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

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

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

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

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

• immigration is good/important
• but it is difficult/time-consuming/uncertain
• so we should make it easier (need examples)

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

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

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

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

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

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

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

Here’s what it wrote:

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

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

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

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

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

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

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

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.

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.

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 Ezra Klein and Derek Thompson 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.”

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.

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.

Selected sources and further reading:

• 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)

I think this is true but irrelevant.

Consider the weather, a prime example of a complex system. We can predict the weather to some extent, but not far out, and even this ability is historically recent. We still can’t control the weather to any significant degree. And yet we are far less at the mercy of the weather today than we were through most of history.

We achieved this not by controlling the weather, but by insulating ourselves from it—figuratively and literally. In agriculture, we irrigate our crops so that we don’t depend on rainfall, and we breed crops to be robust against a range of temperatures. Our buildings and vehicles are climate-controlled. Our roads, bridges, and ports are built to withstand a wide range of weather conditions and events.

Or consider an extreme weather event such as a hurricane. Our cities and infrastructure are not fully robust against them, and we can’t even really predict them, but we can monitor them to get early warning, which gives us a few days to evacuate a city before landfall, protecting lives.

Or consider infectious disease. This is not only a complex system, it is an evolutionary one. There is much about the spread of germs that we can neither predict nor control. But despite this, we have reduced mortality from infectious disease by orders of magnitude, through sanitation, vaccines, and antibiotics. How? It turns out that this complex system has some simple features—and because we are problem-solving animals endowed with symbolic intelligence, we are able to find and exploit them.

Almost all pathogens are transmitted through a small number of pathways: the food we eat, the water we drink, the air we breathe, insects or other animals that bite us, sexual contact, or directly into the body through cuts or other wounds. And almost all of them are killed by sufficient heat or sufficiently harsh chemicals such as acid or bleach. Also, almost none of them can get through certain kinds of barriers, such as latex. Combining these simple facts allows us to create systems of sanitation to keep our food and water clean, to eliminate dangerous insects, to disinfect surfaces and implements, to equip doctors and nurses with masks and gloves.

For the infections that remain, it turns out that a large number of bacterial species share certain basic mechanisms of metabolism and reproduction, which can be disrupted by a small number of antibiotics. And a small number of pathogens once caused a large portion of deaths—such as smallpox, diphtheria, polio, and measles—and for these, we can develop vaccines.

We haven’t completely defeated infectious disease, and perhaps we never will. New pandemics still arise. Bacteria evolve antibiotic resistance. We can sanitize our food and water, but not our air (although that may be coming). But we are far safer from disease than ever before in history, a trend that has been continuing for ~150 years. Even if we never totally solve this problem, we will continually make progress against it.

So I think the idea that we can’t control complex systems is just wrong, at least in the ways that matter to human existence. Indeed, a key lesson of systems engineering is that a system doesn’t need to be perfectly predictable in order to be controllable, it just has to have known variability.¹ We can’t predict the next flood, but we can learn how high a 100-year flood is, and build our levees higher. We can’t predict the composition of iron ore or crude oil that we will find in the ground, but we can devise smelting and refining processes to produce a consistent output. We can’t predict which germs will land on a surgeon’s scalpel, but we know none of them will survive an autoclave.

So we can tame complex systems, and achieve continually increasing (if never absolute or total) mastery over nature. Our success at this is part of the historical record, since most of progress would be impossible without it. The “complex system” objection to the goal of mastery over nature simply doesn’t grapple with these facts.

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

RPI

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

• Held our second annual Progress Conference, featuring speakers including Sam Altman, Blake Scholl, Tyler Cowen, and Michael Kratsios (Director, OSTP). The conference has become the central, must-attend event for the progress community: it is sold out each year, with hundreds on the waitlist, and some attendees report it is literally the best conference they have ever attended.
• Inducted the third cohort of our progress writers fellowship, bringing the total to 74 fellows. Our fellows are having impact: Dean Ball helped draft the Trump administration’s AI Action Plan, Madeline Hart has co-authored a book with the CTO of Palantir on revitalizing the American defense industry, Ryan Puzycki helped legalize single-stair buildings in Austin (a key YIMBY reform), and three other fellows have recently had opinion pieces in the NYT or WSJ.
• Announced our first education initiative: Progress in Medicine, a high school summer career exploration program. I’ve previewed the content for this course and I’m jealous of these kids—I wish I had had something like this when I was a teenager!

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

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

Or see here for other ways to support us.

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

My writing

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

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

The most-liked posts here on Substack were:

• The future of humanity is in management
• How sci-fi can have drama without dystopia or doomerism
• The Unlimited Horizon, part 2
• The Spirit We Lost, part 1
• In defense of slop

The most-commented posts were:

• How much does it cost to back up solar with batteries?
• We should install a thermostat on the Earth
• Where is the YIMBY movement for healthcare?

