information environment. Our most extreme failures to take care of one another cannot be ignored.

"Democracy" has taken a bit of a beating in the past decade. Inherited information and political institutions have been floundering, leaving many longing for the cruel certainties of authoritarianism. Here's the true believer's classic rejoinder: "Sure, the US called itself a democracy then...but *real* democracy has never actually been tried!"

The democratization of online communication has demonstrated the hypocrisy of this view. There was always a moral imperative to be more caring and inclusive; it is now a political necessity. We all hate Facebook, and Facebook (the corporation) is, admittedly, terrible. But to a large extent, we hate Facebook because Facebook is other people.

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Editor’s note: I’m reposting some of my favorite posts from the history of Never Met a Science while on paternity leave. Enjoy!

The high water mark of any movement is also the beginning of its decline. In October 2022, I attended the Effective Altruism Global conference — the first major conference the Effective Altruists held since their explosion onto the mainstream cultural scene thanks to the PR blitz accompanying Will MacAskill’s What We Owe The Future, and the final conference the group would hold before the implosion of EA wunderkind Sam Bankman-Fried’s FTX crypto trading firm in early November. 

The scene at the keynote speech should have been a gigantic red flag. This was the triumphant Washington DC debut of a hot young political movement from Oxford and the Bay, flush with cash and talent, with strong moral commitments to boot. And it concluded with an audience of hundreds of brilliant, altruistic young people, in the auditorium of the Ronald Reagan Building and International Trade Center, getting told (correctly) by Matt fucking Yglesias that their vibes were off.

There have been waves of self-reckoning, tell-alls and takedowns within the EA movement — how, exactly, did they get from “let’s figure out which charities are most efficient at helping people” to deciding that there are only three (in fact, one) ethical principles: 

1. Grow the EA movement

2. Make as much money as possible

In service of 

1. Save humanity from imminent destruction by runaway AI

The downsides of A and B have become clear over the past four months. But, ironically, this same period seems to have vindicated at least the spirit of 1: ChatGPT and related LLMs have thrust AI into public awareness and kickstarted large-scale financial interest in this technology. If AI turns out to be a major technology in the near future, EA’s epistemic investment seems justified. Indeed, a paper arguing for the use of LLMs as “silicon samples” that could replace human subjects has just been published in a prominent Political Science journal; I think this is bad. 

So how do we make sense of this? I don’t think we can, yet. The story of EA is far from finished. But I believe that this group and its descendants will play a major role in shaping the coming decade. The energies they have unleashed will continue to develop, and the direction of this development will be a defining political question. The long era of Boomer Ballast is coming to a close, and the future will be built by whoever is able to step into the vacuum this too-powerful generation leaves behind. 

It’s still too early to tell whether the French Revolution was a success, I’m told, but this is precisely the question at hand: what is the legacy of Liberal Reason in the 21st century? The spatial metaphor of “Left” and “Right” originated in the physical layout of the French National Assembly, with the royalists on the King’s right hand and revolutionaries on his left. These categories are losing their meaning; the emerging dimension of conflict is now pro/anti establishment, local versus global, based versus cringe. 

From a media theory standpoint, the latter is the most important but still downstream of the crucial question of how we should use the internet. Effective Altruists have an answer to this question—raised on the simplified ontology of video games and cutthroat meritocracy, Effective Altruism minmaxed their way to one pole of the dialectic set in motion by the Enlightenment. These are, in a technical sense, the least cool people in the world because the most rational: they are maximally interested in discovering the right rules and then living by them alone. Their ideal is the total realization of the rational liberal subject, the triumph of codes over the sloppy excess of human existence. 

I wrote recently that "If you and your community have not invested serious energy taking advantage of the internet revolution -- if you do not have a concrete set of norms, practices and institutions designed to allow you to use the internet without the internet using you -- you are destined to lose. In fact, you’re not even trying to win."

