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2026 Aug 7

- Neutrinos From Deep Inside Earth Provide a New Picture of the Mantle

PARTICLE PHYSICS | VIEW ON WEBSITE

Neutrinos From Deep Inside Earth Provide a New Picture of the Mantle

By James Dinneen

August 7, 2026

A global constellation of neutrino detectors is creating a never-before-seen view of the radioactive elements that power Earth’s tectonic heat engine.

The JUNO experiment, located outside the city of Guangzhou in China, is expected to report its first geoneutrino detections this year.

JUNO Collaboration

Introduction

In a laboratory 2 kilometers underground, a crane lowers Matt Depatie, a detector technologist, through a hatch into a white-walled cavern filled with about 7,000 tons of ultrapure water that glows as blue as wiper fluid in the light. “Splashdown,” Depatie says over a radio as he steps into an inflatable raft waiting below.

Normally, the cavern is one of the darkest places on Earth, but today, it is lit up for maintenance, offering us a rare chance to see inside. I peer through the hatch at Depatie as he paddles over to examine the submerged experiment. He’s inspecting a house-size detector built to catch some of the most elusive particles known to physics: neutrinos.

This is the SNO+ neutrino experiment, buried deep within the Creighton mine at Snolab, an underground physics laboratory in Sudbury, Canada. SNO+ consists of an acrylic sphere lined with nearly 10,000 sensitive light detectors and filled with 780 tons of oily liquid scintillator, which flashes when lit up by energetic particles. The water around the device and the rock above it shield the detector from the glare of cosmic radiation, allowing the flickers of less common particle interactions to shine through.

Our whole journey down has been part of the crusade to maintain absolute darkness, even beyond the visible spectrum of light. As we descended the main shaft and walked through a rocky tunnel to the lab, our bodies and clothes collected minute amounts of radioactive radon dust. Before entering the main laboratory space, we tossed our mine clothes, showered, and changed into electric blue jumpsuits and hairnets to minimize the contamination we carried in with us. “The showers aren’t for you,” Depatie said as we changed, “they’re for the science.”

Matt Depatie, left, helps maintain the SNO+ detector, which reuses components of the SNO experiment that ran from 1999 to 2006.

James Dinneen

Such extremes are necessary when you’re trying to catch ghosts — in this case, ghosts that may help reveal the secrets of inaccessible regions deep within the Earth.

Radioactive Planet

Neutrinos are the most abundant of all the particles that have mass. But that mass is tiny: just a millionth the mass of an electron. With such little heft and a neutral electromagnetic charge, the particles hardly ever interact with other matter. Trillions of neutrinos — mostly those produced in the sun — pass through our bodies every second, yet after years of hunting them with detectors such as SNO+, researchers have captured only a few hundred thousand of their precious flashes.

Even more elusive — so much so that after decades of searching, scientists have detected only a few hundred of them — are geoneutrinos.

Geoneutrinos are produced in processes that heat the interior of the planet. This heat plays a major role in powering the flow of rocks in the mantle, which shapes everything from plate tectonics to Earth’s magnetic field. It comes from two main sources: heat left over from the planet’s formation, and heat produced by the decay of uranium, thorium, and potassium in the rocks of the mantle and crust. Without this second source, Earth would have long since cooled off and become a tectonically dead planet.

The SNO+ experiment sits deep underground, shielded from cosmic rays beneath 2 kilometers of rock.

Courtesy of Leo Duquette/SNOLAB

In counting geoneutrinos, physicists can get a direct measure of Earth’s vital heat-producing elements. “It’s the one thing we do that focuses on the Earth,” said Ryan Bayes, a particle astrophysicist at Queen’s University in Ontario who works on SNO+. “Everything else we do is more focused on what we receive from other places in the universe.”

The first detection of geoneutrinos, by an instrument in Japan called Kamland, was reported in 2005. In 2009, the Borexino detector in Italy reported catching several dozen more. In November 2025, SNO+ reported its first detection, bumping up the number of observed geoneutrinos by about 50.

Mark Belan/Quanta Magazine

What makes the detections at SNO+ special is the experiment’s location: These are the first geoneutrinos measured in the western hemisphere, offering a new perspective on Earth’s radioactive interior.

Major uncertainties remain in interpreting the results from these experiments, but researchers’ best estimates suggest that each site is measuring a different flux. “It could be that that’s the first hint that the mantle is not uniform,” said Mark Chen, a particle astrophysicist at Queen’s University and director of the SNO+ collaboration.

Geochemists have conventionally assumed that radioactive elements are distributed evenly throughout the mantle, because the flowing rock should mix everything together. But the measurements of geoneutrinos in different locations could hint that this is not the case.

The regions that seem to be producing the most geoneutrinos sit roughly above continent-size blobs of anomalously hot, dense material, known as large low-shear-velocity provinces, or LLSVPs, which seismologists have mapped on either side of the core. One is under Africa, the other under the Pacific Ocean. “There may be deep Earth structures in the mantle that are not understood,” Chen said. “It could be that [they] concentrate some kinds of elements.”

Neutrinos could one day offer new insight into the still mysterious origin of these deep structures and, more broadly, the patterns in the mantle that underlie many aspects of the Earth system. “It really would be a way to make a chemical map of the Earth’s interior,” said William McDonough, a geochemist at the Chinese Academy of Sciences who has long been a leading voice in the search for geoneutrinos.

Catching Geoneutrinos

The outstanding question is whether the geoneutrino measurements reveal differences between the areas of mantle below each experiment, or if the imbalance originates in the way the different experiments count their geoneutrinos. Researchers across the board say there’s still so much uncertainty that it’s impossible to say.

“If we take it at face value, we could say maybe the western hemisphere has a lot more radioactive material in it than the eastern hemisphere,” McDonough said. But there are reasons to be wary of these estimates. “Are the Italians right? Are the Japanese right? Are they both right? Or is something wrong?” he said.

The Borexino experiment, located at Gran Sasso National Laboratory in Italy, was designed primarily to study neutrinos from the sun.

Volker Steger/LNGS-INFN

The uncertainties associated with each detector’s results come from the sorting process that physicists go through to identify geoneutrinos, Bayes said. “They don’t just show up and say, ‘Hi, I’m a geoneutrino.’”

Scientists must eliminate signals from particles with too much energy, particles with the wrong measure of a property called helicity, and particles that they expect to see flowing from nuclear reactors. They must also eliminate geoneutrinos coming from Earth’s crust, with the largest contribution coming from the area within a few hundred kilometers of the detector. When those other detections are subtracted from the total count, the geoneutrino signal from the mantle should be all that’s left.

In general terms, the geoneutrino flux from the mantle seems to be very high at Borexino and very low at Kamland, though the uncertainties are great. A detailed geological interpretation of the SNO+ results is still in the works, Chen said, but so far scientists see a “pretty in-between” mantle below Canada.

A particular challenge for SNO+ scientists is understanding the neutrinos coming from the detector’s surroundings, including a basin formed 1.8 billion years ago by a giant impactor. “There are lots of unknowns in this geological area,” said Virginia Strati, a researcher at the University of Ferrara in Italy who helped develop a local model of radioactivity for Snolab.

The 20-kiloton JUNO detector began operation in August 2025. In addition to catching geoneutrinos, it studies particles that stream from the Taishan and Yangjiang nuclear power plants.

JUNO Collaboration

Uncertainty also comes from estimates of the total amount of radioactive material heating the mantle. The flux of geoneutrinos suggests that these elements could contribute anywhere from just a small percentage of its heat to half of it — a discrepancy equivalent to the output of tens of thousands of nuclear power plants.

Both sources of uncertainty make it even more difficult to detect any differences between the chemical makeup of particular sections of the mantle. The difference in the geoneutrino flux expected from various distributions of radioactive elements “is very small, and is hidden in these uncertainties,” Strati said.

Future Flux

The detection at SNO+ comes at an exciting moment for geoneutrino research: JUNO, another huge neutrino experiment currently collecting data in China, is expected to report its first geoneutrino flux later this year, adding a fourth and notably richer view. With more than 20,000 tons of scintillator, the experiment — buried under a mountain outside the city of Guangzhou — is so large that it is expected to detect more geoneutrinos in its first year than the combined output of Kamland, Borexino, and SNO+ over decades.

Related:

1. How Physicists Track and Trap the Elusive Neutrino

2. Continents of the Underworld Come Into Focus

3. Monster Neutrino Could Be a Messenger of Ancient Black Holes

Clearer estimates of the geoneutrino flux at each experiment could come from more detailed geological data, as well as further geoneutrino counts at each site. However, McDonough says the best thing would be to build a neutrino detector at the bottom of the ocean. It’s an idea McDonough has championed for decades.

Such a detector would be far from continental rocks, which are rich in radioactive elements; oceanic crust is also thinner and more uniform. Crust-related uncertainties go down so much that “you are in mantle-only territory,” he said.

The idea of an ocean-bottom detector, estimated to cost hundreds of millions of dollars, has seen little take-up from government funders to date. But McDonough is hoping he can make something happen in China, which has given the green light to other big geoscience projects. “It’s very possible,” he said. Until then, physicists will keep paddling around for answers deep underground.

2026 Aug 6

- How Does Touch Lead To Pain Or Pleasure?

THE JOY OF WHY | VIEW ON WEBSITE

How Does Touch Lead To Pain Or Pleasure?

By Janna Levin and Steven Strogatz

August 6, 2026

Neuroscientist Ishmail Abdus-Saboor discusses efforts to understand how skin contact can be painful or pleasurable, and what touch-obsessed naked mole rats might teach us about human social behavior.

Chanelle Nibbelink for Quanta Magazine

Introduction

Pain and pleasure seem like simple facts of life, however they are anything but that. Neuroscientists still cannot say why physical pain differs from psychological pain, for instance, or why a loved one’s touch soothes while a stranger’s touch repels.

To explore the science behind these sensations, Janna Levin talked to Ishmail Abdus-Saboor, a neuroscientist at Columbia University’s Zuckerman Institute. Their conversation covers how pain serves an evolutionary purpose, how researchers measure pain and pleasure in the lab despite the absence of any objective biomarker, and how touch functions as a social and emotional signal, not just a sensory one. Abdus-Saboor also describes his work with naked mole rats — a species that barely feels pain, shows no signs of aging, and lives in colonies built almost entirely on touch — and the ethical trade-offs when studying sensations in animals that cannot describe what they feel.

Listen on Apple Podcasts, Spotify, TuneIn or your favorite podcasting app, or you can stream it from Quanta.

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Transcript

[Music plays]

JANNA LEVIN: Hello. Hello out there, I’m Janna Levin.

STEVE STROGATZ: And I’m Steve Strogatz.

LEVIN: And this is The Joy of Why.

STROGATZ: A podcast from Quanta Magazine in which we explore some of the biggest unanswered questions in math and science today.

LEVIN: So Steve, we’ve been talking with Ishmail Abdus-Saboor who’s a professor here at Columbia [University], not a few blocks from me, about skin as an organ and as a vehicle for transmitting both pleasure and pain.

STROGATZ: Mm-hmm. That sounds very interesting.

LEVIN: Yeah. I think it’s interesting that very little is known about pain. I mean, if you think about your own experience, it’s kind of strange when you start to meditate on it. What is it exactly, right? It’s very unpleasant, but other than that, what is it?

STROGATZ: It’s really mysterious, especially when you have pain that doesn’t really relate to tissue damage. Like, sometimes I’ll just be washing something at the sink in the kitchen, and then suddenly I have pain, and I think, “Come on, that’s ridiculous. I didn’t do anything to my back.” And, you know, people will tell you pain is mental. You can sort of talk yourself out of certain pain, which raises the point that pain is not as simple as it might seem at first.

LEVIN: Yeah, and in particular, he studies this at the level of animals. But it’s one of these things that’s very hard for animals to tell you reliably what they’re experiencing. So, a lot of his work is really trying to interpret the animal’s interiority, the animal’s experience of different sensations.

I was very fascinated about biology and biological systems and how animals communicate and cooperated.

STROGATZ: Yeah, I wondered as you were describing this work, is it touch as a means to learn about interiority, or is touch the primary object of interest here?

LEVIN: I mean, I think that that’s an interesting question. Like, with many scientific ambitions, sure, maybe the big goal is consciousness, right? But no, the big goal is always very far off. That’s not the language in which they’re operating.

The language in which they’re operating is data, observations. You know, it’s more immediate to their experiments.

STROGATZ: Well, right. They say science is the art of the solvable, and so we’re trying to restrict ourselves to things where we can make advances, make real progress.

But I have to say, I got a little bit of a queasy feeling when you mentioned pleasure and pain, especially as a person with an animal at home. My dog, Murray, that I love so much.

LEVIN: Yes, I’ve heard about Murray.

STROGATZ:  I know. I’m sure everyone has.

LEVIN: I’ve seen pictures of Murray.

STROGATZ: Okay. Okay. But still, I mean, the thought of pain, you know, and I know there’s a lot of animal rights people among our listeners. So I hope in listening to this episode, I don’t know, what’s the pain part of this gonna be about?

