Now that the a16z newsletter is cooking nicely - we’re publishing every day, it’s growing and maturing into a real publication - one of my fun side projects has been growing its “Culture” section. For anyone who follows my personal substack, you probably know that this is the stuff I most enjoy writing about. We host guest pieces on culture and criticism, book reviews and recommendations, and other pieces that aren’t necessarily business or tech focused, but try to capture what it’s like to be in tech right now in this intense moment.

Here’s what I sent out today, I hope you enjoy it:

A16Z is an optimistic firm. We are excited for the future, we’re excited about technology, and about how people will achieve greatness in their lives.

So it might surprise you, if you know the book by reputation, that I think Infinite Jest belongs in the techno-optimist canon.¹ Infinite Jest - published 30 years ago this month - does not have a reputation of being a “techno-optimist” book. In fact, it’s usually cited as a dystopian book; where advancing technology has brought out the worst of people’s anxieties and addictions.

But Infinite Jest, when you actually read it, is a joyful book. It’s a book about people; it’s about suffering and perseverance and the million points of light that make up a human. It’s a book about seriousness: what it means to surrender to a purpose that’s bigger than you. It’s a book for today’s solo creator, founder, or IC trying to “make it”; it’s a book for talented, but frustrated people; for founders, for posters, for anyone who looks ridiculous but perseveres.

To celebrate its 30th anniversary, I actually read it ² (I only got through 200-ish pages in my first attempt years ago, which is pretty typical, but you need to struggle through them; it’s part of the experience), and my overwhelming impression after putting it down was, ‘Everyone in tech needs to read this book right now.’ Infinite Jest belongs in the startup and founder canon because it is among the great novels written about struggling a lot, and looking painfully ridiculous. Which, the book argues persuasively, are preconditions for seriousness; which is itself a precondition for optimism:

• To be a real optimist, you need to be serious: you need to be a part of something greater than yourself.

• To be serious, you have to be externally focused (striving for something external, in service of others, in surrender to a higher mission), not caught up in your own head.

• To achieve true external focus, you probably need to struggle a lot. And it won’t be the romantic kind of struggling either; you are going to look ridiculous.

These are obvious truths, but they’re hard to see, even when they stare you in the face. And Infinite Jest’s magic comes from its embrace of the ridiculous, which helps you see and accept these lessons: in the moment as a reader, and one day in our own story.

“What’s water?”

Light spoilers ahead; my intent here is to get you to want to read the book, not ruin it for you, but proceed accordingly.

There are four plot strands in the book:

1) The one the book is famous for, which is “The Entertainment” ³: a piece of content so compelling that anyone who watches forgets to eat or drink, and they go insane if it’s taken away from them.

If you’ve heard of anything in the book, it’s probably this theme, as an emblem for “post-scarcity problems.” Infinite Jest is set in a world of abundance, more-or-less (they’ve figured out energy, albeit with some side effects). The principal plot element of The Entertainment helps frame both the abstract world of “post-scarcity” and the present very real phenomenon of addiction. (Most of the characters are battling demons of some sort, mostly drug addictions of various kinds.)

The Entertainment is of great interest to a group of violent Quebec separatists, who want to use it as a terrorist weapon. This particular plot strand helps organize the book intellectually, as a vessel for debate over post-scarcity problems and timeless values: about freedom and individual choice, about individual versus collective rights, and about American exceptionalism.

2) and 3) are the principal plot narratives of the book, which form a classic “upstairs / downstairs” setup. One plotline follows the students at a youth tennis academy, who are all hard-striving tennis protegés (and who have their issues; e.g. smoking too much weed), who despite their troubles do achieve moments of purpose and transcendence in their struggle to make it to “the show” i.e. the professional tennis circuit.⁴

Meanwhile, down the hill from the tennis academy, there’s a halfway house full of people who have had incredibly messed up lives, and whose own journey of suffering and meaning is largely told through their ongoing participation in AA meetings. The AA backdrop tells the same story as the Tennis Academy, of characters suffering from the bleakest of circumstances.

What the tennis players and the halfway house residents have in common is the internal battles they’re fighting. And it’s in the intense moments of fighting for sobriety that you experience the pure gravitas of their everyday heroism. This is where the characters (and, you, the reader) gain such externally oriented empathy, focus and clarity around what it means to be serious - and, accordingly, where a profound and re-born optimism springs forth.

4) Finally, there’s a fourth plot strand which I will not tell you about, to keep the mystery of the book intact.

With this introduction to the basic tenets of the book: why is it worth reading?

DFW’s great gift is in helping us actually see what is right in front of us, and yet hard to describe - like the fish who asks the other fish, “What’s water?” (best known from his 2005 Kenyon College commencement speech, but originally from Infinite Jest, delivered by a biker gang after an AA meeting.)

One way we achieve this escape into the sublime, where we can actually see what’s in front of us, is by diving into absurd side plots, jargonny technical manuals or slangy dialogue, or other framing devices that help us understand the main plot better despite their logical irrelevance. In his Forward to the 20th Anniversary edition of IJ, Tom Boswell writes: “Most great prose writers make the real world seem realer - that’s why we read great prose writers. But Wallace does something weirder, something more astounding: even when you’re not reading him, he trains you to study the real world through the lens of his prose.

The way DFW employs “ridiculousness” is for two purposes. First, because the reader of the book is initially going to be guarded, and defensive, against seeing our own struggle in these characters. By placing some of the book’s most anguishing and pivotal scenes in ridiculous and/or gross circumstances (a critical moment in the plot happens in very funny and disgusting manner in a public bathroom stall), he brings our guard down: when we laugh along, we’re accepting something about ourselves. And second, it helps us accept that our own struggle, our own suffering, can often itself look ridiculous; and that is okay; it is part of being human.

The real world, like Infinite Jest, is full of these lessons:

Not only is this thematically very Infinite Jest-y, but there is one particular Cortisol-spiking Snatch in the book that is critical to the plot, and equally ridiculous as this one

The current Clavicular / Looksmaxxing / World War Mog storyline is a real-world Infinite Jest sideplot ⁵, where people willingly breaking their jaws in pursuit of looksmaxxing tell a story about transcendence through suffering, and about what “seriousness” is. It’s not like this is what actual suffering is - that’s not the point I’m making here, similar to how DFW’s halfway house residents who are genuinely suffering see their struggle sublimely articulated by a tennis coach up the hill.⁶ And it’s not because looksmaxxing as practiced here is a good goal; it’s a ridiculous goal, but it’s taken very seriously.

It’s a useful mirror for a tech community that has gone a cultural 180 over the last decade: from the decadence of the late 2010s and the bewilderment of Covid-era tech largesse, to being suddenly dropped into a race for AGI and into the mission of American Dynamism. Many of us in tech went through an experience like Schtitt’s kids⁷, who needed to get back to being a little fish, and part of a story much bigger than themselves. Back into sweating the details, frantically searching for ways through new, big problems that are suddenly outside of our heads again, with real stakes.⁸

Infinite Jestermaxxing: “I hope suffering happens to you.”

All great novels have a scene where the main character confronts the devil.⁹ These climatic scenes bring the moral thrust of the book to a fine point, where all of the lessons of the previous pages come together to express the author’s moral antithesis. In Infinite Jest, that scene takes place at a group therapy session, lionizing a particularly horrifying idea of self-care. It is the antithesis of AA; it foresaw modern “wellness” culture with clear eyes.

A16Z’s own Katherine Boyle wrote an essay a few years back, “On Seriousness”, making the point that “Seriousness” means believing in a project greater than oneself. David Foster Wallace’s characters form into serious tennis players when they learn to really “see” the lines of the court, the game, and their life’s pursuit as something greater than themself. Clavicular and the jestermaxxers are absurd characters on our current timeline that somehow, illogically but compellingly, help us “see” our own struggle and reincarnation¹⁰. They are looking outward, they are struggling for something, even if that something is ridiculous; arguably because it is ridiculous.

Jensen Huang gave the best version of this:

“One of my great advantages is that I have very low expectations. Most of the Stanford graduates I meet have very high expectations, and you deserve to have high expectations, because you came from a great school. You were very successful; you’re top of your class;

obviously you were able to pay for tuition. And you’re graduating from one of the finest institutions on the planet. You’re surrounded by other kids that are just incredible. You naturally have very high expectations.

People with very high expectations have very low resilience. And unfortunately, resilience matters in success. I don’t know how to teach it to you, except for, I hope suffering happens to you.”

Hence, I’ll restate my case to you why Infinite Jest belongs in the Techno-Optimist canon: because optimism requires seriousness, and seriousness looks painfully ridiculous, in the moments where it counts. The most serious founders often look the most silly! Being a founder means suffering a lot, and looking ridiculous a lot. Being serious about something means being both totally focused on an external, ambitious, hard-to-achieve goal while also flailing around as a complete beginner.

At A16Z, our tennis coach up the hill is this guy Ben Horowitz, who wrote a book called The Hard Thing about Hard Things.¹¹ If you go find your copy, turn to Page 61, and he’ll tell you about the Struggle:

The Struggle is when you wonder why you started the company in the first place.

The Struggle is when people ask why you don’t quit and you don’t know the answer.

The Struggle is when your employees think you are lying and you think they might be right.

The Struggle is when food loses its taste.

David Foster Wallace’s characters certainly know the struggle. Reading fiction is an incredible gift, for that reason. You can learn their lessons from experiencing their pain, and I’m not saying it’s a literal substitute for the pain itself but it’ll make you a whole lot more prepared for when it comes around.¹²

I wrote the other week that the important test of our humanity in the next several years is what we do to help others across the threshold, into a world where everyone is a beginner again, everyone has a blank slate opportunity to be serious about what they want to do with all of this new agency and freedom to do things - but also may lose their jobs, their self image; they will look ridiculous. They will know frustration.

The human greatness that’s to come is going to come from the struggle; it’s how individual people are going to get oriented within the new lines of possibility, like the lines on one of DFW’s tennis courts, and find their own version of greatness.

