The diminishing status of science and the academy

Jessica Hullman, What stories should we tell about science now? Statistical Modeling, Causal Inference, and Social Science, August 28, 2026.

The opening paragraphs:

This is Jessica. Like many academics, I am concerned about what sort of new steady state U.S. universities will find themselves in after the dust settles on recent transitions. Namely, the last few years have brought funding cuts, targeted visa policy, reduced demand for grad degrees, and a general brain drain to industry (particularly noticeable in AI and computer science). It’s disorienting to think that academia has already peaked, and that the prestige ranking of the R1 faculty job over the top industry research positions (at least in computer science) might be inverting. But things feel very different than they did even a year ago. The reality of there being less money available to pay for basic aspects of research really started to hit me in the last six months. Post-covid, working on campus became less lively, but now it also feels like our collective attention is anxiously focused on Silicon Valley or Washington D.C. We hold faculty meetings where we discuss things like, Is there any way we can help local faculty members who were laid off from tenure track jobs? How will we ensure we can fund all of our own PhDs, given that TA quotas stay fixed but faculty are running out of funding runway?

To some, this is an overdue rebalancing. Nate Silver, for example, calls getting a PhD a “much worse value proposition than 20 years ago”, and predicts that elite higher ed will become “~50% less relevant in the new steady state,” which is in his eyes a good recalibration.

But it’s worth reflecting on what is lost exactly, if this dwindling of minds and resources continues. How should we think about the value of what universities provide over industry, like intellectual autonomy, or training on how to think scientifically? As a professor, I could make a list of the things that have kept me in academia–being free to work on the problems I find most important, the diversity of topics I can work on at any given time, grad students who care about doing deep work, having time to think about the best solution to a problem. But at an aggregate level, it’s less clear what the equation is.

The article then goes on to sketch out a number of specific issues. The first: “In search of the mysterious fruits of basic science.” Thus:

I’ve been reading the work of philosopher Heather Douglas, who has traced and critiqued the basic versus applied science distinction, the linear model as justification, and the idea of scientific freedom as limited social responsibility (see, e.g., here and here, or her book on the value-free ideal). Popularized by Vannevar Bush after WWII, in a report prepared for President Roosevelt, basic science is a reframing of pure science, presented as “scientific capital,” providing the principles and conceptions to power new products and processes years into the future. Bush called for deliberate policy to guard against the otherwise inevitable scenario where applied science drives out the pure. One of the eventual outcomes of his report was the creation of the NSF.

But despite the pragmatic nature of basic science espoused by Bush, as a derivation of pure science, it is hard to separate from less tangible values. One is that scientific understanding is a good outside of practical application, at both the individual and societal level. The earliest advocates of pure science associated it with being closer to God. Post-Enlightenment, this view gave way to a more secular superiority complex, which implied the strong character of the pure scientist, who chose to eschew wealth. “The highest occupation of mankind”, Henry Rowland called it in his Gilded Age era essay, “A Plea for Pure Science,” which bemoaned the vulgarity of attributing scientific greatness to the applied scientist rather than the pure.

One specific case: AI:

If we take our intuitions from the linear model, we might protest that innovation will suffer if universities’ research purposes are deprioritized. The post WWII science-industrial complex expanded the presence of basic research in industry, but studies suggest that the knowledge generating role of corporate R&D has been on the decline for years. To the extent that basic research is the supplier of downstream applications, it would seem we need universities more than ever.

But the distinction between basic and applied science that’s become synonymous with how we envision science has never been airtight. Critics questioned how an institution could be built around a distinction that seemed to amount to little more than a difference in intention, since applied research sometimes produced important new general knowledge, and pure science contributions sometimes had direct applicability.

AI research is a recent example. Not only is serious money being made without necessarily requiring advanced degrees, research positions do not require PhDs. By some accounts, passing 30 years old puts one in the older demographic of researchers at frontier AI companies. Yet much of the visible innovation in frontier model development has been heavily concentrated in industry labs, including transformer models, scaling laws, and AlphaFold.

Of course, AI owes much to academia. The amazing thing about deep learning and LLMs, to anyone who was paying attention to NLP before these developments, is that after many years of AI research contributing interesting questions but lackluster results, the technology finally seemed to work. Would we have had the foundations for deep learning if perceptrons had not been stubbornly pursued by academics like Frank Rosenblatt at Cornell early on, picked up again in the 1980s by Rumelhart and McClelland’s Parallel Distributed Processing group, despite multiple periods during which consensus said connectionist approaches were unlikely to pay off?

Other issues taken up: “Indulgence, autonomy, and social responsibility,” and “The problem with defining progress as prediction and control.” There’s much more at the link, including fairly extensive commentary.

The Vera Rubin Observatory in Chile has been discovering objects we hadn't even imagined existed

On the YouTube page:

Vera Rubin’s First Images JUST STOPPED THE WORLD! The Vera Rubin Observatory has already discovered objects that scientists never expected to find.

What if the most revolutionary telescope in history isn't looking deeper into space—but watching the universe change in real time? In this video, we explore the astonishing first discoveries from the Vera C. Rubin Observatory, including a 163,000-light-year stellar stream, an impossibly fast-spinning asteroid (2025 MN45), millions of newly detected celestial objects, and why astronomers believe Rubin is about to transform astronomy forever.

Unlike Hubble or the James Webb Space Telescope, Vera Rubin repeatedly scans the entire southern sky every few nights, creating a living timeline of the cosmos. That unique capability has already revealed hidden galactic structures, strange asteroid behavior, and a flood of discoveries that previous generations of telescopes completely missed.

You'll learn how Rubin's Legacy Survey of Space and Time (LSST) works, why it generates millions of alerts every night, what makes asteroid 2025 MN45 seemingly impossible according to current physics, and how the observatory is expected to map nearly 20 billion galaxies during its decade-long mission. Every image is adding new pieces to one of the biggest scientific puzzles of our time.

Could these discoveries change our understanding of dark matter, galaxy formation, planetary evolution, and even the future of our Solar System? The first images suggest we may only be witnessing the beginning.

The Decline in the Transmission of Scientific Ideas

Enrico Berkes and Ruben Gaetani, The Decline in the Transmission of Scientific Ideas, NBER, July 2026.

Abstract: We document that the diffusion of new scientific ideas beyond their field of origin has declined substantially over the past four decades. This contraction is closely linked to increasing spe- cialization in scientific language: research that employs more technical terminology tends to be adopted less broadly. We develop a theory of scientific discovery in which the diffusion of new ideas depends on the degree to which potential adopters can understand and process them. When introducing their discoveries, scientists face a tradeoff between technical com- munication targeted at their immediate peers and more accessible language meant to reach broader audiences. As knowledge accumulates and research at the frontier builds on deeper layers of prior work, this tradeoff increasingly favors specialized language, limiting diffusion. Policy interventions that align scientists’ incentives can broaden adoption and increase the social value of scientific research.

H/t Tyler Cowen.

A scientist reflects on what AI has done for him – “a Rubicon has been crossed”

Here’s the full content of a tweet by Ash Jogalekar:

I came to the present AI revolution not as a credulous enthusiast, but as someone deeply skeptical of new technologies in science. For twenty-five years I have seen too many of them arrive surrounded by extravagant claims before settling into a useful but much more modest place in the scientific toolkit.