LEVIN: We did talk about this. I mean, this is a very gentle animal lover. It’s really interesting to talk to Ishmail. His experiments, they’re gentle. Maybe, they’ll notice if a paw is retracted, so if it’s uncomfortable, but they’re not torturing these animals.

But even then, I think animal experimentation, even in the most benign sense, is called into question. And he thinks about the ethics of that.

Well, let me introduce our guest. His name is Ishmail Abdus-Saboor. He’s a neuroscientist just down the road at Columbia University Zuckerman Institute, and he studies the skin-brain axis, and in particular, our sense of touch, including gentle touch and soothing touch.

STROGATZ: Fantastic.

Ishmail Abdus-Saboor

[Music plays]

LEVIN: Welcome to The Joy of Why, Ishmail. I’m so glad to speak to you.

ISHMAIL ABDUS-SABOOR: It’s an honor to be here with you as well.

LEVIN: It is a pleasure to get to know colleagues on the same larger campus. I’m very interested in starting with your journey. You grew up in Philadelphia. I read some of your other interviews where you discussed your love of animals, and how at one point you converted the third floor of your home into a year-long science experiment. And maybe I’m exaggerating, but tell me about your initial relationship with animals as a child.

ABDUS-SABOOR: Yeah. Yeah, it’s really joy to be here and, I think, if you were to ask me when I was a kid, what I wanted to do with my life and career, I always said I wanted to become a scientist. You know, I didn’t know any scientists directly, but if I thought about the classes in school, that kept me very excited and energized, and I would watch Animal Planet a lot as a kid and I could just watch nature shows for hours on end.

I had many pets growing up, dogs and cats, but also like lizards and turtles and snakes. You know, I remember, like, as a kid having this subscription to this, like, Turtle Digest sort of a magazine. You know, I was very fascinated about biology and biological systems and how animals communicate and cooperate. I think my science career set in motion in earnest as a freshman in high school, as you alluded to, at Central High School in Philadelphia, as a part of an honors biology class. You know, actually, we didn’t get gym class because to sign up for this honors biology, we had to take two periods of biology. And for me, even as a 14-year-old kid, like, I just jumped at that opportunity. You know, who needs gym? Got teased a little bit.

But, you know, as part of that project, we were able to do this year-long science fair project, and many of the students worked at neighboring universities in Philadelphia, Temple or Drexel, or UPenn. But we also were able to do science at home. So this is what I did. You know, basic rudimentary equipment and things. And the project was actually looking at regeneration in crayfish.

So you know, my parents were very supportive of me and let me take over, you know, the third floor of our house there in the Germantown section of Philly. And, you know, there were hundreds of crayfish and I’m sure it didn’t smell so well up there. But at the time, you know, there was this really big push on, like, supplements and ginseng. And like ginseng was supposed to be like this magical supplement that, like, improved health and memory and all sorts of wonderful things.

So my idea was if I spiked the crayfish, their water with this ginseng herbal supplement, then this could, like, speed up the rate of regeneration because they do have the ability to regenerate lost appendages. So, you know, I got to become a scientist and I, like, trim parts of their appendages and measure the rates of it growing back. And, it was a very exciting time to keep a lab notebook and have hypotheses that I could test and to make graphs and plot my data and do statistical tests to see if there was anything here.

Unfortunately, I don’t quite remember the outcome of those.

LEVIN: You weren’t as diligent with your data analysis as you are now.

ABDUS-SABOOR: Yes, exactly. That’s exactly it.

LEVIN: We actually have something in common. My daughter is obsessed with animals, and at one point we had something like 23 animals in my New York City apartment. It looked like a Petco. There were snakes, lizards, tarantulas. It was insane. Only one time did one animal kill another animal. It’s a real calling, I feel, this interest in animals.

But you ended up studying smaller-scale biology, cellular, molecular. What led you to make that transition from this sort of love of animals to the actual smaller-level biology?

ABDUS-SABOOR:  When I went to college, I thought, you know, again, this love of animals, maybe I wanna become a veterinarian.

So I worked in a number of veterinary clinics and hospitals, and there my experience was, like, helping the vet with spaying and neutering and, and it was very monotonous and, and frankly, quite boring. And I kinda missed, like, this kinda fast-paced nature of biological exploration. So I did another internship my junior year in college at University of Pennsylvania in the Cell and Developmental Biology department, and there we were working on, you know, cells in the hearts of mice, uh, um, proteins in the hearts of mice that are important for cardiac development and function.

Many times we downplay animal intelligence or cognition because we don’t know how to tap into it. We haven’t designed the right test.

And there I got exposed to molecular biology research and working at the bench, and just the culture of science, the whole ethos of the, you know, of scientific discipline at lab meetings and people presenting results and, and just talking about all the open questions and, and being able to, to look at life at the scale of molecules, DNA, RNA, you know, the molecules of life.

I thought that was just very exciting. It wasn’t until a few years later that I moved into, like, neuroscience and sensory neuroscience.

LEVIN: Yeah. There’s this interesting history, painful history — pardon the pun — of our relationship with animals and, sort of denial of the idea that animals are conscious or that they feel pain.

ABDUS-SABOOR: Yes.

LEVIN: Uh, and so going back to Descartes in the 17th century, he infamously performed vivisections when, you know, live animals howling. How could he possibly — and I’m not actually asking you to defend this point of view — but how could he possibly have suggested that the animals were not feeling pain?

What’s your understanding of how we transition from this physical detachment, you know, as our colleague of ours at Quanta said, “They don’t think, therefore they are not.” That was his attitude to accepting that animals feel pain.

ABDUS-SABOOR: This is a wonderful question, and it’s one that I’ve thought a lot about and keeps me and everyone in the lab awake at night. I mean, it’s a part of a broader question, the question of consciousness, right? Do animals have a level of consciousness that we would have?

So if we boil this down to the idea of pain and how it works and where do we draw the line on whether or not animals feel pain? It’s a debate that has raged for many years and I think the modern idea is that you need a brain, you need some central processing unit to have full function and cognition to be able to experience pain.

If you look at lower animals, perhaps, no one denies that they can sense nociception. Nociception, it’s a fancy term for receptors, neurons, out in the peripheral nervous system that can be activated by noxious stimuli. And those signals travel to some central processing units so that the animal knows to like move away. And I think this idea, everyone appreciates. Even simple, you know, bacteria, right, single cell organisms if you put them in an environment that’s not conducive, they’ll move away. They’ll recoil, because they have sensory neurons out in their peripheral nervous system.

Now, we would consider that nociception, but not quite pain. Encapsulate the experience of pain, you have to have a central processing unit whereby you can respond appropriately to subsequent noxious stimuli. There’s some sort of learning and memory. There’s higher level cognition. You understand that this particular stimulus that I’ve received, like, causes me pain, so now I’m going to avoid it.

So then the debate is, if that’s the case, then which animals have it and which animals do not? And I think, as a neuroscience community, by and large the idea has been that many animals, especially lower animals, perhaps do not have that level of higher cognition and consciousness.

You know, a question I get all the time, right, people, especially in the New England area, they ask about lobsters, right? Should I feel bad about throwing this lobster in this hot boiling pot while it’s still alive?

LEVIN: The classic example of slow boiling the lobster.

ABDUS-SABOOR:  Exactly, but then people say, “Well, they tell me they can’t feel any pain, so I shouldn’t feel bad.” And they ask me, “Is that true?” Depends on who you ask, right? And perhaps you have to do the test.

So as a field, like the classical test that we do is like this conditioned place aversion. So does an animal have enough brain real estate to understand that this thing is noxious? They should be able to understand that the environment that they received this noxious stimulus is bad. Such that if you put them in that environment later, they can remember that something I received here was not good and I want to escape or avoid it.

Animals that don’t have that cognitive ability, even though they’ll recoil from something noxious. They don’t have the ability to form the memory, a conscious perception, the negative feeling that is attributed to pain, right?

So this is what people do, and if you can do this test and show that they can make these associations, then people say, “You know what? I think this animal can feel pain, and let’s add that animal to the list.”

LEVIN: This is interesting because the animals you work with specifically have to feel pain to be part of your study but also don’t. And that’s also part of your study. Why they don’t—we’ll come to the naked mole rats.

ABDUS-SABOOR: Yes. We mainly work with rodents, and I think it is accepted that rodents can experience pain or nociception. They will withdraw their paws, like we would withdraw our hands. There is some debate in our field about, again, these higher-level components of pain, the emotional negative valence that’s associated with pain. How much of that are rodents experiencing?

And this is part of, like, I think the importance of some of my work because we’ve taken really detailed behavioral mapping of animals as they experience different stimuli. And we can say actually they respond in ways that they can’t describe to us their ongoing emotional states, but if we can use behavior as a readout of their internal states, they are displaying behaviors very characteristic of negative emotional states of pain and we’re trying to, like, kind of map that in the brain.

LEVIN: It’s a very interesting question that you’re raising because while reading about your work, I was wondering about the ways in which psychological pain differs from physical pain. And beyond differing, how they’re interconnected, right? As you’re describing, if the animal doesn’t have the psychological component, we downgrade it in terms of severity.

ABDUS-SABOOR: That is very true. And you know, I think many times we downplay animal intelligence or cognition because we don’t know how to tap into it. We haven’t designed the right test.

You reminded me of some work from a colleague of mine, Professor Kay Tye at University of California San Diego and also the Salk Institute. In rodents, she’s asking this question, physical pain and emotional, psychological pain do they map or intersect anywhere in the brain? And she designed a really clever test, which she calls the FOMO task in mice, the fear of missing out, where she’ll have mice that are living together. And she puts a divider between them and the animal outside of the divider has to sit there and watch its friends get, like, this chocolate milkshake.

Okay, so the mouse is just watching its friends. The mouse is a little bit hungry, a little bit thirsty, and watching his friends take this milkshake. And her idea is that this is like a psychological form of pain. And then she’s doing recordings in the part of the brain called the insular cortex and asking the question like, does this interact with physical pain? And it appears that it does. Like it alters their threshold to respond to, like, physical pain after going through this emotional psychological pain. And maybe that there’s similar constructs in the brain, and we can even study this in a rodent.

LEVIN: Let’s discuss a little bit of the actual science that goes into this. So, your research in the somatosensory system deals with pain and touch and skin. Can you tell me about some of the brain pathways and the different receptors involved in the different types of sensations?

ABDUS-SABOOR: Sure. So right, as you mentioned, the somatosensory system is our sensory system that mediates touch, pain, itch, temperature, pressure.

LEVIN: And these are all very distinct experiences.

ABDUS-SABOOR: Yes, they are very distinct experiences. Really excitingly for us as a field, if you would look 30 years ago, for example, we didn’t have a lot of the molecular players to even have an entry point into thinking about how does this work.

We had a basic idea of the neuroanatomical pathways. So, you have these peripheral sensory neurons called, uh, they emanate from a structure called the dorsal root ganglia. And we have, like, 30 pairs of these that run alongside our spinal cord. They send one long process to the skin or other internal organs and one process to the spinal cord.

From the spinal cord, a different set of neurons kind of picks up the relay, goes to the brainstem, and then from the base of the brain to other areas throughout the brain.

So, the neuroanatomical pathways, we kind of have had this for quite some time. But as you mentioned, there’s touch, there’s hot, there’s cold, there’s itch, like these are quite distinct. So, it’s where the specificity resides that allows us to exquisitely detect these different sensations.

And you know, our field has had a really nice revolution in the last 15, 20 years where within these sensory neurons out in the peripheral nervous system, we have identified receptor proteins that confer specificity.

And two of the most famous ones I’ll just mention because they were the subject of a Nobel prize in 2021. So, one was the discovery of a receptor protein called, um, TRPV1. Transient Receptor Potential Vanilloid 1. This was discovered in the lab of David Julius, who’s set out to determine, like, how chili peppers, like why do we perceive them as hot? We had an idea of which neurons may do it, but like, how do you explain the “how” at a molecular level?

And so, he designed a really ingenious screen where he cloned receptors into a cell line and basically applied capsaicin, the active ingredient in chili peppers. And he wanted to find cells whereby once you add capsaicin, there can be like an intracellular response. They can be activated in the form of calcium entry into the cell, which is a proxy for, like, neuroactivity.

As a field, I think we’ve made, and are making, really great progress.

And this activity can be conferred by adding a certain receptor protein. So, he screened and basically found this channel that we now call TRPV1, that when it’s expressed on the surface of cells, confers the ability to respond to capsaicin.

We now know, and they showed in that paper, that this same receptor mediates the response to heat. So, this is why, like, capsaicin and chili peppers have this heat sensation because the heat sensing neurons that also respond to capsaicin expressed this one protein.

This paper came out like in 1999, I believe. And it really began a molecular age of pain research because now we had a receptor that we can say, okay, this is how you define a pain neuron or a heat sensing neuron if it expresses this TRPV1 receptor.

LEVIN: So, they’re actually physically specialized neurons. They’re physically different.

ABDUS-SABOOR: They’re physically different than other neurons, by their constitution of the genes and the proteins they express on their surface.