The Effective Altruists are trying to win. So, too, are the Dimes Square / Urbit crowd, the  antithesis of EA: abject aesthetes who care only about vibes, whose ideal is the total annihilation of the liberal subject, the dissolution of the individual into a purely rhizomatic, relational node in a new networked spirituality. This crowd is far messier, but they are broadly illiberal. This includes people like monarchist Curtis Yarvin, aspiring Grima Wormtongue to whichever tech billionaire manages to restore order to our decadent democracy and become God-CEO. Also present is the accelerationist strand, the amphetamine-fueled ravings of anti-humanist philosopher Nick Land. In the same intellectual tradition as self-styled “art extremists” like Charlotte Fang and the rest of the Remilia Collective, this current strikes me as more dangerous because far more plausible: they aspire to fully aestheticized neoliberalism without any of the messy humans slowing down technocapital's runaway feedback loop.

As a liberal conservative, I hope they all fail. As an older American Millennial, I mostly just want to go back to the golden age of my youth, bookended by the fall of the Berlin Wall and Lehman Brothers. But we’re welllll past that, and things are only going to get weirder and weirder. The best weird that I can see involves some kind of synthesis of these two young “movements,” the creation of something genuinely novel rather than gigantism of one latent trend or the other.

I have only the vaguest idea of what this looks like. But I’m hoping that this “scene report” is a step in the right direction. There have been hundreds of scene reports about Dimes Square / Urbit; the medium is the message, so these parties are all vibey, druggy and lacking substance. Indeed, the inability to neatly summarize Dimes Square / Urbit in a single term, the fact that this “movement” consists of little more than runaway media feedback loops, stands in stark contrast to the institutionalized, hierarchical and self-appointed Effective Altruism Movement.  

Scott Alexander notes that EA is addicted to big-picture criticism of itself. That’s true, but only if that criticism is phrased in EA’s preferred medium: immaculately logical posts on the EA forum. The criticism that EA needs cannot be delivered in that medium; the reform that EA needs is media-theoretic, and more fundamentally, aesthetic. Hence, this.

The vibes of EAGlobal were off from the start. I hate DC. I biked down from my hotel on Embassy Row, past the recently-militarized White House to the hideous Ronald Reagan Building and International Trade Center. There was a short line to go through the metal detectors. This struck me as unnecessary, but with that many high-agency young millenarians, you can't be too careful.

Most conferences (and all academic conferences not held in Palo Alto or a castle in, like, Tuscany) are tightly budget-constrained but desperate to project professionalism and gravitas. This was the opposite, with a twist of ethical fanaticism. 

I’d guess they spent over $1k per person, for the venue, catering, and staff. Registration was $200 (£200, in fact, reinforcing the Oxford connection) but included both a sliding scale option to pay less and an option to pay more to subsidize other attendees. You could apply for travel funding, too; after the event, they emailed everyone telling them to submit receipts—they even emailed me, though I hadn't applied. 

There was unlimited free food (catered meals, coffee, fancy snacks, a rainbow spread of La Croix) for over 12 hours a day and unlimited free beer and wine for four hours every night. This open bar would’ve been absolutely slammed at every academic conference I’ve seen, but the half-dozen bartenders were bored all night.

There was also unlimited free swag—and totes, t-shirts and hardcover editions of Will MacAskill's new book, What We Owe The Future. But none of it was fancy, and no one was trying to project gravitas: the strength of their shared moral and epistemological self-importance made projection unnecessary, even redundant. It’s not hard to imagine the famously schlubby SBF fitting in nicely.

The food was all vegan! And not good vegan, just nasty, catered vegan. Faux chicken nuggets. Soysauges. Nut cheese. I drank bitter black coffee all weekend; the two options were oat or soy creamer. The only animal product replacement I was tempted to try were the breakfast "quiches"—crispy on the outside but eerily gooey on the inside, even when lukewarm. 

My first encounter was weird in a way I hadn't realized was possible. As I was chatting with an acquaintance (one of maybe 100 people out of 1,300 who was over thirty years old), a non-gender-conforming person strolled near enough to us to listen to our conversation, but still far enough away to not obviously be trying to participate. I wanted to be welcoming, and shifted my body and focus to include them; no response. So I said "hey! We're talking about social science reform and the impossibility of generalizable knowledge, wanna join?"

