I don’t use AI to draft my writing. These are fine handcrafted artisanal essays, every word placed individually with loving care. I use AI a lot for researching, occasionally for brainstorming, and increasingly for fact-checking. But not for outlining, drafting, or directly editing. This isn’t some principled objection to AI use, or a revulsion at the idea of letting technology intrude on my craft. It has simply never occurred to me to do it. Why? I just don’t think AI writing is good enough today. It’s good at research reports or letters to Congressmen or anything else generic. It isn’t compelling enough to make a good blog post. And it certainly doesn’t have my voice or unique point of view. (I don’t think any AI today has a real point of view.) Here’s an example. In revising “The Progress Agenda” for publication as a chapter in my forthcoming book, I wanted to add a paragraph on immigration. Here was my process. First, in my notes, I wrote down several points I might want to make about immigration, off the top of my head. From that, I wrote a simple structure for the paragraph: immigration is good/important but it is difficult/time-consuming/uncertain so we should make it easier (need examples) At that point I needed data and examples to substantiate these points, which I didn’t have off the top of my head. This is where I turned to AI. I started with the promprt “What are some examples or data points to substantiate the idea that immigration, particularly high-skilled immigration, is important to the US?” I had a conversation with it from there, checking out the sources it pointed me to and asking followup questions until I felt I understood each point well. You can read the whole thing here. Once I had enough material, I went back and summarized what I felt were the most relevant points in my notes, with sources. Then I drafted the paragraph. Here’s what I ended up with (at the end of a section calling for across-the-board regulatory reform): Progress in all of these areas depends on talent, and in the US, a lot of that talent comes through immigration. Immigrants are about 14% of the population, but 43% of doctorate-level scientists & engineers. Immigrants or their children co-founded nearly half of the Fortune 500 list as of 2025, including Amazon, Apple, Google, and NVIDIA, and more than half of US billion-dollar startups, including Stripe, Instacart, and Notion. But we restrict our supply of talent through a slow and burdensome immigration process. The H-1B visa used by many STEM workers is capped at a level far below demand: in 2026, there were almost 339,000 applicants, but only 35% of them were selected. The selections are done by lottery. Once here, the path to permanent residency can be epic, particularly for some nationalities: as of April 2026, some Indians even in the second-highest employment-based preference category (professionals with advanced degrees or “persons of exceptional ability”) have been waiting around 12 years for their green cards. And despite the exceptional success of immigrant entrepreneurs, the US has no startup visa. For the sake of national prosperity and security, the US should have more visas and green cards, including an entrepreneur visa; the ones we have should be better targeted to need, not assigned randomly; and processing should be made more efficient, to eliminate decade-long backlogs. That paragraph isn’t going to win me a Pulitzer, but it’s solid and it does its part in the overall essay. As an experiment, after I wrote this, and without showing it to ChatGPT, I prompted it: Given all this discussion, please draft a paragraph on immigration, arguing that 1. immigration is good/important, 2. but it is difficult/time-consuming/uncertain, 3. so we should make it easier (examples). I want to insert it into this essay, near the end of the first major section, on law/regulation: https://newsletter.rootsofprogress.org/p/the-progress-agenda Here’s what it wrote: Immigration is another area where law and regulation need reform. For over a century, the United States has benefited enormously from immigrants, who have contributed disproportionately to science, engineering, entrepreneurship, and economic growth. Many of our most important technology companies were founded or co-founded by immigrants, and immigrants are heavily overrepresented among PhDs, inventors, and startup founders. Yet our system for admitting and retaining high-skilled talent is slow, cumbersome, and irrational. We educate foreign students in our universities, then subject them to visa lotteries, years-long backlogs, and administrative obstacles if they want to stay and build here. The result is that we needlessly turn away scientists, engineers, and founders, or trap them in long periods of uncertainty, even as we complain of labor shortages and worry about national competitiveness. We should make it far easier for talented people to come to America and remain here: expand and streamline visas for high-skilled workers and entrepreneurs, staple green cards to advanced STEM degrees earned at US universities, recapture unused employment-based green cards, reduce or eliminate per-country caps that create absurd backlogs, and generally treat the attraction of global talent as a national advantage rather than a problem to be managed. I don’t like this nearly as much. It’s more bland and less interesting. The main difference that leaps out at me is that my version wove in many more specific stats and examples. The ChatGPT version stays abstract. I’m sure I could prompt or train a model to write more like me. It would be an interesting exercise to see how many rounds of editorial feedback you’d need to give in order to get it to produce a paragraph much closer to mine (maybe