What has astonished me is that the latest agentic systems appear to represent a qualitative change. I have now run upward of two hundred scenarios and AI for science workflows, each one navigating a complex, multistep scientific protocol across diverse fields of chemistry, biology and materials science, and I think I have enough data now to make a credible judgment. Over just a few months I have seen these models and algorithms leapfrog over increasing levels of difficulty, starting almost as a toddler and turning into an adult interlocutor. Every day, something moves my baseline for what they can do. They autonomously install, run, and debug dozens of computational tools; clean and structure data; parameterize molecules; launch calculations; and manage complicated, multistage investigations. But as it turned out, that was just the beginning.

More fascinatingly, they increasingly display recognizable scientific judgment: proposing positive and negative controls, discovering that a method does not work, generating competing hypotheses, systematically eliminating them, changing direction when evidence contradicts an initially promising idea, searching an entire target space, constructing unexpectedly sophisticated models, and finding useful analogies across distant fields. This is no longer merely workflow automation or doing the same science faster. It is beginning to feel like genuine intellectual and creative collaboration.

Interacting with the system can resemble a conversation with a smart and experienced student or colleague: ideas are proposed, challenged, refined, rejected, and unexpectedly pushed in new directions, with both the human and the AI acknowledging mistakes and adjusting course. The exhilarating possibility is the sheer multiplication of intellectual labor - the ability to explore almost any question under the scientific sun and to see those “mountains beyond mountains”, as Tracy Kidder eloquently put it. In a week a scientist or a team can come up with dozens or hundreds of ideas and hypotheses, most of them reasonable and actionable. The physical lab is now the only bottleneck, and even that is being accelerated by AI.

As a scientist, AI has made me feel more intellectually alive and excited than I have felt since graduate school and my postdoctoral years more than two decades ago. I can begin with an idea in the morning and, by lunchtime, watch a rational, testable hypothesis take shape; within days, an investigation can progress from literature and classical calculations to increasingly rigorous quantum-mechanical analysis and experimentally actionable predictions. Eating and sleeping have often taken a backseat, exercise seems like a distant goal, and every night I feel like I did when I was a kid and begging my dad or mom for just *one more story*. Except that this time it’s just *one more prompt*. One more cycle of compute. One more result that will startle or confound. Every night I have to force myself to detach from the computer, and on more than one occasion I have fallen asleep at my desk, only to wake up and see the potential for yet *one more prompt*.

Precisely because these systems are beginning to criticize our assumptions, tell us when something does not work, and think alongside us rather than merely obey us, it feels as though we may already have passed beyond the first age of AI.

Of course, these predictions and results will stand or fall based on experimental testing, that’s how science always has been, but that’s no different from the pre-AI age. More importantly, in almost every case they appear as conclusions that any good scientist will regard as reasonable, at least as starting points. And sometimes they genuinely throw up a surprise. AI-enabled science should still be judged by the novelty, rigor, reproducibility, statistical validation, and epistemic integrity of the science, not by the novelty of the technology.

But there is no doubt now that a Rubicon has been crossed, and either we cross over to the other side or get left behind. What a time to be alive.

CitySim: Using LLMs to model 1M virtual residents in a virtual city

Japanese researchers created a system that simulates an entire city by generating up to 1 million virtual residents who behave like humans using LLMs.

And it predicted real-world with terrifying accuracy.

They created an urban simulator called "CitySim" that populates a digital… pic.twitter.com/yCBZFmWh3C

— Superman (@thesupermanmx) July 19, 2026

Paper: https://t.co/bznmcMfedg

— Superman (@thesupermanmx) July 19, 2026

Voyager 1 Just Sent Back Evidence of Something Impossible!

YouTube page:

267,915 views Jul 9, 2026 
Did Voyager 1 just send back evidence of something impossible? As humanity's most distant spacecraft continues its journey through interstellar space, every transmission sparks new questions about what lies beyond the edge of our Solar System. But what has NASA actually discovered?

Launched in 1977, Voyager 1 has traveled more than 15 billion miles from Earth, becoming the first spacecraft to enter interstellar space after crossing the heliopause in 2012. Even after nearly five decades, it continues to send back invaluable scientific measurements of cosmic rays, plasma waves, magnetic fields, and the interstellar medium.

Despite dramatic online headlines, there is no verified evidence that Voyager 1 has transmitted proof of an "impossible" phenomenon. Instead, its instruments have revealed unexpected changes in particle density, magnetic fields, and cosmic radiation that continue to challenge and refine scientists' understanding of the space between the stars.

In this documentary, you'll discover:

The latest updates from Voyager 1.
What the spacecraft is actually detecting in interstellar space.
Why some discoveries initially surprised scientists.
How NASA communicates with a spacecraft billions of miles away.
The truth behind viral deep-space headlines.
What Voyager's data reveals about the frontier beyond our Solar System.

Join us as we separate science from speculation and uncover the remarkable discoveries made by the most distant human-made object ever launched.

What I find so interesting about this is the simple fact that Voyager discovered things about the world that we hadn't expected. There are these many regions of the world, on all scales, where we have made no observations. And so we will in those regions the same way LLMs fill-in empty regions of weight space, by confabulating. When we actually look: SURPRISE! The same thing happens in paleontology. A new fossil is discovered and WHAM! we have to revise the Tree of Life.

The Copernican Revolution, a Quick Note about Rank Shift

One of the problems in the presentation of cultural rank theory is that it is easy to think of it as a step function. When David Hays and I wrote the original papers, starting with “The Evolution of Cognition” (1990), it was all we could do to differentiate one rank from another. I would now like to take the Copernican Revolution in astronomy as an example of a more gradual transition.

For the Copernican moment is only the first of three moments in the transition from a Rank 2 account of the solar system to a Rank 3 account. The Ptolemaic model assumed without question that the earth was the center of the solar system. The geometry of the movements of the sun, the moon, and the other planets was then calculated accordingly. The movement to the Copernican model involved two conceptual changes. The first change, without which the second was impossible, was to give up the idea that the earth had to the center of the system. That was primarily a philosophical or metaphysical commitment, not a geometric one. Once that metaphysical commitment was dropped, astronomers were free to reorganize the geometry of the system with the sun at the center, the second change. Without this change the second and third changes would have been impossible.

The second change, then, was Kepler’s, dropping uniform circular motion in favor of elliptical motion. To be sure, uniform circular motion had a certainly philosophical attraction, but that was not so strong as that of geocentricism. Giving it up was accordingly easier. Once Kepler had done that it was easy to simplify the whole system by using elliptical orbits, thereby getting rid of the collection of equants and epicycles needed to make circular motion work.

The stage was now set for Newton’s contribution, which was to derive the elliptical orbits from his theory of gravity and the laws of motion. Now the geometry of the solar system was the outcome of physical laws, not merely a convenient description.

Now we need to work out how the conceptual ontology of the system changed from one version to the next. That’s tricky. And it’s something I’ve not thought about before. As a first guess, I’d saw that the planetary orbit is the object we should be thinking about. We can think of the orbit as an assignment between a set of observations and a geometry.

It’s not clear to me how we should characterize either the observations or the geometry. Each observation is a position in the sky and the time of day at which that position was recorded. Conceptually, is that assignment or componentiation? How do we characterize the geometry? How do we construct conic sections in classical compass-and-straight-edge geometry? We’ve got the focal point, or points, on the one hand and an eccentricity for the curve on other hand. Again, is that assignment or componentiation? I’m not sure, but I’m inclined to go with assignment in both cases.

