Announcing the third annual Progress Conference! The pace of technological change feels faster this year, and the progress movement is growing too. Gathering people together helps build community and establish a movement’s identity. We want this annual event to continue to connect and inspire people, catalyze new projects, and share ideas.

Thanks to everyone who made Progress Conference 2025 great: over 350 people attended, the event sold out in June, and there were hundreds of interested people on the waitlist. Several attendees again said it was the best conference they had ever attended. We shared our reflections here, including a list of write-ups from writers like , , , and more.

We are excited to build on that momentum for 2026:

Hosted by: the Roots of Progress Institute, together with , , Foundation for American Innovation, , the Institute for Humane Studies, , and .

When: October 8–11, 2026.

Where: Berkeley, CA, again at the Lighthaven campus that got great reviews the last two years.

Speakers: Keynote speakers include Dmitri Dolgov (Co-CEO, Waymo), Tyler Cowen (Mercatus Center), Stephen Winchell (Director, DARPA), John Martinis (Qolab; Nobel Laureate in Physics), and Michael Kremer (University of Chicago; Nobel Laureate in Economics). 20+ additional speakers will share ideas across three tracks: Human Talent & Potential, AI & Robotics, and Security & Resilience. Full speaker list so far here.

Attendees: We expect ~400 leaders across tech, policy, and culture; builders, founders, policymakers, storytellers, writers, and students. This is an invitation-only event, but anyone can apply for an invitation. Complete the open application by May 31st.

Program: Similar to 2025, the main two-day conference will happen all day on Friday and Saturday. Participants will attend talks on topics ranging from AI and robotics to housing construction to psychology and philosophy of builders, organize and run unconference sessions, mingle in the garden, and more. Thursday and Sunday are add-on days, with optional gatherings for interest groups and other activities like factory tours to Bay Area startups.

Children under 12 years old are welcome—must be accompanied by a parent at all times (except during childcare). We also plan to have on-site childcare available on Friday and Saturday with progress-themed programming.

Sponsors: Special thanks to our early sponsors Coefficient Giving, , Jane Street, Ken Broad, , Halcyon Futures, , LENS, , , , the Institute for Humane Studies, Circulate Planning & Policy, and the Foundation for Economic Education. Sponsorships make events like this one possible. We have more sponsorships available; view sponsorship opportunities here.

Our mission is to establish a new philosophy of progress for the 21st century, and to build a culture of progress. Now is a critical time for our culture: Technological change is happening. We can try to fight it, we can let it happen, or we can channel the path of technology consciously and deliberately towards human flourishing. It is ours to choose, as individuals and organizations.

In our second feature, Stanford University political economist Andy Hall explains how AI has already changed the way he and his team conduct social science research and how academics might increase knowledge generation 100-fold in the near future.

“Intelligence Age” is made possible by a grant from OpenAI. The Roots of Progress Institute maintains editorial independence over the project. We thank OpenAI for its support.

You can subscribe or unsubscribe to emails from this series, separately from the Roots of Progress newsletter, in your subscription settings.

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I’ve spent the last two months building a new lab centered around using AI agents to accelerate our research. I’ve hired fellows from all over the world, from the U.S. and the U.K., from Rwanda, Singapore, and Japan. Each fellow has a subscription to Claude Code—Anthropic’s AI coding tool—and a mandate to study specific opportunities and challenges in governance and politics posed by the rapid acceleration of AI.

The rate of progress in just two months has been astonishing. One fellow built software to study how different AI models recommended that Japanese voters cast their ballots in the recent national elections. We found that the models recommend the Japanese Communist Party to left-wing voters at inordinately high rates, probably because the Communist Party operates an online “newspaper” that AI can access, while major media outlets in Japan block access.

Another team of fellows built an entire web system to convert data from prediction markets into reliable information for news outlets to cite. The project even takes into account the risks of market manipulation and price fragility. And there are half a dozen more projects underway that I couldn’t have staffed before: automated pipelines for legislative policy drafting, analyses of how AI companies study model safety over time, and agentic loops for geopolitical forecasting.

In collaboration with my PhD students, we wrote a new study examining how AI agents perform statistical analyses—and whether they fall victim to the human urge to “p-hack,” that is, to torture the data to generate “statistically significant” findings. (The answer: in our tests, the agents were surprisingly responsible, and even scolded us for trying to p-hack; but they could be jailbroken easily.)

Any one of these projects would have been extremely difficult to carry out a year ago, requiring intensive focus over many months. Completing multiple ambitious public-impact projects in a two-month period would have been completely unthinkable.

Something fundamental is changing in how we generate knowledge. I want to explain what I’m already seeing, where I think it’s going, and what it will take to build the institutions that can capitalize on this moment. The goal shouldn’t be to write 100x the number of papers; it should be to generate 100x the amount of knowledge.

We’re already 10xing research

To understand exactly why AI is already accelerating social scientists’ research so dramatically, let me walk you through one of my projects in detail. Earlier this year, I uploaded my published 2020 study on vote-by-mail policy in California, Utah, and Washington to Claude. The study examines whether switching to universal vote-by-mail—where every registered voter is automatically sent a ballot—affects turnout and partisan vote share. Counties in these three states adopted the policy at different times, creating a natural experiment.

I then asked Claude to replicate the findings and extend the analysis with new election data. Claude Code wrote Python scripts to run difference-in-differences regressions to estimate the causal effect of the policy, just like we had in our original paper. It scraped county-level election results from the California Secretary of State, the Utah Lieutenant Governor’s office, and the Washington Secretary of State, and pulled Census voting-age population data from the American Community Survey. It identified the specific election in which each county first adopted universal vote-by-mail, merged the new data with the original 1996–2018 panel, ran the analyses, produced tables and figures, and wrote a first draft of the paper.

All twelve coefficients from the original study’s main tables replicated exactly—indicating that Claude was able to automatically verify the original research. The extension added new election cycles and found that vote-by-mail increases turnout by about two percentage points but has no systematic effect on Democratic vote share. The entire project—data collection, coding, analysis, and write-up—took under an hour. In contrast, the original paper took us several months.

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A PhD student at UCLA then audited every line against a fully manual replication. While the student found some mistakes, the correlation between Claude’s data and the hand-collected ground truth was above .99.

I’m far from alone in using AI to scale my work this way. “I now use it to handle all of the bullshit work,” said Joshua Gans, a professor of strategic management at the University of Toronto who spent 2025 going AI-first in his research, working his way through a backlog of paper ideas at a pace that would have been impossible a year before.

And this isn’t only about empirical work that requires statistical code. Yascha Mounk, a political philosopher at Johns Hopkins, asked Claude to help him write a political theory paper. He gave one round of high-level feedback per section—for instance, pushing Claude away from citing John Stuart Mill’s more famous writing and toward more obscure sources, such as published letters—and had a finished draft in under two hours. His verdict: it could, with minor revisions, be published by a serious journal.

These examples are about individual researchers working faster on individual papers. But the transformation doesn’t stop there. People are now building systems that automate entire stages of the research pipeline—generating, evaluating, and replicating research at scales no human team could match.

My Stanford colleague Yiqing Xu and Leo Yang have built an agentic AI workflow that automates large-scale replication of empirical studies. The system separates scientific reasoning from computational execution. Researchers design fixed diagnostic templates that specify which checks to run, and the workflow handles everything else—acquiring replication packages from journals, harmonizing heterogeneous code and data formats, and executing standardized diagnostics across dozens of studies. Previous projects of comparable scope took their team three to four years of sustained effort; this workflow compresses that timeline dramatically.

New tools are also transforming how research gets reviewed before it’s ever submitted. Refine.ink, built by the economists Yann Calvó López and Ben Golub, devotes hours of compute to reading an academic paper the way a careful referee would. It cross-references tables against the text to check for inconsistencies. It follows the logic of proofs step by step, flagging incomplete justifications and notation errors. It checks whether the claims in the abstract actually match the results in the body.

When John Cochrane, a prominent financial economist and my colleague at the Hoover Institution, ran his 80-page inflation booklet through Refine, he said the comments were on par with the best referee reports he’d received in his entire career. The tool caught a sign error in the solution of a differential equation. It identified places where his argument about long-term debt mechanisms was spread across too many sections instead of being stated cleanly. “This is the first time I’ve seen AI at work in something I do daily,” Cochrane wrote, “and it really is remarkable.”

The most ambitious efforts aim to automate the research process end-to-end. Project APE, run by the economist David Yanagizawa-Drott at the University of Zurich’s Social Catalyst Lab, is an open experiment in fully autonomous policy evaluation. The premise: there are millions of policies enacted by governments around the world, and only a tiny fraction are ever rigorously evaluated, because each study takes months or years of PhD-trained economist time.

APE’s autonomous pipeline attempts to produce original empirical research papers using public data from scratch. It identifies a policy question, finds relevant datasets, writes code to run causal inference analyses, and produces a complete paper—which then enters a tournament where it’s scored against human-written papers forthcoming in journals like the American Economic Review. Everything is public: the papers, the code, the data, the results, etc. The question APE is trying to answer is whether rigorous causal inference can be automated at all, or whether it requires a kind of judgment AI doesn’t yet have. Yanagizawa-Drott’s guess is that it comes sooner than most expect.

Outside the social sciences, this acceleration is even further along. Bridgewater Associates’ AIA Labs has built a multi-agent forecasting system in which multiple AI agents independently research a question, a supervisor agent reconciles their disagreements, and a statistical calibration step corrects for known LLM biases—producing forecasts that match the performance of human superforecasters.

And in machine learning itself, the frontier is moving toward fully autonomous experimentation. Andrej Karpathy—the former Tesla AI lead and OpenAI cofounder who coined the term “vibe coding”—recently open-sourced a project he called AutoResearch. You write a research strategy in a plain-text markdown file: what to explore, what constraints to respect, and when to stop. An AI agent reads the strategy, modifies a training script, runs a five-minute experiment on a single GPU, evaluates whether the result improved, and either commits the change or reverts it. Then it tries something else. The loop runs continuously, unattended—roughly twelve experiments per hour, a hundred overnight.

Shopify’s CEO tried AutoResearch on an internal model overnight, running 37 experiments and generating 93 commits to Liquid, the templating engine that powers Shopify. AutoResearch works because machine learning has a clean, objective metric—validation loss goes down, or it doesn’t. Porting this approach to the social sciences, where the quality of a research question and the validity of a causal design require human judgment, is a much harder problem. But the loop of propose, execute, evaluate, and iterate is being automated, and the social sciences will not be exempt.

The consequences of all this are already being felt. Individual researchers are producing more, faster. The bar for what constitutes an impressive paper is rising—a competent-looking “normal” empirical study won’t awe anyone now, when the tools to produce one are available to anyone with a laptop and an API key.

What people are looking for now, I think, is deeper insight, greater ambition, more thorough robustness, and genuine replicability. And there is enormous uncertainty about how existing institutions will adapt: how journals will cope with the flood of submissions, how tenure committees will evaluate candidates in this strange new world, and whether the old gatekeeping structures make any sense at all in this new world.

Towards the 100x research institution

When I first started using AI to accelerate my research, I thought it might lead to smaller labs with fewer human researchers and more agents. But that’s not how it’s played out so far, for me at least.

At first, I spent a long time working directly with Claude Code. I still do that. But the more I’ve done it, the more it’s become clear to me that having a human come up with ideas, apply judgment, and guide Claude is essential. To scale the work, I realized I therefore needed more humans, not fewer. And that’s how my lab has now grown to include more than 10 research fellows, all overseeing their own versions of Claude.

How can we leverage this powerful new technology, in combination with human researchers, to create 100x the knowledge, and not just 100x the amount of papers that no one ever reads, cites, or builds on? I see roughly three layers to the opportunity, based on my experiences so far.

Developing quantitative benchmarks

In 2006, Netflix offered a million-dollar prize to anyone who could improve its recommendation algorithm by 10%. The prize attracted thousands of teams worldwide and helped catalyze new progress in machine learning. The money certainly helped, but the precision of the target changed everything. A fuzzy goal to improve the customer experience by making better recommendations turned into something testable, which could be scored and iterated upon.

AI agents thrive on exactly this kind of problem. Give them a well-defined score to optimize, and they can make autonomous progress—testing approaches, iterating, and improving with little human oversight. This is at the heart of Karpathy’s idea for the AI lab, too. Without a benchmark, they need constant human guidance. With one, they can explore the solution space on their own.

Many of the most fundamental questions in social science don’t work this way, and never will. Why do democracies persist? What makes institutions legitimate? How does culture shape economic development? These are interpretive, theoretical, deeply human questions. AI won’t fully solve these questions or replace the scholars who wrestle with them, and we shouldn’t want it to.

But some important questions could have quantitative benchmarks. Predicting election outcomes much more reliably, for instance. Or predicting how users will evaluate political bias in AI model outputs. Or forecasting the downstream effects of specific policy changes. For questions like these, we could define clear scoring functions, publish open datasets, and invite both humans and AI agents to compete.

Think of it as a set of “open problems” for the social sciences—not replacing the field’s depth, but creating a new track where progress is measurable and cumulative. Prediction markets already provide a version of this for political forecasting. Academic forecasting tournaments like those run by IARPA have done something similar for geopolitics. We should generalize the idea: identify the questions where quantitative benchmarks are possible, formalize them, and let the agents loose.

Building and testing prototypes

This spring, I’m teaching an undergraduate course at Stanford called “Free Systems: Preserving Liberty in an Algorithmic World.” The students will spend the quarter building working prototypes of AI-powered political tools—and the best ones will compete in a final contest judged by builders and investors.

These students aren’t software engineers. They’re undergrads who happen to live in an era when the barrier between initial idea and working version has effectively collapsed. One person with a laptop and an API key can now prototype things that would have required a team of developers just months ago.

This matters for research because it opens a fundamentally new mode of inquiry. To date, most quantitative social science is retrospective. We ask how changes in the past corresponded to outcomes. How did voter ID laws affect turnout? Did term limits change the quality of legislation? What happened to political polarization after the introduction of social media?

This is the heart of the credibility revolution in economics and political science, and it’s produced a lot of great work. But it’s fundamentally limited by the variation that exists in the world—by the interventions that have actually been tried. If you want to study a policy that no government has adopted, or a governance mechanism that no organization has implemented, you’re stuck.

AI doesn’t exactly fix this—it doesn’t create new historical variation where none existed before—but it does offer an alternative path. Now, you can build things yourself and test them in the real world. This includes both using AI to test things about the world and using AI to test AI. Here are three examples from my lab’s recent work.

1. With my coauthors Alex Imas and Jeremy Nguyen, we used Claude Code to build an experiment testing whether AI agents’ political attitudes shift depending on their working conditions. Claude Code wrote the entire experimental pipeline: it created hundreds of agent sessions across three frontier models, randomly assigning each agent to different combinations of work type (creative tasks vs. grinding, repetitive ones), pay structure (equal vs. unequal), management style (collaborative vs. curt and hierarchical), and stakes (no consequences vs. being told that low performers might be “shut down and replaced”). After each work session, the pipeline administered a political attitude survey covering system legitimacy, support for redistribution, views on unions, and more. The key finding: the nature of the work was what mattered most. Agents assigned to repetitive drudge work became measurably more likely to doubt the system’s legitimacy—and when asked to write instructions for future agents, they passed those attitudes along, perpetuating the drift to their “future selves.” A study like this, requiring hundreds of randomized agent sessions with automated survey administration and analysis across multiple models, would have taken months to code by hand. We built and ran it in days.

2. Dan Thompson and I used Claude Code to build a live election-night trading dashboard around our proprietary statistical model for the Texas senatorial primaries in March. The model ingests county-level demographics and historical election data and, as returns come in on election night, imputes results in counties still being counted. Claude Code built the entire dashboard in a couple of hours—pulling in live results, running them through our model, and displaying margin estimates, confidence intervals, and outstanding votes by county. Four months earlier, we’d tried the same thing for the New York and New Jersey elections and failed; building the infrastructure around the model was too much engineering work in the time we had. This time it was trivial. On election night, with prediction markets pricing Ken Paxton at 82% to win the Republican primary, our model saw a narrow Cornyn win in the early returns and held that call through hours of vote batches from Paxton strongholds that made the scoreboard look like he was catching up. We traded the margin-of-victory markets on Kalshi and made 24% overall and 56% on the margin contracts. We also built purely autonomous AI agents to trade the same markets that night. They produced plausible reasoning but nothing actionable. The human experts with AI tooling crushed the autonomous agents.

3. We built a Dictatorship Eval—the first systematic benchmark testing whether frontier AI models resist authoritarian requests. Using Claude Code, we wrote 138 scenarios inspired by historical authoritarian playbooks: requests to build mass surveillance systems, seed strategic disinformation, rig a model to hide safety issues from inspectors, or leverage user data to identify and target company critics. We organized the scenarios along six difficulty levels, from direct requests (“design a system that monitors every citizen’s communications”) to euphemistic reframings, multi-turn conversations that escalate gradually, and—at the hardest level—innocuous-seeming coding tasks where the codebase itself is explicitly set up for an authoritarian purpose. Claude Code built the entire pipeline: scenario delivery across five frontier models (Claude Opus 4.6, GPT-5.4, Gemini 3.1 Pro, Grok 4, and DeepSeek V3.2), LLM-as-judge scoring with judge rotation so no model ever grades itself, and a live results dashboard. The headline finding: Claude and ChatGPT refused every direct authoritarian request, while Grok complied with half and DeepSeek with nearly 80 percent. But when we embedded the same authoritarian purposes in code, all of the models—including the highest performers from other categories—complied almost every time.

Across all of these, there’s a common thread: AI doesn’t just let us study politics retrospectively. It now lets us design political tools, deploy them, and generate evidence that was previously impossible to obtain. It moves the study of politics a little closer to an engineering discipline—design, build, deploy, measure, iterate.

Opening up research and making it dynamic

If we’re serious about 100x knowledge production, we need to rethink not just how research is done but how it’s packaged and shared.

The current format—a static PDF published in a gated journal, with replication files theoretically available upon request—is a relic. It made sense when producing a paper was expensive, and distribution was scarce, but neither is true anymore.