Now, this was in the late 1990s, and there was another pioneering study just a few years later by Ardem Patapoutian’s Lab, who was the co-winner with David Julius on that 2021 Nobel Prize. He did a similar sort of a screen in a different cell line looking for receptor proteins that confer mechanical sensation. Okay. And there they did a slightly different screen where they use RNAi to knock down the expression of receptor proteins in the cell line that they knew was mechanosensitive and could show that without this one protein or two proteins, then the cells no longer responded to mechanical force.

We now call those genes Piezo1 and Piezo2, and for the most part, almost every neuron that we know of that is mechanically sensitive, meaning can convert physical stimulus energy into the language of the nervous system, electrical activity. It’s conferred by this mechanical sensor Piezo. And it’s not just in neurons; it’s in non-neuronal cells.

So here’s another example. I just gave you two examples of they’re sensory neurons, but if this one has  TRPV1, it’s gonna respond to heat, noxious heat. And if this one has Piezo, it’s gonna respond to pressure. And there are other sorts of receptors that confer itch or cold, et cetera.

Now as a field, if I jump ahead to 2026, there was just The Brain Prize, which is the biggest prize in neuroscience, was just awarded in our field again to David Ginty at Harvard and Professor Patrik Ernfors in Europe. They’ve done really pioneering work to show there are at least 15 different classes of these pain, touch, itch neurons that are defined by their expression of different genes, their physiological properties, their expression patterns within the skin. As a field, I think we’ve made, and are making, really great progress.

So you know, what’s left to do, and why am I still employed? You know, I think, as a field, we’ve learned a lot about detection in the periphery. One of the things that really drives me in the work we do in my lab is making a connection between the peripheral nervous system, all these mechanisms in the skin, and how does this connect to the brain where perception resides, right? Making the connection. This body, brain, physiology, and signaling. This is where I think the next wave of major discoveries we’re ripe for in this field.

LEVIN: So here the subject really has shifted from a kind of behavioral science to really hardcore molecular and genetic science. Your particular interest is very specifically skin, as you’re saying, and I know that you’ve quantified a pain scale to try to have a more quantitative way of discussing these things as opposed to just qualitative observations of behavior. Tell me about the pain scale and how that work ties in.

ABDUS-SABOOR: That’s a wonderful question. You know, it was like a side quest actually. So, I’m most at home in genetics and molecular biology. As I mentioned before, studying pain and rodents is challenging. They can’t talk to us, right? And I saw that for me to, like, have precision and understanding the genetic and molecular manipulations, I had to take a step backwards and understand the behavior. And that step backwards has been like 10 plus years of this, like plugging away of trying to make these rodent pain scales.

What we had in the field before some of our studies was just like, you poke an animal with something that you think is painful, and if they respond, they’re in pain. If they don’t, they’re not in pain.

Pain is so complex, right? Just think if someone, like, poked you and looked at whether you lifted your hand, would that be sufficient to explain your pain state? No. It’s so much richer. So, this is what we attempted to do with this rodent pain scale to put a number on their sensation.

LEVIN: Which, of course, doctors ask you to rate your pain scale, which is so fascinating. It’s not something you can measure.

ABDUS-SABOOR: Yes. That gets to another point that keeps us awake in the pain field is that we do not, to this day, you know, April 21, 2026, we do not have a biomarker for pain, alright? This drives us crazy. You cannot get a litmus test. There’s no, like, if this gene is up or if this brain area is on, that means you’re in pain. And if not, you don’t.

We don’t have that. We’re searching. We and everyone in the field is searching, but we don’t have it. In the clinic, we have to rely on self-reports. People largely telling us how they feel. There have been some advances in, like, fMRI and other brain-imaging approaches that looks promising, and maybe we will get to a day where there’s a signature that we can confidently say someone is in pain and not some other state. But we don’t have that yet.

LEVIN: Mm-hmm. As an animal lover and a person who has looked at the complex history of science in relationship to animals, how do you reckon with the ethics of continuing to work with animals?

ABDUS-SABOOR: It’s a tough question, you know. We have an institutional animal care and use committee that every single experiment we do with animals we have to justify, right? And we have to use the lowest amount of animals and do our best to induce the least amount of pain to study the biological process we’re interested in.

It’s a challenge for someone who’s studying pain because we have to like induce the pain to be able to study it, right? One of the benefits I think of our behavioral assays is that because we now get so much resolution, we can test less animals to reach our statistical observations and conclusions. So, this kind of helps with animal welfare.

We try our best to treat the animals humanely and with respect because we do understand that they are giving their lives oftentimes for human benefit. And this is just something that we have to appreciate. We need medicines, we need cures, we need treatments. And oftentimes animal research is a part of that pipeline. And I’m comfortable ethically with that understanding.

Now I know there’s some people who are not. And it’s not upon me to convince people to change their beliefs or thoughts. I respect those. And there have been people who say, “I love the research you do, but ethically this is not for me.” I understand those concerns as well.

But I think, you know, in 2026, we’re still at a point where we need animals to learn how the pain system works and how to design safe ways to relieve chronic pain. And we don’t have better models to do this, so we have to work responsibly and ethically in these animal models.

Now, will that change? Will there be a time where we can use computer models or stimulations or organoid models and learn just as much as we can in animal models? Maybe that day will come and we’ll have to revisit this. Maybe there won’t be a day where we can continue to justify using animals to study pain or in neuroscience research. But I don’t think that day has arrived and they’re still very important conduits to study.

LEVIN: Well, this opens up the question about this fundamental research that you’re doing in a laboratory in an academic setting, how does this transition to therapeutic treatments for human patients?

ABDUS-SABOOR: Yeah, I think the goal of any biomedical researcher such as myself where I’m a basic scientist, curiosity-driven scientist, and I think there is major value in this increasing knowledge for knowledge’s sake, even if it doesn’t have a direct application at the moment that we’ve made the discoveries.

I do believe we have duty to the public and the taxpayers who fund our research to think about how the basic work we’re doing can translate into therapies and cures.

However, in saying that, I do believe we have duty to the public and the taxpayers who fund our research to think about how the basic work we’re doing can translate into therapies and cures and especially for pain, right? There are millions of people who suffer with chronic pain.

The beauty of working in rodents is that many of the genes and molecules, the neuronal pathways I’ve talked about are highly conserved, right? The wiring and the neuroanatomy of the pain system is very much shared between rodents and humans, okay? We really do operate under the space that some of the things we discover can have direct application.

I’ll just mention an exciting example. Some papers in the early 2000s in humans showed that there was a family of patients who can’t feel any pain whatsoever. And there have been these rare cases throughout history of people who just don’t feel any pain. It’s actually, like, not a good thing because many of these patients don’t live long lives. They actually injure themselves. You know, pain is, from an evolutionary perspective, good for our bodies.

LEVIN: Good information.

ABDUS-SABOOR: It’s good information. And especially during development, you learn, like, to not touch that hot stove, right? To not do things that could hurt you.

Anyway, there was a family of people in Pakistan who didn’t feel any pain, so you could do genetic studies and kind of trace the pain and sensitivity. And the scientists went over there and did really heroic work where they sequenced their DNA and they found they all had mutations in a single gene. It’s called Nav1.7. It’s a voltage-gated sodium channel, okay? This channel, this protein, seems to be very specific to pain neurons in the periphery. So if people don’t have a functional version of this protein, they won’t feel pain.

And then conversely, there’s another class of patients that have the exact opposite. It’s called, like, Burning Man syndrome, where they just have spontaneous pain, mainly in their extremities throughout life. It turns out the mutation is in the same protein. This one protein, you don’t have it, no pain. Too much of it, lots of pain.

Same thing happens in rodents and many other mammals we’ve studied. This voltage gated sodium channel is very important for the activity of the neuron propagating an action potential and specifically in pain neurons. And in fact, they’re new drugs that have been improved that actually block this particular receptor and it seems to improve pain for many patients.

There’s another version of this protein called   Nav1.8 that seems to have similar functions. So maybe some of the therapies in the future, we’ll kind of do a double block of both of these proteins.

And this work, a lot of it is going on in rodents, Here’s a one example of a lot of like back and forth and crosstalk between animal studies and actually direct translation.

One of the things we’re very excited about doing in my lab, I mentioned these behavioral tools. And with our behavioral tools we can delineate in rodents the sensory component of pain versus more emotional components of pain. And so there does appear to be an emotional network in the brain — areas like the amygdala, the anterior cingulate cortex, insular cortex, and some other areas that seem to confer an emotional component of pain. How that works, you know, we and others are hard at work.

But the point I wanna make, back to your question about translation. It would be very nice if we could have therapies of the future that don’t target the pain at the level of the peripheral nervous system, at the level of sensation, but alter it at the level of perception and maybe alter the negative emotional state of pain. Because maybe you don’t want a world where you can’t feel any pain, as we mentioned, it does serve some purpose, right?

But, if we can remove the hurting component but keep the sensory intact, then maybe this is how we want to tackle pain. But we have to know how that works at a deep level to be able to kind of target that. But I can envision a future where that’s possible.

LEVIN: It’s so interesting. I mean, I don’t even wanna know how they discovered that this family felt no pain. I don’t even wanna know what happened to them that brought that to the scientific community.

ABDUS-SABOOR: You know, in some countries there are a number of street performers actually who do things – you know, they’ll walk on hot coals or like, do things. And, you know, one of the kids in that family, he would do tricks. He would climb the second-floor building and jump off and just pop right up. And everyone would cheer. And so these sorts of things.

LEVIN: The ability psychologically to overcome pain or to mitigate pain is so fascinating to me because of course, we act like psychological is disconnected from the body. But what we really mean is what the neurons are doing in the brain. And I think that there is this kind of culture of magical thinking that we can transcend pain but there might be a sense in which that’s literally physiologically possible.

ABDUS-SABOOR: That is very true. And what you’re talking at too is one of the reasons I got into pain and pain research. There’s a really nice book called The Challenge of Pain, written by Melzack and Wall, two really pioneering scientists in our field who came up with one of the most important theories in the history of pain: the “gate theory” of pain. But anyway, in this book, they lay out, like, as you’re getting to all these cases where people should be experiencing lots of pain, but they don’t. You know, some people who you can hypnotize them and their pain goes away.

And there are too many anecdotes like this to think that it’s not a real phenomenon. A related phenomenon that we do know a little bit more about, and people are starting to model this in rodents, which I think is really cool, is the placebo effect, which is very strong for pain actually.

If they have a strong expectation and belief that this thing will alter my pain, it can actually work. And conversely, if you tell someone, “I’m gonna give you this treatment and it hasn’t worked for anybody, everyone says it’s crappy, but you’re outta options, and I just wanna try it anyway.” They’ll come back and say, “Doc, you’re right. It didn’t work.” And it could be the same medicine, right?

So this expectation and belief of pain relief is very strong. We now know it definitely taps into our endogenous opioid system, our endogenous system to kind of control pain. It taps into these things. If you block like opioid receptor signaling, you can block many of these effects.

It’s not magic, but it’s acting on defined neuro circuits in the brain. And there are many labs that are hard at work, Greg Corder and Matt Banghart and Greg Scherrer, amongst others to try to map how this is working.

LEVIN: Amazing. I did see that you had talked about the pain addiction connection. Particularly with opioids.

It would be very nice if we could have therapies of the future that don’t target the pain at the level of the peripheral nervous system, but alter it at the level of perception.

ABDUS-SABOOR: Correct.

LEVIN: And what you’re saying is there’s a biological substrate reason why there’s a connection between pain and addiction.

ABDUS-SABOOR: Yes, that’s very true. We have an endogenous opioid system that can be activated by many things, and we have these opioid receptors all throughout our body.

I mean, the discovery of the opioid receptors back in the late 1970s, in rodents, they could stimulate this one area in the brain, the periaqueductal gray. And you could get really strong pain relief. The rat tail would be on the hot plate, and you stimulate this area in the brain. Then they don’t respond, okay? And they found that this area is, like, flooded with opioid receptors. And this response was, like, dependent upon activation of the endogenous opioid system.

Opioids can be powerful relievers of pain and the receptors are everywhere. This is why they also have so many unwanted side effects because they act on the pain system, but they act in the periphery, they act in the bladder, in the gut, in the DRG neurons, the spinal cord. They’re loaded everywhere, so they come with so many unwanted side effects beyond just pain relief.

[Music plays]

STROGATZ: Wow. So many surprising and very immediate things to think about there. I think anyone listening to this can relate.

LEVIN: Mm-hmm. I didn’t really appreciate that the opioids… Of course, I knew they were pain relievers, I guess. I knew that was the story. People were originally going to seek pain relief, but I thought it was a external consequence, you know? I didn’t think it was targeting the same biology.

That was really surprising to me, and that we have this endogenous, natural mechanism that can do the same thing, which is why it suggests we can overcome pain psychologically to some extent, you know? I guess it also just shines a light. There’s a lot we don’t understand about how our own minds work and bodies work.

STROGATZ: I believe it. Watch any magic trick, and you’ll see that. But no, I mean, in something as corporeal as pain, I mean, right? That’s not an illusion. And yet maybe some aspects of it are an illusion. It’s confusing.

