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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I was super excited about this opportunity to engage in serious metascientific theorizing from actors with the knowledge to be able to think seriously about possible futures and the institutional power to actually work towards those futures. We had representatives from established “top” disciplinary journals (Journal of Communication, Sociological Science, American Journal of Political Science) as well as more specialized, dynamic subfield journals (Political Communication, Political Science Research & Methods, Journal of Experimental Political Science, and of course, Journal of Quantitative Description: Digital Media).

I want to emphasize that this kind of interdisciplinary editor-driven collaboration is extremely uncommon; for me, simply getting to share our tacit knowledge was worth the price of admission, and I strongly encourage more editors to organize such events, and funders to help host them. I had a blast. Thanks to everyone who participated.

The result of this meeting is a new working paper that we hope will provide a roadmap for academic institutions weather the coming storm of technological change. It is essential that social scientists take an affirmative stance on this issue, and we believe that journals and editors are the actors best suited to this task. Journals and editors are the primary actors tasked with vetting the quality, validity, relevance and importance of knowledge produced by the academic system.

Peer Review 2027: Scenarios for Academic Publishing in the Age of AI

(note that the following discussion is my gloss of the paper, not necessarily something that every author will agree with)

Accepting the premise that LLMs and associated advances are a potentially revolutionary epistemic technology, we found it essential to think beyond individual policies or reforms. Academic publishing is a dynamic and extremely competitive ecosystem, so thinking about individual cause-and-effect relationships is insufficient; we need to think about different equilibria, different possible futures. These possibilities were evaluated in terms of both normative desirability (after we outlined the various competing goals of peer review and journal publication more broadly) and game-theoretic stability.

While I desperately encourage you to read the whole thing, a key upshot of this exercise is the weakness of a equilibrium in which authors are restricted in their use of AI. Regardless of normative desirability (and I think there are very legitimate concerns about the quality and especially diversity of research which leans heavily on LLMs), there is simply too much of an incentive for individual authors to defect in such an equilibrium, and detection of AI use for many relevant tasks will always be extremely fraught. The rule against plagiarism is a stable equilibrium precisely because detection is so dispositive; AI use functions very differently.

Based on this analysis, we advocate for an equilibrium in which authors are allowed to use AI, and explicitly encouraged to do so for some concrete, practical tasks which we discuss in the paper. But the peer review process is already under heavy strain from increased submission rates; this move will only work if it is accompanied by a collective shift in how much effort we allocate to the task of evaluation. This is where we should increase our investment, in terms of resources and prestige. Although producing knowledge (or, at least, publishing papers) has been the primary coin of the academic realm, as LLMs become able to automate more and more steps of the research process, we need to double down on our most valuable asset: taste.

As I wrote a few weeks ago, if we want things to stay the same (here, if we want humans be in charge of the social science process), things will have to change.

The ad hoc nature of peer review is itself an unstable equilibrium; individual scientists are incentivized to shirk, that is, not to contribute their share of “service to the discipline” in the form of high-quality peer reviews and indeed serving as editors or at least on editorial boards. The system of universal, external peer review has muddled through since it was more or less accidentally invented in the US in the postwar era (a history we touch on in the paper), but it cannot sustain itself if even a small subset of authors supercharge their rate of pdf production .

This means different standards for LLM use by reviews than by authors. We’re not here to make a moralistic stance about which technologies are “good” or “bad”; we’re trying to design a system which is both robust to gamesmanship and which has good outputs. And we think that this is the most important part of the loop to insist on having humans in.

There are a number of institutional innovations that we encourage journals to test out, including the use of LLMs for concrete tasks like computational reproducibility checks, but even with an increase in the total human time spent on knowledge evaluation and moderate efficiency gains, we anticipate that increased production might still be a problem. Here, it may be necessary to impose additional frictions, to offload some of the work of evaluation to the authors themselves; the incentives to simply flood the zone are otherwise irrestible. (See this experiment in a year of “vibe researching” by Joshua Gans…and that was mostly before Claude Code w Opus 4.5!).

Submission fees are one such example, already in use in some disciplines and jouranls. There are of course implications for resource inequalities, but these can be mitigated with targeted exemptions. A stronger option is hard caps on the number of simultaneous submissions by authors, or the number of submissions per year. This option comes with obvious downsides for scientific production, and we do not yet endorse it, but some version of this may prove necessary unless we can get the institutional setup right.

We think that it is essential to move quickly with some of these reforms because the current equilibrium is extremely unstable. Ambiguity abounds around the use of LLMs in social science; as a result, the least scrupulous may be the heaviest adopters. We need to create common knowledge about LLM use, to normalize this use, and to encourage researchers to do so in way that enhances scientific knowledge production. Academic journals are the actors best suited towards guiding social science to a better place.

However, both the path to this equilibrium and the “equilibrium” itself will not serve unless we have the necessary data to adapt to both further technological developments and to the uptake and proliferation of this technology. We need to develop metascientific data streams that we can feed back into the system. This means trace data on submission and publication rates as well as qualitative data from surveys of authors, reviewers, and editors. There is not going to be a single set of policies that will continue to function; the system has to be adaptable, and adaptation is only possible with feedback.

The status quo is no longer tenable, and it’s not like it was perfect, anyway. This is both dangerous (if we do nothing) and exciting (if we can figure out the right things to do, and then do them, and then keep figuring out what the next right things to do are, and then do those too). To quote the final line of the paper:

The peer review system belongs to the social science community as a whole; it is ours to reform, and now is the time to do it.

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Thanks again to all the co-authors and participants in this workshop, as well as the funding from the EUI Research Council which made it possible.

I’ve been trying to argue this throughout the history of this blog, to varying degrees of success. Over the past two years, many others have made the same arugment:

The New Yorker, obviously. The Baffler had a whole issue on post-literacy; on Substack, James Marriott has written the definititive introduction to the topic; Ezra Klein talks about this roughly quarterly on his podcast; an MSNBC MS NOW news anchor wrote a book about it. Even Tyler Cowen is getting in on the post-literary speculation, in a rare admission that new technologies might have bad effects.2

Why now? The world has caught up with the media theorists; we are very clearly living in a post-literate society, and this has very obviously been caused by changing media technology. Ironically, it is only now that we have are in the midst of a new media technological revolution (LLMs) that we can see the impact of the internet and the associated bundle of technologies: smartphones, 3/4/5g, wireless headphones, YouTube, better cameras, TikTok, etc.

So, hooray, I hope we’re all on the same page about this. Now we can move beyond McLuhan and the focus on media per se. We need Flusser’s integrated, dynamic communicology. McLuhan can explain the 20th century but has little to tell us about AI; Flusser should come to be seen as the pre-eminent theorist of AI.

Here is a way in which Flusser’s communicological approach differs from McLuhan’s media theory3 .

This stress on transmission (on what has become the custom to be called media) is unfortunate because it tends to cover up the essential aspect of communication, which is that it transforms humans into accumulators of acquired information.

We need to think understand how communication/media is simply one part of the larger system of human information production, storage and transmission.

This is a limitation of “media theory” per se — the focus on the media itself, whether that be the content or the medium. Like causal social science, this focus on a single component obscures the larger system, doesn’t even acknowledge the reciprocal relationship, cannot accomodate time and therefore does not know how to grapple with processes.

Time is getting away from us. The quote in the title of this post comes from 1985’s Into the Universe of Technical Images, but the phrasing rhymes perfectly with the present. Today’s post is mostly just me extracting quotes from this book, which I now see as Flusser’s most mature. Given the current whirlpool of discussion on Substack around the question of fascism, allow me to begin with Flusser’s contribution on this point:

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.

To see how he gets there, let’s start with what I think is the most compelling paragraph of media theory I’ve ever read:

We have probably never been so incapable of predicting the immediate future…hence, the feelings of emptiness.

We have probably never been so incapable of predicting the immediate future...It is not from fear that we close our eyes to the immediate future; rather we do so because we can’t confront the triumph of the images that flood over us and that we ourselves now partly produce. This triumph doesn’t frighten us; on the contrary, it awakens a feeling of emptiness. Obviously we’re happy that things like work, politics, and art (in short, history in the traditional sense) have no future. We are happy to get rid of all those things that restrict us. But what will be left?…What will we talk about with these people, when we all have the same, centrally programmed information? When we are served by the same central memory? And when we are so neutralized that even as our interests appear to conflict, the conflict has been fed into us from the central memory? Even our arguments are empty chatter…The telematically drawn, dialogic threads will carry no conversations but only empty chatter. And the more they may seem to bring us together, the more they will disperse us into isolated individuals who have nothing to say to one another. They will grind those human bonds such as love and friendship, but also hate and antagonism, down into empty chatter. And although the threads appear to be dialogic, they will in fact make all dialogue superfluous, redundant—hence the feeling of emptiness. (Flusser, 1985)

Even as our interests appear to conflict, the conflict has been fed into us from the central memory. This is exactly how political “conflict” in the current media environment plays out. An event occurs, and each political commentator reacts as their audience has programmed them to react. It’s all empty chatter, what Flusser elsewhere calls “technocratic depoliticization”: although it seems like all discourse has become politicized, it is not ideological in the sense of the great (and terrible) 20th century ideologies, coherent narrative bundles about how groups of people want the world to be — it’s simple reactive, knee-jerk and spastic, electroshocks applied to the corpse of the body politic.

Indeed, Flusser anticipates the death of mass politics and its replacement by consumer-identity groups, in short, by fandoms.

People no longer group themselves according to problems but rather according to technical images…For the sociology of the future, it means that people must be pushed out of the center toward the horizon of the field of inquiry, and this precisely to the extent the discipline seeks to preserve human freedom and dignity.

This is a call for sociology to become more communicological, to focus on the techincal images that structure our impoverished society, as well as the technical structures that store, create and circulate those technical images.

This social structure began to appear only a few decades ago, breaking through the previous social structures like a submarine through ice. As it breaks through, social groups that bound human interaction fall apart. Families, nationalities, classes disintegrate. Sociologists and cultural critics are characteristically more interested in the fall of the earlier social structure than they are in the rise of the new. They pay more attention to the cracking ice than to the rising boat. This is the reason they speak of a decaying society rather than a new society.

New social forms, such as newspaper subscribers, are not interesting. They are not sanctified. There is no higher value ascribed to the relationship between the newspaper, its sender, and its receivers. Those who criticize these new social forms appear to be sidetracked, but in fact, it is exactly these new forms that demand our concentrated attention. For not only are they displacing the old, sanctified forms, they are also consecrating new relationships and new values. If the point of cultural criticism is to maintain and increase human freedom and dignity, then its focus must be on just these new forms. For only if we can recognize the rising fascistic patterns in time to change them may we hope that a humane society could emerge from technical images’ revolt against our inherited social structure.

This insight is at the heart of my scholarly work: understanding precisely the relationship between “subscribers” and creators, albeit on YouTube rather than the newspaper. Flusser correctly understands that this relationship is ascendent, and that this is a structually fascist relationship. Much of his writing in this section is about the problem of “discursive bundles,” making the etymological connection between bundles and fascism. Communicological freedom means ensuring that the individual person (both audience and creator) remains the site of recursion; communicological authoritarianism is when the apparatus itself, the quantified feedback and the algorithmic weights, becomes the site of recursion.4

What about simply logging off, touching grass? Or that “analog media” trend that we only know to be real because of how it is exhaustively discussed on non-analog media? “The energy required to withstand the penetrating force of technical images would project such a person out of the social context.”

The penetrating force of technical images drives their receiver into a corner, puts him under pressure, and this pressure leads him to press keys to make images appear in the corner. It is therefore an optimistic nonsense to claim to be free not to switch the television on, not to order any newspapers, and not to photograph. The energy required to withstand the penetrating force of technical images would project such a person out of the social context. Technical images do isolate those who receive them in corners, but they isolate those few who flee from them even further.

Again, this understanding that the pressure of technical images leads the reciever to “press keys to make images appear in the corner” was written in 1985, well before the spread of the computer to say nothing of the smartphone, to say nothing of Grok-porn. The fact that Flusser’s framework allows him to understand the contours and social consequences of technological “progress” should increase our credence in that framework.

Slop == empy chatter

one could see people synthesizing images on computers, storing them in memory, and transmitting them to others in dialogue. The result is a game of program permutation, that is, empty chatter. In evidence here is a form of distraction at the intellectual, political, and aesthetic level of the nursery…

GenAI images are form of distraction at the intellectual, political, and aesthetic level of the nursery. Sorry folks but this is what I think when I see your substack posts with imageslop. You should just post the prompt — I’d much rather see a box with the text “image of robot sitting in a classroom writing furiously while human students whisper to each other” than whatever happnes when you send that prompt to an LLM.5

On the well-socialized “young Californians”

The destruction of traditional social groups through technical images (e.g., the family through television or nationality through satellites) looks like decadence from the standpoint of the past. Society drifts into corners, into the “lonely mass,” and interpersonal bonds, the social tissue, dissolve. The young Californians who sit in isolation at their computer terminals with their backs to one another have no social awareness. They belong to no family and identify with neither nationality nor class…It is possible to recognize the threads that bind these new people to the senders of technical images. It becomes clear that we are dealing not with an asocial person but with one who is very profoundly socialized, although in a new sense. In fact, we are dealing with people who are so completely socialized that we justifiably fear for their individuality, despite their apparent isolation.