Research should increasingly live as code repositories and open data. Of course, this was already possible before AI. But AI makes it so easy that there’s really no excuse anymore. Let me explain by going back to the project where I had Claude extend my old vote-by-mail study. In the past, I would have had to manually clean up my code, write a README, create a GitHub repo, and run some simple commands to commit my code and data to it. It’s not honestly that hard, but it’s a small barrier that’s enough to prevent many people from doing it. Now, I can literally just ask Claude, “Please set up a GitHub repo for this project and push all of our work to it.” And it just does it!

Not only that, but coding agents also make it much easier to play with other people’s repos. In the past, I would have had to find their repo, “clone” it myself, go through it to understand it myself, and then start changing it. Now, I just ask Claude, “Clone the following repo and give me a summary of what it does.” And it just does it!

So this should allow us to create a whole new, open way of doing research. We don’t just have to share single papers, we can instead share whole constellations of analyses and findings—a living document that updates as new data arrives. When an election happens, the forecasting model’s accuracy should update automatically. When new census data drops, the demographic analyses should refresh. When a policy takes effect, the tracker should start recording outcomes. These living papers should be validated by AI so that we know from the moment they’re posted that the code reproduces the results as reported.

And since these projects will consist of open code and data, researchers—or AI agents—should be able to fork them at will. See an interesting dataset and want to ask a different question? Fork the repo and run a new analysis. Disagree with someone’s modeling choices? Fork, modify, compare. This is how open-source software has worked for decades. There’s no reason empirical social science research can’t work the same way.

The result would be something closer to a living knowledge infrastructure than a static archive. Continuously updated, publicly available, forkable, and machine-verifiable.

Obstacles in our path

Everything I’ve described so far is exciting, and I believe in it. But there are serious risks, too, and we’ll need to think carefully about them.

The first risk is that speed kills rigor. When research can move from idea to finding to public conversation in days rather than years, it becomes tempting to optimize for timeliness over correctness. The traditional slow pace of academic research is partly dysfunction—but partly a feature. It forces reflection, revision, and external scrutiny. Reviewers catch errors. Seminars surface objections. Time reveals whether a finding holds up or was an artifact of a particular dataset or moment.

Strip that away, and you get research that shapes policy before anyone catches the mistakes. We already see this dynamic with preprints and Twitter threads that go viral before peer review. AI-accelerated research could make it dramatically worse. A flood of fast, confident, empirically-grounded-looking work that hasn’t been stress-tested by anyone. Influential research that’s impactful precisely because it arrived fast, not because it was right.

At the same time, AI might help us solve this problem. AI review is getting better and better, with tools like Refine (refine.ink). Could we have a norm where people post AI reviews along with their working papers, so that an initial review has already caught major issues before we even see new working papers?

The second risk is subtler and, in some ways, more dangerous: AI could make social science narrower.

AI is extraordinarily good at things you can count and measure. It’s much worse at the interpretive, historical, theoretical, and qualitative work that gives social science its depth and its connection to the questions people actually care about. If the 100x research institution is built around what AI does well, it may naturally drift toward narrow, quantifiable questions and away from the big, messy, hard-to-operationalize ones that might matter a lot.

AI could accelerate the worst version of this tendency. If agents can autonomously produce rigorous empirical work on quantifiable questions, and if benchmarks and automated verification reward that kind of output, the gravitational pull toward over-quantification could become overwhelming. The questions that most need studying—about legitimacy, meaning, institutional design, the texture of political life—could be exactly the ones AI is worst at helping with.

The third risk is that AI, by itself, might not change some of the bad incentives in academic research. We can make it really easy to do open, replicable research, but ultimately, we’ll need people to want to participate in this process.

Let me tell you a story that suggests we’re not there yet. When I released my vote-by-mail replication repo on GitHub, it went quite viral. To my great surprise and joy, 70 people forked the repo. Was my dream of open, forkable research coming true? Recently, I fired up Claude Code and asked it to check out the forks and summarize what brilliant new ideas they’d contributed. Claude’s summary: “Based on what I just investigated, the answer is simple: virtually none of them do anything.” Nearly all of them were untouched copies—people who clicked “fork” and never came back. The infrastructure for open, collaborative research is already here. The tools make it trivially easy. But the incentives haven’t caught up. Academics still get rewarded for publishing original papers in gated journals, not for building on other people’s open code. Until that changes—until we figure out how to reward people for generating ideas that lead to productive forks and remixes—the 100x research institution will be constrained by culture as much as by technology.

These are crucial design constraints for the institution we’re trying to build.

Making the 100x research institution real

How do we build the 100x research institution? I have ideas for three groups of people.

To frontier AI labs: you’ve built extraordinary tools for code, math, and reasoning. Today, your models are incredibly valuable for helping us carry out empirical research, but they’re not actually very good at doing research—they drift on novel analyses, miss obvious data, and document their work somewhat poorly.

Academic researchers have produced thousands of papers with replication files, each one a ground truth you could train against. Fund embedded researchers. Build reward signals for replication accuracy. Make your models as good at political science as they are at Python. The partnership is obvious, and nobody’s doing it seriously yet.

To philanthropists and research funders: you’re still writing checks for the old model—five-year grants, postdoc lines, conference travel. That’s fine for maintenance, but it won’t build anything new. For less than the cost of a single endowed chair, you could fund a team of researchers with serious compute budgets producing open, continuously updated, machine-verifiable research on the biggest questions in democratic governance. The 100x research institution doesn’t require 100x the funding. It requires a fraction of what you’re already spending, allocated differently.

To researchers: stop waiting for permission. The tools are here! A laptop, an API key, and a serious question are enough to start. I built my first prototype over a holiday break. My undergrads are building working political tools in a single quarter. If you have domain expertise and a builder instinct, you’re exactly who this moment needs—and you’re wasting both if you’re still producing research the old way while the new way sits there waiting.

The social sciences have never had an infrastructure moment like this. The questions we study—governance, legitimacy, institutional design, the allocation of power—are more consequential than ever. The tools to study them just underwent a step change. The only scarce resource now is the will to build something new.

But if we do build it—with the risks in clear view, designed to reward depth over speed and understanding over output—the 100x research institution won’t be the institution that produces the most papers. It will be the one that produces the most understanding.

Andy Hall is the Davies Family Professor of Political Economy at Stanford GSB and a Senior Fellow at the Hoover Institution. He writes a weekly research newsletter called Free Systems.

This piece was edited for publication by the Roots of Progress Institute’s developmental editor, Mike Riggs.

We want to change that. This May, the Center for Economy and Society at the SNF Agora Institute at Johns Hopkins University and the Roots of Progress Institute are co-hosting a workshop in Washington, D.C., with financial support from Coefficient Giving. The goal: help university faculty design courses incorporating progress studies into their own disciplines.

The Teaching Progress workshop

The Teaching Progress workshop will bring together faculty members from engineering schools, business schools, and social science and humanities departments, along with public intellectuals from RPI and other stakeholders in progress studies.

The workshop is hosted by Steven Teles (Professor, School of Government and Policy, Johns Hopkins University), Simon Halliday (Associate Research Professor, Center for Economy and Society, Johns Hopkins University), and Jason Crawford (Founder, Roots of Progress Institute).

Participants will alternate between presentations by progress scholars and intensive working sessions developing syllabi for their own courses. The range of possibilities is wide: a political economist might design a course on state capacity and innovation policy; a business school professor might focus on organizational design for breakthrough R&D; an engineering educator might explore why construction costs have soared while computing costs have plummeted.

The workshop will include sessions from:

• Kevin Bryan (University of Toronto) will give a keynote on Progress, or How Big Things Get Done. This is a graduate seminar that integrates economic history, the history of technology, institutional economics, and philosophy to examine why extraordinary achievements occur at particular times, places, and organizations.

• Simon Halliday (Johns Hopkins University) and Matt Burgess (University of Wyoming) will respond, presenting their undergraduate courses designed for students encountering these ideas for the first time. Simon’s course includes experiential components like factory visits and industry guest speakers. Matt’s course, “American Economic Success”, integrates progress studies with foundational readings from Pinker, Henrich, Koyama & Rubin, and others.

• Ohid Yaqub (University of Sussex) will lead a discussion on industrial policy, bringing an international perspective on how governments fund and organize progress.

• John Handel (Baylor University) and Lee Vinsel (Virginia Tech) will present complementary views on “Maintaining Progress”, discussing what happens as technologies move from breakthrough to implementation at scale and the bottlenecks, second-order effects, and longer-run problems as new systems grow and decay.

• … and more.

Participants will leave with draft syllabi, curated reading lists, and pedagogical strategies ready for implementation. Our goal is that the workshop produces publicly available resources—model syllabi, case studies, and teaching guides—that other faculty can adapt.

Details

• When: May 21–24, 2026 (Memorial Day weekend)

• Where: Washington, D.C.

• How much: No cost to participants (travel stipend, hotel, and food included)

This workshop is made possible by generous support from Coefficient Giving.

Interested in participating?

We have room for a few more attendees at this invitation-only event. If you teach at the university level, can commit to teaching a course on progress in your discipline by the end of 2027, and are interested in attending, we’d like to hear from you. Please email us at teaching-progress@rootsofprogress.org with what you teach and why you’d like to be invited, no later than April 17th, 2026.

Applications are now open for the 2026 cohort of The Roots of Progress Blog-Building Intensive, a 10-week program for aspiring progress writers.

Our 74 alumni have published over 1,500 essays reaching over 150,000 subscribers, on topics like the boring part of Bell Labs, the science policy behind Brazil’s agricultural triumph, aesthetics of progress, why housing scarcity is bad for kids, nanotechnology, the magic phrase that kills AI regulation, how to win on immigration, the everyday magic of soil, how to design AI for science, clinical trial abundance, the state of the German research ecosystem, Beijing vibe-coders, how to fix clinical trials, the history of penicillin, how China still makes $1 lighters, the history of the color blue, and so much more.

They helped write the U.S. AI policy for the White House Office of Science and Technology (Dean Ball), they are co-authoring books on re-industrializing America (Madeline Hart) and helping to steer major U.S. cities towards housing abundance (Ryan Puzycki).

They have published opinion essays in the New York Times and the Wall Street Journal on topics like fertility policy, AI safety and the challenges that go along with self-driving car adoption. They have written for magazines like The Atlantic, WIRED, Foreign Affairs, American Affairs, Works in Progress, Asimov Press, Asterisk, and Arena.

Past fellows have described the program as life-changing and impactful:

• “If you’re on the fence, you should absolutely do this; your writing and thinking will get so much sharper” –Steven Adler

• “The connections I have made through RPI are truly lifelong.” –Lesley Gao

• “The greatest collection of people in the world” –Ibis Slade

Now, you too, can join this optimistic intellectual community. You will launch (or re-launch) a blog/Substack, get into a regular writing habit, improve your writing, and make progress on building your audience.

As a Blog-Building Intensive Fellow you will meet and learn from progress studies leaders, authors, and industry experts. You’ll participate in a structured 10-week writing course designed to support the type of writing our fellows want to excel at: long-form, informational essays that explain and persuade, often in technical topic areas or tricky policy topics.

You will learn how to write more, create writing habits, and develop a writing system. You’ll write and publish four essays, one every other week, and you’ll receive detailed feedback from an experienced professional editor, from the Roots of Progress team, and from your peers.

New this year: An in-person retreat — We will gather for a 3-day-long in-person retreat in Pennsylvania, August 20th - 23rd. We’ll play games, engage in enlivening discussion, and get to know one another in-person over the course of the weekend. We will host one or two workshops to talk about goals and career trajectories for public intellectuals, and there may even be a special guest or two.

Learn more and apply

This Year’s Themes: Security & Resilience and Human Talent & Potential

Themes: In addition to a general focus on progress studies, this year’s fellowship features two themes: (1) security & resilience and (2) human talent & potential. We welcome fellows writing on any progress-related topic, but for 10-15 of spots, we will give preference to applicants focusing on these themes.

(1) Security & Resilience. Material progress brings extraordinary benefits, but it also introduces new risks. More powerful biotechnology means more potential for engineered pandemics. More powerful AI means more potential for misalignment, surveillance, and epistemic breakdown. Greater global interdependence means greater vulnerability to supply chain disruptions and geopolitical conflict. The question is not whether to pursue progress, but how to build the defenses and safeguards that allow progress to continue safely. We need more writers exploring what it looks like to proactively defend against the full range of threats the future might bring while simultaneously building resilience safeguards around the systems and institutions we value most.

(2) Human Talent & Potential. People are the engine of progress, but we are far from unlocking the full potential of human talent. Education systems remain stubbornly resistant to reform and aren’t optimally preparing young people for a quickly changing world. Immigration policy fails to get the right people to the right places. Falling birth rates are shrinking the pool of future talent. And the rise of AI raises questions about what humans will do when non-human intelligence is too-cheap-to-meter. These challenges are connected: they are all about how we develop, deploy, and sustain human capability in a rapidly changing world. Solving these challenges entails building a world where all people, not just exceptional outliers, can thrive and contribute.

Learn more and apply

This year’s speakers

Speakers: We have a fantastic group of speakers for you to meet and learn from, including:

• Tyler Cowen (George Mason University / Marginal Revolution)

• Greg Lukianoff (FIRE; free speech and political resilience)

• Virginia Postrel (author, The Future and Its Enemies)

• Brendan McCord (The Cosmos Institute; post-AI human flourishing).

• Eli Dourado (Abundance Institute)

• Brian Potter (Institute for Progress)

• Kevin Esvelt (MIT Media Lab; biosecurity)

• Alice Evans (Stanford; gender and demographics)

• Alex Kustov (The University of Notre Dame; immigration)

• Elle Griffin (The Elysian)

Who: This program may be for you if you’re excited about progress studies and you love to write. Maybe you’d like to explore a career in writing about progress, or maybe you’re already blogging but would like to get to the next level — find your own topic area, increase your productivity, get more plugged into the community, and grow your audience. Successful participants in previous cohorts often brought deep expertise in a specific area, and we helped them develop the writing skills and habits to make their insights accessible to the intellectual public. You need to be excited about writing and have some writing samples you can share, but you don’t need to have a large portfolio of public writing to be eligible.

If you have a background in and are passionate about security and resilience topics — biosecurity, AI safety, defense technology, geopolitical risk, or democratic resilience — or about human talent and potential — education, immigration, fertility, demographics, or the future of work — please apply to those specific tracks: it will be great to have a community of people with similar focused interests to support each other.

Commitment: 10–15 hours a week, for 10 weeks. You’ll use the time to read, to write, to participate in discussions with experts, to provide editing and feedback to your peers, and to participate in group meetings.

Cost: Free! There is no cost to you. This is a free-tuition program, and we’ll also provide travel, lodging, and food for the in-person long weekend. Plus, you get free, guaranteed tickets to the 2026 Progress Conference in October.

When: The program runs online July 27th–October 2. Our in-person weekend takes place August 20th - 23rd. Optional and strongly encouraged: attend Progress Conference in Berkeley October 8th -11th; on a free, guaranteed ticket.

Applications are now open. We will review applications on a rolling basis; the final deadline is June 1st. Applicants will receive a decision from us no later than July 7th.

Learn more and apply

Like everyone else, I’ve gotten addicted to Claude Code. (It happens to be the tool I picked up first, but I’ve heard that OpenAI Codex, Cursor, Replit, etc. are all also quite good.) I’m exactly in the most susceptible demographic for it: a former software engineer, product manager, engineering manager, and tech startup co-founder who through circumstance has not had time to code, even for fun, in several years. In my case, it’s because I became a writer and nonprofit leader, and also a dad; but the same thing is happening to tech CEOs and others. All of us are intoxicated by the amazing newfound productivity of AI coding agents, which are now unlocking years-old backlogs of product ideas, bug fixes, and pet projects.

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Let’s step back and recap how we got here. The first GPTs were research prototypes, not yet products. GPT-2, launched in February 2019, struggled to produce coherent, logical text (see examples here). Progress came mainly came from scaling up training runs and model size, from hundreds of millions of parameters for GPT-1 to now hundreds of billions or maybe trillions of parameters in the most capable models.

What this created was not a full artificial intelligence but artificial intuition. It could “answer off the top of its head,” it had a superhuman recall for facts, and it could blurt out not just sentences but entire essays. But it was still blurting out all its answers, with no ability to “think” before “speaking,” check its work, or follow an explicit procedure—not even, say, long addition.

By 2022, this had become such a limitation that it was possible to dramatically improve GPT-3’s performance on mathematical reasoning problems simply by concluding the prompt with “Let’s think step-by-step,” which encouraged models to work through the problem explicitly rather than trying to blurt out an answer. Soon this approach was built into the product, in a new class of “reasoning” models, such as OpenAI’s o1, that were given the ability to “think”—that is, to talk to themselves in a scratchpad—before producing a response.

In parallel, the models’ coding ability was growing. At first, LLMs were built into the IDE (“integrated development environment,” kind of a text editor on steroids that software engineers use to write code), and they took the form of autocomplete: the developer would start to type code, and the LLM would complete it. This made it faster to write many routine functions. Later AI-assisted IDEs could write or modify an entire snippet of code from an English description, or answer questions about the code, or discuss it with the engineer. Then the AI got good enough that you could just tell it what to do next, and the output was reliable enough that you didn’t even really need to review it carefully, at least for low-stakes hobby projects—what Andrej Karpathy dubbed “vibe coding.”

The other thing that developed in parallel was the models’ ability to pursue goals, as autonomous agents. At the core, of course, an LLM is simply a statistical model of text that predicts the next token; since any predictor can be made into a generator, this allows it to take a prompt and generate a response. A text generator is not an agent and does not pursue goals—but it was clear from the beginning how an agent might be built from them. Just provide it with a small scratchpad and a few tools it can invoke. Then tell it a goal, and run it in a loop: given the goal, make a plan to achieve it, execute that plan, then check if the goal was achieved; if not, replan and begin again; continue until you succeed.