If you want to enter the future as a prepared, optimistic technologist: pick up Infinite Jest. It’ll make you laugh, it’ll make you grateful, it’ll make you joyful. And, yes, reading it will be an experience in pain and suffering. I wish you ample doses of it.

1. _{See https://a16z.com/the-techno-optimist-manifesto/ for more on Techno Optimism.}

2. _{In a funny bit of contrast, I also re-read The Hard Thing About Hard Things by our own Ben Horowitz at the same time, which was a funny juxtaposition but really worked.}

3. _{A piece of content so compelling that it is used as a terrorist weapon by Quebec separatists (a), in the Year of the Depend Adult Undergarment (b)
   A. Les Assassins des Fauteuils Rollants, known in English as the Wheelchair Assassins, which as an aside demonstrates a recurring joke that surely goes over >90% of the readers’ heads, which is that a great many of the Quebecois French words and idioms are misspelled: e.g. “Rollants” should be “Roulants”
   B. Infinite Jest takes place in a world where calendar years have paid sponsors, similar to college football playoff games. Hence, “Year of the Trial-Sized Adult Dove Bar”, e.g.}

4. _{The elder philosopher drill coach at the academy, Gerhardt Schtitt, frames the book’s concept of virtue (as relayed through his colleague), about how the academy helps those kids not completely lose their minds under the pressure of competition and fame:}

   _{“These kids are here to learn to see. Schtitt’s thing is self-transcendence through pain. These kids, they’re here to get lost in something bigger than them. To have it stay the way it was when they started, the game as something bigger, at first. Then they show talent, start winning, become big fish in their ponds out there in their hometowns, stop being able to get lost inside the game and see. [Messes] with a junior’s head, talent. They pay top dollar to come here and go back to being little fish and to get savaged and feel small and see and develop.”}

   _{Schtitt’s philosophy can be found in greater detail in an early section of the book, where he goes on an exhausting and absolutely disorienting treatise on “seeing within lines” of a tennis court that makes no sense until you’re most of the way through the book, at which point you will try to find it but fail to find the relevant chapter after at least 5 minutes of flipping through pages, for there is no table of context nor chapter numbers in this >1000 page book.}

5. _{Really he’s more of a Cervantes character, but anyway;}

6. _{Or how you, the reader, suffer through pages and pages of detail you do not want to read but you know you have to, because critical plot elements will be buried in what is otherwise a 1000-word footnote on Boston-area drug slang taxonomy - the technique is brutally effective.}

7. _{See note 4.}

8. _{This newsletter is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. Furthermore, this content is not investment advice, nor is it intended for use by any investors or prospective investors in any a16z funds. This newsletter may link to other websites or contain other information obtained from third-party sources - a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. If this content includes third-party advertisements, a16z has not reviewed such advertisements and does not endorse any advertising content or related companies contained therein. Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z; visit https://a16z.com/investment-list/ for a full list of investments. Other important information can be found at a16z.com/disclosures. You’re receiving this newsletter since you opted in earlier; if you would like to opt out of future newsletters you may unsubscribe immediately.}

9. _{Infinite Jest’s devil construct, like many other structural elements of the book, draws heavily from Dostoyevsky’s The Brothers Karamazov (which is my favorite book of all time.) Another major similarity is that the Incandenza brothers (Orin, Hal, Mario) are modeled fairly explicitly on Dimitri, Ivan and Alyosha Karamazov: Orin the sensualist, Hal the literate skeptic, Mario the Christ-like figure; with dad being, you know, the dad.}

10. _{Clavicular was mid jestergooning when a group of Foids came and spiked his Cortisol levels.}

11. _{See note 1.}

12. _{“Since it is so likely that [children] will meet cruel enemies, let them at least have heard of brave knights and heroic courage. - CS Lewis}

As part of joining Andreessen Horowitz, I’m thrilled to be back to writing frequently again. Going forward, I’ll keep publishing personal writing here occasionally - but most I’ll mostly be on the main a16z feed now, which is my responsibility to make great. If you haven’t yet, you can subscribe here:

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Now let’s get to it!

Prediction markets have product-moment fit: they’re perfectly suited for the zeitgeist right now. And I think they carry a torch for a much bigger movement than what you see at face value. “Prediction” is now the entire game. It is the movement and meta-aesthetic that defines our main branch of society going forward; about how we interpret the world and participate in it.

Prediction is as significant a concept as modernism and postmodernism. It’s the defining reframe of our generation, and we’d better understand it:

• Postmodernism was why we built businesses the way we currently do: why we treat innovation as “capital at risk”, why we have the “front end / back end” service model, and other themes that are so pervasive they’re invisible.

• “Prediction” is a distinct break from postmodernism. It is what comes next.

• It can be hard to tell, in the moment, whether you’re looking at the beginning of something or the end of something. Many early AI artifacts that we think are “the future” are actually the final form of the old thing, rendered perfectly at their moment of obsolescence.

• All value creation now is about prediction.

• Prediction markets give us a unit we can understand, like how patents represented progress a century ago, that organized our ambition and represented a stake in the system.

• Prediction helps us break free from postmodern malaise, and gives us a new purpose in the world: we create order in the universe by contributing information.

Postmodernism and its native business model

Our current nostalgia for a time “back when we built things” draws much of its aesthetic from a movement called Modernism, which was the main thing going on in the late 19th and early 20th centuries. We call them “movements” because they are truly grand ideas: multi-decade patterns of perceiving and thinking about art forms, business forms, and national forms; asking, “what is going on here?” at the highest level we can.

Peak modernism was about nation-states asserting their dominance as the new form of human identity and organization. The old multi-ethnic empires like the Habsburgs and Ottomans were dying; art movements like the Romantic period celebrated “unified” peoples, languages, aesthetics, and shared histories. This was a period of time where things in the world were the sum of their parts. We thought about one national heritage, or one body of science.

Nothing lasts forever, and Modernism certainly didn’t. After the pain of the World Wars, we retired that mindset for a subjective one, where identity is “rendered” case by case. Where modernism was about cause, postmodernism emphasized combinatorics: the thing you see is one arrangement of pieces; the thing I see may be another. That’s how we got Freud’s ego-self as a surface rendering of subconscious drives, defining art like Glenn Gould’s Goldberg Variations, and McKinsey. To experience peak postmodernism, watch Friends - first without the laugh track (the pieces themselves) and then with it (the rendered product). If you’d prefer, you can watch any episode of reality TV, or pro wrestling. It’s all the same.

Postmodernism quickly figured out its business model: the “front of house / back of house” abstraction of cost-effective, personalized service at scale. My favourite essay on this is Venkatesh Rao’s The American Cloud, on the Great Back End (the enormous logistical enterprise, hidden from view, that grows our food and heats our homes) becoming abstracted out of view, while coastal consumers interact with pleasantly recreated “situations”, like Starbucks or Whole Foods.

This idea was so powerful that its best biographers were its Marxist critics. In Postmodernism: or, the Cultural Logic of Late Capitalism, Fredric Jameson wrote in a tone that’s so frustrated it betrays a hint of admiration: “Aesthetic production today has become integrated into commodity production generally: the frantic economic urgency of producing fresh waves of even more novel-seeming goods (from clothing to airplanes), at ever greater rates of turnover, now assigns an increasingly essential structural function and position to aesthetic innovation and experimentation.” This is almost a sentence you could find in Atul Gawande’s completely sincere article about why hospitals should operate more like The Cheesecake Factory. It could also describe mobile app development. There’s one coherent idea through it all: the meaning of things is how they’re rendered-for-purpose.

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From “progress” to “innovation”

Modern and postmodern actors think about progress in very different ways. Modernism understood progress as something that simply happened; perpetually and unstoppably. And that way of thinking about the world became a self-fulfilling prophecy: the early 20th century delivered on those promises, as houses lit up with electricity and living standards shot up everywhere. Then in the middle of the 20th century, something interesting happened: we stopped talking about “progress”, and found a new word, “innovation”.

(Longtime newsletter readers may recognize another post featuring this ngram)

In earlier times, “progress” came from either sheer force of will, or from luck (fortuna). But innovation is a postmodern concept: it reorients our goals into units of risk to be assessed, and prudently taken. Forward motion now happens in parallel expeditions, from measured acceptance of capital-at-risk. We rendered the potential out of a combination of bets, into a useful product. In 1957, Peter Drucker wrote in Landmarks of Tomorrow: “Innovation is… a new view of the universe, as one of risk rather than of chance or of certainty. It is a new view of man’s role in the universe; he creates order by taking risks. And this means that innovation, rather than being an assertion of human power, is an acceptance of human responsibility.”

Drucker was not predicting the future; he was trying to describe the present accurately. But he captured the mindset that led Bell Labs and the early Venture Capitalists to seed the beginnings of the future; and for startups to become a financeable endeavour. This is a big part of why Silicon Valley was so aesthetically compatible with what was going on in the rest of the country. Trying lots of small bets, searching for product-market fit, and getting rich at IPO were all postmodern quests. They fit with the rest of what was on TV.

These days in tech, we don’t use the words “progress” or “innovation” as much as we used to. Nowadays we talk about technological adoption. Adoption isn’t a new word; it certainly existed before, but it used to mean something specific about consumer requirements; whereas now it means the entire thing. I think there’s a clue here in where we’re going: that the important essence is not how you choose to engage (the postmodern mindset), but rather when you engage with the snowball rolling down the hill.

Prediction in culture: “I want to feel something, even if it hurts”

Now that our history basics are out of the way, let’s actually talk about prediction; the movement that’s happening now.

“Prediction” has two essential characteristics. The first is what we mean when we talk about prediction markets, or honestly any market. Prediction is the action that markets perform, as information moves around society and directs attention and resources to attractive destinations. Where they go constitutes a kind of prediction. The second essential characteristic of prediction is that computers are getting good at it. Computers now provide a broadly available superstructure for, “given this chain of things, predict a good next thing”, which will keep improving.

