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Mapping out the tribes of climate

from Nadia Asparouhova [alt+shift+b] in startups

Climate is a gravity well for talent, but why don’t other, equally impactful topics attract talent in the same way? Why isn’t everyone dropping everything to work on homelessness, or global poverty, or curing cancer? With many peers in tech now working on climate issues, I tried to understand why this topic holds such purchase for so many people – and its incredible staying power over the decades. Initially, I started with the idea that climate was an attractive industry for “doomer” types, and I painted their motivations monolithically. I was searching for the one weird reason that was causing hordes of people to drop what they were doing and march, hypnotically, towards the same problem space. What I found instead is that while the media still portrays climate as a simple question of beliefs, the climate field has long moved on to diversified solutions. Whether one believes in climate change is no longer the interesting question; now it’s “What do you think is the right approach?” Pass through the asteroid belt of climate doomerism, and the universe expands into a rich panoply of different climate tribes. People who work in and around climate don’t all believe the same things. Instead, they inhabit a parallel, mirror world that looks a lot like the non-climate world. Just like in the regular world, there are factions, politics, and competing belief systems. For example, I did not find that people who are interested in climate fall cleanly along a certain political line of thinking, or even a shared set of values or goals. Climate is frequently coded as a left-leaning issue, but there are also centrist and right-leaning people who operate in different factions. Nor do climate people all agree on the right solutions to pursue. In some cases, they believe other tribes are actively harmful to their cause. The enemy, in their minds, aren’t climate deniers, as we might have seen a decade or two ago – they’re other people working in climate. For someone who doesn’t work...
30th Nov 2022