This is precisely tech twitter, the young californians driven manic by claude code. It mirrors precisely the insight from Dan Wang in this year’s reflection letter, on “Silicon Valley’s soft Leninism. When political winds shift, most people fall in line, most prominently this year as many tech voices embraced the right.” This is complete socialization into the technological fascism of the discursive bundle. There is no way that these young californians will turn away from the technical images — they are building the apocalypse. “Not some series of catastrophes but rather technical images themselves are apocalyptic.”

Growing fat on the feedback

Receivers are not sponges that simply absorb. On the contrary, they must react. On the outside, they must act in accordance with the technical images they have received: buy soap, go on holiday, vote for a political party. However, for the interaction between image and person under discussion here, it is crucial that receivers also react to the received image on the inside. They must feed it. A feedback loop must appear between the image and the receiver, making the images fatter and fatter. The images have feedback channels that run in the opposite direction from the distribution channels and that inform the senders about receivers’ reactions, channels like market research, demography, and political elections. This feedback enables the images to change, to become better and better, and more like the receivers want them to be; that is, the images become more and more like the receivers want them to be so that the receivers can become more and more like the images want them to be.

Market research, demography and political elections are feeble, glacial feedback channels compared to the glaring audience metrics of the ordinal society. A huge amount of our technical, engineering and sociological genius over the past twenty years has gone into hardwiring this feedback loop into the base level of communication. And so the images have become very fat indeed. They are exactly the images that we want them to be; millions of iterations through the hypercompetitive marketplace of attention has produced ever-more-perfect images.

We are rapidly approaching the point of equilibrium, where we are in perfect harmony with the images and nothing in the world needs to change. This is a descent into entropy, model collapse, total entertainment forever. A happy society:

People want to disperse themselves to lose consciousness, to become happy. The present dispersal of society has resulted from a general wish to be happy: we are on the way to a happy society. Shangri La is just around the corner. Everyone is at once a mouth that sucks on the images and an anus that gives the undigested, sucked thing back to the images. Psychoanalysis describes this happiness as the oral–anal phase; cultural analysis calls this happiness “mass culture.” It is happiness at the level of the nursery, intellectually as well as morally and aesthetically. The present dispersal of society can be seen as a move toward this happy twilight condition.

not programmed democracy but democratic programming

society must reconstruct the circuitry of the sender to stop functioning and receiving and instead to program and constantly reprogram the broadcasts. Such a reconstruction is technically possible by means of telematics, which could support a worldwide dialogue about the apparatus. It allows for a broad, worldwide consensus relating to the programming of apparatuses to be reached cybernetically. Technically, the apparatus allows itself to be bent to serve the society. Technically, it could be made to serve a democratic function. But the reconstruction of the circuitry of the sender is not solely a technical but also a political question…Then the global totalitarian apparatus could be avoided, and instruction would be directed dialogically against the apparatus—in other words, not programmed democracy but democratic programming. Only this must happen rather quickly, or the capacities of the apparatuses as a whole will surpass the capacities of the society as a whole.

again

Only this must happen rather quickly, or the capacities of the apparatuses as a whole will surpass the capacities of the society as a whole.

This is the singularity the AI people are talking about. It’s no longer a far-off event; the timelines are being debated in terms of months. Flusser is describing nothing less than democratic alignment, which cannot be a static set of rules or principles; this would represent the end of human freedom and dignity. Alignment must instead be understood as a process, an ongoing democratic conversation, or else the human will already have been eliminated.

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The Leopard is set during Italian unification (the risorgimento) and broadly depicts the way in which cultural and political changes play out in the more remote regions of the young country. The most appealing character, the protagonist’s nephew Tancredi, is a dashing soldier and womanizer who gets the novel’s most memorable line. As the Sicilians worry about how the new regime will impact their way of life, Tancredi offers a pragmatic approach to the problem:1

If we want things to stay as they are, things will have to change.

Academia currently faces such a revolutionary threat with AI.

In the nearly five years I’ve been writing this blog, and the ten in which I’ve been studying digital media, I’ve been consistently skeptical of new technology. I’m not a tech booster, but (or because) I lean towards technological determinism: the view that technology is the most important institution, the one which structures downstream political institutions and thus also beliefs, strategies and outcomes. It is incredibly important that we get the technical layer of our institutional stack right; meanwhile, the default political science view treats technology as an institutional add-on, a minor curiousity.

Here I’m explicitly seeking to marshall all my meager epistemic authority and ideological (anti-tech) legitimacy to the following claim:

AI is now too powerful to ignore. Claude Code with Opus 4.5 is indeed a step change in LLM capacity. If the last time you tried an LLM was 2023, or even, like, October 2025, your understanding of the situation is seriously out of date. If we want human expertise to remain valued and valuable, academic institutions are going to have to change.

It wasn’t obvious that this was going to be the case; I wish that this wasn’t the case. Or at least that first sentence — I’ve been loudly criticizing our institutional setup ever since I got my PhD. There’s an irony that the most intense critics of a system eventually come to appreciate the central value of those systems.

I don’t want academia to be destroyed, but the status quo is simply no longer possible. Our institutions were built around the technology of the printed word; we have accomodated the internet, if not fully embraced it, but AI represents a new level of existential threat. What are some diagnostics?

I have a concrete prediction: this is the year in which we see quantitative evidence of AI’s impact in the production of research. This means at least a 50% increase above a linear extrapolation in the number of papers submitted to journals and grants submitted to funders.2 These systems are already under serious strain.

I’d been fiddling around with a version of this essay for weeks, ever since I started seriously using Claude Code with Opus 4.5, but recent demonstrations by Andy Hall have accelerated some timelines. His claim that Claude more or less one-shotted a paper replication in a weekend was perhaps exaggerated (this was a perfectly selected case and there was clearly a lot of prompt engineering that went into it), but that’s the way to actually make a point in contemporary information ecosystem. It’s an existence proof, and we’re going to see more like it.

From Andy Hall’s post.

Hall proposes some speculative applications of AI, serious possibilities that demand structural reform to implement. Automated replication (upon journal publication) is a no-brainer. This is something that LLMs really can one-shot, even now. I hope to see journals implementing this policy within the year.

In In Favor of Quantitative Description, the post that led to the founding of the JQD:DM, I wrote that:

In a complex and rapidly changing world, social science needs as many time series as possible.

This is precisely what Hall cites as a capacity afforded by AI. We no longer have an excuse to ignore the problem of temporal validity; we need to keep our knowledge up to date. Great! Genuinely, this is great, and we should do it.

But how? Getting this right isn’t a technical problem, it’s an institutional problem. This is why we need metascience, in this case a detailed economic/sociological understanding of the current institutional arrangement. Academia is both conservative and polycentric; our practices have evolved to serve a variety of different purposes, making optimization and reform very difficult.

Hall is unflinching in applying market logic to the situation: AI will proliferate, quickly, through the academic system because “The Economics Demand It.”3 He’s not wrong! There are hundreds of thousands of scientists who competing for a fixed number of positions. The stakes are high; do you get to live near your family? Do you get time to actually do the research you’ve devoted your intellectual life to? Do you get health insurance? At current prices, LLMs can be used to automate a large chunk of the grunt work of academic research, and we’re well past the point of returning to an equilibrium where no one uses them.

If we do nothing, we are going to be flooded. Both with low-quality slop, and with inane, precise, accurate papers; Hall is again correct in pointing to these two risks.4

Herb Simon’s famous quote remains essential:

What information consumes is rather obvious: it consumes the attention of its recipients. Hence a wealth of information creates a poverty of attention and a need to allocate that attention efficiently among the overabundance of information sources that might consume it.

This is the core role that academics must retain. We must refine our taste, our ability to filter through the information with which we will soon be flooded. Perhaps we’ve been flooded for decades, but the problem can always intensify. We simply need to spend more time reading, both old books and new forms of knowledge encoding, more time cultivating our own expertise; we now spend too much time producing knowledge.

I’ve long flirted with the idea that science should simply become a knowledge machine, that we should arrange ourselves as coal-stokers feeding the engine of science that spits out truths which no human mind can verify. This idea is seductive; it is the culmination of contemporary epistemic trends in quantitaitve social science. There are two main downsides:

• How can we know if the knowledge machine breaks down?

• The process of science doesn’t just produce knowledge; it also produces scientists.

Let’s be honest: as a social scientist, I believe in the value of expertise. Our societies will be worse off without social scientists, even if we are replaced by machines which are capable of producing “more” social science knowledge.

But the first concern is a political concern, and thus perhaps even more important. We can build the knowledge machine, but it really matters who owns the knowledge machine.

Democracy has been badly burned by the previous wave of outsourcing a core function to new media technology: social media was never a digital town square, and the democratization it promised in fact only accomplished a delegitimization of existing forms of epistemic authority with no valid replacement. “It would be ridiculous to refer to the electromagnetic field through which the message runs as a republic,” writes Flusser in 1985. Sure, the possibility frontier of what is possible with new technology is larger than it was before, but we need to acknowlege that it takes time for people to collectively hammer out the institutional arrangement that allows for healthy communication.

We eagerly abandoned the decentralized world of the blogosphere and RSS feeds for a platform which allowed us to quantify our cleverness and is now being used to generate unprecedented amounts of what amounts to child porn. Was that a good decision?

So, yes, the current situation with journals publishing static pdfs is indefensibly antiquated. But the current academic polyarchy is actually quite robust; the sluggishness, the institutional conservativism, is necessary for something to have persisted as long as it has.

So I’m opposed to jumping too quickly to radically new institutions reliant on corporate-controlled tech. The risks are too large. But neither can we afford to ignore the situation. We must be proactive in incorporating LLMs into the scientific process in a way that allows us to better serve our fellow citizens in the short rule while retaining our long-term epistemic capacities. If we want things to stay as they are, things will have to change.

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In my recent blogging about The Antimeme Haunting Western Philosophy I discussed how textual media is unable to accomodate the idea of processes, like code. And last week, in What is “Theory”?, I argue that we need to accept that valuable theoretical interventions can be “written” in code — but only if the code actually runs.

Both posts recieved some justifiable pushback: if what I’m saying is true, why do I bother writing it in words? Why not do some coding? Aren’t you a computational social scientist?

Fair enough!

Today, I’m launching No BS Political Science, a periodical (monthly, for now) newsletter summarizing the new publications, conference calls, job openings and other professional announcements from political scientists on BlueSky. The first issue is linked below in this post, and can also be found on this new substack newsletter. If you’re interested, subscribe below; I won’t be posting these on main.

No BS Political Science

Periodical digest of professional political science announcements, presented without networked inequality or parasocial weirdness.

By Kevin Munger

The specific motivation for this initiative is selfish: I just missed out on an awesome conference, right in my substantive area of interest and just a train ride away from Florence. This is a genuine cost of having abandoned the micro-blog ecosystem; I mostly hear about things from friends or from newsletters (shouts-out the PolMeth listserv, one of the most robust and convivial technologies around, now edited by Bruce Desmarais and some of my other former colleagues at Penn State).

I think that micro-blogs, as they have been presented to us by various for-profit corporations, should not be accepted as the only or best or even a defensible way to use the internet. Shouts-out the principled Mastodon users, but I was generally very dissappointed by the X-odus to BlueSky. This was an opportunity to step back and really think about how we, as an academic community, want to use the internet.

But really, everyone was so psychologically locked in to the format that the closest clone to Twitter without the Bad Man won out. I have argued against this, many times, but of course the technology of writing is incompetent for guiding behavior on the internet. I was using the wrong communciation technology. So, again, I’ve taken my own advice and vibe-coded up the pipeline to create this newsletter.

Furthermore, this is only possible because BlueSky makes all of its data freely scrape-able. This is something that social scientists studying social media like, a rejection of Musk’s decision to shut down the free Twitter API that made thousands (millions?) of academic papers possible, including much of what I published early in my career. And I’m sure that every single AI company ingesting the free tokens appreciates it, too.

Because, unfortunately for the cyberlibertarians, openness has failed. As I write in Towards the Post-Naive Internet, we can no longer afford to be naive about the political/market power of the technologically-empowered actors. The Dark Forest is a far safer place than the open internet.

Might a newsletter digest destroy the BlueSky ecosystem in the same way that Craigslist undercut the business model of local newspapers, depriving them of classified ads? That’s crazy grandiose, but it’s directionally accurate.