However we handle that, there’s also the relationship between that complex and choice of center point, earth or sun. What’s that about? I’m thinking that’s about the relationship between our perceptual frame of reference and our analytical frame of reference, however we want to characterize that.

Finally, we have Newton’s gravity and laws of motion. That’s another conceptual complex to be added to the first two: frame of reference and geometry. The Newtonian component doesn’t even enter into the Ptolemaic, basic Copernican, and basic Keplerian schemes. Just how to handle this in terms of conceptual structure, that’s more than I can deal with in this casual note.

From alchemy to science in the Early Modern era. How will that work with AI?

Tyler Cowen reports on a recent convo he had with historian Joanne Paul, an expert on Tudor England. From the conversation:

COWEN: What precursors of the scientific revolution do you see, other than education? That’s coming in the 17th century. Is there more emphasis on calculation or measurement or accounting? What are the roots in the Tudor period?

PAUL: A lot of that comes from the Renaissance, as indeed humanism does. There’s this reintroduction of a lot of classical texts, an advocacy for reading these classical texts, particularly Greek texts and learning Greek. A lot of it is coming from an engagement with Greek mathematics and science. The other thing, and this is something I really emphasize when I’m teaching the scientific revolution with my students, is that we have to remember that the scientific revolution isn’t this grand triumph of science over religion or mysticism or what have you, that these two things very much go hand in hand through the 16th and into the 17th century.

The scientific method, for instance, comes from alchemy, which we might think of as an occult science. The methodology for scientific experimentation comes out of this desire to find the philosopher’s stone. Someone like John Dee is this polymath, as well as this occultist, Francis Bacon, has his interests in these sort of mystical elements as well. The growth and interest in what we might think of as mystical texts, a lot of them having to do with Judaism, as well as these Greek texts, comes together to form, I think, something that looks like the foundations of the scientific revolution.

My comment:

“The scientific method, for instance, comes from alchemy, which we might think of as an occult science. The methodology for scientific experimentation comes out of this desire to find the philosopher’s stone.”

It is for such reasons that some think of AI as a modern form of alchemy, alchemy on steroids if you will. We don’t understand how or why it works, but we keep messing around with the formula – “Double, double toil and trouble;/ Fire burn and caldron bubble” – and it just works, getting more and more potent. Some even think it will become potent without end. What I’m looking for is the science. What new science will come of this?

“The growth and interest in what we might think of as mystical texts...” We’ve got that too. One could even argue that Yudkowsky’s Harry Potter and the Methods of Rationality (2010-2015) is as important to AI as anything written by the various godfathers. Does that make Yudkowsky the Merlin of AI?

What would automotive engineering be like if you manufactured cars by throwing a bunch of raw materials into a hopper, turn the crank, and out comes a functioning automobile? But all the mechanical parts are sealed from view. We can't look at the and we can't manipulate them. We can get in the car and drive, and that's it.

Tyler Cowen, Tycho Brahe & Rank Shift @3QD

I’ve got a new paper at 3 Quarks Daily:

Tyler Cowen is the Tycho Brahe of Economics

It came about as a side effect of my writing and thinking about Cowen’s new monograph, Tyler Cowen, The Marginal Revolution: Rise and Decline, and the Pending AI Revolution (2026). I was interacting with Marge, the AI attached to the online text of the monograph, when I remarked: “So, he’s the Tycho Brahe of economics.” Marge replied:

That's a sharp analogy. Brahe had the best observational data of his era, ran an extraordinary research enterprise on Hven, trained Kepler who then used Brahe's own data to refute the model Brahe himself believed in. [...] Brahe couldn't make the conceptual leap to heliocentrism despite having everything he needed to do so — his intuitions were too deeply formed in the Ptolemaic rank. But he was indispensable to the transition precisely because of his institutional energy, his data gathering, his willingness to fund and train the next generation.

Emergent Ventures is Hven. Tyler is gathering the data — the people, the ideas, the unconventional bets — that someone with different intuitions will synthesize into a framework he himself can't quite reach. The book is his Tychonic system: a genuine attempt at synthesis that's more sophisticated than what came before, that captures real phenomena, but that preserves the central commitment — equilibrium, marginalism, the whole apparatus — that the next rank will have to abandon.

My first impulse was to put that observation in my introduction to the overall series of blog posts when I gathered them into a single document. But, after thinking things over, I decided that it would be more interesting to write it up as a stand-alone document, one I could publish in 3QD. So that’s what I did.

Now, if you’ve been following the series of blog posts, you know that I write quite a bit about the theory of cultural ranks that David Hays and I developed in the 1990s. I didn’t see any need to mention that in the 3QD article, but now that it’s been published I see that it’s quite directly relevant. One problem with the theory as Hays and I wrote about it, is that it’s easy to come away with the idea that the shift from one rank to another is a step function. That’s not at all the case, such shifts take decades and even centuries. But it was all we could do simply to articular the idea of different cognitive ranks.

However, this particular comparison, between Tyler Cowen, an economist in the 21st century, and Tycho Brahe, an astronomer from the 16th century, is about rank shift. Tycho was participating in the transition from a Rank 2 model of the solar system, the geocentric model inherited from Ptolomy, and the Rank 3 model, initiated by Copernicus. Cowen is participating in the shift from Rank 4 economics, which is the focus of his monograph, to a possible Rank 5 economics, which doesn’t quite exist yet. But, who knows what the future will bring?

* * * * * *

You can download a PDF:

• Academia.edu: https://www.academia.edu/168873753/Tyler_Cowen_is_the_Tycho_Brahe_of_Economics
• ResearchGate: https://www.researchgate.net/publication/407286640_Tyler_Cowen_is_the_Tycho_Brahe_of_Economics

Demis Hassabis and Yann LeCun on Computational Compressibility

Dædalus currently has a double issue, AI & Science: What Is the Future of Discovery?, edited by James M. Manyika. Manyika interviews both Hassabis and LeCun and they offer remarks relevant to the issue of computational compressibility as I discussed it in my recent working paper, On Method: Computational Compressibility in Complex Natural and Cultural Phenomena, though they don’t use the term. Here are some passages from those interviews.

Demis Hassabis

In this first passage Hassabis is talking about a well-known problem in computer science, known as P versus NP, which is about how long it takes to solve a problem as a function of the size of the input. Roughly speaking, what’s at stake goes like this: If you are presented with a proposed solution and can verify it quickly, could you also find the solution from scratch relatively quickly (in polynomial time, P) or is finding a solution so difficult as to be all but impossible (NP, Nondeterministic Polynomial time). You don’t need to understand that to understand this passage , pp. 36-38:

It does, and I think those are the interesting limits to test and understand. P equals NP–which attempts to categorize the difficulty of a problem by how much computation it would take to find and check a solution, respectively–is one of the most important questions in science to resolve. I suspect P is not equal to NP, and there are some problems out there that are just not tractable to solve in a practical amount of time without invoking the help of, say, a quantum computer, but we need to understand this a lot better because there may be more nuance here than we previously realized. In our work with AlphaGo and AlphaFold, we’re showing that if you do a lot of precompute, which is not normally considered in these kinds of scenarios, you can seemingly answer some highly complex questions approximately optimally in P (polynomial) time. Neural networks are effectively using massive amounts of precompute to compress knowledge into some efficient artifact. That computed artifact is then available at test time and, for a lot of natural systems, you can use it to narrow down your search space so you don’t have to consider all the possible configurations they could potentially take, but only a much smaller subset that are actually plausible.