With that in mind, let’s go to the present moment and describe some of what we see.

The first observation I will make about popular society is how suddenly and decisively our culture moved towards speculation; not only in stocks and sports betting but also more broadly in life. Meme stocks might only be a few years old, but popular culture preceded them by a decade when Home & Garden TV programming became entirely about house flipping. Large swaths of the population now buy into the idea of “making your own luck” as a more literal virtue than it used to be.

This matters because there is now a meta-awareness (or meta-fantasy?) among the general public that our limited capital and attention could, in fact, find better places to go. There’s a driving energy in this world that everyone is doing this; and therefore, our attention each moment “predicts” the next one in a real sense. Not just forecasting it, but also setting the path dependence that leads us down the road and to a conclusion.

The second observation that I’ll make is how much social media now feels the same way that buying stocks does. Checking if your post is performing feels the same as checking whether your stock is up or down, and that tells you something important about your relationship to both activities.

At the forefront of culture and commerce, flash sales and popup stores are a revealing break from business convention where removing friction is always good. A sneaker drop is all about adding friction to the sales process. That’s why people like them: your place in line is the meaning of the product. Gaming predicted all of this, of course: that’s an industry that completely understands that friction is the product, and anticipation is consumption. The meaning of the game is the path you take through it, trying to feel something.

How early or late you are to something is now an essential component of your relationship to that thing. The timelines and reels that represent “what is going on” are increasingly about a single meta-topic: are you predicting it, or is it predicting you? This has become the main thing that you feel. And it is a complete break from the postmodern aesthetic, where your consumption was wrapped in an unthreatening fuzzy blanket. It doesn’t matter what time of year you arrive at Whole Foods to buy strawberries; the farmstand simulacra is recreated faithfully. The prediction aesthetic is a new thing, and rejects postmodernism: “I want to feel something, even if it hurts.”

“Is this art?”

It can sometimes be hard to tell whether you’re looking at the beginning of something, or at the end of something. In 2008 it sure felt like Obama was the beginning of something significant, but in hindsight he was actually the last president of the 20th century. Similarly, I think many of us thought the scrolling timeline feed was what came after newspapers; but in hindsight, the feed may be the last newspaper.

I remember the moment when AI generated art suddenly burst into public consciousness with Dall-E and Midjourney, and everyone gasped, “this is the beginning of a completely new kind of art!” I think with hindsight we will look at it with nostalgia, because it’ll actually be the last postmodern visual art form. It’s the final, perfect form of “everyone gets to consume the version they would like!”, at the moment it became culturally obsolete.

On that note, you’ll remember a lot of freakout at that time, “In the future, everyone is going to get their own personalized echo chamber of AI-generated news!” which is 1) not what’s happening, and 2) clearly our projection of our anxiety onto the old form factor and its problems. What’s actually much closer to happening is, “Everyone is going to have their own personalized parlay on the same series of reference events.”

So what is an actually new art form? I don’t consider myself a contemporary art connoisseur, but I am pretty online. And there’s a definite art form emerging, which is public predictions that go really well or really badly. For example, this was art:

(If you don’t immediately recognize what this is - it’s a couple who dressed up as Taylor Swift and Travis Kelce for Halloween in 2020, who captioned their Instagram, “I don’t know any world in which Taylor Swift and Travis Kelce would be in the same room together. But apparently in this one they’re married?”) This post, now virally resurfaced, is certified pop art.

READ MORE: Prediction Path Screenshots: a New Kind of Meme

Another kind of art, which may make you mad, is NFTs and even more so Meme Coins. Because, 1) if suggesting something is art makes people Rite of Spring-level furious, you have something interesting; and 2), because they are a pure demonstration of public predictions as art, and the meaning of a thing being when you interact with it.

For example:

That’s art! Andy Warhol famously quipped of the postmodern era, “Everyone gets their 15 minutes of fame.” Today, I think it’s, “Everyone gets one 15-bagger.”

There also doesn’t necessarily need to be a prediction made, per se, to qualify as this kind of art. Just the prediction path itself is sometimes all you need:

Better luck next year, Steve Cohen.

Value creation in the Predictive Era

Every era finds a defining business model. For the modern period, it would be the vertically integrated giants that decided wars. For the postmodern period, it’s the front-of-house, back-of-house simulacrum. Our job today is to imagine what forms of business naturally resonate with where our cultural aesthetic is going.

Crypto was really avant-garde here, because they had to argue with everyone, “Onchain isn’t in the front end or the back end, it’s this new third thing”, that was hard for us to put into words, especially for practical app-building purposes. Culturally speaking, the proposition remains: “for every piece of content, there is now a publicly viewable timeline for whether you own the content or not, and when you bought and sold it”, which produces very silly results when taken at face value, but contains a critical insight. Which is, “We’re all participating in one great online game, and your goal is to be predictive of the game, rather than predicted by the game.”

But the real party started when LLMs became a product in their own right, and suddenly this nebulous thing called “weights” became the most sought-after asset in the world. As AI models start contributing to business decisions, showing ads, and influencing the culture, we now have a tangible place where “everyone has their own personalized parlay on the same series of reference events” is now no longer about gambling; it’s about the whole timeline of your life. And in many cases, the house is actually trying to help you win.

“Gen” was the best word I could find at the time; “Prediction” is much better.

I think back on Packy’s Great Online Game piece a lot, because this was a piece that was basically about training LLMs, except for it was written in 2021, about ZIRP Online Citizenry. “Being good at the Internet” is now a highly practical and monetizable career - it used to be, too, but less legibly. Today, it’s easier to understand why being online is naturally monetizable: because prediction is it; prediction is the entire meta-game, from meme coins to model training, and the farther upstream you are in the information flow, the more likely you’ll be predictive of the game as opposed to being predicted by the game. It is suddenly not-so-ridiculous when Matthew Prince of Cloudflare asserts, “the way you’ll get paid on the internet now is by writing things that the AI does not yet know.” We’re not there yet, but there is a plausible path to it.

To read the rest of the piece, head over to a16z.substack.com:

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I’m heading to Andreessen Horowitz!

After five years at Shopify, I’m moving onto my next adventure in life, and joining a16z. Shopify is an incredible place, and Tobi is one of the great founders of our era. I’ll remember those years as one of the golden chapters of my life. Now, onwards!

I’m joining a16z as Editor at Large, and I’ll be responsible for the written output of the firm. A16z has always prioritized great thinking and writing as a deliberate instrument of the firm’s purpose and power. It starts with Ben and Marc: both generationally talented high-agency thinkers, writers, and company builders. And there’s a burning new energy in the firm today, brought by Erik Torenberg - who I’ll work with closely as he takes a16z’s narrative presence to a new level of ambition.

Writing is power transfer technology

I’ve had a few different vocations over the years - I’ve been a founder, touring musician, venture associate, worked at Shopify - but most people on the internet know me from my writing online. I’ve been blogging since my early twenties, and in that time I’ve seen different online content metas come and go - the golden years of VC blogs like AVC and Haystack, the Medium years, now Substack; the rise of “Go direct” and the crisis of traditional media.

What hasn’t changed is how valuable great writing can be. Something special happens when you give someone words to express an idea they always knew, but couldn’t articulate: you give them power. And it didn’t cost you anything.

“Power transfer technology” is what the business of VC is. Why does a VC firm care about content? It can’t just be to advertise the firm; or promote their partners and their theses. Those are both consequences of success, but they can’t be the actual goal.

The primary objective of a VC firm’s content, particularly their written output, should be to give founders power. The goal is to give them writing that transforms them into someone with more legitimacy, which is what power really is about.

Traditional media sometimes helps you accomplish this. A well-written op ed, thoughtfully crafted, can serve this purpose. But I think bloggers are naturally better at this craft, because bloggers have to grow their franchises under the constraint of not having built-in distribution. If you’ve mastered the craft of writing online, you know something important about how to reach and influence people, and what exactly it is you offer to an audience that gives them power.

Blogging is a trade

Winning, for bloggers, means writing the reference take on a good topic. My favourite example of this is how Byrne Hobart broke out with his piece on the 30-year mortgage. It’s kind of surprising that this kind of post had such influence - it’s wonky, it’s not written for a general audience whatsoever. But it turns out that people think and talk about their mortgages a lot, and like to feel competent when they do. Reading that piece equips them with a kind of legitimacy to speak on the topic.

One lesson hiding in plain sight here is that most of the audience of any successful post does not actually read it. They are told it by someone who did read it. There’s a primary audience who carefully reads the piece and does the cognitive work of “restructuring their consciousness” (see Walter Ong, Orality and Literacy) around good writing. And then there’s a secondary audience, who are re-told the content, either verbally (including group chats, podcasts, Youtube) or in other oral formats like Twitter.

(This is broadly true both inside and outside of organizations; e.g. a lot of work goes into writing an annual plan, which only a small number of people actually read, but a lot of people are “re-told” in some way.)

The primary audience gets something out of this sequence of events: they get power. This is the great secret of writing in public: the writer and primary audience both put in effort (to pack and unpack the idea); and they jointly reap the rewards, which is the legitimacy earned when the idea gets subsequently retold verbally to the wider secondary audience.

This is why, paradoxically, to reach the widest audience, you write to the narrow audience. Your objective as a writer is to give your primary audience material they’ll want to re-tell. They do the work of translating it to wider audiences in specific contexts; you do the general articulation in rich detail.

The secret of magic is to transform the magician

Today, there’s an amazing idea-sharing format that’s swallowed a lot of the “smart people discourse” on the internet, which is podcasting. The rise of podcasts has been astounding to watch, in the six years since I wrote The Audio Revolution. And a16z has both a great past and a great future in this media format, especially with Erik at the helm and seeing the caliber of talent he’s bringing on board his New Media team.

But podcasts are not enough on their own. Great writing, which has gone through the crucible of thinking and editing, transfers something into the reader, and transforms something within the reader, that talking does not.