On the other hand, it looks like Bluesky has already peaked.

Who cares. Platforms come and go, it’s a mistake to invest too much time in networked capital unless it’s transportable (the single best argument for Substack is that authors can keep the email lists of subscribers). We must be nimble, adaptive in how we use the internet. And we should be durable, resilient: develop our own digital real estate that is beyond the profit motive (or worse, the political whims) of corporations. LLMs are a new chance for us to get it right: a radical break in how we speak with and through the internet.

For-profit, closed LLMs are a crutch, a bridge (I used Claude to vibe code the scraper, and exactly $.50 of API credits for classification of this batch), but in the long run, we need to be developing and maintaining our own versions of these models.

I should note that the the current iteration of this newsletter is really pretty bad. I learned a lot by doing it, and that’s part of the point. We can dream up better ways the internet might be, but only by taking actions where we are the locus of recursion can we can actually perform the behavior of moving towards these better places. (If you think I might’ve missed your account and want to be included in the digest, input your Bluesky handle here.)

For now, I think that having the option to catch up on recent professional developments in political science in one place, without the networked inequality or parasocial weirdness that comes along with micro-blogging, is a net positive. If you agree, you can subscribe here:

No BS Political Science

Periodical digest of professional political science announcements, presented without networked inequality or parasocial weirdness.

By Kevin Munger

And here’s the first issue!

No BS Political Science

No BS Political Science (December 2025)

For a discussion of this project, see this post on Never Met a Science…

Read more

4 months ago · Kevin Munger

This was already immediately useful for me — I otherwise would’ve totally missed this call for the The Italian Conference On Computational Social Science, which I didn't know about but looks awesome.

I also saw the following articles that look especially interesting to me:

https://www.nature.com/articles/s41562-025-02358-4

https://www.pnas.org/doi/full/10.1073/pnas.2515516122

https://www.sciencedirect.com/science/chapter/handbook/pii/S3051167425000061?via%3Dihub

https://ideas.repec.org/p/osf/socarx/d5yx2_v1.html

Hope you find some useful links as well!

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I bring this up now because apparently the last month has seen a Substack whirlpool around the topic of “continental” versus “analytical” theory. In accordance with my communicological model, this discussion quickly became meaningless — and it has nothing to do with my current topic.

What — literally, what — is theory?

I’m deeply grateful to my current colleagues at EUI. They’re brilliant, but more unusually, they’re both curious and serious. Contemporary academia makes this combination all too rare, but something about the institutional setup at EUI has enabled an intellectual community unlike anything I’ve experienced elsewhere.

As evidence, I was invited to present a talk on “Meta-Science, Methodology and Time” to our monthly internal seminar series. 100% of the faculty in the department showed up, as well as dozens of students and postdocs. Forgive my sentimentality, but I simply cannot communicate how rare an experience this is. I’ve been blogging about these topics for five years, as a necessary intellectual outlet—it’s been difficult to find interlocutors in mainstream political science, to say nothing of actually publishing these kind of ideas in peer-reviewed journals.

The talk itself was a synthesis of those blog posts, refined into what I hope is a coherent argument. I want to discuss what proved to be the most controversial part of the talk: the need for theory. The standard story in social science is that any empirical work is only as good as the theory it tests.

But compared to most of my colleagues, I’m skeptical about the value of theory as it is currently practiced — for reasons discussed in Knausgaard Discovers the Computer , theory is limited by the capacity of the medium in which it is encoded and communicated, which is, generally, natural language.

The best illustration of this problem comes (surprise!) from Stafford Beer:

The human brain can “handle perhaps three quantified variables in our heads.” This is a hardware constraint. If we insist on using this hardware and the associated software (natural langauge), this is the limit of what social science can hope to accomplish; if we want to study complex questions, we will only ever be able to come up with vague answers. Alternatively, and this is the direction suggested by the progress of social science methodology, we can produce precise answers to very simple questions.

Put another way, philosophy of social science1 must grapple with “The Bitter Lesson” from artificial intelligence research. The recent revolution in AI progress has implications for methodology and applied epistemology. The symbolic approach to AI — attempting to translate our natural language theories directly into the langauge of the machine — has been routed by the “scaling approach” of pouring in ever-more data and compute. Sutton says that “building in how we think we think does not work in the long run.”

This is directly analogous to the role of natural langauge theory in social science, which insists on building in how we think the social world works. The problem goes back to Beer’s sketch above: a “theory” is produced by a “theorist,” and must therefore be something that fits inside of a single human’s head. We can fit (perhaps!) three quantified variables in there, but can we recognize ecological systems? We cannot.

For social science to transcend the limitations of the single human brain, Sutton’s insight must be incorporated into social science. At a minimum, any serious philosophy of (quantitative)2 social science needs an explanation for why “The Bitter Lesson” is irrelevant. Quant social scientists who wish to proceed as they were trained more than a decade ago: if you haven’t reconsidered your approach in light of the machine learning (sigh, “AI”) revolution, your approach is far less likely to be correct than it originally was!

I want to be clear that I am deeply sympathetic to the goals of the theorist. I wish this was how the world worked! I wish social science were tractable at a level of precision I could fit in my head!3

Another way to phrase this criticism we might call “the demarcation problem” of theory. The traditional demarcation problem in the philosophy of sceince is the question of how to distinguish between science and non-science. For natural-language theory4, the demarcation question is how to distinguish theory from non-theory.

This is tricky because because both theory and non-theory take the form of sentences. Worse, both theory and non-theory sentences are essential to the practice and strucutre of research; the latter include methodological details and what are sometimes called “auxiliary hypotheses” about the set of things that all must be true for a given empirical test of a theory to be falsifiable; the Duhem-Quine thesis demonstrates the seriousness of the problem of ever falsifying a theory rather than one of the non-theory sentences surrounding it.

If social scientists want to continue to emphasize the centrality of theory, I would really like to see more meta-scientific attention to this question. For example, hypotheses are generally numbered at typeset differently, emphasizing their importance to the flow of a scientific paper and making it easier for readers to track subsequent statistical results back to those hypotheses. In contrast, the “theory section” is generally a few pages of dense text filled with citations which then, someone, are condensed into the specific, enumerated hypotheses and then set aside.5

This pdf formatting decision reflects our field’s meta-scientific preference for internal validity over knowledge generation: the mapping between the hypotheses and results is precise and rigorous, but the mapping between the theory and the hypotheses is, let’s say, pre-scientific.

Pro-theory social scientists often tell me, “well no in fact we take theory very seriously, we teach our students theory-first, our papers are all well-motivated theoretically, and as a reviewer I spend a lot of time evaluating the theoretical motivation of the empirical tests.”

To which I say, how’s that going? Is it working? Is it working better than it was in the past? For which theories does it work better or worse? What is the test-retest validity of the crucial scientific process of deriving empirical hypotheses from theory? Do people even agree about what “theory” means, or how many theories there are?

The turn towards empirical metascience means that we should not accept the practices that we have inherited from the past — that we should expand the scope of methodology to include all of the practices that go into producing a paper, not just the practices associated with data manipulation. At present, I believe that there are serious gains to be made even with “barefoot” empirical metascience — simple description of all of these practices and an straightforward evaluation of whether those practices are functional or flawed, whether they work at all.

But empirical metascience must ultimately be complemented by theoretical metascience. And this, I think, is an importantly different type of thing; it requires us to think about what we’re trying to do, to have an outcome-based evaluation of science rather than the purely process-based evaluation we have implicitly adopted.

Science is understood as a recursive machine which begins with theory. From this theory, we look to methodology to tell us which scientific actions are valid; we perform some of these valid actions, and then some modification to aspect of the theory is spit out. This is the core loop of science. Science itself is thus valid if (and only if!) it correctly combines theory and methods.

This is the heart of my critique of both philosophy of science and methodology. These traditions are fundamentally inward-facing. They are long, logical conversations in which progress is made by identifying flaws in previous arguments. They point inwards, to what has been said before, rather than outwards, to the world.

But the world is changing, in important ways. The theory-first paradigm is insensitive to changes in the world; science lacks the sense organs to detect changes in the world, except through the tortously long path through theory. Indeed, at present, the only reason to care about some development in the world is if it allows us to refine the pre-existing theories.

I argue that science needs more apertures to the world, to develop new organs that allow us to interact directly with the world. This can happen at the beginning of the scientific loop, with more descriptive work that causes us to update both our theories and our auxiliary hypotheses. As I write in The YouTube Apparatus, this can help us decide which theories to prioritize, something which the core scientific loop is unable to do rigorously, if at all. The aperture to the world can come at the end of the loop, too, in the form of making specific predictions about the future. This allows us to understand the overall value of our efforts; our theory might be valid internally but if changes in the world swamp the predictive lift from the theory then maybe we should abandon this theory for one in which we can actually hope to apply to the world.

These are valuable reforms that I believe will help at the margin. But, ultimately, I agree that “theory” is essential to science, even axiomatic. What is theory, to return to the titular question — but more importantly, what should theory be? Here the philosophy of technology / STS becomes a critical complement to philosophy/methodology.

Feel free to stop reading now if this kind of thing makes you upset.

The world has become enframed by technology, in Heidegger’s term. Knausgaard does not cite Heidegger in the Harper’s essay from a few weeks ago, and I think this omission is on purpose. He writes that “I cannot find an outside to technology,” a direct invocation of enframing. He also concludes the essay by quoting Holderlin, “Where the danger is, also grows the saving power.” This quote is famously applied to technology in Heidegger’s The Question Concerning Technology.6

Technology imposes a technical way of seeing the world, as instrumentally valuable to humans. Technology is literally creating the world, and creating humans. Humanity is axiomatically unnatural, the descendents of the apes who went insane (in Flusser’s memorable prhase), and we have been recursively re-arranging more and more of Earth’s inorganic molecules to create our increasingly unnatural world. This world, in turn, in-forms the subsequent humans.

More recently, software has eaten the world, has changed the sites of recursion and accelerated the feedback loops. Forget de-skilling, this process is de-souling humanity, and ensouling the machines. From Yuk Hui:

We can imagine a spiral form, in its every circular movement, which determines its becoming partially from the past circular movements, which still extends their effects as ideas and impressions. This image corresponds to the soul. What is called the soul is the capacity of coming back to itself in order to know itself and determine itself.

Theory-as-natural-language, especially when printed on dead trees, cements the human as the site of recursion, is a powerful way to inform the world while retaining our soul. But technological progress makes this core loop incompetent, in the technical sense proposed by Ashby and Beer: the information processing capacity of this core loop is radically insufficient to the complexity of the systems it seeks to understand and govern.

Theory as artefacts, and specifically as engineered non-digital objects, is a necessary move. One way to think of this is as an elevation of the status of engineering, normally denigrated as science’s degenerate (because atheoretical) handmaiden. The problem is that this theory is self-limiting; the tinkerer learns the language of the tool and can speak with the tool, but only directly from their body to the world, rather than to other humans. Media are necessary for recursive scalability.

So we arrive at the necessity of theory as code. The digital world is the site of a huge percentage of human intellectual effort, but most of us have avoided developing the capacity to speak the language of the internet. We can speak to other people through the internet, but because we cannot speak with the internet, this communication is mediated by the apparatus; we cannot speak to each other except through the apparatus.

For example, Drew Dimmery recently migrated his blog from Substack to his own server, and describes the protocol-level interventions that he has coded to be part of this new blog — for example, Semantic Scholar integration that automatically pulls and formats his academic publications, a fully open-source newsletter publication pipeline, and a very detailed discussion of specialized fonts.

This code is metascientific theory. But so is Google Scholar, ChatGPT, Overleaf, X, Bluesky, Editorial Manager, ScholarOne, Substack, ResearchGate…all of this code produces the world in which science is conducted. Scientific communication is encoded and passed through these platforms, none of which are neutral. (Not even, apparently, the fonts). The actual science that is done is not independent of the code in which it is transmitted. Empirical metascience will help us understand the impacts of those different codes according to criteria we decide are important, but the actual meaning of this theory as code can only be understood be people who speak this langauge.

Avery unsatisfying coda: given the (natural language) theory-first paradigm in science, the code-as-theory can only be evaluated if it is cashed out into a sentence, one of those special sentences which we call a theory. That doesn’t work. This is theory that cannot be thought in natural language.

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Topline data shows that use of the big three platforms has pretty much plateaued, and that TikTok continues to grow at roughly the same rate as Instagram did during its expansion.

(Note that this analyzes adults, so no data on under-18s, and that this is done via carefully executed surveys, so bots and non-representative respondents aren’t a concern.1 Also note that this is people who ever use each platform — this isn’t saying anything about the intensity/duration of usage).

