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Sundar Pichai: Google’s Frontier Imperative, Compute Scarcity Dilemmas, Future AI Products and Businesses
Alphabet CEO Sundar Pichai has little patience for doubts about Google’s willingness or capacity to compete at the frontier of AI. His company’s latest Gemini 4 Argon model — long-promised and recently delivered — beats OpenAI and Anthropic’s latest public models on coding, math, science, and knowledge-work benchmarks and has placed Google’s AI back among the world’s best. Amid chatter that Google was fine with good-enough lightweight ‘Flash’ models, Pichai pushed back hard against any suggestion that company is limiting its research ambitions. “We won’t do it any other way,” he told me in a Big Technology Podcast interview recorded this week at Google’s Mountain View headquarters. Pursuing cutting-edge research is costly for Google, requiring compute and chips that otherwise could be sold at a high markup by its cloud business. But Pichai said allocating resources to frontier AI development is “ground zero” for Google. “Our cloud business is at any given moment in time forgoing short-term revenues because we are making compute trade-offs to support frontier AI development,” Pichai told me. In our conversation, we discussed Argon (which Pichai says processed three times as many tokens as Gemini 3 in the 72 hours after launch), AI’s role in developing better models, the AI interface of the future, competition with OpenAI, Google’s partnerships with Anthropic and Apple, data centers in space, and the nature of his optimism. You can read the full conversation, lightly edited for length and clarity, below, or listen to it on Apple Podcasts, Spotify, YouTube, or your podcast app of choice. Alex Kantrowitz: Google is back at the frontier with Gemini 4 Argon, which beats OpenAI’s and Anthropic’s public models on benchmarks spanning coding, math, science, and knowledge work. The conventional wisdom was that Google had given up on the frontier to focus on fast, good-enough models for its products. That was wrong? Sundar Pichai: Very wrong. Look, we are so committed to being at the frontier. It’s in our DNA. We won’t do it any other way. We’ve been thinking about the AI frontier for well over a decade now. The frontier is very dynamic. It’s very competitive. It’s very exciting. I couldn’t be more pleased with how Argon has come together. We took our time with it. We made sure we tested it safely and deployed it carefully. We took a bit longer than our other models, given its capabilities. Across the board, be it for agentic work, knowledge work, coding, creative tasks, Argon is one of the really good frontier models. The first 72 hours since we launched, it’s almost up to 25 trillion tokens used, which is about three times what Gemini 3.0 had in the first 72 hours. And we are capacity constrained as we are serving it. Excited, but there are always areas….. You know, the frontier is very dynamic, and you have to keep evolving the models. There are areas which we can see we need to improve as well. We’ll be at work on all of that as well. Which areas? On any given model, Argon would be across the board better at everything if you just look even two months back. But the space is so evolving. Even on coding, for example, there are pockets where we know we need to do better, and there’s clear feedback. We’ll make it better. The argument for standard models is that they’re already good enough to build great products, with the frontier valuable only for certain tasks. Meta built Muse with good models rather than the best, and Google could put standard models into Maps, Gmail, and Search. Why isn’t that sufficient? It’s definitely not how I have thought about it at all. We are deeply committed to driving the state of the art and actually pushing where this technology can go, all the way from transformers. That’s a journey we’ve been on. You will see us continue to do that. When you’re at the frontier, you get an earlier look into the emerging capabilities, earlier than others, which helps you better prepare for the future. We are fortunate to have a wide set of opportunities across consumer and enterprise. Some of our cloud customers, for sure, want the model at the frontier. The frontier models will be better at developing the next set of models in an era of recursive self-improvement. I would argue it matters there. Internally, we have definitely seen a steep point of inflection by deploying Argon to our developers. It definitely tackles problems which our previous models could not do. Being at the frontier puts you at the innovation frontier and helps you drive that as well. There is room for models of all sizes and capabilities. That’s why we did Flash, Flash-Lite, etc. I agree with you that you can develop very good product experiences with not necessarily the most frontier model, because they tend to be slower and more expensive too. But we care about the Pareto frontier, and we’re committed to doing all of that. You said developing frontier models gives you a look at what’s coming. What do you see on the horizon? What makes this moment so exciting is you build something which you think is a culmination of months of work, and you put it out. You already see clear headroom ahead. We’ve all been working on these agentic, long-horizon capabilities, but clearly the frontier is going to have multiple of these agents working together to do things. That’s an example of an exciting paradigm underway. It’s also clear to me that users, both in consumer and enterprise, will have access to some multi-agent framework for them, working all the time doing things. It feels like a moment of infinite possibilities. Sergey Brin said one of Argon’s core innovations was relying more on the model to help develop the model. Yet incidents involving OpenAI’s bots breaking containment and Google’s bots accessing a site they shouldn’t have suggest these systems still aren’t perfectly aligned. Isn’t there reasonable cause for concern about accelerating toward recursive self-improvement? There are three parts to your question. Let’s first talk about RSI itself. You’re right, Sergey mentioned RSI being a critical contributor as we are developing Argon. But to be very clear, today, in a continuum, we are pretty far from the end state of RSI or something like that. The right way to describe it is you have RSI loops along with AI researchers in various aspects of model development. You almost have agents helping AI researchers in some automated way work together to build these models. It’s good to at least understand where we are today, and it’s super exciting. I expect that continuum to keep making progress as we go into next year. The second question is around safety. Security comes from defense in depth. You’re talking about alignment, obviously. We all are doing a lot of work to make sure the models are as aligned as possible. But it’s an active area of research, and there are clear gaps. That’s not the only thing we rely on. You have the work you do on alignment, but beyond that, we put a lot of effort into real-time monitoring as these models are working. Making sure you have good telemetry into what’s happening so that you can intervene in real time if you see unexpected behaviors. How you do that, the architecture with which you do that, how you make sure it’s all sandboxed and isolated in a way that it can continue doing its job during real-time monitoring — these are all areas of investment. Obviously, when we deploy these models, giving customers, enterprises, control, both with real-time monitoring, but their ability to decide where they deploy, what type of data which models ever have access to, etc. All that work is very, very important, and we are doing a lot of that. As an industry, we need to constantly up our game and take the time to do all this stuff, including dedicating the compute required to make sure you can safely deploy these models. The third part is: Is it all enough? This is why there are a lot of conversations in the industry, and I think it’s reasonable to be worried. The right way to approach something you worry about is early on thinking about what more we can do. As an industry, you’re definitely seeing increased levels of work on alignment, testing for safety, etc., as we deploy these models. The next step would be developing better standards so there are cross-industry standards of how we do these things, including the ability to evaluate using independent evaluators. If you look at areas like financial controls, we have accounting standards, we have independent auditors. Those are all the next natural steps for us as an industry to work toward, and regulation will end up playing a role as we go through all of that as well. Google pursues both language models and world models, while some other AI companies like OpenAI have focused more heavily on LLMs as the path to AGI. Why is world-model research important to you? As humans, you obviously perceive the world around you, and you’re taking all those inputs. Making sure these models are able to understand the laws of physics, simulate the world in high fidelity. All that, I think, are key enabling blocks for intelligence. We are working across all possible applications of this. Take an area like robotics. It’s not an accident: If you look at embodied reasoning benchmarks, Argon is state of the art. That all comes from our experience building world models. Inherently, I would expect there will be architectures which will converge all of this in the future. But for us, we view this as, today, this jagged frontier that is intelligence. We want to be at the frontier of all the possible approaches, and that will help us both drive better research, better insights, and