I know a lot of smart people who correctly intuit that blogging is powerful, but can’t justify it on a return-on-effort basis compared to other ways of getting your message out. This is a mistake. Writing matters.

It takes more effort to read something than to listen to it. (And much more to write something than to talk about it.) The cognitive work of writing and reading is a real cost, but the benefit is that you get “restructured consciousness” from factoring and negotiating with the idea in written form. When you read an important idea and put in the work to understand it, you gain the subconscious competence and legitimacy to talk about it: you are transformed into someone with more power. Whereas, when you hear an idea, you can usually repeat the idea but not with the same authority. The deep competency is not transferred to the same degree.

This is an important idea to understand in a world where most of our information diet is shifting towards oral communication in various forms - “the internet village.” Now, people have been complaining that “nobody reads anymore” for decades - the point here is, the benefits of reading have never been more disproportionate, nor less obvious.

The farther we move towards being a default-verbal information culture, the more powerful the trade between writer and primary reader, but the less obvious it is to do. (Why write things if people would rather watch or listen?) If your goal is “I want to maximize the number of people who receive my message” then oral formats like podcasts, Youtube, and Twitter will appear like the obvious choice. But if you articulate the goal as “I want to give people power” then clearly you’ve got to write something down.

Founders are customers for legitimacy

I remember some days when I was a founder and no one would take me seriously, except for when I could produce a blog post from someone like Paul Graham or Semil Shah and speak to that idea. Something incredible would happen in those moments - people would actually listen to me, as if I’d suddenly become magic. Magic works because it is communication. The founder equipped with the VC’s writing should communicate something higher-signal than the founder alone. Ask, “what must be true of the content for this statement to become real?” and you’ve got a good guide for what kinds of things to write.

This relationship between VC and founder scales all the way up to real power-politics: the job of the VC firm is to be the “legitimacy bank” where founders (and other high-agency people) can go to take out legitimacy on credit, or make a legitimacy deposit. I find this to be a wonderful way to frame the founder-VC relationship because it does NOT imply the VCs are the “grownups” in some patronizing sense: it celebrates legitimacy as a thing that VCs and founders incept together, just like how blogging is thing that the writers and their primary readers incept together.

This is why VC blogs were such a good product in their heyday. As the “free tier” of the VC, it naturally frames the relationship as one where legitimacy is jointly incepted (by the writer and primary reader), not as one that’s benevolently bestowed.

The meta is different now, but the purpose is the same, and I think a16z is probably the best spot in the world to pursue that craft and that thesis. I’m incredibly fortunate to get to join this group of talented investors and company builders, towards a mission that’s never been more important: giving the world’s founders the power they need to build a bright future.

Let’s go!

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But I am not delighted about what it foreshadows, which I wasn’t able to articulate well in the moment but I’ll explain in this post. I think this event marks the breaking of the startup bundle: perhaps an inevitability in the new economics of the AI talent wars, but a bad thing for startups and for the innovation economy overall.

The hardest thing a startup has to do is solve a coordination problem between different people, who have different goals and make different contributions. You may not have ever thought of it this way, but startups employ a timeless economic coordination mechanism - the bundle (like your cable bundle) - as a way of aligning founders, VCs and employees to invest together and win together. If this bundle falls apart, as the cable bundle did, we all lose in much the same way.

Bundling benefits both buyers and sellers

Time to review our bundle basics, which means going back to Chris Dixon’s classic post from 2012. Read it if you haven’t, it’s worth your time; then come back here.

To recap: the basic intuition around bundles starts by acknowledging buyers’ heterogenous preferences: different people are after different things. Some buyers love history and less so sports: they’d happily pay $10 monthly for the history channel, but only $3 for the sports channel. Other buyers are the opposite. The seller’s dilemma is how to price these channels: at $3, 10, or somewhere in between? None are ideal.

The smart move is to combine the channels into a single bundle offering at $13. Buyers overall get more product, at a price they happily pay, with more consumer surplus on top of it. Sellers overall get to sell more product, and minimize deadweight loss due to non-consumption. Everybody is happy.

I wrote about this a few years back with respect to another familiar purchase: cars. To me, there is an obvious reason why higher-end SUVs have exploded in popularity over the last decade, at the expense of other more varied kinds of cars. It’s because a performance SUV is a bundle of a bunch of things you want in a car (e.g. Minivan plus Fun to Drive) and bundling it into a single, expensive product draws out more overall consumption from buyers, letting carmakers invest more back into that premium product.

The key to bundle economics is that the benefits of bundling are proportional to how varied the buyers’ preferences are. The steeper the demand curve across all of the individual bundle components (e.g. individual TV stations), the more powerful the combined economics of a single subscription, because there is more deadweight loss that can be squeezed out by the combined bundle.

A startup is a bundle

So, bear with me; we’re going to describe a startup in these same terms. Specifically, we’re looking at the transaction of pricing and selling equity. Optimally priced equity is the thing that makes the startup go.

Startups have a product called “upside”. That’s all they are, at first. And the founder has to create a market for that upside. They are the sellers; investors (which include angels and VCs, but also include employees of all kinds) are the buyers.

All of these different customers have distinct concepts of what they think upside is. They have different utility functions of “I’m interested in paying X to get Y” where ‘Paying X’ means giving your money or time in exchange for equity, and ‘Getting Y’ means working towards, and being rewarded by, liquidity scenarios.

So a VC on their own might prefer to pay a lower valuation (and put in less cash), in order to hire less people, fund a smaller company but where they own more, and aim for acquirable cash flows. An employee, conversely, might rather get more equity percentage in the company for their work, raise less venture capital, and explicitly hope for the company getting acquired for its engineering talent. Each person might be individually more interested in a more bespoke version of the company and its upside potential, but that’s more targeted to what they want.

In other words, each individual buying the equity has their own preference for what they’d want to pay, and the kind of upside potential path they’d want to pursue. On the other hand, the founder’s aim is they want to maximize the total amount of company potential they can create. They want to make one big beautiful company.

And so, what founders do is offer all of the kinds of investors and employees, more or less, the same bundle offering. (Yes, VCs get protection in the preferred stock, but the upside scenario converts to common.) This is particularly important for employees, where no matter if you’re junior or an ambitious VP, you all get the same stock. The stock is a bundle. Everyone buys the combined product, paying a higher price for the bundle. And like the cable bundle, the founder can now put more resources into the combined product, and maximize the overall offering (the potential to win.)

There is one oversimplification going on here, which you may have spotted, which is the assumption that a startup is like a consumer product in that, the higher the price and the more money you raise, the more the startup’s chance of success. (Because all of the money collected from people buying in goes towards realizing the upside.) Of course it’s not as simple as that, but for the purpose of this argument I think you should accept it as true - because the ability to coordinate others and raise money is the thing that founders must be able to do here, just like “sell TV subscriptions” is the thing that the cable company must do.

What happened with Windsurf?

So, here’s a rough sequence of what happened. (And, to reiterate, I have no inside info here, I may be wrong or incomplete about details; this about the general gist and narrative.)

OpenAI proposes to acquire Windsurf (an AI coding IDE that it makes reasonable sense for them to acquire.) For various reasons, possibly because Microsoft put pressure to kill it, the deal with OpenAI falls apart and instead, the founders and a few senior employees walk to Google for a massive pay package. In exchange, Google gives Windsurf a pile of money for a hand-wavey non-exclusive license to the technology (so, basically, gave them a pile of money). The pile of money gets distributed between investors and tenured employees, and then subsequently the remainder of the company got acquired by Cognition.

Early on, there was a reflexive cynical read here (which I’d had) that the founders were pulling a fast one: parlaying an acquisition term sheet into themselves being acquired for a massive pay package, not for the company’s benefit but for their own. (And supposedly defensible under ‘this my free will employment, you can’t enforce a non-compete, and you can’t sue me personally.”) This take didn’t tangibly materialize, and thank goodness, because that would’ve set an absolutely terrible precedent of behaviour and I’m glad that it clearly was not set. (Although some, like Delian above, retain the cynical take and that’s justified.)

There is another read, which came into vogue a few hours later as details emerged, which went “these kind of weird negotiated disbursements of a startup’s component value is a weird and shitty consequence of regulatory M&A scrutiny.” If regulators won’t let Google buy other startups for antitrust reasons, then this is a way around that. Also, fine - if you want a team’s talent and you can either get it now or after months of antitrust review, obviously you’d rather have it now.

After a few days’ reflection, I have a different way to articulate the issue. It’s that the war for AI talent, and the new sky-high stakes involved in securing hot researchers, is putting pressure on the startup bundle. It tempts everybody involved to unbundle the different components of upside - too good on their own not to pursue.

The AI talent wars unbundle the startup

So what we have here, I think, is the “unbundled disbursement of a startup’s upside.” That is a weird and new thing to contemplate. Yes, everybody got paid here and that’s great. But let’s look at the precedent it sets, which is that the valuable employees of a company can be acquired separately from the business of the company. And if each can be negotiated separately, in an upside scenario, then each can be negotiated for at the outset, when the equity is first issued.

We used to all accept that a startup was one cohesive thing - aside from in some downside scenarios. (You can have unhappy liquidity events where the startup gets sold for spare parts in some way.) But in happy scenarios, the arrangement we all accepted was that successful liquidity came as a singleton event. One company (or the public market) bought you, it bought common stock, you all win together.

Now we have this curious thing which is an upside liquidation event in parts. The founder and some employees got their payment in one kind of way, the rest of the company and the investors got their payment in another kind of way. And maybe everyone is happy. Sure! But this is like one group of people enjoying just the history channel, and the other group of people enjoying just the sports channel. They start wondering, “Maybe I could specifically pay for the sports channel?”

The analogy starts to become more relevant if term sheets start changing to accommodate or anticipate these kinds of unbundled disbursements, and employees start asking (or looking) for bespoke kinds of upside. This is the unbundling of the startup as a singleton product, being replaced by a new kind of upside that’s tailored around the kind of work and the kind of reward that an individual contributor might want. This is like subscribing to the niche streaming service. It’s exactly what you want, and you’re paying a price you think is fair, and getting a product that’s what you asked for.