The most surprising development, to me, is the rapid expansion of Reddit usage — it basically doubled from 2019 to 2025. After like fifteen years of casual scrolling, I finally and definitively kicked my Reddit habit last year, so I don’t really know how the platform experience has changed, but it’s definitely unusual for a platform to have persisted for so long in a given niche and only now, twenty years after it was founded, reach a quarter of the US adult population. We’ll discuss this in a bit.

Given the attention dynamics online, of course, the headlines derived from this Pew report focus on the status of Elon Musk’s platform. Usage of Twitter peaked at 24% of US adults in 2018, had been pretty much constant since then, with a small dip to 21% in the 2025 report. The tech press saw this as a win.

the XX-odus

But these topline numbers fail to capture the piece of evidence in the title of this article: X is now the most male-dominated social media platform in the US. I scraped the gender usage data from the time series of Pew Reports and calculated the gender ratio for each platform in each year, and X in 2025 has a male-female imbalance which is less extreme only than late-2010s Reddit.

YouTube is used by essentially everyone, so there’s no gender gap; Facebook and Instagram have a moderate but stable imbalance in favor of women; TikTok is actually the most gender-imbalanced platform, with about 50% more usage among women than men, but again this imbalance is stable.2

The real action comes from Reddit and X. We see now that the former’s overall growth was driven primarily by increasing usage among women, bringing Reddit much closer to gender parity. In contrast, where Twitter use was equal in 2018 and slightly favored men from 2019 to 2021, the Elon Musk era has seen a steady erosion of female participation. Never in the history of modern social media has one gender so decisely abandoned a platform.

Why does this matter? Aside from explaining a lot about the vibes on X, I’m increasingly convinced that gender polarization, especially among young people, is both:

1. Upstream of contemporary ideological developments, on both “the left” and “the right”3

2. Driven by gender fragmentation in online spaces

Most young people really don’t care about politics, but they do care, a lot, about gender, romance, sex. The less they’re learning about and experiencing this offline, the more intense things get online — and this is a feedback loop, where these online conversations are driven by the most gender-embittered individuals who spend the most time online, developing narratives and explanations for how and why the world is conspiring against their gender. Political “entrepreneurs”4 then come along and coalesce these greiveances into (psuedo)-ideological frameworks which then, potentially, interact with electoral politics.

I recently cohosted (with my colleague Léa Pessin) an interdisciplinary workshop at EUI about youth gender polarization and internet fragmentation; we’re working on writing up the results, but we’re convinced that this is a major new socio-political trend that academics should be paying attention to.

What I really want to avoid is an analysis of “the Manosphere” that is a reprisal of the way that internet scholars in the late 2010s studied “the alt-right,” as a simple contagion of evil that must be surveilled and quarantined. As academics, we need to understand the underlying dynamics that give rise to these trends; we’re the only actors in society with the remit to do so.

More broadly, “the Manosphere” has for years been much too large for this strategy to be successful, even for political activists. There is no way to build a successful political coalition that writes off a huge chunk of the population, especially if they are young and not yet fully politicized.

And so as always I think that the first step is to develop a robust quantitative description of who is using different platforms and for what. That X is now the most male-dominated social media platform in the US is one small but important fact upon which I hope to continue to build a more comprehensive understasnding.

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(Sorry this post was so breezy and fun; the next one will not be, don’t worry.)

1. Literally, technically, seriously: what is a computer?

2. ????

3. I need to learn about Stafford Beer

Conflict of interest warning: I’m a huge KOK fan. In grad school, I was in a multi-year book club where we read the later books in his magnum opus My Struggle hexology and discussed them in bars around Brooklyn; for reasons that are now obscure to me, there was also an event in Red Hook where he played the drums in a band fronted by his brother, Yngve, a frequent character in his autofictional novels. I can’t forget the image of this tall, handsome Norwegian competently but uncomfortably playing a drumset that was much too small for him.

In the Harper’s essay, Knausgaard provides a useful reframing of my central question, “why are things so weird?”:

My current problem, what I struggle with, is that I cannot find an outside to technology. It is as if the outside had disappeared, as if it were no longer a possible place…It feels as if the whole world has been transformed into images of the world and has thus been drawn into the human realm, which now encompasses everything. There is no place, no thing, no person or phenomenon that I cannot obtain as image or information.

Karl Ove: if you liked Beer, you need to read some Flusser. As I wrote in “The Discourse” is the Cybernetic Event Horizon of Human Freedom, “one non-tenure-relevant priority in the coming year is Flusser boosterism.” And it seems to be working!

But in fact, Knausgaard’s characteristic restraint allows him to work methodically on the more fundamental questions before going headlong into abstruse media theory. He brings us back into his own memory, of his first experience of a computer, how inhuman but still unimportant it felt. Decades later, the computer is still inhuman but now ubiquitous, seemingly omnipotent. He decides to figure out what a computer is, what computation is.

This allows Knausgaard to make an impressive move for a writer of his breadth and erudition. He was able to recognize that he had been raised a wordcel: axiomatically committed to the media technology of reading and writing to understand and control the world, even as this technology is weakened and surpassed by other media technologies.

In the Nineties I studied literature, art history, and aesthetics, completely convinced that what I studied was about human nature, life, and the true fabric of existence...Back then, much of literary studies was about structuralism, poststructuralism, and deconstructionism. In many instances, this meant that texts were understood to be isolated objects…They were a kind of closed system of signs whose meaning arose in the differences between them, rather than in the extratextual reality they pointed to. It was fantastic. Signifier and signified, signifying and signification, phenotext and genotext, denotation and connotation! But it was signs that we sat hunched over, it was signs we related to, so that what we were doing was basically a kind of encoding and decoding, while it was the poor souls at the natural-science department who were out at sea or in the woods or out in the fields…Their approach toward nature may have been reductive, but at least they were looking at it. How did I not realize this back then? How could I have been living under the illusion that I was the one in touch with nature, with human nature, when in fact I was just messing around with signs and abstractions?

The same thing has happened to me. It helps that I have had as an anchor the same bookstore for over twenty years now: the Reader’s Corner, on Hillsborough Street, in downtown Raleigh. It’s right across the street from Cup a Joe, where you could still smoke inside when I was a teenager. At that point, I used to only look in the classic literature section. I was reading fiction, especially Russian stuff, mostly 19th century. The impetus was pretension born from precocity, but the effect was the same: I developed a much broader understanding of the human condition by reading the modern classics.1

Then, in college down the road in Chapel Hill, I started to get into “theory.” Reader’s Corner is also where I bought a copy of Baudrillard’s Cool Memories II, which set my understanding of “theory” back a few years. All of the cool kids and hot girls in my scene were at least talking about being into “theory.” I had read a bit of literary theory in class for my Russian literature minor, mostly Bakhtin and Nabokov’s commentary on Gogol, but the real stuff turned out to be a special kind of theory: French theory. Critical theory. Foucault and Barthes and, yes, Baudrillard, who Latour would go on to call “a French general, no, a marshal of critique” (derogatory).

What I didn’t realize is that this particular Baudrillard book is in fact French theory at its Frenchiest and theoriest…

It was so ridiculous that I gave up on the enterprise of “theory” entirely. Which was a great idea. They wouldn’t let me buy the vintage copies of Playboy until I turned 18, but I wish they’d had a much stricter policy for French theory.

Later, in my 20s, I started reading more nonfiction, across the social sciences and sociology. Serious stuff, for understanding the social world. Knausgaard describes the same experience:

I didn’t read science books. I didn’t know why I couldn’t, but I couldn’t. Philosophy, yes. Sociology, yes. History, memoirs, biographies, yes. Biology? Physics? Astronomy? No.

All this changed only in my forties, when I started on a project that tried to break down the barriers between literature and life. It had slowly dawned on me that it was the world I was interested in, life here, existence, and not the literature about it, which was just one approach among many. I had gotten the two mixed up.

This has happened to me as well, over the past five years in which I’ve been increasingly obsessed with cybernetics. Now, I skip fiction and social science, skim the philosophy section but mostly spend my time in the science, engineering technology sections, odd hybrid bookshelves that range from chemistry textbooks and old guides for computing in SAS to Thomas Kuhn and Marvin Minsky. Reader’s Corner is right next to the campus of NC State, an excellent STEM-focused university, so these shelves tend to be well-stocked.

Side note: I’ve fallen deep down the rabbit hole of book collecting, particularly first editions. Pyschoanalytically, this fetishization of books as physical objects is a transparent and feeble response to the decay of wider literary culture. But, still, books aren’t pure/virtual information vessels, their materiality and durability is part of what has allowed them to succeed as a media technology.

The imperative to collect first editions fits in nicely with the arc of intellectual history and the early cybernetic/computational period I’m most interested in. It’s not really possible for me to be collecting first editions of Hegel, but first editions of Flusser and Beer are still manageable. It’s immensely satisfying to experience the precise passage of time from when these ideas were first circulated to the present, when they sit on my bookshelf.

Returning to the article, it takes a bit of a picaresque turn, as Knausgaard becomes fascinated by the passage in James Bridle’s book Ways of Being in which Stafford Beer attempts to use a pond to control a factory. Again, the exact same Beerian anecdote had a huge infleunce on me, in my case recounted through Andrew Pickering’s fascinating history/sociology of British cybernetics, The Cybernetic Brain: Sketches of Another Future.

Knausgaard decides to track down Bridle and demand an answer to the question

“Like I said, I’ve spent my whole life being utterly ignorant when it comes to technology,” I said. “I’ve just recently started getting interested in it, but I don’t get it. I don’t understand it. Your book . . . I was really astonished by it. So I thought you would be the right person to ask. What is computing?”

“What is computing?” James said, sounding a bit taken aback.

I felt my face grow hot. It was like asking an author what a book is, or a director what movies do.

It’s essential to observe that the central theoretical discussion in the article is interspersed with that classic deadpan Knausgaardian description (“James took a bottle of sunscreen from a backpack, applied some, handed it to me, and I did the same…We had long since finished the coffee, and I poured some of the water into the glasses.”). The material world is always present in the conversations, reinforcing the fact that they are not happening in some Platonic realm of ideas.

This style mirrors the content of Bridle’s explanation of computation as something that happens in the world, reinforced by Beer’s experiments (often with Gordon Pask) in building a pond computer or in “growing an ear.” The latter is a perfect illustration of the limitations of computation within an artificial space: we pre-specify all of the inputs, the computer can perform some optimization within this artificial space, but nothing more.

In contrast, Beer and Pask’s biochemical computer is able to grow an ear because it is a physical object, directly interfacing with the world and therefore able to detect the physcial properties of sound. Digital computers (without a microphone) will not change if you blast them with sound waves: they are not adaptable with respect to the world, only with respect to the inputs we provide them.

Pickering calls this “ontological theater,” experiments in the phsyical world that allow us to understand processes. This is precisely the Antimeme Haunting Western Philosophy, the kind of thing that cannot be expressed within this media technology. Knausgaard beautifully limns the parallel between 90s-wordcel theoretical (post/decon)structuralism and the naive reification of the computer: they are both taking place within powerful but fragile artificial worlds, with sharply constrained capacity to adapt to changes in the real world.

Obviously, I don’t think that natural langauge theory is useless; like Knausgaard, it has constituted me, I cannot navigate the world without it.2 But is essential that we develop new kinds of theories, expressed and encoded in different mediums, to enact our freedom, to escape the Whirlpool of the Artificial. To be effective in the 21st century, “theory” cannot use the same technology as it did in the 19th. We pick up this theme next week.

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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 consequences of this decision are becoming increasingly obvious. We’ve past peak social media; everything is becoming slop, gambling, addiction. The open web is increasingly a dead internet, inhabited by bots or — much worse — humans who have been conditioned into behaving like bots. And that’s just what we can see. The really degrading stuff is happening in the privacy of LLM-backed chat interfaces, where humans can spiral into insanity without even a minimal tether to social reality.

As Flusser anticipates, in Post-History: “what we dread is the inexorable progress of culture. Today, to engage oneself with freedom, and more radically, to engage oneself in the survival of the human species on the face of the Earth, implies strategieas in order to delay progress. This reaction is today the only dignified one.” We can now observe, in the brutal progress towards a meaningless internet, the necessity of Heidegger’s step-back, of the refusal to take things as they are.

The upside is that there have been some communities, of technologists and artists, thinking seriously about the problem of how to use the web for their own ends. The Mozilla Foundation recently published a visual essay by Severin Matusek, Nick Houde and Paloma Moniz about The Post-Naive Internet, where we’re finally moving past the failed idealism of John Perry Barlow and the associated California Ideology.

The tech companies that own the major platforms have become explicitly political actors, abandoning the liberatarian pretense of value neutrality and free speech; the US, China and other nation-states are engaging ever-more-explicitly in Cold War 2, where digital soveriegnty and a weaponized world economy are the battlefield. Power, predictably, is being brought to bear.