LEVIN: Yeah, I don’t know that it’s an illusion. I mean, things are triggered. There are signals, right? But you can flood the receptors with other chemicals. I mean, that’s really what it sounded like to me. And it has an interesting evolutionary role, right, pain in terms of survival. It’s important that we don’t go running if our foot’s broken, and it is very disadvantageous to survival to have no pain receptors, and they know that there are certain people who don’t feel pain. So just appreciate when your back hurts at the sink, Steve.

STROGATZ: Yes, I see. I should be grateful for my pain receptors.

LEVIN: Exactly. Well, after the break, we’re gonna leave pain behind, and we’re gonna talk about something a little more pleasant, and that is the wonderful naked mole rat.

[Music plays]

LEVIN: Welcome back to The Joy of Why. We’re speaking with neuroscientist Ishmail Abdus-Saboor, who studies the brain and our sense of touch.

I do want to turn to your important work in antithesis to pain, which is about gentle touch and social touch and pleasing touch. So, I’m both curious what drew you in this other direction, and of course how the receptors involved in gentle touch or stroking touch are different from pain receptors and why this is so important in the social fabric of possibly both animals and humans.

ABDUS-SABOOR: Great question. You know, as I was ending my postdoctoral studies at UPenn and opening my lab about eight years ago, I started to think about, okay, who would want to come work in my research lab? And if I only have projects studying pain, maybe that could be off-putting. Could be some people who would prefer to study, you know, social touch, appetitive, something good. And that was the case, yeah.

I wanna pay homage to a colleague of mine, David Anderson, at Caltech. He published this paper that I read as a postdoc, and it was just so fascinating and exciting because what they had discovered was that there was a population of neurons that seemed to be activated by stroking touch on the mouse’s skin, consistent with these being pleasurable social touch detectors.

And that paper, they didn’t look in the brain, and they hadn’t linked it to like any behavior. So, I thought this was right for someone with my background and genetics and molecular manipulations, really careful analysis of behavior and also linking that with brain imaging. So, the thing that differs between these neurons and this pathway and pain is that it’s a different molecular population. These neurons happen to express, at least in a mouse, this gene called MRGPRB4. It’s a tongue twister. They do express Piezo channels, this other mechano sensor I told you that’s important. So, they’re definitely mechanosensory neurons.

What makes them special is their constellation of genes they express that’s different than some of the other classes that mediate other types of somatosensation. Their wiring where they’re, that they innervate the hairy skin areas that are, like, normally touched. The neurons that they’re connected to in the spinal cord and from spinal cord to the brain. Basically, the whole pathway from skin to brain is gonna be different than a pathway for like hot or cold or pain, okay. So, the whole, like, circuit, the anatomy, the wiring is totally different.

LEVIN: Wow. So highly specialized again.

ABDUS-SABOOR: Highly specialized again. For example, if you stimulate, like, a pain pathway to animals, like, quickly they give you signs that this is painful and they don’t like it, they avoid it. But with this pathway, they give us signs that they actually like it. They want to spend time in environments where this pathway is stimulated. And if we look at, for example, dopamine release in the brain’s reward center, we can see that stimulating this pathway in the skin leads to like this dopamine release or if we genetically ablated these neurons, we see social touch behaviors are greatly kind of diminished.

Now, some of our ongoing studies where we’re really excited, because this gets to your question again about, like, why it’s important and translational impact in humans. You know, we think this pathway is also important for the ability of touch to relieve stress and anxiety and depression.

When you’re down or going through some negative emotion, just think about a hug from a loved one, a parent, a friend. It can really calm you down, and make you feel better. We appreciate this phenomenon, but how it works at a molecular, cellular and neuronal level is almost completely unknown. The skin to brain pathway for social touch that we’ve been discovering and elucidating, we think could be important for that. And we have really strong evidence. It’s unpublished, but hopefully, you know, maybe later in this year we’ll submit this paper showing that activating this pathway can relieve negative states, which is really exciting. And we do think it has direct therapeutic potential. Maybe we can even think about treating diseases of the brain, chronic stress, not in the brain but by targeting neurons in the skin.

I have to appreciate my limitations as a molecular biologist, neuroscientist. Some of these questions may be slightly outside of the realm of where we can approach.

LEVIN: Wow. Fascinating. Now there’s this research that you’ve done where you have genetically altered mice to become sensitive to blue light so that instead of physically stroking them, you can illuminate them with this blue light, and they have the experience of being gently touched. So, I have many aspects to this question.

One is, what a crazy thing to do. That’s not a question. And the other is, you know, are we going to genetically alter human beings so that they can do blue light therapy as part of this, you know, attempt to heal certain disorders of the mind?

ABDUS-SABOOR: Yeah, it would be nice if we could get there one day. So, this technology is called optogenetics and I think it is one of the most important technologies in all of neuroscience. And hopefully there’s a Nobel prize on its way for some of our colleagues.

But anyway, this shows that the beauty of, like, evolutionary biology and looking across diverse species, there’s this protein and algae that allows the algae to propel through these muddy ponds to blue light. It’s an ion channel, a non-selective cation channel, which just basically means there’s a part of the protein that when blue light hits it, the channel opens, it allows positive ions to flood into the cell that expresses this protein.

This is, like, perfect for neurons. Neurons are electrically excitable cells that fire to positive ionic current flowing into the neuron. So, if you can put this special protein on the surface of your neuron through some sort of viral genetic engineering, you can confer the ability to directly activate these neurons in this way.

So, part of this project that we did some genetic engineering crossing mice together to put this blue light sensitive protein only in these neurons. And we can just shine light directly to the skin, as you mentioned to confer behavior.

Now, to do this in humans, one would have to get to a point where you could do, like, gene engineering to add this protein to cell types of interest.

People are doing this now to treat – this is not our work – but to treat like forms of retinal degeneration and blindness actually. It’s really incredible. So maybe there is a pathway for using this technology to treat people.

LEVIN: You were discussing similarities in how certain neural networks are working for mice and for human beings in some of these complex systems. But you famously work with naked mole rats and who doesn’t love a great naked mole rat colony? Please, for those who are not in the know of the extraordinary naked mole rat, can you describe them a little bit for us and, why their colonies are so unusual in the rodent kingdom.

ABDUS-SABOOR: Yeah, they’re amongst the most fascinating animals I’ve ever encountered. Some days we’re just like, “What are we studying?” Some days we literally just sit there and just stare at them.

LEVIN: Watch them?

ABDUS-SABOOR: Yes, yes, yes. Because their biology is so fascinating, but some of the things that really excite us about them.

So for one, they don’t feel many forms of pain, for reasons that are not fully clear. They seem to be immune or recalcitrant, unable to get cancer. They’re long lived for rodents, so they live about 30 to 40 years.

LEVIN: Wow, that’s a long lived rodent.

ABDUS-SABOOR: Yeah, yeah. Most mice and rats, they only live one to two years. Yeah. Yeah. And they don’t really have traditional signs of aging. So, when they die, they just die. You can’t look at ’em and age them. They don’t seem to have any like cognitive or physical decline. They just die.

And they’re highly social. And this is the thing that we really keyed in on my lab to date. In the last three plus years or so, we’ve been working with them. They’re actually the most social animals in the entire mammalian kingdom. So, one colony can contain dozens or hundreds of animals all living together and a really tight-knit colony. And they’re driven by this really single dominant queen, who’s the matriarch that kind of guides the whole society. And all the other animals are essentially workers and drones and are reproductively suppressed in the presence of the queen. She’s the only one who mates with one or two like breeding males in a colony. And she continues to give birth her entire life. So, no menopause or signs of slowing down. And so, as she has a litter, they never leave. The colony just gets bigger and bigger and bigger, okay. They’ve decided that it’s best for their survival of their colony to do everything together, okay.

They chiefly come from East Africa, Kenya, Ethiopia. They live completely underground as well. And the leading hypothesis is that, you know, maybe in this dry, arid desert climate, where food and resources are not plentiful, that it was easier to have a communal form of living such that some animals are foraging and hoping to stumble upon a tuber, like a sweet potato. And if they do, like the whole colony is alerted and they all can feed off that together.

LEVIN: It’s insect-like.

ABDUS-SABOOR: It’s very insect-like or bee-like, but they’re mammals, which is very interesting.

So, you know, we want to know like, how does this pain insensitivity work? How does you know this highly social life, like how is it set up? How is it maintained? How do they even recognize one another, especially because they’re essentially blind?

LEVIN:  And they’re hairless, so there’s a lot of skin-to-skin contact.

ABDUS-SABOOR: There’s a lot of skin-to-skin contact. They crawl right over each other. They’re always touching. Even if you make the colony really big and you go to look at ’em, they’re always on top of each other. They just are always touching, you know?

And from our early studies and observations, it appears that, not saying other senses are not important, but touch is certainly outsized. You know, most of their sensory cortex seems to be dedicated to touch. So, they’ve lost the visual parts of their sensory cortex because they’re blind. But the somatosensory cortex, the touch cortex, has like encroached upon it.

So, we think they’re like touch specialists and they might be able to do things with touch that other seeing animals do with vision. Like communicate and recognize one another and know who it is that they’re interacting with.

LEVIN: Wow. Fascinating. And so, is there a sense in which we’re trying to understand human social interactions through touch by studying the naked mole rat? Or are these just two disparate fields of study?

ABDUS-SABOOR: I hope they’re not disparate fields of study. One could even make the case that their long-term stable relationships are more akin to human societies than a mouse, which is the predominant model system used to study these things, right?

We have long-term stable relationships. You know, people for 10 or 20 years, or you see a friend from college or whatever, you remember them. And these sorts of dynamic relationships that we see in their colonies we don’t see like in other animals. So, I think there are principles that we hope are very similar and could teach us about societies.

You know, I think one very exciting idea is that if we can learn like the principles, the genes, the molecules, the neural circuits, the neural networks that they use to like cooperate with one another and use shared resources like maybe some of that can be used to like inform human societies and how we can better cooperate given limited resources, for example.

LEVIN: In human societies, touch is a sensitive issue. Pardon the pun. But there’s an important role, obviously, that social touch plays, both aversion and appeal. Are you thinking about this in human society or is that kind of a meta level that you dream about maybe when you’re walking down the street, but isn’t really part of your actual research?

ABDUS-SABOOR: No, we do think about that. That question we’re actually trying to address on the mouse side of the lab, not in the naked mole rats. Maybe in the naked mole rats one day. But right now, we are working on this.

It’s really fascinating as you mentioned, right. If you’re in the comfort of your home and a loved one strokes you on the back or arm, it may feel good, but if you know you’re on the 1 train or something and someone touches you like that, right? You wanna recoil, and you know it’s not gonna feel good. But it could be the same touch to the skin. So then how does your brain know that this touch is in a good context and this is a bad context?

There must be some sort of gating in the brain that allows you to quickly approximate how you should respond. And how does that all work? It’s unclear, but I think we have good approaches to kind of study this.

Those are the moments that I really live for, where we can go from not knowing to – at least at that moment – we might be the only people in the world who have appreciated something.

Back to the mouse, I talked about those neurons we discovered as part of like that positive balance, social reward pathway. Now if we give animals mice that same stimulation, but we pair it, they’ve learned to associate with something negative like a shock. And now we do that optogenetic stimulation to activate the neurons. The animals don’t have a positive response; they have a negative response. So, we can easily dial in, like, good touch, bad touch in the animals and read this out.

So now we’re saying, “Well, how does this look in the brain?”, okay. There’s one area in the brain that we’ve become very excited about called the orbital frontal cortex, a frontal area, the front of the brain that appears to integrate sensory components and high level like learning and planning. And we think there might be neuro ensembles that are talking to other areas of the brain to let the animal know, like if the animal’s in a good state for a touch to be perceived as good, or if the animal’s somewhere where they’re afraid or anxious or have had a bad experience with it, such that now this gate to like the positive violence networks won’t be activated. So early days of this project, but we’re trying to figure out how this works at the level of the brain.

LEVIN: A lot of what you’re raising taps into, going back to metaphysics. So we began with Descartes, “I think therefore I am,” you know, consciousness is all important. Going down deep into genetics, molecular biology, neuroscience, and now it’s kind of to my mind, the questions start to come back again of how these neurons translate into this mental world, this interiority, this experience.

And I guess I just wonder how much at this scientific level you feel we can approach this really difficult question of why does it feel like something?

ABDUS-SABOOR: It is a wonderful question. And one of the things I’ll say is that I also have to appreciate my limitations as a molecular biologist, neuroscientist, right? Some of these questions may be slightly outside of the realm of where we can approach, you know, especially using a mouse model, right? I think there are mechanistic studies we can do in animals, but I do not think we can reach the highest levels of these high-level cognition and consciousness in a rodent model.

And this is where I think folks like me, we need to be interacting with cognitive scientists, people in psychology, people in other spaces. There’s too much of a disconnect between neuroscience and psychology. We’re not talking to one another, you know, but we’re both studying the brain and the mind, right. The neuroscientists tend to use animal models and are doing the work I talked about very mechanistic. Whereas the psychologists are doing really beautiful work, but they’re more tapped into, you know, the human experience.