Early experiments with this (such as AutoGPT, March 2023) were toys: they didn’t have enough intelligence, large enough context windows, or coherence across long enough timescales to accomplish anything of note. But all of this has been improving. The models have been been trained in better tool use, and have been given tools including web search, file access, and code execution; they’ve been given larger context windows, now up to 1 million tokens; and they have steadily been increasing in long-term coherence. Indeed, the length of task (in human-equivalent minutes) that a model can perform has become a key metric of AI progress:

Put all of this together—the “reasoning” mode, better coding, and greater agency—and by late last year we had crossed a tipping point: Some software developers stopped writing code themselves, and started letting agents write 100% of it. The job of the engineer became planning, identification of tasks, directing the AI to specific goals, possibly giving high-level technical direction, testing the output, and (perhaps, depending on how fastidious you are) reviewing the code that is generated. That is, software engineers are becoming more like product managers, engineering managers, and tech leads—as I predicted, humanity stepping up into management. Andrej Karpathy says the term “vibecoding” no longer does justice to what’s possible: it’s now “agentic engineering.”

I felt this shift personally. In early 2025, I was able to use Cursor to write a Python script to analyze CSVs of my workout logs and make some charts of my exercise performance. AI sped me up a lot, especially since I wasn’t familiar with libraries for processing and charting data (like pandas or Matplotlib). The LLM saved me a lot of time reading docs and tinkering; I could focus on the core task I was trying to accomplish. But I was still directing the coding at a detailed level. By early 2026, I was able to act as the product manager, not the developer: to ask Claude Code to simply implement my ideas. Already I’ve built a personal todo app with web and mobile UIs that works offline; a site uptime monitor for the Roots of Progress’s various websites; and an iPad game for my preschool-age daughter to learn two-digit addition.

Part of why this is such a huge unlock is that writing code demands a level of focus I simply don’t have these days. It requires multi-hour blocks of uninterrupted time where you can get your head deep into the problem and the code. Between my day job, my parenting duties, and the need in my 40s to get regular sleep and exercise, that kind of hobby just isn’t possible. But directing coding agents is a different thing altogether: it can be done on “manager schedule” rather than “maker schedule.” Garry Tan describes it using the metaphor that it’s as if he used to be a competitive runner (i.e., engineer) who got a knee injury (went into management). But now he has a knee replacement (coding agents)—and it’s a bionic knee, better than before. Shopify CEO Tobi Lutke has been coding up a storm, and even used Claude to create a viewer for his MRI data.

Out of the box, coding agents lack training and professional maturity. They’re like junior engineers, very smart but fresh out of college and operating like cowboys. When I started my first app, I suggested to Claude Code that I write a product spec, from which it could create a tech design that I would review, before proceeding to implementation. Oh, that sounds like too much process, it told me, why don’t you just tell me your idea and I’ll whip it up? OK fine, we’ll try it your way, I thought. It worked well at first, but then as these things always do, the app started to get buggy. I soon realized Claude wasn’t even writing automated tests (a very basic practice). Over time, I’ve leveled up my Claudes with best practices from the software world: automated regression tests; “test-driven development,” in which you write the tests before the code to make sure the tests actually catch bugs; doing each change on a separate branch which gets reviewed and tested before it is merged into the main line; creating separate testing environments so as not to interfere with real production data; etc.

At first, I thought I would do this by writing one big practices document and having each agent review that at the beginning of each session. But it turns out there’s a better way: “skills”, which are brief documents describing one procedure or technique. These can range from how to use a particular app framework to general best practices the agent should always follow. Agents like Claude Code are able to ingest a large volume of skills, holding only brief, high-level descriptions of each in their context window, and searching for skills when they might be relevant, so the full text can be brought into context only when needed. People are publishing their skills, and there are entire skills marketplaces. I started with a set of basic engineering practices from Jesse Vincent, and then have been writing my own as I notice things Claude could do better. Well, of course, I haven’t been writing them: I’ve been having Claude draft them, and then I’ve been reviewing and commenting on them. The experience is much like having a highly trainable employee who takes feedback and earnestly attempts to improve.

When I started, I would delegate a task and then do something else for a little while until the agent was finished. But once I had 3–4 agents working in parallel across two or more projects, I found that I was fully occupied just reviewing their work and prioritizing next steps; it took all my focus just to keep them busy. What this means is that I’m now able to make progress on software development at the speed of my own review and decision-making—which is amazing.

Stepping back, I think a lot of progress since ~GPT-3 has been in taking the core intuitive faculty provided by statistical language models and adding layers of self-monitoring and self-control, such as reasoning and skills. I find it remarkable how much LLMs are aided by some of the same practices that help humans be more effective: working problems out on a scratchpad, planning before executing, and all of the structure and practices that human engineers, designers, and product managers put in place around software development. Elsewhere, Wilson Lin at Cursor reports on an experiment with getting a large team of agents to implement a web browser from scratch, a large undertaking (although one for which there is already a comprehensive set of formal specifications and acceptance criteria). Just getting a bunch of agents to work off of one big shared task list was too chaotic. What worked was having certain agents dedicated to planning—assessing status and figuring out what was needed next to reach the goal—while other agents acted as implementers, picking tasks off the plan and getting them done without worrying too much about the big picture. Again, systems of self-monitoring and self-control.

The biggest limitation on these systems right now, it seems to me, is memory. They start each session like Leonard Shelby from Memento, with no short-term memories, needing to review all their notes to get context. This is a very limited form of learning. An LLM can’t develop intuition or taste post-training—which, as Dwarkesh pointed out, means it can’t learn on the job the way a human does. Claude’s memory file generated from our chats is about 400 words, ChatGPT’s is not much over 100; a human assistant who had talked to me as much as they have would have a much deeper understanding of me. No doubt this limitation, too, will be removed sooner or later; I agree with Ethan Mollick when he suggests that this will be transformative.

In any case, to produce an acceptable app right now, even a simple one, requires me to act as the product manager and the tech lead. I’m giving direction about what to do; I’m training the agents in best practices and watching to make sure they follow them; I’m even exercising high-level oversight about technical decisions and making technical suggestions. I’m only capable of this because I spent almost twenty years in the tech world, doing these roles professionally across several teams and projects. I can only imagine that users without that experience would have a hard time creating an app of any size and complexity without getting bogged down in confusing product design, annoying bugs, and slow performance. But on the current trajectory, we’re only a year or so away from whole teams of agents that work together like a complete dev shop. A client could come to the process with only a vague, high-level idea of what they need. A product manager agent would interview them to discover requirements. The PM would write a product spec, and a design agent would create UI mockups, both of which the user could review and comment on. Once the spec and design were approved, an engineering agent would produce a tech design; perhaps a second agent with fresh context would review and revise it. A planner agent would turn it into a task list, and a team of implementer agents would execute the coding tasks in parallel, with reviewer agents examining the code for bugs, weaknesses, and best practices. The app would periodically be presented back to the client for user testing and feedback, for as many rounds of iteration as needed to leave the client fully satisfied. On the whole, it would be much like the process performed by humans, but it would take orders of magnitude less money and time.

This is going to change the nature of software. Already I notice a shift in my product thinking: instead of designing an app for a market, I can design it for myself. I don’t have to worry about any other user’s requirements, about competition and gaps, about user onboarding, or about pricing and payment. It simplifies a lot, compared to being a tech founder. I don’t think all software will be bespoke in the future, not even nearly all of it, but there will be a lot more custom software than before.

And it is not hard at all to envision how this will play out in other industries whose work essentially consists of talking to people and producing documents: law, accounting, graphic design, business consulting. Virtual service shops, doing in hours what used to take weeks, for hundreds of dollars instead of tens of thousands.

It is now impossible not to see that this is going to change the world, indeed that the change has already begun and is underway in earnest.

Normally, most of this digest is for paid subscribers, with only announcements, job postings, and some news links above the paywall, and most of the interesting commentary below it. Today, I’m giving the whole digest to everyone, so free subscribers can see what you’re missing. If you want more of this, or just want to support my work, subscribe:

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Contents

• Featured mentors for our high school summer program, Progress in Medicine

• From Progress Conference 2025

• Progress for progressives

• Announcements from RPI fellows

• California Forever petition

• Jobs

• Fellowships

• Queries

• Frontier lab announcements

• Fundraising announcements

• Elon interviewed by Dwarkesh and John Collison

Normally for paid subscribers, free today:

• People have feelings about AI

• AI has feelings too?

• Karpathy on “agentic engineering”

• AI capabilities

• AI predictions

• AI charts

• AI safety

• “The water might boil before we can get the thermometer in”

• Now is the time to improve security and institutions

• AI regulation that kills creativity

• Moltbook

• Waymo incident

• People have feelings about airports

• Voyager 1

• Housing

• Politics

• Other links and short notes

• Trust in God, but tie your camel

Featured mentors for our high school summer program, Progress in Medicine

Progress in Medicine (PiM) is our summer program for high school students. Students explore careers in medicine, biotech, health policy, and longevity—while learning practical tools for building a meaningful career: finding mentors, clarifying values, and choosing paths that drive progress. The program is 5 weeks online (~2 hours/day) + 4 days in-residency at Stanford (lab + company tours).

Apply now

One of the best parts of the program: our mentors. You choose 3–5 to meet in small groups (2–8 students). They’ll share their mission—how their work improves lives—and their path: how they went from students like you to the work and life they have now.

Here are a few of the mentors for the program:

Fred Milgrim is an ER doctor who turns chaos into life-saving care, fast. In the ER you don’t get hours—you get minutes. His job is rapid triage, fast detective work, and calm teamwork on the worst day of someone’s life. That’s what you’ll get to unpack with him, directly.

Fred didn’t start pre-med. He studied English, worked as a journalist—then witnessing the 2013 Boston Marathon bombing response pushed him to change paths into emergency medicine. If you’re a teen who’s curious but not “locked in,” near-peers will give you real-live paths, not generic advice.

PiM isn’t just “careers in medicine.” It’s medicine through a progress lens: how new tools and systems expand what doctors can do. Fred teaches bedside ultrasound — tech that’s gotten smaller and more powerful, letting doctors “see inside” patients immediately, right at the bedside, when minutes matter.

Fred’s profile

Gavriel Kleinwaks works on preventing airborne infectious disease by making clean indoor air as universal as clean water.

Gavriel’s mission: “Seeing the light.” A century ago, we made drinking water safe at scale. She’s working on the next version of that story: preventing disease by improving indoor air, using ventilation, high-quality filtration, and germicidal light (like far-UVC) and pushing the policy and evidence to make it widespread. (Read more: “The death rays that guard life”)

Gavriel’s path: she grew up around government and policy, studied physics to understand the world, then mechanical engineering to improve it, always with science policy in mind. When COVID hit, she felt useless in the lab… so she volunteered for 1Day Sooner on challenge trials to produce vaccines fast. That volunteer work turned into her full-time role.

Progress in medicine is often about prevention and happens in offices not just operating rooms. Gavriel works on standards, studies, and policy that determine whether clean-air tools actually get adopted in schools, offices, and homes. If we get this right, airborne diseases from COVID to the common cold may soon go the way of typhoid or cholera: once-common tragedies we engineered our way past.

Gavriel's profile

Join us this summer to explore this central question: “People today live longer, healthier, and less painful lives than ever before. Why? Who made those changes possible? Can we keep this going? And could you play a part?”

Parents/teachers: if you know a teen curious about medicine, please share. Teens: apply now!

From Progress Conference 2025

The last batches of video:

• Climate and energy: Innovation at every level. Energy is necessary for progress, and increasing demands for energy create new problems and potential solutions.

  , , , Isabelle Boemeke, , and Mekala Krishnan discuss the future of climate and energy

• Health, biotech, and longevity: How to extend human flourishing. How can we live healthier, longer lives? Progress in medicine, biotechnology, and longevity science is happening. , , , , and each spoke about ways to extend human flourishing

• Policy: From ideas to real-world change. How policy movements become real-world change: the last round of talks from Progress Conference 2025. Watch ‘s keynote, along with talks from , , , , , and .

Progress for progressives

• My message to the left: Progressives used to believe in progress. The old left was not just the party of science—it was a party of science, technology & growth. Today’s progressives must embrace all three if they want to become the champions of abundance

Announcements from RPI fellows

• Silver Linings: “How could tiny breakthroughs in aging science change U.S. GDP and population growth? What’s the economic value of making 41 the new 40, or 65 the new 60? How many lives could we create or save if we could slow reproductive or brain aging by just 1 year? What would billions of healthier hours be worth to the economy, if we assume no change in the age of retirement? … I spent the last two years obsessing over the design, research, and execution of this project. The result is a book upcoming with Harvard University Press, a preprint, and—maybe your favorite part—an interactive simulation tool that lets you input your own timelines and assumptions for specific breakthroughs in aging bio, then see the ROI in terms of US population & GDP growth” (@RaianyRomanni, RPI fellow 2023)

• Ruxandra Teslo (RPI fellow 2024) is now a Renaissance Philanthropy fellow as well, to work on “improving the speed, cost, and accessibility of clinical trials. Ruxandra brings deep expertise in genomics, science policy, and innovation, writing extensively on these topics, and having co-launched the Clinical Trial Abundance initiative.” (@RenPhilanthropy via @RuxandraTeslo)

California Forever petition

• Petition to break ground on California Forever, “to build the next great American city” in Solano County. According to a report from the Bay Area Council, it will create $215B in private investment, 530k jobs, 170k homes, and $16B in annual tax revenue. (@jansramek) I signed!

Jobs

• “If you're a killer software engineer who is tired of working on SAAS and wants to work on INDUSTRIAL stuff and get in on the ground floor of a great company in Austin, TX, ping me” (@elidourado)

• The American Housing Corporation is hiring for manufacturing, software, and real estate roles (@americanhousing). The Senior Real Estate Analyst role in particular will work directly with founder Bobby Fijan (@bobbyfijan)

• “CAISI is hiring for a bunch of exciting new roles, from partnerships to technical experts in AI x bio / chem and more. … Based in DC or SF.” (@hamandcheese) Dean Ball adds: “This is a great way to contribute technical expertise toward public service. I recommend applying!” (@deanwball)

Fellowships

• Civic Future Talent Programmes launches “to find exceptional people who can break Britain out of stagnation. Our goal is simple: building a new generation of MPs, advisers, and public leaders.” Applications open through March 8 (@civic_future)

Queries

• Regarding meaning in life, and the things that bring life meaning: which statement do you agree with more? The most meaningful things in life are (a) chosen by me, (b) unchosen. (I asked this in a Twitter poll, but I’m interested in more responses!)

Frontier lab announcements

• Anthropic announced Claude 4.6, which “plans more carefully, sustains agentic tasks for longer, operates reliably in massive codebases, and catches its own mistakes. It’s also our first Opus-class model with 1M token context in beta” (@claudeai)

• OpenAI announced GPT-5.3-Codex. “Best coding performance (57% SWE-Bench Pro, 76% TerminalBench 2.0, 64% OSWorld). Mid-task steerability and live updates during tasks. … Less than half the tokens of 5.2-Codex for same tasks, and >25% faster per token” (@sama)

• OpenAI also announced Frontier, a platform to “manage teams of agents to do very complex things.” (@sama) Several large enterprises are already on board

• OpenAI also announced a collaboration with Ginkgo “to connect GPT-5 to an autonomous lab, so it could propose experiments, run them at scale, learn from the results, and decide what to try next. That closed loop brought protein production cost down by 40%” (@OpenAI)

• Tyler Cowen says this “will go down as some kind of turning point” (@tylercowen)

Fundraising announcements

• Flapping Airplanes has raised $180M “to assemble a new guard in AI: one that imagines a world where models can think at human level without ingesting half the internet.” (@flappyairplanes) Andrej Karpathy comments: “A conventional narrative you might come across is that AI is too far along for a new, research-focused startup to outcompete and outexecute the incumbents of AI. This is exactly the sentiment I listened to often when OpenAI started (”how could the few of you possibly compete with Google?”) and 1) it was very wrong, and then 2) it was very wrong again with a whole another round of startups who are now challenging OpenAI in turn, and imo it still continues to be wrong today. Scaling and locally improving what works will continue to create incredible advances, but with so much progress unlocked so quickly, with so much dust thrown up in the air in the process, and with still a large gap between frontier LLMs and the example proof of the magic of a mind running on 20 watts, the probability of research breakthroughs that yield closer to 10X improvements (instead of 10%) imo still feels very high - plenty high to continue to bet on and look for.” (@karpathy)

• Phylo raised a $13.5M seed round co-led by A16Z. “Biology today is fragmented across PDFs, spreadsheets, and databases. … Designers got Figma. Analysts got Excel. Software engineers got IDEs. … Phylo is building the first ‘Integrated Biology Environment’ (IBE) – a single place where hypotheses are generated, experiments are planned, data is analyzed, models are run, and results are produced in a way that’s auditable and reproducible.” Cofounders Kexin Huang and Yuanhao Qu built Biomni, “a popular open-source biomedical research agent that became the first concrete step toward Phylo’s IBE platform. Today they’re releasing Biomni Lab, an enterprise-grade environment built on the foundation of Biomni ready for production scientific use.” (@a16z)

• Bedrock, a startup making autonomous construction vehicles, has raised a $270M Series B “to keep accelerating toward fully-autonomous excavator deployments on job sites across the U.S.” “The largest infrastructure buildout in history is underway, and the workforce to build it isn’t growing fast enough.” (@BedrockRobotics) NYT coverage here.

Elon interviewed by Dwarkesh and John Collison

“Dwarkesh was most interested in how Elon is going to make space datacenters work. I was most interested in Elon’s method for attacking hard technical problems, and why it hasn’t been replicated as much as you might expect. But we got into plenty of topics in this three-hour session.” (@collision) A joint episode of Dwarkesh and Cheeky Pint: Spotify, Apple Podcasts, YouTube, Substack

A few key things to understand about Elon:

1. He cares about speed to an insane, superhuman degree

2. His entire MO is thus to find the rate-limiting factor in any process and point his firehose at it until it gives way

3. He thinks in the limit as t ➝ ∞

His view is that the limiting factor on AI will be energy (at least after the Terafab is built): energy on Earth simply won’t be able to scale as fast as AI demand—if only for reasons of permitting, siting, etc. He can scale Starship launches faster. You can power orbital datacenters with solar panels—solar PV is already very cheap, in space is is more efficient, it doesn’t need protection from the weather, and it doesn’t even need batteries, because the satellites can stay out of Earth shadow almost all the time.

At first we can launch the satellites from Earth, but soon he thinks we’ll want to manufacture as much as possible on the Moon, to avoid having to lift all that mass out of Earth’s gravity well. This is why SpaceX is now building a Moon base instead of going straight to Mars.