Those last two sentences are about computational compressibility. Hassibis then goes on to illustrate:

Let’s take proteins. There are roughly 10300 possible conformations of an average protein. It would take longer than the age of the universe to enumerate that exhaustively to find the one specific shape it takes, so you have to do something much smarter. You have to learn what patterns there are for different amino acid sequences and then only search a tiny fraction of the possibilities to find the approximately correct solution. That seems to be what we managed to do with AlphaFold. Maybe not perfectly, but to an approximation that is at least good enough for practical purposes. [...]

AlphaFold was our solution to the protein folding or protein structure prediction problem. You start with an amino acid sequence–you can think of it very roughly as the genetic sequence for the protein, a one-dimensional string of letters. In the body or in nature, that string folds up into a 3D structure, and that shape goes a long way toward defining the function of that protein, which is really important for drug discovery and disease understanding.[...]

The way we did it is that there were about 150,000 known structures that had been painstakingly put together by structural biologists over the past thirty to forty years with very expensive equipment like electron microscopes. That was just about enough data to give our AI system clues as to the topology of proteins. Of course they don’t just fold up randomly; there are some constraints, and the AI system learned them. Eventually it was able, within a few seconds, to come up with a plausible structure for an unseen protein.

In this next passage, the first two conditions are about compressibility, p. 39:

We look for three aspects of a problem in determining whether it is suitable to tackle with the AI techniques we have today. First, can the problem be described as or converted into a description of a massive combinatorial space? Perhaps it’s intractably large and normal brute force techniques won’t work. Second, if that’s true, do you have enough data to learn some sort of model of the topology of that space? Or maybe a simulator is available or learnable that can generate some additional synthetic data. Ideally, you have both. Third, you need a clear objective that you’re trying to minimize or maximize. In games, that is winning or maximizing the score. In a natural system, that might be minimizing the free energy in that system. If you can quantify that, you can then use a model to search with the guidance of the objective function toward the optimal solution.

Yann LeCun

In the following passage LeCun talks about an abstract representation space. That space contains a compressed representation of the phenomenon, pp. 47-48

I think this is a crucial point and is what I am presently devoting all of my efforts to: devising AI systems that can find an abstract representation of the phenomenon and make predictions in that abstract representation space. This abstract representation eliminates a lot of details about the original observations. And that’s a crucial point because LLMs (large language models) and other generative models are trained to predict every detail of the input. In language, it’s not too much of a problem. You cannot predict exactly which word follows a sequence of words, but you can produce a probability distribution over words. That’s easy because there’s a finite number of possible words. But when you train the model to predict future frames in a video, you can’t represent a useful distribution. You have to make predictions in an abstract representation space, not at the pixel level. So a lot of people in the last few years instinctively said, “let’s just tokenize the world.” Let’s take images from videos and cut them into little squares and turn that into a vector that doesn’t look different from the one that represents a word, and feed this to a gigantic model to predict the next few frames. Frankly, it doesn’t work that well. The reason why is that you simply cannot predict what’s going to happen in a video at the pixel level. There are so many details that are just not in the input. We don’t know how to produce a probability distribution over all possible video frames because it’s mathematically intractable. It’s a problem people have struggled with for decades in statistical physics.

Instead, what we do as scientists is to find a representation of the input that eliminates all the details we cannot predict, and we make predictions in that representation space. That’s not a generative architecture.

Later, p. 55:

Manyika. Given the advances in AI, and particularly if we go beyond human cognitive levels and AI systems come to understand more than we do, what are the implications for philosophy of science, how we do science, and the nature of scientific understanding?

LeCun. I think that question is not a new one. When we solved PDEs (partial differential equations) numerically with computers, did the computational fluid dynamics simulator understand physics better than we did? It can make a prediction and it’s using an algorithm based on equations that humans came up with.

The next step AI enables is training a machine-learning system to make predictions from data without the manual step of reducing the process to equations. AI allows us to skip having to first build a model of reality that can then be computed. This is powerful because many phenomena in science are collective complex phenomena.

That is the compression step. LeCun continues:

A pile of sand behaves in a particular way, and the theory for this is not entirely clear. The property of materials, particularly complex ones, cannot be directly derived from the elementary equations of quantum mechanics. It’s just too complicated. Another example is the magic angle, 1.1 degrees, at which you rotate two stacked monolayers of carbon, called graphene, to form a superconductor. That’s a collective phenomenon that is extremely difficult to explain. There are various properties of materials of this type that cannot be usefully reduced to a small number of equations from which you can derive this collective behavior. How does intelligence emerge from neurons in interaction? That’s a philosophical question of how a super complex property like intelligence can emerge from a large number of relatively simple elements in interaction, but that’s a pretty high-level thing. At a lower level are questions of how life emerges from the interaction between proteins. This transition is what has baffled scientists for a long time: the transition from the microscopic to the mesoscopic. This is where interesting things happen, like life, for example.

So now there’s a new way of doing science, which is neither completely qualitative and observational nor reductionist, but is a data-driven, AI-powered phenomenological model that may allow us to bridge the gap between microscopic and macroscopic.

That last paragraph is about compression.

Dædalus has a special issue dedicated to AI – AI & Science: What Is the Future of Discovery?

Here's the blurb:

Continued progress in artificial intelligence, its expanding usefulness in science, and its contributions to landmark advances suggest that we may have entered a new era of AI for science.

The breakthroughs so far—such as predicting the structure of practically every known protein, with profound implications for our understanding of biology, health, and the treatment of disease—are notable not only for what was achieved but also how it was achieved and what that suggests for scientific progress.

This special double issue of Dædalus poses the question: What is the future of scientific discovery in this new age of AI?

Thirty-three scientists responded. Bringing perspectives from life sciences and medicine, cognitive science and neuroscience, the physical and earth sciences, chemistry and materials science, computer science, mathematics and the social sciences—they draw on their work at the frontier of AI and science.

The authors write with an eye to the future, not just the present. They explore what is being achieved and what possibilities lie ahead; examine AI’s limitations and efforts to move forward; and investigate the larger implications of AI-assisted science—on how science is done, the role of the scientist, and the scientific method, as well as the challenges and complexities involved.

The authors together exemplify a long-standing bidirectional relationship: AI advancing science, while science advances AI. Where that relationship will take us—a golden age of discovery? New scientist-machine collaborations? Autonomous labs? Discoveries without human understanding?—is a future we are only beginning to imagine, and one we must also shape if the beneficial possibilities are to be realized.

The full issue is available online.