No individual person can be blamed for wanting this, and in particular, you can’t really be upset with the superstar AI talent for whom the unbundled upside event is so obviously better. You can’t blame an individual for breaking off the bundle if they’re acting rationally and in good faith. You can’t blame superstar AI talent for whatever they can negotiate in their pay packages.

But if startup funding goes the way of the cable bundle - replaced by a more individually crafted mishmash of upside possibilities - I don’t think that’s a good thing. Because the job for founders gets much harder. You lose the economic benefit of the bundle, which was avoiding all of the deadweight loss due to non-consumption that you get when everyone is after their own individual thing. The irony is that the very thing that makes bundles work - the heterogeneity of buyers’ preferences - is itself the thing that breaks the bundle.

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It’s been a tough week for the AI haters; not only because of Ghibli Day (if you aren’t in the loop, congratulations for touching grass, but also open any Twitter feed and start scrolling) but also since Dwarkesh’s new book The Scaling Era is now out on Stripe Press, chronicling the oral history of AI so far. And the overall vibe this week got me thinking about an old series of essays I wrote at Social Capital - the Scarcity and Abundance series.

Those old Scarcity and Abundance posts were actually two different series I wrote at Social Capital, one in 2016 called “Emergent Layers” that iterated on The Innovator’s Dilemma, and a second in 2017 about positive feedback loops and Red Queen’s Races in environments of abundance. I think they hold up, 8 years later. They describe 2025 pretty darn well.

I’m gonna revisit them today. Now that we’re solidly in Agent S-curve somewhere - and you should read Dwarkesh and others for how that happened, not me - how do we start sorting out what resulting behaviour is actually new, what will continue to feel forced, and what’s next up that’s non-obvious? Three themes stood out to me, on reread, as plausible narratives for the next few years:

1. Affirming the idea of “light versus heavy businesses”, and how we think about software businesses and their profit potential

2. The eclipse of “Code as Capital” by a new metaphor that’s closer to “Code as Labour”

3. Agents that execute work outside of firms are more interesting to me than those that execute work within firms.

Let’s go!

Understanding Abundance

It’s been 8 years, so let’s do a refresher on the two series I’m talking about:

2017: Emergent Layers

2018: Understanding Abundance

The two series each tell a part of the story of how you get virtuous cycles that drive the cost of some critical input lower by orders of magnitude - Moore’s Law, Network Connectivity, and now the cost of reasoning and inference. In each case, consumers violently adopt a new S-curve when they are both overserved by what exists already (and like the new thing because it’s cheaper), and also underserved by the existing solution (and like the new thing because it’s better).

• Real S curves are pulled forward by market demand that violently appears, almost as if out of thin air. But at the beginning of the cycle, customers are jaded and “overserved” by existing products and incumbent businesses. Their value propositions are complex, and the customer has to consider many different factors in selecting a product. These are not the kind of starting points from which you get explosive new growth; something new has to happen.

• The spark that ignites an adoption curve is when a technological unlock (“newly abundant resource”) suddenly offers end users a new kind of product, sometimes a single killer feature, which is “disruptive” in the sense that it focuses on a single, newly practical value proposition compared to incumbents and ignores the “legacy” checkboxes. The decision to use the product is a quick “Yes or No” decision, not a check box ordeal.

  

• A virtuous cycle kicks off where increasingly diverse, highly specified solutions both a) pull infrastructure forward, and b) make consumer choice even easier for product adoption (you can just try things, and it’s a yes/no decision as to whether you want to or not.)

• A new “n of 1” company emerges for each of these cycles (approximately every 6 years?), that captures the scarce resource. It is not a mystery who the n of 1 company is. Everybody buys their stock, and they make a gazillion dollars.

  

• The interesting action lies in what people do with the newly abundant resource. It’s where the new behaviour is that really defines the era. For example, in a previous era of the web where Google was the n of 1 company, all of the interesting action was in Adwords and online advertising, where a new kind of “underserved customer” emerged with basically infinite demand to put the product do useful work, in markets like travel and ecommerce.

• Legacy companies don’t suddenly wither; they remain profitable for a long time, and are often put forth as “beneficiaries” of the new trend. But their product offerings can feel forced; the new tech compels them to overserve their customers even more, rather than deliver something truly new. (i.e. “now your email is sorted by AI relevance, and not by date!”) While it’s tempting to judge the new technology by these annoying intrusions, they are not the main story.

With that recap in place, we can pencil out the current state of software and AI-driven product roadmap development onto four quadrants: 1) the “Legacy” action that feels forced and tired, 2) the newly abundant resource that’s suddenly too cheap to meter, 3) the customers who can’t get enough of it, and 4) Nvidia.

Profit potential and the Red Queen’s Race

The first point to make here is to just ask the question: in these two quadrants where all of the action is happening, who is going to actually make money over the long run? There’s no doubt that real value is getting created for customers, but it’s less obvious which kinds of businesses are poised to capture defensible margin. Switching costs are just really, really low - which is, again, one of the bona fide hallmarks of the abundance cycle that I wrote about years ago. There are some whispers this week (that I won’t link to, because they’re just rumours) of various prominent AI startups being not-so-truthful about their revenue growth, which I think says something about the general mood: not that people are cheating, but that people are skeptical about the durability of any of these businesses.

AI today is a Red Queen’s Race all right: where you have to keep running faster and faster just in order to keep in place. The result is a harder challenge for both the pointy businesses (to stay ahead of consumer expectations) and the utility businesses (to keep deploying capital at immense scale, into things like data centres) to actually capture much of the value they’re creating, yet a hard cycle for either of them to escape.

(From 2017)

Amjad Masad of Replit, one of the shining stars of the Newly Abundant quadrant, shared on TBPN why even quick revenue is no guarantee of upward success anymore: the switching cost is so low between these different (and all excellent) products that practically all of the immense value being created is ending up as consumer surplus. Which is a great thing! Good luck picking winners on a valuation-adjusted basis, though.

(While we’re here, can we give a shout out to the Technology Brothers for creating such a great franchise, in such a short period of time? I’ve never seen anything like it. Hats off.)

We’re seeing the best example yet of “pointy businesses and utility businesses” being driven by a relentless cycle of specialization and differentiation, underlying infrastructure pull-forward, and consumer choice getting easier and easier. Amjad, understandably, talks about having to deliver “the whole burger” for customers as a way to remain sticky and differentiated. I expect the accelerating treadmill to keep accelerating for some time - I’ve never seen a Red Queen’s Race like this.

Peak “Code as Capital”?

Meanwhile, if we refocus our attention to the companies that already were big and advantaged, we see another classic “Scarcity and Abundance” theme play out predictably: the idea of the overserved customer getting even more overserved as a compulsive response from the incumbents.

From CNET

Very early on in technological S-curves, a narrative is often put forth that incumbents stand to benefit the most from this change - “this isn’t disruptive, it’s sustaining!” This was notably true for AI. There was a pretty conventional take, not long ago, that went: “AI is all about data, which the big companies have. And it’s going to be all about making existing products more contextually useful, which the big companies are best positioned to exploit.”

That’s not really what happened though, is it. The most beloved new products - ChatGPT, Claude, Cursor, Replit - are all startups. To the degree that tech giants’ versions do get adopted, as far as I can tell this is only from distribution advantages and not from any product advantage. Furthermore, (with some exceptions, like NotebookLM), the big tech giants with all our user data, all of the “contextual advantage” that was supposedly so important, compulsively ship a lot of annoying, forced features that no one really wants. (To their credit, the big tech giants have other pots on the stove that are way more beloved and successful, like Llama.)

Knowing all of our user context has not yet been the advantage people thought it was. Users are perfectly happy, it turns out, to bring the context they care about to the product, and then use the product as a focused tool to accomplish a specific task. For all the ink spilled about AI as an “orchestration tool” that knows my context and saves me time, more of the actual AI uptake has been focused on doing the end tasks. I expect that to continue.

If you try to put your finger on what exactly distinguishes the products that feel magical from those that feel like slop, you’ll notice something interesting. The pointless incumbent products - Genmoji or Text Summaries from Apple, “contextual” anything from Google and Microsoft, that kind of stuff - express an ethos of maximizing the value of a software asset of some kind. There’s a whiff of attitude where “the codebase is the capital”, and the point of all these AI tools is to keep drilling for undiscovered value in the asset.

Contrast this to everywhere I see people ravenously using new tools, like Cursor for coding, OpenAI Operator, or even fairly “basic” uses like lawyers using NotebookLM to summarize case documents in a way they can listen to in the car. There is no concept of an “asset” here; the value of the product to the user does not really depend on rich context, network effects, or some other obvious software incumbency. The software is just doing work, and the work is tangibly value-additive, even if it requires some human supervision.

It might be a bit too flippant to say, “We’re evolving from a mindset where the codebase is capital (the past few decades of software) and into a mindset where code is labor.” But this is a blog post, so it suits the medium. And it suits today’s energy: new projects and startups are writing a lot more code on the basis of “does this make me money now” (what Simon Wardley would’ve once called “worth-driven development”): the codebase is more like a workforce to be trained than like a factory line to be architected. You still want to put thought in it, but it’s a different kind of thoughtfulness. And you expect profit generation out of it a lot more aggressively than with patient capital deployment.

If you’ve gotten this far and wondered why the logo I put in the “underserved customers” box is Y Combinator, this is why - because new startups are arguably the most overserved by the existing paradigm of “your codebase is your capital.” As a new company, you don’t need, or want, “capital” as such! You want signal and growth, and if that can be accomplished by “labor” rather than by capital, all the better, if the numbers work. It’s no great mystery why the current batch of YC companies is earning more revenue than any other cohort, by far - it’s almost like a manufacturer suddenly discovering an ultra-low-wage and reasonably highly skilled workforce available for hire. You’re not going to overcapitalize this opportunity; you’re just going to put your foot on the gas and go.