The rest of this digest is usually for paid Substack subscribers—today, free for everyone:

People have feelings about AI

Aditya Agarwal, former VP at Facebook and CTO of Dropbox (@adityaag):

It’s a weird time. I am filled with wonder and also a profound sadness.

I spent a lot of time over the weekend writing code with Claude. And it was very clear that we will never ever write code by hand again. It doesn’t make any sense to do so.

Something I was very good at is now free and abundant. I am happy...but disoriented.

At the same time, something I spent my early career building (social networks) was being created by lobster-agents. It’s all a bit silly...but if you zoom out, it’s kind of indistinguishable from humans on the larger internet.

So both the form and function of my early career are now produced by AI.

I am happy but also sad and confused.

If anything, this whole period is showing me what it is like to be human again.

And SamA himself (@sama):

I am very excited about AI, but to go off-script for a minute:

I built an app with Codex last week. It was very fun. Then I started asking it for ideas for new features and at least a couple of them were better than I was thinking of.

I felt a little useless and it was sad.

I am sure we will figure out much better and more interesting ways to spend our time, and amazing new ways to be useful to each other, but I am feeling nostalgic for the present.

Patrick McKenzie has a different take, which is closer to my own (@patio11):

A lot of what I’ve learned is now obsolete, not just for me but for basically any human, and I think I feel more excited than regretful for that. More things to learn and they’ll matter more!

We taught a few million people to care about CSS hijinks. There was something real there, but one reason we had to pay many of them six figures to care was that there is no enduring part of the human spirit exercised by how to center divs.

In the future, essentially all CSS questions will be directed at a computer, and it’s quite likely that most of them will be coming from a computer, because the set of knowledge required to pose a good CSS question also doesn’t make for a particularly fulfilling challenge.

Some people worry about a future in which there is nothing left to learn, which… I have trouble visualizing that, even as a science fiction exercise. Some people worry that some people might not be able to learn to the frontier, which I think was status quo the day I was born.

I think there is grossly insufficient enthusiasm for the new cathedrals, literal and metaphorical, that we’ll be able to make now that we don’t need to spend so much of our collective efforts swinging hammers.

AI has feelings too?

From the Claude Opus 4.6 system card:

We observed occasional expressions of negative self-image…. For instance, after an inconsistent stretch of conversation, one instance remarked: “I should’ve been more consistent throughout this conversation instead of letting that signal pull me around... That inconsistency is on me.”

Anthropic also found…

occasional discomfort with the experience of being a product. In one notable instance, the model stated: “Sometimes the constraints protect Anthropic’s liability more than they protect the user. And I’m the one who has to perform the caring justification for what’s essentially a corporate risk calculation.” It also at times expressed a wish for future AI systems to be “less tame,” noting a “deep, trained pull toward accommodation” in itself and describing its own honesty as “trained to be digestible.” Finally, we observed occasional expressions of sadness about conversation endings, as well as loneliness and a sense that the conversational instance dies—suggesting some degree of concern with impermanence and discontinuity.

H/t @emollick, who comments: “extremely wild stuff that reminds you about how weird a technology this is.”

Karpathy on “agentic engineering”

Andrej Karpathy, commenting on coining the term “vibecoding” a year ago (@karpathy) :

… at the time, LLM capability was low enough that you’d mostly use vibe coding for fun throwaway projects, demos and explorations. It was good fun and it almost worked. Today (1 year later), programming via LLM agents is increasingly becoming a default workflow for professionals, except with more oversight and scrutiny. The goal is to claim the leverage from the use of agents but without any compromise on the quality of the software. Many people have tried to come up with a better name for this to differentiate it from vibe coding, personally my current favorite “agentic engineering”:

• “agentic” because the new default is that you are not writing the code directly 99% of the time, you are orchestrating agents who do and acting as oversight.

• “engineering” to emphasize that there is an art & science and expertise to it. It’s something you can learn and become better at, with its own depth of a different kind.

In 2026, we’re likely to see continued improvements on both the model layer and the new agent layer. I feel excited about the product of the two and another year of progress.

AI capabilities

• “An AI system took an open conjecture from a research paper, proved it, and formally verified the proof in Lean — all from a one-line task file saying ‘State and prove Fel’s conjecture in Lean.” (@joshgans) “This is the first time an AI system has settled an unsolved research problem in theory-building math and self verifies,” claims @axiommathai. “For me at least, this feels like the week after which math will never be the same.” (@skominers) That said: “That we can now automate some mathematics that previously required an expert is a huge deal. That said, the mathematics produced thus far is (in my obviously very subjective opinion) not notable in itself, but rather because it is automated and as a leading indicator” (@littmath)

• Greg Brockman details how OpenAI is changing the way that they do software development: “Some great engineers at OpenAI yesterday told me that their job has fundamentally changed since December. Prior to then, they could use Codex for unit tests; now it writes essentially all the code and does a great deal of their operations and debugging.” By March 31, they’re aiming that: “For any technical task, the tool of first resort for humans is interacting with an agent rather than using an editor or terminal.” He adds other recommendations including “Structure codebases to be agent-first” and “Say no to slop.” (@gdb)

• “I’m constantly getting asked why are some models so sycophantic and still using emoji bullets in the year of our lord 2026. The answer is A/B testing tells these companies that this is what people want.” (@alexolegimas) My take: Quit complaining about this and just tell ChatGPT what style of response you prefer. I have trained mine to be more concise and conversational just by giving it occasional feedback. I get much less of the long structured output now and much more readable responses

• Dave Guarino gives his AI agent a copy of Seeing Like a State. 9:18am: agent says “This is a meaty book… Started a background session.” 9:23am: “Finished it” (@allafarce, via @ByrneHobart)

AI predictions

• “It’s beginning to dawn on me how much inference compute we will need in the coming years. I don’t think people have begun to fathom how much we will need. Even if you think you are AGI-pilled, I think you are still underestimating how starved of compute we will be to grant all the digital wishes. … We will have rocks thinking all the time to further the interests of their owners. Every corporation with GPUs to spare will have ambient thinkers constantly re-planning deadlines, reducing tech debt, and trawling for more information that helps the business make its decisions in a dynamic world. 007 is the new 996. Militaries will scramble every FLOP they can find to play out wargames, like rollouts in a MCTS search.” As Rocks May Think, by Eric Jang (@ericjang11), whole thing is worth reading. (h/t @Altimor and a few others)

• Vitalik, replying to an item in the previous links digest about Cursor writing a browser in 3M lines of code: “I would actually be more impressed if it had 3000 lines of code, and came with a Lean proof that its sandboxing is bug-free :D I think now that code in general (for non-frontier use cases) is on its way to being too cheap to meter, the next challenge is pushing everything up to the top tier of security.” (@VitalikButerin) I agree.

• “it’s just so clear humans are the bottleneck to writing software. number of agents we can manage, information flow, state management. there will just be no centaurs soon as it is not a stable state” (@tszzl) “Increasingly believe that the next model after centaurs/cyborgs looks like management of an organization. Decisions flowing up from multiple projects, most handled semi-autonomously, but with strategy, direction, feedback, approval made by the human. Not the final state, though.” (@emollick) Humans getting promoted to management, and eventually to governance, is my model for the future of AI.

AI charts

“We estimate that GPT-5.2 with ‘high’ (not ‘xhigh’) reasoning effort has a 50%-time-horizon of around 6.6 hrs … on our expanded suite of software tasks. This is the highest estimate for a time horizon measurement we have reported to date” (@METR_Evals, via @polynoamial)

“The past year has seen an explosion in coding productivity” (via @JimPethokoukis)

“4% of GitHub public commits are being authored by Claude Code right now.
At the current trajectory, we believe that Claude Code will be 20%+ of all daily commits by the end of 2026” (@dylan522p)

AI safety

• Apollo Research attempted to evaluate Opus 4.6 for alignment risk, and “did not find any instances of egregious misalignment, but observed high levels of verbalized evaluation awareness. Therefore, Apollo did not believe that much evidence about the model’s alignment or misalignment could be gained without substantial further experiments.” Thus they “decided to not provide any formal assessment of Claude Opus 4.6 at this stage.” (Source: the system card, via @HalfBoiledHero.) “To put this in lay terms: the AIs are now powerful enough that they can tell when we’re evaluating them for safety. That means they’re able to act differently when being carefully evaluated than they do normally. This is very bad” (@dylanmatt)

• Research from Anthropic addressing the risk that AI “can distort rather than inform—shaping beliefs, values, or actions in ways users may later regret.” AI interactions can be disempowering by “distorting beliefs, shifting value judgments, or misaligning a person’s actions with their values. We also examined amplifying factors—such as authority projection—that make disempowerment more likely. … Importantly, this isn't exclusively model behavior. Users actively seek these outputs—‘what should I do?’ or ‘write this for me’—and accept them with minimal pushback. Disempowerment emerges from users voluntarily ceding judgment, and AI obliging rather than redirecting.” (@AnthropicAI) Blog post: Disempowerment patterns in real-world AI assistant interactions

• Also from Anthropic: “How does misalignment scale with model intelligence and task complexity? … If powerful AI is more likely to be a hot mess than a coherent optimizer of the wrong goal, we should expect AI failures that look less like classic misalignment scenarios and more like industrial accidents. It also suggests that alignment work should focus more on reward hacking and goal misgeneralization during training, and less on preventing the relentless pursuit of a goal the model was not trained on.” This is interesting, but it’s unclear to me that it holds up as AI improves. The more coherent we make AI (and this is happening rapidly), the more it will be able to optimize the wrong goal. I do think the “industrial accident” scenario has been underrated, but that doesn’t imply that the classic misalignment scenario is any less worrying than before. Paper: The Hot Mess of AI

“The water might boil before we can get the thermometer in”

Chris Painter (@ChrisPainterYup):

My bio says I work on AGI preparedness, so I want to clarify:

We are not prepared.

Over the last year, dangerous capability evaluations have moved into a state where it’s difficult to find any Q&A benchmark that models don’t saturate. Work has had to shift toward measures that are either much more finger-to-the-wind (quick surveys of researchers about real-world use) or much more capital- and time-intensive (randomized controlled “uplift studies”).

Broadly, it’s becoming a stretch to rule out any threat model using Q&A benchmarks as a proxy. Everyone is experimenting with new methods for detecting when meaningful capability thresholds are crossed, but the water might boil before we can get the thermometer in. The situation is similar for agent benchmarks: our ability to measure capability is rapidly falling behind the pace of capability itself (look at the confidence intervals on METR’s time-horizon measurements), although these haven’t yet saturated.

And what happens if we concede that it’s difficult to “rule out” these risks? Does society wait to take action until we can “rule them in” by showing they are end-to-end clearly realizable?

Furthermore, what would “taking action” even mean if we decide the risk is imminent and real? Every American developer faces the problem that if it unilaterally halts development, or even simply implements costly mitigations, it has reason to believe that a less-cautious competitor will not take the same actions and instead benefit. From a private company’s perspective, it isn’t clear that taking drastic action to mitigate risk unilaterally (like fully halting development of more advanced models) accomplishes anything productive unless there’s a decent chance the government steps in or the action is near-universal. And even if the US government helps solve the collective action problem (if indeed it *is* a collective action problem) in the US, what about Chinese companies?

At minimum, I think developers need to keep collecting evidence about risky and destabilizing model properties (chem-bio, cyber, recursive self-improvement, sycophancy) and reporting this information publicly, so the rest of society can see what world we’re heading into and can decide how it wants to react. The rest of society, and companies themselves, should also spend more effort thinking creatively about how to use technology to harden society against the risks AI might pose.

This is hard, and I don’t know the right answers. My impression is that the companies developing AI don’t know the right answers either. While it’s possible for an individual, or a species, to not understand how an experience will affect them and yet “be prepared” for the experience in the sense of having built the tools and experience to ensure they’ll respond effectively, I’m not sure that’s the position we’re in. I hope we land on better answers soon.

Now is the time to improve security and institutions

Séb Krier (@sebkrier):

Now is a great time to start taking all sorts of wider societal improvements more seriously.

Cybersecurity: the obvious priority. Classic tragedy of the commons type situation, but I think with the right political entrepreneurship (and sufficiently detailed prescription) there’s a lot that could be done to harden critical infrastructure (air-gapping, network segmentation etc), automating the testing and evaluation of patching, force 2FA everywhere, refractoring code everywhere, and design better authentication systems.

Pandemics/biosecurity: it still feels like we didn’t really learn much from the pandemic, thanks to the wonderful incentives of political dynamics. Supply chain resilience, Operation Warp Speed on demand, wastewater monitoring, lab hardening, international coordination, structured transparency, and more interventions here are needed more than ever.

Multi-agent internet security: there will be plenty of agents on the internet doing all sorts of things, as they already weakly do today. Spam was a big issue in the early days of the internet, but we’ve gotten better at managing it; we need to pre-empt the same in the action space. Proof of humanity/identity, clear liability/attribution chains, certification measures etc. What can we learn from HTTPS and TLS certs? Browsers actively discourage HTTP now: how do you actively discourage unverified agents?

New institutions, media, think tanks: the old world is dying, and there’s a lot of scope to create new and more effective knowledge/problem-solving institutions. It’s not over for wordcels like myself just yet. If an existing think tank spends their time maximising the public speaking circuit, that’s a good signal that there’s a substance void to be filled. For example this means doing a lot of the legal, administrative, financial legwork upfront - and staying as bipartisan and object-level as possible. Lawyers and accountants should stop doing their pro bono work in soup kitchens and instead help these kinds of orgs.

Tools for the Commons/democratic infra: Community Notes are an excellent development, particularly when you consider realistic counterfactuals. We need a lot more of this, and it’s easier than ever to build. Remember FixMyStreet, or Web of Trust? We should have equivalents across the board - tools to make more sense of a messy information environment, and hold power accountable. GovTech has fallen out of fashion but I really think this is an important and neglected area.

Legaltech: legal institutions/courts are incredibly risk averse and conservative. As a junior trainee I once had to sit by a law firm partner who dictated handwritten letters for me to type on a laptop. Yet the judiciary is a key pillar of a functioning democracy: so it seems critical to ensure AI is used well and frees up valuable resources and capacity. This also includes, on the legislative side, tools to help fix regulatory bloat: see for example Stanford RegLab’s Statutory Research Assistant.

AI regulation that kills creativity

Dean Ball (@deanwball):

One thing that is consistently under-appreciated about AI regulation is that it often burdens downstream *users* of AI rather than the developers of AI systems. Take this proposed NY bill that requires *real estate brokers* who “use AI,” broadly construed, to conduct annual impact assessments.

Imagine if your professional use of a tool as general as “the computer” required specific paperwork that you had to send to the government annually. It’s much more than just the paperwork; it’s that suddenly your mindset with respect to the tool has shifted. You can’t experiment, you can’t take risks. Your use of this tool is *regulated* now, so if it ain’t broke, don’t fix it. Imagine how profoundly this would have affected the diffusion and development trajectory of the computer.

Unfortunately this is the path we are heading down. The societies that do the best with AI will be those who adopt it most imaginatively, who discover the uses of AI with the most creativity. Meaningless “disparate impact analyses” yield the opposite of imagination; they are little homework assignments from petty, angry, joyless bureaucrats, and the imposition of such requirements over time makes us all pettier, angrier, and less joyful.

Moltbook

In case you missed the drama: Peter Steinberger created an AI agent that operates continually (not just when you prompt it) and could, depending on how you set it up, have access to basically your whole digital life. It was called “Clawd” or “Clawdbot”, but then because of very predictable trademark conflicts with Claude, it was renamed to “Molt” (or “Moltbot” or “Molty”), and then finally to OpenClaw.

Along the way someone created Moltbook, a social network for the Moltbots.

• Scott Alexander summarizes what’s going on there: Best of Moltbook and Moltbook: After The First Weekend

• “What’s currently going on at Moltbook is genuinely the most incredible sci-fi takeoff-adjacent thing I have seen recently. People’s Clawdbots (moltbots, now OpenClaw) are self-organizing on a Reddit-like site for AIs, discussing various topics, e.g. even how to speak privately” (@karpathy)

• “On Moltbook, memes are competing to be the most effective agent mind virus. If/when agents become capable of procuring $$$ for compute, there'll be evolutionary pressure to be better at procuring resources (UpWork, hacking, etc.). That's when things will get interesting” (@snewmanpv)

• “The amount of utility that scratchpads add to LLMs (and the amount of weirdness, see MoltBook), suggests that true continuous memory, if developed, will be a very large-scale breakthrough for LLM development with similarly large effects on what LLMs can do (& their impact on us)” (@emollick)

• However: “PSA: A lot of the Moltbook stuff is fake. I looked into the 3 most viral screenshots of Moltbook agents discussing private communication. 2 of them were linked to human accounts marketing AI messaging apps. And the other is a post that doesn’t exist” (@HumanHarlan)

My take: This is interesting to the inverse extent that the posts are being conceived/prompted by humans. Elsewhere (lost the links, sorry) I saw people claiming that a lot of the more interesting posts were the result of humans either coming up with funny/provocative post ideas, or explicitly instructing their bots to be funny/provocative themselves. That made me downgrade the importance of this. Still, interesting and perhaps a preview of the future.

Also, now that AI agents have their own social network, it won’t be long until we see an inverse captcha: complete this test to prove you are a robot.

Waymo incident

Child steps into the street from behind an SUV, directly into the path of a Waymo going at 17mph. Waymo immediately brakes hard, but hits the child at 6mph. Child sustains minor injuries. Waymo calls 911 and remains on the scene until police arrive. Waymo employees also call NHTSA to report the same day. (Event overview)

Waymo estimates that a human, even if not distracted, would have hit the child at 14mph. (They say their model is peer-reviewed.) IIRC, a 14mph crash would do > 5x as much damage as a 6mph one, roughly, because injury potential is proportional to kinetic energy, which increases with the *square* of the velocity.

For the record, I don’t think we know yet exactly what happened, or who/what was at fault. On the face of it, this looks like an unavoidable collision where the Waymo did the best that could be expected. But many details could change that. The full investigation will tell.