only one or two?), or whether you could come up with a general style guide or set of skills that would get it to produce better writing the first time. (In the general case, it would have to be much more than “include specific numbers and examples,” because this paragraph is not one that shows off my unique voice.) I am unmotivated to tinker with this. Frankly, I’m kind of a diva about my writing. I don’t co-author with other people (I kind of dread the idea). I almost never allow guest posts on my blog. I don’t even let other people write social media posts for me. I flatter myself that my voice is inimitable. When someone sends me a draft of something that is supposed to come from me, more often than not I just rewrite it completely. (In contrast, I’m very happy to have AI write code for me.) There’s also the fact that when I publish any writing under my own name, I feel that I am taking on a sacred responsibility. Any writing in my name has to be correct, to the best of my knowledge and ability. It has to not only say true things, it has to select the most important things to include, and take the most interesting and illuminating angles on them. When I sign a piece, I’m not just taking responsibility for errors, I’m taking responsibility for the entire worldview and perspective that gave rise to the piece. And I simply can’t do that unless I wrote it. And the writing process does more than just produce a piece of writing. I learn a lot from researching, outlining, drafting, and revising, and the knowledge and worldview that is built up in my head through that process is part of the outcome. What I learn helps me decide what to research and write about next, serves as examples in future essays, and provides the substance of interviews that I give. Even if I could crank out perfect essays using AI, I couldn’t be a public intellectual that way. If I did have a way to produce truly excellent writing with AI, I might publish it, but not with my name as the author. My role then would not be author but editor. This is, indeed, the future I predicted for writers, and it might be a direction I go in the future. But if I do, you’ll know. It’ll be right there in the byline.

I get a lot of pushback to the idea that humanity can “master” nature. Nature is a complex system, I am told, and therefore unpredictable, uncontrollable, unruly. I think this is true but irrelevant. Consider the weather, a prime example of a complex system. We can predict the weather to some extent, but not far out, and even this ability is historically recent. We still can’t control the weather to any significant degree. And yet we are far less at the mercy of the weather today than we were through most of history. We achieved this not by controlling the weather, but by insulating ourselves from it—figuratively and literally. In agriculture, we irrigate our crops so that we don’t depend on rainfall, and we breed crops to be robust against a range of temperatures. Our buildings and vehicles are climate-controlled. Our roads, bridges, and ports are built to withstand a wide range of weather conditions and events. Or consider an extreme weather event such as a hurricane. Our cities and infrastructure are not fully robust against them, and we can’t even really predict them, but we can monitor them to get early warning, which gives us a few days to evacuate a city before landfall, protecting lives. Or consider infectious disease. This is not only a complex system, it is an evolutionary one. There is much about the spread of germs that we can neither predict nor control. But despite this, we have reduced mortality from infectious disease by orders of magnitude, through sanitation, vaccines, and antibiotics. How? It turns out that this complex system has some simple features—and because we are problem-solving animals endowed with symbolic intelligence, we are able to find and exploit them. Almost all pathogens are transmitted through a small number of pathways: the food we eat, the water we drink, the air we breathe, insects or other animals that bite us, sexual contact, or directly into the body through cuts or other wounds. And almost all of them are killed by sufficient heat or sufficiently harsh chemicals such as acid or bleach. Also, almost none of them can get through certain kinds of barriers, such as latex. Combining these simple facts allows us to create systems of sanitation to keep our food and water clean, to eliminate dangerous insects, to disinfect surfaces and implements, to equip doctors and nurses with masks and gloves. For the infections that remain, it turns out that a large number of bacterial species share certain basic mechanisms of metabolism and reproduction, which can be disrupted by a small number of antibiotics. And a small number of pathogens once caused a large portion of deaths—such as smallpox, diphtheria, polio, and measles—and for these, we can develop vaccines. We haven’t completely defeated infectious disease, and perhaps we never will. New pandemics still arise. Bacteria evolve antibiotic resistance. We can sanitize our food and water, but not our air (although that may be coming). But we are far safer from disease than ever before in history, a trend that has been continuing for ~150 years. Even if we never totally solve this problem, we will continually make progress against it. So I think the idea that we can’t control complex systems is just wrong, at least in the ways that matter to human existence. Indeed, a key lesson of systems engineering is that a system doesn’t need to be perfectly predictable in order to be controllable, it just has to have known variability.1 We can’t predict the next flood, but we can learn how high a 100-year flood is, and build our levees higher. We can’t predict the composition of iron ore or crude oil that we will find in the ground, but we can devise smelting and refining processes to produce a consistent output. We can’t predict which germs will land on a surgeon’s scalpel, but we know none of them will survive an autoclave. So we can tame complex systems, and achieve continually increasing (if never absolute or total) mastery over nature. Our success at this is part of the historical record, since most of progress would be impossible without it. The “complex system” objection to the goal of mastery over nature simply doesn’t grapple with these facts. Eric Drexler makes this point at length in Radical Abundance. ↩

