• Head of Communications

• Program Manager, Fellowships

Both are full-time, fully remote (North America–based).

Head of Communications

The role

We are seeking a Head of Communications, to grow our online audience and tell them about everything we’re saying and doing.

You will start with a great brand. RPI is known as a central organization in the progress movement. Our founder and president, Jason Crawford, is recognized as a leader in the movement, and has been interviewed as a spokesman for progress by BBC and Vox. Our annual Progress Conference is the main event for the community, with headliners including billionaire founders and Nobel laureates; it is sold out every year with hundreds on the waitlist. Our Substack has 60,000 subscribers, and Jason has over 43,000 Twitter followers.

You will also have a lot of great material to work with: regular essays and threads from Jason and our fellows; dozens of hours of recorded talks coming out of every conference; and announcements from our fellowship, conference, and university and high school programs. Biggest of all, next year, we’ll be launching Jason’s book, The Progress Manifesto.

You’ll report directly to Jason, and will work closely with him and with the program leads. We’re a small team with Silicon Valley DNA; we operate with high autonomy and minimal process. You will amplify the work of this fantastic team.

You will uphold the highest standards for taste and quality. No slop, no clickbait, no feeding the outrage machine. We write the kind of posts we want to read. We don’t pollute the epistemic commons—we clean it up.

And all of this in service of one of the most important missions in the world today: building a culture of progress and restoring a bold, ambitious vision of the future.

What you will own and do

You will own our audiences on Substack and social media, and all communications about our programs. You will also own the launch of The Progress Manifesto, forthcoming from MIT Press in Spring 2027. Your key metrics will be audience growth, engagement, and book sales.

You will have broad latitude to devise comms strategies and tactics. At the same time, you must own execution: you will have a budget for contractors like video production, but otherwise you will be a one-person comms team.

This is not a PR role: it’s about managing our own audience, not about getting external media mentions or interviews. However, when we have a major media campaign, like the book launch, you will work with an external PR firm who does those things.

Day-to-day

Examples of day-to-day tasks you might perform include:

• Creating a launch plan for The Progress Manifesto, each year’s Progress Conference, each cohort of the Blog-Building Intensive, etc.

• Drafting announcements, updates, and retrospectives for all our programs (or editing drafts written by the program leads) and publishing them

• Drafting and publishing the links digest (which we would like to put out more regularly!)

• Publishing essays and turning them into social posts and threads

• Managing production for promotional videos, like this one for our fellowship

• Publishing the talks from the Progress Conference, including creating short clips for social media

• And much more that you will figure out!

Apply Now

About you

You should:

• Be very online: you don’t have to post a lot or have a large audience yourself, but you should read a lot of social media and blogs/newsletters

• Be familiar with the progress community and its writers

• Have good taste in essays and talks

• Be a good copy-writer (but not necessarily an essayist)

• Have backbone: we touch on controversial topics

• Be able to draft and execute a comms plan

• Be quantitative enough to look at an audience metrics dashboard and optimize it

No prior experience in communications roles is strictly required, although experience in relevant roles is valued!

Location and travel

We’re a distributed team, and you can work from anywhere that has reasonable overlap with US timezones. You’ll be expected to travel at least a few times a year for our annual conference and possibly other events, and for our annual team meeting.

Application process

We’ll review applications on a rolling basis. Promising applications will get an intro call from Jason. To get an offer, expect that you will interview with several members of the team, and possibly do a small amount of prep or writing offline.

This role can start immediately. We aim to fill it by mid-October at the latest.

Apply Now

Program Manager, Fellowships

The role

We’re looking for a program manager to cultivate a community of progress writers.

The Roots of Progress is a nonprofit dedicated to building a culture of progress and restoring a bold, ambitious vision of the future. As program manager, you will support and empower the intellectuals who generating the ideas that form the foundation of the progress studies movement.

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.

As program manager you will be responsible for the operations of our flagship fellowship program, the Blog-Building Intensive, along with a new program, the Audience-Building Intensive. You also provide 1:1 support to our fellows to help accelerate their careers as public intellectuals.

