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When we make something new, people often ask "why don't you just add that to Basecamp?" There are a number of reasons, depending on what it is. But, broadly, making something brand new gives you latitude (and attitude) to explore new tech and design approaches. It's the opposite of grafting something on to a heavier, larger system that already exists. The gravity of existing decisions in current systems requires so much energy to reach escape velocity that you tend to conform rather than explore. Essentially you're bent back to where you started, rather than arcing out towards a new horizon. New can be wrong, but it's always interesting. And that in itself is worth it. Because in the end, even if the whole new thing doesn't work out, individual elements, explorations, and executions discovered along the way can make their way back into other things you're already doing. Or something else new down the road. These bits would have been undiscovered had you never set out for new territory in the first place. Ultimately, a big part of making something new is simply thinking something new. -Jason
Whenever I talk about working in real-time, making decisions as you go, figuring things out now rather than before, I get a question like this... "If you don't have a backlog, or deep sets of prioritized, ranked items, how do you decide what to do next?" My answer: The same way you do when your made your list. You make decisions. We just make decisions about what to work on next as we go, looking forward, rather than making decisions as we went, looking backwards. Why work from what /seemed/ like a good idea before? Instead, work from appears to be a good idea now. You have more information now — why not use it? It's always baffled me how people who pluck work from long lists of past decisions think you can't make those same kinds of decisions now instead. It's all yay/nay decisions. Same process. Before wasn't magical. Before was just now, then. Why not look at now, now? Now is a far more accurate version of next. The backlog way is based on what you thought then. The non-backlog way is based on what you think now. I'll take now. One's stale, one's fresh. We'll take fresh. Then is further, now is closer. There's nothing special about having made decisions already. They aren't better, they aren't more accurate, they aren't more substantial just because they've been made. What they are, however, is older and often outdated. If you've got to believe in something, I'd suggest putting more faith in now. -Jason
One of the great lessons of nature: Randomness is the most beautiful thing. Every forest, every field, every place untouched by humans is full of randomness. Nothing lines up, a million different shapes, sprouting seeds burst where the winds — or birds — randomly drop them. Stones strewn by water, ice, gravity, and wind, all acting on their own in their own ways. Things that just stop and stay. Until they move somehow, another day. The way the light falls, the dapples that hit the dirt. The shades of shades of shades of green and gold that work no matter what's behind it. The way the wind carries whatever's light enough for liftoff. The negative space between the leaves. Colliding clouds. The random wave that catches light from the predictable sun. The water's surface like a shuffled blanket. Collect the undergrowth in your hand. Lift it up. Drop it on the ground. It's always beautiful. However it comes together, or however it stays apart, you never look at it and say that doesn't line up or those colors don't work or there's simply too much stuff or I don't know where to look. Nature's out of line. Just right. You too. -Jason
Not too long ago, we dedicated a 6-week cycle to improving Basecamp's onboarding flows. The aim was to increase conversion from trial to paid by smoothing out the initial experience of getting going, doing a better job of quick-teaching the basics, and making a few things a little bit easier each step of the way. At a high level, these were the projects in that 6-week period: Adding a sample Getting Started project with steps and basic education. Streamlined project creation (reducing it to one step from the previous multi-step process) and exposed all the tools upfront. We also removed the wizard option. Revamped and simplified the blank slates that introduce unused tools. Refreshed the sample project for creating a podcast (great cross-functional example people could relate to). Sped up creating a new account (people used to have to wait a few seconds while the sample projects were generated). Added an email reminder that the trial was ending soon. Dropped the other sample project so we didn't overwhelm with examples. Rewrote the Hey! menu onboarding messages/tips so they were easier to follow. The result? A huge 30% increase in conversion. 30%! As anyone who works in this field knows, conversion improvements (without tricking people) are usually a grind. You're typically thrilled to see any results, and often have to parlay small single digit improvements into more single digit improvements, hoping to eventually hit double digits. Yet somehow, this time, we managed to find our way to a 30% increase. And we have no idea how. And we don't care to find out. What was it exactly? Was it just one of the things that really mattered? All of the improvements together? A handful of small improvements that tipped into something bigger? Don't know, don't care. We spent six weeks on the work, we did our best, and something worked out really, really well. We're thrilled with the outcome, and that's enough for us. That was the point in the end, wasn't it? You could make the argument that we should have tried each thing separately, measured each impact, and then decided where to go next (or known when to stop). You could make the argument that changing so many things at once makes it impossible to know which variables actually mattered. You could have argued we should have been more rigorous in our evaluation so we could learn something fundamental we could apply to a future project. You could argue all those things. While you were debating those points, spending months teasing out answers, or testing each change in high-traffic succession for statistical significance, we were already basking in the results and moving our product teams to the next project. In six weeks all the work was done, we did our best, and it worked. The point wasn't to know, it was to do. And it was done.