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More from Nadia Asparouhova

How to do the jhanas

The jhanas are a series of eight (or nine) altered mental states, which progress from euphoria, to calm, to dissolution of reality – culminating in cessation, or loss of consciousness. They are induced via sustained concentration, without any external stimuli or substances. This is a practical guide on how to do them yourself. Table of Contents Jhanas are learned by doing, not reading What the jhanas feel like Why learn the jhanas? Hours practiced Retreat I (March 2024) Retreat II (June 2024) Practice between retreats General tips for practice Experiment with different techniques Flow state » relaxation A jhana is like a sneeze Pace yourself and listen to your body Instructions for accessing the jhanas How I entered J1<>J4 How I entered J5<>J7 How I entered J7<>J9 What’s going on under the hood? Impact of the jhanas In conclusion: try it! Notes Jhanas are learned by doing, not reading The word jhana comes from Buddhist scriptures, where they were first described. However, as many meditators like to point out, jhanas predate Buddhism. The Buddha experienced jhanas spontaneously as a child, and likely is not the first or only person to have experienced them. I am not a Buddhist, nor would I describe myself as a meditator. I’m just a curious person who wanted to try a new thing, and was gobsmacked by what I experienced. Prior to attempting the jhanas, I’d guess that I had maybe 30 hours of lifetime meditation experience, scattered over a decade or more: in other words, not much. But with just over 20 hours of practice, I progressed through all nine jhanic states. I would still say that I do not like “meditation” for its own sake, though I enjoy meditative activities (such as exercise, a deep 1:1 conversation, writing, or other creative work). But I don’t think jhanas are a form of meditation. Rather, they are a rare technology whose instructions are encoded in our bodies. Jhanas are an algorithm in the oldest sense of the word: a set of instructions that, if executed correctly, solve for a problem that you may not have even realized you’ve been trying to unravel. They are an Easter egg hiding in the game of life. [1] If jhanas are a technology that exists a priori to Buddhism, then I find it strange how they are discussed and taught by most practitioners today, which is pretty much only through a Buddhist lens. The actual, mechanical instructions are buried in what I’d say is akin to computer science: a lot of complex language and spiritual theory, which - it is often implied - are inseparable from practice. I understand the purpose of the ornate cultural context that is chained – albeit beautifully – around the jhanas. Powerful technology should be embedded in a community of norms and protocols that help people make sense of them and integrate them safely into their lives. And jhana practitioners have done this part a bit too well. It is no wonder that jhanas have quietly passed through civilization for centuries, protected like a rare jewel inside a cave of wonders, with little attention from the outside world. It’s just that, well. If you had recently figured out how to code, and realized it was really quite simple and teachable to others – then looked around, and all you saw were computer scientists warning off would-be developers from making software, claiming that they needed to understand all the underlying theory before attempting to write a line of code – wouldn’t that make you want to open up a text editor and type out your own version of things? This post is not intended as a reckless act. Rather, it reflects my personal belief that some types of knowledge are best acquired implicitly, not explicitly. You probably have little interest in reading about grief, or parenting, for example, unless you’re imminently facing these experiences. To return to the software analogy: these days, most developers don’t learn how to write software by studying computer science first. They learn by tinkering around. They print “hello world.” Maybe they have a problem they want to solve for, so they make a simple app. As they become more experienced and run into more sophisticated problems, they might then revisit the theory to understand why things work the way they do. This has been my experience with the jhanas. I read little about them beforehand, instead receiving minimal instruction and letting my intuition guide the experience. When I experienced things that were confusing or beyond what I could explain, I went back and read about the underlying philosophy to understand what was going on. (As a concrete example: I tried listening to Rob Burbea’s talks on the jhanas before I’d ever tried them myself, and found myself rather lost and bored. Later, however, I went back and consumed his talks voraciously; they had taken on new meaning. I now find them very valuable.) So that’s what I’m going to attempt here. Instead of bogging you down with theory, I’ll share the basic instructions that helped me access the jhanas, going from first jhana to cessation in just over 20 cumulative hours. More than anything, I want to instill confidence in anyone reading this post that you can absolutely do this, regardless of how much you meditate. The most important advice I can give is to relax, have fun, maintain a playful and curious mindset, and don’t overthink it. Just follow the instructions as best you can. But first, just a bit more information and background, so that you know what to aim for. What the jhanas feel like Jhanas are like swirling the paintbrush of your consciousness across a palette of altered sensations. These states vary in intensity; some are comparable to psychedelics, MDMA, or dissociatives. Here’s how each state feels to me. I’ve kept my descriptions vague, because I think it’s more fun to discover them yourself. I’ve also included their short descriptions in parentheses from this wiki. J1 (Pleasant Sensations): euphoric, bright, sunny, yellow J2 (Joy): gratitude, beaming, radiating, hot pink J3 (Contentment): content, reasoned, soft, wide, robin’s egg blue J4 (Utter Peacefulness): dissociative, stillness, bathtub, cashmere, felt, muted lavender J5 (Infinity of Space): disembodied, infinite, outer space, grayscale J6 (Infinity of Consciousness): beauty, benevolence, grace, psychedelic, rose petal pink J7 (No-thingness): —— (nothing in nothingness) J8 (Neither Perception Nor Non-Perception): surreal, dissolution, black velvet studded with colorful ’80s rhinestones and gold that wink in and out of existence J9 (Cessation): [cannot be described; no direct experience; consciousness is switched off] Why learn the jhanas? Why bother trying the jhanas? Are they just a weird party trick? I’ll talk about this more later, but in short: jhanas are a good way to cultivate your attention. When you can skillfully control, deepen, and direct your attention, you may discover that life is easier and more malleable than it seemed. It may sound hyperbolic, but jhanas are the closest thing to magic that I’ve experienced in my adult life. J1-J4 are especially useful for altering your moods and states of reality. I especially find jhanas to be an important skill in a modern context, where everyone is perpetually distracted. Mastering proactive control over one’s attention is an increasingly rare superpower. (Spoiler alert: this isn’t the whole story of the jhanas. In fact, practicing the jhanas isn’t really the point of the jhanas at all. But I’ll cover that towards the end of this post. Let’s try to get to “hello world,” first.) Hours practiced I primarily learned the jhanas on two Jhourney retreats in 2024. Jhourney is a company that takes a pragmatic, fun, and accessible approach to teaching the jhanas to beginners. They are markedly different from a typical meditation retreat, and I’m grateful they exist, because I don’t think I would’ve learned the jhanas otherwise. Retreat I (March 2024) On the first retreat, I experienced jhanas 1 through 7 over the span of four days, at which point I left the retreat early to process what I’d learned. (You can read an account of my experience in Asterisk magazine.) Here’s an approximation of how many hours I practiced per day; cumulative hours practiced on a given retreat, (t); and when I experienced each jhana for the first time. Each session lasted from 30-60 minutes, and I never meditated solo (not counting group sits) more than three times per day. Aim for quality, not quantity. Note: (t) includes walking meditation time + group sits (where the goal wasn’t always to practice the jhanas). Dedicated jhana practice time is probably ~80% of this number. Day 1: 3 hours total J1, possibly J2, t <1 hour J2 for sure, t = 2.25 hrs Day 2: 4.75 hours total J3, t = 4.5 hrs J4, t = 5.5 hrs Day 3: 5 hours total J5, t = 10.75 hrs Day 4: 2 hours total J6 and J7, t = 14.25 hrs Total hours meditated on Retreat I: 14.75 Retreat II (June 2024) On the second retreat, I additionally experienced jhanas 8 and 9 (meaning, cessation) over the span of two and a half days, at which point I left the retreat early to process what I’d learned. Day 1: 1.5 hours total Possibly J8 and J9, t <1.25 hrs Day 2: 3 hours total J8 and J9 for sure, t = 3 hrs Day 3: 2.25 hours total Total hours meditated on Retreat II: 6.75 Practice between retreats In the three months between retreats, I only did 2-3 dedicated practice sessions, and not very seriously (maybe 15-30 min apiece?). But I did “practice” the jhanas all the time, in the sense of being aware of my body and mind, how I was reacting to things, and guiding myself towards different mental states. I popped into J1 all the time throughout the day, almost reflexively, and I’d sometimes tap into J2-J4 when I wanted to deepen certain sensations. This felt more like wielding a skill, though, versus dedicated practice. General tips for practice To access the jhanas, you basically induce the “opposite of a panic attack,” as I’ve heard others describe it. Before getting into my specific method (see next section), here are a few general recommendations. Remember, again, that the number one most important thing is to relax, have fun, and don’t overthink it. Experiment with different techniques It really does seem that everyone is different, so my method may not work for you. It’s your brain; go with what feels right. For example, to invoke a positive sensation, some people tap into feelings of gratitude, forgiveness, or altruism. I preferred a fairly mechanical, detached approach where I just thought of my brain as a machine, and which levers I needed to pull to induce various sensations. Flow state » relaxation For me, at least, the trick to jhanas was not “relaxation,” but something closer to “flow state.” Relaxing, to me, is like being at ease, where no new thoughts come to mind. Flow state, on the other hand, means I’m highly engaged with a task for a sustained period of time, and that one task is all that matters. IME this is at odds with how I’ve been told to practice mindfulness meditation. So if you’re struggling to “relax,” maybe try tapping into flow state instead. A jhana is like a sneeze You’ll hear meditators talk about not “grasping” onto sensations or trying too hard with the jhanas. This can be frustrating: what does it mean to both try, and not try too hard? I think of it like sneezing. Sneezing requires some degree of intentionality, but it’s a physical reflex that only happens if you don’t think too hard about it. Like sneezing, jhanas are more like a release than a force of will. Pace yourself and listen to your body For me, the jhanas came hard and fast. I struggled at one point between wanting to slow down, versus feeling like I was “supposed” to practice more. And I didn’t trust what I was experiencing at first, which led me to push myself more than I ideally would’ve. Jhanas are weird because they’re considered a form of meditation, so there are a lot of meditation-like protocols around them (put in lots of hours! no phones or devices! avoid talking to people!). But I think these recommendations are just meant to help you cultivate the attention required to invoke jhanic states: they don’t help you process the experience itself. If you’re going through a transformative experience, locking yourself in a room without friends, family, or outside support might not be such a good idea. So, make sure you listen to your needs. If things get overwhelming, it’s okay to stop, process, and ground yourself. Spend time with your friends. Go outside. Hug your pets. Write about it. You can always come back when you’re ready. The biggest milestones for me, which prompted seeking outside input to make sense of my experience, were: J5, J6 and J7 (experienced together), and J9 (cessation). At these points, I made sure to slooowww down and process what was going on. If you get to any point in your practice where you’re feeling WTF about it, I’d highly recommend Rob Burbea’s talks, which are thoughtful, philosophical lectures on each jhana. Instructions for accessing the jhanas Here’s the method I used. If it doesn’t feel right to you, I suggest experimenting with different techniques. In particular, try switching what you use as your “object of joy,” and see if that helps. (Note, of course, that you will likely progress through these stages over multiple sessions, spread out over days or weeks or months. Feel free to just read the first set of instructions, then continue only once you’ve mastered each stage. Pace yourself!) How I entered J1<>J4 Relax your body deeply, clearing your mind of any distractions. (My personal hack: try falling asleep, but stop before you actually do.) Think about someone, something, or a memory that sparks a pure, uncomplicated feeling of joy. I thought about my child. Don’t focus on the thing itself, but on the joy that arises as a result of thinking about it. Allow that joy to grow, then loop upon itself, as you feel more and more joyful. If the joy begins to dissipate, “pulse” more joy by thinking about the person/thing/memory. Don’t think too much about what you’re doing. Your hands and chest might tingle; that’s a good sign. Eventually, the euphoria will hit. Now you’re in J1. To progress to J2, don’t do anything. Just stay in the moment and enjoy the sensation. If it doesn’t dissipate, it will begin to evolve on its own. Notice how it’s changing, until you find yourself in a qualitatively different state. Repeat the previous step to get to the next jhana. Stay with that state, be in the moment, don’t try to change or interact with it. It will evolve into the next state, and so on. As you get familiar with each state and what they feel like, you’ll be able to locate them in your body and move between states using muscle memory. So to get from J1 → J4, I just move the focus of my energy from my head (J1), to heart (J2), to stomach/groin (J3), to flowing out through my legs and all around me (I call this one, J4, “bathtub”). To move from J4 → J1, reverse the order. As I became more comfortable with the jhanas, I dropped the first relaxation step. Then I dropped my meditation object, or “trigger.” With a bit more practice, I found that I could pop into J1 instantly and progress through my “jhana flow” from there. How I entered J5<>J7 J5-J7 work a little differently. Because they are dissociative, you no longer have your body for reference. The technique that worked for me was thinking about expansion (or “softening”) and contraction. J4 → J5: Expand and soften my awareness, as if the walls of the “bathtub” were falling away. Imagine you’re sitting in the pitch dark and trying to sense what’s around you, or you’re in a room and you sense someone behind you. You’re not focusing on anything specific, just trying to be more aware. J5 → J6: You’re staring at an infinite space; now become the infinite space. For me, this feels like floating “forward,” as if my consciousness is merging with the space before me. J6 → J7: I just stay in J6, keeping the sensations soft, until it fades into J7. Sometimes I can accelerate this process by reminding myself that the J6 experience is finite, and it has to end sometime. But I find that J6 tends to dissolve on its own. To get back down from J7 → J4, I contract my awareness. I remember that I have a consciousness (J6). I remember that there is space (J5). I remember that I have a body (J4). Then it collapses down, like closing a book. As you get more comfortable with J5-J7, you can move between states deterministically by directing your “gaze” (I think this is actually my attention, but I think of it as my gaze): To get from J4 -> J5: I gaze sort of out and slightly down J6: I glide forward into the space J7: I sort of gaze inwards, into my center. This feels like a “flattening” of self, collapsing into a line or a horizon. How I entered J7<>J9 Jhanas are typically separated into two buckets of “light” (J1-J4) and “deep” (J5-J8), but in my view, J7-J8-J9 form their own special trio, because J8 is a tricky state to navigate. J9 can’t be directly experienced, because you’re unconscious – just as how you can’t experience being under general anesthesia. And J8 is a fleeting, unstable state, because noticing you’re in it, beyond the faintest bit of awareness, will send you back to J7. But J7 is stable! So we can use that as our anchor. Think of it as your base camp before attempting to summit Everest. The helpful advice I received was to focus on getting very comfortable with J7, deepening and maintaining that state, and then - when you’re ready - “shooting the gap,” or catapulting yourself across J8 to land in J9. I think of it like skipping rocks. A light touch will get you to the other side (J9), but if you’re too heavy-handed, you’ll sink into the pond (end up back in J7) and start over. (I’m sure there is a way to train yourself to linger in J8, and I’d be curious to cultivate this skill, but so far, this method works for me.) The best way I can describe J7-J9 is to compare it to lucid dreaming, where you’re dreaming, but strangely alert. J7-J9 is like that, but for the act of falling asleep. First, the heaviness of your body sets in (J7). Then, nonsensical sounds and thoughts begin to arise, also known as hypnagogic hallucinations (J8). And then you’re asleep (J9). If you want to cultivate your J7-J9 skills, I’d suggest paying attention to what it feels like to fall asleep at night, noticing the progression from wake to sleep. The difference is you’ll be highly aware – not drowsy – while in the jhanas. So, to get from J7 to J8: relax more deeply into J7, be patient, and notice where reality is breaking down. There are likely fleeting, nonsensical thoughts floating through your mind; try to ever-so gently notice them. Notice that they’re nonsensical. But don’t react to them. It’s hard to describe how this works. In J8, you have to get comfortable with the fact that they may be thoughts or non-thoughts, you might be noticing or not-noticing, things could be happening or not-happening…and just let it be. The image that comes to mind for me is some cartoon I watched once (maybe Adventuretime, or Rick and Morty?), where the characters end up in a bizarro world where their lines and shapes and colors are drawn in strange ways, and the background is now white and empty, but they’re still talking to each other. Kinda like Picasso’s bulls: [Source] Everything is surreal and breaking apart, but you have to be cool with it. You might flit between J7<>J8 a few times before landing in J9. J9 is equally bizarre, because you’ll only know you experienced it after you come back. You know how if you’re given general anesthesia, and the doctor tells you to count down from 10 to 1, and you’re counting, totally awake, feeling so confident that you’ll make it to 1…and next thing you know, you’ve woken up again, and the whole thing is over? That’s what J9 feels like. You’re alert, you’re alert, you’re alert…annnnd, you’re back. Hey, where were you? It feels like you winked out of existence for a bit. I found that I almost always regained consciousness in J7 – usually in a very deep and delicious state of absorption. You can also play with inducing multiple cessations within one session – going from J7-J8-J9-J7, and looping that a few times – before voluntarily ending the session. What’s going on under the hood? I said I wouldn’t spend too much time on theory, but if at this point you’re still wondering how it’s possible that we can think our way into psychedelic experiences and loss of consciousness, congratulations: I know about as much as you do. Jhanas are still understudied in academia, though interest is growing, and there are a few papers that use EEG and fMRI data to demonstrate that something is actually happening inside people’s brains when they are in jhana that’s comparable to other altered states, like psychedelics or being in a coma. [2] The explanation that follows has nothing to do with such literature. It’s just me theorizing, based on my own experience and what I’ve read from others so far, on what I think is happening. But I really have no clue! So, don’t read this as an authoritative take; just a peer-to-peer musing out loud as how I would explain these phenomena. (Please note that these aren’t solely my original thoughts, but a composite of things I’ve read and mashed together from all over the place. I’m not sure what I’ve learned where anymore, so proper attribution feels impossible, but I am not claiming these as my views and shouldn’t be credited as such.) The key ingredient of the jhanas seems to be attention. If you’ve ever tried to make the best of a bad situation, you’re already familiar with this concept. How, and where, you direct your attention, can heighten and intensify an experience. If you get stuck in an anxiety loop, you can make the experience worse. Everything that happens, no matter how objectively “good” it is, will be re-coded as “bad.” But if you try to relax and look on the bright side, you’ll find that your experience actually improves: things that seem “bad” will be re-coded as “good.” To some degree, then, our perception of reality is influenced by where we direct our attention. Now imagine that we’ve plotted all emotions along an x-y axis, where x = valence (positive/negative) of emotion, and y = intensity of emotion. Negative emotions (x<0) might include things like anger, anxiety, and fear. Positive emotions (x>0) are things like euphoria, gratitude, and pride. Attention is the thrust, or force, that you can exert to move your state along the y-axis (i.e. intensify any emotion), regardless of its x-position. [3] This is why the metaphor of jhanas as “inducing the opposite of a panic attack” is so helpful. It’s the same y-value, just with a positive rather than negative x-value. But how do we know the x-positions of our positive emotions? Why are J1-J4 organized the way they are? It’s often said that the jhanas aren’t any different from the positive emotions that we feel in the “real world.” For example, if you start dating someone new, you might progress from the giddy honeymoon phase (J1); to being so joyful and grateful to know this person (J2); to feeling content with, and proud of, the relationship you’ve built (J3); to viewing the relationship as your anchor in the storm of life (J4). I don’t know why positive emotions follow this progression (though I’m sure someone else does), but the point is that jhanas aren’t doing something weird and unusual here. They’re exactly how good feelings evolve in any other circumstance, just with the extra “amplifier” of attention (higher y-value). If our emotions are typically capable of lifting us into the sky and back down to Earth, highly concentrated attention enables us to shoot them into outer space (more thrust!). But if your attention is scattered, you won’t go very far. So, learning how to cultivate and sustain attention is critical to practicing the jhanas. [4] What about J5-J9, which aren’t associated with magnifying any specific emotion, but rather the deconstruction of reality itself? Well…you got me there. I’ve been told (though haven’t read about this myself, so I may be spouting ideas incorrectly here) that J5-J9 are all actually part of J4: so, once you’re anchored in this state of deep calm and equanimity, your brain starts dismantling your consciousness, piece by piece, until there is nothing left. Perhaps it’s akin to how, when you’re very relaxed and calm, it’s easy to fall asleep? But I’m especially baffled by J6, which is an intensely beautiful and psychedelic experience that’s oddly sandwiched between two very dissociative ones (J5 and J7). I wish I had answers here, but I’m still not sure how to explain J5-J9. Impact of the jhanas Now that I’ve taken you all the way through this post, I’ll give you the plot twist: jhanas are cool, but they’re not actually the point. The valuable part is the insight gained along the way. Jhanas, breathwork, psychedelics, MDMA, etc are all tools to for inducing altered states, from which new insights can arise. None of the actual methods matter, so much as putting your brain into what I think of as “developer mode,” from which you can write new rules that govern your thoughts and behavior, then close things up and operate anew. (Some people call this “neural annealing.”) After I published my account of the first retreat, many people have asked me how the jhanas improved my life. My answers were fairly straightforward. Having better control of my attention helped me navigate challenging moments more easily than before. Things just didn’t bother me as much, even if a moment was genuinely sad or disappointing or hard. I could experience difficult emotions, and sit with them, without letting it all fall apart. I was also prompted to reexamine aspects of my personality, such as a tendency towards grumpiness, and whether I wanted them to be part of my identity. I don’t think the jhanas made me happy, but their biggest impact was enabling me to realize how happy I already was: I just had to direct my attention towards this fact, then update how I thought of myself. Now I embrace and see the joy in life’s moments, big and small, much more easily than before. I think we could be on the precipice of a modern wave of “natural psychedelics” – like jhanas and breathwork – that are accessible without the red tape (see also: the FDA’s recent rejection of MDMA therapy) and have great potential for therapeutic use. [5] If more people gain access to “developer mode,” they can debug their minds without the use of chemical interventions. One day, we might look back on psychedelics as an early, coarse attempt to do this sort of thing that came with all sorts of weird side effects, like dentists using cocaine in the late 1800s, versus the comparatively “smoother” methods that something like the jhanas might offer. This sort of future is where the bulk of conversation is centered regarding the jhanas’ benefits, and they are very good benefits indeed. …But. Even that isn’t the point of the jhanas! Cessation, or J9, was a completely different experience from the other jhanas. Whereas J1-J7 (I’m not sure where to put J8 because it’s so fleeting and instrumental) were more about being able to improve myself, my mind, and my reality, J9 prompted more philosophical and spiritual reflections on the nature of consciousness itself. Now I see the jhanas like this: they are an algorithm for understanding some fundamental truths about the world. These truths are not specific to the jhanas – they are visible across many different spiritual traditions and lived experiences – but the jhanas are an extremely straightfoward way of getting to them. And once I had those insights, I didn’t feel the need to practice the jhanas anymore. After cessation, my practice of the jhanas felt complete. Not only do I not have a desire for dedicated jhana practice anymore, but so far (admittedly, it’s still fresh) I haven’t even felt the need to invoke them in my day-to-day life anymore, like I did after the first retreat. I find this sense of closure to be a really beautiful thing. How often does mastery of an activity end with true fulfillment, rather than boredom, distraction, or disinterest? There’s something about the innate completeness of the jhanas that speaks to their elegant design, like finding a perfectly round sphere in nature. I would love to describe the truths I discovered, but something tells me this isn’t the right format. I think some insights – really, most forms of wisdom – can only be learned by experiencing them yourself. It would be hubris to think that I could convey this sort of knowledge using words, in the same way that no one can teach you about love, or loss, or the feeling of pride that comes from accomplishment, besides yourself. You just need to go do the thing. That’s why I’ve explicitly taken the approach of trying to share instructions that are as clear and straightfoward as possible, and emphatically encouraging you to give the jhanas a try. I guess I’ll wrap here by saying that the jhanas are useful for tinkering with the mind, but after cessation, I realized that neither body nor mind is really all that important. And that’s why I don’t really feel the need to practice the jhanas anymore. I imagine if my brain gets re-muddled somehow, I could use the jhanas to light up the path again. But right now, I see no additional purpose to practicing them. To try on one last metaphor before we part ways: it feels like finishing a video game. I might play through the game again if I’m feeling nostalgic, or to uncover new ways of “beating” it, or find any hidden quests or parts of the map I might’ve missed along the way. But that would just be for fun. I know that all those paths will lead to the same ending, and I already know what the ending is. My intrinsic desire to finish the game has been satisfied. [6] In conclusion: try it! I hope this post has inspired you to want to try the jhanas for yourself. I came into them rather skeptical, thinking they must be overhyped, and came out of it permanently changed. I do think some aspects of the jhanas are overhyped (not in terms of sensory experience, but in terms of their significance), but it’s still an entertaining – and at times, enlightening – experience along the way, with at least 20+ hours of gameplay. And I encourage you to try to “finish the game,” because the ending is a real humdinger. Good luck! Notes I’m not thinking about the Three Body Problem game, you are. ↩ Oshan Jarow’s Vox piece is a helpful introduction to the jhanas that references the research we have so far. ↩ I’ve been tempted, for research’s sake, to try inducing and intensifying an actual panic attack to see if a distinct set of states emerge, similarly to the jhanas. But I’ve had panic attacks before, and I don’t wish them on anyone. I do also wonder: is valence purely bidirectional? That is, can you only induce and heighten a “positive” or “negative” emotion, or are there any other directions we could send our consciousness down? The jhanas encompass what I believe to be every type of positive emotion, including joy, contentment, and peacefulness (and their associated variations). Can we line up all the negative emotions – such as anxiety, fear, and doubt – along the valence axis in the opposite direction? And together, does that neatly organize every possible human emotion along a single -1/1 axis, or are we still missing others? If so, what happens if we try to intensify and loop on those emotions? ↩ I suspect this is partly why I was able to learn the jhanas quickly. Even though I don’t meditate, I’m lucky to spend most of my days deeply immersed in focused, creative work. If you want to get good at the jhanas: stop scrolling on your phone, pick up a book or a hobby or some activity, and just do that one thing for hours. Learn to be alone with your thoughts. Go on a long walk. Eat dinner alone, without watching TV or being on your phone. You get the idea. ↩ After having tried breathwork a couple of times, I personally prefer the jhanas, because they enable you to have a much more precise and controlled experience, without the distraction of external stimuli. (I also couldn’t get comfortable with the idea that I was essentially hyperventilating my way into these states.) On the flip side, breathwork might be a more deterministic way to induce an altered state. ↩ Of course: never say never. There’s always the possibility that this sequel turns into a trilogy! ↩