The links digest is back, baby! I got so busy writing The Techno-Humanist Manifesto this year that after May I stopped doing the links digest and my monthly reading updates. I’m bringing them back now (although we’ll see what frequency I can keep up). This one covers the last two or three weeks. But first… A year-end call to support our work I write this newsletter as part of my job running the Roots of Progress Institute (RPI). RPI is a nonprofit, supported by your subscriptions and donations. If you enjoy my writing, or appreciate programs like our conference, writer’s fellowship, and high school program, consider making a donation: If you can give $100, upgrade to an annual Substack subscription If you can give $500, make it a founder subscription If you can give $1000 or more, support us on Patreon, or see here for PayPal and other methods, including DAF and crypto To those who already donate, thank you for making this possible! We now return you to your regularly scheduled links digest… Much of this content originated on social media. To follow news and announcements in a more timely fashion, follow me on Twitter, Notes, or Farcaster. Contents Progress in Medicine, a career exploration summer program for high schoolers Progress Conference 2025 My writing From RPI fellows Jobs Grants & fellowships Events Miscellaneous opportunities Queries Announcements For paid subscribers: What is worthy and valuable? Claude’s soul Self-driving cars are a public health imperative Slop from the 1700s The genius of Jeff Dean Everything has to be invented AI Manufacturing Science Health Politics Other links and short notes Progress in Medicine, a career exploration summer program for high schoolers We recently announced a new summer program for high school students: “Discover careers in medicine, biology, and related fields while developing practical tools and strategies for building a meaningful life and career—learning how to find mentors, identify your values, and build a career you love that drives the world forward.” I’ve previewed the content for this course and I’m jealous of these kids—I wish I had had something like this. We’re going to undo the doomerism that teens pick up in school and inspire them with an ambitious vision of the future. Applications open now. Please share with any high schoolers or parents. Progress Conference 2025 Our writeup: Reflections on Progress Conference 2025 Big Think released a special issue after the conference, “The Engine of Progress,” featuring articles from Boom CEO Blake Scholl, futurist Peter Leyden, and six RPI fellows We’ve also started to release video of the talks, including: Tyler Cowen interviewing Sam Altman and Blake Scholl AI Protopia: Ideas for how AI can improve the world (track) New cities, housing policy, and lessons from the YIMBY movement (track) More to come! My writing “Progress” and “abundance”: “Abundance” tends to be more wonkish, oriented towards DC and policy. “Progress” is interested in regulatory reform and efficiency, but also in ambitious future technologies, and it’s more focused on ideas and culture. But the movements overlap 80–90% In defense of slop: When the cost of creation falls, the volume of production greatly expands, but the average quality necessarily falls. This overall process, however, will usher in a golden age of creativity and experimentation From RPI fellows Ruxandra Teslo (RPI fellow 2024) and Jack Scannell have written “a manifesto on reviving pharma productivity … Public debates focus on improving science or loosening approval. We argue there’s real leverage in optimizing the middle part of the drug discovery funnel: Clinical Trials.” (@RuxandraTeslo) Article: To Get More Effective Drugs, We Need More Human Trials. Elsewhere, Ruxandra comments on the need for health policy to focus more on the supply side, saying: “The reason why I felt empowered to propose things related to supply-side is because of the ideological influence of the Progress Studies movement (Roots of Progress, Jason Crawford)” (@RuxandraTeslo) Dean Ball (RPI fellow 2024) interviewed by Rob Wiblin on the 80,000 Hours Podcast. Rob says of Dean that “unlike many new AI commentators he’s a true intellectual and a blogger at heart — not a shallow ideologue or corporate mouthpiece. So he doesn’t wave away concerns and predict a smooth simple ride.” (@robertwiblin) Podcast on Apple, YouTube, Spotify Andrew Miller writes for the WSJ about the inevitable growing pains of adopting self-driving cars: Remember When the Information Superhighway Was a Metaphor? (via @AndrewMillerYYZ) Jobs Astera Neuro (just announced, see below!) is looking for a COO: “This is an all-hands-on-deck effort as we build a new paradigm for systems neuroscience” (@doristsao) Astera Institute is also hiring an Open Science Data Steward “to help our researchers manage, share, and facilitate new solutions for their open data” (@PracheeAC) Monumental Labs is hiring two Business Development VPs: “One will focus on large-scale building projects and city developments. Another will focus on developing new