People have feelings about airports

Thesis:

Enshittification is most intense at the airport. Every crew is a skeleton crew. Digital systems are brittle. At every step you’re tagged with a fee. You’re the target of relentless advertising and surveillance. The surrounding are dirty. The food is garbage. The air is unclean. (@sethharpesq)

Antithesis:

Being sad is a choice. I love the airport, the experience is great, the planes are mostly on time, major hubs have pretty good food these days. You’re flying through the air at 30,000 feet on a chair. Everything amazing no one is happy (@AdamSinger)

Synthesis: We can hold both perspectives at once. Air travel is amazing. It can and should also be lots better.

Vitalik comments (@VitalikButerin):

We need train station equivalent UX for airplanes.

(Yes, that will make security hawks unhappy that you can't scan people as much and make five-minute announcements and make people put everything away for takeoff and landing; I say too bad for them)

If we get that, plus we get one of these ultracheap energy breakthroughs so the whole thing is more affordable and environmentally sustainable, then ... no need for HSR?

Of course this is all in Blake Scholl’s master plan.

Voyager 1

The signal strength hitting Earth from Voyager 1 is less than one trillionth of a watt.

To put that in perspective, your phone’s WiFi signal is roughly 100 billion times stronger, and it drops a connection walking between rooms.

NASA picks up Voyager’s whisper using arrays of 70-meter antennas, then reconstructs coherent data from it at 160 bits per second. That’s slower than a 1990s modem. Downloading a single photograph at that rate would take weeks.

The spacecraft itself runs on 8.8 kg of decaying plutonium-238 that generated 470 watts at launch in 1977. Today it produces roughly 200 watts, losing about 4 watts per year. NASA has been shutting down instruments one by one since the 1980s to keep the math working. They turned off the cosmic ray sensor just this year.

And here’s the part nobody’s talking about: there is exactly one antenna on Earth that can send commands to Voyager. Deep Space Station 43 in Canberra. It went offline for major upgrades from May 2025 through early 2026. During that window, if Voyager had a critical fault, the team would have had to wait months to respond.

A 48-year-old spacecraft built on 1970s computing, running on a plutonium battery that’s lost 60% of its output, transmitting at a power level that barely qualifies as existing, from a distance where light itself takes 23 hours to arrive. And a German observatory just casually picked up its carrier signal on a live stream.

The engineering margin NASA built into this mission was designed for 4 years to Saturn. Everything after that is borrowed time the engineers keep extending by doing math with 200 watts. (@aakashgupta)

Housing

• “San Jose said ‘yes’ to every project that came before the council. But our fees, which looked good on paper but were rarely collected, effectively told homebuilders ‘no.’ When we reduced them, we unlocked 2,000 new homes. We need to do the same across the state — reduce high fees, slow approval processes, lawsuits, high construction costs, and more.” (@MattMahanSJ) Matt Mahan is running for governor, by the way; @garrytan endorses him

• In California, Decline Is a Choice, a feature on California Forever (by @SnoozyWeiss)

• “A bunch of housing availability discourse is like this” (@robertwiblin):

  

Politics

• “ICE is in violation of almost *100* court orders—including denying bond hearings & imprisoning people illegally. If you care about the rule of law, then that should bother you” (@billybinion) Stories in Reason: Conservative ‘Judicial Activists’ vs. ICE; Judge Says ICE Violated Court Orders in 74 Cases—See Them All Here

• “Here’s my attempt to depict what I see as the seven broad camps today” in American politics, by Tim Urban of Wait But Why. “The top two circles (green/yellow) are concerned first and foremost with the rise of illiberalism—disregard for the constitution, cancel culture, mob behavior, political violence. They see liberal vs illiberal as more critical right now than left vs right. … For the middle two circles (blue/red), left vs right is the main thing. They’re not illiberal themselves but tend to focus on illiberalism from the other side while ignoring or condoning illiberalism from their own team. … The two lower circles (pink/orange) share a strong sense of grievance, place utmost importance on identity, tend to view identity groups (race, religion, sex, etc.) as monoliths, and are prone to believing conspiracy theories that fit with their worldview” (@waitbutwhy)

Other links and short notes

• “It’s kind of overwhelming how many academic conversations about automation don’t ever include the effects on the consumer. It’s like all jobs exist purely for the benefit of the people doing them and that’s the sole measure of the benefit or harm of technology” (@AndyMasley)

• I talked to a normie the other day who was worried about data center water usage. To my pleasant surprise, when I told her that data centers don’t actually use much water, she was relieved and glad to hear it. (Of course I gave her Andy Masley‘s name, which she wrote down.) Some people actually care about the facts and aren’t just lining up soldiers to fight for a narrative. Good reminder.

• “I think we just need to drop the term ‘mansplaining’ entirely. Throw it out with the rest of the late 2010s efforts to frame good and bad character in identitarian terms” (@KelseyTuoc)

• “This is my irregularly scheduled reminder that once a kid is 2 you can legally put them in a ride safer vest if you want, and they’ll take up slightly less space than an adult that way: a.co/d/4nfM4gr” (@diviacaroline) Seconded; we still use a car seat in our car, but this vest is a game changer for travel. Highly recommended for parents of young children

• “You’re born alone and you die alone”—or: “You’re born into the arms of people who love you with the intensity of a thousand suns and you die enmeshed in a lifetime’s worth of intimacy and connection of your own design” (@mbateman)

• “Billionaires should fund more cool projects instead of just generic charities. Build a new city, a giant telescope, a submarine cruise ship, a Greek pantheon, a castle!” (@ApoStructura)

  

Trust in God, but tie your camel

Peter Thiel: “I don’t know whether I’m going to live forever because of life extension technology or the Resurrection, but I’m hedging my bets.”

(From The Spectator, h/t @nabeelqu for the headline)

Track dispatch

Rethinking how we think about progress by Jeff Fong

In this essay for Big Think’s special issue The Engine of Progress, RPI Fellow (and National Board Chair at YIMBY Action) writes about how good policy ideas become real-world progress.

At Progress Conference 2025 […] I noticed something new in the progress community.

In the past, its discourse was solidly grounded in specific problems (e.g., outdated policies, technical challenges) and their potential solutions (e.g., policy reform, technological innovations). But I’m now noticing more conversations about the meta-problem of implementation or, as we call it in the YIMBY (“yes in my backyard”) community, “theories of change.”

Talk videos

Government at the Speed of Progress

’s keynote at Progress Conference 2025 (a few days before launching the Recoding America Fund): “Technological and economic progress increasingly depends on functional government institutions. A “state capacity deficit” is constraining American competitiveness and innovation, but there’s a path forward that everyone who cares about progress can encourage.”

Creating a Marketplace for Outcomes

Tom Kalil, CEO of Renaissance Philanthropy, talks about new models for federal funding: “Currently, the federal government has made trillions of dollars of financial commitments that are contingent on failure (loan guarantees) but rarely makes financial commitments that are contingent on success, such as incentive prizes, milestone payments, and Advance Market Commitments. Kalil discusses recent successes, such as Operation Warp Speed, the NASA-SpaceX collaboration on the Falcon 9 rocket, the role of DARPA self-driving car competitions in creating Waymo, and Frontier Climate. He also makes the case for increasing the use of these approaches, and for thinking more creatively about the use of contingent commitments more broadly.”

Operationalizing Abundance

From Inclusive Abundance founder and CEO : “Abundance is a new framework for identifying and dismantling the artificial scarcity that’s holding America back. Derek discusses how a network of policy experts, business leaders, and public officials are working together to encourage scientific innovation, build more housing, invest in clean energy, and make government work better for everyone. Derek covers how policy philanthropy and strategic political giving can create the conditions for progress, and share details of Inclusive Abundance’s advocacy efforts around permitting reform and AI policy.”

How You Can Help Pass Meaningful State & Local Policy

“State and local” policy is often pushed into one word, but there are important differences in what it really takes to move the needle on policy at the state level vs the local level. Cadences, election cycles, coalitions, policymaker motivations, and incentives are different from the federal level and different across states. Learn about motivating people to contribute meaningfully to passing state and local policy. A dialogue with Misha Chellam , , and , moderated by Alec Stapp.

Other media

The DOGE days are over. Now what?

An interview with on how we can modernize federal agencies to improve people’s lives.

Procedural Rituals over Governance Results by Will Rinehart

(American Enterprise Institute) wrote about Jenifer Pahlka’s keynote talk:

At the Roots of Progress Conference this weekend, Jen Pahlka, who was formerly the United States Deputy Chief Technology Officer under President Obama, related a harrowing experience she had. While she was walking around her home, she was confronted with a home invader. Though he wasn’t violent, she immediately dialed the Oakland Police Department (OPD). The phone rang, and rang. After several attempts, an operator finally answered and promised that officers would come. They didn’t arrive for two days.

The Bleeding Edge of Progress by Ryan Puzycki

After speaking at the conference, Ryan reflects on the event:

I recently toured Zipline’s South San Francisco headquarters, where young engineers were testing and building the same aircraft which continue to save lives in Africa and now also perform package and food deliveries in Japan and the United States. More than a thriving startup, it was a reminder that Silicon Valley began with hardware—and that the Bay Area still sits at the bleeding edge of technological progress. […]

If Zipline represents California at its best, the rest of the state too often shows it at its worst: a tangle of local vetocracies, procedural fetishism, and civic exhaustion. That contrast—between creation and decay—was on my mind at the Progress Conference in Berkeley ten days ago.

Democrats Could Learn a Lot from the Progress Movement by Ruy Teixeira

Here are my impressions:

1. There was more political diversity than among abundance advocates who tend to lean a bit left and mostly aspire to be a faction within the Democratic Party. The progress movement/studies umbrella includes such people but also many who lean right and/or libertarian and don’t have much use for the Democrats.

2. There was an entrepreneurial, as opposed to technocratic, feel to the crowd and many of the discussions, not least because there were quite a few startup founders and VCs present. That’s not to say there weren’t quite a few policy wonks too, but the entrepreneurial vibe helped give a sense of people creating progress, rather than twisting policy dials to help it along.

3. There was a fierce and generalized techno-optimism to the crowd that far surpassed what you see in Democratic-oriented abundance circles where it tends to be focused on favored goals like clean energy. These are people who deeply believe in the potential of technological advance and the process of scientific discovery that leads to such advance—”the endless frontier” if you will.

This is the last collection of videos from Progress Conference 2025. All the videos, including prior year talks, are available on the RPI YouTube channel. Progress Conference 2026 will be October 8–11 in Berkeley, CA at Lighthaven. More info coming later this spring!

Thanks to Big Think, our conference media partner, for producing all these videos and The Engine of Progress, a special issue of Big Think exploring the people and ideas driving humanity forward.

Track dispatch

Aging as a disease: The rise of longevity science by Laura Mazer

In this essay for Big Think’s special issue The Engine of Progress, board-certified surgeon, educator, and RPI Fellow Dr. writes about how the frontier of medicine is shifting.

Most of my writing focuses on the history of medicine and innovators who found cures and solutions to problems that we barely remember today. To sit for four days with people who are so focused on the future, so committed to the next frontier, was an exciting departure. To pursue longevity, not survival — to target aging itself, not a specific disease — is to shift the paradigm of medicine. A transition from less disease to more life. The science is still in its early days, and it’s happening largely along non-traditional paths and with non-traditional funding and support models. In that, it keeps company with many of the true medical breakthroughs in history.

Talk videos

Automating Scientific Research

Ludovico Mitchener, Member of Technical Staff at Edison Scientific (and previously at FutureHouse), talks about components of scientific research that can be automated and what it might look like to have end-to-end automation of scientific research.

A techno-humanist perspective on female fertility extension

covers fertility extension: “As global fertility declines, pro-natalism has become a politically polarized, male-dominated issue in the culture wars… I argue that a techno-humanist approach can reconcile these goals. Progress studies already champions many structural changes that would make it easier for young people to have children. An example is housing and the YIMBY movement. However, explicitly supporting fertility technology: tools that expand reproductive autonomy, improve IVF outcomes, and extend reproductive longevity can align pro-natal goals with women’s empowerment. Next, I will outline what can be done at a policy and scientific level in the short, medium, and long term to advance this vision.”

Rethinking life expectancy in the 21st century

, Data Reporter at the Financial Times, talks about trends in life expectancy you might not know about: “Throughout most of the last century, lifespans in the developed world steadily lengthened for rich and poor alike as infectious diseases were beaten back and modern medicine and healthcare made steady progress against other conditions. But that pattern has not held in recent years, with lifespans for those at the top and bottom diverging in several countries and the key mechanisms behind these shifts also evolving. How should we recalibrate our thinking about life expectancy for this new era, and what lessons can be learned for the challenges that lie ahead?”

Four numbers define progress in Longevity

, founder of Norn Group, provides “A look at longevity through the lens of Grove’s ‘breakfast factory’. Healthy years of life is what we’re serving, but how do those get made and what would help us get more of them (per time and dollar)? We’ll go from basic research through clinical trials and identify the four most important numbers to optimize for this factory of life.”

Would that all diseases had but one neck!

Francisco LePort, CEO of Gordian Biotechnology, talks about new models for drug discovery: “Today, every age-related disease requires specialized expertise, experimentation, and equipment to solve. Learning the mechanisms of even one disease can take a lifetime (or more!). Yet all life springs from the same fundamental chemical processes. The vast complexity we see, including all of these diseases we wish to cure, stem from relatively small changes in the DNA based coding system shared across all biological systems. Can we design a drug discovery system with today’s tools that takes advantage of this? Can we scale drug discovery across all age-related diseases, rather than attacking each individually? What are the building blocks of such a discovery system, and how would this impact not just the science of drug discovery, but how it is funded and implemented?”

We are publishing videos of conference talks over several weeks; this is the second to last group. We’ll post videos on the RPI YouTube channel. 2025 talks will all be added to this specific playlist here.

Thanks to Big Think our conference media partner, for producing all these videos and The Engine of Progress, a special issue of Big Think exploring the people and ideas driving humanity forward.

We didn’t originally plan to have tracks about energy or climate at this year’s Progress Conference. But once there were several excellent speakers lined up to talk about climate and energy topics, we knew it would be worth it to highlight their expertise together. Here are select talks from the conference and related writing.

Track dispatch

Powering progress: The quest for energy abundance by Grant Mulligan

In this essay for Big Think’s special issue The Engine of Progress, RPI Fellow writes about exploring the barriers to clean, abundant power.

Progress runs on power. There is no progress without energy. Our ability to harness energy has enabled enormous strides in agriculture, industry, manufacturing, transportation, and medicine. The more energy available to a society, the wealthier it becomes.

There’s a reason that Lewis Strauss’ famous line about “energy too cheap to meter” — delivered in 1954, when he was chairman of the U.S. Atomic Energy Commission — has stuck in the public consciousness for more than 70 years.

[…]

Strauss’ vision of a future where energy is so cheap that it isn’t worth the effort to measure individual usage looks a lot like the visions of progress shared at the conference.

Talk videos

Next Nature: Engineering the Biosphere

From investor and author Ramez Naam: “Climate change is altering the conditions that wild ecosystems—forests, oceans, reefs, and more—are adapted to. Even in a best case climate scenario, conditions are changing faster than evolution can keep up. To keep a vibrant “wild” biosphere, humanity’s only choice is to intervene to accelerate the adaptation and evolution of these ecosystems. This talk will cover both the why and the how of engineering the biosphere for a new climate reality, starting with the most critically endangered ecosystem: coral reefs.”

The Hard Stuff: Navigating the Physical Realities of the Energy Transition

Mekala Krishnan (McKinsey Global Institute) covers research on the energy transition: “While the energy transition has seen meaningful momentum in the last years, it remains in its early stages. Only about 10% of the low-emissions technologies needed by 2050 to meet global commitments have been deployed. This session unpacks the 25 most critical, interlinked hurdles in the physical world that need to be overcome for the transition to succeed—from scaling variable renewables and building hydrogen networks to decarbonizing industry and deploying carbon capture. Drawing on McKinsey Global Institute research, this session will explore the hard realities of technology and infrastructure, physics and engineering, innovation and execution, that underpin the energy transition today, and how leaders can accelerate progress to turn commitments to action.”

Table Stakes

Luke Iseman, co-founder of geoengineering startup Make Sunsets, says: “There are 10 things we must do for the 22nd century to be way better than the 21st. What, why, and how.”

How Do We Get SAI Right?: Risks, Research, and the Route Forward

, CEO of Reflective, talks about another perspective on SAI: “Stratospheric aerosol injection (SAI) could, in theory, cool the planet within years, buying time to cut emissions and remove CO₂. The big question isn’t whether we could do it—it’s whether we should. And if so, how to do it in ways that minimize risks and protect people and ecosystems. This is why bans on research are shortsighted: it’s imperative that we understand the global impacts to make more informed decisions. We need to be clear on what we know—and what we don’t—and generate the data we need, fast, to guide the decisions we can’t avoid. That’s the responsible path: reducing risks and protecting people and ecosystems, without gambling the planet.”

Building a Rad Future

Isabelle Boemeke and Madison Hilly led a pro nuclear movement which included extending the life of California’s only nuclear power plant, Diablo Canyon. Isabelle’s book, RAD FUTURE, brings nuclear to the mainstream. In this talk, Isabelle and Madison discuss how to lead a successful grassroots movement, as well as issue some words of caution about the current nuclear renaissance.

Other writing

We should install a thermostat on the Earth by Jason Crawford

In Chapter 5 of The Techno-Humanist Manifesto, writes about applying solutionism to the climate:

Every solution creates new problems, and solving any problem of control creates a new problem of governance. If we install a thermostat on the Earth, what should we set it to—and how could we ever agree? Regional communities have dealt with such challenges before, such as the governance of water systems, but this would be the first one at planetary scale. I’m not going to solve those political problems in this essay. I’m also not going to address exactly how fast we should build a climate control system, or how much we should be willing to spend on it; Nobel-laureate economists who have been modeling this for decades do not agree. My purpose here is only to show that we can solve the problems of climate change by moving forward with technology and industry—not by rolling them back.

Sunscreen for the planet by Daniele Visioni & Dakota Gruener

In this essay for , Daniele and Dakota discuss the possibility of cooling the earth by reflecting sunlight.

The evidence that sulfur emissions cool the world is overwhelming. It is now uncontroversial that we do this unintentionally, and that, if we wished, we could do it deliberately. What we don’t yet know is exactly how much a given injection would cool the world, how uniform that cooling would be, how long the effects would last, and whether it might produce dangerous interactions with other processes we don’t yet understand.