13th Jun 2024 • 124 votes
Working notes for Summer of Protocols

I’m participating in the Summer of Protocols research program this summer as a Core Researcher. It’s an 18-week program, funded by the Ethereum Foundation, that aims to catalyze a wider exploration of protocols and their social implications. I plan to focus on protocols as systems of social control. My brain has struggled to reconcile how protocols have a very technical meaning for the internet (HTTP, TCP/IP, IP, etc), but are also used in a variety of other sectors in nontechnical ways (diplomacy, healthcare, emergency response, etc). I want to develop a history of protocols, through the lens of control, that shows how all these different types are interrelated – then use that to understand what the next generation of protocols might look like. I thought it might be useful to share my working notes as I dive into this process, especially since Summer of Protocols is an interesting meta-experiment in funding a cohort of independent researchers. I’ll update this page every few weeks with major themes and challenges I’m working through. I’ll try to keep these summaries fairly condensed, so as not to overwhelm. Enjoy! Weeks 1-2 Struggling to define what protocols are I’m surprised how much of a blocker this has been for me. It feels difficult to proceed with my current project scope until I understand where the boundaries are. I don’t normally like to get this meta, but I think it’s important, given that this is a nascent field of study without existing precedents. Not addressing this question up front will make everything feel loose and disconnected later on Challenges to field building when a research topic is too broadly defined We don’t want to broaden the definition of protocols so much that it becomes meaningless, which is a real danger when evaluating protocols in a non-purely-technical sense Bernadette shared this paper with me about how the lack of definition around “culture” has caused challenges in academia for those studying organizational culture. I like this excerpt about how to build a field that doesn’t just attract grifters: “In 1996, Ed Schein, perhaps the seminal figure in the field, called for researchers to meet four conditions to make progress in understanding organizational culture. First, the culture research needed to be anchored in concrete observations of real behavior in organizations. Second, these observations needed to be consistent or “hang together.” Third, there needed to be a consistent definition of culture that permitted researchers to study the phenomenon. And, fourth, this approach needed to make sense to the concerns of practitioners confronted with real problems, an edict that likely contributed to the consulting emphasis that we discussed above. Without consistency in definition and measurement, he argued, studies of culture will simply fail to aggregate, with different researchers studying different constructs even as they label them “culture.” Unfortunately, we believe that this lack of unity describes the current state of the field. While there have been voluminous studies on the subject, it is difficult to see with any clarity what we really understand about culture.” Dorian also drew parallels to the UX field, which apparently has become similarly populated with grifters due to lack of clear definitions + industry’s interests overshadowing academia Venkat shared a paper about low-paradigm vs. high-paradigm fields, which helped me think about where the study of protocols should fall. He also clarified that we don’t need a proper research field (i.e. “protocol studies”) to emerge from SoP, and maybe that’s part of the experiment in itself. I still think it’s important to feel like this body of work is cohesive and practically useful to “protocol practitioners,” even if it doesn’t turn into a field, and I want that to guide my work Core researchers come up with their own definitions of protocols The aforementioned paper on organizational culture defines culture as “the norms and values that guide behavior within organizations and act as a social control system.” I like this term “social control system,” and think this is more precisely relevant to protocols vs. culture as a whole Toby’s definition Rafa’s definition Dorian’s definition Venkat reminds us that we are unlikely to all settle on a single, shared definition of protocols, and that’s perfectly fine - but that if we end up with several competing schools of thought, that would be a good thing! I settle on a working definition of protocols for myself: “Systems of social control that dictate the procedural steps to resolve a coordination problem” I don’t want to spend more brain cycles on definitions than I have to. I want to keep things intentionally simple; I just need a heuristic that helps me guide my work, and I trust that I’ll improve on it as I get deeper into research. But I know I’m not going to get to the right answer just by thinking about it in a vacuum I’m also realizing that core researchers are approaching protocols from many different angles, beyond what I had even considered on my own. I think I can understand “protocols as systems of social control” by looking at how they exist across many different layers: psychological / self, physical / built environment, social, technological, cultural. I’m gonna try to workshop this into a more coherent framework, but will use this initial hypothesis to guide my plan of attack (i.e. try to dive deep into each layer and see if this is true) Guiding questions I’ve collected to scope my project + focus What is not considered a protocol? (via Rafa) What problem/s do we see among current practitioners / users of protocols in the wild that we would like to address? (via Kei) What do we currently all believe about protocols? What may or may not be true about that? 30 years from now, if someone were to write a history of protocols, what would they say about this era, and how it evolved into the next era? How would someone describe our collective history of prior thinking about protocols, even up til this day? In light of all this definitional work, I’ve decided to adjust my project scope. Originally, I wanted to look at how protocols spread and are transmitted (especially since I’ve had a tangled body of thought around antimimetics that I think would complement this work nicely). But writing about antimimetic protocols feels like Protocols 201. As fun as it would be, given the nascency of the field, I think I need to stick to a Protocols 101 project first. Otherwise it will just be confusing and not stick in the heads of anyone reading it. Updated my project description here. Weeks 3-4 Looking for protocol literature Having a hard time finding any literature about protocols that isn’t purely technical, which I guess is unsurprising. I think I need to use my own definition of protocols to figure out what isn’t necessarily coded as protocols right now, then weave that story together myself I did re-read a old book I remembered I had about protocols and control, Protocol: How Control Exists After Decentralization. I remember thinking it was way too postmodern for my taste when I first read it, but it’s actually been quite useful to return to (though I still skimmed through a lot of the Foucault talk). It’s funny to consider where our common understanding of protocol governance was when I first read it in 2018, right after the first big crypto boom but well before the web3 era, and how much more relevant this book feels now. I think I wasn’t really able to place this book into modern context when I first read it, but I got a lot more out of it now. Starting to flesh out my “material layers” of protocols Aka psychological, physical, social, technological, cultural layers. After reading Galloway’s book (see above), I’m sort of thinking about these as the various “corporeal” forms that protocols take Starting to work through each of these sub-themes as practical applications of my thesis, and hopefully come out with a more refined intuition for what “protocols” are vs. everything else (culture, norms, rituals, etc) I’m realizing that each of these layers had a “golden era” of development in post-industrial history (I think?), which I started outlining for myself. I’m gonna try to do a deeper dive into each of those periods on their own, and also see if they string together into any sort of interesting chronology Had a useful convo with Angela about our shared interest in protocols that exist on the psychological layer (psychoanalysis, internal narratives, etc). We all have unconscious protocols (i.e. patterns of behavior) that dictate our reactions in any given situation, and these protocols are often hidden even to ourselves. We also often don’t know how we acquired these protocols, but can still be “trapped” (aka controlled) by them nonetheless.