My hunch with this crowd is to just dive into the deep end. They might say "oh nah I don't care about that" or "can you give me some context" or some other blunter-than-normal response; this tolerance for information-dense socializing is a real strength of the community norms. While I enjoy social niceties, I'm also...neurocognitively aligned enough for maximum information upload/download.

Or so I thought. Our young interlocutor said something to the effect that it was impossible, but that we still had a moral obligation to try. 

I said, "You're saying social science is impossible?"

"No. Communication."

We tried to get on the requisite level but apparently couldn't hold their interest; after a few minutes of impossibility, they abruptly turned, picked up an unflavored La Croix, and walked away. Communication may be impossible, after all. 

The first night, I got tipsy on two glasses of pinot grigio after eating just olives for dinner. To be clear, I have a lot of respect for EA’s philosophical commitment to animal welfare, but it was stupid of me to go to this conference while doing keto. For what I'm assuming were covid reasons, the food was all individually packaged, so I had to carry four of these massive clamshells of 9 olives each to a seminar on forecasting and prediction markets by an energetic young co-founder Manifold Markets. I’ve long been a fan of prediction markets, and they’re a major part of the epistemic technology of EA.

The seminar turned out to be on the 8th floor of a tower at the opposite end of the building, and I walked over with two adults (people over 30) and a passel of bubbly teenagers. I teach undergrads, so I'm used to the nervous energy of a mixed-gender group in a social state of exception, but there was something else at play here. They barely seemed able to register our existence. And of course they weren't cool, not at all; they seemed animated by the confidence of their knowledge, the confidence that no one over 30 could possibly know anything of interest.

The hippie Boomers accomplished this youthism in the 60s through the confluence of sex, drugs (psychedelic), benign economic conditions, and the existential threat of nuclear war. The EAs have the internet, drugs (nootropic), malign economic conditions and the existential threat of AI. And a big part of how they use the internet is through online prediction markets like Manifold.

This technology solves a genuine problem. The standard of predictions in punditry is abysmal. The metascientist Phil Tetlock argues that the status quo prevents us from ever learning from our mistakes. Thanks to him and to the next generation of epistemic reformers, people are open to a wide range of new possible institutions. 

But the EAs are once again a few steps ahead. They've already considered the options and are all the way in on prediction markets. First championed by iconoclastic economist Robin Hanson, prediction markets work by commodifying the central tendencies of individual thought, aggregating the heterogeneous judgments of all multitudes of otherwise disconnected humans into auguries about binary states of the world. 

"Legacy" prediction markets, already multiple years old, suffer from the authoritarianism of the agenda setter: the people who run the thing are the ones who choose the questions, and perhaps worse, are the ones who choose the specifics of the prediction and how to decide whether it came true. Manifold’s value proposition is the standard neoliberal reform: introduce competition to protect the consumer and brand recognition as the regulatory mechanism. 

Our assignment in the workshop was to collaborate with our tablemates to create a prediction market on the Manifold app. I proposed that our prediction should be that an article about this event would be published online in the next week. I bought the "yes" and watched the price of "yes" plummet as the smart money said that nobody gave a shit. Some critics of prediction markets about specific, short-term events worry that they encourage insider trading—like bookies paying off boxers to throw the bout. Defenders of prediction markets think that insider trading is great: it brings the information into the noosphere faster than events themselves take place. 

In either event, insider trading doesn't always work. Sometimes the insider overestimates their ability to sit down and write.

But what if I had phrased the question slightly differently? "Will the NYT mention the words "effective altruism" more than 100 times this year?" This prediction seems to be getting at the same underlying concept but operationalizes it in a very different way. My prediction was wrong; the alternatively phrased prediction would have been right, very right, but for an unexpected reason.