develop a much better AGI model, is how we think about it. Some people argue Google should focus on selling its computing resources rather than using them for AI research. Cloud wants to sell compute at a high markup, while researchers need a very big computer to build the best models. How do you balance those demands? Ground zero for us is making sure we are investing in the compute that’s needed to be at the AI frontier. In 2015, I set the company to be AI-first. AI is that key horizontal ingredient across everything we do, all the way from Search to Cloud to Waymo. That is ground zero for us, making sure both for research and developing the models that we have enough compute needed. Beyond that, we have these independent businesses which each have their own long-range plans. They’re using those long-range plans to project forward, and we plan for compute for all of them. The cloud team, they have a business and they have a projection and they have demand signals. They have committed contracts. With that help, we can plan on a long-term compute trajectory for them. We’re fortunate to have this wide set of opportunities. There’s no company in the world which isn’t making trade-offs at any given moment in time. But the first principles we operate on is making sure we have enough compute to invest in the frontier, and that’s non-negotiable, and everything else works on top. Remember, we are building our own compute, our own data centers. There are long lead times. The compute we have today is a function of decisions I made in 2024. Even if you think back to a year from today, last year the agentic coding takeoff hadn’t happened. Today, obviously, when you sit and look at anything, everyone is short—not just us, other companies—and everyone wishes you had two times what you had planned for. That comes with the territory. At any given moment in time, we are planning for what we would need two to three years out in the future. You’re going to find some decisions you make may look suboptimal two years out, depending on where the state of the art is. I don’t spend too much time thinking about it. The way I think about compute allocation is making sure Google DeepMind, both for research and frontier model development, is cutting edge in terms of compute access. After that, we plan for all our businesses. It’s great we can provide our compute to other people too, like we have done. It allows us to scale that up, get real tests for it in the widest application set possible, test it in the real world, and make sure we are at the cutting edge of technology at every layer of our AI stack as well. Ben Horowitz relayed on a podcast that Andy Jassy had said compute was sold out until 2028. Are you seeing the same thing? If today a customer is coming out and asking us for compute in ’27, that is incredibly difficult to do. Even last year we went out and leased some short-term compute to accommodate some customers and help them out. But yes, we are all dealing with similar timelines. Every major technology shift seems to involve overbuilding, from railroads to fiber. Do you think that’s happening here? It depends on the time frames you’re looking at. This level of build, if I take a long enough time frame, it makes complete sense to me. We are at early stages of the most consequential technology ever, and this is not like fiber. It’s not dark. We are using it. We are capacity constrained on a daily basis. The applications, the underlying technology is improving rapidly. But running a company, the way I work through the moment is you look at your demand signals. You look at your committed revenue opportunities that you’re getting in the form of contracts. You balance that with the underlying technical capability, or internally you balance how we are using it internally, the model trajectories. Based on all that, you take a disciplined approach—a confident, aggressive, but disciplined approach—to plan out compute. But when you talk about the broader economy as a whole, you’re having people operate with various levels of risk. I focus on what I can control as a company and plan through it. If your question is, can there be an overbuild? Yes, because you can have some constrained components. Remember, what matters is a set of everything you need to build out that compute. Memory, electricity, electricians…. Land. Like 2,000 components or more. You could get constrained at some point. There could be things which cause bottlenecks, and then you could have overbuilds. There could be external shocks to the economy. There are macro risks. All that is possible at any given time. But am I confident that the compute we are planning for, we will have uses for it given enough time? Absolutely, yes. I was in the room when you said AI would be as profound as fire and electricity. If you believe that, shouldn’t you give researchers the biggest possible computer, even at the expense of today’s cloud profits? We’re all doing some form of that. Our cloud business is at any given moment in time forgoing short-term revenues because we are making compute trade-offs to support frontier AI development. What you’re seeing is true already. But you want to be thoughtful. Even our AI researchers have some constraints, which are also good. That’s what inspires creativity. That’s what helps you come up with a more efficient algorithmic breakthrough, maybe to use less memory. What we are committed to is to invest what’s needed to stay at the frontier. OpenAI President Greg Brockman told me a few months ago that the ideal AI product might have no interface at all—just as natural as conversation. What do you think the interface looks like in the age of AI? Computing historically has been humans adapting to computing. With AI, it’ll be the other way around. The technology will adapt seamlessly to how humans live and go about their day-to-day life. You want to be able to speak to that AI when you want to. It has context. It can be persistently running somewhere, give you continuity. You’re not dealing with a lot of complexity. It’s natural. All that makes sense to me. That doesn’t mean it won’t have any interface, however. Nowadays, even on my phone, Gemini voice models are so good. I can just press the microphone button. I can just ramble. That’s why we call it Rambler. I can ramble. It intelligently corrects with my personal context. Even my friends’ names, weird names, it gets it right, even if it first guesses it wrong. It’s amazing. I’m not going in, backspacing, editing once I finish saying it. It’s remarkable how good it’s gotten. But there are going to be moments you will need an interface. You may want to entertain yourself. You may want to watch a video. You may ask your AI to create something, and you want to watch it, consume it. The way I think about it is, you want it to be as seamless and as natural as possible, and that you are not doing the hard work adapting to whatever the interface is. The technology is adapting to how you’re working at any given moment in time. When I asked Sam Altman about competition from Google, he argued that OpenAI was building AI-native products and implied Google was bolting AI onto existing ones. Do you accept that framing, and can a “bolt-on” compete with an AI-native product? First of all, it’s a very dynamic moment. We are all pushing each other. OpenAI has pushed us as a company too. This is not just we are doing something to them. Far from it. Yes, Google went into its own version of a code red too. In the AI moment, all of us have to respond with varying levels of urgency at any given moment in time. We are constantly building new products as well. Gemini was built from the ground up as a product—the Gemini app I’m talking about. Or tomorrow, as all of us are adapting to agents, are you bolting agents onto ChatGPT, or are they building it natively? How would you think about that? You can always put these questions back. I don’t think of it that way. I think of it as you want to build world-class product experiences that are AI-native and they are winning. Some of it is adapting products, some of it is rethinking new experiences. They will all be part of how you approach this moment. How do you expect to make money from these AI interfaces? If an agent recommends a product and I buy it, should it take a cut of the sale, even if that’s disclosed? I’m going to put enterprise aside. Consumers are going to increasingly value this technology. Take software developers today. Developers are willing to pay for these coding models because it really helps make them more productive. Nobody would want to code the way they were coding three years ago, or even the same time last year. When you’re delivering value, there’s value to be had. I’ve never seen examples where consumer products really scale to billions of users, where people are using it intensely, and haven’t had monetization to go along with it. If you’re providing value to people, there is inherent value to be had. The nature of it may change. You’re asking a scenario in which agents are doing stuff for you. At the end of the day, you will have commercial needs. The agents need to fulfill those commercial needs. The providers on the other side are going to see value in that. There’s going to be some mechanism by which it gets fleshed out. That’s what an efficient system does. But what matters is that you’re building those world-class consumer experiences. I promised myself one product feature request: Can you make Gemini-powered search in Gmail as good as searching Gmail through ChatGPT? What’s going on here? One of the exciting things we announced recently is the unified Gemini agent for enterprise, and we’re bringing that to consumers. These are persistent agents which run 24/7 in the cloud. We’re spinning