The more we can talk to one another, I think we’ll be able to address these higher-level problems. And this is part of the institute I’m in, the Zuckerman Mind Brain Behavior Institute. Maybe this is a plug for our institute, but you know, this is something that we try to do because this is the only way we’re gonna solve the brain and the mind by bringing people together who think across skills.

LEVIN: Fascinating. I think you’ve portrayed this very well in your responses and in this conversation, which has just been so intriguing. But there’s a question we’d like to ask here at The Joy of Why, and that is what brings you the most joy or fulfillment in your work?

ABDUS-SABOOR:  Yeah, I think I would answer that in two ways.

So one is just discovery, okay? When we have that kinda aha moment, that moment of insight where we say, “Oh my goodness, this is it.” For example, something we’re very excited about that we all can relate to is lactation, nursing that occurs in mammals, right? Now, we’ve known for a long time that, for example, with nursing, there’s a suckling stimulus on the skin from the child to the mom or the pup to the mom, and that physical suckling stimulus is enough to mediate milk release.

There’s like this neuroendocrine reflex that goes from the skin up to the brain back to the tissue. The milk comes down. The baby, the pup gets it. It all is initiated by, in part by touch, okay? But we had no idea, like what are the touch neurons that mediate that? In the last few months, we’ve been able to discover and put in a molecular handle. We found the neurons that mediate this. When we made this discovery, it was just like jumping for joy. It’s like the thing that we and others have been looking for for a very long time, like we cracked it, and the data looks so beautiful and convincing. Like, those are the moments that I really live for, where we can go from just not knowing to – at least at that moment – we might be the only people in the world who, like, have appreciated something.

Those are the moments that we live for. So, discovery is the thing that really drives me and brings joy to my life. And mentorship, right? Helping people achieve their dreams and goals in life and kinda working alongside them, I also live for that, too, and sometimes that’s just as like exciting for me as discovery.

LEVIN: Thank you so much for taking the time to bring those ideas out of the lab and share them with us and with our audience. It’s just been a pleasure, Ishmail. Thank you.

ABDUS-SABOOR: Thank you. You’ve been great. you’ve really pulled a lot out of me today. Thank you.

STROGATZ: I’m smiling at that, and so are you, Janna. We can see each other here, and I just, I had a feeling that he would say something about, “At that moment, I’m the only person,” or, “My team is the only team in the world.” There is something about that. It’s not exactly vanity, but I think every scientist feels that, every mathematician, right?

I mean, is that why you were smiling when he said that?

LEVIN: Oh, yeah. Well, also he was such a lovely person, you know? And you just, you enjoyed that he enjoyed it. You know that he had this wonderful moment of discovery and I also thought it was incredibly fascinating how he was talking about the  speciation of the actual neurons. Like, they’re specialized, and they’re not these generic neurons, but they’re intended for these purposes. I mean, I just thought that was also really fascinating, and you can imagine that moment of discovery was just tremendous for them.

Anyway, wonderful stuff. I’ll give you a hug next time I see you.

STROGATZ: Very good. I might recoil. Touch is a big thing for me. So don’t take it personally.

LEVIN: Oh my God. That’s hilarious. I’m gonna come at you with such a bear hug next time.

STROGATZ: Come at me!

Related:

1. Pleasure or Pain? He Maps the Neural Circuits That Decide.

2. Medicine Nobel Prize Goes to Temperature and Touch Discoveries

3. Touch, Our Most Complex Sense, Is a Landscape of Cellular Sensors

4. Scientists Reveal Structure of Pain Sensor

[Music plays]

STROGATZ: If you’re enjoying The Joy of Why and you’re not already subscribed, hit the subscribe or follow button wherever you’re listening. You can also leave a review for the show. It helps people find this podcast. Find articles, newsletters, videos and more at QuantaMagazine.org.

LEVIN: The Joy of Why is a podcast from Quanta Magazine, an editorially independent publication supported by the Simons Foundation. Funding decisions by the Simons Foundation have no influence on the selection of topics, guests or other editorial decisions in this podcast or in Quanta Magazine.

The Joy of Why is produced by PRX Productions. The production team is Caitlin Faulds, Jade Abdul-Malik, Genevieve Sponsler, and Merritt Jacob. The Executive Producer of PRX Productions is Jocelyn Gonzales. Edwin Ochoa is our project manager.

From Quanta Magazine, Simon Frantz and Samir Patel provided editorial guidance, with support from Samuel Velasco, Simone Barr, and Michael Kanyongolo. Samir Patel is Quanta’s Editor-in-Chief.

The episode art is by Chanelle Nibbelink and our logo is by Jaki King and Kristina Armitage. Special thanks to Garth Avery at the Cornell Broadcast Studio.

I’m your host, Janna Levin. If you have any questions or comments, please email us at [email protected]. Thanks for listening!

[Music fades]

2026 Aug 5

- Corals Spin Tiny Vortices to Get Oxygen, but Not if It’s Too Hot

MARINE BIOLOGY | VIEW ON WEBSITE

Corals Spin Tiny Vortices to Get Oxygen, but Not if It’s Too Hot

By Marlowe Starling

August 5, 2026

New research is helping biologists understand how an overlooked aspect of coral physiology may affect their fate under climate change.

Fluorescent, oxygen-reactive nanoparticles are caught in swirls of water created by a coral’s cilia.

Cesar Pacharres/colored by Quanta Magazine

Introduction

At first glance, corals present as little more than colorful rocks — piles of lobes, stalagmites, and branches poking out from the seafloor.

They are anything but. Corals are complex creatures that form enduring colonies, and just like other animals they need oxygen to live. Across the living surface of coral, a frantic dance of survival takes place, invisible to our eyes and unknown to science before 2014. The tiny dancers are hairlike cilia, and new research into these microscopic structures is revealing just how active corals are in determining their own fate.

Corals aren’t fortunate enough to have a consistent supply of oxygen, and they can’t change location to seek it out. During the day, the tiny polyps that make up a coral colony get plenty of oxygen from the symbiotic algae that photosynthesize within their tissues. But at night that process stops, and a coral polyp’s only source of oxygen is the water around it. Then it’s do or die for the cilia. Using mechanisms scientists are still trying to understand, the cilia wave around to generate fast-moving vortices of water that circulate oxygen to the coral’s outer tissues, in addition to helping keep the colonies free of sediment.

A study published in Science in May 2026 provides new insight into how organisms with no brain or musculoskeletal system can generate and regulate this process — and what happens when the water around them warms up. Warmer water naturally carries less oxygen, which prompts corals to move their cilia faster and faster, as if gasping for breath. Above a certain temperature, the system starts to work against itself; the furious beating of cilia uses up any oxygen the coral’s tissues can absorb, and then the polyps can suffocate in the less oxygenated water. Biophysicists, marine biologists, mathematicians, and modelers are now collaborating to better understand the physiological and hydrodynamic forces at work, and how they correlate with bleaching patterns, coral disease, and mass die-offs.

This dynamic picture is somewhat new to scientists, who have long used corals’ symbiotic algal partners as indicators of their health. Cilia may serve as a more direct signal, said Rachel Alderdice, a marine biologist who studies coral stress biomarkers and genomics at the University of Konstanz in Germany and was not involved in the research. “It’s these finer details that could help us understand why some corals bleach and others don’t, [even when] they sit right beside each other.”

Swirling To Survive

Every coral colony is cushioned by a thin boundary layer of water whose movement is slowed by friction at the coral’s surface. Researchers assumed that corals were passive with respect to the slow-moving boundary layer, simply relying on natural diffusion through it to provide nutrients and oxygen.

A colony of Porites lutea, a stony coral, continues to grow slowly in waters off the Samoan Islands.

National Marine Sanctuaries

Then, in 2014, a team from the Massachusetts Institute of Technology and the Weizmann Institute of Science published a groundbreaking study showing that coral cilia interact with the boundary layer by rapidly whipping about to generate swirls of fresh, oxygenated seawater. Until a decade ago, scientists thought of these cilia merely as brooms that move mucus and sweep away waste particles and other debris. The research not only modeled the tiny vortices created by the cilia for the first time, but also revealed the cilia’s importance for survival and metabolism.

At first, the microbiologist and environmental engineer Orr Shapiro, who led the 2014 work at MIT as a postdoctoral fellow, was interested in how microbes that infect corals and cause disease follow concentration gradients, a process called chemotaxis. Under the microscope, he noticed something weird: In the boundary layer, particles were swirling around and mixing together — not at all like the passive diffusion he had been expecting.

It could be oxygen that’s been the smoking gun all along.

David Suggett, King Abdullah University of Science and Technology

“That was to me, and I think later on to the entire field, sort of a paradigm shift,” said Shapiro, now a researcher at the Volcani Institute in Israel. It became clear that the boundary layer wasn’t static, but rather a dynamic zone, and one where cilia were creating their own turbulence. The realization inspired Shapiro’s team to go off on a tangent, mapping the flow of oxygen to coral tissues via cilia. “It really transformed how we understand this [micro]environment, because suddenly the diffusion is no longer really important,” Shapiro said.

Diffusion is the default route for nutrients traveling through water, but it’s painfully slow. It can take as long as four minutes for oxygen to travel just 1 millimeter. That’s why the fast-moving flows created by cilia are so important: because naturally flowing water slows down near the coral’s surface, and corals consume oxygen faster than diffusion can supply it.

It is a system that delivers enough oxygen, despite the cilia’s energy consumption. But there’s a downside: Oxygen dwindles as temperature climbs. That’s when corals run into trouble.

Hyperventilating Under Water

Scientists have a clear understanding of one thing that happens to corals when water gets too hot: bleaching. As water temperatures rise, a coral’s symbiotic algae become stressed and release molecules that are toxic to the coral in large quantities. To protect itself, the coral expels its own algae — a primary food, energy, and oxygen source — and soon loses its color. It’s a slow death and an increasingly common occurrence as heat waves sweep across the world’s reefs.

But sometimes, some corals on a reef bleach while others don’t, and in other cases corals under heat stress die without expelling their algae.

An international team of microbiologists, engineers, and physiologists was eager to understand how heat affects cilia, and whether this could explain different types of coral death.

In the experiment, researchers exposed stony coral to various water temperatures. Fluorescent nanoparticles revealed the flow of oxygenated water.

Courtesy of Cesar Pacherres

“We’re living in a world right now of extreme scenarios,” said Cesar Pacherres, a co-author of the new study and a marine biologist at the University of Copenhagen who studies fluid dynamics. As marine heat waves become more common, and as a particularly strong El Niño year threatens to catalyze a fifth global bleaching event in late 2026, “corals can experience an increase in temperature of several degrees in a time frame of a few hours,” Pacherres said.

To explore heat’s impact on cilia, the team ran a series of 24-hour experiments. They exposed aquarium-raised stony coral called Porites lutea to incrementally higher water temperatures up to 39 degrees Celsius (more than 102 degrees Fahrenheit) — an extreme scenario, but one that could occur. They kept the tanks dark to better observe how cilia transport oxygen when algae aren’t producing any.

Every hour, the researchers recorded the microscopic cilia with a high-speed camera to capture how frequently they moved as they were exposed to warmer and warmer water. The team also used a technology called SensPIV to track oxygen flow. Fluorescent, oxygen-reactive nanoparticles “traced” the water swirls, creating a vivid map of how oxygen concentrations corresponded to the cilia’s vortices.

Visualizing the movement of oxygen was key to the team’s findings. In warmer water, corals burned energy faster, which increased their demand for oxygen — prompting the cilia to dance faster. But there’s only so much dissolved oxygen available in the surrounding water, and as temperatures climbed, the cilia started to send oxygen-deficient water toward the coral in their frenzy. “The oxygen demand of the coral increased faster than the increased swirling of the water,” said co-author Michael Kühl, a marine microbiologist at the University of Copenhagen.

When the water approached 37 degrees Celsius (the temperature of the human body), the cilia started to slow down. Past 39 degrees Celsius, they shut down altogether, and the coral died. These were the findings they reported in Science in May 2026.

Coral cilia move normally in water heated to 27 degrees Celsius (left). At 39 degrees Celsius (right), cilia motion slowed and eventually stopped.

Courtesy of Cesar Pacherres

Pacherres cautioned against interpreting these temperature limits as a standard threshold; each species of coral is adapted to its own range of daily temperature fluctuations. Even so, climate change has started to push many corals toward their respective limits, with record-high surface water temperatures nearing 38 degrees Celsius in places such as Florida.

Why the cilia move faster in hotter water remains a mystery. Perhaps seawater’s viscosity — which decreases as temperature increases, making the water thinner —affects ciliary movements, Shapiro said. Corals lack a central nervous system and brain, but they do have neurons — inside what’s called a nerve net — to process sensory information. Perhaps the coral senses heat or lack of oxygen, and some biological mechanism then triggers the faster beating. “It’s physics, biology, engineering, all mixed up together, which is what makes it really interesting,” he said.