Prediction markets require a disembodied, transcendent conception of knowledge. For problems where human institutions have already reduced the complexity of the world – things like “Who will be elected President in 2024?” – this strategy is just about the best we can do. But it does not generalize, and shouldn’t be forced to. This is not how embodied knowledge works, which is to say, it is not how humans work. Confronted by the internet, the quintessentially print-based ideology of liberal reason can either fold or double the fuck down. Effective Altruism did the latter. 

I spent the rest of the evening chastely flirting with an overdressed mother of three with a PhD who worked at the CDC. She was amused by the youthful energy of the event and bemused by the ideology; she was mostly there to recruit high-end talent, of which there was plenty. But she confessed a genuine sympathy for the movement. She had been raised a fundamentalist Christian, and this was the best secular replacement she had found. I could certainly see the parallels.

The next morning, I biked down to a Whole Foods near GWU and demolished a pound of scrambled eggs and sausage. I can concede the long-term nutritional viability and ethical superiority of a vegan diet, but I could not face debating the AI apocalypse for 12 hours on a diet of crudites, beet hummus and vegan gruel (is gruel always vegan?).

My first semi-official “one-on-one” was weird in exactly the way I expected for the conference: a PhD student on indefinite leave to work full time on the AI Alignment problem. They were intellectually interested in my pitch for science reform, they thought I had diagnosed the problem well and that my action plan sounded plausible --- but that none of this could possibly matter unless it led to progress on the AI alignment problem in the next ten years, at which point the future of humanity would be irrevocably decided: Skynet or Star Trek. 

I am not exaggerating. To be slightly more charitable to this strand of EA thinking, my interlocutor was convinced that spending as much of their professional and intellectual efforts in addressing the AI Alignment problem would maximize the positive longterm utility they could generate for humanity. 

To their credit, they had put their money where their mouth was, fully abandoning the institutional warren of the PhD student for the brave new world of an AI alignment nonprofit. Unfortunately for them, their mouth (and now their money) turned out to be full of hot air from Sam Bankman-Fried's ass. Perhaps this was the concept of asymmetric risk in action—if you succeed, you've saved the human race; if you get bamboozled by a charismatic (?) grifter, the worst consequence is that you've got to slink back to grad school

I spent most of the day in these 25-minute one-on-one meetings that we had been strongly encouraged to set up in advance.  The conference app was dramatically superior to anything I'd used before. Everyone had a profile and we were algorithmically recommended to each other, like eHarmony for saving the world. At basically any other conference I've been to, everyone would have been too socially unsure to play along. Here, I got plenty of meeting requests, sent several myself, all of which were accepted. It all just worked — a genuinely impressive accomplishment of technology and community norms.

Most of the people I talked to were academics of various stripes, and we had normal-ish, productive conversations. I met an ex-Amish guy who started college at 24, not knowing arithmetic, who had benefited dramatically from Khan academy and was just starting a PhD in applied Number Theory; I was impressed, but wondered if he told his life story in each of these meetings, and why. I met a private high school teacher who had developed course materials for a seminar on existential risk; a normal, earnest guy just trying to Do Good, Better (™). I met some anarchist programmers developing epistemic software, and a woman who had just started an elite MD/PhD just trying to Do Good, The Best.

There were also a few sessions with EA VIPs. A highlight was a virtuosic Q&A session with Tyler Cowen that covered huge intellectual territory in his high-information-density style; I asked him if the US should ban TikTok. 

For dinner, I rode an e-scooter to the Whole Foods down by the Navy Yard, had chicken wings and a few hard seltzers at the in-house bar, then watched the Eagles game. I can't imagine what would have happened if I had tried to talk about football back at EA. It was time for a meeting about designing better epistemic institutions. This was my wheelhouse—I know this subsubsubfield of social science as well as anyone. I'll spare you the details, but I had a blast and three glasses of pinot grigio poured almost to the brim by the army of amiable but entirely unoccupied bartenders. 