up a micro VM effectively for every query, and from the runtime, the harness, the model—it’s all a headless service. It’ll work everywhere, and it has your context with you. You can address this agent whether you’re in Workspace, in Search, in Gemini. Agents are going to drive this unification from a customer experience, which people are looking for. When you have a broad product portfolio and you’re moving forward fast, there are always areas where you’re moving forward faster than some other areas. That’s valid feedback. But we are putting a lot of time and effort into making sure, as we build out our agentic experience, that unification and that capability works well across all these services. People have asked where Google’s equivalent of Muse or Dots is. Do you anticipate creating a new product to compete with them? Our goal with Gemini is to be a personal, proactive, and powerful assistant. That’s what we are working toward. You saw us describe the early versions of it at Google I/O this year with Spark. Effectively, already, if you open the Gemini app as a Pro or an Ultra user, you see Chat and Task, and they start giving you that agentic experience. With Argon now, that capability is dramatically accelerated. What we announced recently in our enterprise event, we are bringing that to consumers as well. Soon in Gemini, you will be able to ask something, and it’s a continuum. You don’t need to think about whether you’re asking a chat or an agentic task. It’ll just automatically do the right thing, and it can work persistently in the background securely and get things done for you. Already, empirically, when I look at what’s coming into Gemini in our app, last year you would have tagged them and most of them would have been chats. But a steadily growing percentage is already agentic tasks in Gemini. Yes, we are going to bring personal agents to our consumers. It’ll work in the Gemini app. It’ll work in Google Search. It’ll work in Gmail. You can address it, call it, etc. We want to make sure we’re getting it right. It’ll be powered by Argon, and we are very excited to bring it to our users. Google has invested and committed tens of billions of dollars to Anthropic. What do you see in Dario Amodei that makes you want such a deep investment and partnership? Very early on, when we looked at the founding team of Anthropic, it was very clear to us this was a world-class team which was involved in some of the earliest GPT models. We were quickly able to assess that they would be at the frontier. We’ve always tried to identify and partner with companies which would be at the cutting edge of technology. We’ve done that with SpaceX. We’ve done that with Anthropic. That’s what we saw, and we had a chance to partner. Is your partnership with Apple to license Gemini for Siri expanding, contracting, or standing still? For both us and Apple, that partnership was about translating the capabilities of AI into better experiences for users. I thought Apple has done a phenomenal job of incorporating—they customize these models and build it into Siri—and it’s been great to see the reception. We are committed to doing more. You’re going to do more with them? There’s nothing specific to comment, but it’s a partnership. It’s working well. What have you learned so far about the feasibility of data centers in space? It’s a very exciting project. Projects like this remind me—I still recall that moment in the desert for the DARPA Grand Challenge, watching the Stanley car compete in it, which ended up being the basis for Waymo. With all these technologies, you’re at a moment in time where it looks a bit like sci-fi, but 10 to 20 years down the line, it’s actually working in a meaningful way. I feel the same in space. We’ve always believed in betting big on these technologies. Quantum has been a great example of it. We’re probably 10 years into the journey. Waymo, we are almost 20 years into the journey. Space, we are two to three years into the journey. It’s clear to me that it’s an approach which will make a lot of sense in the future. Being in sub-synchronous orbits with 24/7 access to the most abundant form of energy we have access to will make a lot of sense for this exponential increase in compute that we are going to need. Having said that, this is a challenging project. The team has disciplined milestones. It was a super exciting milestone to send the TPUs up to space. Sometime, as we are speaking, we are expecting a “hello world” moment from them. I look forward to seeing that happen, and we’ll continue investing here too. You previously said 25% of the code written within Google was written by AI. What’s the number today? That number in our world today is meaningless in the sense that every part of what we now touch is influenced by AI deeply. What we are tracking with Argon, for example, it’s been a point of inflection. People are using Argon to really go tackle complex projects, including projects which affect millions of lines of code. In the four weeks since we have launched Argon, we have seen a 30% increase in CLs, and more importantly, almost a similar increase—a one-third decrease in rollbacks. People are writing code more productively and better code with it. We are tracking it with newer metrics now. How are you dealing with opposition to data centers? If you can’t build in the U.S. the way you want to, where else do you go? We’re very committed to building our data centers very responsibly. When I look at our largest data centers in some of the communities where we have been the longest, I visit them, I talk to people. There are people who come and tell us their kids have gone to college because of our investments in these communities, and we’ve grown in those communities. To me, this is about doing it right. Engaging with the communities early, doing it transparently. We are committed to making sure we are bringing incremental power, and we cover 100% of the cost of our power needs for these projects. It’s understanding the impact on neighborhoods and making sure you’re working thoroughly to address them. Done rightly, you can continue making progress. But we are constantly taking feedback into account. I do worry not everyone operates the same way. People have been surprised by data centers showing up in their backyard. Those are all important. As an industry, we should continue to do better. But part of the reason, in a 20-year time frame, we are thinking about things like data centers in space is making sure we are able to put these data centers where maybe the trade-offs will be different. What unproven area of AI is Google working hardest to crack? Is it continual learning—getting models to keep updating after release—or something else? There are multiple parallel paths. You saw with Argon we have a million output tokens. With our Gemini models early on, we had a one-million context window on the input side. These are all examples of breakthroughs we are constantly working on. How do you think about synthetic data? How do you think about continual learning? How do you think about more efficient architectures? We do dedicate a meaningful percentage of our compute to these research bets too. It always goes hand in hand, including our world models. Those are all bets we are placing. Sometimes, as part of these bets, there are ideas that emerge which just make their way into the mainline models. Maybe we don’t fully talk about it, but we have multiple steps in these areas. Working in technology requires optimism that building something will improve the world, but those bets can leave real consequences when they fail. I recently returned to a town in El Salvador where enthusiasm for Bitcoin had given way to unfinished construction and something resembling a ghost town. How do you maintain the optimism you spoke about in your Stanford commencement address while accounting for those risks? I genuinely have a deep conviction that improving technology and improving access to technology makes people’s lives better. It’s been my personal experience in my life, out of which comes that deep conviction, optimism. But with that, I’ve always felt humanity’s journey is always about working hard to harness that technology to make it work well for society. The optimism is not an optimism that you just build whatever you want, whenever. You work hard to make sure you’re building technology in the service of humanity, to make the human condition better. That’s got to be the defining goal as we are approaching it. With that framework, I’m very, very optimistic. We are in very early stages of the kind of progress that we are going to see on many areas. We’re all talking about areas like healthcare. A lot of the progress is ahead. The ability to make things in a fundamentally cheaper way on many things where things have been getting expensive for a long, long time, discovering new materials, etc., which really bend the curve—it’s tough to step today and understand the impact of all that, but they’re going to be very profound. I’m very optimistic, but we have to be rightfully concerned about all the ways this technology can go wrong. Sundar, I know you don’t do many of these. Thank you. Thanks, Alex. Appreciate it.
Live Nation Entertainment (LYV) Stock Stays Hard To Read After Its 93% Run
Live Nation Entertainment has seen its share price advance strongly over the past few years, which naturally puts the focus on whether the current US$168.77 level is supported by what the business can generate in cash. With the stock giving back some ground in recent weeks, the question now is how that recent share move lines up against the value implied by its cash flows. Over the past 3 years the stock has returned 92.9%, which raises the issue of whether the market is now asking investors...