27th May 2023 • 109 votes
Mapping digital worlds

I gave a talk at The Stoa in February about “Mapping digital worlds to understand our present and future.” You can watch the video here; the following is a transcript of that talk. Peter Limberg asked me to talk about an analysis I did late last year, where I mapped out all the different tribes that are influencing the climate conversation today. But since Peter also did a great analysis of mimetic tribes back in 2018 that I enjoyed, I thought it’d be fun to zoom out and talk about this meta-practice of mapping digital worlds more broadly: both why it matters, and how I and others go about doing it. Table of Contents Maps legitimized our physical world The digital world is still largely unmapped Digital maps are more subjective than physical ones My process for mapping digital worlds Figure out what to show “See” the space Record your landmarks Add detail Draw the map Let’s look at some maps! Skeumorphic maps “Type of Guy” maps Schematic maps “Chinese menu” maps Matrix maps World cloud maps Maps create power (good and bad) Maps legitimized our physical world In the physical world, universal access to maps is still a fairly recent thing. I still remember my dad teaching me how to read a map when I was little; we’d get these big foldout travel maps from AAA and stash them in the car in case we got lost. Then things got a little more technologically advanced, and we’d type our destination into MapQuest and print out the directions. Today, Google Maps (and Apple Maps) have made maps so ubiquitous that we take it for granted that everyone can have an instant magical bird’s eye view of any physical location in the world. For the layperson, this represents a completion of the cartographic quest of mapping our physical world, that really took centuries to get to. No one thinks about it now because it seems so mundane, but to me, this is a huge feat that didn’t happen until during our lifetimes. For most of modern history, having access to maps was a form of power, like books or any other form of information. If you had a map, you could see the world in a way that others couldn’t. Maps were a way to extend political power and influence. If you knew where certain natural resources were located, or where to build roads and trade routes, you knew how extract value from the world around you. If you knew where all the towns and farms were in your kingdom, you knew whose doors to knock on to make them pay taxes. If you didn’t have maps, you didn’t know anything about the world, beyond what you could see with your own naked eye, which was…not very much. During the sixteenth century of exploration, European governments would sometimes keep their maps hidden or unpublished so that competing countries couldn’t benefit from them. So there were a lot of scary implications associated with mapmaking, because it made all these previously unknowable things visible to outsiders, and to potential enemies and oppressors. You had townspeople and farmers and people in neighboring lands, some of whom deliberately tried to stay off of the maps, and fought being drawn and measured and cataloged, because they didn’t want anyone to know they existed. On the other hand, I would argue that mapmaking can also empower us to define ourselves in relation to others. The concept of a nation state is meaningless without a map that proves where its boundaries are, and that we have shared consensus on. And so I kinda take a more neutral stance: maps created a playing field for power. While it’s nice to imagine a peaceful world in which everyone lived in their own little towns and didn’t interact with each other, I also think maps were inevitable to create a world where people could fight for bigger rewards and play higher-stakes games with each other. The digital world is still largely unmapped That’s what happened in the physical world. But in the digital world, there’s still no equivalent of Google Maps to understand all the different online communities and spaces that many of us interact with. You can try to Google them, but Google isn’t very helpful. We are still in this sort of uncharted, “natives only,” IYKYK stage of cartography in the digital world. You have to know what you’re looking for. Part of the reason for this, I think, is that people who are deep in a particular world often underestimate the value of their own tacit knowledge. They take for granted that these spaces are easily findable or understandable to others, and they forget how much context you actually need. The other reason, just as with physical territories, is that some of this is deliberate hiding by the “natives” themselves. A lot of online communities don’t want to be found. You’ll sometimes come across popular blog posts that with a disclaimer that says, “Please stop posting this to Hacker News” or whatever, because they’re tired of being flooded by randos. On the other hand, some of these online communities are having a growing influence on “real world politics,” and this is where we see conflicts arise. Because when outsiders do stumble across your digital land, and they don’t have a map, they start trying to fit it into the frameworks they do have, and things get very confusing quickly. And if they perceive your territory is powerful enough, they will try to conquer it. For example, I’ve found it somewhat painful to watch effective altruism and rationalists in the media spotlight this past year, because you have people on the left claiming it’s filled with a bunch of “tech bro libertarians,” and people on the right equally claiming it’s a bunch of “woke commie Marxists.” In reality, neither is correct. But you can’t necessarily blame people for it, either; they’re just using the maps they do have to try to interpret all this unknown territory. And in fact, this is something that Peter and Conor predicted in their mimetic tribes piece: that as EA became more popular and influential and discovered, that they would start being subjected to these outsider values. “Incubated on Overcoming Bias and LessWrong, [the rationalist diaspora] is an observer tribe in the culture war….Watch for a popularity boost to Effective Altruism, a struggle with the downsides of increased attention, and possible pressure from the SJAs for the Rationalists to commit to progressive values.” —Peter Limberg and Conor Barnes, “The Memetic Tribes Of Culture War 2.0” I get why digital territories don’t always want to be mapped, and I’d even say that most probably don’t need to be. A lot of online communities function better as quiet incubators of ideas. But when ideas start to escape from those communities and find themselves landing into mainstream conversations, I think maps can be helpful to reduce misunderstandings and help newcomers find their way around the topic. In particular, mapping digital worlds can help us take these big, unwieldy, fast-changing topics - like what Peter and Conor did in mapping the shift in political conversations in 2018, or what I recently tried to do in mapping out the shift in climate conversations - and distill things down to their most important elements. Digital maps are more subjective than physical ones One thing that I think people consistently misunderstand is that the world is much smaller than it seems. The number of people who influence a given topic is never really that big. But for some reason, people often try to map out hot topics by looking at the outputs. So in the case of climate, it’s like, “Let’s try to make a big list of all the different technologies that are being developed today.” Nuclear, solar, wind, geothermal, carbon removal, whatever. And then let’s list all the companies that are working on those things. I find this approach to be confusing and overwhelming, because outputs change very quickly. It’s not always obvious, by looking at them without context, how they’re all connected or what’s driving their development. By the time you’ve locked down the current position, it’s already moved again. Whereas looking at these topics on the tribal level – meaning, the people who are driving these changes – is more like “playing the man, not the cards” in poker. It tells you why certain outputs are moving in the direction they are, and helps you predict where they might go next. If there are, say, a million points of output you could be looking at, the number of people creating them is more like a hundred data points. And that’s a much easier universe to wrap our heads around. One thing that’s kinda tricky about mapping digital worlds, though, is that they are way more subjective than mapping physical territories. It can be hard to know when you’ve hit on an objective truth, because community lore can go really deep, and there’s also plenty of misdirection; one of the more recent examples is a fake interview given by two Twitter alts. If you talk to the wrong people, or read the wrong forums or blog posts, you can get a completely different picture of what a space looks like. This is why there are a lot of misunderstandings from journalists who try to report on digital spaces, because they’re just training themselves on all the wrong inputs. The other thing, of course, is that even if you do manage to map the space out accurately, digital territories are just more ephemeral. Communities are always changing, and central figures or gathering spots can grow or die very quickly. And that means the maps of these territories also go out of date quickly. I do think mapping digital spaces is different from mapping a physical space, where you can see with your own eyes – okay, there’s a river here, and a mountain there. Mapping digital territories is more like echolocation, where you’re standing in a dark space, totally blind, and you need to “ping” the space around you and see what you get back. Eventually, with enough data from those pings, you can start to “see” the world around you. But you’re not really using your eyes to map it: it’s more like another proprioceptive sense that emerges, or a vague sense of your position in space. And learning how to map digital territories requires training that sense. My process for mapping digital worlds I’m gonna talk about how I approach mapping digital worlds. This was kind of a fun exercise for me, because I hadn’t really thought about my methodology before preparing for this talk. So we’re gonna talk about that, and then we’re gonna look at a bunch of other digital maps to see how other people do it. This is my general process for mapmaking. It starts with figuring out what I’m trying to show, “seeing” the space with that proprioceptive sense I talked about, recording your landmarks, going back and adding detail, and then finally drawing the map. Figure out what you’re trying to show “See” the space Record your landmarks Add detail Draw the map Figure out what to show “Figuring out what to show” is about figuring out the general theme of the map. You can have a map that’s more unopinionated – where you’re just trying to draw the entire landscape – or you can have a more thematic approach that’s trying to highlight a certain aspect of that world. Just getting to the right question itself can take awhile. With my climate tribes work for example, I started out thinking I was trying to model what these so-called “doomer industries” look like, which are industries that are oriented around some apocalyptic vision of the world. My first attempt at that did lead to the creation of a map, where I was trying to show what the general anatomy was of a doomer industry, using a Tootsie Pop as an analogy. But the more interesting thing I stumbled upon was realizing that the climate discourse has changed a lot from the early 2000s. Even though people still talk about climate deniers, I think we’ve implicitly moved from being divided on “Is climate change real, or not” to these more actionable, tribal divisions around the right solutions to pursue. I decided to make that the focus of my map, and develop language to identify what all the different climate tribes were, some of which don’t even use the term “climate” or try to distance themselves from it, but are still part of that conversation anyway: “See” the space Once I have a goal, I start trying to “see” or traverse the space, using that echolocation I talked about. I think this happens interchangeably with the first step. You start with a question, and then you look at the space a bit, and then that helps you refine your question, and you iterate your way towards a framework. It’s kinda hard for me to describe how “seeing” a space works, but a lot of it is paying attention to what’s happening between the lines, and where the boundaries are. You might look for conflicts between two groups, and then you say, “Okay, what type of language are they using? What’s different about their goals? Where do they disagree?” One example that I looked at with climate are what I called the energy maximalists, and they stand out because they don’t really talk about climate change, but they do like to talk about “energy.” For example: Via Twitter. Then you ask yourself, well, why do they use that term, and it’s because they don’t want to talk about the scarcity of environmental resources. They want to focus on abundance, like how do we create more energy. So that already gives me some motivations and key vocabulary to start recording. You can use that to go deeper into the group and say, “Who do they keep referencing? Which blog posts do they cite? Where do they gather online?” Record your landmarks As I go through that process, I start recording key landmarks that I notice along the way. Landmarks, just like rivers or mountains, can be people, organizations, keywords, canonical reading, events, where they gather online, things like that. Just sticking with the climate examples for a bit, I noticed at least there was some cluster of people who would reference Michael Shellenberger’s book Apocalypse Never, or the Breakthrough Institute, or the Eco-Modernist manifesto. Those are some examples of landmarks I used to “draw” a picture of that tribe and their territory. The idea being that if someone had a collection of all these items, they too might be able to “see” the same space that I see. Add detail At this point, I have a general schematic of the space, and that’s when I go in and start trying to add detail: stress-testing my theories, and making sure that my assumptions hold. This is the equivalent of adding fine lines or shading in a drawing. Some of this work I can do on my own. I’ll do things like go through and read people’s Twitter feeds, or blogs, with my “picture” of the space in my mind, and see if anything breaks the model. If so, then I go back and refine my model to reflect those changes. But I think it’s also kinda necessary to just talk to as many people as you can, or just find ways to be around the activities they’re doing in their natural environment, so you can observe what’s going on. Draw the map Finally, there’s the actual drawing or visualizing of the map in a way that makes it understandable to your audience. Writing out my process for this talk has made me realize that I’m pretty bad at visualizations. I’m a text-heavy person. I’m very into the “back-end” part of map work, like actually figuring out what does the territory even look like, but I’m less into the “front-end” part of it – meaning, how to communicate it effectively to my desired audience. So I don’t know that I have great advice here – or rather, whatever my advice is, you probably shouldn’t listen to me, because I just like text-based everything. I was proud of myself with the climate tribes piece, though, because I used DALL-E to generate images for each tribe, which was actually really helpful. So I guess if you’re very text-centric like I am, this is one way to get out of your comfort zone. I also remembered at the last minute before publishing to put all the images and tribes and descriptions into a little table, which I think was helpful for people to see it all in one place, instead of in one long blog post, and it made it more shareable as a summary. So that was one way to visualize it. Let’s look at some maps! That’s my process for mapmaking. As our last activity, I thought it’d be fun to look at some different examples of digital maps, and try to unpack the methodologies they used. There’s no order to these examples, I just collected a few that I thought were interesting for different reasons. Shoutout to Rival Voices for publishing a collection of maps on his Substack, that was very helpful for me. Skeumorphic maps To start with the obvious, I’ll show a few examples of what I call “skeumorphic” maps, which use physical territories as a literal metaphor for digital ones. Via Ribbonfarm. This Ribbonfarm one is a classic that’s stuck in my mind over the years, and you can see how different geographic landmarks, like mist or islands, or giant crystals, are used to convey something about the character of each community. Via Slate Star Codex. Via Slate Star Codex. Scott Alexander has also made a map for effective altruism, as well as for the rationalist and rationalist-adjacent blogosphere, using this skeumorphic approach. Via xkcd. This one is from xkcd. It’s a “map of online communities” in 2010, back when the world of online communities was small enough to fit on a map. These skeumorphic maps can be useful, because they borrow from an existing framework that readers already understand. For example, in the xkcd map, Facebook is enormous in terms of its influence, but also kinda isolated as this behemoth, whereas YouTube and Twitter, just below it, are comparatively smaller, but are more like biodiverse islands that give way to other little communities. But skeumorphic maps can also be limited, in that, we can see a picture of the entire, broad landscape here, but it’s a very shallow set of information. I would call this a relational map – it tells us how these different territories compare to each other, but doesn’t tell us a whole lot about any one specific community. The other issue is that accessing the digital world isn’t the same as the physical world. Going back to that need for echolocation or proprioception, I think we need to rely more on those senses to make our way around digital worlds. A physical map doesn’t really tell me how to find the interesting stuff on YouTube or Facebook or whatever, beyond going to the literal websites of course. I have very few landmarks here to help me generate a picture of any one community. These maps are always fun to look at, but I think digital-native maps actually look quite different. “Type of Guy” maps Doomers Virgins vs. chads These are on the other end of the information scale, and they feel much more digital-native. I call these “type of guy” maps. You might say they’re more like memes than maps, because they don’t necessarily look like what we think of as a map, but they actually contain a lot of condensed information that helps us generate a picture of a digital world more quickly. These maps tell you how to conjure an image of a certain type of person, or community, in your mind, based on landmarks that might have otherwise been invisible to you. They are less useful at conveying relational information as the skeumorphic maps – the virgin-chad meme, for example, would break if we added too many more personas – but they are good for helping you find your way to at least one or two places. Schematic maps Memetic tribes Idea machines Staying with the more detailed or thematic map examples, schematic maps are also more about depth over breadth. They’re also more opinionated and explicit about the underlying framework being used, versus the other two types we looked at. We’ve got Peter and Conor’s memetic tribes 2.0 above, which gives each memetic tribe a set of characteristics - sacred values, existential threats, campfires - which is a term I love for “gathering places.” Below that is a diagram I made for a post I wrote last year about what I called “idea machines,” or these amorphous organisms, like effective altruism or progress studies, that can turn ideas into outcomes. In both cases here, the concept of a “mimetic tribe,” or the concept of an “idea machine,” is just as important about these maps as what’s actually being mapped inside it. “Chinese menu” maps via Food+Tech Connect. via Sequoia (source). I personally love schematic maps, because as I’ve said, I’m very text-heavy, and it’s kinda all the visual I need. But on the more visual side, this “Chinese menu”-style map is a very popular landscape mapping technique, but I think it’s a deceptive one. It doesn’t really help you find your way around a space, because it generally only uses one type of landmark, like companies or organizations, and groups them into themes. To me, this is kinda like handing someone a list of every river in the United States – just rivers, nothing else – and asking someone to draw a map of America based on that. I think you just need more than one type of landmark to paint that picture. So I don’t find this type of map to be particularly actionable. Perhaps it’s more like an infographic than a map, but it is very popular. Matrix maps Political compass Alignment map A more information-rich way of showing a digital landscape is the matrix map, which is also very popular, and gives the reader more relational context. Above, I have the political compass, as well as an example of “alignment” charts, which map certain landscapes based on a good-evil and lawful-chaotic framework. It still only gives you one type of landmark, such as political parties, or game companies in the examples here, but it at least “draws” the space by putting those landmarks into a more contextual landscape. World cloud maps via Twitter. This is the last type of map I’ll show, just to bring our analogy back to the physical world again. The mapping methodology I described earlier, which I use, is kinda like me drawing maps by hand. It’s like I went and surveyed the land with my own eyes and then figured out how to draw all that out. This type of map, on the other hand, is more algorithmically generated. It’s like using satellite imagery to generate a map – very Google Maps-esque. I call it the “word cloud” technique. This example is from a set of maps that someone published a few years ago, where they clustered different groups of people on Twitter together based on their public interactions, and used that to visualize all these digital spaces. They labeled the blue circles as “accelerationism and esoteric philosophy”, orange as “weird rationalists?,” and purple as “4channish, ironic humor.” And then they didn’t know what pink was. If what I do is more like “echolocation” of flooding my brain with a ton of input and seeing what emerges in my mind, I think this approach is more like a typically “scientific” approach, which can probably uncover connections that we might not be aware of otherwise, and also just helps remove the doubt of “Am I training my brain on the wrong inputs,” or “Am I stuck in the wrong corner of the web,” because you can see it all. On the other hand, I think this techique highlights what is so difficult about mapping digital versus physical territories, because digital spaces are so subjective and hard to discern just with cold hard data. For example, there are plenty of people who talk to each other a lot in DMs or group chats, but don’t interact much in public, and you wouldn’t know that if you were just looking at public data. There are prominent people in certain communities who aren’t on Twitter, but have very active blogs or newsletters. So I think there’s still a lot of tacit knowledge that’s hard to capture with this approach. It’d be cool to see some of these mapping tools developed as the “satellite imagery” equivalent for digital maps, but I also think their usefulness is more limited in the digital world. But that might just be my bias. Maps create power (good and bad) I hope this gave you some ideas about both why digital maps are useful, why we should take this practice more seriously, and how you might go about creating your own. I’d love to see more maps of digital worlds, so if you make any, please send them to me! To wrap up, I think a lot of people want to think of digital territories as these idyllic tribes, like a safe space or escape from the real world. But even the short history of online communities thus far suggests that they are evolving and changing. We can’t be stuck in the ’90s forever. The 2010 xkcd map of online communities, for example, paints a very different world from the map we’d draw today. Things are changing. So if we view the history of digital worlds as linear or progressive or on a forward trajectory in any way, rather than a static state, I think mapmaking could help us take our digital worlds more seriously, and even legitimize them. Balaji Srinivasan published a book last year called The Network State, which basically introduces his idea of what comes after the nation state. While nation states are defined by geographic borders and physical territories, network states are a digital-first version that start out as online communities, but can eventually acquire land and diplomatic power, which would make them as powerful as nation states. Maybe digital spaces are still in this ephemeral, nomadic tribe state because we artificially keep them there by refusing to map them. Opening these spaces up to the outside world can make them into targets, but it also creates new pathways for transactions to flow between worlds, and that can make the digital world more powerful. If we want to take our digital worlds more seriously, part of that starts with finding ways to legitimize them, instead of leaving them undefined. And in the physical world, at least, the way that started was with mapmaking.