It's 9:15pm and I realize I'm hammered, sitting in the overlit atrium of the Ronald Reagan Building and International Trade Center. The center of the room is covered with beanbags (what is it with these people and beanbags?), which are covered in Effective Altruists. Some of them are sprawled on the floor, deep in conversations that I can barely imagine. I’m sitting on a couch next to an aspiring reactionary; his takes are tepid (wokeism is slowing down AI progress; a bit more virtue ethics and a bit less utilitarianism) but the conservative intellectual flank of EA is so wide open that he’s made a bit of a splash. 

Eliezer Yudkowksy—perhaps the most important intellectual in the Rationalism movement, related to and now co-evolving with Effective Altruism—stalks the room, just waiting for an acolyte to pluck up the courage to approach him. They do, in constant succession. Again, this is a big improvement over standard academic conferences, where senior professors hold forth and grad students need to be introduced by someone of higher status, like a Victorian period drama complete with rank and pedigree.  

Rather than the aristocratic approach, EAs rely on illegibility through mountains of text and elaborate jargon. If you try to argue with someone on the Rationalist LessWrong forum and you haven’t read Yudkowksy’s “The Sequences,” you’ve got no chance. It’s like today’s Marxists, for whom reading the Gundrisse is a pre-requisite for deciding whether to vote for Bernie or work on mutual aid projects.

The Urbit Yin to The Sequences' Yang is Yarvin's "A Gentle Introduction to Unqualified Reservations." These are the hardcore weedout texts, the Organic Chemistry of internet political theory. Each of them realized the need for an on-ramp to their philosophy.

Yarvin's is "An Open Letter to Open-Minded Progressives." Self-aware, honeyed propaganda: the Red Pill, not for the losers who can't handle today's gender relations, but for the losers who can't handle liberal democracy. 120,000 words—nearly twice as long as my book, and about 20% longer than The Hobbit. 

Yudkowsky's version is infinitely cringier and over 5x as long. "Harry Potter and the Methods of Rationalism" (HPMOR) has 100,000 more words than the Lord of the Rings trilogy and the same number of fans within the EA community (all of them). The difference is that Yarvin's prolix transgression is merely Proof-of-Work for the Urbit "I Like Art"-type kids, while HPMOR is literally supposed to replace religion, or culture, as a system of thought for adolescents.

From one perspective, Yudkowsky’s peripatetic interactions with admirers echoed the classical form of the old master sparring with young disciples, the very picture of Socratic dialogue. But this ideal only works if new information is generated through dialogue, if the neophytes come with a wealth of individual knowledge to be refined in the master’s system. The failure mode of EA is that of the cult: if the system is too successful, too totalizing, it creates epistemic closure and the dominance of discourse over dialogue. I’m still a few Sequences short, so I didn’t dare intrude, opting to go walk around the Washington Monument with the kids smoking weed and listening to Bad Bunny on shitty bluetooth speakers.

Sunday is more of the same: failing to palliate my hangover with a tureen of black coffee and seven coconut LaCroix, alternating between normal and insane conversations, roaming the Ronald Reagan Building and International Trade Center. I spot a professor I know from my normal research; unsure how to proceed, I default to awkwardly using the app to schedule the last time slot of the day with him. 

I assumed that he was also a secret EA sympathizer; turns out that he was just there to see a talk by a recent Econ Nobel Prize winner that was relevant to his research. The first thing he asked me was “What the hell is going on here?” Not wanting to reveal my power level, I just said I was there to talk about metascience and that this was weird for me too. We watched the teens mill about the atrium, their energy undiluted, before the closing keynote: a dialogue between Yglesias and Kelsey Piper, a journalist from Vox. If you recognize Piper’s name, that may very well be because she’s the editor of Vox’s SBF-funded Future Perfect vertical, who parlayed her cozy relationship with the FTX founder into an astonishingly frank interview over Twitter DM just as Bankman-Fried was settling into his role as the company’s “now-disgraced former CEO.” 

By this point, I had fully adopted the EAs’ infectious sense of their own moral rectitude and boundless capacity to change the world. This was a poor mental state in which to listen to a self-satisfied DC insider who did not seem to appreciate the gravity of what The Movement was about to accomplish, and the room was underwhelmed by Yglesias.