This why I think it’s cute and possibly helpful to think of this period right now as Peak ‘Code as Capital.’ The true capital asset is becoming the big models and the chips; things you can finance, things you can rent. Going forward, we likely won’t romanticize individual companies or projects’ codebases as The Asset to the same degree, and I think is a double-edged but interesting development.

The Ronald Coase Theory of Agents

The last idea I have here is likely going to be the controversial one. I’m actually above-average skeptical about the ability of AI agents to radically increase “useful work done” inside of big companies - but in contrast, I think there’ll be wild and unpredictable new value created in the interstitial space between companies. Let me explain:

We talked about Coase’s Theory of the Firm the other day, and I keep thinking back to it as an old idea with fresh relevance here. If you don’t recall Coase’s work, “The Nature of the Firm”, it goes like this:

• Some kinds of economic activity gets naturally organized inside firms, whereas other kinds get naturally organized between firms.

• Markets generally work well, and strict classical economics would suggest that most productive work, over time, ought to be conducted by free economic agents and settled by market mechanisms.

• But this doesn’t always happen. There are some kinds of costs - the costs of searching for information, the costs of protecting trade secrets, the costs of coordinating complex sequences of events - that are more easily born within hierarchical companies than by free markets. Hence, “the boundary of a firm” spontaneously forms at the interface between work that economically makes sense to in-source versus outsource.

There’s one additional element here: the “O-Ring theory of productivity”. The O-Ring theory (named after the Challenger space shuttle disaster, where the failure of a single part ultimately doomed the flight) is about national economic development, but applies well to firms too: it argues that productive output is ultimately limited by the weakest link in a chain of economic activity, which in complex systems can be hard to identify. This idea fits really well with Coase. It says, look, firms naturally tend to in-source work that is highly susceptible to the O-Ring problem - the work that is complex and really value-additive.

Ultimately, the valuable work done inside companies is the work where human accountability is the primary bulwark against the O-ring problem. It acknowledges that complex decisions and accepted constraints need to have human owners, and that relationships between humans (notably, but obviously not exclusively, in reporting relationships) are ultimately the valuable stuff that companies are made of.

Okay, so, what does this have to do with AI agents? Well, my suspicion is as follows. One, combinations of AI and Human work could be notably vulnerable to the O-Ring problem. Their utility in executing a chain of tasks is only as valuable as the weakest link, very obviously. (If there’s one hallucinatory or badly-interfaced step anywhere that isn’t caught, you have to throw out the whole thing.) Firms have self-selected for solving O-Ring problems, over decades. That’s just what firms are.

So I think you could construct a bear thesis that the closer a workflow is to core to of an average business, the less suitable agents are to help, for reasons that are readily explained by Coase’s theory to the firm. Maybe not for the very best companies, who truly master their internal agents, but for the average company to whom AI Agents doing real work is essentially a kind of outsourcing. I’m not quite as pessimistic as that, but I agree with Tyler Cowen’s post that any hope for 10% GDP growth or whatever to emerge out of existing firms (which, even in a very disruptive environment, make up most of GDP) is too hopeful.

This sounds pessimistic about the value that can be added by agents, but I’m actually arguing the opposite is true - the most interesting new kinds of work are at the “periphery”. Work that takes place out in the market, where there’s plenty of skin in the game, and more importantly, that the economy has already selected, via Coase’s theory, as an environment where the lower-trust, higher-agency, direct-consequences kind of transactions were naturally sorted. The world of commerce and trading, payments, insurance, capital markets, what have you - these are all areas that have already evolved heavy lines of defence in dealing with unexpected surprise (and the antifragility to exploit it). AI interoperability is moving ahead by leaps and bounds with Model Context Protocol, which is an important step for an emergent layer of work from independent agent composability. If you want to talk “Emergent Layers” and look at what’s next, this is where I’d spend my time.

And, of course, (I think you may see where this is going) - we have an excellent playground all set up where you can speed run all of those experiments to find out which ones work:

We’re seeing real traction on the idea that “stablecoins are the natural way that AI agents are going to make payments to each other”, but there’s another way to think about the opportunity here that I wrote about in the Abundance series in 2017, which is that cheap blockspace is the natural place where independent agents are going to write state. This is the radically interesting thing: the fact that we now have cheap public state and execution environments (like Solana and Base) allows AI agents (call them “onchain agents”, if you like) to just be independent, stateless, functional actors that “can just do things”. Some of the most important APIs for agents are going to be ones like Thirdweb, where agents can just use a traditional Web 2.0 api to read and write to shared, neutral state (blockspace) without needing their own RPC or anything. The result is an additional layer of composability that augments MCP, since there’s a valid, permissionless, shared state to work with.

From Thirdweb MCP Server 0.1 Beta

The end result looks like a virtuous abundance cycle where on one side (the right side, below) you have onchain agents that are able to just do things and be stateless actors (because actual state, like account balances and what have you, is just stored onchain in a neutral third place outside the boundaries of any company.) And on the left hand side, you have consumers of their work, which could be people or frankly could be other agents, but in either case, the decision to consume an agent’s work is a simple yes/no answer: “Is this exactly what I want? Yes/no!”

Here’s how I illustrated that idea in 2017:

Back when I wrote this, quite a few people told me it was word salad hallucination, but I distinctly remember one person - who is now a Pretty Important Person in AI - taking the time to email me that the thesis was really insightful. We’re not there yet, but I think we can see it. Can you see it?

I’ll leave it there. Thanks for reading if you got this far, and I wish you a great time playing, building, and joyfully enjoying this strange new abundance in the software world.

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Scenario One: the Trump economic gambit is a good strategy, but they’re executing badly

Scenario Two: it’s a bad strategy, and they’re executing badly

Scenario Three: It’s a bad strategy, but they’re executing well

Scenario Four: it’s a good strategy, and they’re executing well

These are entirely fake axioms, but over the past month I think there are four distinct narratives out in the world that each support one of these quadrants. In the interest of learning in public (and just sharing what we think is going on live in real time - I wish I’d done this at the start of Covid), I’m going to try and write down my understanding of “what are the storylines going on here.”

These four scenarios are not mutually exclusive; the truth of the matter certainly contains elements of all four. If you’re following the news, then most of this won’t be brand new information or novel synthesis. It’s my attempt to organize my thoughts, with the full knowledge that a) I’m obviously out of my depth there, but hey, who isn’t; and b) new information is coming to light all the time, but we’re two months in so we do have something to work with.

It should go without saying - but I’ll say it anyway - that this isn’t investing advice and if you trade based on anything I write in this blog, you’re a bozo.

The starting assumption: we’re all working off the Miran paper

I’m making one big assumption with all of these, which is that the Stephen Miran paper A user’s guide to restructuring the global trading system, the more-or-less consensus inspiration for a possible “Mar-a-Lago accord”, is what most people are looking at as their crib sheet for reading signals. (Miran now chairs Trump’s council of economic advisers, so this seems like a reasonable assumption around the table.)

Miran is not a solo voice here; he’s a part of a recent chorus (including the thought-provoking book Trade Wars are Class Wars, which everyone should read, even if you don’t agree with it) that describes a bigger problem than American manufacturing decline. The problem is the US Dollar’s reserve currency status:

“The root of the economic imbalances lies in persistent dollar overvaluation that prevents the balancing of international trade, and this overvaluation is driven by inelastic demand for reserve assets. As global GDP grows, it becomes increasingly burdensome for the United States to finance the provision of reserve assets and the defence umbrella, as the manufacturing and tradeable sectors bear the brunt of the costs.”

Miran is describing a reflexive feedback loop:

1. For the past decade, global savings has disproportionately poured into US dollars, bonds, and equities - because of rate disparities (e.g. between Yellen’s Fed versus negative interest rates abroad), perceived safety & stability of US assets, and plain old chasing scarce growth.

2. This pushed up prices of US assets, which tangibly gave American companies a cost of capital advantage. Arguably more importantly, soaring stock prices gave company execs confidence to grow business (hiring; Capex) and soaring stock prices also made US consumers giddily consume a lot more stuff - much of which came from abroad.

3. This perceived outperformance of American investment opportunity reinforces global savers’ propensity to park their money in the S&P, rather than anywhere else. Rinse, repeat.

4. The problem is, this positive cycle accelerates our collective deficit spending.

5. At some point, this bubble bursts, badly. Everyone loses confidence in the US dollar, asset prices collapse, we can’t fund our military anymore, China steps in, it’s a disaster.

The Trump team believes that radical action needs to be taken to get us out of this cycle. Trump clearly authentically believes this; he’s been saying it since the 80s. That definitely puts it into the category of “too early is the same as wrong”, but maybe it’s finally right. This is also a team that really believes in the power of stories. They know how important it is to name things, label characters and plot devices, so that the voting public can track a narrative of their choice. What are the various storylines we’ve seen so far, and how are they going?

“3/3/3” (Good Strategy, Bad Execution)

The first story goes, okay, the first step to fixing this has to be fixing the deficit. Trump and the republicans have a mandate to make big moves here. We have a unique opportunity to freeze and reverse US Federal spending and bloat, Elon refactors the US civil service for a new generation, and the economy accelerates. If we can do those things, we can grow our way out of the debt problem and get the US back on better fiscal footing. This plan was memorably described in Bessent’s “3/3/3” plan: 3% real growth, 3% deficit, 3 million additional domestic barrels of oil.

(I’ll brush by the fact that one of these can’t work if the Permian won’t cooperate. But the goal of “we want cheaper energy and cheaper input costs” is valid and possible, maybe they’ll get barrel-equivalents from natural gas liquids, they could get it pseudo-domestically from Canada, etc, so let’s forgive it for now and move on, since it’s not the point of this post.)

The general idea of “grow revenues and cut costs” does actually work across a variety of situations! But the thing is, you have to do both of those things, fast. And so far there’s no sign of either.

From Brendan Duke on Twitter: so far, DOGE’s impact on outlays has been imperceptible.

