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When evaluating possible design solutions, people tend to overlook options that subtract something from the existing design rather than add something to it, even if the subtractive options would be better (and would make the overall design or system simpler, which has its own benefits). One obvious follow-up question to think about is how this can be addressed in designing systems and organizations and their incentive structures. Musk is famously adamant that you should delete more, but outside of a leader who drills this into everything, I’m not sure how best to institutionalize it.

Alchian criticizes rational profit-maximization as something a firm can’t really do in practical reality, and turns to evolution instead. Only firms that happen to take actions that generate profits survive, the rest “dies”. The outcome is that it looks as if existing firms were actively maximizing their profits. In this framework, it doesn’t really matter if they calculated their optimal behavior, bumbled around and got lucky, or decided to copy what was working for someone else without even knowing why it worked for them. What can I say, I’m a sucker for evolutionary framings. I also really like the role attributed to imitation, spend five minutes on LinkedIn or in a lecture about corporate innovation and it feels on the dot.

An extremely formal theory of design, the formality of which Alexander himself apparently called misguided later on. Design, according to this, is about eliminating misfit between the form of what you are designing and its context (IIRC he explicitly said you can only solve this via negativa). To eliminate all misfit, you map out which of your design variables interact, break the graph down into densely connected but externally loosely coupled subsets, solve each of these in isolation, then put the design solutions together into a whole. It seems extremely obvious that you are not going to do this set-/graph-theoretically for any real-world design problem, but I find the intuition behind the approach interesting and useful anyway. He also claims that traditional cultures have done this “unselfconsciously” by way of cultural evolution, but “modern” designers are operating “selfconsciously” (more rooted in rational attempts at problem-solving) — I don’t find this part particularly relevant, a traditional culture hasn’t had to optimize global logistics or design a CPU, so it’s only natural that the approaches differ.

Alon takes the framework of dynamical systems to understanding physiology and diseases. The parts I’ve read so far go beyond showing how a certain condition arises, and make an argument for why the underlying system is constructed in such a way as to be vulnerable to the condition. I don’t have enough of a background here to judge how fruitful this approach is in clinical practice or pharmacology, but it feels like one of those things that is obviously the right theoretical lens. You’re not supposed to judge a book by its cover, but the cover alone feels extremely insightful.

Be aware that if you read two volumes of his short stories in short succession, you will probably get a bit tired of how clever Borges gets with things.

I don’t really have a “favorite” book, but this is probably the one that most absorbed me and tickled my brain just right. It is also the only one I happen to have a perfect music pairing for.

The titular “Story of Your Life” was the basis of the 2016 movie “Arrival”. Chiang’s short stories are probably the closest contemporary thing I’ve read to Borges and Calvino, highly recommended.

An essay about Duncan’s approach to hiring. I like it for the underlying approach moreso than any of the specific advice he gives: The skill you should be cultivating is seeing yourself and other people clearly, everything else follows from that. Selecting a candidate, then, is less about testing them, and more about figuring them out so you can see where they fit, and whether that’s the position you are trying to fill. I’ve had very little experience sitting on the employer’s side of job interviews, but as a candidate I’ve rarely felt like the person on the other side of the table was actually curious about me. And besides how the interaction feels, I agree that this is how you actually get signal from talking to people.

How do you succesfully achieve large-scale ventures? According to Flyvbjerg, you make them modular (instead of turning each part into a bespoke subproject) and you go fast. Modularity enables learning curves (each module you roll out makes you better at rolling out the next), allows you to experiment cheaply on some of the modules (which in turn feeds back as learning), and delivers value incrementally. Speed allows you to avoid variance: Every year your project goes on is further from the initial conditions under which you planned it and deemed it worthwhile, which increases risks. The empirical argument is about as thin as you’d expect of a business paper, but I like the framework and think there’s a lot to learn from it.

One of the best texts on org structure and management, written in the context of feminist activist groups in the 1970s, but applicable a lot more broadly. A lot of orgs pride themselves on not being hierarchical or overly formal, thinking what happens in absence of such structure is more just and meritocratic. Freeman argues that there is no such thing as a group without structure, and what actually arises in such cases are opaque, unaccountable, and hard to navigate informal structures of influence and power. Structurelessness, then, is the cop-out of people who don’t want to deal with creating structures, or bring a naive kind of skepticism of any and all authority. This doesn’t mean you should put in place a dictator and count on them staying benevolent, or make everything uber-rigid — but you should make structures legible, so they can be navigated on equal footing by everyone while they are working, and deliberately criticized and changed when they no longer are.

The history of molecular biology from roughly the 1930s to 1970s. Be warned, this is not a breeze to read, but it is one of the most remarkable science books I ever picked up. Reading this is about as close as you can get to having a front-row seat to how scientists go from ignorance and confusion to insight and unifying theory without being one yourself. I want to do this one justice and write more about it at some point, ping me if you see this after 2026 is over and I still haven’t!

I had been warned about the absurdity of Kafka as something to be wary of, like it would be a huge slog to get through. Turns out it’s actually hilarious.

Should be read with two things in mind: First, it deliberately focuses on the “systems” part of management (largely to the exclusion of interpersonal aspects). Second, it comes from a particular context that has many properties that won’t apply to most organizations (extremely fast-scaling venture-backed tech startups). With these caveats, I liked it precisely because of how tactical and systems-focused it was. A lot of the material has previously been covered on the author’s blog. Some favorites include the point that teams are an abstraction, and his playbook on tackling migrations, which applies well beyond software.