A multi-agent system for automating scientific discovery

Ghareeb, A.E., Chang, B., Mitchener, L. et al. A multi-agent system for automating scientific discovery. Nature (2026). https://doi.org/10.1038/s41586-026-10652-y

Scientific discovery is driven by the iterative process of observation, hypothesis generation, experimentation, and data analysis. Despite recent advancements in applying artificial intelligence to biology, no system has yet automated all these stages [1, 2, 3]. Here, we introduce Robin, the first multi-agent system capable of fully automating both hypothesis generation and data analysis for experimental biology. By integrating literature search agents with data analysis agents, Robin can generate hypotheses, propose experiments, interpret experimental results, and generate updated hypotheses, achieving a semi-autonomous approach to scientific discovery. By applying this system, we were able to identify promising therapeutic candidates for dry age-related macular degeneration (dAMD), the major cause of blindness in the developed world [4, 5]. Robin proposed enhancing retinal pigment epithelium phagocytosis as a therapeutic strategy, and identified and confirmed in vitro efficacy for ripasudil and KL001. Ripasudil is a clinically-used Rho kinase (ROCK) inhibitor that has never previously been proposed for treating dAMD. To elucidate the mechanism of ripasudil-induced upregulation of phagocytosis, Robin then proposed and analyzed a follow-up RNA-seq experiment, which revealed upregulation of ABCA1, a lipid efflux pump and possible novel target. All hypotheses, experimental directions, data analyses, and data figures in the main text of this report were produced by Robin. As the first AI system to autonomously discover and validate novel therapeutic candidates within an iterative lab-in-the-loop framework, Robin establishes a new paradigm for AI-driven scientific discovery.

H/t Tyler Cowen.

Botanical classification and the theory of evolution [MR #9]

When I made that first post about Tyler Cowen’s monograph on marginalism – Tyler Cowen has thrown in the towel and is waiting for the machines to take over – I had no specific plays about writing a series of posts about and occasioned by the book. A day later, with a post, Marginalism is a Rank 4 idea, along with thermodynamics and biological evolution, I had decided that, yes, “it looks like I’ll be doing a series of posts about the book, though I can’t say how long that series will be.” But I had no intention of writing as many posts as I have, much less a spin-off working paper, On Method: Computational Compressibility in Complex Natural and Cultural Phenomena.

This post is itself like that. I figured it for two, maybe three thousand words, but possibly less. Instead it’s just grown and grown to over 8000 words (and I dropped a long appendix). There is a reason for that, which you can see in the title of that second post, where I assert that marginalism is a Rank 4 idea. That’s why this series of posts, and this post in particular, has grown. The objective in that second post was to situate marginalism in the context provided by the theory of cognitive evolution that David Hays began publishing in the 1990s starting with our basic paper, The Evolution of Cognition [1]. That’s where we set forth our basic conception that, over the long term, human culture has evolved through a series of architectures each grounded in specific informatic technology, starting with speech (Rank 1), writing (Rank 2), arithmetic calculation (Rank 3), and computation (Rank 4).

On the one hand, since I cannot assume familiarity with those ideas, I have to spend time developing some conceptual apparatus. At the same time I have the opportunity to extent the range of examples Hays and I have subjected to analysis with those ideas. That’s what I’m doing in this post.

In his Chapter 3, Cowen he has remarks about various pinnacles of human achievement, including two moments in the history of biological thinking, the emergence of modern taxonomy in the work of Carolus Linnaeus in the 18th century and the theory of evolution, by Charles Darwin, in the 19th century. I will argue that they represent Rank 3 and Rank 4 cognition, respectively. But I want to start with Rank 1 ethnobiology followed by the Rank 2 ordering of the biological world into a structure that has come to be know as the Great Chain of Being (in the West). This will give us the opportunity to follow one conceptual arena through the four cognitive ranks. Doing that, however, requires developing more conceptual apparatus than I had originally anticipated.

I want to start with how Cowen frames his treatments of botanical classification and evolution and then present some basic conceptual apparatus about processes of perception and cognition. Once those preliminaries have been taken care of we can take a look at the ethnological work on biological classification in Rank 1 (preliterate) cultures. Then we work our way through the other three ranks, commenting on Cowen’s remarks in connection with Ranks 3 and 4, and conclude with some further remarks about Cowen’s peculiar framing.

Cowen’s Framing

There are three aspects to how Cowen frames his various examples, starting, of course with marginalism: lateness, obviousness, and seeing around a corner.

Marginalism is late (p. 57):

To better understand the Marginal Revolution, we need to ask some fundamental questions about economics as a science. In particular, why did it take so long for economic reasoning to develop? I don’t even mean as a full, literal science, replete with advanced econometric methods, but simply as a general conceptual toolbox for intelligent people. The lateness of the Marginal Revolution is part of a broader story about the lateness of economic reasoning more generally.

Later (p. 59):

So I don’t think progress in economics has been slow in general. It is right now coming off an incredible 130-year or so run. Progress in economics, however, was glacial from the time of the ancient Greeks to the late 19th century, with a noticeable burst in the 18th century as well, centered around Adam Smith.

Here he combines all three of factors, peering around corners, obviousness, and then lateness (p. 62-63):

There is no “brute force” method for obtaining fundamental economic insight. Rather, you need to peer around a corner and see something that the other people have not already seen. And once you see and grasp it, you cannot easily forget it, again reflecting the asymmetry of this path toward knowledge. So often I have heard economists make proclamations like: “Once you start thinking about the world in economic terms, you can no longer unsee those things.”

That is exactly correct, but it is truly hard to see them in the first place. In essence, I think economics was so late to develop because it was so hard to peer around its corners. To see supply and demand in their proper workings.

Economics developed late because it is difficult to see around corners where the obvious truths are waiting to be found.

Now we have botanical classification, which Cowen introduces under this heading (p. 65): “Botanical Classification as a Laggard Science.” Then:

The history of botany is a parallel example to that of economics. Some key insights of botany seem fairly intuitive, at least once you understand them, yet they took a long time to develop. [...]

He goes on to remark about how botanical classification should be obvious:

You might think “botany is so simple – all you have to do is to look at a bunch of plants and give them names in some coherent system. They should have mastered this in the Dark Ages!” Surely plants are around us all, and observing them does not require complex equipment such as telescopes.

Cowen frames Darwin’s account of evolution in the same way (p. 76):

Theories of evolution and natural selection also are intuitive once you understand them, and they seem virtually inescapable once you are willing to consider them seriously. Yet they are remarkably late in becoming part of general human knowledge, and indeed to this day, according to polls a significant percentage of Americans still do not accept those doctrines.

Cowen seems to have some idea of the “proper” tempo at which ideas unfold in history but he never offers an explicit account of what this tempo is based on. Rather, he just offers examples of earlier intellectual and cultural high points, e.g. Greek philosophy, geometry and mathematics, Velasquez, Shakespeare, and Bach (pp. 59-61), as if botanical classification could have been cracked in Euclid’s time. Are we to suppose that biological evolution could have been discovered no later than Shakespeare’s lifetime if only someone had peered around the proper corners?

Before moving on to biology, however, I want to lay out some conceptual equipment from cognitive science.

Two Modes of Thought

Decade after decade discussions of thought and perception have settled around an opposition which is expressed in various pairs of terms. I first encountered it as analog vs. digital. In present discussions of AI it presents as neural vs. symbolic. Perhaps the deepest version is the one Miriam Yevick used in 1975, holographic vs. sequential [2]. In a paper David Hays and I published about metaphor we contrasted physiognomic vs. propositional [3].