During the earliest days of DOGE there was a tremendous “never bet against Elon, he is uniquely capable of getting this done” vibe out there that has, if not disappeared, at least attenuated. This blog is not about DOGE and I’m offering no takes on it here, other than stating what I think is obvious: other cabinet leaders (particularly Rubio) sound like they have had it with Elon, and Trump has started publicly walking back his authority and promises in recent days. On a pure dollars and cents basis it could still contribute real savings, and find a more moderate path to Jean Chretien style cuts. But betting markets are now forecasting that the overall impact will be modest.

Meanwhile in congress, things aren’t exactly going to plan on the “3% deficit” front either. On the “what promises are we willing to break” front, it doesn’t look like congress collectively feels the need or the legitimacy to break any. The wild card could still be military cuts - in keeping with the theme of “Europe has to hire its own security now”, plus in a technology transition, budget cuts actually help you get off the old tired defence primes and onto Anduril, so this might actually go okay. Let’s assume for now they do get some military cuts. Still, they are a long, long way off from anything resembling fiscal balance, and if they can’t fix it now, they’re not going to find new political capital somewhere to fix it later. It’s now or never; so I guess never. You’re left with “Monetize the US balance sheet” style gimmicks, which (after a one-time gold mark-to-market, neat, great) basically means selling off US federal land at 10 cents on the dollar, which would be a sad thing indeed.

Overall, this is the least intriguing of the four stories. New administration, new congress, new mandate to actually make progress on the deficit this time, and yet, it’s the same old story. If this were the only story, it’d be a letdown start to the Trump 2.0 economy. Don’t worry, though, we have three more to go.

“Tariffs made America rich” (Bad strategy, bad execution)

So far this has been the objectively funniest storyline of the four, although “funniest” has to be asterisked with “reputationally and progressively catastrophic.” It’s also probably the most authentic to Trump’s core character. He’s loved tariffs his entire life; he always thinks America is getting ripped off all the time, particularly by those damn Canadians. So this time around, a major pillar of the Trump platform is using protectionist tariffs to restore manufacturing back to America, in a way that simultaneously creates a lot of jobs, but also uses robots not people, since they were low paying jobs, which is why they went abroad in the first place. (???) Or something like that.

The purpose of these tariffs, as outlined by Miran, is dual-use: as a durable trade balancing mechanism that can be put in place long-term, but also as a short-term whack on the head, from which you can prompt countries one at a time to take steps that America wants, like currency revaluation. As the world’s largest consumer market by far, America ought to be able to divide and conquer its trading partners, reshore a bunch of domestic manufacturing, and win so much we get sick of winning, etc.

Not everyone is buying it:

Yes, if you stick with this policy for a long time, you might live to see the benefits - I am sympathetic to the idea of “if you can’t make steel, you aren’t a country” in some form. But the costs of this knee-jerk protectionism are immediate, and they sting, so you’d better be prepared to stay the course and stick it out.

If fully implemented, you’re going to raise something like 100 Billion dollars a year. Which is real money, but nowhere close to what you’d need for any kind of sustained major tax cut, let alone replacing the income tax altogether, as Trump initially spitballed as a way to drum up enthusiasm. And in exchange, you’re going to drag GDP growth by a half a percent or so, and in a particularly regressive way. (50 basis points of GDP, compounded annually, is an enormous cost.) That’s not a great trade. Moreover, you make the west as a whole less competitive, which should be the main point to all of this.

Living in Canada, I’m getting a fairly Canadian-skewed lens on what the tariff rollout has looked like so far. And from my vantage point, it looks like a comic book disaster. They’re putting on tariffs, but now they’re delayed; now they’re back, now they’re all mostly exempt, now the ones that hurt the US the most are on double. (Seriously - tariffs on aluminum, they day they’re declaring a national electricity grid emergency? Do any of them know how you make aluminum?)

The other problem with this strategy is Howard Lutnick. My working model of Lutnick - “the final boss of sell-side guys” - is that he is a genie of some sort, summoned out of a lamp, and determined to grant Trump’s wish of psychologically overcoming the jealousy of seeing the Biden guys actually succeed at some amount of manufacturing restoring. He has a supernatural gift to make things feel personal, not business.

You can imagine an alternate scenario where Trump ran the same tariff plan through a different messenger, and it’d be doing a whole lot better than it is now. What should’ve happened was Trump himself being chief bad cop, and a charming lead negotiator. In a recent David Remnick interview with historian Stephen Kotzin, they reminded us of Trump’s “Good cop bad cop” dynamic that he repeatedly set up in his first term: with our enemies, Trump cozies up as good cop, and lets his staff make the threats. With allies, he’s the bully, while his staff got productive work done. He could’ve run that here, and had it work great! Instead, Lutnick has just gotten everyone to hate him, and they’ve burned through a lot of credibility.

The other thing here is, just a regular recession, combined with some reshoring incentives, might’ve actually done a lot of help on the trade balance front. But these tariffs, if carried through, make inflation worse, make cutting rates harder, hurt your manufacturers in the short term as they have to pay more for all of their imported components… yeah I’m out on this one. Tariff policy, as carried out so far, officially rings the bell for “bad idea, worse execution.”

So those were the two “bad execution” categories. We’re failing to cut spending or get in fiscal shape; we’re failing at the “tariffs made America rich” McKinley story. What’s more interesting - and anxiety-provoking - is the framing in which it’s being executed well.

“If you’re first out the door, it’s not called panicking.” (Bad strategy, good execution)

This is the interesting one.

So, let’s go back to the principal Miran assertion, “we have to take action right now to get the dollar lower.” The problem with the current setup is that the US dollar and US assets have gone up when the global economy is strong (because we run a big deficit, other countries accumulate a lot of dollars, and keep them in dollars because that’s where they can invest them safely and profitably), but the US dollar also goes up when the global economy is weak (it’s the safe asset), so there’s no mechanism to get the dollar down.

Unless - and here’s where the Joker plan comes into view - the purpose of the plan is to throw things into complete chaos. Not garden variety recession fears, where people flee into the dollar. But chaos around “We can’t trust the United States anymore.” Sam Hammond has a thread around possible sequencing here; alternately, Yanis Varoufakis has a simplified version. We’ve already seen some early steps:

1. Europe received J.D. Vance’s message loud and clear that they can’t count on the US anymore, and have to massively raise their defence spending. Canada received Lutnick’s message that they can’t do high-trust business with the US anymore, have to vote Liberal now, and likely will spend on massive infrastructure projects to bring its natural resources to new markets. And so on.

2. The US announces an offer they can’t refuse: “Hey, we’re asking all big creditors to move into ultra-long duration treasuries that don’t pay any coupon; oh and if you want any military protection from us, you won’t say no.”

3. Then, the thinking goes, you get “a controlled retreat from the global dollar savings system, on America’s terms”. Other countries can’t just leave (it takes a long time to build an army!), but they want to: they don’t trust America anymore, they have their own stuff to spend on. That equals leverage to redraw a Mar-a-Lago type deal.

I think the big problem with this approach is that it leaves US businesses pretty vulnerable to a shock that is both bigger and less controllable than anyone in the White House is bargaining for. The problem is that the current system has two reflexive feedback loops in place:

• First is the reflexive loop of money-get-sucked-in: savers all around the world perceive US assets as both safe and growing; so they park their money there, which makes number go up, which makes their perception come true. (While earnings have gone up over the past decade, S&P gains are more than half from multiple expansion.)

• Second is the reflexive loop of Stock-is-up,-let’s-spend-money: the basic idea here being, the CEOs and management teams who make capital allocation decisions are primarily looking at their stock price as a signal for, “Are things going well, and should we invest more money back into the business?”. The perception (the stock price) meaningfully drives the reality (hiring, Capex), in both directions.

So, here’s what this means. If this plan comes true and there’s a big rotation of foreign money out of US stocks and into international ones (into foreign spending on their own domestic priorities), the US economy is not going to move like a market that was at equilibrium with the rest of the world - it is going to snap like a whip. This is the problem with reflexivity: if the flow of money stops coming in, and stock prices go down, CEOs suddenly get pessimistic (about their stock and also about all of the tariff uncertainty, etc), you see hiring projections go down, Capex go down, revenue guidance go down. And that can kick off a vicious cycle in the other direction. Reflexivity rewarded us in one direction with “the S&P basically goes up double digits every single year, and sometimes goes up by like, 25%”, but it could punish us in the opposite direction with an equally serious magnitude of outflows.

And, of course, we have a president who famously does not look at the stock market as his report card. Nope, not at all.

The other problem with this approach is you have to stop spending so much money domestically in order for it to work (see # 1). But that certainly doesn’t seem to be happening at a rate that will move the needle. So if you have a tanking stock market that meaningfully translates into sandbagged US growth, and you aren’t actually making any progress on the deficit, then other countries have just as much leverage over you - your ability to coordinate “controlled slow retreat from dollar-as-savings, but still have dollar-as-unit-of-global-exchange” just gets too fraught.

For this strategy to work, you have to really buy into the idea of “if you’re first out the door, it’s not called panicking.” (I hope you all know where that line is from without having to click the link.) I obviously understand why the Trump team refuses to sleepwalk into a scenario where other countries are dictating the terms and the pace of de-dollarization. But this kind of high-stakes negotiation will only work if you’re coming at it from a position of strength on all cylinders. Given that #1 and #2 are going badly, then I’d categorize this thread of the story as, “successfully pursuing a bad idea” rather than “badly pursuing a successful idea”, although I’d forgive you if you mentally categorize this as #1 instead. (In fairness, they have to go after them all at the same time; you don’t get to sequence them. Still, though.)

Anyway, all of this seems bad, bad bad. Is there anything we can find in our fourth quadrant?

“No Crying in the Casino” / Good strategy, good execution

It is unbelievably tempting in times like these to start listening to the doomer podcasts, start regularly checking the gold price, and buy into a theory that “it’s all going to collapse”. It’s good to be open-minded, but there’s a reason why the doomer cranks are historically wrong: the American economy itself is still pretty strong, and not only is it strong, it’s pretty resilient compared to any other alternative. The stock market is not the economy.