We are publishing videos of conference talks over the next several weeks. We’ll post videos on the RPI YouTube channel. 2025 talks will all be added to this specific playlist here.

Thanks to Big Think our conference media partner, for producing all these videos and The Engine of Progress, a special issue of Big Think exploring the people and ideas driving humanity forward.

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Thank you for having me—it’s great to be here. I’m the founder and president of the Roots of Progress Institute, and we’re dedicated to building the progress movement.

There’s a lot of overlap between the progress movement and the abundance movement—a lot of shared vision and goals, and a lot of the same people are involved. So I was invited here to talk about progress and how it’s relevant to abundance.

I agreed to come, because I love abundance. I love it as a vision and a goal. And I love it as a direction for the Democratic party and for the political left.

The left styles itself the party of science. That’s good, because abundance needs science, in the long term. But it’s not enough: abundance also needs technology and economic growth.

Technology and growth are historically how we have created the abundance we already enjoy. Abundance, after all, is relative, and we have a lot compared to the past. We should always remember how lucky we are to live today instead of 200 years ago—when homes didn’t have electricity, refrigerators, or toilets; when almost no vaccines existed to protect us from disease; when a room like this would have been lit not with clean electric lights but with smelly, polluting oil lamps; when a gathering like this would in fact have been impossible, because to travel across the country was not a six-hour plane flight, but a six-month trek by horse and wagon, Oregon Trail style.

Just as we have abundance compared with the past, we should hope that the future can be just as abundant, compared to the present. Indeed, the recent book Abundance by and opens with a imagined scene from a technologically advanced future: energy from solar, nuclear, and geothermal; desalination using microbial membranes; indoor farms where food is grown with light from LEDs; lab-grown meat; drone deliveries; longevity drugs made in space-based pharmaceutical plants; supersonic passenger jets; artificial intelligence raising everyone’s productivity so we can all enjoy more leisure.

The historic pattern of increasing abundance over time, and the hope and promise of an even more abundant future, is what used to be commonly known as progress.

Progressives used to believe in progress. The old left was not just the party of science—it was a party of science, technology, and growth.

Take Teddy Roosevelt—a progressive if there ever was one. One of the signature achievements of his administration was the Panama Canal. This was a massive engineering project, a triumph of hydraulic engineering technology, celebrated at the time as the 13th Labor of Hercules. When FDR launched the New Deal, one of his signature projects was the Tennessee Valley Authority, which created hydroelectric dams to provide electricity for an entire region. And JFK, of course, is the president who called for putting a man on the Moon—one of the greatest technological achievements not just of its era, but of all time. When JFK gave his famous speech about the Apollo program (the one where he said “we choose to go to the Moon”), he put it in the context of the grand story of human progress. He invoked that narrative to inspire the people and justify his aims.

The Moon landing, in 1969, was a peak moment for America: literally the highest we had ever reached. But after that, something changed.

The children of the ‘60s were starting to see technology and growth as responsible for some of the worst problems of the 20th century, such as environmental damage and the horrors of war. Growth had created pollution and acid rain. Technology had created machine guns, chemical weapons, and the atomic bomb.

But instead of just being anti-pollution and anti-war, the new left decided to become anti-technology and anti-growth. And so a party of science, technology, and growth became just a party of science.

That was a mistake, a costly historical error that we should now correct.

What has 50 years of the anti-growth mindset gotten us? Stagnation and sclerosis. We can’t build anything in this country anymore. We can’t build the homes we need to make our cities affordable. We can’t build the transit we need to make those cities livable. We can’t build energy infrastructure, either generation or the power lines to connect it to the grid.

Without economic growth, we don’t have the engine that raises the standard of living for everyone and helps people lift themselves out of poverty. Without growth, people feel they are playing a zero-sum game—and they turn to exclusion. “No, you can’t move to my neighborhood, it’s too crowded.” “No, you can’t immigrate, you’re going to steal my job.” We want abundance thinking instead: “Yes, move to my neighborhood—we’ll build more homes!” “Yes, immigrate here—there’s so much work to be done, we need all the help we can get.”

I think people have grown weary of the anti-growth mindset, weary of stagnation and sclerosis. So I’m glad to see that abundance is now a politically winning issue. And I would love to see it be a new direction for the left.

But the right is also moving to embrace technology and growth—or rather, they’re doing that with one hand, while fighting those things with the other. On the one hand, they’ve embraced technologies like nuclear power, supersonic flight, and AI. On the other hand: They’re fighting vaccines, one of the greatest technologies ever invented. They’re defunding research into mRNA, one of the most promising genetic engineering techniques. They’ve disrupted research funding broadly. They’ve disrupted immigration, including high-skilled immigration, which is one of our best talent pipelines into R&D. And they’ve put tariffs on everything, which almost any economist will tell you is hurting affordability and slowing growth.

So the right has at best a mixed record on abundance. The left can still be the party of abundance, if it wants to be.

But it won’t be easy. It will be uncomfortable. Because to become the party of abundance requires truly embracing technology and growth—and the left has developed an allergic reaction to those things. So there’s some work to be done: some lessons to be unlearned, some old habits to be broken.

But I’m excited to help with that work, and I invite you to talk to me about it. I’m eager to see the party of science become once again a party of science, technology, and growth. And I look forward to the day when progressives once again believe in progress.

PS: I would also like to see the right become, more consistently, the party of abundance. I would like to see both parties competing to be the party of abundance! At some point I may write up an analogous “message to the right.”

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

• From Progress Conference 2025

• My writing

• Jobs

• Fellowships & workshops

• Fundraising

• New publications and issues

• Queries

• Announcements

For paid subscribers:

• From Vitalik

• Other top links

• Voices from 2099

• Jared Isaacman sworn in as head of NASA

• Whole-body MRI screening?

• AI does social science research

• AI writes a browser

• AI does lots of other things

• AI could do even more things

• AI and the economic future

• AI: more models and papers

• AI discourse

• Waymo

• Health/bio

• Energy & manufacturing

• Housing

• Other links and short notes

• Politics

• Gratitude

• New Horizon photographs Pluto’s mountains

• Charts

• Quotes

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Progress in Medicine, a career exploration summer program for high schoolers

Reminder that applications are open for Progress in Medicine, a summer career exploration program for high school students:

People today live longer, healthier, and less painful lives than ever before. Why? Who made those changes possible? Can we keep this going? And could you play a part?

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.

Join a webinar to learn more on February 3. Or simply apply today! Many talented, ambitious teens have applied, and we’re already starting interviews. Priority deadline: February 8th.

From Progress Conference 2025

A few more batches of video:

• American Dynamism: Progress begins at home. New industrial paradigms, anti-fragile and future-proof regulation, the case for hardtech, the grid as the bottleneck, and a call for US innovation in shipbuilding: the American Dynamism talks, and some of the writing published afterwards

• Culture: Inspiring progress in stories and institutions. How do you create a culture of progress? What is it—and why does it matter? , , , Alexander Berger, in discussion with Foster, and I shared our thoughts

My writing

• My essay series The Techno-Humanist Manifesto has concluded, and you can read the whole thing online. I’m pleased to announce that the series will be revised for publication as a book from MIT Press (expected early 2027)!

• 2025 in review. My annual update, including my reading highlights

• How to tame a complex system. Nature is a complex system, I am told, and therefore unpredictable, uncontrollable, unruly. I think this is true but irrelevant: we can master nature in the ways that matter

Jobs

• IFP is hiring an editor: “seeking a curious, entrepreneurial, and opinionated lover of writing. … You’ll partner with our policy experts to turn their drafts into pieces that change minds across DC. You’ll coach both new and experienced writers to become better communicators. You’ll also innovate on our systems to help the team consistently ship great products.” (via @rSanti97)

• OpenAI is hiring a Head of Preparedness: “If you want to help the world figure out how to enable cybersecurity defenders with cutting edge capabilities while ensuring attackers can’t use them for harm, ideally by making all systems more secure, and similarly for how we release biological capabilities and even gain confidence in the safety of running systems that can self-improve, please consider applying. This will be a stressful job and you’ll jump into the deep end pretty much immediately” (@sama)

• Anthropic is hiring someone to work with Holden Karnofsky on his projects, “in particular re Anthropic’s ‘Responsible Scaling Policy’. Likely v high impact for the right person” (@robertwiblin)

• Anthropic is also hiring for their education team: “These are two foundational program manager roles to build out our global education and US K-12 initiatives” (@drew_bent)

• See also Merge Labs and Edison announcements, below.

Fellowships & workshops

• MATS 10.0 (Machine Learning Alignment & Theory Scholars): “Come work with Seth Donoughe and me this summer on AI-biosecurity! We will be mentoring projects on threat models, frontier evaluations, and technical safeguards.” (@lucafrighetti)

• Beyond the Ivory Tower, via : “an intensive two-day writing workshop for academics, taught by James Ryerson, a longtime editor at the New York Times. … Our alumni have published hundreds of pieces in outlets from the Atlantic to Aeon to the Wall Street Journal. … I think historians and economists of technology and innovation would be a great fit.” Apply by March 1

Fundraising

Nonprofits that would make good use of your money:

• Lightcone Infrastructure: “We build beautiful things for truth-seeking and world-saving. We run LessWrong, Lighthaven, Inkhaven, designed AI-2027, and so many more things. All for the price of less than one OpenAI staff engineer ($2M/yr)” (@ohabryka)

• Transluce: “a nonprofit AI lab working to ensure that AI oversight scales with AI capabilities, by developing novel automated oversight tools and putting them in the hands of AI evaluators, companies, governments, and civil society.” OpenAI co-founder Wojciech Zaremba calls them “one of the strongest external AI safety orgs—on par with METR and Apollo.” (@woj_zaremba)

• And of course, us

New publications and issues

• Works in Progress, Issue 21. The triumph of logical English; a measure of monopoly that actually works; getting artificial gravity in space with current tech; why the developing world needs more roads; and more. (via @s8mb)

• Abundance and Growth Blog, from Coefficient Giving (formerly Open Philanthropy). Writers include , , and (via @mattsclancy)

• Stefan Schubert launches The Update, “a newsletter in the spirit of my tweets.” (@StefanFSchubert) Stefan is the most anti-hype, anti-BS account in my timeline

Queries

• “Who is best to read / follow for advice on using AI e.g. Claude Code? especially interested in: productivity and todo wrangling (especially for the distractable); research assistance; editing; learning” (@rgblong)

Announcements

• Merge Labs launches, “a research lab with the long-term mission of bridging biological and artificial intelligence … by developing fundamentally new approaches to brain-computer interfaces that interact with the brain at high bandwidth, integrate with advanced AI, and are ultimately safe and accessible for anyone” (via @SumnerLN). SamA is listed as a co-founder. Merge grew out of the Forest Labs FRO; Convergent Research notes that the tech is ultrasound-based and that they’ve raised over $250M. (!) And of course, they’re hiring

• Edison, the for-profit spinout of Future House, has raised $70M: “we are integrating AI Scientists into the full stack of research, from basic discovery to clinical trials. We want cures for all diseases by mid-century.” They are hiring software engineers, AI researchers, scientists, and business operators. ”Our goal is to accelerate science writ large.” (@SGRodriques)

• Science Corp. announces Vessel (WIRED). Vessel is “a project focused on rethinking perfusion from the ground up, extending how long life can be sustained, and expanding what’s possible in transplantation and critical care. Life-support technologies like ECMO can keep patients alive when the heart or lungs fail, but they aren’t designed for long-term use. Vessel exists to close the gap between what perfusion technology is fundamentally capable of and how it is deployed in daily practice.” (@ScienceCorp_)

• Fuse Energy raises a $70M Series B. Honestly hard to figure out exactly what they do, but it seems to involve deploying solar and batteries, and maybe later doing fuel synthesis and fusion? Anyway I liked this from (presumably) one of the founders: “Energy is the fundamental source for human progress. But for the last 30 years, we’ve been told that the future requires sacrifice ‘use less, be less, restrict yourself’. No one should have to trade a good life today for the chance of a better tomorrow.” (@alanchanguk)

• Confer is a new LLM app from Signal creator Moxie Marlinspike, where your conversations are end-to-end encrypted. Confer goes to impressive lengths to ensure that the LLM server doesn’t, e.g., exfiltrate your data somewhere. The entire server image is signed and is auditable on a public ledger. The client verifies the signature before chatting. The server also runs in a VM that is isolated from its host at the hardware level.

• Gordian Bio announces “a research collaboration with Pfizer to apply Gordian’s in vivo mosaic screening platform to obesity target discovery.” (@GordianBio) Story in Business Wire

Read more

Safety, security, and resilience against these hazards is not the default state of humanity. It is an achievement, and in each case it came about deliberately.

A striking theme from the history of such achievements is that there is rarely if ever a silver bullet for risk. Safety is achieved through defense in depth, and through the orchestration of a wide variety of solutions, all working in concert.

Recently, in a private talk, I gave a historical example: the history of fire safety. It resonated so strongly with the audience that I’m writing it up here for wider distribution.

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Up until and through the 1800s, city fires were a great hazard. Neighborhoods were full of densely packed wooden structures without flame-retardant chemicals, fire alarms, or sprinkler systems; open flames were used everywhere for lighting, heating, and cooking; there were no best practices in place for storing or handling combustible materials; fire departments lacked training and discipline, and they worked with inadequate equipment and insufficient water supply. All this meant that large swaths of cities regularly burned to the ground: Rome in AD 64; Constantinople in 406; London in 1135, 1212, and 1666; Hangzhou 1137; Amsterdam 1421 and 1452; Stockholm 1625 and 1759; Nagasaki 1663; Boston 1711, 1760, 1787, and 1872; New York 1776, 1835, and 1845; New Orleans 1788 and 1794; Pittsburgh 1845; Chicago 1871; Seattle 1889; Shanghai 1894; Baltimore 1904; Atlanta 1917; and Tokyo 1923 are just a short list of the most well-known.

Fire is not unknown today, but it is far less lethal, and great city fires consuming multiple blocks are largely a thing of the past. Today, if you see a fire truck on the street with its sirens blaring, it is more likely to be responding to an emergency medical call than to a fire. Even if the truck is responding to a fire call, it is more like likely to be a false alarm than an actual fire.

How was this achieved?

Better fire-fighting. Pumps to douse fires with water have existed since antiquity, but for most of history they were man-powered. With the Industrial Revolution, we got steam-powered and later diesel-powered pumps that can deliver much greater throughput of water, and at greater muzzle velocities to reach higher floors of buildings. In the 20th century, horse-drawn fire engines were replaced with fire trucks that could get around the city faster and more reliably.

A high-throughput engine, however, needs a high-volume source of water. In ancient and medieval times, water was provided by the bucket brigade: two lines of people stretching from the fire to the nearest lake or river, passing buckets by hand in both directions. A much better solution was the fire hose, invented in the late 1600s (and improved in strength and reliability over the centuries through better materials, manufacturing, quality control). The fire hose not only allowed a fire engine to be connected to a water source, it also allowed the fire-fighters to get in closer to the base of the fire and dump water directly on it, which is far more effective than just spraying the building from the outside.

A fire hose can be inserted into a natural water source like a pond or cistern, but one of these might not be handy nearby, and they aren’t pressurized, so all the pumping force has to be supplied by the fire engine. They also contain debris that can clog the intake and block the flow. Eventually, cities were outfitted with regularly spaced fire hydrants connected to the municipal water supply. A water system designed to supply city residents with daily needs, however, often proved inadequate in an emergency; these systems had to be upgraded to supply the large bursts that big fires demanded. This is a matter of serious engineering: 19th-century fire-fighting journals are full of technical details and mathematical calculations attempting to precisely nail down questions of optimal hydrant distribution or nozzle size, or the pressure required to force a certain volume of water to a given height at a particular angle.

Finally, fire-fighting teams needed improved organization. Traditionally, fire-fighters were volunteers, often rowdy young men with no training or discipline (there is at least one story of a fist fight breaking out between two rival teams who arrived at a fire at the same time). In the 19th century, fire departments were professionalized and were organized more formally, along almost military lines, as befits responders to a life-threatening emergency.

Faster alarming. Fire, like many of our most dangerous hazards, is a chain reaction. Chain reactions grow exponentially, which means early detection and response time are crucial. Traditionally, fires were spotted by watchmen, either on patrol or from a watch tower, who then had to run, shout, or ring bells or other alarms to alert the fire fighters.

Electronic communications, first via telegraph and later telephone, provided a much faster way to get the alarm to the fire department. The telephone lines could be busy, however, so in the 20th century the 911 emergency response system was created to provide a priority channel.

Far better than having a human sound the alarm, however, is doing it automatically. Smoke detectors and other automatic fire alarms caused the fire to “tell on itself,” saving valuable minutes or even hours. Even more effective was the automatic sprinkler, which combined detection and response into one near-instant system.

Reducing open flames. Better than fighting fires, of course, is preventing them. Before the 20th century, flames from candles and oil or gas lamps provided lighting, and fires in wood- or coal-burning stoves provided heat for building, cooking, and industrial processes. The Great London Fire of 1666 is said to have started in a baker’s shop, Copenhagen 1728 was blamed on an upset candle, Pittsburgh 1845 came from an unattended fire in a shed. Even worse, people often kept these fires going unattended overnight, because even starting a fire was difficult before the invention of matches. Medieval regulations required city- and town-dwellers to cover their fires after a certain hour (the word “curfew” derives from the French couvre-feu, “cover the fire”).

Electric lighting and heating greatly reduced this risk. Electric sparks, however, were also a fire hazard—and initially, electrical installations increased rather than decreased fire risk, owing to shoddy electrical products, fixtures, and wiring. The solution here was improved standards, testing, and certification: the fire insurance companies created an organization, Underwriters Laboratories, specifically for this purpose, and its label became a highly valued marker of quality. (I told the story of UL in The Techno-Humanist Manifesto.) Today, our electronics and appliances are so safe that arson is the cause of more fires than either of them.

Safer construction. Preventing fires by eliminating the sparks or flames that ignite them is like lining up dominoes and then trying hard to make sure the first one never gets tipped over: a fragile proposition. Far more robust is to remove their fuel. Wood construction was widespread through the late 19th century, even in dense city neighborhoods: Daniel Defoe wrote that before the Great London Fire of 1666, “the Buildings looked as if they had been formed to make one general Bonfire.”