A Polyp Puzzle

Scientists are now investigating cilia physiology and the boundary layer as a whole. “It’s much more complex than we thought,” Shapiro said.

In another paper co-authored by Pacherres and Kühl in May 2026, they found that cilia’s vortices resemble corkscrews. The turbulence pushes unwanted particles away from the coral’s surface while redirecting nutrients toward polyps’ mouths.

Under a microscope it’s possible to see the density and arrangement of cilia on a coral surface.

Courtesy of Cesar Pacherres

The cilia on each polyp, they found, are arranged in hexagonal units that keep ciliary movement streamlined, which helps explain how cilia can coordinate their movement to produce predictable swirls. “It demonstrates that coral skeletal architecture and living tissue are functionally integrated,” Pacherres said.

Combined, the studies recontextualize how ciliary movement, previously overlooked, gives corals some stability and buffers them against environmental change, he said.

The relationship between cilia and bleaching remains ambiguous, said Alderdice, who was not involved in the studies. But testing cilia under bleaching conditions without heat — by using red light, for example, which can also trigger bleaching — would help answer this question.

Playing Catch-Up

Scientists knew from the 2014 work that cilia help corals self-ventilate when water flow is low, said David Suggett, a marine biologist at King Abdullah University of Science and Technology who wasn’t involved in the research. “But until this point, we had been focusing on molecular and metabolic machinery” to understand how corals evolved to deal with low-oxygen conditions, he said. “We hadn’t really appreciated that there are these behavioral-physiological mechanisms at play.”

For Suggett, the research raises a critical question: What does this say about how corals will deal with future climates?

Sometimes, corals of a single reef don’t bleach evenly, with some individuals dying off while their neighbors persist. In new research led by Suggett’s colleague Tadd Truscott, marine biologists are finding that these patchy bleaching patterns are correlated with areas of reduced water flow — areas therefore receiving less oxygen.

Related:

1. The ‘Elegant’ Math Model That Could Help Rescue Coral Reefs

2. Ecologists Struggle to Get a Grip on ‘Keystone Species’

3. Underground Cells Make ‘Dark Oxygen’ Without Light

Next, Kühl and Pacherres’ team wants to test corals under different conditions — especially under normal light-dark cycles — and to better understand the mechanism driving ciliary beating. Is it a molecular process that activates their movement? The physics of warmer seawater? A bit of both, or something else?

It’s only in the past decade that scientists have realized the role of deoxygenation in coral health, Suggett said. Ocean acidification took center stage as the primary concern for corals in the early 2000s. In hindsight, deoxygenation was a much bigger issue, he said. The new research suggests that deoxygenation, not just bleaching, is a fatal threat to corals resulting from a hotter climate.

“We’re playing massive catch-up,” Suggett said. “The amount of information we’re gathering quickly is demonstrating just what a problem for corals it is — so much so that we’re starting to really revisit long-standing paradigms of the role of other environmental factors, like temperature and light, where in fact, it could be oxygen that’s been the smoking gun all along.”

2026 Aug 3

- Why the Legendary Erdős Problems Are Falling to AI

ARTIFICIAL INTELLIGENCE | VIEW ON WEBSITE

Why the Legendary Erdős Problems Are Falling to AI

By Konstantin Kakaes

August 3, 2026

AI’s greatest mathematical successes have come from answers to problems posed by a mid-20th century iconoclast. By examining what makes the Erdős problems unique, mathematicians are trying to understand how AI might change the rest of math.

DVDP for Quanta Magazine

On May 20, 2026, OpenAI made an announcement that shook the mathematical world. An internal AI model — one not available to the public — had come up with a counterexample to the “unit distance” problem, a conjecture made in 1946 by Paul Erdős, the prolific, itinerant Hungarian mathematician.

Erdős posed thousands of questions, but this one was special: It was both simple to explain and mathematically deep. It was the first historically significant proof to come from an AI model. Though the model’s result wasn’t definitive — human mathematicians would substantially improve on it within weeks — it was innovative, bringing in ideas from a distant branch of math that no one had successfully applied to this problem before. And it was influential: Within a few days, related techniques were used to solve other important problems.

Then on August 1, OpenAI announced that an unreleased model named Astra made 10 additional mathematical advances, including finding solutions to three more problems posed by Erdős.

Many mathematicians have hailed developments such as these as a phase transition in the mathematical capability of AI models. These models are “changing dramatically the way mathematical research is being done,” said Noga Alon of Princeton University, who has solved dozens of Erdős problems over his decades-long career.

Paul Erdős, one of the most prolific mathematicians in history, was deeply whimsical when it came to mathematics, and deeply cynical when it came to authority.

Photo by George Csicsery from the documentary N is a Number: A Portrait of Paul Erdős ©1993. All Rights Reserved.

Erdős and his conjectures have long fascinated mathematicians. He traveled constantly — living out of a suitcase for years at a time, staying with friends, owning almost nothing. He rattled off problems in published papers and letters to mathematicians around the world, often attaching prize money that he would pay out of pocket to the first person to come up with a solution. The reward might be a token $10 or $25, or, for problems he considered important or difficult, it could range into the thousands. Erdős died of a heart attack in 1996 while attending a math conference in Warsaw, but a nonprofit foundation based in Iowa has promised to make good on his bounties.

He was a beloved figure, but also a downright weird one. He only wore silk, and he avoided the touch of other people. Deeply cynical about authority, he gave away most of the money he earned and relied on a friend to manage his finances and other practical affairs. He referred to God as the “Supreme Fascist” and fueled his incessant output of mathematical ideas with a steady diet of amphetamines. It is a strange irony of history that the problems he suggested have now become a central proving ground — and, in effect, a series of PR coups — for the world’s biggest and most powerful technology companies.

But in all likelihood none of this would have happened had it not been for an English mathematician named Thomas Bloom.

Many Meetings

Like Erdős, Bloom was interested in both number theory and combinatorics. His focus has been an area called arithmetic combinatorics, which lies at the intersection of the two. After getting his doctorate in 2014, Bloom established himself as a rising star in the field, landing a prestigious fellowship from Britain’s Royal Society, which let him work at almost any university he wanted to. (He’s now at the University of Manchester.)

Bloom has liked Erdős’ style for as long as he can remember. But he always found it hard to keep track of which problems had been solved and which had been forgotten entirely. So in early 2023, he decided to gather as many problems as he could into a list.

He intended it for his own use. But “I thought it would be easier if I could access it wherever I was,” he said; he figured he “might as well make a website, kind of with the expectation that maybe nobody would use it.” He gathered a couple hundred problems and launched erdosproblems.com. Bloom used ChatGPT to write the Python code that ran the website, which was, at the time, a remarkable thing for a large language model to be able to do. Using one to collaborate on the math itself still seemed like only a distant possibility.

Thomas Bloom’s website of Erdős problems became a home for mathematics at its best. Then AI came on the scene.

Courtesy of Thomas Bloom

His goal was not just to cross items off a list. He wondered if “modern day mathematics, often using techniques unknown by Erdős, could clear up many of these more obscure problems,” he wrote in a blog post. “We will then be left with a core of interesting, difficult problems, which can serve to demonstrate the limits of our knowledge.”

Bloom did crucial work in curating the list: Sometimes Erdős stated problems in ambiguous or unclear ways, and Bloom figured out what the most sensible version of each problem should be. He kept adding problems to the site, and gradually its audience grew. Over the course of 2024 and the first eight months of 2025, the statuses of 111 problems on the list were changed from “open” to “solved” (although some of these had been solved years earlier, and their status change reflected the rediscovery or verification of a proof).

Then, in August 2025, some colleagues suggested that Bloom add a commenting function, so that people could talk about problems they were interested in. He was able to do so quickly, using ChatGPT to write the code. By now he’d cataloged nearly 1,000 problems.

Bloom’s timing was good. He made it possible for like-minded people to talk to one another, and that “really let a community build up,” he said. For the most part, comments were sporadic — a problem might attract a single comment pointing out an example or noting how hard the problem looked. But activity steadily grew, and some problems catalyzed nuanced mathematical discussions between strangers.

“Tom probably never really realized this, but for me it’s honestly changed my life,” said Wouter van Doorn, the fourth-most-prolific commenter on Bloom’s website. Like many people who became active on the site in the autumn of 2025, van Doorn isn’t exactly a professional mathematician. He works “for a company that gets hired by other companies to do customer service support,” as he put it. But he isn’t exactly an amateur either — a decade prior, he almost completed a master’s degree in math at KU Leuven in Belgium. In 2024, spurred in part by how capable he saw LLMs getting, he took a six-month leave of absence from work to focus on math. At the time, while he didn’t particularly want to use AI, he remembers thinking, “Right now I’m still better at mathematics than an AI is, but who knows what it’ll be in a year, two years, five years? If I want to finish these projects, and I want them to be mine, now is the time.”

Problem 1102

We say that $latex A \subseteq \mathbb{N}$ has property $latex P$ if, for all $latex n \geq 1$, there are only finitely many $latex a \in A$ such that $latex n + a$ is square-free. We say that $latex A$ has property $latex Q$ if there are infinitely many $latex n$ such that $latex n + a$ is square-free for all $latex a < n$. How fast must sequences $latex A = \{a_1 < a_2 < \cdots\}$ with properties $latex P$ or $latex Q$ increase?

And so, in October 2025, van Doorn, now back at his day job, left the first comment on the page for Problem 1102. The problem, which Erdős posed in 1981, asks about properties of sets of “square-free” integers — that is, integers that have no repeated prime factors. (For instance, 30 is square-free because it is equal to 2 × 3 × 5, but 18 is not, because it is equal to 2 × 3 × 3; the 3 repeats.)

In early November, van Doorn shared progress toward an answer — which he’d figured out without relying on AI — as a comment on the problem page.

Later that day, another commenter on the site replied, claiming he had found a flaw in van Doorn’s argument. The two traded remarks in rapid succession, and van Doorn convinced his interlocutor that his argument was correct. “I see how your argument works now. Nice!” the other mathematician replied. That other mathematician was Terence Tao, a professor at the University of California, Los Angeles who is arguably the best-known mathematician alive today, and inarguably one of the most influential. (Not incidentally, when Tao was just 10 years old, he crossed paths with Erdős.)

Bloom’s website, which has the look and feel of an earlier time, was becoming an example of the internet at its democratic best. “This entire collaboration would not have been possible without Tom’s website and the comments section there,” van Doorn said. It didn’t matter if you had tenure or not, if you were young or old, if you were at a fancy university or even at a university at all. If you wanted to work on math and had good ideas, you could find people to collaborate with.

But as the winter set in — around the same time that van Doorn found himself collaborating with Terry Tao — things started to change.

Journey to the Cross-Roads

Kevin Barreto and Liam Price, both in their early 20s, became friends in the summer of 2025 on a Discord server dedicated to AI. Barreto is currently an undergraduate at the University of Cambridge; Price studied some math in college but left before finishing. In December, convinced that the newest AI models might succeed in resolving some Erdős problems, the pair started throwing batches of problems at them. They realized early on that if they told GPT-5.2 that a problem’s answer wasn’t known, it wouldn’t make much headway, so as Barreto put it, they learned how to “prompt it in a very particular way, gaslighting it into thinking the problem is easier than it actually is.”

Problem 333

Let $latex A \subseteq \mathbb{N}$ be a set of density zero. Does there exist a $latex B$ such that $latex A \subseteq B + B$ and $latex |B \cap \{1, \ldots, N\}| = o(N^{1/2})$ for all large $latex N$?

They had what they thought was their first triumph on Erdős Problem 333. Early on Christmas morning, Barreto posted a proof to Bloom’s website, writing, “We believe, to the best of our knowledge, this is the first case of an LLM fully autonomously resolving an Erdős problem, not previously resolved by humans.” Even though 333, which dealt with the sums of sets of integers, was not a particularly important problem, solving it with AI still felt important.

But a few hours later, another user pointed out that Erdős himself had provided a resolution to 333 in a paper published in 1977. Barreto owned up to the mistake. “My formal request to all members of the website is to put greater focus on literature search on the problems currently marked as open,” he wrote. “As someone who has fallen for this twice now, it’s quite gut-wrenching.”

Problem 728

Let $latex C > 0$ and $latex \epsilon > 0$ be sufficiently small. Are there infinitely many integers $latex a, b, n$ with $latex a \geq \epsilon n$ and $latex b \geq \epsilon n$ such that $latex a!b! \mid n!(a + b – n)!$ and $latex a + b > n + C \log n$?

Undeterred, he and Price kept at it, and by January 4, 2026, they’d used GPT-5.2 Pro to find a solution to Erdős 728, a problem about when certain numbers are divisible by other numbers. This time nobody could find prior work already proving it. Barreto used another AI tool called Aristotle (developed by a startup called Harmonic) to certify that the proof held together logically. Nat Sothanaphan, a software engineer and the only forum participant more prolific than Bloom, Tao, and van Doorn, had ChatGPT write up the formalized result and posted it online.