In contrast to the curdled ideals of universalism, The Post-Naive Internet is local, collective, community-owned: “Instead of fighting for grand alternatives like the free and open internet for everyone, the post-naive generation builds digital infrastructure primarily for their communities and their needs.” The universal, as Laclau says, has no content of its own, but can only emerge from the particular through a political process of contestation. This is the problem with abstract universalist dreams, and why I think the post-naive, prefigurative approach to building a new internet is the best way forward.

Here’s their map:

I’m encouraged personal because the map demonstrates a number of intersections with my own internet-intellectual trajectory in ways that go beyond mere coincidence; I’m hoping that the density of interconnections here is leading towards as a new canon, or at least a new intellectual movement, a new model. So let me explain how a few of these things work, and why you should be paying attention.

I started connecting with these online communities during the pandemic, largely because I was dissatisfied with the way in which the quantititative political science literature was approaching the study of social media and particulary emerging, video-based platforms. We were applying old models to new phenomena for the purpose of testing if the old models still held. For sociological reasons (the people who are experts in the old models are now senior academics), the answer tended to be yes. But regardless of the answer, this approach has things backwards: we should be trying to understand the new phenomena, which the online world produces at an amazing clip. In the deepening political-epistemic crisis of Trump’s re-election and the mask-off nature of big tech, the remaining mainstream media has finally started catching on.

A few weeks ago, for example, The New Yorker released an article about the podcaster, artist and internet research Joshua Citarella, arguing for the value of Citarella’s twin projects of investigating youth radicalization online and in building a broader coalition of left-leaning media figures in contrast to the purity politics of the past decade

I’ve been assigning Citarella’s book in my PhD seminar on media and politics since the spring of 2021.1 It’s a fantastic example of the qualitative descriptive work being done by internet-native non-academics, observing important emerging phenomena which academia is too slow and still too internet-naive to grasp, the kind of thing that I argued in Deepfakes and Thirst Traps that we desperately need more of.

I came to Citarella’s work through Do Not Research, a platform for writing, visual art, internet culture research which he was instrumental in founding. Do Not Research started as a Discord server in 2020, which I joined later that year. They also publish amazing art/culture/theory pieces on their website, (the design of the original is incredible, but they’ve since mostly moved to Substack) including a recent profile of New Models.

New Models is the “post-naive” internet space I’ve spent the most time engaging with, and a crucial part of my intellectual development over the past five years. The Discord community is vibrant, serious, engaging — especially during the height of the pandemic, when Twitter was melting down, having access to an online community free of ads, engagement-bait and crazy people was invaluable.

The first article New Models posted on their feed was James Bridle’s self-published text from 2017, “Something is Wrong on the Internet.” This text also features prominently in my explanation of the feedback loops at the heart of the YouTube Apparatus, in my last book. This mechanism is elaborated in much more detail by Flusser in Communicology, as I attempt to explain in this beautiful, News Model-produced video lecture, which was co-funded by Citarella and Do Not Research:

The basic model of New Models and many other relevant post-naive internet institutions is that of the dark forest. The metaphor comes from Cixin Liu’s sci-fi trilogy, and is explicated in The Dark Forest Anthology of the Internet, published by Metalabel, another crucial node in this ecosystem. The key is to balance discoverability with the health of the internal community. Crypto enthusiasts did a lot of blue-sky thinking on this problem, but as is usually the case, there’s a simpler solution that doesn’t require the blockchain: have some light-leak signal in order to recruit new members (generally, a podcast), and then have a paywalled, moderated internal forum (generally, Discord) for discussion and community.

The original sin of the internet, as Ethan Zuckerman famously wrote in 2014, was having it funded by advertisements. This was downstream of the technical decision not to build micro-payments into the structure of the web — this allowed it grow, to progress much faster, but we now see what all that progress has gotten us. The current technical systems for payments and distribution are imperfect; here, crypto might one day prove genuinely useful. But at the end of the day, we have to pay something for the internet to work.

This, by the way, is the business model of Substack. An incredible analysis by Andrew Thompson in Components (itself a post-naive internet institution), suggests that Substack’s payment-based approach has managed to realize (at least for now) the long tail of paid internet content. In contrast to the winner-take-all nature of ad-driven platforms, we see a much more even distribution of platform revenue across producers of different levels of popularity.

I’m encouraged by this, and also to notice so many more of my colleagues joining Substack recently. It’s not the silver-bullet solution to the decaying web, but it fits into the post-naive internet ecosystem much more naturally than does, say, Elon Musk’s platform, or Sam Altman’s platform, or Mark Zuckerberg’s platforms.

But also — subscribe to New Models, subscribe to Do Not research, seek out or build your own post-naive internet spaces. This is the only way to even be trying to win.

To close, here’s a rare photo of Stafford Beer that the New Models crew found to accompany my most recent appearance on their podcast; here’s the teaser link, subscribe to hear the whole thing. It gets weird.

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Second, just published in Nature: Scientific Data, is The Digitally Accountable Public Representation Database, with my amazing colleagues at Pitt and Penn State. Please check it out, it’s an amazing resource for studying political communication in the US. Publishing large, targeted datasets of digital communication is one of my core commitments as a computational social scientist; I think this kind of work should be prioritized over expensive ad hoc approaches because it enables so much downstream work. Along with the Upworthy Research Archive, this is my second publication in Nature: Scientific Data, and I encourage my colleauges working in computational social science to consider this journal, perhaps if they have something that they think is valuable but doesn’t quite make the cut at the JQD.

Third, and related — we just found out that the past two winners of the APSA Information Technology Policy section award for best published article were published in the Journal of Quantitative Description: Digital Media. Congrats again to the authors of Inequalities in Online Representation: Who Follows Their Own Member of Congress on Twitter? for the 2024 award, and to the authors of (Mis)measurement of Political Content Exposure within the Smartphone Ecosystem for the 2025 award. In the five years since I proposed the idea for the journal on this blog, the field has seen a significant shift towards descriptive research; I like to think that the JQD has played a small part in this, so thanks to my co-editors and all our fantastic authors and reviewers for making this happen.

FYI, this post is primarily of interest to my fellow academics, we’ll be back with more media theory next week.

It’s been just over a year since I started a new job at the European University Institute in Florence. At the time, it felt like a bit of a risky move, giving up a tenure-track job in the US for a time-limited professorship in an academic environment which I understood less and in which I had fewer professional connections.

The job itself has proved amazing. I’m very happy here. I’m working on my Italian (to the extent that it allows me to make puns like the one in the title), having interdisciplinary conversations with sociologists, deepening my knowledge of the history and philosophy of science, and training a new generation of computational social scientists. It’s all I wanted in an academic gig.

But the contrast with the situation in US academia I did not expect. The first eight months of the Trump administration have proved catastrophic, and while things to have stabilized somewhat as the Eye has turned elsewhere for the moment, rehabilitation of the old status quo is impossible.

Overall, the best way forward (at least among things I have any degree of control over) is to simply continue to insist on academic freedom, not only in principle but in the actual practice of cultivating institutions and networks that deepen our connection and commitment to each other. If we believed in the work before, we should continue to do so now: sometimes I feel bit useless, going down philosophical rabbit holes while the world burns, but Trump hasn’t outlawed intellectual curiosity just yet, and it’d be a shame to do that work for him.

Given that I know many academics currently working in the US who might prefer not to continue working in the US, I’ll first share what I’ve learned about the European job market. I’m particularly sympathetic to the situation of non-US citizens still at early career stages.

I’ll then turn towards something which seems to have paradoxically increased European integration: broad-based stereotypes of the culture of these different systems.

Job Market Advice for European Academia

First — there isn’t really a “European job market” in the way that there is a US job market. There are a variety of different national systems with respective rules and priorities; the timing of hiring, particularly for postdocs, is not at all coordinated but rather seems to depend on funding from grants etc.

Most people seem to want to stay where they’re from, in several senses. The “academic incest taboo” against hiring one’s one PhD student is weaker in many places (though this seems to be changing somewhat). At a regional level, especially in compact places with functional train networks (and in Germany, specifically), people are more likely to continue living where they did their PhD/postdoc and then commute rather than to up and move. And nationally, given national pension systems and language communities, it’s somewhat rare to move countries even for a tenure-track job. This tendency is especially pronounced for rich, northern countries (Denmark, Sweden) and for proud, parochial countries (France, France).

More and more institutions are trying to transcend their national systems. The CIVICA network, of which EUI is a part, is aiming towards this goal, and it seems to be working. There is a tension here, though: becoming more international can in some sense mean simply becoming more American.

Building a genuinely international, European system seems laudable; I’m in favor of multipolarity and I agree that it requires aggregation beyond the medium-small European nations. But nationalism remains robust. Anti-EU countries want no part of this, and are in some cases even increasing domestic funding with the caveat that they want to prioritize research into, eg, Hungarian-ness. And the idea is spreading. Even more liberal democracies, like the Netherlands, have been pushing for a re-nationalization of higher education, by insisting that more classes be taught in Dutch.

Practical advice:

• Things are (even) less meritocratic than in the US. It feels necessary to know people, to be known by people, to have a pre-existing relationship with an institution. I’ve noticed PhD students and postdocs wanting and expecting to spend a few weeks “visiting” at other institutions in way that seems not productivity-maximizing, and I think thi is because it’s actually rational on the relevant job market margin.

• That said, to the extent that procedures are meritocratic/objective, the criterion is more likely to be raw quantity of publications, with a bit of a boost for higher-ranked publications. In the US this is basically reversed, to the point where early-career researchers can face a penalty for publishing too many papers (especially if they lack a tight theoretical focus) and there is a huge premium for young scholars having at least one top publication.

• It’s even less likely to get a permanent job directly after finishing a PhD than it is in the US. The postdoc/non-permanent researcher ecosystem is extremely robust. Many (most?) of these are ERC-funded “project” postdocs where most of your time is spent at the discretion of the PI.

• Related, the field of non-professor, public sector, permanent researcher jobs seems to be larger than in the US. These seem essentially impossible to get for a complete outsider, but it’s worth pointing them out as a solid career option soaking up some of many postdocs who numerically won’t be getting a job as a professor.

• More of the higher process happens before the job ad is even posted. Rather than having a delegated committee make recommendations to the whole department (who then votes), only the hiring committee has any formal power in selecting candidates for job talks and for selecting who to make offers to.

General European Academia Observations

These observations mostly fall into two categories: European academia is socially much more relaxed / convivial; and European academia is less rationalized / their approach to research is less instrumental.

• Conferences tend to be held during the week, rather than on the weekend. This one really snuck up on me. I’m used to conferences of the form “Arrive Friday, dinner, full day Saturday, half-day Sunday and leave.” My experience in Europe has been more “Arrive Wednesday, full day Thursday, half-day Friday and leave” or even, “Arrive Monday, full day Tuesday and Wednesday, leave Thusday.” The general idea is that conferences are work, and that work doesn’t happen on the weekend.

• Longer lunches, an hour minimum, with an additional coffee break built in. Seems like 1.5 hours away from the desk is the norm. For more formal work lunches, this is more like 2 hours and wine is often involved. The modal lunch I had when I was a visiting fellow at Princeton was catered and consumed during a presentation; this seems dramatically less common in Europe.

• There is much more semi-formal or even formal hierarchy. PhD students, postdocs/long-term researchers and professors are each on very different planes; this seems to mostly be due to the funding structure. In the US, junior positions are mostly funded collectively by institutions, giving these people more formal autonomy. The power of senior faculty is more discreet and perhaps more terrifying, coming discontinuously through the all-important vote on tenure.

  The European formal hierarchy is accompanied by more informal flatness — it seems more common to hang out across levels, certainly people are less worried about drinking and socializing. This is perhaps due to the formal hierarchy and generally stricter labor protections.

• More people in Europe seem to be genuinely interested in the intellectual questions underlying empirical research. The increasingly binding meritocratic filters through which US academics have to pass (elite undergrad, good grades, maybe a pre-doc, top GREs, elite grad school, elite advisor, top publications) introduces a harsh selection mechanism for academics willing to approach their jobs instrumentally, to minmax their way past those filters. This is not to say that meritocracy is purely a bad thing: compared to “holistic” evaluations of character, standardized evaluations help overcome (but do not unilaterally obliviate) existing inequalities. But every selection mechanism comes with its own cost.

• At least partially as a result, academics here seem to be less monomaniacally motivated to grind out work. There’s lots of presentations and events to attend, and attendence is very high, even if If a paper doesn’t work, it just doesn’t work; there’s less of a need to salvage a publication if you don’t really believe in the project. There’s plenty of anxiety about the future, but not the kind of anxiety that arises from not knowing whether you’ll have health insurance next year.