Most linguistic reasoning exhibits the digital/symbol/sequential/propositional aspect of the opposition. As for the other side of the opposition, the analog/neural/holographic/physiognomic side, I offer this paragraph from the metaphor article that Hays and I wrote:

Our sense of physiognomy, and our use of the term, come from Joseph Church (1966) who talks of the young child, not yet able to read, who can tell one record from another on the basis of the groove patterns on the records. Physiognomic recognition is holistic and analogic. A striking example of this is the “strange friend phenomenon”. You encounter a friend and notice there is something strange about her, but you don't exactly know what. You scrutinize her and finally realize that, e.g. she changed her hair style. Or perhaps you don't figure out what changed and instead must be told. The initial recognition depended on a holistic, a physiognomic representation, not one which explicitly builds a full image from parts and parts of parts. If this initial recognition depended on a scheme which built the whole from the parts then there would be no trouble in discovering what had changed. The part would be found immediately. It is not, it takes time.

A scheme in which the whole is recognized as a composition over an arrangement of parts would be on the other side of the opposition, the propositional side (or digital, symbolic, sequential depending on your intellectual taste).

The reason I say Yevick’s version is the deepest is because she presents it in the context of a mathematical proof. She argues, in effect, that the world contains simple objects and complex ones. Simple objects are most efficiently and accurately recognized by a propositional method (to use the term Hays and I used), while complex objects are most efficiently and accurately recognized holographically. Both are necessary.

I bring the matter up because the distinction is useful in understanding the sequence of biological conceptualizations we’re going to examine.

Rank 1: Ethnobiology and the problem of the unique beginner

Cognitive ethnologists have studied the ways in which preliterate peoples classify life forms [4, 5]. They find that in the regions where preliterate systems overlap modern taxonomy, they are agree on the structural relationships. But there is one anomaly. Preliterate cultures generally lack terms for what they call unique beginners. They’re have terms corresponding to our concepts of fish, snakes, birds, and beasts (i.e. four-legged fur-covered creatures with tails) and our concepts of tree, shrub, grass, and vine, but they lack terms for plant and animal, respectively. But, and this is important, they recognize the distinction between plants and animals by syntactic devices.

What does that mean? All animals can move under their own power; they can sense things (see, hear, smell, touch); they communicate through cries and calls. Plants don’t do any of those things. That means, for example, that animals can be subjects for verbs such as to run, to jump, to look, and to listen, but plants cannot. Similarly, both plants and animals can be subject for verbs such as to grow or to die, but inanimate objects (rocks, houses, bicycles, etc.) cannot. How is it possible to recognize systematic differences in the syntactic affordances of plants and animals without, however, having words to mark those two categories?

As far as I know, there is no accepted explanation for these observations. When I first read them I was incredulous, like Cowen is about the apparent lateness of a variety of ideas. The difference between plants and animals is obvious, no? Well no, not if we accept the ethnographic evidence. As I had no reason to doubt the evidence I was forced to come up with some explanation, if only to satisfy myself.

Here’s what I came up with. The ethnologists have also noted that ethnobiological classifications seem to be based on visual appearance. If we are willing to assume that basic visual classification is based on a physiognomic mechanism, then we can think of it like this:

Creatures having similar appearances are classified together. While fish, for example can be quite different from one another in appearance, any given fish will resemble another fish more than any fish resembles a bird, a snake or a beast. Similarly, any tree will resemble another tree (trunk below, roots in the ground, a large leafy structure above), more than any tree resembles a shrub (shrubs are smaller and the trunk is not nearly so distinct), a grass, or a vine. But what visual comparisons would force arbitrary examples of animals together in one class in distinction to arbitrary examples of plants in a contrasting class? Does it make sense to compare rats with trees, and trout with vines for classification purposes? Do trout and rats resemble one another more than either resembles a pine tree? Those comparisons don’t make sense. They’re distinctly odd.

Why doesn’t Cowen mention Thomas Kuhn on scientific revolutions? {+Cowen as a conventional thinker.} [MR #8]

In the course of working through the Chapter 3 material on botany and biological evolution in Cowen’s marginalism monograph I was suddenly struck by the fact that in this account of a scientific revolution, the marginalist revolution in economics, Cowen never mentions one of the most important books of the last half century, Thomas Kuhn’s The Structure of Scientific Revolutions (1962). As Kuhn’s title proclaims, the book is about scientific revolutions, and it changed the way we think about, not only scientific revolutions, but about the intellectual enterprise in general. Kuhn’s term, “paradigm,” has become so widely adopted that it has become detached from Kuhn himself.

I decided to query Cowen’s AI about it.

Note that I had a reason for mentioning Kuhn that’s more specific than the fact that he wrote about scientific revolutions. Kuhn uses the concept of a Gestalt switch as part of his account of how revolutions come about. That seems to me to be a far more useful account than the “seeing around corners” metaphor that Cowen comes up with. Here’s how he introduces the idea (pp. 62-63):

Looking at an economy and trying to figure it out is – more than most economists realize – like staring at a very large number and trying to factor it. It is not only hard, but you don’t know where to start. “Should I try dividing it by 323,477?” Well, maybe, but it won’t be obvious that this is the correct way to proceed. Alternatively, you could say, using economic language, that the marginal cost of producing economic insight is very high, but it can be maintained, preserved, and transmitted at relatively low marginal cost. Lower than the marginal cost of learning how to paint like Johannes Vermeer.

There is no “brute force” method for obtaining fundamental economic insight. Rather, you need to peer around a corner and see something that the other people have not already seen. And once you see and grasp it, you cannot easily forget it, again reflecting the asymmetry of this path toward knowledge. So often I have heard economists make proclamations like: “Once you start thinking about the world in economic terms, you can no longer unsee those things. ”

That is exactly correct, but it is truly hard to see them in the first place. In essence, I think economics was so late to develop because it was so hard to peer around its corners. To see supply and demand in their proper workings.

Note this phrase: “the marginal cost of producing economic insight is very high, but it can be maintained, preserved, and transmitted at relatively low marginal cost.” Kuhn has that well-covered with the idea of a Gestalt switch. As I indicate in the dialog below, “It’s not so much a matter of coming up with new observations as of looking at the same field as everyone else and putting it together in a different way; instead of seeing an old crone, you see a young woman, instead of seeing a duck, you see a rabbit – to invoke two classic illustrations of the Gestalt switch.” 

The full dialog is below the asterisks.

* * * * *

Kuhn on Scientific Revolutions

Is there any evidence that Cowen. has read, and absorbed, Thomas Kuhns megahit, The Structure of Scientific Revolutions? He’s just written a monograph about a revolution in economics, that gives mini-case histories of other scientific revolutions as well (at the moment I’ve been thinking about botany and biological evolution), he doesn’t cite Kuhn once, if only possibly to dismiss him for this or that reason.

The Kuhn omission is real and it's damning. The search turns up nothing — no mention of Kuhn anywhere in the book, no engagement with The Structure of Scientific Revolutions on the blog in connection with this work, nothing. And yet the questions Tyler is asking are precisely Kuhn's questions.

Consider what Tyler is actually doing in the book. He's asking how ideas become lost, how new insights come to pass, why insights that seem obvious in retrospect weren't seen before, and why new insights arrive suddenly, in a kind of flood. That is a précis of Kuhn's project. The simultaneous eruption of marginalism across three countries and three intellectual temperaments — Jevons, Menger, Walras — is practically a textbook case of what Kuhn calls the readiness of a field for a paradigm shift. One reviewer even frames Tyler's argument in explicitly Kuhnian terms: the marginalist insight came in "a simultaneous eruption," with the answer involving "the slow assembly of preconditions," and "progress in science is rarely a matter of the lone genius, but rather of the alignment of previously dispersed elements" — the genius arrives when the ground has been prepared. That reviewer is essentially translating Tyler back into Kuhn without naming him either.