Studying Science in an Anti-Scientific Time
This Chronicle Review Deep Cut was first published in 2025. By Julianne Werlin Thanks for reading The Chronicle Review! Subscribe for free to receive new posts and support my work. Subscribe The last two decades have not been kind to science studies. Already bruised and battered by the “science wars” of the 1990s, by the 2000s sociologists of science — who had long argued that science had to be understood in its human context — were encountering the funhouse-mirror version of their own views in right-wing critiques of climate scientists. It was enough to give some prominent scholars pause. “Was I wrong to participate in the invention of this field known as science studies?” asked Bruno Latour, the most famous sociologist of science in the world, in 2003. “Should we apologize for having been wrong all along? Or should we rather bring the sword of criticism to criticism itself and do a bit of soul-searching here: What were we really after when we were so intent on showing the social construction of scientific facts?” In response, Latour proposed a change of method and attitude. Science studies, now chastened and contrite, would no longer seek to strip facts of their authority, but to enrich and enhance them. What this meant in practice was never fully clear. But his diagnosis of problems resonated, even if his solutions did not. Latour’s exercise in public hand-wringing signaled a shift in mood. By 2016, with the first election of Donald Trump, the idea that skepticism of science was a right-wing position, not a left-wing one, had become a commonplace, discussed eagerly in The New York Times. For liberals, vaccine denial during the Covid era and Trump’s second-term attacks on the funding of scientific research, covered extensively in these pages, added a new urgency to the defense of science on both practical and principled grounds. There was little appetite for a sociology or philosophy of science that emphasized the messiness and missteps of the scientific process rather than its integrity and triumphs. The distinguished historian of science Peter Dear’s new book, The World as We Know It: From Natural Philosophy to Modern Science (Princeton University Press, 2025), which seeks to tell the story of 18th- and 19th-century science for a general public, may seem comfortably remote from these bitter contests. In its 16 short chapters, Dear draws swift, sure sketches of major discoveries from Newton to Einstein, ably guiding the reader through debates on stellar nebulae, animal taxonomy, atomic chemistry, evolution, and other flashpoints in the history of science. But though a work of history, Dear’s book must still negotiate our polarized present. In a blurb on its back cover, the historian of biology Lynn K. Nyhart alludes to the “present moment, when scientific knowledge is being swamped by misinformation and its institutions are under siege.” Dear himself, in his conclusion, refers to the “yard signs” and “bumper stickers” that proclaim “Science Is Real.” One antidote to both anti-science propaganda and to reductive slogans in science’s defense is a robust history and philosophy of science: exactly what Dear’s scholarship has long offered. But his new book illustrates how difficult such work has become. Dear, an emeritus professor of history at Cornell University, is best known as a creative and philosophically minded historian of the Scientific Revolution. His edited volume The Literary Structure of Scientific Argument (1991) analyzed the rhetoric of Galileo’s thought experiments to brilliant effect. Discipline and Experience: The Mathematical Way in the Scientific Revolution (1995) was a major contribution to the analysis of scientific experiments in early modern Europe and beyond, while Revolutionizing the Sciences: European Knowledge in Transition, 1500-1700 (2001), now in its third edition, remains one of the best overviews of the profound epistemological upheavals of 16th- and 17th-century science. But Dear has also ventured into later periods. Nearly two decades ago, in The Intelligibility of Nature: How Science Makes Sense of the World (2006), he turned to the 18th and 19th centuries to sketch a history and theory of scientific knowledge. Aimed at a generalist audience, Dear’s simple prose belied serious philosophical ambitions. Science, Dear argued, has two aspects: “natural philosophy,” the quest to understand the true nature of the universe, and “instrumentality,” the applied skills and technologies that allow us to manipulate the world for human advantage. Both strands are ancient, but the innovation of the phenomenon we call “science” was to yoke them together. As Francis Bacon wrote, “Human knowledge and human power meet in one; for where the cause is not known, the effect cannot be produced,” an insight typically abridged to “knowledge is power.” Or, in Bertrand Russell’s characteristically blunt phrase, science has two functions, “1. to enable us to know things, and 2., to enable us to do things.” So far, so conventional. But Dear took issue with this familiar story in one respect. Bacon’s formula implied that theory and practice could be fused in a single method. In fact, Dear argued, the quest to understand nature and the desire to manipulate it were coupled only loosely. They could come together, but they could also pull apart. Focusing on natural philosophy, the “knowing” half of the scientific circle, he used a series of case studies from cosmology, taxonomy, chemistry, electromagnetism, and quantum theory to show how theories changed over time, in dialogue with practice, but never reducible to it. Take the case of matter, the basic substrate of the physical world. In the 18th and 19th centuries, scientists not only disagreed about what it was, but also about how well it could be known. In 1789, the chemist Antoine Lavoisier dismissed speculation about the atomic composition of chemicals as “discussions entirely of a metaphysical nature,” as he sought to orient chemistry away from philosophical abstraction and toward laboratory results. But in the 19th century, atoms returned with a vengeance in the atomic chemistry of John Dalton, who wanted to explain the underlying structure of nature. It was not just that Dalton and Lavoisier had a different set of experimental results, or even a different theory, Dear showed. What counted as an explanation had changed. In arguing that scientific theories depended on their intelligibility to human beings with complex arrays of beliefs and commitments, Dear was in sympathy with science studies. Key figures such as the sociologist Steven Shapin emphasized the importance of psychological mechanisms like trust in the formation and acceptance of scientific ideas. But in focusing on the scientific pursuit of knowledge, not its politics or economics, Dear resisted the more cynical perspective of some of his colleagues. The Intelligibility of Nature strikes a very different note than Shapin’s collection of a few years later, Never Pure: Historical Studies of Science as If It Was Produced by People With Bodies, Situated in Time, Space, Culture, and Society, and Struggling for Credibility and Authority (2010), to name just one example. Walking a very fine line, Dear depicted science as driven at once by complex social dynamics and by the intellectual power of real discoveries. It was a testament not only to his own acumen but also to the vitality of the field that he was able to do so. Nearly two decades later, The World as We Know It returns to the questions broached in The Intelligibility of Nature. Like Dear’s earlier study, it begins with the Newtonian cosmos and ends with debates between Einstein and Bohr on quantum physics. Beyond its terminal figures, many of the same characters appear in both studies, including the naturalists John Ray and the Comte de Buffon; the chemists Étienne François Geoffroy, Antoine Lavoisier, and John Dalton; as well as Charles Darwin and Michael Faraday, among others. Its subject is nearly identical: As in The Intelligibility of Nature, The World as We Know It considers natural philosophy as opposed to instrumentality, here defined as the “desire to create a picture of what the world is really like — the world as God knows it — rather than simply having instrumental or operational control over it.” The two books also overlap in insights — and sometimes even in language. The reader who has learned in The Intelligibility of Nature that Buffon’s method of classifying animals “reflected his belief in the importance of the senses and empiricism in learning about nature (a doctrine associated with Newton and the philosopher John Locke)” will experience a sense of déjà vu on reading, in The World as We Know It, that the classification “reflects Buffon’s commitment to the epistemological stance of Newton and his philosophical underlaborer John Locke, who stressed the role of the senses in creating natural knowledge.” Later in the passage, we read that, “because of its stress on understanding the ways of life of animals in their environments, Buffon’s approach could almost be labeled (anachronistically) as ‘ecological.’” And again, “an anachronistic way of putting it would be to say that Buffon recommends an ecological mode of understanding.” Likewise, in The Intelligibility of Nature we read of Dalton that he was initially drawn to meteorology, and that: He did a bit of experimental work on these things, especially having to do with the water-holding capacity of gases, but his real interest lay in understanding what, at an underlying natural-philosophical level, was really going on physically.” Whereas, in The World as We Know It: He did experimental work on