• I’ve noticed less methodological dogmatism, and a greater willingness to engage in foundational epistemological debates about what exactly we’re doing. Given that I’m extremely interested in having these kinds of debates, I’m having a great time. But I admit that it comes with a cost. The unquestioning American pragmatism is great at just getting shit done. For early career researchers in particular, it can be dangerous to get too introspective too soon.

All of these observations are vulgar generalizations derived from anecdotal observation. The best way to puncture these stereotypes is with more direct contact between scholars across cultures. As the world seems to be turning back inward, it’s essential that we retain a commitment to scientific openness and cross-national collaboration.

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This credibility revolution made progress by insisting on “credible” causal inference. Central to this movement was the widespread adoption of randomized controlled trials (RCTs), natural experiments, and other quasi-experimental methods that prioritized internal validity.

Anyone reading a social science blog today is familiar with RCTs. They’re widely (but not universally) believed to be the “gold standard” research method, and while I am one of the critics of this label, today I advance an even more radical claim about RCTs: not only are they not the gold standard, they’re not even a research method.

At least not, as we shall see, in one specific but extremely important sense.

Dialectical scientific progress rolls on. Where we once had a crisis of internal validity (the impetus for the credibility revolution), we now have a crisis of external validity. We can conduct an essentially perfect RCT in one time and place; we can be certain that the treatment group and the control group have different outcomes, and that this effect was definitely caused by the treatment and the treatment alone. But this effect cannot be generalized to other times and places. This perfect golden knowledge nugget cannot yet be melted down with other knowledge nuggets. No human mind or machine learning algorithm can forge these nuggets into a holy grail: the ability to reliably predict (with anything near the level of precision of the original studies) what will happen when the treatment from the RCT is deployed again, in the real world.

Statisticians and practicing social scientists are valiantly applying their tools to this problem, but there are many issues. One of the biggest hurdles to conducting large-scale field experiments is ethical: people think, for good reason, that mad social scientists shouldn’t be able to run around conducting experiments on society. At a minimum, there should be guidelines: when possible, make sure to get informed consent from research subjects; don’t put subjects at risk of unnecessary harm; if trialling something that is expected to cause benefits (like a conditional cash transfer experiment), be sure to give the same benefits to the subjects in the control group after the experiment ends; don’t use AI to make up stories about being a rape victim in order to change people’s view on Reddit; but most importantly, make sure to get IRB approval.

Institutional Review Boards (IRBs) are the university-level entities tasked with ensuring the ethics of “human subjects research.”1 They were standardized by the 1991 “Common Rule”, updated in 2018 to expedite some of the cumbersome approval process. As this is an official legal document, these terms have precise and exhaustively defined meanings. Much of the document is devoted to defining “human subjects”; as anyone who has submitted something to an IRB knows, there are different levels of protections and restrictions involved with working with vulnerable populations (children, prisoners) and for more intrusive interventions (testing a new drug versus asking a survey question). There are, unfortunately, tragic historical examples for each line of this definition.

Less attention is given to the remainder of the phrase “human subjects research.” “Research” here is defined as a ‘systematic investigation...designed to develop or contribute to generalizable knowledge’ – per section 46.102(i).

This seem innocuous – especially from the perspective of RCTs as the gold standard method. But given what we now know about the limits of external validity, we can’t be sure. According to my epistemic commitments, the results of RCTs are not generalizable. They are, therefore, not research, and are thus exempt from IRB approval.2

This would be tendentious trolling if I didn’t believe it. If RCTs aren’t research, what are they? Should we do them? Why?

The value of RCTs comes not from the control group but from the treatment group -- from the action. They are best understood as a formal kind of performance art. Each society enjoys performances in the idiom of its respective culture. Martial societies find meaning in ritual combat; religious societies in pious displays of devotion or spiritual rapture.

Our society is scientific, even scientistic. We appreciate the performance of scientific rituals, big data crunching and demonstrations of control over nature or our fellow citizens. Social science experiments don’t provide us with airtight guarantees about what will happen in the future – and it’s just as well that they don’t, because such guarantees are incompatible with democracy and human freedom. What these experiments provide us is the ability to see the social world in a different way.

In a review of the Meta2020 academic partnership, I wrote that

My aesthetic appreciation for these experiments cannot be overstated. They are simply beautiful. Thinking as I sometimes do of social science experiments as performance art, the craft and the vision on display is deeply satisfying.

I find this perspective liberating. “you can just do stuff” has become a rallying cry for the Silicon Valley anti-progressives, who feel constrained by both social norms and government regulations. On this point I agree with them: we need to recover the spirit of individual initiative outside of instititutional constraint. The “founder mode”/start-up/a16z impulse is central to the American ethos, and it is genuinely a political problem that this impulse is at present finds its most fertile soil in AI and defense startups but seems to wither in government, universities and general intellectual culture.

One of the key battlegrounds in the emerging ideological split between the center-left credentialed establishment and the Silicon Valley right is about the role of science. Each side is explicitly in favor of science, but they differ in their conception of what science means.

The SV people have explicitly embraced the scientist as a radical truth-teller and handmaiden to technological progress, a cross between Galileo tweeting “E pur si muove!” and von Neumann inventing game theory and then confidently telling Truman that game theory says we have to genocide the Russians with nukes. In contrast, the credentialed establishment tells us to “trust the science” as if this were a static, eternally valid scripture rather than a process for developing expertise and refining our beliefs.

The bureaucratization of “ethics” represented by the IRB certainly moves academic science in the latter direction. Scott Alexander has a shocking story of his experience with a medical IRB, and while I haven’t experienced anything so dramatic, the scientific-entreprenuerial experience is significantly dimmed by spending six months going back and forth explaining that, say, Twitch users are psuedononymous already and that asking them to give me their email address so they can subsequently opt out their data being used in an experiment is in fact introducing more risk of de-anonymization than simply letting this bureaucratic box go un-checked.

To re-democratize science, we need to encourage everyone to believe that they can just do stuff — that they can and should try to understand how to improve their local situation by trying things out. That is, that you can literally Do Your Own Research. The idea that only a very specific subset of human actions contributes to “generalizable knowledge,” and that this formally-defined research is something defined, controlled, and gatekept by government-aligned academics, enervates the non-specialist and (in the internet era) inevitably fuels anti-establishment backlash.

Tech startups are allowed and financially encouraged to run uncontrolled “experiments” on hundreds of millions of people. They “try things out” all the time; social media was a massive, poorly-designed experiment, and now they’re just letting chatbots rip throughout the most alienated and lonely members of society. They don’t aspire to “generalizable knowledge,” so they aren’t doing “human subjects research,” so no ethics review is necessary.

The message of the IRB is that the ethical risk of an RCT comes from the addition of a control group to the nihilistic rollout of a massive new social technology. Personally, I disagree that RCTs generate “generalizable knowledge” to a categorically different degree than does other forms of systematic inquiry. This ethico-methodological straightjacket is bad for science and bad for society. The Silicon Valley message is nominally more free-spirited but in practice produces an equally restricted science aimed only at maximizing Monthly Active Users in service of shareholder value.

I’ll give the final word to John Dewey:

The future of our civilisation depends upon the widening spread and deepening hold of the scientific habit of mind.

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I’ve been accidentally tracking one of the great antimemetic artists across Europe this summer.1 There are pictures and videos below, but in true antimemetic fashion, his greatest work no longer exists.

Daniel Soar explains, in review for the LRB, the situation with “Jean Tinguely’s self-immolating sculpture La Vittoria, or ‘Suicide of the Machine’”:

On the night of 28 November 1970, in front of the Duomo in Milan, a sheet of purple drapery was removed to reveal a ten-metre-high golden penis, with a pair of massive golden papier-mâché balls on the plinth at its base. When darkness fell, a firecracker went off, and then another, as sparks and smoke issued from the tip, with louder explosions following, rockets shooting out everywhere, until the whole thing was a tower of flames erupting into the sky. Somewhere in the crowd a man sang ‘O Sole Mio’; within half an hour the structure had burned down.

This is not a video of that event.

In a vindication of Tinguely’s sexually explicit motif, Sora is unable to produce any but the most symbolist of 10-meter-tall golden penis.

Given that this series of blog posts might as well be titled “Footnotes to McLuhan”,2 I would be remiss to not reiterate his point about the centrality of the visual in Western culture.

Like books, paintings and sculpture are ontologically stable. They persist; many people across time can be said to encounter “the same” painting or sculpture. Modern museum practices are optimized to keep them stable, in fact: careful controls over light, temperature and moisture. If the object is somehow “damaged” (that is, if it changes), museums invest serious resources in restoring them. The aim is to remove these objects from the temporal realm, making them, as it were, divine.

McLuhan argues that this externalization / eternalization of art is part and parcel of the Rennaisance package. Perspective is a key technical innovation in Rennaisance painting: it literally adds depth, but in so doing imposes a fixed position for the viewer, outside of the scene. This position is reified by museums, treating both the content and the object of the painting as external and eternal.

The visual-intellectual ecosystem which includes both painting and written art criticism promulgated through printed (rather than handwritten) text allowed for the progressive development of the Rennaissance memeplex of philosophy, science and visual art. Institutions like museums, libraries and universities housed and channeled the media through which this knowledge could flow.

And of course the Rennaissance was an important period in the consolidation of whatever came to be called Western intellectual culture. The high points remain the most famous artworks in the world. The Mona Lisa and Michelangelo’s David are some of the most fundamental visual memes in Western culture. Walking around Florence, you can find aprons, t-shirts and towels with any number of unlikely objects in place of the fig leaf on David’s dick.

The centrality of 500 year-old paintings and sculptures to the contemporary imaginary of “Western art” is obviously something that practicing artists have been rebelling against for at least a century now. Dada, Lettrism, Fluxus, and particularly Situationism — these art movements were explicitly opposed to the hegemony of legacy mediums and intitutions. These and other movements are an antimemetic stream within the art world, known and studied by serious artists and theorists but impossible to scale within the mainstream art world of public-facing museums.

This “central tendency” of Western art has proven resilient. The mediums of paintings and sculpture are ideal for art-as-commodity. Paintings, in particular, are nice, medium-sized objects that can be transported without too much difficulty. They can be hung on the wall, visibly displaying both taste and wealth without taking up any valuable floor space. Within a museum, they are easily photographed by visitors looking for a selfie; they can be easily remixed within contemporary digital media.

Performance art is generally the most anti-memetic medium. Performances take place exactly once; some performance artists, like the Situationists, performed their actions in public spaces rather than art galleries, making it unclear that “Art” is happening.

The exception proves the rule. The most famous performance artist today is undoubtedly Marina Abramović. Before her turn towards wellness entrepreneurship, her most famous work was The Artist is Present, also the subject of a popular documentary. The work consists of her sitting in the MOMA for hours and hours on end, while a single person can sit in a chair across from her, making eye contact for as long as they want. Her performance is to literally turn herself into an object in a museum, forcing the viewer to adopt a specific perspective. To transcend its antimeme status and be viewable on Amazon Prime Video, performance art has to become legible by turning itself into painting or sculpture.

Tinguely’s ‘Suicide of the Machine’ is obviously neither external nor eternal. It cannot be purchased and it cannot be used to signal good bourgeois taste. It took place in public, allowing viewers any point of view they could phyiscally achieve — but only for a half-hour in 1970.

Differently from the Situationsists, however, Tinguely’s work uses the medium not of the body but of the machine.

I was struck by an oddly editorialized passage in the Wikipedia page about Alexander Calder:

Calder’s turning away in the early 1930s from his motor-powered works in favor of the wind-driven mobile as marking a decisive moment in Modernism’s abandonment of its earlier commitment to the machine as a critical and potentially expressive new element in human affairs. According to this viewpoint, the mobile also marked an abandonment of Modernism’s larger goal of a rapprochement with science and engineering, and with unfortunate long-term implications for contemporary art.

Calder is an extremely famous contemporary artist. His “mobiles” delight, the way an infant looks up at dangling ornaments from a crib, and his monumental sculptures serve the function of “tasteful painting” for giant corporate buildings around the world.3 He was also a major influence on Tinguely, who very much embraced the machine as an expressive new element in human affairs.

Tinguely happened to be the focus of a major retrospective at the newly-refinished Grand Palais in Paris, along with his short-time wife and long-time muse, Niki de Saint Phalle. I knew some of her work, which is great; she burst onto the art scene in the 60s with a series called Tirs in which she made paintings by shooting a gun. I had never heard of Tinguely. But, in fact, I’d seen the famous Tinguely-Saint Phalle collaboration, the Stravinsky fountain, outside of the Centre Pompidou:

Tinguely mostly made whimsical, aggressively noisy clanking machines; some of them self-destructed, others were paraded around the streets of New York and Paris to interact with the public.

This video, for example, shows him being approximately as cool as I’ve ever seen someone be:

He’s basically minting paper NFTs with a Rube-Goldberg-ass machine, deftly smoking a cigarette and handing out unique machine-drawings to extremely charmed women.