• Our Blog-Building Intensive Program is a 10-week-long online program designed to accelerate the careers of progress intellectuals. We admit 25-35 fellows each year through a competitive application process. During the program, we host daily sessions with our fellows that include writing lessons, guest speakers, and feedback sessions. We also provide editorial support to help our fellows write and publish >4 essays during the program. Successful fellows establish a regular writing cadence, become skilled at communicating complex ideas to a general intellectual audience, and are embedded in the broader progress community.

• Our Audience-Building Intensive Program is a 5-month-long program designed to meet the needs of intellectuals with an established writing practice. The program includes modules on Op-Ed writing, public speaking, developing a paid publication, and optimizing published writing for engagement and growth. As with the Blog-Building Intensive Program, our developmental editor provides ongoing editorial support to our fellows. The ABI builds on BBI to increase the impact our writers have: larger, better-matched audiences, more viral and higher-impact publications, and skills at monetizing writing or leveraging expertise into other ways to make writing a (larger) part of their careers.

This role reports to Emma McAleavy, who leads our fellowship programs. It is a full-time, fully remote position, with a couple of multi-day trips required every quarter.

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About You

You love managing programs that bring people together, enabling them to learn and form communities. You’re excited about working in a small team. You are thrilled when your work enables others to succeed. And: you’d be delighted to apply these skills toward enabling human progress to continue and even accelerate.

• Do you enjoy organizing and project management? You have substantial experience managing a project or program. You’re energized by figuring out what it takes to deliver a delightful experience for a program or an event. Perhaps you have led online cohort-based programs before. Or an in-person program like a retreat, a camp, or an educational program. Or maybe you’ve organized a larger-scale, more complex event that took months of prep and planning. Whatever your specific background, you are comfortable using wide variety of tools and tactics to ensure an exceptional participant experience.

• Are you an indefatigable champion for other people? Can you listen deeply to someone and help them identify goals and blockers? Are you obsessed with helping other people achieve their potential? Are you excited to roll up your sleeves and offer practical support: a letter of recommendation, a nomination to an exciting opportunity, edits on a fellowship application? Do you like to help people develop a concrete strategy for accomplishing their goals, and then help them execute on this strategy?

• Do you have an ownership mentality? You thrive in a work environment with clear objectives and regular kind-and-candid, growth-oriented feedback. You take full ownership of your area, planning your own work and communicating proactively with your teammates. You love finding efficient ways to do things and dislike bureaucracy.

• Are you passionate about ideas in general and human progress in particular? You believe, like us, that ideas shape history and that writers, researchers, storytellers, and educators need a team behind them so they can do their best work and have the most impact. You’re fascinated by the amazing progress we’ve made in the last 200 years, lifting most of humanity out of poverty, and you are eager to support the intellectual entrepreneurs who explain this progress and advocate for policies and programs that will keep it going. You don’t aspire to be an intellectual yourself, yet you admire their work and want to amplify their impact.

Day-to-Day

As the Program Manager for the blog-building fellowship, your main focus will be:

• Running the annual 10-week Blog-Building Intensive. You’ll own the entire program, including managing partners (e.g., our editors and advisors), running marketing and program outreach, managing the application and selection process for the 25-35 fellows, executing the 10-week online program, and planning and running the three-day in-person retreat. You may also teach or co-teach the writing curriculum that we’ve developed. We’ve run this program three times, and have perfected many aspects of it. But, as program manager, your job will be to continuously solicit and incorporate feedback to continue improving the program. This program typically runs sometime between July-October, with applications open May-June.

• Running the 5-month Audience-Building Intensive program. As with the Blog-Building Intensive program, you will own the entire program. You will manage the advisors we contract with to teach various modules, and you will teach or co-teach some of the modules yourself. You will manage the application and selection process, and suggest improvements to the program based on fellow feedback and outcomes. We have run this program once with alumni of the Blog-Building Intensive program. We may open this program up to the general public in its second year. This program has run only once, January–June; future scheduling TBD.