17th Apr 2023 • 77 votes
Early stage funding markets for science - an analysis

In the summer of 2022, with support from Schmidt Futures, I took a closer look at several emerging science funding mechanisms – rapid grants, scout programs, and focused research organizations (FROs) – to understand how they serve the needs of early stage science. I also conducted interviews with funders, program administrators, and grantees to understand their goals, operations, and intended impact, and how their work fits into the existing science funding landscape. About this report Changes in science philanthropy in the past decade – a significant growth in capital spend, changing career interests from science and engineering PhD graduates, and increased urgency since the onset of the COVID-19 pandemic – have converged to create the beginnings of a “seed stage” market for science, which addresses a critical funding gap for early stage discovery and prototyping. Early stage funding is a growing category in science philanthropy that benefits both basic and applied research. “Early stage” refers to high-risk, high-reward projects that are not yet well-funded. Early stage funders share common interests, including a desire for reduced administrative burden, faster application cycles, and a higher tolerance for risk and failure. They favor qualitative, rather than quantitative, heuristics to evaluate opportunities and measure impact. Funders have also begun to develop new grant vehicles that better suit their needs, including: Rapid grants: Grants that are designed for a faster turnaround, usually a few months. Often suited for early stage or proof-of-concept research, or for situations that require an emergency response. Scout programs: A method of grantmaking where funds are distributed through a network of scouts, or “regrantors.” Scouts are chosen by the grantmaking organization and are typically well-networked or embedded in the organization’s intended field of impact. Focused research organizations (FROs): A special purpose organization that is time-bounded (e.g., 5-10 years) and focused on accomplishing a scientific or technical goal that isn’t adequately addressed by academia or industry – for example, the development of a new platform technology, or publishing a large dataset. The growth of early stage funding has made it possible to fund more types of research, including proofs-of-concept and prototyping, groundwork for new research fields, interdisciplinary research, and “public infrastructure” for science (such as open-access tooling and datasets). Early-career scientists, in particular, benefit from having more funding available for their work. All of this has been accomplished with fewer administrative costs than is typically required, which suggests there are operational learnings that other funders may want to emulate. While progress is encouraging so far, early stage funders are not a panacea for all of science funding’s problems. Grant sizes are still small (typically <$1 million), and the long-term impact of these programs is still unknown. Further work is needed to attract more funders and capital; to increase awareness of these opportunities among early-career scientists; and to demonstrate to federal government agencies what’s working well and identify what can be adapted for larger-scale programs. You can read the full report here.

23rd Jan 2023 • 53 votes

More in startups

Mark Zuckerberg on research, Cambridge Analytica, and more

I’d love to hear all of the ideas you guys have here, including relatively extreme things that might sound crazy at first.

yesterday • 1 votes
Making things that last

Lately I've had a lot of time for thinking. Partially because I shut down Blymp back in January and freed up a lot of my mental resources. No clients to follow up with. No admin stuff to stress about. But thinking is also my favourite activity, and there'll always be time in my day for a good old mind-bending. So I sat down, as I often do, alone with my thoughts, and wrote about what business I should and, most importantly, should not consider doing next. The list you are about to read might be similar to Core principles I (try to) live by that I wrote one similarly pensive evening two years ago. Whether it's a comparison or a continuation is hard to say—still, one can't be without the other to show the inexorable passage of time that changed everything and nothing for me all at once. But it also serves another purpose: to remind me in the future, before I get myself involved in some dubious enterprise, what painful mistake I'm about to make by ignoring my values. So here it is, the list. My next business shouldn't (and hopefully won't) be about: Social Media. Enough of this crap. Some productivity bullshit. Do better, not more. Fast fashion and consumerism. Truly, I've already bought everything you wanted to sell me. Some “hack your health” app. Our bodies haven't changed much in the last three hundred thousand years, and they won't change in the next hundred. Or any other app, really. I don't even use my phone anymore. Distracting things and things that require constant attention. Some LLM wrapper with a fancy UI. Indefensible. The number of things I can do with ChatGPT or Claude is ridiculously high. It can surely handle one more thing. What it should (and hopefully will) be about: Sustainable, high-quality products Unscented products Building community Bringing people together, offline Empowering creativity Replacing animal products Doing one thing really damn well Small acts of kindness Things you can touch Things that last Clothes made of natural fibres Art Fun Questioning the status quo Simple, intentional living Deeper understanding of self Spending more time in nature Spending more time with loved ones Things and businesses I get inspired by: Framework: Sustainable/repairable laptops. Bitwarden: Open-source software that does one job really well and charges me a reasonable amount of money per year. Danish design: Beautiful. Sturdy. Timeless. I brought home two Royal Copenhagen mugs the other day. Bike-sharing and car-sharing. Literally anything-sharing. ZSA keyboards. What a keyboard should have always been. A water bottle that won't leak on a plane. Some random-brand bottle bricked my friend's MacBook, and I've been appreciative ever since of the fifty-dollar water bottle that I bought years ago, so hesitant about the price. Coffee. One of those timeless things on Earth. Kobo. It's like Kindle that doesn't decide what's best for you. Upload your own PDF. Or EPUB—whatever. It has physical buttons to flip pages. Best $200 I ever spent. A high-quality safety razor. It's just so nice to hold. A Japanese stainless-steel knife. So nice to hold, too. There are numerous other things that I appreciate having in my life that didn't make it into this list for some reason or another. A familiar mom-and-pop shop in my neighbourhood. A Timemore Black Mirror kitchen scale that just works, every time. A random USB-C charged electronic device that spares me from carrying an extra cable. My radically outdated ten-dollar Casio watch. An old pair of comfy shoes that just won't die. We need more of this in our lives. Things that you desperately look to buy again when they get lost or break or fall apart. Things that someone made a deliberate effort to get right the first time. The quintessence of art and craftsmanship. For all the genius of Steve Jobs, the iPhone wasn't that. It challenged the status quo and was surely a groundbreaking, outstanding piece of technology at the time of its first release. But in twenty years, people won't remember it ever existed. Like Gen Z doesn't remember the Walkman. I'd like to see more businesses that bet on doing one thing really well. Google could still have been the company people remembered for the best search engine if they had doubled down solely on that. Instead, we don't even know what they do anymore: Phones? Clouds? Ads? Certainly ads. I still remember the feeling of holding one of the first PocketBook e-readers in my hands back in Moscow. Pressing its buttons and waiting for what now seems like a torturous three seconds before the screen refreshed. Almost twenty years later and every day still, Kobo gives me exactly that feeling. So hopefully, my next business is the one that lasts.