these things, especially concerning the water-holding capacity of gases — although not at a very refined level quantitatively — but he was continually trying to develop a theoretical conception of what was happening physically. The final full chapter of each book closes on the same resigned note. Intelligibility: These days, most serious work on the natural philosophical underpinnings and implications of quantum mechanics is performed not by physicists but by philosophers of science. Among scientific practitioners themselves, Bohr’s attempt to use instrumentality as sufficient grounds for a respectable science has met with a high degree of success. And again, in The World as We Know It: Bohr’s and Einstein’s concerns are now questions that are mostly discussed by philosophers of science rather than by physicists themselves. The marginalization of these sorts of questions amounts to the demise of natural philosophy, at least in physics, in the 20th century. Quantum mechanics “works,” and that’s good enough for most scientists who encounter it. Examples could be multiplied. When handling the same subject matter, some echoes are inevitable. Scholars should not feel that because they have written “Isaac Newton died in 1727” in one book, in the next they must describe him as perishing, expiring, or meeting his maker. But the repetitions of language and arguments (not to mention six reproduced images) threaded throughout The World as We Know It are more extensive than that. They should have been caught by Princeton University Press or its reviewers, and revised. Because the two books have so much in common, it is easy to see where they diverge. The World as We Know It is a less theoretical work than its predecessor. The sociology of knowledge that organized The Intelligibility of Nature has been reduced in scale and folded into readings and examples. The taut interplay between history and theory, which gave the earlier study so much interest, is there, but it is subtle. In the earlier book, the case studies demonstrated theoretical claims about the character and development of science. In the later work, each narrative of discovery is an end in itself. Insights emerge from the history, but they do not control the organization of the study. With the theory muted, it can be hard to understand the logic uniting Dear’s sprawling history of more than two centuries of scientific knowledge. The 16 brisk chapters cover an enormous amount of material. But the closer the volume edges to comprehensiveness, the more obvious the missing elements become. A chapter on “Institutions and Pedagogy,” describing the birth of the research university, illustrates the problem. Inserted roughly halfway through the book, the unnumbered chapter is designed as an “entr’acte,” in acknowledgement that it does not fit the study’s organization around scientific discoveries. Yet as Dear is well aware, no general history of the development of modern science can bracket its 19th-century institutionalization and professionalization. It was the 19th-century university, after all, that gave us the “scientist,” a term coined by the Cambridge philosopher William Whewell. Dear’s history of scientific discoveries implies a wider social and institutional history, but it remains largely subterranean, only occasionally extruded under the pressure of the narrative. It must be said that the balance is not all on the side of the earlier study. There are some advantages to the more historical, less theoretical organization of The World as We Know It. Dear’s accounts of paradigm shifts are remarkably lucid, no small accomplishment given that they span fields and centuries. He makes excellent use of the wealth of new scholarship on Darwin and his influences, to which he has himself contributed. It is satisfying to see puzzle pieces from Buffon’s natural history, Georges Cuvier’s animal taxonomy, Thomas Robert Malthus’s demography, and Charles Lyell’s geology recombined seamlessly in the Darwinian jigsaw. Likewise, Dear’s discussion of stellar nebulae, divided between chapters two and 15, shows vividly how Newton, Kant, Herschel, and Hubble reimagined insights and observations as they sought to discover whether the diffuse light that filtered through early telescopes was a glowing, gaseous substance or discrete but distant stars. Yet despite such pleasures, the book remains a more modest work than The Intelligibility of Nature. Dear has clearly not changed his views about the history of science. But in the two decades that have elapsed, the discipline and the world around it have changed, making it harder to write a sociologically and philosophically ambitious history of science for the general reader. That is a shame, because in the age of Silicon Valley and biotechnology, both the reciprocity and the tensions Dear identified between the epistemological and instrumental ambitions of science are more evident than ever. In recent years, some of the most exciting new books on the history of science in early modernity have focused precisely on this pressure point. Important studies such as Pamela H. Smith’s From Lived Experience to the Written Word (2022), James Poskett’s Horizons: The Global Origins of Modern Science (2022), and Vera Keller’s The Interlopers: Early Stuart Projects and the Undisciplining of Knowledge (2023) all shed new light on how early modern science negotiated the relationship between its instrumental and epistemological ambitions, or in Russell’s phrase, knowing and doing. There is every opportunity for a philosophically informed approach to draw new insights from this wealth of material. Dear, unfortunately, has not done so in The World as We Know It. But for future scholars of the history of science, his body of work over nearly four decades may provide just the model. Julianne Werlin is an associate professor of English at Duke University. 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Chartbook 464 The"China squeeze" - the politics of development counterfactuals.
There are three types of China shock discourse. Each has a distinct temporal register. China shock 1.0 was a phrase coined by US academics in the 2010s to retrospectively summarize the impact on particular US labour markets, which they identified through painstaking empirical investigation. China shock 2.0 on everyone’s lips in Europe in the mid 2020s is an alarm raised by trans-Atlantic policy thinkers warning of a coming disaster which they project onto Europe’s ailing car industry. The third type of China shock discourse - commonly known as the “China squeeze” launched most recently by Indian economists Shoumitro Chatterjee and Arvind Subramanian in the pages of Foreign Affairs - mobilizes a different, more speculative temporal register: the counterfactual realm of alternate history. The China squeeze argument asks not what has happened, or what is going to happen, but what might have been. Specifically, what options for economic development might have been open to Global South economies like India or the economies of sub-Saharan Africa, had it not been for China’s spectacular industrialization since the late 1990s and the persistent competitiveness of China’s manufacture in low-skilled segments. Chatterjee and Subramanian’s argument is that whilst China has built a powerful position in electrical machinery and other high-skill manufacturing, which is now delivering China Shock 2.0, it also continues to run a trade surplus in low-skill, labour-intensive manufacturing. This surplus is large and has remained largely unchanged in fifteen years. Indeed, in the 2010s, in trade with low-income countries, China’s surplus rapidly increased. On their reading, this squeezes low-income producers in low-skill sectors in which they “should” be able to compete. Source: “China’s Mercantilist Squeeze on Developing Countries” Shoumitro Chatterjee and Arvind Subramanian. Peterson Institute for International Economics 26-7 May 2026 Chatterjee and Subramanian’s wording is telling: (emphasis mine) The canonical trajectory of economic development in the post-World War II period is for countries to transition from agriculture to manufacturing, first in low-skilled manufacturing and then later on to high-skill manufacturing (Herrendorf, Rogerson, and Valentinyi 2013). At the global level, this implies that high-income countries (HICs) that once specialized in low-skilled activities cede manufacturing export space to poorer countries that come along to occupy that space. In 1965, advanced economies accounted for about 60 percent of global value-added exports in four key low-skilled manufacturing sectors, i.e., apparel, leather, textiles, and footwear. Over time this share declined and began to be occupied first by the East Asian tigers and, beginning in the late 1990s, by China. Today China accounts for about 50 percent of the global market. The question is whether China, with its rising prosperity, is continuing to behave like its richer counterparts and ceding export space to poorer followers or to behave unlike them and continuing to occupy space. In terms of the conceptual maneuvers it performs, of the three China shock discourses, the “China squeeze” argument is by far the most ambitious. After all, it bases its critique of China on a counterfactual imagining of the world. Like any counterfactual argument, to construct this alternative reality it must rely on a heuristic model. To make their argument Chatterjee and Subramanian mobilize two key claims. The first reference point is the actual history of rich-country industrialization, what they refer to as the “canonical path”. The idea is that countries transition from agriculture to light industry and low-skilled