• Individual alumni fellow and alumni support. One of the most delightful parts of the role is meeting and engaging with our Roots of Progress fellows, who are an amazing group of ambitious intellectuals—the type of students every teacher dreams of! You will provide individual and customized support to the fellows. Is a fellow writing a book? You could find yourself helping them refine a query letter. Are they looking for job? You might be called upon to write letters of introduction and help them connect with the right people. Does a fellow want to publish an op-ed? You might identify editors and help them prepare their pitch. Whatever the fellows want to do, as part of their work as public intellectuals, your job is to help them do it.

• Running additional programs. The Blog-Building Intensive Program, the Audience-Building Intensive program and ad-hoc fellow support will require ~70% of your time. The other 30% will be dedicated to other programs. This could including helping to run our Progress in Medicine program for High School students, or adding a second Blog-Building Intensive cohort during the year, or supporting specific events like the Progress Conference during critical times.

In this role, you’ll work closely with Emma McAleavy, your manager, Mike Riggs, our developmental editor, as well as our partners (e.g., instructors we bring on for the program, and other progress organizations who support our fellows in a variety of ways).

Apply Now

Application process

We’ll review applications on a rolling basis beginning June 1st. We’ll conduct first round interviews in early June. Finalists will be asked to complete a take-home assignment and participate in two final round interviews.

We intend to hire for this role by August 1st at the latest.

Apply Now

About the Roots of Progress Institute

The Roots of Progress Institute is a nonprofit dedicated to building a culture of progress and restoring a bold, ambitious vision of the future. Our programs this year include:

• Progress Conference, the main annual gathering for the progress community

• The Blog-Building Intensive, a fellowship program for progress writers

• Progress in Medicine, a summer program for high school students

• Teaching Progress, a workshop for university professors who want to teach ideas of progress

Our founder and president, Jason Crawford, writes and speaks about the history and philosophy of progress, including in his forthcoming book The Progress Manifesto.

Why care about progress? The progress of the last few centuries—in science, technology, industry, and the economy—is one of the greatest achievements of humanity. But progress is not automatic or inevitable. We must understand its causes so that we can keep it going, and even accelerate it.

In order to make progress, we must believe that it is possible and desirable. The 19th century believed in the power of technology and industry to better humanity, but in the 20th century, this belief gave way to skepticism and distrust. We need a new philosophy of progress for the 21st century, and beyond.

Read more about our mission and the bios of our team.

It is in this context, from the perspective of the bloody millennia of mankind's history, that I want you to look at the birth of a miracle: the United States of America. If it is ever proper for man to kneel, we should kneel when we read the Declaration of Independence.

Ayn Rand, “A Nation’s Unity”

A bonus post for Independence Day 250:

In a 1972 speech, Ayn Rand put the founding of America into the grand historical context of the problem of human violence. I love this because it is a story of progress. Just as with the history of agriculture, or energy, or health, there is a fundamental human need—the need for peace and stability—and a succession of iterative improvements, culminating in this case with the best system we have found so far: democratically representative government in a constitutionally-limited republic. America was the solution to a problem, and a brilliantly-engineered one.

You can listen to the full speech online, but I couldn’t find a transcript anywhere. Below, for paid subscribers, is a transcript I made of the key passage (about five and a half minutes, or some 700 words).

Read more

America’s Independence Day is not the day the war began, nor the day it ended. It’s not the ascension of a leader, let alone his birthday. It is the day we declared our independence, in writing—the day we laid down the principles and the justification for our existence. It is a moral and philosophical holiday.

The Declaration of Independence is the greatest political document ever written, and the founding of America is a monumental human achievement. After thousands of years of monarchy, in almost every large-scale society, for most of human history—after all the tyrants and all the bloody wars of succession—a small group of daring leaders made a bold experiment in a new and better form of government. And they changed the world, setting off a chain of revolutions and reforms: today most of the Western world enjoys representative government, and most of its monarchies are constitutional or ceremonial.

The Founders did not know, could not have known, what would come of their experiment. They hoped that it would survive, that their fledgling nation would not become subordinated to the much stronger and constantly warring great powers of Europe, that the union of several independent states would last and not descend into fractious squabbling. They could never have guessed that the nation they birthed would go on to become the largest and most powerful economy on Earth, that we would lead the world in science and invention, that we would fight and win two world wars, that we would plant our flag on the Moon.