Some of Tinguely’s machines self-destructed on purpose, but others simply wore down over time. Art in motion is far more difficult to maintain than is static art; motorized art even moreso, due to the increasely violent movement it makes possible. The more complicated the technology, the less likely that spare parts or expertise will persist to maintain it. This is true for all varieties of pre-digital audio/video art, as in the case of one of Flusser’s favorite visual artists, Nam June Paik.

As a result, the machines on display were only turned on for one minute out of every 15. My favorite, and most convient for my thesis, was his series of four “philosophers” who influenced his work:

Of the four works on display (Bergson, Jacob Burckhardt, Rousseau and Heidegger) only Bergson was not moving.4 Amazed by this coincidence I tried to take a video at the correct angle, but by then all motion had ceased.

Soar, from the LRB article cited in the intro, notes that

Bergson is a rotating and swaying doll or spinning top with copper-basket pineapple head, turning around a central axis while also oscillating from side to side – of course the philosopher of mind in motion does a complex dance. Ingeniously, the sculpture’s form also resembles Bergson’s diagram – the inverted cone – with the apex representing the present moment and the stacked segments layers of memory.

Personally, I thought it looked like a stawberry. The coolest one was probably Rousseau, also the most problematic one, but the circular motion of the wheel made the caricature of the native american move in an extemely satisfying fashion.

So I was stunned to encounter this unmistakeable circular motion again, in a documentary about the Austrian artist Friedrich Hundertwasser showing at the Kunst Haus Wien, the museum desinged by Hundertwasser!

In fact, the final presentation of Tinguely’s work during his lifetime was the inaugural show at the Kunst Haus Wien, in 1991. There is a clear affinity between these two artists; Hundertwasser was engaged in what I can only describe (and not elaborate on, in this overlong post) as antimemetic architecture.

Today, perhaps, the hegemony of painting and sculpture is being eroded. Not in terms of museum visitors, surely, but in the form of the technology for preserving and remixng the processes at the heart of this formerly antimemetic strand of art practice.

Most interesting contemporary art, as Carly Busta and Lil Internet have been arguing at New Models, as Mat Dryhurst and Holly Herndon have very effectively demonstrated in practice, and as Venkatesh Rao has done some heavy theorizing on, takes the form of protocols. The medium of computer code turns writing into a protocol, interaction with the world through sensors and effectors, allowing for the generation of novel outputs in response to novel inputs.

These practices might allow for the development of an alternative memeplex, to supplant rather than merely rebel against the central tendency enshrined in the Rennaisance. Protocols aren’t ontological stable; to preserve them in a musuem is nonsensical. Tinguely’s machines communicate their meaning through action, and are the victims of entropy. Protocols work only through interaction with the world or with the viewer — who is, of course, no longer merely a viewer.

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In the spirit of Arvind Narayanan and Sayash Kapoor’s essential intervention into the breathless AI discorse, AI as Normal Technology, I want to share three ways in which LLMs and AI more broadly have intersected my research as a political scientist studying digital media.

These are modest contributions. But they are, I think, real. Surveying the past 2.75 years, since ChatGPT was released, there has been a huge amount of social science research of the form, “Hey Look, ChatGPT Can Do This!” or “No, ChatGPT Can’t Do That.” Very little of this research is relevant today, even sixth months later; the knowledge decays to zero too quickly.

Much of my metascientific theorizing is about setting the academic agenda. After watching several, accelerating hype cycles around digital media, I continue to think it essential that we bring more rigor to the question of What questions should we ask? The answers are only as good as the questions. And if the questions take the form of, “Is This NYTimes Oped True? We Spent 3 Years Designing A Rigorous Research Strategy And We Think The Answer Is…Probably Not But Maybe A Little”, or “We Beta Tested This Corporation’s New Digital Product For Free,” we have no hope of making scientific progress.

The temporal validity issues that I have been screaming about in the study of digital media are radically accelerated when it comes to LLMs. Try to submit an article evaluating, say, the ideological bias of a given LLM. Spend the bare-minimum 6 months from concieving the article to sending it out for peer review; enjoy the reviewer saying, “Oh but I read on X that the new version of this model is better.” And the reviewer isn’t even wrong! It’s the institutions of academic knowledge production that are the problem.

So, my three complementary contributions. A peer-reviewed study of how academics understand AI vs Machine Learning (ML), an updated policy about studying LLMs in the academic journal I edit, and an essay in the latest issue of the APSA Experiments Section Newsletter.

Thanks to a grant from the Penn State Center for Socially Responsible AI, my colleague Sarah Rajtmajer and I set out to study how academics understand “AI” and “ML” in their research. Our paper, Social Scientists on the Role of AI in Research, has recently been accepted for publication in the 2025 proceedings of the AAAI/ACM AI, Ethics, and Society Conference (AIES).

This research was directed Sarah and her team of grad students, who did an amazing job with the qualitative interviewing and coding the quantitative results. My role in the study involved a survey experiment: we asked respondents a series of questions about their use of AI/ML technology, but we randomized each subject to get the exact same questions about either AI or ML, throughout the whole survey. Topline results are striking:

Perhaps even more interesting are the descriptive correlates of the answers to these four questions. Each coefficient should be read with respect to the reference category specified on the Y-axis, with a * signifying a significant difference. So, for example, in the top left panel, we see that female academics use AI significantly less frequently than do men — the magnitude of the coefficient is similar for ML, as well, but the comparison falls just short of significance. Women are also significantly less familiar with both AI and ML; we find no differneces around race.

The most striking results, to me, have to do with the interaction between academic seniority and these two different technologies. PhD students use AI significantly more frequently than either Assistant or Full Professors — but they are significantly less familiar with ML than are Assistant Professors (and, almost, Fulls).

The largest magnitude effects have to do with Acceptance — PhD students are significantly more likely to accept the use of AI in their research domain than are all three levels of Professors. This is mirrored in their estimates of the usefulness of AI — PhD students report it to be significantly more useful than either Full or Assistnat Professors. There are no differences by academic rank on any of these two estimates (of acceptance or usefulness) of ML.

These specific quantitative results are, of course, of limited temporal validity. What enhances their value is their combination with a series of semi-structured interviews that allow us to understand what our survey partipants actually think about these technologies. There’s a ton of insights in the paper, so I’ll just share my favorite quote here:

Hype chasing is a major issue because it “sucks the oxygen” out of the room in terms of resources and time. Furthermore, it enables bad actors to plug up publication pipelines with garbage. Not to mention generative text and images are changing the incentive structures of producing content online – it will be very difficult to find content actually produced by people who care about the content next to a mountain of botshit. – AI Survey

The area of social science over which I have the most control is the academic journal I co-edit, the Journal of Quantitative Description: Digital Media. Given our topical focus on digital media, we have needed to figure out to deal with the wave of studies about LLMs that looms over the next few years. After deliberating over the summer, we have established our standards for studies about LLMs. The full policy is here, including our position on the use of LLMs by reviewers, but I want to highlight a passage that illuminates the key issues we debated.

We are interested in quantitative descriptions of how LLMs are used, experienced, or integrated into digital communication environments. Relevant topics may include, but are not limited to:

*Patterns of LLM usage on social platforms, in content creation, or in communication workflows (more broadly, survey data about how people are using LLMs).

*Descriptions of user behavior when interacting with LLM-based tools (e.g., analyses of observational data from user engagement with chatbots, AI companions, writing assistants, or search engines).

*Or, conversely, user-based descriptions of the output of LLMs as a form of digital media, e.g. trace data studies of the media content produced by LLMs in response to actual user inputs.

This is the positive part; these are the kinds of studies we actively really want to see being conducted. This is is what we mean by AI as normal science—in the field of digital media, LLMs provide a novel modality by which citizens encounter information online. We want to start answering normal social science questions:

• Who are these people?

• How do they interact with LLMs?

• What information do LLMs provide to these people’s interactions?

• How is content hosted/generated by LLMs circulating around other parts of the internet?

These are very important questions! Moreover, they treat LLMs seriously but not as a something that we intrinsically care about. If the LLM generates something in response to an artificial, researcher-generated prompt, we don’t care about it — unless we understand which real people might be seeing that response.

We’re trying to get out in front of what I will have to say was my least favorite type of submission to recieve at the JQD over the past four years: “We Scraped Some Tweets and Ran a Topic Model.”

• The sampling frame is usually under-specified

• The justification for studying Twitter was usually not explicit; the true motivation in practice was that “it’s trendy” and then that “it’s easy”

• The inductive nature of the model means that it will always produce something, with few safegaurds to understand if that something is meaningful or stable

So I don’t want to be reviewing a bunch of papers of the form “We Asked ChatGPT Some Stuff, Here’s What It Said.” I don’t care if it said something similar to what humans might have said; I don’t care if it said something different from what humans might have said. If the only humans to read the output are academic researchers, I personally don’t care about it1, and it doesn’t meet our criteria as interesting qua digital media. It’s fine for computer scientists or linguistics diagnosing algorithms to care about it, but that’s not what the JQD:DM is about.

We have an exception, where we’re thinking of LLMs as a form of recommendation algorithm.

LLM “audits” will only be considered if they have a significant cross-model or (ideally) geographical or over-time component. That is, there must be some real world variation of interest. This Google Search audit is an example of a minimal variation we might consider for an LLM audit: https://journalqd.org/article/view/2752

If researchers can control the LLM interaction process in a way that mirrors important variations in how humans might use those LLMs, we’re interested. I’m personally the most interested in the over-time component. Rapid progress in the field makes it absurd to publish the cross-sectional results of an LLM audit; the temporal validity is too low.

So, if you’re interested in doing research about LLMs along the lines we encourage, please send us an LOI! We just returned from our summer hiatus and we’re back in business at https://journalqd.org/loi.

Finally, in the Poli Sci corner, the second edition of the APSA Experiments Section Newsletter I co-edit with Krissy Lunz Trujillo has just been released. There are section announcements for the upcoming APSA meeting, for those interested, and five essays about experimental sample concerns across different types of recruitment methods.

My essay summarizes recent developments in the cat-and-mouse game of making sure that online respondents in surveys and survey experiments are real people, with the actual demographic characteristics they report, paying attention. We’ve developed many kinds of attention checks and tricks; survey respondents, mostly just trying to make money, have developed work-arounds. And now they can use LLMs.

A report from May 2025’s meeting of AAPOR, summarized in this blog post, demonstrates how serious the problem is. To reproduce their bullet points:

• Operator successfully answers all the most common types of survey questions

• Operator is very good at image recognition

• Operator makes honeypot and prompt injection questions obsolete

• Operator passed all the attention checks [they] included in the study

• Operator will be consistent over the course of an interview

• If you directly ask Operator if it's an AI or a human, it will lie to you every time

So…how about those attention checks?

There’s more in the essay, but this is the part I find most interesting:

Thinking meta-scientifically, social scientists must follow the lead of computer scientists in “establishing baselines and ongoing measurements.” The rapid progress in the development of AI tools like LLMs was only made possible by the field’s adoption of “frictionless reproducibility,” in the term of Donoho (2023): “The emergence of frictionless reproducibility flows from 3 data science principles that matured together after decades of work by many technologists and numerous research communities. The mature principles involve data sharing, code sharing, and competitive challenges, however implemented in the particularly strong form of frictionless open services.”

Political scientists have embraced the first two tenets; we would do well to consider the public competitive challenges (the “benchmarks”) whenever appropriate. But institutional reform is both the most important and most challenging step. In the age of LLMs, the validity of attention checks and specific online survey providers are methodological inputs that must be understood to drift, across time, at a faster rate than we can publish papers. Bare-bones open-source accounts of relevant subject attention and survey provider diagnostics give us a better chance at keeping up.

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The antimeme is the message of the medium, defined negatively.

We can identify what the medium is telling us by observing what it is unable to tell us.

Today I apply this insight to Western philosophy, in service of a question which I’ve been trying to answer for years on this blog: why does no one talk about cybernetics anymore? I’ve written about cybernetics many times, in The Tragedy of Stafford Beer and The Whirlpool of the Artificial, and I’m convinced that it offers essential mental tools for navigating the modern world.

But there is an obvious objection. Cybernetics was once extremely popular (Wiener’s book Cybernetics was a best-seller) — if people knew about it and it was so obviously useful, why aren’t they still using it?

One answer is that people are in fact using cybernetics all the time, in the military OODA loop, disciplines like ecology and climate science, in business operations research, and in tech operations via the agile methodology. Another answer is that cybernetics came along just a bit too early, before we had the sensors, computers and effectors for it to flourish.