manufacturing and from there up the value chain. To make this useful as a standard of comparison and a means to construct a normative standard, they adjust sectoral export shares for two factors: 1. the underlying level of economic development of the exporting country, as measured by national income and 2. the degree to which the sector in question is internationalized. The idea being that for a high income country to have a high export share in low-skill sectors is anomalous and even more so if that particular product is highly internationalized and thus suitable for being “conceded” to lower-income competitors. Where should this process end? Here Chatterjee and Subramanian introduce their second benchmark. Their normative idea of a global development path is one in which, in due course, low-skill manufacturing exports are distributed in proportion to a country’s share in the global labour force. They imagine, in other words, a world of proportional, polycentric global development without much national specialization in low-skill manufacturing. A useful benchmark is that, in labor-intensive manufacturing sectors, a country’s share of world exports should be broadly commensurate with its share of the world’s low-skilled labor endowment. … In the Heckscher-Ohlin model, this would be true if value added per worker and export propensities, i.e., fraction of output that is exported, are the same across countries. It is this pleasant liberal vista of orderly global development, progressing towards a general distribution of low-skilled manufacturing against which they judge reality and conclude that the low-income countries are suffering a “China squeeze”. China is not exiting low-skill sectors fast enough Allowing for its gdp level and the degree of globalization, China’s share of low-skill manufacturing exports is far higher than it “should” be. Relative to population, its low-skill share of exports is far too high. That in turn yields the conclusion that huge numbers of jobs - the authors suggest tens of millions - are being hoarded by China. For the four sectors captured in figure 4, this excess in value-added exports is $110 billion in 2022. For all low-skilled sectors overall captured in appendix figure D.1, excess value-added exports amount to $365 billion. The crowding out of LMIC exports by China is therefore substantial. Both these claims have in common that they are strong structuralisms. The first is that development paths ought to resemble each other. The second is that broadly speaking as far as low-skilled manufacturing is concerned, all countries are alike. If the world currently diverges from the distribution of trade implied by these assumptions, if China is not following the advanced economy path and has huge exports, this must be the result of “policy”, which the authors then put in question as illegitimate or unfair. Thus the naturalistic construction of a canonical path leading to a “level” distribution of low-skilled manufacturing reveals itself to be a polemical tool. From the point of view of China shock polemics, this discourse is no doubt welcome. But as an account of economic history, it must surely be regarded as nothing short of bamboozling. Development paths do not follow a regular, natural sequence. As David Fishman put it aptly, “development is not a queue”. They are always and everywhere, through and through political. Development is not so much a process, as a project. Each such project is more or less well adjusted to its context. As Gerschenkron taught uneven and combined development implies differentiation rather than simple imitation on a single parth. It may be true that Europe and the US exited relatively early from low-skill work. But clearly technology has changed. As C&S themselves point out, China no longer enjoys a low wage environment. Do they really believe that such huge differences can be accounted for either by industrial policy, which as they admit does not operate powerfully in low-skill sectors, or by exchange rate manipulation? What is overwhelmingly dominant in explaining the persistence of “low-skill” manufacturing in China are surely the network effects of complex supply chains. Indeed the label of “low-skill” may miss the point. The degree of globalization which Chatterjee and Subramanian decide to conjure away in their model of the development path - to allow for the fact that Europe and American textiles exporters did not enjoy just-in-time logistics in the 1950s - obscures the point that the networks through which Chinese factories supply the world are themselves key parts of the low-skill industrialization project. China isn’t knocking out 1970s style “cheap”. It has redefined the entire product cycle. C&S’s casual reference to “T-shirts” is telling. In today’s “fast fashion”, there is weight on both terms. Fashion and fast. China’s success in delivering on both at unprecedentedly high speed and at unprecedentedly low cost, isn’t a low-skill operation at all. If we see development trajectories not as the serial repetition of the same, but as distinctive, crafted projects that cumulate in complex agglomerations of skill, technology and labor, then this explains why the assumption that low-skilled manufacturing should follow the distribution of population flies in the face of all experience. As countless cases attest, low-wage, low-income developed economies are not by themselves the makings of a competitive production base. As Leon Liao puts it quite fairly: “International trade has never been allocated according to population. A country’s population gives it no natural claim to an equivalent share of global exports." And the remarkable fact is that Subramanian alone and with co-authors has long been making this point, not to pillory China, as is the case of the tendentious _Foreign Affair_s and Peterson pieces, but to highlight the great counterpart to China’s industrial success, namely India’s relative failure. The much-quoted Foreign Affairs piece showed low-skill export shares only for China, not for India. If you put both countries in the picture as Chaterjee and Subramanian have done on other occasions, the picture looks like this. China is far above the line, but India, almost as large in terms of population, is no less anomalous, just in the opposite direction. As far as global populations are concerned, and the imagining of tens of millions of “squeezed” jobs, it is this Sino-Indian pair that account for a huge share of the action. What this graph reveals is not China’s cheating, but India’s “greatest development failure”, at least this is the argument made Arvind Subramanian, this time in co-authorship with Devesh Kapur, in their major book, A Sixth of Humanity. Independent India’s Development Odyssey which appeared with Oxford University Press earlier this year. In this highly critical work on India’s development, China is repeatedly evoked, not to criticize but as a benchmark against which to illuminate what India’s political economy has not delivered i.e. large-scale formal employment for hundreds of millions of citizens. Kapur and Subramanian leave no doubt, if you want to understand why China, not India was the main driver of manufacturing globalization from the 1980s, don’t look to slots opening up in the division of labour thanks to the liberal mindedness of advanced economies, look to the contrasting political economy of the Maoist PRC and India’s post-independence political economy. As Kapur and Subramian note: The great divergence between China and India did not begin in 1978 with the Deng reforms—it was well underway by then. In one of the great ironies of modern history, the conditions created by Maoism turned out to be a great launching pad for industrial globalization. As the comparison of India and China in 1980 makes clear, to imagine manufacturing would be equally distributed when populations are so differently endowed in terms of educational and nutritional is fanciful. Here is the data from Kapur and Subramanian, data which I explored back at the very beginning of Chartbook. As Kapur and Subramanian insist, the striking fact is that even as India’s growth accelerated from the 1980s, structural change has not. Formal employment in India lags woefully behind. Why? As Kapur and Subramanian note, the path-dependent impact of India’s poor early start was devastating. The list of factors they mention is long and weighty: We argue for a new five-fold explanation applicable at different stages of Indian history: in the initial stages, the legacy of two key pre-1980s policies, the neglect of agriculture and the strangling of the domestic private sector, were critical. The failure of broad-based agricultural growth militated against rural industrialization, which would have been devoted to satisfying local needs that would have necessarily been labour intensive. Then, when the boom came after 1980, the thicket of regulation and broader labour protections that were legislated prematurely in a nascent democracy, underpinned by a deep ideological aversion to private capital in general and big capital specifically, became a barrier to attaining the size that would be necessary to compete globally. Similarly, the reforms after 1991, while wide-ranging, did not quite encompass non-tradeable inputs critical to manufacturing, namely, land, logistics infrastructure and power. A combination of low investment (logistics infrastructure), regulation (land) and the continued monopoly supply by state governments (power), combined with residual socialism, kept costs at 75–100 per cent above efficient supply. These factors were compounded on the supply side by the government, which dominated the formal labour