This 250th anniversary, then, should be a time of reverence and a time of joy. But today, some of those who feel the most reverence are also those who feel the least joy.

I understand why. We have not achieved the ideal of “a government of laws and not of men.” Our institutions have become bloated and sclerotic. We once again face a geopolitical rival with an authoritarian regime; collectivism was not defeated in the Cold War after all.

Some say that America is in a period of decadence, faltering and directionless. Some liken our condition to the last stage of the Roman Republic. Some say that this is the way of the world—empires rise and they fall.

But the American spirit is the opposite of such fatalism. I cannot, will not, resign myself to American stagnation, sclerosis, and decline.

As great as the Declaration was, the greatness of America is not only that we declared and won our independence. It is also in all the achievements since. That we unified the colonies into a strong federal system, under a brilliant Constitution. That we made the Louisiana Purchase and expanded into the West. That we abolished slavery. That we invented the reaper, the light bulb, the telephone, the airplane, the assembly line, the television, the computer, the Internet. That we gave a home to so many immigrants, from so many places, that they called us a “melting pot.” That we built the transcontinental railroad, the Panama Canal, the interstate highway system. That we gave women the vote. That we gave the world jazz and Hollywood. That we defeated the Nazis and faced down the Soviets. That we eliminated polio and led the eradication of smallpox.

Nor did the Declaration birth a perfect nation. We have always faced, and will always face, problems and threats both from without and from within. We have been plagued by corruption and scandal, rocked by banking crises, torn by civil war. We have known the fear of atomic warfare and of Sputniks orbiting overhead. Again and again, we have fought ourselves: over equal rights for all races and sexes, over whether to welcome foreigners or exclude them, over Prohibition.

When I look at the history of America, I don’t see a story of decline. I see a story of progress—however messy and hard-won. I see a long stumbling towards the light. That is the true way of the world.

And I see the spirit of America still strong among those who have not forgotten. Those who are building rockets and supersonic jets and nuclear power plants; those who are curing cancer and obesity and aging; those who are still tirelessly defending the rule of law, separation of powers, and freedom of speech. And I see Americans, sometimes in the crudest possible way, groping towards rediscovering our national self-esteem.

In America’s darkest hour, less than a century after its birth, Lincoln asked his countrymen to rededicate themselves to cause of liberty, and to the great unfinished work in front of them. He called on them to resolve that the nation would see a rebirth of freedom. Now, after a quarter-millennium, it is time for us to do that once more.

May the next 250 years do justice to the progress of the last 250.

We launched Progress in Medicine (PiM) in December as a test, a pilot program aimed at answering the question: How can we inspire young people to choose builder careers that advance human progress?

Our hypothesis when we designed PiM was this: Schools generally are ill-equipped to help teens figure out their life purpose—and teens and parents feel this problem urgently enough that they are willing to invest time and money. While many other great summer programs exist, none offered what we would have wanted for our own teens: a hybrid model that enabled teens to go deep on content and community building, without requiring them to commit their entire summer to one camp. Plus, few selective programs go broad: many competitive, ambitious programs zero in on one specialty, rather than helping teens explore a wide range of careers to see where they might fit. We thought teens needed to learn not just the what of a specific discipline, but get the framework of the why and how. That means learning the story of historical progress in medicine and bio, including the people who made it happen. It also means meeting the people on the current frontiers, and learning where they as young people can build a career that matters.

This is what Progress in Medicine set out to do, and now that the program has started, we’re even more excited about its potential to solve a real parent-student problem and have meaningful impact on the broader world.

Without much marketing (one ad in an online summer camp listing space, plus a couple of emails to organizations like HOSA, a health care network for young people, plus our own network), we received well over 100 applications. The 30 students, ages 14–18, who we admitted in this inaugural class come from all over the US, Canada, and four international countries. Some are interested in becoming neurosurgeons, pediatricians, cancer researchers, and biomedical engineers; others are just curious about medicine and eager to figure out where they might fit. Some are already medical builders, launching an app with 10,000 users or putting together business plans for moonshot ideas; others have built in other areas (AI tools, computers, robots); and others have volunteered or had challenging summer jobs.