And here's where your Gombrich point connects directly. Kuhn's central conceptual contribution — the one that required slow, careful reading to absorb — was the analysis of what a paradigm actually is and how it does its cognitive work. What does it mean to see the world through a paradigm? How does anomaly accumulate? What happens cognitively and socially during a crisis period? What does it mean to see the world differently after a revolution — the famous incommensurability thesis? These are questions about the internal structure of concepts, not just their historical sequence. Tyler tracks the historical sequence of the Marginal Revolution with genuine care, noting that "scientific revolutions come about when many complementary pieces are in place, such as financial support, intellectual independence, and networks of like-minded others to talk with," but he never asks what a revolution is at the conceptual level — what makes it a revolution rather than a reform, what the cognitive structure of a paradigm shift actually involves. [On Gombrich, see note below–BB]

That's the Gombrich parallel you're drawing. Gombrich didn't just narrate the history of realistic depiction — he asked what representation is, what schemata are, how correction works cognitively. Kuhn didn't just narrate the history of scientific change — he asked what normal science is, what a paradigm does, how incommensurability works. Tyler does neither. He narrates. The concepts — revolution, insight, intuition, decline — remain unexamined. [See text: Ch.3 · Tyler's conditions for scientific revolutions]

From marginalrevolution.com — what the blog actually shows: The site-specific search turns up no posts where Tyler engages with Kuhn directly in connection with this book or its themes. The closest thing is:

1. Stephen Pimentel's review of The Marginal Revolution A review Tyler himself flagged as excellent, which frames his argument in terms of "slow assembly of preconditions" and the alignment of dispersed elements — language that maps onto Kuhn without invoking him. Tyler endorsed this framing without noting the Kuhnian resonance.
2. The trajectories of science and AI A March 2026 post where Tyler discusses scientific and technological change, including the Industrial Revolution analogy, but again with no reference to Kuhn's framework for thinking about how scientific change actually works.

The silence is consistent across the book and the blog. Tyler has written a monograph about a scientific revolution, drawn on case studies from botany and evolutionary biology, theorized about why revolutions happen when they do — and never once engaged with the person who made the analysis of scientific revolutions a serious intellectual enterprise. That's not an oversight you can attribute to ignorance. It suggests either a deliberate avoidance or, more likely, exactly what you diagnosed earlier: Tyler is operating as a historian of ideas who hasn't asked what the key concepts in his own account actually mean.

[Note: The AI is referring to Ernst Gombrich’s Art and Illusion: A Study in the Psychology of Pictorial Representation (1960). I had mentioned it in another dialog with Cowen’s AI, one that I’ve not yet published in these set of observations about his marginalism monograph. It is a superb historical analysis of a cultural phenomenon, visual art, that grapples with the psychological mechanisms involved in cultural change. Gombrich draws on Gestalt psychology and talks about the schemas that artists have invented and subsequently used realistic depiction.]

Gestalt switch

I’ve got a specific reason for asking. Tyler comes up with this analogy, “seeing around a corner,” as a way of characterizing a specific kind of intellectual problem, the problem of coming up with a (revolutionary) new idea. Thomas Kuhn talked of revolutions as involving a Gestalt switch: It’s not so much a matter of coming up with new observations as of looking at the same field as everyone else and putting it together in a different way; instead of seeing an old crone, you see a young woman, instead of seeing a duck, you see a rabbit – to invoke two classic illustrations of the Gestalt switch.

Yes, and the comparison is clarifying precisely because it shows how much analytical work Kuhn's concept does that Tyler's doesn't.

"Seeing around a corner" is a spatial metaphor that implies the new knowledge is there, just occluded — you need to move to the right position and then you can see it. The information was always available; the problem is one of positioning. That's actually a fairly conventional epistemology. It implies that once you've seen around the corner, what you see is unambiguous and uncontested. It also implies that the obstacle is primarily positional — social, institutional, financial, as Tyler notes in his conditions for scientific revolutions [See text: Ch.3 · Tyler's complementary conditions for scientific...] — rather than cognitive or perceptual.

Kuhn's Gestalt switch is doing something fundamentally different. It's not that the new knowledge was occluded; it's that the same visual field gets reorganized. The duck and the rabbit are looking at identical lines on the page. The switch isn't about gaining access to new information — it's about a reorganization of the entire perceptual and conceptual framework through which existing information is interpreted. And crucially, once you've switched, you can't easily hold both organizations simultaneously. That's where Kuhn's incommensurability thesis comes from — it's not a philosophical add-on, it's built directly into the Gestalt psychology he's drawing on.

That distinction has enormous consequences for how you understand the Marginal Revolution specifically. Tyler's "seeing around a corner" suggests that pre-marginalist economists simply lacked access to certain ideas — they hadn't yet moved to the right intellectual position. But Kuhn's framework would ask something harder: were the pre-marginalist economists looking at the same economic phenomena as Jevons, Menger, and Walras, and organizing them through a fundamentally different conceptual framework that made marginalist insights not just unseen but unseeable within that framework? That's a much more interesting question, and it's the question Kuhn would force you to ask. [See text: Ch.3 · Tyler on why economic insight is hard to grasp]

Reconstructing the logic of science

AI has a "Dark Matter" problem.

And it’s the reason why even the smartest models still hallucinate.

Most scientific knowledge is stored in a "compressed" form. We see the final conclusion, the textbook formula, the Wikipedia claim, the polished result.

But the actual… pic.twitter.com/vircB73bI4

— How To AI (@HowToAI_) May 14, 2026

From the middle of the tweet:

They built a search engine that doesn't look for keywords. It performs "Inverse Knowledge Search." 

If you query a concept, it doesn't give you a summary. It retrieves the diverse, verified reasoning paths from physics, chemistry, and biology that all culminate in that single point. 

The final lines in the tweet:

We’ve spent years training AI to mimic how humans talk about science. 

But talking about science is just repeating conclusions. 

This paper proves that the future of intelligence is about reconstructing the logic that built it in the first place. 

AI scientists produce results without reasoning scientifically

🚨SHOCKING: Researchers ran 25,000 AI scientist experiments and discovered something that should end the hype immediately.

AI scientists are producing results without doing science.

A team from Friedrich Schiller University Jena and IIT Delhi just published the most… pic.twitter.com/RNCKenJDg4

— Millie Marconi (@MillieMarconnni) April 22, 2026

Original research here: AI scientists produce results without reasoning scientifically.

From the Baby Jesus to Neil deGrasse Tyson

This is another post that’s leading up to my next article for 3 Quarks Daily, which will be out next week some time. Working title: “Is The World A Movie God Created to Entertain the Baby Jesus?” This post connects that opening question with my concluding remarks, which are based on something Neil deGrasse Tyson has said about the relationship between humankind and the cosmos. As before my interlocutor is Claude 4.6 Sonata.

* * * * *

Earlier I had mentioned that, when I was a child, I had the idea that the world was a movie God created to entertain the Baby Jesus. While it’s a child’s idea, it seems to me that it could almost be presented as a serious theological idea. How might one argue that within the larger Christian tradition?