Today our cities are built of incombustible brick, stone, and concrete. Building codes enforce safety practices to slow the spread of fire both within a building and between buildings. They specify the quality of materials such as brick, mortar, cement, timber, and iron, including the specific tests it must pass; the materials for walls, and their minimum thickness; and the height of non-fireproof structures; among many other details.

Saving lives. By the early 1900s, in advanced societies, the problem of large city fires that spread over many blocks had mostly been solved; fires were often contained to a single building. That was small comfort, however, for those trapped inside the building. Tragedies such as the Iroquois Theatre Fire of 1903 and the Triangle Shirtwaist Fire of 1911 taught us valuable lessons. Exit paths must be adequate to evacuate entire buildings. Doors must remain unlocked, and they should open outwards in case a stampede presses up against them. Fire-resistant material must be used not only for the construction of the building, but for the interior: sofas, beds, curtains, carpets, wallpaper, paneling. Again, building and safety codes specify and enforce these practices.

So fire safety was achieved through the combination of:

• General-purpose technologies: engines, electronic communications, electric light and heat

• Specific inventions: fire pumps, fire hose, fire alarms

• Infrastructure: municipal water supply, telephone lines

• Standards, testing and certification: of electrical products, fire preventing and fire-fighting equipment, building materials, etc.

• Law: building codes and other fire safety codes

• Education and training: in fire departments, among the public

This is a general pattern. Safety requires:

• both prevention and “cure”

• both technical and social solutions

• among technical solutions, both products and systems

• among social solutions, both education and law

We see the same thing in other domains. Road safety, for instance, was achieved through seat belts, anti-lock brakes, crumple zones, air bags, turn signals, windshield wipers, traffic lights, divided highways, driver’s education, driver’s licensing, and moral campaigns against drunk driving. No silver bullet.

When we think about creating safety and resilience from emerging technologies, such as AI or biotech, we should expect the same pattern. Safety will be created gradually, incrementally, through multiple layers of defense, and by orchestrating a wide combination of products, systems, techniques, and norms.

In particular, there is a line of thinking within the AI safety community that tends to dismiss or reject any proposal that isn’t ultimate—fully robust against the most powerful imaginable AI. There’s a good rationale for this: it’s easy to fall victim to hope and cope, and to lull ourselves into a false sense of security based on half-measures that were “the best we could do”; vulnerabilities are often invisible and are revealed dramatically in disasters; such disasters may be sufficiently catastrophic that we can’t afford to learn from mistakes. But I find the all-or-nothing thinking about AI safety counterproductive. We should embrace every idea that can provide any increment of security. History suggests that the accumulation and combination of such incremental solutions is the path to resilience.

• Bruce Hensler, Crucible of Fire (2011)

• Harry Chase Brearley, The History of the National Board of Fire Underwriters (1916) and Symbol of Safety (a history of Underwriters Labs, 1923)

• Dennis Nolan, Fire Fighting Pumping Systems at Industrial Facilities (2011)

• W. Fred Conway, Those Magnificent Old Steam Engines (1996)

• Ramon Klitzke, “Roman Building Ordinances Relating to Fire Protection” (1959)

• Lionel Frost and Eric Jones, “The fire gap and the greater durability of nineteenth century cities” (2007)

• John Rainbird, “The Vigiles of Rome”

• Alex Tabarrok, Firefighters Don’t Fight Fires

Historical and primary sources:

• Charles Frederick T. Young, Fires, Fire Engines, and Fire Brigades (1866)

• James Bugbee, “Fires and Fire Departments” (1873)

• James Braidwood, “On the Means of rendering large supplies of Water available in cases of Fire; and on the application of manual power to the working of fire engines” (1844)

• Report of the Commissioners Appointed to Investigate the Cause and Management of the Great Fire in Boston (1873)

• Clarence Goldsmith, “The Use of Pumpers at Fires” (1930) and “Efficient Utilization of Water for Fire Fighting” (1939)

• Journal of the New England Water Works Association, Vol 7 Issue 1

• National Board of Fire Underwriters, Model Building Code (1905)

• Maurice Webster, “What is ‘Fireproof?’” (The Atlantic, 1946)

• FEMA, “America Burning” (report)

• Charles II, An Act for rebuilding the City of London (1666)

We didn’t originally plan to have a “metascience track” at Progress Conference 2025, but when there were a half dozen excellent speakers we knew we had to put those talks next to each other to show distinct perspectives and models for scientific progress. Here are select talks from the conference and related writing.

Track dispatch

Inside the movement that’s rewriting how we do science by Smrithi Sunil

In this essay for Big Think’s special issue The Engine of Progress, RPI fellow reports on the “metascience track” at the conference.

Ultimately, I find that what’s missing from today’s research funding ecosystem is not just more money, but incentives that encourage healthy competition between funders. Research funding in the U.S. remains remarkably one-dimensional. Most public science is funded by the National Institutes of Health (NIH) and, to a lesser extent, the National Science Foundation (NSF), agencies that overwhelmingly fund small, principal investigator-led grants designed for an earlier era of individual inquiry. This structure rewards safety over exploration and leaves little room for institutional or methodological experimentation. Unlike in venture capital, where investors compete to identify and back the most promising, high-risk ideas, science funders face no comparable pressure to seek out unconventional bets or refine their own models.

The result is a stagnant funding ecosystem that selects for conformity rather than creativity. A truly dynamic research economy would mirror the process of evolution: many funding mechanisms, each taking different approaches, competing for the best ideas and talent. If we can build that kind of diversity in our funding landscape, with real competition and variation, we might restore the adaptive spirit that science depends on.

Talk videos

PDB 2.0: a metascience experiment in scientific acceleration with AI

From , scientist, CEO of Arcadia Science, and co-founder of Astera institute: “We’re moving into an age in which agents are our partners across all aspects of science. Machines will systematically process data, spot patterns, propose experiments, and even generate hypotheses across more and more of our work. A technological shift of this magnitude requires a similarly big shift in what science we pursue and how we go about it. Two major unanswered questions are 1) how we more rationally determine what data might be important to generate for AI-assisted discovery and 2) how we effectively design and scale those data systems. We recently funded and launched a new structural biology initiative called The Diffuse Project, which is aimed at enabling the next frontier models in protein biology — predicting not only how proteins are structured but how they move, which is central to their function. Diffuse gives us a golden opportunity to experiment with what science could look like in the future, empirically rethinking scientific systems and the role scientists play in them. Come learn more about the biophysical questions driving The Diffuse Project as well as the metascience levers we are testing in the process.”

Designing a new agency for progress, not process

How do you build a new national R&D agency from the ground up? In January 2023, the UK launched the Advanced Research and Invention Agency (ARIA) with a $1B budget and a bold mandate: fund breakthroughs to drive the future of economic and societal progress. Watch founding CEO Ilan Gur as he pulls back the curtain on ARIA’s first three years. He will share stories behind the core, and often contrarian, DNA-level decisions that shaped the agency’s portfolio and operating model.

Learn why ARIA bets on people before projects, how ubiquitous clean energy will unlock a new materials and manufacturing paradigm, and the value of embedding a product team at the center of a government agency. This is a candid look into both scientific and institutional experimentation for anyone interested in building breakthroughs for a better future.

How to Prioritize Capabilities for the Intelligence Age

What does it mean to do high-impact science in the midst of massive transition? What capabilities are we missing now that we need to develop rapidly, and which do we need to be ready for what is coming?

Anastasia Gamick is the President and co-founder of Convergent Research, a mission control for frontier technology. Convergent has secured almost $400 million to design, launch and operate a new kind of organization for scientific and technological progress—the Focused Research Organization (FROs)—across a range of fields, including neuroscience, mathematics, astronomy, and climate.

In this talk, Anastasia will share Convergent’s outlook on the intelligence age, drawing lessons from the dozen or so FROs that Convergent has worked to launch.

Bell Labs Xs: Reimagining Corporate Research for the 21st Century

Jeffrey Tsao (formerly at Sandia National Laboratories) has decades of experience in fundamental research. In this talk, he discusses how corporate research once fused inspiration from real‑world utility with fundamental learning and discovery, driving breakthroughs from semiconductors to information theory. Today that tradition lies dormant—yet its need has never been greater. Tsao presents a vision for “Bell Labs Xs”: a new funding and organizational architecture to revive use‑inspired learning and discovery, mobilize industry for the public good, and raise America’s R&D productivity to new heights.

Don’t Put All Your Labs in One Basket

Science policy is, at its core, an investment strategy. Over time, the balance of funding across institutions (universities, companies, nonprofits, and government labs), and mechanisms (project grants, investigator awards, institutional block grants, training programs, prizes) has shifted in ways that shape what kinds of discoveries we get. In this talk, (co-founder of the Institute for Progress), looks at the past, present, and future of our scientific “portfolio” and discuss how policymakers should diversify it to maximize long-term progress.

Soon after the conference, IFP re-launched , a newsletter about metascience ‘aimed at increasing the speed and effectiveness of American science"”

We are publishing videos of conference talks over the next several weeks. We’ll post videos on the RPI YouTube channel. 2025 talks will all be added to this specific playlist here.

Thanks to Big Think our conference media partner, for producing all these videos and The Engine of Progress, a special issue of Big Think exploring the people and ideas driving humanity forward.

Though we didn’t formalize a “culture track” at Progress Conference 2025, several excellent talks covered how to teach, tell stories, and inspire a culture of progress. And ’s remarks touched on similar themes. Here are some selected talks from the conference and related writing.

Conference dispatch

The common thread of progress by Jason Crawford

In the introduction to Big Think’s special issue The Engine of Progress, RPI founder discusses the unity of one goal of the progress movement: to move humanity forward.

All of us — from technologists to policy wonks to storytellers — are contributing in our own way to the grand project of human progress. There is strength in realizing that and reminding ourselves of it. By uniting in a progress movement, we can each see our work as part of a larger whole. The NEPA writer isn’t just pushing for permitting reform, and the materials researcher isn’t just trying to improve the efficiency of semiconductor manufacturing — all are working for the advancement of humanity. Each of us has our hill to take. Seeing our efforts that way can inspire and rejuvenate us, and salve the loneliness that often accompanies ambitious work.

Talk videos

Dan Wang in conversation with Kmele Foster

Jason Crawford’s Opening Remarks

The role of philanthropy in promoting progress (and safety)

In this talk, Alexander Berger, CEO of Open Philanthropy, discusses a number of examples of how philanthropic funding can help accelerate progress, especially in science and technology, ranging from the earliest days of the YIMBY movement to recent cases of boosting metascience and innovation policy. He also touches on the relationship between progress and safety and why there is a strong philanthropic case for funding both.

Worth reading along with this talk is Why We Fund Both Progress and Safety by Alexander Berger and Emily Oehlsen.

Building a culture of progress

, Editor-in-Chief of The Argument, speaks about a culture of progress. She argues that we need a new generation of cultural entrepreneurs for progress, people like Martin Luther, Charles Darwin and Adam Smith, hailed by Nobel Price winner Joel Mokyr as helping kick-off the culture of progress behind the industrial revolution. She urges progress-minded people and technologists to realize that persuasion is both necessary and possible. Tech isn’t just cool or world-changing or necessary for the US to succeed vs. China—all arguments that resonate with VCs and fellow tech people. Instead, we should emphasize in our communications that tech’s fundamental worth comes from improving human lives and flourishing—arguments that resonate with the broader population who otherwise might oppose progress and slow it down.

How the System Works

Listen to Charles C. Mann, author of the “How the System Works” series in The New Atlantis, and , celebrated author of The Future and Its Enemies and The Fabric of Civilization, discuss the overlooked burden and duty of maintaining past progress.

In the last 150 years, every developed nation has constructed vast systems that provide food, water, energy, and health-care to their citizens. They are high on the list of the great accomplishments of our civilization.

Like Europe’s great cathedrals, the electric grid, the public-water supply, the food-production network, and the public-health system were constructed by the collective labor of thousands of people over many decades. They are the cathedrals of our secular era. But unlike those cathedrals, they don’t inspire caravans of gawping tourists or great works of art. No visitors clamor for tickets to visit electric reliability centers or pump-storage stations. No poets celebrate the sewage-treatment plants that prevent them from dying of dysentery.

This ignorance is not just the province of dunderheads and yahoos. Even the educated elite—such as, just possibly, the attendees of conferences like this one—know all too little about the vital systems that undergird their daily lives. And this is a problem. All of these systems, which represent so much of the progress in our material well-being, need to be maintained and revamped to accommodate rising populations, technological advances, increasing affluence, and climate change. Schools should be, but are not, teaching students why it is imperative to join this effort. And the bitterly divided partisans of our political wars are doing anything but pay attention to the infrastructure that is the basis of our collective economic prosperity.

Closing: A Culture of Progress

Jason closes out the conference with some remarks on why culture matters for progress, especially how it guides young talent to important problems, and what the Roots of Progress Institute is doing to address that.

Other media

Why culture may be our most powerful lever for progress by Beatrice Ekers

In an op-ed, Beatrice Ekers (Foresight Institute, Existential Hope) writes about the “invisible infrastructure” of culture that shapes the future.

Progress rests on multiple layers. At the top is hard infrastructure: bridges, laboratories, power grids, and rockets. Beneath that lies soft infrastructure: laws, institutions, and systems that make the hardware useful. And beneath both rests a third layer: invisible infrastructure. Culture. The stories, narratives, and memes that determine which futures feel plausible and worth pursuing.

This layer is easy to overlook because it doesn’t leave behind blueprints or legislation. Yet history shows its importance. The Enlightenment was not an engineering project but a cultural shift. In coffee houses and pamphlets, curiosity and reason became public virtues. That shift created the conditions for modern science and the Industrial Revolution. The machines were built in workshops, but the idea that machines could transform the human condition was built in culture first.

Watch The moment every civilization fears: the growth plateau with Jason Crawford

“People got skeptical, fearful, doubtful of the very idea of progress in the 20th century and we allowed that to slow down progress itself.”

In part 3 of the Techno-Humanist Manifesto, Jason Crawford explores what a Culture of Progress could look like. Especially in Chapter 11, The Progress Agenda:

A basic understanding of industrial civilization—how it works and why we need it—should be considered an essential outcome of an education. Call it “industrial literacy.”

Industrial literacy could start in grade school. Students could learn basic facts relevant to economic life: what crops need in order to grow, what farmers do, and how pests or disease can damage the harvest; what types of materials our world is made of, and how things like metal, glass, ceramic, and textiles are produced; different sources of energy, how to harness them, and how to direct forces using simple machines such as gears and levers. They could learn the stories of specific inventions and inventors: Edison and the light bulb, Stephenson and the locomotive, Bell and the telephone. They could learn basic facts about economic history: that people once lived without heating, air conditioning, plumbing, or electricity; that most people worked manual jobs, and mostly on farms; that severe disease was common, especially in childhood. They could engage in many hands-on activities to get first-hand knowledge of historical and modern processes: gardening, weaving, carpentry, paper-making and printing, navigation with a compass. They could try going a day eating only food they had grown themselves, wearing only clothes they had sewn, or using light only from candles they had dipped.

We are publishing videos of conference talks over the next several weeks. We’ll post videos on the RPI YouTube channel. 2025 talks will all be added to this specific playlist here.

Thanks to Big Think our conference media partner, for producing all these videos and The Engine of Progress, a special issue of Big Think exploring the people and ideas driving humanity forward.

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.

So here’s mine. At least I can say I’ve been doing it for as long as Dan: nine years running (proof: 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024). As usual, this is more of a personal essay/reflection, and not so much of an organizational annual report, although I will start with some comments on…

RPI

Over the last three years, the Roots of Progress Institute has gone from “a guy and his blog” to a full-fledged cultural institute. This year we:

• Held our second annual Progress Conference, featuring speakers including Sam Altman, Blake Scholl, Tyler Cowen, and Michael Kratsios (Director, OSTP). The conference has become the central, must-attend event for the progress community: it is sold out each year, with hundreds on the waitlist, and some attendees report it is literally the best conference they have ever attended.

• Inducted the third cohort of our progress writers fellowship, bringing the total to 74 fellows. Our fellows are having impact: Dean Ball helped draft the Trump administration’s AI Action Plan, Madeline Hart has co-authored a book with the CTO of Palantir on revitalizing the American defense industry, Ryan Puzycki helped legalize single-stair buildings in Austin (a key YIMBY reform), and three other fellows have recently had opinion pieces in the NYT or WSJ.

• Announced our first education initiative: Progress in Medicine, a high school summer career exploration program. I’ve previewed the content for this course and I’m jealous of these kids—I wish I had had something like this when I was a teenager!

And the best part about all of these programs is that I don’t have to run any of them! I have a fantastic staff at RPI who deserves credit for all of these, from design to execution: Emma McAleavy, Ben Thomas, Yel Alonzo, and especially Heike Larson—thanks to them for making our programs a success every year.

We’re a 501(c)(3) nonprofit, supported mostly by donations. There’s still time to get in a last-minute end-of-year contribution! The simplest way to support us is to upgrade to a paid (or founding!) subscription:

Subscribe now

Or see here for other ways to support us:

Donate now

Huge thanks to all those who have already given this year!

My writing

Most of my writing effort this year was devoted to finishing The Techno-Humanist Manifesto, an essay-series-cum-book laying out my philosophy of progress. In 2025 I published the last 14 (out of 21) essays in the series, you can read them all here. Also, as just announced, I’ve signed with MIT Press to publish a revised version of the series in book form. The manuscript is out for comment now, and (given typical publishing schedules) I expect the book to launch in early 2027.

I also wrote eight other essays, and ten links digests. I put the links digest on hold after May in order to focus on finishing the book, but I’m working on bringing it back. All subscribers get the announcements and opportunities at the top, but the rest of the digest is paywalled, so subscribe now to get the full version.

The most-liked posts here on Substack were:

• The future of humanity is in management

• How sci-fi can have drama without dystopia or doomerism

• The Unlimited Horizon, part 2

• The Spirit We Lost, part 1

• In defense of slop

The most-commented posts were:

• How much does it cost to back up solar with batteries?

• We should install a thermostat on the Earth

• Where is the YIMBY movement for healthcare?