markets for stone sculpture, including public sculpture, landscape etc.” (@mspringut) Jason Kelly at Ginkgo Bioworks is “personally hiring for scientists that are automation freaks. Not that you run a high throughput screening platform but rather that you believe we should automate all lab work” (@jrkelly) Lulu Cheng Meservey is hiring a “puckish troublemaker” for special projects. “This is a real job with excellent pay, benefits, and budget. Your responsibilities will be to conceive of interesting ideas and make them happen in the real world, often sub rosa” (@lulumeservey) Grants & fellowships Edison Grants from Future House to run their AI-for-science tools: “Today, we’re launching our first round of Edison Grants. These fast grants will provide 20,000 credits (100 Kosmos runs) and significant engineering support to researchers looking to use Kosmos and our other agents in their research.” (@SGBodriques) Foresight Institute’s AI Nodes for Science & Safety: “If you’re working on AI for science or safety, apply for funding, office space in Berlin & Bay Area, or compute by Dec 31!” (@allisondman via @foresightinst) Events I’ll be speaking at The Festival of Progressive Abundance, LA, Jan 30–Feb 1 (@YIMBYDems) The Economics of Ideas, Science, and Innovation Online short course for PhD students, hosted by IFP, is back for the third time (@mattsclancy). Online, Feb 3–April 30 Miscellaneous opportunities a16z Build: “A dinner series and community for founders, technologists, and operators figuring out what they want to build next — and who they want to build it with. … It’s not an accelerator, or even a structured program. … Instead, we focus on one thing: creating small, repeatable environments where people with ambition, ability, and similar timing spend enough time together that trust compounds, decisions get easier.” (@david__booth) Vast’s Call for Research Proposals: “Vast is opening access to microgravity research aboard Haven-1 Lab, the world’s first crewed commercial space-based research and manufacturing facility” (@vast, h/t @juanbenet) A long-running project with HBO to make a series about the early days of Elon Musk and SpaceX has died. The series was based on Ashlee Vance’s biography, and he’s still interested in doing something with this: “If there are serious offers out there to make something amazing, my mind and inbox are open” (@ashleevance) Manjari Narayan (@NeuroStats) is looking for a co author to collaborate on one or more explainers about surrogate endpoints and other proxies in health and bio—including why we waste time and money on those that don’t work and how we can do better. She is the domain expert, all you have to bring is the ability to make technical topics readable and accessible to a non-specialist audience. Reply or DM me and I’ll connect you Queries “It’s ‘well-known’ that science is upstream of abundance… I’ve found it surprisingly difficult to find strong general discussion of this link between science and our ability to act. … The best discussions I know are probably Solow-Romer from the economics literature, and Deutsch (grounded in physics, but broader). What else is worth reading?” (@michael_nielsen) Announcements NSF launches a Tech Labs Initiative “to launch and scale a new generation of transformative independent research organizations to advance breakthrough science.” Caleb Watney, writing in the WSJ, calls it “one of the most ambitious experiments in federal science funding in 75 years. … the goal is to invest ~$1 billion to seed new institutions of science and technology for the 21st century.” (@calebwatney) Seems like big news! Astera Neuro launches, a neuroscience research program led by Doris Tsao. “We’re seeking to understand how the brain constructs conscious experience and what those principles could teach us about building intelligence. Jed McCaleb and I are all-in on this effort.” (@seemaychou) Ricursive Intelligence launches, “a frontier AI lab creating a recursive self-improving loop between AI and the hardware that fuels it. Today, chip design takes 2-3 years and requires thousands of human experts. We will reduce that to weeks.” (@annadgoldie) Coverage in the WSJ: This AI Startup Wants to Remake the $800 Billion Chip Industry Boom Supersonic launches Superpower: “a 42MW natural gas turbine optimized for AI datacenters, built on our supersonic technology. Superpower launches with a 1.21GW order from Crusoe.” (@bscholl) Aeroderivative generator turbines are not new, but Boom’s has much better performance on hot days Cuby launches “a factory-in-a-box” for home construction: “a mobile, rapidly deployable manufacturing platform that can land almost anywhere and start producing home components locally. … Components are manufactured just-in-time, packaged, palletized, and sent last-mile for staged assembly. … Full vertical integration from digital design → factory → site.” (@AGampel1) I’m still unclear whether this is going to be the thing that finally works in this space, but Brian Potter is a fan, which is a strong signal! OpenAI announces FrontierScience, a new eval that “measures PhD-level scientific reasoning across physics, chemistry, and biology” (@OpenAI) Antares raises a $96M Series B “to build and deploy our microreactors … paving the way for our first reactor demonstration in 2026. Two years in: 60 people, three states, a 145,000-sq-ft facility, and contracts across DoW, NASA, and others” (@AntaresNuclear) GPT-5.2 Pro (X-High) scores 90.5% on the ARC-AGI-1 eval, at $11.64/task. “A year ago, we verified a preview of an unreleased version of OpenAI o3 (High) that scored 88% on ARC-AGI-1 at est. $4.5k/task … This represents a ~390X efficiency improvement in one year” (@arcprize) To read the rest, subscribe on Substack.