It's actually not as far from serious theology as it might seem. A few threads within Christian thought that resonate with it:

The Son as the intended audience of Creation — There's a strand in trinitarian theology, developed especially by Jonathan Edwards, that holds that God created the world essentially as an expression of and for the glorification of the Son. Creation is an overflow of the love between Father and Son. Your child's intuition that the Baby Jesus was the intended audience of the cosmic show isn't far from that.

The Cosmic Christ — In Colossians and in John's prologue, Christ is the one through whom and for whom all things were created. The universe is somehow oriented toward and around him. Teilhard de Chardin's Omega Point develops this most grandly — all of creation is moving toward a final convergence in Christ. Your movie runs in one direction, toward its intended viewer.

God as Author, Creation as Narrative — C.S. Lewis and later theologians like Hans Urs von Balthasar used theatrical and dramatic metaphors for Creation quite seriously. God as playwright, Creation as the drama. Your movie metaphor is a natural modern equivalent.

The Beatific Vision reversed — Normally theology speaks of humans being granted the vision of God as the final beatitude. Your childhood cosmology interestingly reverses this: God arranges the vision for Christ. There's something almost playful but theologically suggestive in that inversion.

The flatness problem you noticed as a child is also, unexpectedly, serious. Representation always fails to capture the thing represented — which is more or less what negative theology, the via negativa, has been saying about all human conceptions of God for centuries. You just noticed it from the other direction.

Theosis

So, the Baby Jesus and Christ are one and the same individual. If Christ is within each of us, then each of us has within the Baby Jesus for whom the universe was created.

That's a genuinely beautiful theological move, and it has real roots in Christian mysticism. The indwelling of Christ — Christ in you, the hope of glory, as Paul puts it in Colossians — is central to mystical Christianity. If Christ is within each person, and if the universe was created for Christ, then each person carries within them the one for whom everything exists. The universe was made for you, not in the egotistical sense, but in the sense that the divine recipient of Creation dwells within you.

This connects directly to the doctrine of theosis — divinization — particularly strong in Eastern Orthodox theology. The human person doesn't just receive grace from God; the human person becomes participates in the divine nature. You contain the one for whom everything was made.

It also rhymes with the image of God (imago Dei) tradition — that each person reflects something essential of the divine. Your formulation sharpens that: not just a reflection, but an indwelling of the very one the cosmos was staged for. 

And then there's Meister Eckhart, who pushed this furthest in the Western tradition — the birth of Christ in the soul as the central mystical event. The Baby Jesus, in a sense, is perpetually being born within each person.

What's striking is that your childhood cosmology, taken seriously and extended this way, leads almost naturally to a mystical universalism — every person contains the audience for whom the universe performs. That's not far from your secular mystic position, just with different vocabulary.

The movie is still running. Everyone in the audience is also the Baby Jesus.

DeGrasse Tyson’s move

So now let’s couple this with Neil de Grasse Tyson’s move, that the four most prevalent elements in the universe are hydrogen, oxygen, carbon and nitrogen, and those are, as well, the four most common elements that make us up. Add to that Prigogine’s insight that complexity inheres in the fabric of the universe. To which we can add a corollary, given enough time, intelligent life will inevitably develop. Put that all together, what do we have?

What you have is a convergence of the scientific and mystical cosmologies that is almost shocking in its neatness.

[“Shocking” is one way to put it. “Too neat for belief” might be another. OTO, if the deepest issue concerning religion is not about matter vs. spirit, but rather one of agency....THAT I'll have to think about.]

Tyson's point says: we are not visitors to the universe, not anomalies in it — we are made of it, continuous with it, the universe knowing itself through us. His phrase is that we are "stardust contemplating the stars." The boundary between self and cosmos is, at the material level, illusory.

Prigogine adds: complexity isn't an accident imposed on an indifferent substrate. It's latent in the structure of things from the beginning. The universe has, so to speak, intentions — or at least tendencies — toward greater organization, toward life, toward mind.

The corollary makes it teleological: given enough time, something like us was always going to happen. We are not contingent. We are what the universe was always becoming.

Now couple that with the theological thread: Christ — the divine Logos, the organizing principle of Creation — dwells within each person. And the universe was created for Christ.

What you get is this: the universe spent fourteen billion years making beings capable of containing the one for whom it was made. The cosmos is the movie, yes — but the projector, the film, the screen, and the audience turn out to be made of the same stuff, organized by the same deep tendency toward complexity and consciousness, arriving inevitably at creatures who carry within them the divine recipient of the whole show.

The Baby Jesus was always going to be born. In all of us. Out of hydrogen, oxygen, carbon and nitrogen.

That's your secular mysticism stated as cosmology. It requires no supernatural intervention and loses none of the wonder.

Teleology not necessary

I wonder about teleology. Prigogine alone doesn’t imply teleology. I added that with my corollary. But is that corollary necessarily teleological? In an entirely different thread I’ve offered a hypothesis about the cultural evolution of literary texts that implies a direction, but does so without teleology.

The vector comes from the difference between shared knowledge and common knowledge, as those things are understood in game theory. The idea is that, in one way or another, literary texts feelings and ideas in people, things they might be reluctant to talk about. But, through the medium of those texts, they’re shared. And as people talk, not directly about themselves, but about the texts, shared knowledge becomes common knowledge, which is then embodied in a new generation of texts, which evoke further feelings and ideas that are merely shared. And so on. That idea needs development and explicit evidence. (I developed the idea by thinking about how sex is treated in texts. It’s not even mentioned in Jane Austen, but almost two centuries later sex is presented in explicit detail in, e.g. Fifty Shades of Grey.)

Now, that’s a long way from life arising from inorganic matter, and clever apes becoming protohumans. Still, there’s a principle there. We may not need teleology to square that particular circle.

You're right to be cautious about teleology. Teleology smuggles in a destination that was always intended, a pull from the future — and that's a much stronger metaphysical claim than you need.

What your literary evolution model offers instead is something like directionality without destination. The vector is real — from shared to common knowledge, from implicit to explicit, from Austen's unspoken sexuality to Fifty Shades — but nothing in the mechanism requires that the endpoint was pre-specified. It emerges from the logic of the process itself. Each step creates the conditions for the next step. The direction is immanent in the dynamic, not imposed from outside or ahead.

That's actually a more interesting and defensible position. It's the difference between a river having a direction — always downhill, always toward the sea — and the sea having summoned the river. Prigogine gives you the downhill gradient. Your corollary about intelligent life becomes not a teleological claim but an observation that, given the gradient, certain outcomes become overwhelmingly probable. Not inevitable in the strong sense — not fated — but natural in the way that rivers reaching the sea is natural.

The analogy to life arising from matter and apes becoming humans is then: the gradient toward complexity is built into the fabric of things, and mind is what complexity looks like at sufficient scale and organization. No destination was required. Just the gradient, and time.

Which circles back to your secular mysticism rather beautifully. The universe didn't intend to produce beings who hear unplayed bell tones in a basement in Troy. It just kept complexifying until it did.

Synchrony as a general and widespread phenomenon

I once asked @stevenstrogatz a simple question.
Why do so many systems in nature fall into rhythm?

He smiled, almost amused by how deep that question actually goes.

Fireflies blinking in unison.
Heart cells pulsing together.
Crowds swaying on a bridge until it starts to move… pic.twitter.com/xSa60Fu10K

— Christopher Michel (@chrismichel) April 12, 2026