There is merit to both answers, but neither explains why nobody talks about cybernetics — by which I mean, why nobody writes about cybernetics, why there are millions of Substack posts about AI compared to just a handful by people like me, Maxim Raginsky, Ben Recht, Dan Davies and Henry Farrell.1

Davies specifically notes this forgetting and re-discovering of cybernetics in a discussion about anarchist anthropologist James C. Scott, calling this cycle “intellectual carcinization – the phenomenon whereby people from other fields reinvent some of the important principles of management cybernetics simply because they’re studying the same problems and the underlying mathematical structures are there to be found.”2 But again: if the answers are there to be found, why do they keep getting forgotten from the canon of intellectual writing?

The media-theoretic/antimemetic answer is that it’s really hard to write about cybernetics; the topic resists encoding in text. It’s best understood in action, which naturally makes it difficult to mediatize. Among media modalities, the best cybernetic knowledge comes from the medium which involves action: video games. It’s hard to imagine a better introduction to cybernetics than a game of Factorio.

But video games are so successful in this by creating an artificial world with idealized ontologies and user interfaces that allow you to “feel the feedback.” For cybernetics to be applicable beyond these artificial worlds to the world most people care about, the most effective media technology remains the baroque cyclical diagram.

Cybernetics is, per Wikipedia, the study of circular causal processes such as feedback and recursion, where the effects of a system’s actions (its outputs) return as inputs to that system, influencing subsequent action. The crucial word here is processes — rather than studying static entitities, cybernetics is the study of the process by which entities change and are changed by their environment. This flies in the face of the classical ontology, which the Wiki page about process philosophy explains:

“Since the time of Plato and Aristotle, classical ontology has posited ordinary world reality as constituted of enduring substances, to which transient processes are ontologically subordinate, if they are not denied.”

The river is a classic object for process philosophy because it is not, in fact, an object. It is a process. A river cannot stand still. The molecules comprising the river are always different. “A river” is the process of objects (H20) flowing through the same location. Heraclitus says that “Everything changes and nothing remains still ... and ... you cannot step twice into the same stream.”

Processes are the antimeme haunting Western philosophy — where philosophy is defined by the technology of static, linear, lengthy written text.3 Cybernetics is, put another way, the analytical study of complex processes, and thus especially difficult for this medium to encode.

The ontology implied by linear text is one of stable entities bouncing off of each other. Nouns and verbs. Fixed entities taking actions or being acted upon. Standard nouns refer to types, proper nouns refer to unique instantiations.

Imagine any popular ethical thought experiment. It involves some actor in a situation with some well-defined entities. You’re walking alongside a river, there’s a drowning child, and you are wearing a fancy suit. The ethical decision about whether to save the child hinges on how fancy your suit is, your estimate of the physiognomy of the child (he looks like he might be >95th percentile criminality :-/ ) and your monetary discount rate divided by your timeline for AI X-risk…

All of those parameters are fascinating, for a certain type of person, but before you pull out your calculators and start multiplying probabilities by consequent utiles, take a second to recall that the medium (of the thought experiment) is the message: the message is that the world consists of entities like “suits” and “rivers,” that a few sentences are sufficient to define a human’s experience of the world, and that those humans have an essentially unlimited amount of time to perform those calculations.

To me, it is time that most concretely differentiates process philosophy from classical philosophy. Despite considerable effort, I personally cannot transcend my Cartesian intellectual formation; I cannot will myself to really think of the world consisting of processes rather than substances. But I have thought about time a lot, specifically about how the scientific process is capable of studying phenomena which change slowly but not those which change quickly. The idea of a “scientific fact” implies that the thing is ontologically stable — that it’s going to stick around for a while!

Reflexively, we should consider the durability and reproducibility of the printed books and journals by which science and philosophy are communicated. These mediums are themselves ontologically stable; machine-printed texts last a long time, and even if the invidual object decays, the exact same text can be re-produced with the iron press. The “spirit” of the text is eternal, and the “body” of the book has a lifespan longer than that of the human. Books are very intuitively things, not processes, and it is natural that the world communicable through books seems to be composed of things.

Western process philosophy can reasonably be said to begin with Heraclitus, who is probably most famous for the idiom that “man cannot step into the same river twice.” This idea is central to Herclitus’ thinking; he believes the whole world to be made of flux, never being but always becoming.

Anti-ironically,4 this most famous quote of his defies our received definition of “a quote” as an exact and eternal thing. Wikipedia notes three different surviving formulations:

"On those who step into the same rivers, different and different waters flow" – Arius Didymus, quoted in Stobaeus^[at]

"We both step and do not step into the same river, we both are and are not" – Heraclitus Homericus, Homeric Allegories^[au]

"It is not possible to step into the same river twice" – Plutarch, On the E at Delphi^[av]

The point of the aphorism is not to memorize it or to deconstruct it, this isn’t gospel5 — the point is to think with it, so the exact text is simply less important than its capacity to be applied, to be implemented in a cognitive process. Why is Heraclitus’ process philosophy an antimeme? Well, it wasn’t at the time — he was very influential in the more dialogic, speaking-focus Anciet Greek intellectual society, including on Aristotle. His relative obscurity today reflects the fact that none of his writing exists, except in quotation from others. Remembering that an antimeme is only defined with respect to the medium in which it is encoded, “one copy of papyrus” turns everything into an antimeme eventually.

The “river” remains difficult for standard natural philosophy to absorb. Consider Hume’s billiard balls, the famous example he uses to develop the definition of causation which became central to logical positivism and the recieved understanding by practicing scientists. One billiard ball is moving, rolls into the other one; they exchange position and momentum but they are not fundamentally changed by any of this action. The heavenly orbs that served as the ideal-typical objects of Newtonian physics and consequent scientific intuitions are not changing, from the temporal perspective of the human scientist with a telescope.

Hegel is the most famous classical capital-P Philosopher who can best be characterized as doing process philosophy; fittingly, he is often derided as an obscuratinist mystic by subsequent analytical/positivist philosophers. I’m not about to change anyone’s mind about Hegel in this blog post (it’s not like I’ve managed to actually read him), but I will point to Yuk Hui’s re-reading of Hegel through the lens of cybernetics in Recursivity and Contingency and especially Machine and Sovereignty as an important influence on my thinking.6

Hui argues that the process philosophers7 were hamstrung by their geometry and the associated tools-of-thought. Pre-scientific, mystical traditions think in terms of circles, of eternal return to the present; scientific/mechanistic thinkers, and people communicating with books, are trained to think in terms of linear progress. What both camps are missing is the idea of recursion, in Hui’s framework — which I think is the same idea that Flusser anticipates in Communicology, which he calls circular progress.

Recursion is only possible if our ontology allows for change. The stability of classical ontology, like the billiard balls of linear historical progress, makes it impossible for something to go around in a circle, end up in the same place, and yet be fundamentally changed by the experience. I said earlier that video games are the best medium for understanding processes, but even better might be code: the language of the current world, in which the idea of recursion is central.

But back to the antimeme: my story is more compelling if I can identify other places where the idea of processes sprang up—and why it sprang up, where it came from, if not from the mainline tradition of Western philosophy—and then disappeared. The clearest case is Henri Bergson, who was an intellectual celebrity in the early 20th century before a rapid decrease in popularity; he was more recently rehabilitated by Deleuze, and the past twenty years has seen a strong re-appreciation for his work.

Bergson’s most famous concepts are of durée and elan vital. The former reconceptualizes time, allowing for a subjective understanding rather than the standard “clock-time”; the latter assigns agency to every living thing, an agency which implies a goal. This inverts the causality of classical physics, because the cause (the goal) is temporally after the effect (the actions the being takes to achieve their goal).

But where did Bergson get his ideas, so far from the mainstream of philosophy? From the natural sciences, specifically from the idea of evolution. First, from Herbert Spencer, and then from Charles Darwin.8 The idea that the human beings (and all living beings) are the result of some living thing changing into another thing is, obviously, a problem for classical ontology. Evolution is very much a process, even if the 19th-century theorists didn’t quite understand how it worked.

This is why evolutionary theory was such a shock to the Western intellectual tradition. If God created every being fully formed — the Medieval Great Chain of Being — then we can imagine the mechanistic theory of causality playing out and explaining everything as a cause of the first Great Effect. This is the Deism of the clockmaker God, who wound up the clock but has since let it tick according to the laws of Newtonian physics.9

Bergson was heavily criticized by mainstream philosophers at the time; his vitalism was seen as obscuritanist mysticism to analytical logicians. Wikipedia’s list of “those who explicitly criticized Bergson” reads as a who’s-who of midcentury philosophy.10 He did win the 1927 Nobel Prize….in Literature, not exactly an endorsement of analytical rigor.

Overall, Bergson-as-philosophical-antimeme can be understood by one pole of Asparouhova’s original antimeme framework: he was simply too popular, his ideas burned through the public so fast that the intellectual establishment simply derided him rather than thoughtfully engaging with or extending his work. Suzanne Guerlac makes the point:

Bergson achieved enormous popular success in this context, often due to the emotional appeal of his ideas. But he did not have the equivalent of graduate students who might have become rigorous interpreters of his thought. Thus Bergson's philosophy—in principle open and nonsystematic—was easily borrowed piecemeal and altered by enthusiastic admirers

So Bergson suffered from perhaps an excess of memetic success: he was an intellectual celebrity in his lifetime. This was exacerbated by his unusual academic position at the College de France, where he did not have PhD students but rather gave popular public lecture. Spending time lecturing certainly took time away from the further development of his thinking — his public base of support may have insulated from the need to integrate his framework with that of his critics. His work was an antimeme within the philosophical community of written books becuase it was too much of a meme in popular culture.

Alfred North Whitehead represents the opposite pole. Whitehead cites Bergson and his protégés, the American pragmatists James and Dewey, in the preface of his magnum opus. Process and Reality is one of the most respected philosophical texts of the 20th century, and the explicit modern origin of the “process philosophy” that I’ve been tracing back to Heraclitus…but, in true antimeme fashion, nobody reads it today.

I need to emphasize the ridiculousness of Whitehead’s intellectual trajectory. The man was a mathematician of the first class, writing (with his student Bertrand Russell) the Principia Mathematica, a seminal contribution to mathematical logic. In his 50s, his mathematical edge a bit blunted, he became an important university administrator and Dean of the Faculty of Science at the University of London. For most academics, that’s a life sentence.

Whitehead, instead, “turned to philosophy” in his late 50s, despite no formal training on the subject. Wikipedia notes that “So impressive and different was Whitehead’s philosophy that in 1924 he was invited to join the faculty at Harvard University as a professor of philosophy at 63 years of age…This is not to say that Whitehead’s thought was widely accepted or even well understood. His philosophical work is generally considered to be among the most difficult to understand in all of the Western canon”, and provides the following incredible anecdote:

Eddington was a marvellous popular lecturer who had enthralled an audience of 600 for his entire course. The same audience turned up to Whitehead’s first lecture but it was completely unintelligible, not merely to the world at large but to the elect. My father remarked to me afterwards that if he had not known Whitehead well he would have suspected that it was an imposter making it up as he went along... The audience at subsequent lectures was only about half a dozen in all.

This is the other end of the memetic spectrum: Whitehead’s process philosophy was so difficult to understand that it could never really spread at all.

I think these two poles of popularity can explain subsequent cases of process philosophy as antimeme. Norbert Wiener’s Cyberntetics was a bestseller — but it was filled, filled with mathematical jargon and equations. People might’ve bought the book, but there’s no way they fully incorporated the ideas; the meme spread too quickly to be fully integrated within intellectual culture, turning the original idea into an unrigorous caricature.

On the other pole are all sorts of cranks who have written something too involved/baroque to be picked up by other thinkers. This is a failure mode of cybernetics, when the baroque diagrams are embedded in books; they’re too hard to evaluate or use. This certainly happened to my hero Stafford Beer, who followed cybernetics all the way from the height of management consulting to a semi-mystic hylozoism, as I describe in The Tragedy of Stafford Beer.

And yet this is still happening! Internet cranks like the pseudonymous Slime Mold Time Mold are attempting to introduce “a new paradigm in psychology”: by which they mean, just do cybernetics. Other internet cranks, like myself, are saying we should just do cybernetics to understand political communication on social media, like in The YouTube Apparatus, from Cambridge University Press.

We’ll see if anything comes of it. I’m skeptical — unless the idea can be encoded in a different, more (inter)active medium.

I’d be remiss not to mention one of the more famous instances of cybernetics in Western philosophy.

In Heidegger’s final interview (conducted with the agreement that it only be published after his death) “Only a God Can Save Us,” he explains the end of philosophy:

Hiedegger: Philosophy [today] dissolves into individual sciences: psychology, logic, political science.

SPIEGEL: And what now takes the place of philosophy?

Heidegger: Cybernetics.

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It’s crazy how much more famous Spencer was than Darwin during their lifetimes…and also just how recent Darwin’s fame is. “Charles Darwin” is, largely, a meme from 1990s/2000s culture wars about science and religion — even if Darwinism/evolutionary theory is not.