This week showed us that we selected well. We had great (on-time, camera-on!) attendance in our four two-hour long Zoom sessions. Teens maxed out their break-out room chats, completed their online assignments, and flooded our Zoom chat with their answers. For our first advisor session on sanitation, 22 of our students asked a wide range of questions of Dr. Jake Swett, Executive Director and founder of Blueprint Biosecurity—and their questions showed how deeply many had researched Dr. Swett and his company.

The Progress in Medicine program is led by Laura Mazer, a Stanford-trained surgeon turned educator, who writes about the history of Medicine on Substack. PiM has three parts:

• Four online weeks, two hours per day. Teens learn about the history of medicine and bio via case studies (sanitation, infectious diseases, cancer, research ethics, obesity, longevity). They meet with expert advisors and ask them questions. They learn life skills—such as how to be a good mentee and get the most out of mentors. They then apply those skills, for example, by meeting in small groups with young professionals who have agreed to be near-peer mentors.

• A four-day in-person residency at Stanford. Teens will tour university facilities and labs, as well as Bay-Area biotech companies. They’ll gather for a celebratory reception at The Institute at Salesforce Tower, where they’ll meet with inspiring adults involved in medicine and biotech, and the broader Bay Area progress movement. They’ll get to experience dorm life and meet Stanford students, and spend time with each other to deepen their relationships.

• A reflection and writing week. Back home and online, teens will write up what they learned. With guidance by our on-staff editor, Mike Riggs, they’ll synthesize their insights about the world of medicine, biotech, and health, and their growing awareness of their own interests into a personal narrative which can form the basis for a college application essay.

When we launched this program we had two big questions:

• Are teens and parents eager to explore careers through a wider, progress-oriented lens?

• Can we help raise ambitions and broaden perspectives of talented teens in a way that empowers them to choose great-fit careers that tackle some of humanity’s biggest challenges?

We won’t have the full answers for a few more weeks, as PiM runs through late July. But we do know this: 30 teens and their parents are in the program. Two-thirds of the families have paid $2,000 to participate, and one-third of the students attend on scholarships. We’re grateful to our donors who funded the scholarships (without asking for name recognition): your generous donations enabled us to make admissions decisions need-blind, and give these teens a unique summer opportunity.

This summer, we will learn a lot. If Progress in Medicine ends up being the success we now think it can be, it’s a first step in a larger program to help orient teens toward a solutionist mindset and toward builder careers that fill their lives with meaning and contribute toward a better future for all of us. Stay tuned: we’ll have lots of plans to build on this first pilot program!

For now, join us in congratulating these teens on a great first week. We’re excited for the next five weeks we get to spend with them, and can’t wait to meet them in person during the Stanford in-residency in mid-July!

Questions the students asked of Dr. Swett. They were more insightful and better researched than the questions we’ve heard at many adult conferences!

I use AI a lot for researching, occasionally for brainstorming, and increasingly for fact-checking. But not for outlining, drafting, or directly editing.

This isn’t some principled objection to AI use, or a revulsion at the idea of letting technology intrude on my craft. It has simply never occurred to me to do it.

Why? I just don’t think AI writing is good enough today. It’s good at research reports or letters to Congressmen or anything else generic. It isn’t compelling enough to make a good blog post. And it certainly doesn’t have my voice or unique point of view. (I don’t think any AI today has a real point of view.)

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Here’s an example. In revising “The Progress Agenda” for publication as a chapter in my forthcoming book, I wanted to add a paragraph on immigration.

Here was my process. First, in my notes, I wrote down several points I might want to make about immigration, off the top of my head. From that, I wrote a simple structure for the paragraph:

1. immigration is good/important

2. but it is difficult/time-consuming/uncertain

3. so we should make it easier (need examples)

At that point I needed data and examples to substantiate these points, which I didn’t have off the top of my head. This is where I turned to AI. I started with the promprt “What are some examples or data points to substantiate the idea that immigration, particularly high-skilled immigration, is important to the US?” I had a conversation with it from there, checking out the sources it pointed me to and asking followup questions until I felt I understood each point well. You can read the whole thing here.