market and established exorbitantly high wages at the lower end of the skill spectrum. This made their employment in the labour-intensive sector by the private sector less attractive. Finally, when the services sector and its exports boomed, wages soared even further via a Dutch Disease effect: one successful tradeable sector killed and cannibalized another less-skilled tradable sector, namely, manufacturing. Thus, scarcity-inducing policies of the planning era, precocious democracy and a Gandhian aversion to size; inefficient, monopolistic supply of critical non-tradable inputs; the behemoth of government; and super-successful skill-intensive services successively undermined India’s unskilled labour employment opportunities. The employment failure seems to have been over-determined. … Less quantifiable but no less important are some of the sociological factors that have undermined manufacturing and factory jobs. Take the impact of the success of the IT sector—located in urban areas with better working conditions, such as air-conditioned offices, good schools for children and attractive entertainment options. It became a more attractive place for engineering and management graduates to flock to. … Consider manufacturing and female employment. Indian society makes it difficult for women to work, reflected in India’s unusually low female labour force participation (elaborated in Chapter XIII). Indian society, in particular the routine harassment of women in public spaces, makes it even more difficult for them to travel long distances to take up work.37 Combined with onerous regulations on creating hostels and dormitories for women—which, in turn, prevents multiple shifts of operation—factories need to be located in areas with a large female labour pool. While firms can take apparel plants to female labour or bring them to the plants by building large-scale dormitories, this raises costs. If there was a case for subsidies to encourage labour-intensive manufacturing, housing subsidies to build dormitories for women workers would be a good place to start. All of this meant, that when opportunities did open up, in part thanks to adjustments in China, India was in no position to take advantage. Here is the account of the 2020s from Kapur and Subramanian. It stands in dizzying contrast to the one-sided, “China squeeze” argument delivered in the Peterson and Foreign Affairs pieces: In 2022, for example, India’s share of global exports in clothing (the canonical labour-intensive sector) was 3.1 per cent compared to 32 per cent for China. This is extremely disappointing because China started vacating export space in this sector after the Global Financial Crisis as its wages started rising; it then vacated further space in the aftermath of geopolitical developments that led investors to seek alternative investment locations. Between its peak and today, China lost about 7.5 percentage points of global export share (worth about $43 billion in 2022); over this period, India’s export share remained stagnant at 3 per cent, while that of Vietnam and Bangladesh rose by 3.5 to 4 percentage points. China’s loss did not translate into any gain for India in terms of low-skill exports and employment. Put differently, when the conditions for dynamism and prosperity were established after 1991, and even more tantalizingly when the global export opportunities opened up, it was as if India was a high-wage economy and only skilled labour could benefit from the dynamism. It had converted its physically abundant unskilled workforce into an economically expensive resource, penalizing its use. It is not my mission here to argue for or against Kapur and Subramanian’s interpretation of India’s development trajectory. The point in this context is simply that the “China squeeze” such as it is can only be understood, in relation to the heavy burden of domestic constraints that Kapur and Subramanian so unsparingly enumerate. I am concentrating here on India because it is the great counterpart to China and the unspoken reference point of the “China squeeze” discourse. But the point is more general. Can anyone with a straight face suggest that the main problems afflicting the development of South Africa or Nigeria, the great centers of mass underemployment in sub-saharan Africa, are cheap competition from China driven by Beijing’s industrial policy and an undervalued RMB? Likewise, is the main problem of the American working-class the threat of Chinese competition or the manifest crisis of social reproduction in the United States itself? The point here, as in the discourse around China shock 2.0 in Europe, is to insist that we should not normalize the world and pathologize China, without actually investigating what is going on in America’s industrial wastelands, the German car industry, or India’s lopsided labour market. One-sided pathologization should be understood for what it is, a polemical gesture. Concepts like China shock and China squeeze are, what Reinhart Koselleck, following Carl Schmitt, would have described as “asymmetrische Kampfbegriffe”, or “asymmetrical counter-concepts” or “asymmetrical combat concepts”. If the function of arguments like the “China squeeze” is to alert elites in the rest of the world of the need to rejuvenate and energize their development projects, so be it. But let us see the resulting discourses as the mobilizing interventions that they are. Interestingly, a significant slice of China’s public sphere - the so-called industrial party - have been obsessed for the last twenty years with precisely these questions of counterfactual economic history. Since the mid 2000s, a massive online discourse developed around an alternative history in which Chinese techno-patriots were asked to imagine how they would proceed if they had the possibility of traveling back in time to transport modern technology into the Ming dynasty (1368-1644) thus averting the “great divergence” of the 18th and 19th centuries. It is a fantasy of course, but also a goad. The point was not directed at Westerners. The alternate history is dramatically nationalist, but few n the West had even heard of it until recently. The adherents of the industrial party are not liberal moralists. They don’t project natural ladders of international development and ask why others don’t cede space. They take the Realpolitik of global economic development for granted. Their pointe was directed against fellow Chinese. The rallying cry of the alternative narrative was this: Now that we are beginning to gain steam and push forward on our national development path, now that we are breaking out of the inferior position assigned to us even in the early years following WTO accession, do not lose yourself in humanistic imaginings about better futures. In the spirit of Deng, they called on China to embrace the hard truth of development. Perhaps one might of the Industrial Party as a cultural trend born out of desire for symbolic revenge against those who had inflicted the century of humiliation. In terms of the various temporalities of the “China shock” - retrospective, prospective and counterfactual - one might think of the Industrial Policy discourse as truly speculative, conjuring up the China shock that never was. Or perhaps one might better think of it as imagining a world in which a powerful China was no shock at all, but simply a defining fact of modern history. I love putting out Chartbook. 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The Beauty Of Settled Science
Commenters convinced me I made a subtle mis-step in my framing of Psychology Research Is Mostly Fine. My framing emphasized that social priming studies had failed to replicate, but that many other subfields of psychology hadn’t. This naturally led commenters to point to failed replications in other subfields. Although none of these were as bad as social priming, few or none were entirely problem-free. Instead of emphasizing the difference between social priming and other subfields, I should have emphasized the difference between settled science and novel research. Here are some of my favorite psychology findings: The brain is sculpted by reinforcement learning, and a key driver of this learning is a prediction-error-based algorithm located in the nucleus accumbens and implemented through dopamine. Psychedelic drugs exert their effects through 5-HT2A receptors, pointing to a role for these receptors in some sort of construct like the balance between priors and new evidence. Certain cognitive functions are localized to certain brain areas (for example, impulse control to the frontal lobes; linguistic structure to Broca’s area, linguistic meaning to Wernicke’s area. But injure one of these areas, or render a certain function useless (eg a blind person having no more use for their visual cortex), and these mappings can gradually shift - suggesting that neurons are general-purpose compute whose specialization is a contingent fact about convenience and efficiency rather than a necessity. Most personality traits and general capabilities are about 50% genetic and 50% random, with little role for family environment or childhood history, with a few notable exceptions like partisan preference. Recent research complicates this story, but the further you go from this basic picture, the gnarlier the chains of indirect causation you require. Neurons fire together in “brain waves”, whose exact frequency corresponds with the character of consciousness; These divide neurons into natural subpopulations and linked graphs. The gamma rhythm corresponds surprisingly well to items in conscious awareness. GABA receptors seem to be involved in something like relaxation. By hitting different GABA