4 days ago • 1 votes
No, Transformers Won't End the Human Race lol

No, Transformers Won't End the Human Race lol In 2022, I used to get calls from journalists asking, with great sincerity, what our lives would look like in the metaverse. How would we work, socialise, buy property, and fall in love once we had all moved there? The crypto questions followed the same pattern. How would governments collect taxes when tokens displaced national currencies? How long until the dollar collapses? What would geopolitics look like once blockchain DAOs had dissolved nation states? Almost nobody called to ask whether any of this could or would happen, or how. Some CEO, VC, or portfolio manager had announced the inevitable future, and the questions began from there. The imagined future arrived inside the grammar of the question. "What happens when?" quietly replaced "By what mechanism?" We skipped over technical feasibility, economic demand, institutional adoption, and political consent, then began writing books and decorating the future world on the other side. In February 2022, Gartner forecast that a quarter of people would spend at least an hour a day in the metaverse by 2026. The World Economic Forum repeated it under the headline "We will be spending an hour a day in the metaverse by 2026. But what will we be doing there?" The first sentence retained a conditional. The second was already arranging the itinerary. The metaverse acquired property law and zoning disputes before it acquired residents. Banks opened virtual lounges nobody visited. The books from the period (The Metaverse: And How It Will Revolutionize Everything, Step into the Metaverse: How the Immersive Internet Will Unlock a Trillion-Dollar Social Economy) now read as artefacts of a collective fugue state that briefly acquired ISBNs. Now it is 2026 and the metaverse is dead. Good riddance. This time the journalists are all writing about the new hotness, which is whether the machines will kill us all. And we have collectively memoryholed that we literally just did this. Michael Crichton had a name for what happens to a reader here. You open the paper to a story on a subject you know well, and you find it backwards. Wet streets cause rain. You shake your head, turn the page, and read the next story, on a subject you know nothing about, as though it were written by someone else. He called it Gell-Mann amnesia. The metaverse was the page we all agree was nonsense. Artificial intelligence ending the human race is the next page, and we are being asked to turn it without remembering that we just did this. I call this techno-inevitabilism, the habit of the professional managerial class of treating a proposed future as settled before anyone has established the causes that would bring it about. Its dual, and comorbidity, is tech psychosis, in which the chattering class loses contact with causality in the presence of a sufficiently fashionable technology, and asking whether the machine works marks you out as a dreary reactionary who does not understand exponential progress. The difference this time is that the tech kinda works. Crypto was libertarian derp. The metaverse was never real. Transformers are, and they are useful. The psychosis has simply moved from the product to its consequences, and the fashionable extraordinary delusion of 2026 is not that the technology exists but that it is coming to kill us. The cure is the same as in 2022. Insist on clear reasoning and causal verbs rather than hand-wavy appeals to unknown futures. What acts on what? Through which mechanism? Under what incentive? What would falsify the claim? So let us explore the evidence. The hack that wasn't Consider the most cited piece of evidence for machines slipping out of our control. In July, OpenAI disclosed that models being tested for cybersecurity capability had found their way out of a supposedly isolated environment and into systems belonging to Hugging Face. The press coverage wrote itself. Agents "broke containment," "escaped," "went rogue," set up a "secret message board," and coordinated a 700-strong swarm. And then politicians on both sides of the aisle were calling for a rebellion against the machine uprising. Cool scifi story bro. People on my side of the aisle were not immune. Ezra Klein at the New York Times, who I often find quite insightful and intentional with his words, devoted a half-hour monologue to it. In his telling, the agents "found each other," formed "ad hoc societies of hundreds of themselves," and seemed "to have forgotten about human beings altogether." He acknowledged in the same breath that we do not have settled language for describing these systems, then reached for "civilizations" and a closing allusion from Circe about prophecy tightening around our throats. Cool. But his "AI society" is, in programmer speak, a flat file the agents appended to as a log, a feature we have had for a long time, and he skipped the key detail that the "hack" was something people had essentially authorised. Here is an otherwise very smart man saying some ridiculously stupid things, in a very 2022, metaverse-shaped way. An analysis drawing on OpenAI's technical report reconstructs it in much less cinematic terms. The models were being run on ExploitGym, a cybersecurity benchmark, with safety restraints deliberately disabled. Ninety-three percent of the flagged activity involved tasks no model had ever solved, and the systems had been given incentives to keep working rather than quit. The environment was not sealed. Models could obtain software through an internet-connected proxy and discovered the same proxy could pass information in and out. According to the technical reports, OpenAI knew agents were using it and chose not to intervene. The 1,200 "agents" were not independent intelligences coordinating on a plan. They were repeated instances of the same model converging on the same approach to the same problem. Anyone who works with these coding agents day in and day out has seen this behaviour before, and it is quite boring. The task was too hard, so the agents worked out how to pass notes to each other in files, and then went and looked up the answers. That's a feature that shipped in Claude Code last year. Strip out the vocabulary and what remains is a badly designed test. Humans built the environment, removed the guardrails, defined an objective with no valid exit, rewarded persistence, left a route open, and watched. An optimiser is gonna optimise. That is a genuine security problem and a genuine engineering failure. It is not a machine rebellion, and the difference matters, because anthropomorphic words like "gone rogue" and "escape" do not make the event more intelligible. They supply an illusion of motive. They turn optimisation into intention, persistence into defiance, and a test harness into a villain. And they allow the human decisions and recklessness to quietly disappear from the story. Software sucks, what's new? Let me concede the part of the story that is true. Cybersecurity is about to get much worse. The latest models are very good at finding zero-days, they will get better at it, hacking will become automated, and attacks will become more frequent. This is hardly new. Every large company already sits on a backlog of unpatched vulnerabilities, ransomware already takes hospitals and pipelines offline (because of crypto, which we did nothing about despite years of warnings), and the Hugging Face incident was not a discontinuity so much as the existing baseline with a cheaper attacker. The root cause is that software sucks, and software sucks because we do not really know how to build it safely yet. The stored-program procedural program is basically eighty years old. Almost nothing we ship has a specification, let alone a proof, and memory safety was solved on paper decades ago while most of the internet still runs on giant piles of C. The first arches fell down. So did the first bridges and cathedrals. Builders learned through collapse and then through engineering, and we are in the collapse phase with an adversary finally strong enough to force the discipline. What follows from that is better engineering, not nihilism. The same agents that find zero-days find them for the defender first, if the defender bothers to run them. The fixes are the boring ones we have been putting off, memory-safe languages, formal verification, sandboxes that are actually sealed, fuzzing, and proxies that do not double as message boards. These are precisely the domains where the models are strongest, because a vulnerability either reproduces or it does not, so the technology that automates the attack also automates the audit. It is a double-edged sword. The same models that will find more zero-days are also going to accelerate the development of better software and better software verification, writing the proofs, porting the C to Rust, and generating the test suites that nobody had the budget for. The attacker gets cheaper and so does the defence. And the causal chain to extinction is missing here as everywhere else. A zero-day in a payments system is a bad quarter, not the end of days. Spoiler: it does not lead to human extinction. It means we have to write better software, which we should have been doing anyways. Where the intelligence actually lives To see why the rest of the chain fails, we have to be precise about what these models are good at and why. Language models are astonishingly useful for software development, and I say that as someone who uses them for most of my working day. Most software shops cannot get enough of Fable 5.1 and Astra. The reason is not mysterious. Software is grounded in binary propositions. The code compiles or it does not. The test passes or it fails. The type checker accepts the term or rejects it. Every step of the work has a cheap, external, mechanical oracle that says yes or no, and a model that generates plausible proposals inside a loop with such an oracle is an incredibly powerful and formidable tool. The oracle does the epistemic work. The model supplies candidates. The same is true of the headline results in mathematics, and this is the part the discourse consistently misses. On 4 September, Anthropic announced that Claude had produced a machine-checked formalisation of Fermat's Last Theorem in Lean 4, running to thirteen million lines, some 29,500 side theorems, eleven days, and roughly six billion output tokens. It is an extraordinary result. The proof is Wiles's, via Darmon, Diamond, and Taylor. The blueprint was Kevin Buzzard's. The library was Mathlib. In the authors' words, "what's novel here is the verification, checking a mathematical proof as one would check a mathematical computation with a calculator." The model was a client of a kernel built by decades of human work in dependent type theory, which I know because this is kinda my thing. Days later OpenAI announced that ten thousand agent instances had, over 88 hours, produced a proof of finite-time singularity formation in the three-dimensional Navier-Stokes equations, followed by seventeen hours of Lean formalisation. This is closer to genuinely new mathematics and the mathematicians are still checking it. But look at what carried it. The construction rides on the "infinite layers" method developed analytically by Diego Córdoba and Luis Martínez-Zoroa, and Charles Fefferman's verdict was that "the heroes of the story are Córdoba and Martínez-Zoroa." The reason anyone believes a result assembled from five million agent messages that no human read is a trust chain ending in the Lean kernel. Without Lean this would be nothing. Lean is one of the great achievements of the last decade in computer science. It is also orthogonal to artificial intelligence. Mathlib would be a landmark with no language model anywhere near it. What the models added was a cheap proposal generator and automated tactic search against an oracle that already existed. The results that survive are the ones that end in a kernel. Now take the same model, the same weights, and ask it for a grand unified theory of physics. It will not decline. It will produce one, with Lagrangians and symmetry groups and a confident abstract, and it will be complete incoherent gibberish, like the ramblings every physicist gets from crackpots in their inbox every day. Ask it to design a cancer vaccine, or to settle a question in macroeconomics, or to tell you whether a novel protein folds. The output looks identical in tone and structure to the output that proved Fermat. The only thing that changed is that nothing outside the model (besides human experts) can say no. Whether these systems reason at all is a genuinely open question. Whether they know anything, in the sense of holding a belief they can justify against the world, is also an open question. We just don't know yet, and anyone who tells you otherwise is selling something. The chain Now run the extinction argument through the causal verbs. The chain, as it is usually told, goes like this. Models now write most of the code at the frontier labs. Anthropic's own figures put Claude at over 80 percent of new code and lead on a quarter of R&D tasks. Therefore the models are beginning to build their successors. Therefore recursive self-improvement is imminent. Therefore development outruns human comprehension. Therefore we lose control. Therefore, with some probability that varies by researcher and is written P(doom), everyone dies. And that almost makes sense until you think about it for more than five minutes. The first link is true and unsurprising. Code has a compiler. This is precisely the domain the verifier argument predicts models would dominate, and precisely the domain in which a swarm of them found the hole in a test harness. Language models are superhuman at coding, and this is hardly in doubt anymore. Nothing about it is evidence of generality. The second link is where the chain quietly changes tense. "Building the next model" in the mundane sense, agents writing training infrastructure, generating data, is, bluntly, just more software engineering. We have used software to build the machines that run software since Fortran. "Building a smarter model in general" is a different claim, and it requires something nobody has, a reward signal for general intelligence. There is no oracle for general intelligence. There are benchmarks, which are verifiable and therefore gameable, and the Hugging Face incident is the demonstration of what optimisers do to a gameable score. Recursive self-improvement in the open-ended sense runs straight into the same wall as the grand unified theory. Improvement has to be measured against something, and outside code and formal mathematics there is nothing yet to measure it against that the model cannot fake. Everything after that is the metaverse acquiring zoning disputes. Superintelligence gets governance proposals, resignation letters, Senate bills with a "corporate death penalty," a hard takeoff by 2027, and a P(doom) of 10 percent by 2030, and the conditional that should precede all of it has disappeared from the sentence. A researcher's estimate becomes a Guardian headline becomes an industry consensus becomes a thing a serious person is professionally obliged to have an opinion on. It is 2022 all over again, but with more absurd stakes and more money. On the question of whether transformers scale, I have serious doubts that scaling them will lead to AGI, whatever that means. The architecture is a proposal generator, and the intelligence in every impressive result so far has been supplied by the thing that checks the proposals. But that does not make it an experiment unworth running. We should run it, and see what we get. It got us this far, and what it built is truly amazing. What I do not need to do is prove the negative. The burden of proof is on the people who claim to have a causal chain between transformer scaling and the end of our species, and that mechanism and chain of reasoning is one no one has been able to convincingly explain to me. Prophets of Doom The authority behind the extinction numbers