Precision. Certainty. Specificity. Everyone wants to know exactly what and exactly when, and they want a statistic attached to corroborate it. But numbers are rarely answers — just as projects are rarely math problems. Where are we in this process exactly? How far along in this project are we exactly? Where does everything stand absolutely? There are no equal signs after those statements. There are all sorts of things in life that you want but can’t have. Exactly is one of them. Creative work thrives on subjective interpretation. You can establish an end date that’s fixed, but where you are is really up to you. Yes, in some specific businesses with ultra-specific standardized widget-making processes, it’s possible to expect and require perfectly precise forecasts. But that’s almost certainly not yours. The quest for knowing exactly has preoccupied entire industries with impossible answers, and pushed entire categories of tools towards the wrong trends. You can find out where things stand. But the answer is a human one, not a digital one. It’s fuzzy, it’s a bit abstract, and it’s more a feeling than a figure. It’s taking stock of all the things that can’t be measured, and speaking or writing the actual answer, not pointing to an abstract number. “63” means nothing. “We think next Tuesday” means something. As long as humans are making things, it’s best to ride along with human nature. To follow that seam, rather than to cross it. This is what we’ve been doing as of late in Basecamp. We’ve been adding imprecise, humanizing features to project management. If you want false precision, you have plenty of other choices. If you want a system that represents what’s really going on, here we are. For example, take Basecamp’s Hill Charts and Move The Needle features. They are visual representations of where individual scopes of work, or entire projects stand, based on the flexibility and subjectivity of someone’s mind and hand, not a computer’s representation of rigidity. Both tools use graphical markers — either a dot or a needle — that people physically move around and place on a subjective scale based on an understanding of where something stands. It’s not precise, it’s approximate. “About here.” “Feels like we’re this far along”. That imprecise wiggle room is also known as the truth. You can see how both features work here... Hill Charts: https://www.youtube.com/watch?v=sZKJD-l3W6E Move The Needle: https://www.youtube.com/watch?v=D0urtIQrFyQ Subjectivity is a good thing — a human thing. It’s with the grain. Subjectivity and productivity are partners, not adversaries. Over time we’ll be adding more imprecision like this to Basecamp. The more we can reflect reality, not a false premise, the better off teams will be. We know that, we believe that, and we’ll continue to promote that. -Jason
More in life
This is a weird moment. I have a hunch that we’ll look back later and tell ourselves stories in which it seemed obvious where things were going. So I wanted to lay down a little time capsule and register just how confusing everything felt at the time. This is a personal view from the trenches. 1. If you’re a normal person, you may feel that AI has been good at math for a long time.1 But for “real” math, this is roughly what it felt like: When Feeling 2024 LLMs are toys Summer 2025 LLMs are interesting toys Early Autumn 2025 LLMs can do useful stuff sometimes Mid Autumn 2025 …a lot of stuff Late Autumn 2025 …a lot Early 2026 holy god Even having been astonished by AI progress in the past, it was still astonishing to see AI move in a matter of months from a mildly-useful toy to something that many people now consider almost impossible to do without. 2. Personally, I’d say that current AI makes me around 50% more productive at “doing math”. That’s despite the fact that AI can now often do in a few minutes things that would have taken me hours to do myself. The main reason is that my concept of “doing math” involves various steps, like coming up with ideas for theorems, trying to prove them, writing things down, reorganizing everything to make it more clear, and so on. AI is good at some parts of this, but still pretty bad at others, so the overall speedup isn’t as high as you might expect. (Even if planes could go 10× faster, your door-to-door travel time from your apartment in London to your friend’s place in Rome wouldn’t decrease that much, because you still have to get to the airport and wait to board.) 3. But then, I’m still (sort of; more on this later) trying to follow my old “doing math” workflow, just automating certain parts of it. That can’t be right, can it? If we found a way to make batteries at 1% of the current cost, we wouldn’t just say, “cool, batteries are cheap now”. We’d find all sorts of new ways to use batteries that didn’t make sense before, and change the ways we generate power to account for all these new batteries. Probably our whole energy system would end up transformed. Analogously, AI being good at proofs should lead to a very different equilibrium. But what is that?2 4. It seems healthy, at the moment, to maintain a fluid view of what “doing math” means. But at bare minimum it surely involves understanding the math that “I” am “doing”. So long as that’s part of the workflow, it would seem to impose a limit on how quickly “I” could “do” math, no matter how good AI is. One obvious conclusion would be that my tiny organic brain should be removed from the workflow entirely. Maybe! But it’s interesting to speculate about how much faster things could go while still keeping a human brain in the loop. I think the answer is: A lot. 5. Contrary to what you may have heard, humans are not obsolete for math. Not yet. The most obvious reason is that AI is still bad at writing. People often complain that AI writing overuses various patterns or lacks a unique voice or whatever. I don’t disagree, but I always felt like there was a deeper issue that I couldn’t quite put my finger on. With math, this becomes very clear: The problem is it doesn’t make any goddamned sense. Really, I think it’s deeper than being bad “at writing”. It seems like AI is bad at thinking in certain ways that manifest as bad writing. 