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

• The Progress Agenda

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

Social media

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

• One model for Elon is that he is perpetually operating in wartime mode: Move as fast as possible, bring overwhelming force, extract superhuman effort, accept collateral damage. A thread. For some reason many people seemed to think I was praising or defending this: I wasn’t, just observing.
• Grocery stores have price transparency and open competition, which health services don’t (replying to a wrong-headed comment by Rep. Jake Auchincloss)
• Everything has to be invented—even buttons
• The moment the Boom XB-1 test plane went supersonic
• “Daddy, why is a hundred a big number?”, and other difficult questions from my 3yo
• “On paper, titanium should be the world’s #1 structural metal”
• When Tyler Cowen just flat-out says that tariffs are bad, you know they are very straightforwardly bad
• A great moment from a legendary interview with Morris Chang of TSMC, on the Acquired podcast
• Waymo achieves 80–90% reduction across all crash/injury metrics. If there were some intervention that achieved this, safety advocates would be demanding its wholesale adoption immediately, even if it were costly or inconvenient

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

Speaking and events

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

• I spoke at “d/acc Day” alongside Vitalik Buterin, Juan Benet, Mary Lou Jepsen, Allison Duettmann, and others. My talk was “d/acc: The first 150 years”: a whirlwind tour of how society has thought about progress, decentralization and defense over the last century and a half
• I gave a short talk at Social Science Foo Camp titled “The Fourth Age of Humanity?”, based on ideas that I later wrote up in The Flywheel
• I did a fun Interintellect salon with Virginia Postrel based on her essay “The World of Tomorrow”
• I hosted a discussion series at Edge Esmeralda with the aim of envisioning the future. Each day there was a ~90-minutes session with a theme like AI, health & bio, or energy
• I went to Mojave to watch the first supersonic flight of the Boom XB-1 test plane. Here’s some video I took of the plane taxiing down the runway, and then the pilot getting out after landing and shaking hands with Boom founder Blake Scholl

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

Reading

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

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

Books and other book-length things I read

Or read at least most of:

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

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

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

To read the rest, subscribe on Substack.

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

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

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

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

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

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

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

• More experimentation. It can be hard to predict how good or great a piece of writing, art, music or video is going to be. Major Hollywood pictures can be disliked by audiences, critics, or both; books often fail to make money or even pay out their advances. Conversely, sometimes an unknown creator comes out with a work that is initially ignored but goes on to fame and/or fortune. Lowering the bar for creation allows for more experiments, more chances to create something high-quality.
• Removal of the gatekeepers. If it’s hard to predict or evaluate what is good, who decides? Editors, producers, etc., who act as gatekeepers to the means of production and distribution. But gatekeepers are imperfect predictors, and they have blind spots. Harry Potter was rejected by twelve publishers before finding one that would take it: how many potentially great books never found that one editor to champion them, and never saw the light of day? Today there are far fewer gatekeepers for writing or podcasting, but they still exist in music and movies; AI will gradually remove these.
• More chance for people to make a start. E.g., there are many good bloggers who never would have gotten started if they had to first find a job as a journalist.
• More runway for works to find their audience. My writing had about 50 subscribers for the first two and a half years (now well over 50,000). Dwarkesh Patel was “was 2 months away from quitting the podcast for 2 years” before becoming a rocket-ship success. Lowering the cost of production allows these experiments to be incubated for years, kept alive by love and sweat, until they evolve into a more valuable form or catch their break.
• More content for niche audiences. When content is expensive, it has to serve a large audience, and everything converges on bland mainstream taste. When the only significant cost is one creator’s time, it only has to find 1,000 True Fans, and there is much more room for a broad and varied menu to serve many different palates.
• More diversity of content and format. When content is expensive, and gate-kept, it becomes the work of Trained Professionals, who are Serious People, and it should follow Formal Conventions. No serious magazine editor would approve a column that ranges widely across psychiatry, philosophy, politics, science, and epistemology, covering everything from book reviews to academic papers to online controversies. But that’s Scott Alexander, and he’s one of the best and most successful writers of our generation.
• Freedom from the tyranny of finance. When content is expensive, it becomes the domain of large corporations, who have a duty to their shareholders and who frequently succumb to the ruthless logic of financial returns. Hollywood today has found the safest returns in sequels, remakes, and the endless continuation of franchises such as Star Wars or the MCU. Low costs give you more ability to work for the love of the craft and for the sake of the art.

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

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

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

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

Short answer: They overlap 80–90%, and if you’re outside both of them you should probably think of them as variations on the same thing. If you look at the Abundance conference and the Progress Conference, for example, there’s a good amount of overlap in the speakers, attendees, and topics.

What are the differences?