Price developed a methodology for how to ask LLMs to solve open questions. First, he would ask a chatbot for a solution. Then he would feed that solution into a fresh instance of the chatbot, asking it to check the previous chatbot’s work. He’d repeat this process until he had what looked like a workable solution. (This echoes some of the work that companies have been doing internally to create what they call harnesses or scaffolds, which automate the sort of iteration that Price does by hand.)

Barreto and Price’s papers represent just a fraction of the many Erdős problems solved at least in part by AI over the past few months. There are multiple reasons why these problems in particular have become such a fertile test bed for LLMs. The primary one is that, by and large, Erdős problems are in number theory, combinatorics, and graph theory, all areas of math that have proved more accessible than others to large language models. The problems also vary widely in difficulty and mathematical significance. This variation makes them appropriate for a nascent technology whose abilities also vary widely.

Many of Erdős’ problems had a monetary value attached to them from their moment of inception, a playful incentive from a wandering eccentric. But now, as the problems have become an informal benchmark for AI, their solutions are being discussed in terms of their “per-problem cost” — the price of the tokens needed to solve them.

Photo by George Csicsery from the documentary N is a Number: A Portrait of Paul Erdős ©1993. All Rights Reserved.

“A lot of my recent papers should be mostly credited to AI,” van Doorn said. “The ideas involved were ideas I did not come up with myself.” Like many people active on the Erdős site, van Doorn is excited about the way LLMs are allowing him to do more things more quickly. “If I read an idea by an LLM, I digest it, try to understand it, simplify it, and generalize it,” he said. He uses AI to better understand the math.

Not everyone holds themselves to this standard. “A big problem is AI is being used a lot by people who aren’t mathematicians, who don’t have a huge mathematical background and are not capable of verifying the output,” Bloom said. “They like to move fast, ask their AI to check it, it grows and grows. We’re seeing a lot more of these 100- to 200-page papers that people are posting. ‘I solved this theorem; I got AI to generate the proof and check the proof and write the paper.’ But no human has read it, and no human is going to read it. It’s a huge challenge now.”

Problem 1196

Is it true that, for any $latex x$, if $latex A \subset [x, \infty)$ is a primitive set of integers (so that no distinct elements of $latex A$ divide each other) then $latex \displaystyle\sum_{a \in A} \frac{1}{a \log a} < 1 + o(1),$ where the $latex o(1)$ term $latex \to 0$ as $latex x \to \infty$?

By Price’s own assessment, he doesn’t have enough mathematical understanding to verify the solutions he ultimately coaxes from the LLMs. But with Barreto’s help, he’s been able to find mathematicians knowledgeable and willing enough to check the results. Both Price and Barreto are co-authors with Tao, Jared Duker Lichtman of Stanford University, and other accomplished mathematicians on a May 2026 paper resolving Erdős Problem 1196, one of their more significant results. (1196 asks about the possible size of so-called primitive sets — collections of integers, such as {2, 5, 9, 21}, in which no number divides any other.)

Bloom was surprised that despite lots of attention from OpenAI, Google DeepMind, and several startups, most of the new results had come from hobbyists and undergraduates using publicly available LLMs, not from corporate labs using more advanced internal models.

But that would change a few weeks later, on May 20, 2026, when OpenAI announced that they had solved one of the most well known Erdős problems of all, the unit distance problem.

Many Partings

In the first months of 2026, the major tech companies began to see opportunity in erdosproblems.com. As Lichtman explained, “Erdős had over 1,000 papers. They were scattered.” An institute in Hungary had collected scanned images of many of the papers, but nobody had collected all the problems. “This kind of single repository that anyone can access — labs realized that this could effectively be a benchmark.”

In January, a team of 24 researchers led by Google DeepMind shared a paper solving four problems and finding old, forgotten solutions to nine more, after “using Gemini to systematically evaluate 700 conjectures labeled ‘Open’ in Bloom’s Erdős Problems database.” In May, a separate DeepMind team of 21 researchers announced that “our most capable agent autonomously resolved 9 of 353 open Erdős problems at the per-problem cost of a few hundred dollars.” (As of this article’s publication, Bloom’s database contains 565 solved problems and 652 open ones, but the DeepMind team narrowed their search to problems that have been written in formal logic.)

And, on May 20, OpenAI shared a solution to the unit distance problem, along with a blog post explaining the work and a companion paper that featured nine world-class mathematicians commenting on the correctness of the proof and the importance of what had been done (as well as presenting a streamlined human version of the result). Mathematicians had generally believed that Erdős’ conjecture — about how many evenly spaced points can be placed on a plane — was correct. To general surprise, OpenAI’s internal model found a counterexample. To do so, it had found a sophisticated way to use tools from an area of math called algebraic number theory. As Jacob Tsimerman of the University of Toronto wrote in the companion article, “This is a really impressive piece of work. … It is definitely an intimidating construction.”

In the same article, Tim Gowers of Cambridge and the Collège de France wrote that “if a human had written the paper and submitted it to the Annals of Mathematics and I had been asked for a quick opinion, I would have recommended acceptance without any hesitation. No previous AI-generated proof has come close to that.”

The author on the paper that presented the original solution was given simply as “OpenAI.”

Terence Tao was 10 years old when he met Erdős.

Billy Grace Tao

Later, using techniques related to the ones the AI model had applied to the unit distance problem, a group of four mathematicians, including Bloom, disproved a version of another long-standing Erdős conjecture. The “sum-product” conjecture proposed that if you have sets of numbers, either their sum or their product must grow quickly. The mathematicians found a set of real numbers for which both the sum and the product grow more slowly than expected. The conjecture for integers remains open.

Figuring out what impact AI will have on math and mathematicians means not only looking to its most important results, but also examining how it changes the everyday practice of solving quotidian problems. Noga Alon, the Princeton mathematician, estimates that he has solved a few dozen Erdős problems over his career. He has now stopped trying. “Once AI started to solve them, there is no point anymore,” he said. Terry Tao has stepped away from the Erdős problem community to focus on getting work done.

Van Doorn, who for now still has his day job at a customer service company, said that LLMs “are clearly better at thinking and doing math than I am. I don’t hold a candle to current AI systems.” However, he added, “the eventual proofs that I write are simpler, more general, and easier to read for other people than the thing that ChatGPT came up with.” AI has indisputably boosted his productivity, and he’s still having fun. “If you want to play piano, you aren’t going to hire a piano-playing machine that does it better than you. You will play the piano because you like playing the piano. I enjoy thinking about numbers, doing math, writing papers. I’m not going to hire a paper-making machine that does it for me.”

Related:

1. The AI Revolution in Math Has Arrived

2. Mathematical Beauty, Truth and Proof in the Age of AI

3. When ChatGPT Broke an Entire Field: An Oral History

For van Doorn, there is joy to be found in digesting the responses he gets from LLMs. “I’ve been doing a lot of math recently thanks to the Erdős-problems community. It used to be the case I just did everything all by myself, struggled alone in my room. I don’t know how it happened, but nowadays people contact me saying, ‘I have this idea. Do you want to join me thinking about this?’”

The increasing capability of AI has made it easier for people like Price or van Doorn, with less mathematical training, to solve puzzles, while making those puzzles less interesting to people like Alon who have devoted a lifetime to understanding them.

Nonetheless, “many and maybe most good mathematicians will use AI,” Alon said. He notes that a number of first-rate mathematicians have left academia to work at AI companies, not only because they are well paid to do so but because “maybe this is where the action now is.” In July 2026, on the same day that Tsimerman was awarded the Fields Medal, the highest honor in math, he announced that he was leaving academia for a job at OpenAI.

What Is the Unit Distance Problem?

Say you want to arrange points in a plane to create pairs of points that are the same distance apart. How can you create the most pairs?

If you arrange your points so that they form polygons like squares and pentagons, you will get the same number of pairs as you have points.

Mark Belan/Quanta Magazine

But if you put points in a lattice, you can create substantially more equidistant pairs.

Erdős conjectured that it wasn’t possible to do substantially better than this. For 80 years, mathematicians generally believed he was correct.

But Erdős was mistaken. OpenAI found a pattern (similar to the one shown below) that, for a given number of points, produces more pairs than a lattice can.

Mark Belan/Quanta Magazine; source: Kai Williams

Mark Belan/Quanta Magazine; source: Kai Williams

2026 Jul 31

- Is AI Reasoning Right for the Wrong Reasons?

QUALIA | VIEW ON WEBSITE

Is AI Reasoning Right for the Wrong Reasons?

By John Pavlus

July 31, 2026

The idea that artificial intelligence can “reason” is more intuitive than ever. But intuitions can be wrong, and the science is far from settled.

Celsius Pictor for Quanta Magazine

Introduction

I’ll just say it: What the hell is going on with AI “reasoning”?

Sorry for the air quotes. That punctuational side-eye was more common in 2024, when the specially trained cousins of LLMs now known as “large reasoning models,” or LRMs, were still new. Nowadays it may seem downright churlish, though, given that a “general-purpose reasoning model” from OpenAI solved a famous open mathematical research problem in one shot in May 2026. Still, I’m not sure how else to acknowledge my intellectual whiplash over the scientific interpretation of what these AI systems are actually doing.

Reasoning comes in many technically defined forms, but the basic procedure is easily recognizable: arriving at a sound conclusion by linking together intermediate steps that logically follow from each other. We do this with thoughts; LRMs use so-called chains of thought, a term of art for the streams of synthetic text that the models emit before arriving at an answer to a complex query. One minute, the idea that AI could reason via these chains was being prominently and credibly critiqued (by a team of researchers from Apple) as an “Illusion of Thinking” subject to “complete accuracy collapse” under surprisingly simple conditions. The next minute, LRMs were bagging gold medals at the International Mathematical Olympiad, a feat so challenging that “even very successful mathematicians and scientists may well highlight [it] on their CVs all their lives,” as the scientist and AI critic Gary Marcus and Ernest Davis wrote in 2025. If that’s not a sign of “real” reasoning, what is?

In philosophy, “qualia” refers to the subjective qualities of our experience: what it’s like for Alice to see blue or for Bob to feel delighted. Qualia are “the ways things seem to us,” as the late philosopher Daniel Dennett put it. In these essays, our columnists follow their curiosity, and explore important but not necessarily answerable scientific questions.

But wait — soon after, more research, from the Santa Fe Institute, showed that LRMs can crush even carefully designed benchmarks for reasoning (like a collection of analogy-like visual puzzles) using mere “surface-level ‘shortcuts.’” What they were doing looked less like generalizable reasoning than just gaming the system. Then, as if on cue, another “hold my beer” moment: Google DeepMind and the mathematician Terence Tao (the GOAT!) used AI to rediscover or improve the solutions to 67 problems “spanning mathematical analysis, combinatorics, geometry, and number theory.” Deal with it, haters!

What about additional evidence that LRMs can’t reason reliably, even when they possess the necessary algorithm and computational budget to do so, and suffer from a list of scientifically documented failure states long enough to use as a Slip ’N Slide? Whatever — I guess that’s just “jagged intelligence” for you (AI-speak for “when it works, it works”).

And so it went from late 2025 into 2026. I’ve been a science journalist for 20 years and an AI journalist for half of that, so I know better than to expect tidy consistency out of rapidly advancing research. But even for me, this back-and-forth has been a bit much. To quote Al Pacino in The Insider, “I’m getting two things: pissed off, and curious.” I don’t believe there’s fraud to be found here. I just want to know which way is up. Can AI reasoning somehow be both BS and not at the same time? And if so, how on Earth does that work?

I knew just who to call first.

Melanie Mitchell’s career in AI stretches back to the 1980s, but lately she’s earned a reputation as an au courant AI truth teller, penning lucid explainers for Science and her widely read newsletter, as well as conducting research at the Santa Fe Institute. (The study about “surface-level ‘shortcuts’” is hers.) When I asked her what we actually know about AI reasoning, her answer was brief enough to fit on an index card.

“Number one: It works. It improves things,” she said, referring to LRMs’ superior accuracy on reasoning tasks compared to LLMs. “Number two: The actual text that’s generated” — i.e., the chain of thought that every LRM is trained to produce to improve its performance — “isn’t necessarily faithful to what’s going on [inside the model]. And number three: A lot of that text isn’t even useful. You can actually take it out.”

Let’s unpack numbers two and three, because that’s where the superposition of “BS and not” actually lives. Chains of thought were half-discovered, half-devised in 2022 as a prompting hack for LLMs: Provide them with examples of written-out reasoning (or, famously, just ask them to “think step by step”), and they’ll suddenly give less boneheaded answers to simple logic and math problems. LRMs, starting with OpenAI’s o1 model in 2024, are trained to automate this trick by generating such prompts — also called reasoning traces or thinking tokens — and then feeding them back to themselves. Because LRMs are essentially just language models, those extra bits of text create what looks convincingly like a paper trail of the model’s “thought process.”