6. When novices do math, they often create “spaghetti-code math”: Imagine a long document with lots of equations, but if you point at any one equation, it’s hard to say what assumptions underpin it, or how it fits into the bigger picture. Any random idea that was mentioned 17 pages ago might be used again at any time with no warning. With experience, people learn to break complex arguments down into modular pieces with a good “API”, in the sense that the assumptions and conclusions for each part are relatively simple. Somehow, AI has developed these amazing technical skills, while still being bad at creating abstractions So the arguments it gives are long and tangled and bewildering. This is what makes AI math seem so alien. 7. Now I know what you’re thinking. Maybe AI isn’t bad at creating abstractions. It’s just bad at creating abstractions that work for you, human with tiny organic brain. From a distance, that seems plausible. And it might be true to some degree. I have high-bandwidth access to a human brain. It’s not crazy to imagine that this gives me an advantage in predicting how a human brain would react to a given presentation of ideas. But still, I claim that current AI is objectively bad at creating abstractions, regardless of if the consumer of those abstractions is human or artificial. Here’s why: If I ask AI to prove something hard, it will produce a proof that starts simple and gradually descends into a rampage of complexity. If I tell it to break that proof down into self-contained pieces, it will produce lemmas that each take a full page just to state. If I ask, “Why? Why have you done this to me?”, the AI will say, “This is as simple as it gets. It’s not that bad. What’s your problem?” But if I argue with the AI for a while, I can start to understand the basic contours of the argument. And I’ll ask, “Why don’t you first just try to prove blah using only assumptions blahblah, without worrying about blahblahblah? Wouldn’t that be easier?” Often, this doesn’t work, either because my idea was bad, or because the AI isn’t smart enough to do it. (Which one? Who knows!) But after a few attempts, I can usually find something that that works, and the AI can use it to re-write the proof in a somewhat simpler way. If I repeat this many times, I can gradually chip away at the complexity. But maybe that’s still just making the proof easier for humans to understand? No! For one, this usually makes the proof shorter, and sometimes dramatically shorter. More importantly, during this process, someone (maybe me, maybe the AI) will often realize that there’s a different proof strategy that gives a stronger result. Instead of proving x < 9.7, now we can prove x < 7.1. Thus, artisanal human abstractions are “objectively” useful. 8. Some people might claim that it’s already possible to automate some of what I’m doing with some multi-agent Rube Goldberg thing. I’m not sure, because my experience has been that investing lots of time into clever AGENTS.MD or custom skills or harnesses isn’t a great use of time. That stuff is somewhat helpful, but things are changing so fast that the knowledge has a short half-life. Also, the majority of information about these tricks seems to come from people who are excited about the technology itself. On the other hand, the automator told me to try typing, “ask independent agents for an adversarial review”, and I’ve found those seven words to be quite helpful. I wish I could find a source with boring best practices for people who are happy to stay away from the bleeding edge. 9. Another reason AI doesn’t improve my productivity more is that it is often the green light, the orgastic future that recedes before me. I’ll ask AI to do something, and it will almost seem to do it. So I’ll ask for changes, and it seems to help, but it’s still not quite right. Hours later, I’ve accomplished nothing and learned nothing. Eventually, I start over, either doing it myself or with AI on a very tight leash, and I’m done in 30 minutes. Each time this happens I feel foolish and tell myself not to do it again. But it’s very hard to judge in real-time. 10. Of course, I’m not claiming that AI is intrinsically bad at writing or thinking or abstraction-making. It’s gotten better at all of these and I expect it will continue to do so. All I’m saying is that as of Autumn 2026, humans still have a lot to contribute, and it’s not just better exposition. 11. Did AI actually get much better at math in Autumn 2025? Or perhaps, in the grand scheme of things, human math ability falls into a narrow range? 12. Some people are responding to the new reality by gleefully using AI to generate lots of semi-OK-ish papers and puking them up on arXiv. I understand the incentives that drive this. In some sense, it seems like the logical response to the new reality. (After inventing cars, we started taking a lot more low-value trips.) But I don’t think this is socially useful, and I tend to think everyone will soon agree that this is embarrassing. But… I’m not sure. Maybe it is socially useful? And I find myself feeling vaguely jealous of the people who are wired this way, who instead of feeling blinkered, see opportunity and plunder. 