• “Abundance” tends to be more wonkish, oriented towards DC and policy, and focuses on reforming regulations and institutions with a goal of efficiency and being able to build stuff again.
• “Progress” is interested in regulatory reform and efficiency, but it’s also interested in ambitious future technologies, from longevity to nanotech. It’s also more focused on ideas and culture, and on history, philosophy, and economics, in addition to just policy.

Again, just look at the conferences: Abundance is held in DC; Progress Conference in the San Francisco area.

In “The Progress Agenda” I sketched out three broad cause areas for the progress movement: regulatory reform, research institutions, and culture. To my mind, the “abundance agenda” is basically the first of those. So I think of the abundance movement as a part of the progress movement.

After this year’s Progress Conference, Charles Mann suggested: “Abundance wants to make sure everyone has a house. Progress wants to make those houses better.” But IMO, the progress movement is interested in both of those things, so that’s not how I think of the distinction.

Some more background for those who are interested:

“Progress” took off in 2019 when Patrick Collison and Tyler Cowen coined the term “progress studies” in The Atlantic. They proposed it as a field of study, but the article galvanized a movement. (The movement has taken off more than the field, so I tend to talk about the progress movement or progress community rather than “progress studies.”)

There were precursors. Steven Pinker’s Enlightenment Now (2018), David Deutsch’s The Beginning of Infinity (2011), and Virginia Postrel’s The Future and Its Enemies (1998) all had progress as a central concept (two of those have “Progress” in the subtitle). Marian Tupy started HumanProgress.org back around 2012. Peter Thiel, Tyler Cowen, and Robert Gordon were all talking about growth vs. stagnation in the 2010s, although they emphasized those concepts more than “progress.” Max Roser at Our World in Data since 2011, and Hans Rosling before him, worked to communicate the reality of progress, especially global development. Of course, the idea of progress has a history going back centuries at least.

Organizations and projects that put “progress” in the name include the Institute for Progress, Works in Progress magazine, the Human Progress project, and of course us, the Roots of Progress Institute.

“Abundance” was popularized by Derek Thompson, who coined the term “abundance agenda” in a 2022 Atlantic piece. It has since taken off politically, especially with Derek and Ezra Klein’s book titled Abundance that launched this year. Again, there were precursors, including Peter Diamandis’s book also titled Abundance from 2012.

Derek and Ezra credit the progress movement as one of their influences in this podcast.

Things with “abundance” in the name include the Abundance Institute, the Abundance Network, the Metropolitan Abundance Project, the Inclusive Abundance Initiative, and even for a while an Abundance Caucus, which got renamed to the Build America Caucus (because “abundance” has become a partisan term on Capitol Hill, owing to Derek and Ezra being Democrats).

Further reading: Ruy Teixeira captured some of the differences between the progress and abundance movements in his post on the Progress Conference; Steve Teles at Niskanen offers a different take on various factions who have adopted “abundance” ideas.

In “The Progress Agenda,” I mentioned children’s books. One in particular caught people’s attention: You Will Go to the Moon, from 1959 (h/t Virginia Postrel via her blog).

I’ve now heard from a few people who are buying used copies for their kids. Snag yours for Christmas before they’re all gone.

Here are a few more progress-related books I know of—some old and out of print, some quite recent; some I’ve read and can recommend, some I’m just intrigued by.

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Up Goes the Skyscraper!

This one opens with a clear statement of human needs: “Thousands of people want to work and live on the empty city block. It is a small space for so many people. A skyscraper must be built.” And it concludes by saying “Look up at the skyscraper … it is beautiful.”

It goes through many details—I skip the smaller print for my 4-year-old. She loved learning about I-beams and H-beams, however, and she wondered why there were no G-beams or J-beams. (H/t Luca Gattoni-Celli for this one.)

Evidently there is an updated version of this book (I haven’t seen it yet), and it is only one of dozens of “Explore the World” books by the same author, including How a House is Built, Tool Book. and From Seed to Plant. I think my daughter will get some of these for Christmas.

John Deere, That’s Who!

The story of the invention of the polished steel plow. This is a story of industriousness and resilience: Deere is a hardworking blacksmith who has to find a new home after his workshop burns down. It’s also a story of inventiveness: when the farmers in his new town are having trouble plowing through the thick, sticky mud in their fields, Deere creates a slick, lightweight plow out of polished steel. (H/t Gregory Salmieri.)

Magic Ramen: The Story of Momofuku Ando

The invention of instant ramen might seem an unlikely topic, but it’s actually a very well-told story that draws out the problem-solving process (in the way that Anton Howes talked about here).