Once I had enough material, I went back and summarized what I felt were the most relevant points in my notes, with sources. Then I drafted the paragraph.

Here’s what I ended up with (at the end of a section calling for across-the-board regulatory reform):

Progress in all of these areas depends on talent, and in the US, a lot of that talent comes through immigration. Immigrants are about 14% of the population, but 43% of doctorate-level scientists & engineers. Immigrants or their children co-founded nearly half of the Fortune 500 list as of 2025, including Amazon, Apple, Google, and NVIDIA, and more than half of US billion-dollar startups, including Stripe, Instacart, and Notion. But we restrict our supply of talent through a slow and burdensome immigration process. The H-1B visa used by many STEM workers is capped at a level far below demand: in 2026, there were almost 339,000 applicants, but only 35% of them were selected. The selections are done by lottery. Once here, the path to permanent residency can be epic, particularly for some nationalities: as of April 2026, some Indians even in the second-highest employment-based preference category (professionals with advanced degrees or “persons of exceptional ability”) have been waiting around 12 years for their green cards. And despite the exceptional success of immigrant entrepreneurs, the US has no startup visa. For the sake of national prosperity and security, the US should have more visas and green cards, including an entrepreneur visa; the ones we have should be better targeted to need, not assigned randomly; and processing should be made more efficient, to eliminate decade-long backlogs.

That paragraph isn’t going to win me a Pulitzer, but it’s solid and it does its part in the overall essay.

As an experiment, after I wrote this, and without showing it to ChatGPT, I prompted it:

Given all this discussion, please draft a paragraph on immigration, arguing that 1. immigration is good/important, 2. but it is difficult/time-consuming/uncertain, 3. so we should make it easier (examples). I want to insert it into this essay, near the end of the first major section, on law/regulation: https://newsletter.rootsofprogress.org/p/the-progress-agenda

Here’s what it wrote:

Immigration is another area where law and regulation need reform. For over a century, the United States has benefited enormously from immigrants, who have contributed disproportionately to science, engineering, entrepreneurship, and economic growth. Many of our most important technology companies were founded or co-founded by immigrants, and immigrants are heavily overrepresented among PhDs, inventors, and startup founders. Yet our system for admitting and retaining high-skilled talent is slow, cumbersome, and irrational. We educate foreign students in our universities, then subject them to visa lotteries, years-long backlogs, and administrative obstacles if they want to stay and build here. The result is that we needlessly turn away scientists, engineers, and founders, or trap them in long periods of uncertainty, even as we complain of labor shortages and worry about national competitiveness. We should make it far easier for talented people to come to America and remain here: expand and streamline visas for high-skilled workers and entrepreneurs, staple green cards to advanced STEM degrees earned at US universities, recapture unused employment-based green cards, reduce or eliminate per-country caps that create absurd backlogs, and generally treat the attraction of global talent as a national advantage rather than a problem to be managed.

I don’t like this nearly as much. It’s more bland and less interesting. The main difference that leaps out at me is that my version wove in many more specific stats and examples. The ChatGPT version stays abstract.

I’m sure I could prompt or train a model to write more like me. It would be an interesting exercise to see how many rounds of editorial feedback you’d need to give in order to get it to produce a paragraph much closer to mine (maybe only one or two?), or whether you could come up with a general style guide or set of skills that would get it to produce better writing the first time. (In the general case, it would have to be much more than “include specific numbers and examples,” because this paragraph is not one that shows off my unique voice.)

I am unmotivated to tinker with this. Frankly, I’m kind of a diva about my writing. I don’t co-author with other people (I kind of dread the idea). I almost never allow guest posts on my blog. I don’t even let other people write social media posts for me. I flatter myself that my voice is inimitable. When someone sends me a draft of something that is supposed to come from me, more often than not I just rewrite it completely. (In contrast, I’m very happy to have AI write code for me.)