Highly specific to its US/Silicon Valley context as well as very tactical and concrete, much like the other Larson book I read. Good insight into what staff roles actually look like (I wasn’t aware of the breadth/types before reading this). The “Operating at Staff” chapter had useful and actionable advice for any leadership role that included at least some engineering or even technical product work. I previously encountered similar topics in the context of companies about as far from SV big tech culture as you could get while still touching computers, and I think it would have been a very handy reference there or anywhere in between. Mostly skipped the concrete career advice as it wasn’t immediately relevant to me, so I can’t judge its usefulness.

On second read, I kind of agree with the contrarian interpretation.

I once read Asimov’s “Foundation” and felt transported into the mind of a very smart and somewhat autistic twelve-year-old. Le Guin’s humanity and nuance are the antithesis to that. The love story is great too!

Less a guide for trading, more a tour of the associated worldview. I’ve encountered much of this already over the years by way of economics, interviews with people with a finance background, and “general online exposure”. This is the most concise summary of this kind of reasoning and the associated mental models I’ve seen so far. Recommended reading, even if you keep your money in a cheap broad index fund, as you probably should and which reading this will make you more likely to do.

A large part of what underpins my worldview and politics is summed up in this essay series. The achievements of modernity are mind-bogglingly amazing, but we don’t realize it because their comforts and the infrastructure that supports them are to us as water is to fish. Things could, of course, be a lot better, and we shouldn’t stop trying to fix the bad parts, and trying to get to a point where more of us get to enjoy the good parts. That being said, it annoys me to no end when people act like we used to live in Eden until their favorite bogeyman came along and messed it up. The fix, as so often, is curiosity about how things actually work.

This is the canonical book explaining timeless and domain-agnostic principles of good design. Unfortunately it also seems to fail to apply its own insight to writing and is terribly disorganized. Looking past that, I still found it worthwhile as an introduction.

Norman argues against calls for making tools and technology simple. He distinguishes between things being complex and things being complicated. Complexity, in his view, is an inherent feature of many domains and tasks, or simply of reality, and any tool worth anything in dealing with reality has to match that complexity to be useful. Good design, then, does not simplify the inherent complexity of reality (it can’t), it makes it easier for humans to make sense of and engage with it. It’s been a while since I read this and the details are hazy, and the complicated/complex thing is semantic nitpicking, but regardless of the words you choose the distinction feels like a really important one and matters a lot if you want to design anything useful.

Duttweiler was the founder of Switzerland’s largest supermarket chain, which (of course) is organized as a federation of cooperatives. Randomly found this in my university’s free library one day. The writing is so-so, and far from impartial, but I stuck with it and was rewarded. The origin story of Migros, and Duttweiler’s various other ventures, are a pretty inspiring example of entrepreneurial spirit winning against incumbents trying everything to entrench the comfortable-for-them status quo, majorly improving the situation for consumers. I also did not fully realize before how weirdly protectionist and cronyist Switzerland used to be, e.g. enacting a law (German) that prohibited chains from opening new stores or expanding existing ones.

Roth concerns himself mainly with the design of matching markets. Unlike “normal” markets, the focus is on both sides choosing who they can or want to transact with — students and colleges, employees and employers, organ donors and recipients, etc. The book outlines some common failure modes of matching markets: 1. There’s not enough participants on either side (lack of “thickness”). 2. There’s too much evaluation to be done (“congestion”) and search costs go through the roof. 3. Markets “unravel” because decisions get pushed too early, before participants can adequately evaluate. 4. Mechanisms make it unsafe for participants to express their true preferences, so they have to game the market to get a good outcome. His main fixes are centralized clearinghouses which aggregate participants on both sides (thickness) and enforce simultaneous clearing (no unraveling), and making market mechanisms strategyproof so people are incentivized to reveal their true preferences. Importantly, this only works if the market produces stable outcomes that people don’t want to defect from — otherwise, they will circumvent the process. Roth also introduces repugnance as a reason why some markets (e.g. organ donations) can’t use prices as an allocation mechanism.

Sacks has recently been somewhat disgraced for apparently making up a lot of stuff in his case studies. Luckily I mainly knew him as the extremely colorful and unhinged character of this and his other memoir, so I don’t think my view shifted much and I’d probably still recommend reading this if you like the genre.

Maybe more meme than book at this point. For the gist, read vgr’s post about the central idea of “legibility”. He hints at parallels in tech (waterfall project planning), but I often find myself thinking about this when someone wants to add 20 strictly-validated form fields across 15 rigid workflow steps in some tool, hoping it will make reality conform to the platonic ideal of the process, but most likely just making people hate their job and put everything important in the unstructured “notes” field instead.

Three-part series on the question “what are mental illnesses, really”. Casual debates around this abound — “what even is normal”, etc., but then people still go out and try getting a black-and-white diagnosis of ADHD. This series of posts does a great job exploring the topic. The first part covers taxometrics, a set of methods you use to figure out whether some condition is more likely to be caused by something underlying you have or don’t have, or is just the tail end of trait variation. The second part looks at psychiatric conditions through the lens of dynamical systems. The third part asks whether conditions are more likely to be a failure of evolution, or one side of some tradeoff.

Good exploration of a reality missed by the naive programmer: Information has properties that are socially and/or legally relevant that are not directly present in the information itself. For example, provenance of source code matters — if I copy a small function verbatim in violation of a license, that’s meaningfully different from if I happen to write it myself the same way someone else did before me. Probably an interesting re-read now in light of training of and content generation using AI.

I did not “get” biology in school either — I did well enough, but it felt like memorization and I never really cared about it. I discovered it for real in my 20s, after falling down a bit of a rabbit hole via a hacker type’s youtube video on a DIY genetic engineering project, and this incredible essay really captured my feelings about the subject once things started clicking into place and I grokked its significance.