Ando has a clear goal of creating a ready meal that people could make easily and quickly. He has to solve one problem after another, and he does it by tinkering and experimenting. First he has to find a good recipe for noodles that don’t stick or fall apart; then he has to figure out how to get the soup flavor into the noodles; and finally he has to figure out how to make it cook in just a few minutes using boiling water from a kettle, instead of cooking for several minutes on a stove. At each stage he doesn’t know what to do and tries many things that don’t work before finally hitting on the solution. A story of vision, drive and persistence, made tangible and real. (I confess to editorializing a bit by inserting at each stage, “and he was very frustrated, but he didn’t give up. He kept trying.”)

Working Boats: An Inside Look at Ten Amazing Watercraft

This one is beautifully illustrated and does not skimp on the detail. I didn’t realize there were so many specialized boats, seemingly one for every species of seafood, as well as tugboats, fire boats, and more. (H/t Katherine Boyle via Twitter.)

Note for parents: most of the books above are probably best for ~6- to 7-year-olds, but I was able to adapt them for my daughter when she was only 3 or 4 by simplifying the language and explaining some of the background concepts.

Le Livre des Progrès

This one is in French and I don’t know if there’s an English translation, but it’s so remarkable I had to mention it. It’s literally titled “The Book of Progress,” and every page shows how we used to do things, and how we do them now.

Here’s a sample page, headlined “From the plow to the tractor.” The page opposite is about construction machines:

Here’s another sample page, “From the broom to the washing machine”:

I’d love an English translation of this.

“We Were There”

“We Were There” is a series of chapter books for older kids, written in the 1950s and ‘60s, basically historical fiction meant to teach history. The books include We Were There at the First Airplane Flight (pictured above), We Were There at the Driving of the Golden Spike (about the completion of the transcontinental railroad), and We Were There at the Opening of the Erie Canal. I haven’t read these yet to evaluate them.

Those are the most remarkable I’ve found so far. What else is out there? Please suggest more in the comments on Substack (ideally not just science explainers, of which there are plenty, but stories of invention or discovery, or explainers about industrial civilization and infrastructure).

This responsibility exists at all levels: from society as a whole, to institutions, to families, down to each individual. Companies should strive to design healthier products—snack foods that aren’t calorie-dense, smartphones with screen time controls built in to the operating system. There is a role for law and regulation as well, but that is a blunt instrument: there is no way to force people to eat a healthy diet, or to ensure that students don’t cheat on their homework, without instituting a draconian regime that prevents many legitimate uses as well. Ultimately part of the responsibility will always rest with individuals and families. The reality, although it makes some people uncomfortable, is that individual choices matter, and some choices are better than others.

I am reminded of a study on whether higher incomes make people happier. You might have heard that more money does not make people happier past an annual income of about $75k. Later research found that that was only true for the unhappiest people: among moderately happy people, the log-linear relationship of income to happiness continued well past $75k, and in the happiest people, it actually accelerated. So there was a divergence in happiness at higher income levels, a sort of inverse Anna Karenina pattern: poor people are all alike in unhappiness, but wealthy people are each happy or unhappy in their own way. This matches my intuitions: if you are deeply unhappy, you likely have a problem that money can’t solve, such as low self-esteem or bad relationships; if you are very happy, then you probably also know how to spend your money wisely and well on things you will truly enjoy. It would be interesting to test those intuitions with further research and to determine what exactly people are doing differently that causes the happiness divergence.

Similarly, instead of simply asking whether social media makes us anxious or depressed, we should also ask how much divergence there is in these outcomes, and what makes for the difference. Some people, I assume, turn off notifications, limit their screen time, put away their phones at dinner, mute annoying people and topics, and seek out voices and channels that teach them something or bring them cheer. Others, I imagine, passively submit to the algorithm, or worse, let media feed their addictions and anxieties. A comparative study could explore the differences and give guidance to media consumers.

In short, we should take an active or agentic perspective on the effects of technology and our relationship to it, rather than a passive or fatalistic one. Instead of viewing technology as an external force that acts on us, we should view it as opening up a new landscape of choices and possibilities, which we must navigate. Nir Eyal’s book Indistractable is an example, as is Brink Lindsey’s call for a media temperance movement.

We should also take a dynamic rather than static perspective on the question. New technology often demands adjustments in behavior and institutions: it changes our environment, and we must adapt. For thousands of years manual labor was routine, and the greatest risk of food was famine—so no one had to be counseled to diet or exercise, and mothers would always encourage their children to eat up. Times have changed.

These changes create problems, as we discover that old habits and patterns no longer serve us well. But they are better thought of as growing pains to be gotten through, rather than as an invasion to be repelled.

When we shift from a static, passive framing to a dynamic, agentic one, we can have a more productive conversation. Instead of debating whether any given technology is inherently good or bad—the answer is almost always neither—we can instead discuss how best to adapt to new environments and navigate new landscapes. And we can recognize the responsibility we all have, at every level, to do so.