The vibes were off, he said - we all need to learn to wear a suit, shake someone’s hand and look them in the eye. And what a cynic! To paraphrase:

So you’re the new kids on the block, trying to make a splash, throw some money around and become serious players in Washington. You’re talking all this stuff about saving the world, pandemic prevention and artificial intelligence. The average lobbyist/political flack isn’t going to blink, they’re just going to ask “So what do these guys really want? This moral philosophy stuff is just a cover for looser crypto regulations, right?”

As I said, the story of EA is far from over. I hope they can adapt, to preserve the beautiful impulse and channel their youthful energies into still-radical but more human-scale activities. More Hume and less Mill, of course, but also more vibes and less brute-force cognition. You’re all very smart, much smarter than the people with power, but as long as human freedom persists, the wicked problems will always remain out of reach.

Because the ultimate tragedy for this group of maximally unorthodox, ambitious and well-intentioned young people would be to remain nothing more and nothing less than what Matt Yglesias expected.

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If you liked or hated this, check out this recent conversation with my friends at New Models, the internet community that is cool and rational.

Léa and I are immensely happy and everyone is healthy, thankfully. We have learned that the purpose of a baby is not to sleep, apparently, but it’s a blessing to get to watch the buds on the trees turn to flower day by day as we stroll around Florence.

EUI offers a reasonable parental leave option, and I want to be explicit that I’ll be taking a full 2.5 months off of work, becuase I think it’s important to normalize paternity leave in pursuit of gender equality, especially in a field in which work can creep into every moment of the day. Already, three weeks of parenting a newborn feels like much more of a departure from the world of academic work than any vacation I’ve taken since I started down this path.

So if everyone could chill out a bit about AI while I’m gone, that’d be great, thanks.

The readership of this blog has grown considerably over the past five years, so I’ll be posting some bangers from the archives while I’m on leave. Thanks for reading — Kevin

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I had run some analysis about social media use and political attitudes using earlier waves of the American National Election Survey and wanted to update the analysis when the 2024 survey results dropped in December. This seemed like an ideal case for Claude Code. I put my old code, the new data and the codebook for the new data into a directory on my desktop and just told Claude to update the analysis.

It worked perfectly for several of the analyses. It modified my code, with simple explanations of what it was doing and why. The trends were interesting, something I plan to write about soon.

But it said that it couldn’t do the extension of the feeling thermometer measures for liberals and conservatives because there was an error in the data. As a good Bayesian, I thought, that’s super unlikely, there must be an error in the code? But no.

Here’s a link to the 4-page error report that Claude produced for me, which I shared with the PIs of the ANES.1 The various analyses are pedantic to the point of being passive-aggressive, showing the identical main result in three different ways.

I had to go back and forth with it a few times, specifying that it should conduct the analysis to test whether it was the liberal feeling thermometer or the conservative feeling thermometer that was overwritten by the other one; turns out, it’s the liberal feeling thermometer that was copied.

What most impressed my about this experience was the fact that Claude was in no way instructed to check for errors, and nor should it have “expected” to find any. This is the gold standard dataset in American political science. I’m sure I would’ve noticed the error on my own when I saw this measure of polarization drop down to exactly zero, but the fact that it refused to even do the analysis is extremely impressive.

….on the other hand, when I asked it explicitly to look for any other errors in the ANES, it came up with a bunch of nonsense. The negative numbers it flagged as errors are explicitly explained in the codebook to be various forms of non-response, and the fact that certain questions weren’t asked in 2024 is also a basic fact of these kind of panel surveys. The explicit instructions were far less helpful than the provision of example code. This has been my experience working with Claude Code for academic research in general: the more specific, domain-relevant examples you can provide, the more it acts like you want it to.

This kind of basic data processing and quality control is an ideal case for incorporating AI tools — exactly the kind of thing we call for in Peer Review 2027.

That’s it for today…I’ll leave you with a preview of next week’s Flusser post…

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