There’s also the fact that when I publish any writing under my own name, I feel that I am taking on a sacred responsibility. Any writing in my name has to be correct, to the best of my knowledge and ability. It has to not only say true things, it has to select the most important things to include, and take the most interesting and illuminating angles on them. When I sign a piece, I’m not just taking responsibility for errors, I’m taking responsibility for the entire worldview and perspective that gave rise to the piece. And I simply can’t do that unless I wrote it.

And the writing process does more than just produce a piece of writing. I learn a lot from researching, outlining, drafting, and revising, and the knowledge and worldview that is built up in my head through that process is part of the outcome. What I learn helps me decide what to research and write about next, serves as examples in future essays, and provides the substance of interviews that I give. Even if I could crank out perfect essays using AI, I couldn’t be a public intellectual that way.

If I did have a way to produce truly excellent writing with AI, I might publish it, but not with my name as the author. My role then would not be author but editor. This is, indeed, the future I predicted for writers, and it might be a direction I go in the future. But if I do, you’ll know. It’ll be right there in the byline.

You can read more about this year’s program or apply here. The application deadline is this Monday, June 1.

Our fellows have sung the program’s praises, and you can hear directly from them about how it changed their lives in the video below.

You can also watch the recording of yesterday's Substack Live session. We talked about the origin of the Blog-Building Intensive and how it emerged out of Jason’s own blog. We talked about how we designed it, and how it has evolved over the last three years. And we talked about what it’s like to participate in the program.

For this year’s Blog-Building Intensive Program, we have two tracks: Human Talent & Potential and Security & Resilience. We welcome fellows writing on any progress-related topic, but for 10-15 spots, we will give preference to applicants focusing on these themes.

Over the course of 10 weeks, fellows will participate in our bespoke writing curriculum, work 1:1 with our developmental editor Mike Riggs, workshop their writing with their peers, and hear from some of the leading lights of the progress studies movement, Virginia Postrel, Tyler Cowen, Eli Dourado, Brian Potter, Brendan McCord, Alexander Kustov, Elle Griffin, Alice Evans, Greg Lukianoff, and Kevin Esvelt.

• 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: 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 27–October 2. Our in-person weekend takes place August 20–23. Optional and strongly encouraged: attend Progress Conference in Berkeley October 8–11; 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.

You can read more about this year’s program or apply here. The application deadline is this Monday, June 1.

Join us on Substack Live

You have 5 more days to submit your application to the fourth cohort of our Blog-Building Intensive program, a 10-week program for aspiring progress writers.

You can read more about this year’s program or apply here.

You may have seen alumni singing the program’s praises on Substack over the last few weeks. Among the many complementary things they’ve said about the program:

• “The Roots of Progress fellowship changed my life.”— Grant Mulligan

• “I will push you out of your comfort zone in the best ways.”— Colleen Smith

• “The Roots of Progress fellowship was, and continues to be, one of the most important and impactful things I’ve ever done as a writer.” — Elle Griffin

• “I feel I can improve my craft by being part of this community and, most importantly, has increased my already high optimism about humanity!” — Rhishi Pethe

Our alumni have published over 1,500 essays on their own blogs and Substack publications. And their work has appeared in the New York Times, The Washington Post, The Atlantic, and the Wall Street Journal, where they’ve written on topics such as fertility policy, AI safety, and the challenges that accompany self-driving car adoption. They have also written for magazines like WIRED, Foreign Affairs, American Affairs, Works in Progress, Asimov Press, Asterisk, and Arena.

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This year, we have two tracks: Human Talent & Potential and Security & Resilience. We welcome fellows writing on any progress-related topic, but for 10-15 spots, we will give preference to applicants focusing on these themes.

Over the course of 10 weeks, fellows will participate in our bespoke writing curriculum, work 1:1 with our developmental editor Mike Riggs, workshop their writing with their peers, and hear from some of the leading lights of the progress studies movement, including , , , , , , , , , and .

• 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.

You can read more about this year’s program or apply here.

Join us on Substack Live at 2 pm PT / 5 pm ET. We’ll be answering questions about the Blog-Building Intensive program: how it works, who it’s for, and what you’ll get out of participating.

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What’s going on in the world, and what matters right now? I’ll cover this in two parts: science/tech, and politics.

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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, , 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.

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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)