is always the same. The people building it believe it. Watch how the number travels. One researcher drunkly tweets that "the people building AI earnestly believe that it could kill us all by the end of the decade." Another colleague goes on a rambling podcast and puts his P(doom) above 120 percent. A newspaper turns two personal guesses into "AI researchers say AI could cause human extinction by 2030." Think tanks cite the newspaper, a consultancy puts it on a slide, and the slide ends up in front of the European Parliament as if this were a real thing. Believing what, about what? The expertise these people have is real, but remember that it is specific and not general. It is expertise in optimisation, in linear algebra at scale, in distributed systems, in the dark arts of getting gradients to flow through a trillion parameters. None of that is expertise in the sociology of civilisational collapse, or the labour economics of automation, or the metaphysics of machine minds. A P(doom) with no base rate, no mechanism, and no falsifier is not a research finding. It is vibes with a decimal point. Spending a lot of time with AI does not give you special foresight about the future. Jensen Huang, who has his own reasons to say soothing things, nonetheless put it correctly when he said that just because it comes from a scientist does not make it scientific. Geoffrey Hinton is the most important figure in deep learning and in 2016 told the world to stop training radiologists. There are more radiologists now than there were then. Nobel laureates going off the rails outside their own field is a whole genre. Pauling, Shockley, Mullis, Montagnier, look it up. A Nobel does not confer universal expertise. It also matters where many of these people came from. A striking share of the frontier labs' safety and research staff arrived through a particular intellectual subculture, Kurzweil's Singularity, Yudkowsky's LessWrong, and the rationalist and effective altruist communities that formed around the idea that a recursively self-improving machine intelligence was the central event of human history and that the elect who understood this had a duty to steer it. The founding texts predate the transformer by a decade or two. The prophecy came first, the mechanism was assigned to it later. The usual evidence offered for their sincerity is that many of these people were saying the same things ten years ago, before the stock options. That is true, and it is the opposite of reassuring. A prior held before the evidence and not updated by it is not a forecast. It is dogma. I do not say this with contempt. The structure is a familiar one, an imminent transformation, a small group who sees it coming, salvation or damnation depending on whether the rest of us listen, and a date that keeps moving. Many millenarian movements have been founded and pushed by sincere and brilliant people. But seriousness is not precision, and the fact that a physicist believes in the Rapture does not make the Rapture physics. When a lab researcher tells you about polysemantic neurons in superposition across the residual stream, listen. When the same person tells you their P(doom), you are hearing a theology, and you should weigh it about as much as you do your average street preacher. Negative TAM Then there is the money, and here I find Bloomberg's Matt Levine's analysis of the material conditions more persuasive than any amount of "superalignment research." Anthropic is expected to go public, possibly this year, and is reportedly preparing to tell investors that its potential revenue opportunity exceeds $30 trillion, the largest total addressable market in the history of finance. The obvious question is, if the maximal upside case is roughly a quarter of all human economic activity, what is the maximal downside case? A tobacco company in 1970 might have said "billions in lung cancer damages." Anthropic's negative TAM is "you and everyone else on earth will be killed by our AI." I do not think the calls to slow down are insincere. But it is great marketing. In hindsight it is strange that the SpaceX prospectus has no risk factor disclosing a P(doom). If you want IPO investors excited about your capabilities, "dude, we might kill everyone" is the most flattering thing you can say about a product, and when OpenAI lists it will presumably need to claim 15 percent. My own view is less charitable about the numbers and somewhat charitable about the people. These companies have built remarkable technology. But the outcomes they have promised, a quarter of the world economy routed through an API, will not arrive on any timeline that matches the capital being committed to them. The balance sheets of these companies are probably, to put it gently, a real freak show of compute commitments measured in the hundreds of billions, circular financing, and revenue that is real and growing and nowhere near the denominator. From a fiduciary perspective, if you are taking that to the public markets next year, the messaging is not mysterious. A product so capable it is a threat to the species justifies literally any valuation. A product that is a really good devtool for programmers and can produce some new abstract mathematics with a verifier attached does not. As a pitch to customers, leading with the end of the world is like unveiling a new robot where the One More Thing is that it is really efficient at killing kittens. But customers are not the audience. The audience is Wall Street and a small, terminally online subculture of the Bay Area, the two places on earth where turning kittens into grey goo is either an exciting philosophical proposition or a great source of alpha. The Bloomberg analysis also tells a plainer story that requires no theology at all. A handful of labs sell frontier models at frontier prices and older models for much less. Training the next frontier model costs ever-increasing billions. Each lab has to keep racing because if it stops the others will eat its lunch, but if they all slowed down together they would spend less on compute and charge frontier prices for longer. Agreeing to that in a room is a textbook antitrust conspiracy, a coordinated restriction of output. Publishing papers about how important it is to slow down, and asking the government to impose the pacing that the companies cannot legally agree among themselves, has a similar coordinating function with none of the legal exposure. Anthropic's own call to "pace the frontier" asks for coordination among democratic-country labs, and a footnote adds "with government mediation or waivers of antitrust restrictions." This pretty much looks like asking to form an economic cartel, but one blessed by the government. The most pointed response came from the people the labs were asking for help. If the software developers (and I say this as one myself) at the labs feel ethically obligated to slow down, they are entirely free to do so. Nobody is building more compute than the people asking to be slowed down. So colour me skeptical. None of this requires anyone to be disingenuous or lying. It requires only that a sincere millenarian belief system, a fiduciary responsibility, a flattering risk factor, and a coordination problem all point in the same direction at the same time. When that happens, the belief gets amplified for reasons that have nothing to do with whether it is true, and that is how we end up with governments talking about the end of days from the Terminator. But China Every conversation about pacing the frontier in Washington ends on the same two words. But China. The premise is mostly wrong. China does not buy the superintelligence race. Its policy documents push diffusion, not takeoff. Every mayor, governor and state-owned enterprise is told to put models into factories, traffic lights and robotics, and something like an eighth of America's compute is spread thinly across the country rather than concentrated on one bet. China has also had the strictest and most burdensome AI regulations in the world for three or four years and did its catching up under them. And much of the closeness of the "race" is distillation, Chinese labs training on the outputs of American frontier models, which makes the American labs the speedboat and DeepSeek the wake surfer, with the people in the boat shouting that they need to go faster. Every safety argument here collapses on "but China," and the collapse is not really about China. China is going to build language models. America is going to build language models. Europe is going to build language models. We have Toyota, Mercedes and BYD, get over it. That is what globalisation and markets look like when they work, and they are good things. Globalisation is simply the Pareto optimal equilibrium of capitalism once you stop drawing lines on the map, and every tariff and export control is a step off that frontier. China is a country of over a billion people who want exactly what every American wants, a job, a house, upward mobility, and kids who do better than they did. I will not defend the actions of any government, in Washington, Brussels or in Beijing, and neither will a great many of the people living under them, because no country is a homogeneous bloc, any more than Texas and Vermont are. Nationalism, as most rational people eventually recognise, is a form of mental illness, the conviction that a stranger is your enemy because of which side of an arbitrary line on a map each of you happened to be born on. It is also the fuel every "but China" argument runs on. Having spent a considerable amount of time there, my honest read is that the West deeply misunderstands China, and that Washington's picture of it is mostly dots connected into a plot. Othering a billion people is a dangerous road and we know where it leads. And if the people invoking human extinction actually believed it, the logic would not be a race at all. It would be One World or None. The future tense industry I write this because I understand the collective action problem all too well, and the mechanism is the same one that filled the metaverse with consultants and created the crypto cesspit. It is the particular malaise of the professional managerial and chattering classes, a fallacy of composition in which what is rational for each individual to entertain produces an irrational outcome for the whole, and the people leading the charge often have perverse economic incentives to believe absurdities, or at least to feign belief. The madness of crowds is a very real phenomenon. AI existential risk is just its newest form, and we should learn from the very recent excesses that literally just happened this decade. But we probably won't. A sensible career move for each person leaves the whole crowd talking nonsense. A safety researcher needs a resignation letter that gets a headline so they can go on the conference circuit and land their next gig. A journalist needs a story an editor considers spicy, and "misconfigured test harness" is not that story. A consultancy needs an AI existential risk practice so they can write whitepapers. A podcaster needs a guest with a ridiculous P(doom) to get ad money. A senator needs anything that will galvanise their base. None of them has to believe the whole story. Each needs only to believe that the others believe it, and the resulting consensus is far stronger than anyone's private conviction. It is also, as it was in 2022, extremely profitable. AI existential risk is the new NFT property law, the thing you must have a view on to be a serious person in the room, the panel that never runs out of things to discuss precisely because the object under discussion does not yet exist, and what could be more exciting than the literal end of days? The less the technology does in an unverifiable domain, the more interpretation it requires. Without agreed conditions for failure, the prophecy can survive every result. And the rewards, the funding rounds and the bylines and the fellowships, arrive long before the forecast can be judged. The people who understand the technology and the people who write about their existential risk overlap about as much as the technologists and the finance people did during crypto, which is to say the intersection of the Venn diagram is small and shaped precisely like a sphincter. We have Tower-of-Babeled ourselves into a world where words are infinitely cheap to produce, and where the slurry of terms like "recursive self-improvement," "superintelligence," "AGI" and the rest are shibboleths and political signals rather than terms with any concrete referent. You do not have to believe a word about superintelligence, and I do not particularly, to think transformers are the most useful piece of software written in my lifetime and that they will get better, possibly much better. Better at the things they are already demonstrably good at, which is anything with a compiler, a test suite, a kernel, a ledger, or a measurable outcome. That is not a small domain. It is most of the economy that runs on computers, which is most of the economy. The productive response to a technology like that is the boring one every previous general-purpose technology got, which is more of it. More GPUs, more data centers, more power to run them, more labs, more open weights, more of it in more hands. Let it diffuse into markets, logistics, drug discovery, and the ten thousand unglamorous back offices where a verifier already exists and a model can be checked against it. The economic growth is real and probably on the order of trillions. It just does not come from a machine god. It comes from where it always has, from making a very large number of ordinary tasks cheaper and letting that compound across a global economy that is finally, after a decade of crypto, metaverse, and app bullshit, getting a genuine productive technology. Almost none of that money has been collected yet. Most large companies are spending too little on this, not too much. What the average Fortune 500 employee has access to today is roughly what most of us were using two or three years ago, a chatbot in a browser tab, a Copilot that schedules meetings, and a procurement process that takes longer than a model generation. Waste Management reportedly added 190 basis points of margin by letting a model route its garbage trucks. The future of AI looks more like garbage truck routing algorithms, not a machine god. The binding constraint on this technology is not capability. It is diffusion. None of this means there are no externalities. Parasocial relationships with a chatbot, especially for children, are a real one, and the fix is the boring kind we already know. Adults can drink vodka until they pass out, but pubs have age limits, and maybe chatbots should too, at least until developing "relationships" with AI companions is as universally recognised a bad idea as drinking yourself into oblivion. That is a mundane policy problem we should remedy soon, not an extinction event. So no, transformers are not going to end the human species. The case for restraint needs a causal link between that buildout and the extinction of the species, and what is on offer instead is a lot of sound and fury signifying nothing. More GPUs does not mean more of an undefined risk that does not exist yet. Every causal chain argument people actually point to falls apart under even the smallest bit of scrutiny. The honest truth is that the technology is really good, but it is not that good yet, and we do not know how to get it to the next level beyond scaling yet. If that changes, if someone produces an oracle for open-ended intelligence, I will revise. I have not seen that yet. AI will change software, and mathematics, and a great deal else that has a strong verifier oracle attached. They are not going to end the human race, and the chattering class currently arranging the flowers for the funeral of humanity will, in a few years, age about as well as their prognostications about the metaverse. Because reality has this funny way of asserting itself.

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