Except it’s not that simple. A growing body of academic and industry research has cast doubt on whether these “intermediate tokens” are a faithful representation of an LRM’s inner workings. Instead of being auditable receipts or accurate reports, they can appear more like what the Arizona State University researcher Subbarao Kambhampati calls “mumblings” — bits of language, yes, but ones whose meaning may be entirely incidental to any reasoning that might have occurred. Kambhampati’s lab showed in 2025 that fully replacing a model’s correct “traces” with incorrect or irrelevant ones didn’t degrade its performance on a formal reasoning task. Meanwhile, training the model only on correct trace data still led it to occasionally generate invalid records of its reasoning — even when it produced a correct solution to the original problem it was given. A 2024 paper from researchers at New York University showed that “meaningless filler tokens” — literally, strings of dots — could function effectively in place of a human-readable “chain of thought.”

William Merrill, one of the authors on that paper and currently a professor at the Toyota Technological Institute at Chicago, put the matter plainly: “There’s no guarantee the chain of thought has to be meaningful in any sense.” Pavel Izmailov, a researcher at NYU who also works for Anthropic (and was part of its original reasoning-model team), said he doubts that reinforcement learning — a typical training method for LRMs — even incentivizes models to produce faithful chains of thought in the first place. “I mean, maybe it will,” he told me. “But I would say the chances are not very high.”

OK, so the linguistic content of reasoning traces may be dubious. But surely the tokens themselves must play a role in producing the model’s outputs? (Think of a pinball machine: It runs on coins, not the words “In God We Trust.”)

Not so fast. A 2025 paper from Northeastern University and the University of California, Berkeley on frontier open-source LRMs showed that between 30% and 60% of their “thinking steps” had “minimal causal impact” on the answers the models produced to benchmark math questions. Chop half of them out, and a model’s performance barely suffers. “We want to be careful when we review these chain-of-thought prompts because they may not be linked to the final output,” said Weiyan Shi, one of the study’s authors.

So reasoning traces, the very things that supposedly distinguish LRMs from the mere next-word-predicting LLMs, are not necessarily either meaningful or causal to a model’s … reasoning? I’m no philosopher, but this seems to stretch the meaning of “reasoning” beyond its tensile strength. Kambhampati’s research group sounded frankly fed up in the title of their position paper on the subject (presented at the 2026 International Conference on Machine Learning, one of the field’s most prestigious academic gatherings): “Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Traces!”

To be clear, Kambhampati, a former president of the Association for the Advancement of Artificial Intelligence, with a background in AI planning algorithms, doesn’t deny that LRMs can work (when they work). “We are in wondrous times,” he told me, when I asked what he thought of OpenAI’s 2026 victory in solving the famous unit distance problem in math. If he has a bone to pick, it’s with what he sees as a rush in both academia and industry to embrace overly convenient explanations.

A fake theory is worse than admitting that we don’t have a theory.

Subbarao Kambhampati, Arizona State University

“Many ideas that have been proposed [about] the sources of strength [of these models] have been misunderstood or mischaracterized,” he said. “There’s this general mindset that says, ‘Let’s go ahead and claim certain abilities, because eventually that might become true anyway.’ And my sense is: That’s not science. That is investment.”

On the other side of the AI-reasoning fence, the disdain seems to be mutual. “These ‘scientific’ papers from last summer — I would put this in big, big air quotes,” said Sébastien Bubeck, a member of OpenAI’s technical staff (and a prominent evangelist for the company’s reasoning models among scientists and mathematicians). He called earlier Apple results critiquing AI reasoning “wrong,” claiming that they were due to a training quirk in models that are now obsolete. “Modern models starting with GPT-5.5 do not suffer from this issue,” he said. “It would be interesting to revisit those results.” (Apple did not make its researchers available for interviews.)

Here’s the thing: Nobody denies that AI reasoning models can, indeed, produce significant and accurate results. Furthermore, every researcher I spoke to acknowledged that negative findings about the models’ capabilities on certain reasoning tasks (especially those of smaller, open-source LRMs) may not always generalize to the latest-and-greatest AI products. Their inner workings remain trade secrets. But if we’re disinclined (as I am) to simply dismiss contradictory evidence about the mechanisms driving AI reasoning, the question remains: How do we account for it?

Kambhampati, as it turns out, is interested in doing exactly that. “I’m not negative. I just sound negative because everybody else is way too positive,” he said. “In science, you have to actually understand what the current thing does and what it cannot do.”

One straightforward reason state-of-the-art LRMs work, he told me (a point also echoed by Mitchell), is that they’re often surrounded by “normal” software that guides and verifies their outputs. Agentic AI systems, which have transformed software engineering since the fall of 2025, work this way. So does Google DeepMind’s AlphaProof Nexus, which relies on Lean, an automated theorem-proving tool. But Kambhampati is more interested in making sense of stand-alone reasoning models that rely solely on their self-generated reasoning traces — “the ‘think’ part,” he said.

The “think” part is what OpenAI, for one, is doubling down on. When I asked Bubeck if the splashy unit distance proof was produced with methods outside the LRM’s own chain of thought — perhaps with Lean verifying its results — he seemed to find the question almost nonsensical.

“It’s not like we’re making a mystery of it,” he said. “We have released the chain of thought. You can just go and look at it. The whole point is that the model is reasoning like a human would. And when humans reason, we don’t use Lean.” Technically, OpenAI released a “rewritten summary” of the model’s chain of thought produced by two human experts using Codex, another OpenAI model. Since 2024, the company has not publicly revealed “raw” chains of thought from its reasoning models, a policy also adopted by Google DeepMind and Anthropic.

Kambhampati’s analysis begins in a surprisingly similar place: with the idea that LRMs are just LLMs with more specific training. “There is no extra magic,” he said. But he diverges sharply from there. “It doesn’t make sense to me that an LLM would actually do a step-by-step description of what it is [reasoning] before giving the solution — because that’s a much harder task than just guessing the solution, given the way that LLMs are trained.”

His working hypothesis is that an LRM, like its LLM precursors, performs what he calls “approximate retrieval” across its vast training corpus: “somewhere in the middle” between pattern matching and reasoning, he said, but closer to the former. The role of “thinking tokens,” then, isn’t to narrate an actual chain of thought (because there isn’t one). Instead, it’s to load up the model’s context window in a way that makes it more likely to predict, or “approximately retrieve,” reasoning-shaped strings of text.

Kambhampati compared this process to mumbling words to yourself to jog your memory: It barely matters what the words are (though related ones may help), as long as they knock loose something useful. An LRM’s vast “memory” includes all the call-and-response-like examples of written reasoning it was trained on, mulched into numerical “embeddings” that encode their similarities and differences (plus other inscrutable associations) as geometric relationships in a high-dimensional space. Probabilistically arriving at an answer within that space may involve intermediate tokens whose embeddings map to coherent-looking “thoughts” in plain English, but not necessarily. They could be bits of other languages. They could be fake exclamations like “aha.” Under the right conditions, they could just be dots.

You want the right answer for the right reason, so you can trust these things.

Melanie Mitchell, Santa Fe Institute

“Whether the [embedding] actually corresponds to a single word or not” — much less a faithful reasoning process — “is beside the point,” Kambhampati said.

This framing could help explain both the odd “BS”-ness of some chains of thought and the fact that they can elicit accurate outputs anyway. It would also neatly account for LRMs’ steady improvement in coding and math — what AI researchers call “verifiable domains.” Code runs, or it doesn’t; proofs are either correct or not. These binary conditions and the written steps associated with them can create convenient training signals for LRMs. The model doesn’t have to learn or reliably apply a general reasoning process, Kambhampati said; it just has to absorb enough examples of what the steps look like to predictively mimic them on its way to “stitching together” a plausible result that can then be verified.

The limit of a reasoning model’s training and step-following capability, known as the “inference horizon,” Kambhampati added, was what Apple researchers exposed with their “Illusion of Thinking” paper in 2025. Newer models have appeared to push this horizon further, albeit jaggedly. “Most of the time they probably are not learning the algorithm” associated with a reasoning process, he said. It’s much likelier that they are leveraging an ever-enlarging set of examples and clever reward signals.

Kambhampati hardly considers his case closed, and neither do I. But it’s a start — and one I find plausible, given that other researchers have also used similar “it’s the training, stupid” approaches to demystify AI behavior. Still, there was an elephant left in the room: How much does it matter whether or not we can accurately observe, characterize, and validate the processes at work inside large reasoning models?

The honest answer, according to Mitchell, is that it depends. “Think of AlphaFold,” she said, referring to Google’s AI tool for predicting protein structures. “It’s doing some kind of incredibly complex statistical associations. We don’t know what they are, but they seem to work. These things are [already] black boxes, even without a ‘reasoning trace.’” If LRMs can supercharge mathematics research the way AlphaFold did for computational biology, this line of thinking goes, why not embrace them, idiosyncrasies and all, and just verify the results? “My perspective is: We’re trying to be useful. We’re trying to build these models so that they can solve problems that matter, so that we actually accelerate scientific research,” said Bubeck. “It’s more interesting and more productive to talk about what they can do, rather than, ‘Oh, but they can only do that because of X [reasons].’”

But as Mitchell also points out, the possibility that an LRM could be “right for the wrong reasons” has an obvious relevance to the future of doing research. “You want the right answer for the right reason, so you can trust these things,” she said, and not just in verifiable domains.

Tal Linzen, a researcher at NYU and Google whose Computation and Psycholinguistics Lab published results similar to Apple’s “Illusion of Thinking” paper, said that “you want an AI system to be able to apply an algorithm reliably, regardless of whether you call [it] reasoning or not.” Treating chains of thought too reverently — even when their results are verifiable — could also prevent scientists from discovering even better ways of biasing LRMs toward accurate outputs. “We may be leaving some opportunities unexplored,” said Pradeep Dasigi, a researcher who helped train open LRMs at the Allen Institute for Artificial Intelligence. Kambhampati, unsurprisingly, puts it in even starker terms: Taking the meaning of AI reasoning traces seriously, he said, was a scientific “rabbit hole,” akin to believing in geocentrism or the ether.

Harsh, perhaps, but he has a point. Those incorrect mental models made intuitive sense at the time, just as chains of thought do now. When an LRM produces a correct answer — along with pages of “thoughts” showing how it got the result — intuition tells us that the two must be linked. It’s hard to imagine that process and outcome may have little to do with each other. But in the 1990s (in an episode Mitchell and Izmailov both brought up), it was hard to imagine how brute-force search could beat world champ Garry Kasparov at chess. And in 2023, it was hard to intuit how a giant pile of matrix multiplications could write in iambic pentameter. For most of us, these just weren’t thinkable thoughts. Until, suddenly, they were.

In summer 2024, just months before the first LRM appeared, Mitchell turned me on to a concept that I keep returning to in my AI reporting: “wishful mnemonics.” The phrase was first used all the way back in 1976 by the computer scientist Drew McDermott, in a paper with the epically grouchy title “Artificial Intelligence Meets Natural Stupidity.” I’ll quote the same passage Mitchell did:

A major source of simple-mindedness in AI programs is the use of mnemonics like “UNDERSTAND” or “GOAL” to refer to programs and data structures. … If a researcher … calls the main loop of his program “UNDERSTAND,” he is (until proven innocent) merely begging the question. He may mislead a lot of people, most prominently himself. … What he should do instead is refer to this main loop as “G0034,” and see if he can convince himself or anyone else that G0034 implements some part of understanding. … Many instructive examples of wishful mnemonics by AI researchers come to mind once you see the point.

This is how I make sense of AI reasoning. LRMs, chains of thought, thinking tokens: It’s wishful mnemonics all the way down — a heady mix of shorthand and suspended disbelief, like Oprah-style “manifesting” with a computer science spin. This isn’t necessarily a dig; all novel research likely requires some version of this mindset just to get off the ground. It certainly doesn’t mean AI reasoning can’t or doesn’t work. But the “wishful” part seems to be as powerful as ever.

Related:

1. The AI Revolution in Math Has Arrived

2. Why Do We Tell Ourselves Scary Stories About AI?

3. AI Comes Up With Bizarre Physics Experiments. But They Work.

“We react to language in a way that is very anthropomorphizing. That’s just the way that we humans work,” Mitchell told me. Much of the contentious research activity around AI reasoning, she said, “is par for the course. But in other ways, there’s a lot of very unscientific aspects to it.” Or, as Kambhampati put it, “A fake theory is worse than admitting that we don’t have a theory.”

In any case, we have to call it something while we figure out what it is. I don’t foresee always reaching for the air quotes around AI reasoning, any more than I’d put them around the “horse” in horsepower. LRMs are like engines: They require fuel, emit exhaust, and go fast. Still, when I describe the oomph my Toyota can deliver when I step on the gas, it’s not because I believe there are little hooves pounding away under the hood. Until a clearer scientific account emerges of what’s going on under the hood of AI reasoning models, I’ll regard their horsepower in a similar spirit — even as the engines roar.

Table of Contents

Quanta Magazine: 2026 Aug 7

2026 Aug 7

Neutrinos From Deep Inside Earth Provide a New Picture of the Mantle

2026 Aug 6

How Does Touch Lead To Pain Or Pleasure?

2026 Aug 5

Corals Spin Tiny Vortices to Get Oxygen, but Not if It’s Too Hot

2026 Aug 3

Why the Legendary Erdős Problems Are Falling to AI

2026 Jul 31

Is AI Reasoning Right for the Wrong Reasons?