13. Want an optimistic pro-AI, pro-progress take? OK. If I take my old “doing math” workflow, AI makes it strictly better. A lot better. It’s like having a hyper-conscientious intern that is smarter than me in many ways, works at incredible speed, and knows approximately everything. Yes, AI sucks at writing and making abstractions. But if I’m disciplined and try to mostly do the math “myself” while asking the AI for help, I can explore ideas much faster than before, make far fewer mistakes, and produce something just as clear as before, while developing just as much understanding. I can ask, “What happens if you do a Kofuni expansion?”, and get an answer instantly. I can ask for illustrations or interactive visualizations and see them instantly. Arguably, we’re incredibly lucky to be alive at this moment, when these new tools have opened up this new territory to explore. 14. And isn’t that what I wanted? Back in 2024, I bemoaned that what AI most seemed to struggle with was getting the facts straight: Perhaps the old science fiction robots were a projection of our fantasies. I want my style and voice to be precious and uncopyable. I want an AI assistant that will tirelessly hunt down facts while I provide creativity and panache and magic. But that’s not how it is. So we should remember: In a scenario where AI is useful but humans retain some comparative advantage, we might not like where that comparative advantage lies. At least with math, and at least for the moment, I get to be Kirk, while the AI is Spock. (Even with other types of writing, my impression is that since 2024, AI has improved more along Spock dimensions than along Kirk dimensions.) So that’s nice. 15. In the last few months, it frequently happens that a collaborator will use AI to generate something, and then send it to me and ask me to check if it’s useful. I can see why this seems reasonable. (Really, I do.) But when it happens a lot, one can’t help but wonder what the point of the human collaborator is. 16. On the other hand—with creative writing, my advice is not to use AI to write, at all. Ask questions if you want, but write every damned word yourself.3 With math, I don’t feel like such a hard line makes sense. It seems that creating something good still requires a lot of human involvement, to the point of agonizing over notation, repeatedly re-organizing things, and heavily editing individual sentences. Still, I find that if people make that effort, they can create something just as good as they could by hand, while still being perhaps 50% AI written.4 So I don’t mind if people send me “centaur math”, provided they take responsibility for the contents. The problem is that when working with AI, it takes constant paranoid vigilance to avoid letting things slip beyond your understanding. I often find myself wanting to awkwardly ask people, “OK, you obviously used AI to make this… which is OK… but how much of this do you vouch for? If we have a meeting to discuss it, will you crumble the moment I start asking questions?” 17. When I’m in abstraction-creation mode, I feel like I’m doing something intelligent. When yell at AI about bad notation and writing, I feel like I’m providing taste. But I often find myself giving feedback that seems incredibly basic. Things like: This is a mess, clean it up. Or: Do you need that assumption? Or: If you can’t solve this problem, why don’t you create a simpler version and try to solve it first? On the one hand, I guess a lot of what good human managers do amounts to giving very basic feedback. On the other hand, it’s hard to imagine that I have much durable advantage in doing this kind of thing. So I wonder how much durable advantage I have in abstraction-making or taste, too. 18. A common reaction to AI getting better at proofs is that humans need to focus more on exposition. This has an obvious Bourdieu-esque criticism: Human painters used to strongly value photo-realism, but after cameras were invented they convinced everyone that, oops, photo-realism isn’t important, what matters is expression, which coincidentally cameras can do. Isn’t a shift towards exposition in math just a way for humans to move the goalposts and justify their own continued relevance? The only credible way I see to avoid that criticism seems to be to bite the bullet and say that, in fact, math should have already have focused more on exposition, even before AI. Personally, I strongly believe that this is true. 19. I think the biggest reason I’m not more productive is: What’s the point? In the past, if you came up with an elegant proof, you’d tell yourself, “I made a thing! Other people will read and use my thing! I am advancing human progress.” You can still tell yourself that now. But you have to wonder, will anyone actually read your proof? Or will they just ask another AI to explain it to them? Will your proof help their AI explain it better? For how long? If you just waited a year, would some other AI produce that same proof independently? Alternatively, if you’re a normal person, you might think that LLMs still struggle with basic arithmetic? ↩ AI progress is so fast, compared to society’s ability to adjust, that we may never actually see what “the current equilibrium” looks like. ↩ This essay, if you’re wondering, was created by writing every damned word myself and not asking AI anything. ↩ You could have a whole debate about what that precentage means. It’s not the number of words written by AI that really matters, in my opinion, so much as who is responsible for the overall structure of the ideas. ↩
I always liked stories about eccentric royals doing mad things. Queen Christina of Sweden reportedly had a miniature cannon made for firing tiny cannonballs at fleas in her bedroom. Louis XIV made a proto-reality-show of his daily routines, allowing an audience of courtiers the honor of watching him dress, bathe, and use the toilet. Peter the Great famously returned from […]
The (Re)insurance AI Landscape
to become the version of yourself needed in the next chapter