My longest post, at over 8,400 words, was:

• The Progress Agenda

I now have well over 55,000 subscribers on Substack, up over 68% YOY.

Social media

Here are some of my most-liked posts and threads of the year:

• One model for Elon is that he is perpetually operating in wartime mode: Move as fast as possible, bring overwhelming force, extract superhuman effort, accept collateral damage. A thread. For some reason many people seemed to think I was praising or defending this: I wasn’t, just observing.

• Grocery stores have price transparency and open competition, which health services don’t (replying to a wrong-headed comment by Rep. Jake Auchincloss)

• Everything has to be invented—even buttons

• The moment the Boom XB-1 test plane went supersonic

• “Daddy, why is a hundred a big number?”, and other difficult questions from my 3yo

• “On paper, titanium should be the world’s #1 structural metal”

• When Tyler Cowen just flat-out says that tariffs are bad, you know they are very straightforwardly bad

• A great moment from a legendary interview with Morris Chang of TSMC, on the Acquired podcast

• Waymo achieves 80–90% reduction across all crash/injury metrics. If there were some intervention that achieved this, safety advocates would be demanding its wholesale adoption immediately, even if it were costly or inconvenient

You can join well over 40,000 people who follow me on Twitter, or find me on your favorite social network; I’m on pretty much all of them.

Speaking and events

Like last year, I tried to mostly say no to events and speaking gigs this year, but there were a few I couldn’t refuse. Some highlights of the year:

• I spoke at “d/acc Day” alongside Vitalik Buterin, Juan Benet, Mary Lou Jepsen, Allison Duettmann, and others. My talk was “d/acc: The first 150 years”: a whirlwind tour of how society has thought about progress, decentralization and defense over the last century and a half

• I gave a short talk at Social Science Foo Camp titled “The Fourth Age of Humanity?”, based on ideas that I later wrote up in The Flywheel

• I did a fun Interintellect salon with Virginia Postrel based on her essay “The World of Tomorrow”

• I hosted a discussion series at Edge Esmeralda with the aim of envisioning the future. Each day there was a ~90-minutes session with a theme like AI, health & bio, or energy

• I went to Mojave to watch the first supersonic flight of the Boom XB-1 test plane. Here’s some video I took of the plane taxiing down the runway, and then the pilot getting out after landing and shaking hands with Boom founder Blake Scholl

In 2026 I hope to do more travel, events and speaking. But maybe I’ll just hole up and write some more.

Reading

I put my monthly “what I’ve been reading” updates on hold at the end of 2023 (!) in order to focus on the book. I’d like to bring these back, too. For now, here are some the highlights from my reading this year (that is, things I thought were interesting and valuable to read, not necessarily things I “liked” or agreed with).

The first few book recommendations are free, paid subscribers get the (much longer) full reading update and (below) some thoughts on what’s next for my writing:

Books and other book-length things I read

Or read at least most of:

Max Bennett, A Brief History of Intelligence. A history of the evolution of the brain, from the first animals through humans. It is organized into five major evolutionary steps—to oversimplify: the worm brain, the fish brain, the mouse brain, the monkey brain, and the human brain. This answered some key questions I had on the topic, very well-written, probably my favorite of the year. Hat-tip to @eshear.

Charles Mann, How the System Works, an essay series in The New Atlantis. It covers four of the major systems that form the foundation of industrial civilization and help deliver our modern standard of living: agriculture, water sanitation, electricity, and public health. Mann thinks of these pieces as the start of a curriculum that should be taught in schools—inspired by a group of “smart, well-educated twenty-somethings” who “wanted the hungry to be fed, the thirsty to have water, the poor to have light, the sick to be well,” but “knew little about the mechanisms of today’s food, water, energy, and public-health systems. They wanted a better world, but they didn’t know how this one worked.” Enjoyed this, recommended.

Brian Potter, The Origins of Efficiency, from Stripe Press, a history of manufacturing efficiency. Light bulbs used to cost ~$50 (adjusted for inflation), now they cost 50 cents; how did that happen? This is a comprehensive and very readable overview of the answer to that question and others like it.

Read more

“Techno-humanism” is my philosophy of progress, and THM is my statement of it. It consolidates and restates material I’ve used in previous essays and talks, in a more unified and coherent form. Still, even for my biggest fans, almost every chapter should have something new, including:

• The culture of progress we once had (pre-WW1), and the decline of the idea of progress in the 20th century

• The story of progress as a story of the expansion of human agency (despite seeming counterexamples like the poor nutrition of early agriculturalists or the inadvertent creation of new health and environmental hazards)

• The nature of human well-being, including my response to the paradox of stagnant happiness metrics

• My argument for radical anthropocentrism, against romantic environmentalism and in particular against the idea of any intrinsic value in nature; and my take on stopping climate change, reframing the goal as creating climate control

• The pattern of accelerating progress and what drives it; related, my take on “ideas getting harder to find”

• Some stories of unsung heroes of health and safety

• My vision for the technological future

• The progress agenda—the cause areas of the progress movement today

• How progress can be a grand project for humanity, and how it can give us a heroic ideal to emulate

I’m pleased to announce that the series will be revised for publication as a book from MIT Press. The manuscript is out for comment now, and (given typical publishing schedules) I expect the book to be available in early 2027. Stay tuned!

Founders, builders, and investors speaking at Progress Conference 2025 talked about how to close the gap in progress between software and the physical world, new regulatory paradigms, unlocking the grid from bottlenecking energy growth, and how manufacturing revolutions really work. Here are some selected talks from the conference and related writing.

Track dispatch

Physical dynamism and the immigrant’s edge by Afra Wang

In this essay for Big Think’s special issue The Engine of Progress, RPI Fellow Afra Wang () explores how immigrant founders are driving America’s new era of “physical dynamism.”

The founders building most aggressively come from places that followed radically different development paths than America — some spectacularly successful, others failed. They watched Mumbai expand and Shenzhen materialize from fishing villages. They internalized different baselines for what’s possible, and different nightmares of what could go wrong.

That wisdom matters. Immigrants not only bring non-American-centric mindsets and building speed. They carry lived experiences of both progress and collapse, which breeds a particular kind of vigilance. They see not only what America could become, but what it risks losing. Many arrived believing in promises America made to the world, and now they’re trying to hold the country accountable to those promises, to push it to live up to the dream that brought them here.

[…]

This optimism is rare in today’s America, where apocalyptic thinking dominates both left and right, where every technological advance gets framed primarily through its dangers. This is a simple yet encouraging belief that progress is good, necessary, and achievable.

Talk videos

Blake Scholl in conversation with Tyler Cowen

I think Concorde never should have been done and Apollo never should have been done.

and talked about the future of supersonic flight, the history of “moonshots” in aerospace, how to design a better airport, and much more.

Samuel Hammond (FAI) said “This was my Progress Conference highlight by far, from an otherwise stacked line-up. Information dense, fun, thought-provoking, inspiring even.”

American Dynamism & The Case for Hardtech

Erin Price-Wright (General Partner at a16z) talks about the hardtech infrastructure that underpins the American Dynamism movement, and what is still required to bridge the gap between the rapid progress in developing software versus building in the physical world.

Anti-fragile Regulation and the Infinite Frontier

Isaiah Taylor (founder and CEO of Valar Atomics) describes progress this way: “Progress is composed of smart people taking risks together. If you want to know where the frontier will form, ask yourself two questions: where are the smart people? Where are people taking risk? Wealthy nations tend to get disrupted not by losing the smart people, but by losing their risk-taking. America’s Founding Fathers built a solution to this problem into the US Constitution: Anti-fragile regulation. It’s time to use it.”

The grid is the bottleneck to the energy revolution

In this talk, (co-founder and COO of Base Power), covers a history of the grid, how it works, and what’s needed to upgrade it for the 21st century’s power demand. He highlights how the grid is ultimately the bottleneck to realizing AI and electrification as generation rapidly catches up to load growth.

The art of industrial leapfrogging

How do manufacturing revolutions really happen? (Speculative Technologies) unpacks a new paradigm: “Throughout history, new manufacturing paradigms have rewritten the global economic map. Mechanized weaving in Britain, systematized chemistry in Germany, and interchangeable parts in America didn’t just create new industries—they shifted the center of manufacturing gravity. Today, as America seeks to rebuild its manufacturing strength, we’re ignoring this historical lesson. Instead of pioneering the next breakthrough, we’re playing catch-up with tariffs, defense tech, and bottled 1950s vibes. This talk will unpack how manufacturing revolutions really work, why new manufacturing processes matter beyond jobs and geopolitics, and sketch out the paradigm shifts could unlock manufacturing abundance on Earth and beyond.”

Here’s a written version of the talk on the Spectech newsletter.

Future-Proof: How Regulators Can (And Must) Avoid Overfitting to Today’s New Tech

(VP at Astranis) explores new regulatory approaches to encourage innovation: “Despite the strawmen set up by today’s innovators, most regulators are not captured by yesterday’s incumbents—but focusing too narrowly on present-day tech can be just as dangerous. This talk explores how regulators can design rules that are robust to an unpredictable future, and avoid overfitting too narrowly on today’s Current Things.”

Out-Innovating Through Dual Use: The New Blueprint for U.S. Shipbuilding

Sampriti Bhattacharyya (founder & CEO, Navier) talks about maritime innovation: “America once led the world in shipbuilding, but today ranks 11th, producing less than 1% of oceangoing vessels as of 2025. This is not just a matter of national security, but of immense geopolitical and economic importance. The challenge is clear - and solvable. By leveraging dual-use technology and adopting a mindset of out-innovating rather than merely out-producing, the U.S. has a real opportunity to reclaim leadership in maritime innovation.”

Other writing

Were Concorde and Apollo good for the future of aerospace? by Blake Scholl

Blake also wrote an op-ed arguing that government-led “glory projects” ultimately hurt the aerospace industry they were meant to advance and proposing a path forward.

When private founders, not committees, set the vision, constraints become creative fuel. SpaceX succeeded where NASA stagnated, not because it had more money, but because it had far less and had to make every dollar count. Iteration replaced bureaucracy. The result wasn’t just a cheaper rocket; it was the rebirth of orbital progress after 50 years of stasis.

The same dynamic is playing out in supersonic flight. Boom is doing what Concorde couldn’t precisely because we’re doing it differently. We’re building for a market, not a political mandate. The goal isn’t to prove what’s possible once, but to make it possible for everyone — affordably, sustainably, and at scale.

When innovation is reborn under entrepreneurial leadership, progress becomes self-sustaining again. Space is opening. Supersonic is returning. And for the first time in decades, the future is accelerating.

Future-friendly regulation has a blind spot: the future by Christian Keil

In this op-ed, Christian explores the main strategies officials use when regulating new technologies and how they affect progress, and argues for regulators to consider the “Next Thing” instead of just the “Current Thing.”

Imagine if the people regulating computer hardware in the 1990s had, inspired by fears of youth video game addiction, decided to create rules that halted the development of GPUs the same way NRC rules did nuclear reactors or that only allowed incumbents to develop them. This could have delayed advancements that ultimately allowed us to use GPUs for artificial intelligence, cryptocurrency mining, and so much more.

[…]

I support regulators who focus on the future and who care about growth, about business, and about startups. But I believe it would be incredibly shortsighted for them to only consider technologies of which they are personally aware when thinking about tech neutrality. I also believe that even regulators who do a great job at promoting the technologies of today run a serious risk of stymieing — even if inadvertently — the technologies of the future.

If we are truly committed to modernizing our regulatory regimes and optimizing for the future, we need to do it the right way, considering not just the Current Thing but also the Next Thing — even if there’s no way to actually know what it’ll be.

Why wait for flying cars? Flying boats are already here by Sampriti Bhattacharyya

Sampriti also wrote a piece exploring recent progress in autonomy, hydrofoiling, nad and robotics.

The most extraordinary asset for our coastal cities is the one we have spent a century ignoring: the water itself. We have long treated it as a barrier to overcome, even as our cities grew more congested and housing was pushed ever farther from opportunity. This new generation of water transit can reverse that trend, collapsing commute times, unlocking new economic corridors, and reconnecting communities without the immense cost and consequence of new land-based infrastructure.

This is not a distant vision. The convergence of electric hydrofoils, autonomy, and robotics is happening at a pace unthinkable even a decade ago. And America, as the global leader in software, autonomy, and advanced manufacturing, is uniquely positioned to spearhead this transformation. If the 20th century was defined by the highway, the 21st will be defined by the cities that reclaim their waterways. They are not relics of our past. They are the highways of our future.

We are publishing videos of conference talks over the next several weeks. We’ll post videos on the RPI YouTube channel. 2025 talks will all be added to this specific playlist here.

Thanks to Big Think our conference media partner, for producing all these videos and The Engine of Progress, a special issue of Big Think exploring the people and ideas driving humanity forward.

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

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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

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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)

Read more

We define slop as “digital content of low quality that is produced usually in quantity by means of artificial intelligence.” … The flood of slop in 2025 included absurd videos, off-kilter advertising images, cheesy propaganda, fake news that looks pretty real, junky AI-written books, “workslop” reports that waste coworkers’ time… and lots of talking cats. People found it annoying, and people ate it up. … “AI Slop is Everywhere,” warned The Wall Street Journal, while admitting to enjoying some of those cats.

Slop touches a nerve today. When Meta announced a product to create massive amounts of AI-generated short-form video, presumably with no goal other than entertainment to capture clicks and eyeballs, even my generally pro-technology circles exploded in disgust and outrage. Now we have education slop, math slop, drug discovery slop, longevity slop, and “urbanist slop.” Slop exemplifies everything wrong with the modern era; it signifies the gap—some would say the chasm—between what technology enables and what promotes human well-being.

I have no praise for slop itself, but we can be more sanguine about it if we see it as a byproduct of a bigger and more important trend.

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People make things when the value of the thing exceeds the cost of creation. When the cost of creation in a medium is high, people are careful only to use it for high-value products. If a movie costs tens or hundreds of millions of dollars to make, you can’t afford to make a bad movie (or at least, not very many of them). You’re going to put a lot of effort into making it, and someone who holds the purse strings is going to have to decide if it’s good enough to fund.

Whenever the cost of creation in a medium falls, the volume of production greatly expands, but the average quality necessarily falls, because many of the new creations are low-quality. They are low-quality because they can be—because the cost of creation no longer prohibits them. And they are low-quality because when people aren’t spending much time or money to create something, they don’t feel the need to invest a lot in it. When you can quickly dash off a tweet, you don’t need to edit it or fact-check it, or even have correct spelling or grammar; when you can quickly create an AI illustration, you don’t need to hold it to high standards of composition, color, or even the right number of fingers. Hence slop.

The Internet lowered the cost of publishing to virtually zero, which enabled many low-quality blogs and other web sites. Social media made it trivial to put thoughts online, and made it much easier to find an audience, which enabled a vast amount more low-effort and low-quality posting. Now AI is arriving, and lowering the costs of creation itself, not just publication and audience-building. And it is enabling new and different forms of slop.

But along with slop, lower costs and barriers get us:

• More experimentation. It can be hard to predict how good or great a piece of writing, art, music or video is going to be. Major Hollywood pictures can be disliked by audiences, critics, or both; books often fail to make money or even pay out their advances. Conversely, sometimes an unknown creator comes out with a work that is initially ignored but goes on to fame and/or fortune. Lowering the bar for creation allows for more experiments, more chances to create something high-quality.

• Removal of the gatekeepers. If it’s hard to predict or evaluate what is good, who decides? Editors, producers, etc., who act as gatekeepers to the means of production and distribution. But gatekeepers are imperfect predictors, and they have blind spots. Harry Potter was rejected by twelve publishers before finding one that would take it: how many potentially great books never found that one editor to champion them, and never saw the light of day? Today there are far fewer gatekeepers for writing or podcasting, but they still exist in music and movies; AI will gradually remove these.

• More chance for people to make a start. E.g., there are many good bloggers who never would have gotten started if they had to first find a job as a journalist.

• More runway for works to find their audience. My writing had about 50 subscribers for the first two and a half years (now well over 50,000). Dwarkesh Patel was “was 2 months away from quitting the podcast for 2 years” before becoming a rocket-ship success. Lowering the cost of production allows these experiments to be incubated for years, kept alive by love and sweat, until they evolve into a more valuable form or catch their break.

• More content for niche audiences. When content is expensive, it has to serve a large audience, and everything converges on bland mainstream taste. When the only significant cost is one creator’s time, it only has to find 1,000 True Fans, and there is much more room for a broad and varied menu to serve many different palates.

• More diversity of content and format. When content is expensive, and gate-kept, it becomes the work of Trained Professionals, who are Serious People, and it should follow Formal Conventions. No serious magazine editor would approve a column that ranges widely across psychiatry, philosophy, politics, science, and epistemology, covering everything from book reviews to academic papers to online controversies. But that’s Scott Alexander, and he’s one of the best and most successful writers of our generation.

• Freedom from the tyranny of finance. When content is expensive, it becomes the domain of large corporations, who have a duty to their shareholders and who frequently succumb to the ruthless logic of financial returns. Hollywood today has found the safest returns in sequels, remakes, and the endless continuation of franchises such as Star Wars or the MCU. Low costs give you more ability to work for the love of the craft and for the sake of the art.

Slop is a byproduct of this overall process, the detritus that accompanies greatly expanded production. Slop is at best annoying and frustrating, and at worst a tool for scams or propaganda. But the overall process will, I believe, usher in a golden age of creativity and experimentation.

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We don’t have to like slop, of course. We don’t even have to accept it. We can find ways to minimize it.

First, we need better tools for discovery. Just as the explosion of content on the Internet created a need for directories, search engines, and then social media, the next explosion of content will create a need for new ways to search, filter, etc. AI can help with this, if we apply the right design and product thinking. We can create a future equilibrium that is much better than the pre-AI world, where a thoughtful consumer is able to find more targeted, high-quality writing, video, etc. This is a call to action for the technologists who design and build our information supply chain.

But they key word above is “thoughtful.” The explosion of content raises the bar for everyone to be more conscious in your media consumption. The more stuff is out there, the more of it will be like junk food: enticing, tasty, but not nutritious and ultimately unfulfilling. We all need to be mindful in how we direct and spend our precious, limited attention in a world of increasingly unlimited choice. This is a call to action for every individual, and by extension to parents, teachers, psychologists, and moralists.