receptors in different ways, you can modulate the exact type of relaxation you produce. Hitting GABA-A directly is too powerful and can cause dangerous levels of sedation, but allosteric modulation of GABA is an incredibly fruitful strategy that underlies drugs from benzos to barbituates to alcohol. If you know your GABA-A subunits, you can target specific subtypes of relaxation; for example, Ambien hits GABA-A1 and induces sleep. Memory divides somewhat fuzzily into bins of immediate-sensory, short-term, and long-term, with separate retrieval and storage functions, and separate systems for declarative vs. procedural learning. These can be harmed selectively, usually by hippocampal injury, so that some patients can remember their life stories but not form new long-term memories, or learn new skills like bike-riding but not new facts. The exact substrate of memory is unclear but seems to involve new synaptic connections between neurons in at least some way. The ability of modern psychology and psychiatry to treat mental illness is real but limited. SSRIs work for half or slightly fewer than half of patients; antipsychotics work with extreme side effects; benzodiazpines work but produce addiction and tolerance in many (not all) people. Most supplements are stuck in Poor Quality Study Replication Crisis Hell, but melatonin and l-theanine stand out as especially promising. Psychotherapy works with moderate effect size, but (with a few exceptions) seems to be more about the relationship with the provider than the details of the therapy itself. Mental hospitalization can sometimes defuse immediate crises but work less well than mental hospitals would like, with some studies showing overall negative effects. Schizophrenia is mostly genetic, but risk also increases with prenatal infection (especially rubella) and obstetric complications; there is suggestive evidence for roles of social stress and drug use (with psychedelics and marijuana making things worse, and nicotine if anything making things better). Bipolar and schizophrenia are so closely linked on a pathophysiological level that it seems like they might be the same disease; on the clinical level, they do seem to follow two separate courses/prognoses but can sometimes surprise clinicians by eg someone who seemed condemned to permanent schizophrenia unexpectedly recovering. The brain’s native reinforcement learning algorithms cash out in the phenomena of classical and operant conditioning; different reinforcement schedules reinforce at different levels, which can be exploited by bad actors to make especially compelling products (eg slot machines). None of these are immune to doubt. A few are speculative; one or two are genuinely unorthodox. But they’re all a step above one p = 0.049 paper that might not replicate. If they get overturned, they’ll be overturned by the same sort of genuine scientific shift that replaced Ptolemy with Copernicus or Lamarck with Darwin, not some sort of sordid discovery about someone entering random numbers into a spreadsheet. In 2008, Eliezer Yudkowsky wrote about The Beauty Of Settled Science: If you only care about scientific issues that are controversial, you will end up with a head stuffed full of garbage. The media thinks that only the cutting edge of science is worth reporting on. How often do you see headlines like “General Relativity still governing planetary orbits” or “Phlogiston theory remains false”? So, by the time anything is solid science, it is no longer a breaking headline. “Newsworthy” science is often based on the thinnest of evidence and wrong half the time—if it were not on the uttermost fringes of the scientific frontier, it would not be breaking news. Scientific controversies are problems so difficult that even people who’ve spent years mastering the field can still fool themselves. That’s what makes for the heated arguments that attract all the media attention. Worse, if you aren’t in the field and part of the game, controversies aren’t even fun. Don’t be embarrassed to read elementary science textbooks, either. If you want to pretend to be sophisticated, go find a play to sneer at. If you just want to have fun, remember that simplicity is at the core of scientific beauty [...] A steady diet of science news is bad for you: You are what you eat, and if you eat only science reporting on fluid situations, without a solid textbook now and then, your brain will turn to liquid. Psychology isn’t centrally the new paper in Annals Of Recently-Breaking News saying that an n=5 study showing that large portion sizes increase support for terrorism. Psychology is centrally ideas like brain waves, GABA receptors, reinforcement learning, twin studies, and connectionism. People who think differently are worrying too much about BREAKING NEWS!!!, and should stop. The key distinction isn’t “recent research in social psychology” vs. “recent research in cognitive/bio/behavioral psychology”, as my essay last week unfortunately implied. It’s recent research in general vs. the deposit of settled science, battle-tested hypotheses, and carefully-earmarked speculation. And part of the reason social priming in particular was such a disaster was that it defied that heuristic, building itself up over hundreds of studies until it approached or even entered the pantheon of settled science - before collapsing (5-HTTLPR was also sort of like this). This isn’t a claim that settled science is always right - Freud was “settled science” for a long time - just that there’s an intuitive distinction between the level of credence you should have in the existence of power posing vs. the existence of Broca’s Area. (I compounded this error by calling the last post “Psychology Research Is Mostly Fine”, which suggested I was talking about the new research rather than the settled science. I actually meant to distinguish between research-psychology-the-field vs. clinical-psychology-aka-therapy, but this framing unintentionally pushed against the point I wanted to make) Some commenters on the previous article understood this point, but challenged its weight. It’s very cool, they said, to know how many neurons are in which brain lobe, but the main way that Psychology affected their lives was through bad clickbait studies used to buttress dumb policies; some p = 0.049 study says that exposure to microaggressions makes minorities’ brains shrivel into raisins, and now their company has to implement punitive DEI or be sued for hazardous work conditions. I won’t dispute this as an accurate description of how the sausage gets made, but it’s worth examining the trains of thought that connect this observation to a campaign against psychology in general: The dumb woke psychology was wrong and bad, and I don’t have good heuristics for distinguishing dumb woke psychology from other psychology, so I’m going to assume it’s all wrong and bad. The dumb woke psychology was wrong and bad; it seems useful to punish/discredit psychology as a whole to assist my campaign against it, so for propaganda purposes I will try to discredit psychology as a whole. To people in the first camp, my response is: come on, your heuristics aren’t that bad. “Microaggressions makes minorities’ brains shrivel up like raisins” is in the woke category; “the hippocampal theta rhythm seems to be associated with both memory and pathfinding, suggesting an unexpected yet surprisingly compelling relationship between the two” is in the not-woke category, don’t play dumb here. To people in the second camp, my response is: deliberately exaggerated propaganda campaigns are bad and this is a confession, not an excuse. Inability to view science through anything but a political lens was what got us into this problem; doubling down on it is a temporary solution but a recipe for permanent disaster. If you pronounce entire fields of science to be acceptable collateral damage in your campaign against bad political actors, you will alienate not just scientists but patients, clinicians, and general lovers of truth. Instead, you can say the thing you mean - that woke partisans shouldn’t try to occupy and exploit the otherwise-noble science of psychology for their own political gain - and wage the same campaign with a halo rather than a comic-book-villain goatee. From the font of oracular wisdom that is Weird Sun Twitter: [ Vessel Of Spirit@VesselOfSpirit i love you all conditionally but i love the good part of you unconditionally. even if it becomes a very small part 2:11 AM · Jan 22, 2015 1 Reply · 5 Reposts · 29 Likes ](https://x.com/VesselOfSpirit/status/558084551772471298) This is how I feel about psychology1, too, but with a touch more optimism. The good part of psychology is enormous, a hoard of endless treasures. You just have to look past the headlines! 1 To play devil’s advocate - should one always be so magnamious? Astrology? Phrenology? These seem different in that their entire orientation was flawed from the start; they didn’t simply fail to find the correct constellation-personality connections, the whole project of searching for connections between constellations and personality was doomed from the start. More difficult might be asking the same question about education research, which is notoriously bad. I don’t know enough about education research to know if there’s some settled science of correct and enlightening results lurking in the background beyond the day-to-day froth of poorly-conducted and politically-motivated studies. I think if it’s 90% bad, 10% good, it’s correct to call it bad as a compact way of communicating a useful truth; I think psychology is better than that, but admit it’s a tough judgment call.
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