Many technologies can be used in both healthy and unhealthy ways. You can indulge in food to the point of obesity, or even make it the subject of anxiety. Media can keep us informed, but it can also steal our focus and drain our energy, especially social media. AI can help students learn, or it can help them avoid learning. Technology itself has no agency to choose between these paths; we do. This responsibility exists at all levels: from society as a whole, to institutions, to families, down to each individual. Companies should strive to design healthier products—snack foods that aren’t calorie-dense, smartphones with screen time controls built in to the operating system. There is a role for law and regulation as well, but that is a blunt instrument: there is no way to force people to eat a healthy diet, or to ensure that students don’t cheat on their homework, without instituting a draconian regime that prevents many legitimate uses as well. Ultimately part of the responsibility will always rest with individuals and families. The reality, although it makes some people uncomfortable, is that individual choices matter, and some choices are better than others. I am reminded of a study on whether higher incomes make people happier. You might have heard that more money does not make people happier past an annual income of about $75k. Later research found that that was only true for the unhappiest people: among moderately happy people, the log-linear relationship of income to happiness continued well past $75k, and in the happiest people, it actually accelerated. So there was a divergence in happiness at higher income levels, a sort of inverse Anna Karenina pattern: poor people are all alike in unhappiness, but wealthy people are each happy or unhappy in their own way. This matches my intutions: if you are deeply unhappy, you likely have a problem that money can’t solve, such as low self-esteem or bad relationships; if you are very happy, then you probably also know how to spend your money wisely and well on things you will truly enjoy. It would be interesting to test those intuitions with further research and to determine what exactly people are doing differently that causes the happiness divergence. Similarly, instead of simply asking whether social media makes us anxious or depressed, we should also ask how much divergence there is in these outcomes, and what makes for the difference. Some people, I assume, turn off notifications, limit their screen time, put away their phones at dinner, mute annoying people and topics, and seek out voices and channels that teach them something or bring them cheer. Others, I imagine, passively submit to the algorithm, or worse, let media feed their addictions and anxieties. A comparative study could explore the differences and give guidance to media consumers. In short, we should take an active or agentic perspective on the effects of technology and our relationship to it, rather than a passive or fatalistic one. Instead of viewing technology as an external force that acts on us, we should view it as opening up a new landscape of choices and possibilities, which we must navigate. Nir Eyal’s book Indistractable is an example, as is Brink Lindsey’s call for a media temperance movement. We should also take a dynamic rather than static perspective on the question. New technology often demands adjustments in behavior and institutions: it changes our environment, and we must adapt. For thousands of years manual labor was routine, and the greatest risk of food was famine—so no one had to be counseled to diet or exercise, and mothers would always encourage their children to eat up. Times have changed. These changes create problems, as we discover that old habits and patterns no longer serve us well. But they are better thought of as growing pains to be gotten through, rather than as an invasion to be repelled. When we shift from a static, passive framing to a dynamic, agentic one, we can have a more productive conversation. Instead of debating whether any given technology is inherently good or bad—the answer is almost always neither—we can instead discuss how best to adapt to new environments and navigate new landscapes. And we can recognize the responsibility we all have, at every level, to do so.