Moreover, let’s go back to Trump and Bessent’s stated goals: for rates, the dollar, oil, and inflation to all be going down. Well?

(From a longer post from Gavin, a perennially thoughtful and reasonable source.)

Sorry if this sounds obtuse, but… what if they’re getting it? We got our stock market correction, particularly in tech, which is down anywhere from 15 to 35% from the election highs, but no one thinks that any of these companies are actually in any sort of danger. Market stress has nudged some people into bonds; their focus on the 10-year is going fine.

Meanwhile, Europe has answered the call, loud and clear, to not only fund their militaries but also to move past the “Debt Brake” era and grow into an alternate western consumer / builder superpower again. We’re getting some overseas investment into what they want to see (American manufacturing production) rather than what they don’t (the S&P).

None of those things, by any means, are sufficient to get America out of the trouble that it’s in. But we are still early into this term, and we have to remind ourselves that there’s a lot we’re not seeing as of yet. That could mean things are even worse than the headlines suggest, but there is no shortage of essays and takes to that effect. I’m not smart enough to say I am following it any better than the average person who follows smart people on Twitter. You’re allowed to say it’s hazy!

Okay so, what do we think?

If I were to weigh all of these together, I think the one aspect of this you can safely bank on is that we’re not seeing sub 3% deficits any time soon. Every other story, like tariffs or equity rotations or the alchemy of rates, has too many unknowns for me. But congress isn’t likely, or able, to pull off a Hail Mary or hidden ball trick the way the executive branch might. And, as much as you should never underestimate the guy, the range of plausible DOGE savings is considerably smaller than it was a month ago. Still could help, but it won’t carry most of the water.

Again, not investing advice. Stay safe out there, get lots of opinions from a wide range of sources, get primary source material when you can; and stay curious. Like the 70s, when we got to test every economic theory at once, we’re about to get some real data on how political and economic power actually work now, about which we could only grasp and speculate just recently. And maybe we’ll look back on this period of time and all of this will be a footnote to something even bigger.

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If you’ve made it this far: thanks for reading! I’m back writing here for a limited time, while on parental leave from Shopify. For email updates you can subscribe here on Substack, or find an archived copy on alexdanco.com.

There’s some counterintuitive but deep intuition that things can feel higher signal when they are 99% false but 1% true, versus it they are 99% true but 1% false. A grain of truth within a crazy statement can seize our attention more powerfully than a reasonable argument with blemishes here and there.

There are many variations of this: like how a tiny pinch of salt brings out more flavour in a dish than a larger dose. Or how the 1% of pro wrestling that’s off-script brings the whole show to life, whereas a football game that’s 1% fixed by refs (but still 99% genuine) would be immediately declared fraudulent.

Sometimes homeopathic doses do have stronger effects than conventional ones; at least in the world of information and meaning. I think you should be curious about why this is, if you want to be information-literate in today’s world. You want to be in tune with how powerful that 1% signal is, but also have the presence of mind to not fall for things you shouldn’t.

To get to the bottom of this, in this post we’ll spend some time on what exactly information is. We’ll learn it through the kids game of 20 Questions, and the grown up activity of blind tasting wine, to get some intuition around why those grains of truth disproportionately draw our attention.

Information is a verb, not a noun

A lot of people today don’t know who John Perry Barlow is, which is a cosmic oversight. His life is more interesting than I can summarize here, but as a young man he became the Grateful Dead’s lyricist, and headed up the contingent of Deadheads who helped shape the early internet. He cofounded the Electronic Frontier Foundation in 1990, and his writing is closely associated (and often confused with) Stewart Brand, and with famous lines like “Information wants to be free”. (With the eternal clarification, “free as in speech, not as in beer.”)

Buried inside one of Barlow’s essays on digital property rights, there’s a profound paragraph that I’ll copy here in full:

Information Is a Verb, Not a Noun. Freed of its containers, information is obviously not a thing. In fact, it is something which happens in the field of interaction between minds or objects or other pieces of information. Gregory Bateson, expanding on the information theory of Claude Shannon, said, "Information is a difference which makes a difference." Thus, information only really exists in the [delta]. The making of that difference is an activity within a relationship. Information is an action which occupies time rather than a state of being which occupies physical space, as is the case with hard goods. It is the pitch, not the baseball, the dance, not the dancer.

We’d joke today that “Barlow is a just Bateson wrapper, and Bateson is just a Shannon wrapper”: a lot of roads lead you back to Information Theory. The definition of Information as “a difference that makes a difference” took me years to really understand, even with alternate phrasings like, “Information isn’t what you say, it’s what they hear”. I finally wrapped my head around it after reading Simon DeDeo’s excellent paper Information Theory for Intelligent People. I absolutely love this paper. I find it so clear and intuitive, I find myself rereading it every six months minimum, and I’ll draw from it extensively in the following explanation.

“I’m thinking of a person”

My older kids have reached the age where we can play Twenty Questions at the dinner table. (We play it very simply: one player thinks of a person, and the others have to guess who from Yes or No questions.) After playing for a little while, you learn that the main way you can “get better” at the game is to know something about the other player and tailor your questions appropriately. You can talk about an optimal strategy for each kid: for our older one, her schoolteachers are a promising lead; for the middle child, try asking about her classmates first.

This is the moment where Information Theory enters the picture. I wrote a post a few years ago that walks you through the math (it’s actually pretty approachable) so I won’t repeat it here. All you’ll need is a bit of intuition around probability distributions, which 20 Questions provides for us handily.

Let’s do an ultra-simple example: p(Mom) = 0.25, p(Dad) = .25, p(Big sister) = .25, p(Little sister) = .25. You can quickly work out that this distribution, P(x), takes exactly two Yes/No questions to solve. Now let’s suppose Dad’s ineligible, and Mom’s twice as likely. So we have a new distribution Q(x): q(Mom) = .5, q(Big sister) = .25, q(Little sister) = .25. You should intuit that this should take less than two Yes/No questions on average, because if you guess “is it Mom” right off the bat, half the time you’d be right. Shannon’s equation lets us calculate, from known probabilities of Q(x), that our average question tree length will be 1.5.

Once that’s comfortable, let’s go ahead and play a real game. (We’ll reuse our P and Q variables.) For child one and two, we’ll define distributions P(x) and Q(x): the set of people that they routinely select for you to guess, and the relative likelihood of each. We can say a few things about these distributions:

• The older kid knows more people and is less predictable. We might expect the average question tree length to take, say, twelve questions for the older kid versus eight or nine for our middle child, on average. So we can say we expect P(x) take 3.5 more guesses than Q(x) on average. Each yes/no question resolves one bit of information - a unit of resolution of uncertainty, in base 2. (It’s base 2 because we’re asking Yes or No questions, and we assume that at some point we reach a satisfactory level of yes/no detail that no further questioning is required. This is called “Coarse graining”, and DeDeo’s paper has a great explanation of it.)

• The older kid will reliably think of a child ~50% of the time and an adult 50% of the time, so we can meaningfully talk about 1 bit of uncertainty to Child/Adult that we must suss out with our questioning. In contrast, our middle child usually thinks of a kid rather than a grownup, so there is less than one bit of uncertainty there. (Go back to our simple example earlier if it’s not obvious why.) If she thought of a kid every single time, which is close to what we empirically observe, then there’s actually zero bits of uncertainty in Child/Adult, so we could safely skip the question. We might skip straight to a finer-grained question: “Are they in preschool?”, a poor strategy for the older kid but optimal for the younger.
  

Why does this work? As the guesser, the fact that we know something about her map of the world - how big it is and how it’s coded - means our guessing process can take fewer questions on average. It has less uncertainty to resolve. That quantity of “uncertainty-to-resolve” is called H(X), or Entropy. It is not an absolute quantity, it is a Delta; and it’s measured in Bits. This is very much in the same world of Bayesian probabilities, and managing priories; but I find the Shannon lens gives an important emphasis on “Uncertainty-to-resolve” so we’ll keep going with it.

Surprise!

Now, let’s imagine we’re playing the game with a blindfold on, and you don’t know which kid you’re playing against. Pay close attention here because this is the critical step for understanding what Barlow and Bateson are getting at with “Information is a verb, not a noun.”

So in this blindfolded version, we don’t know if we’re interrogating P(x) or Q(x); but we must commit to a questioning strategy at the outset. So let’s say we choose a questioning strategy that’s optimized for the older kid’s probably distribution P(x), but then over the course of our questioning we discover that, actually, we’ve got the younger kid. Rats! We were inefficient with our question tree. We can talk about the additional work we must do of asking questions when we were expecting one distribution, P(x), but actually encounter another one, Q(x).

Note carefully that these additionally incurred questions are not because the distributions are contain different amounts of latent uncertainty. It’s because our questioning itself had been non-optimal, and now has to be recalibrated. That additional uncertainty has a formal name, “Kullbach-Leibler Divergence”. And it measures the extra uncertainty, in bits, that we incur in the discovery that we are dealing with a different distribution than we expected, and it’s a signal to focus our effort in a new place. The everyday name for this is “Surprise.”

These moments of surprise are when we actually learn things. It’s the moment when you realize that your map of the world - your internal model of the current situation - does not actually match the facts on the ground, and we need to switch up our map. Whatever answer surprised us with, “this is actually Kid 1, not Kid 2”, was a difference that made a difference. To spell it out: it was a difference in observed versus expected values, that makes a difference in our selection of coding tree, which can be measured in bits of newly appreciated possibility associated with the new uncertainty.

I’ve written about this before, in a post called The Audio Revolution. The relevant part was about feed-forward information processing, and how most of the time, the world we perceive is actually a pre-populated model of objects and spaces from our working memory, which we then “error-check” against a low-bandwidth sample of real sensory information. When you’re in a familiar environment, like your living room in your house, you draw on a richly defined, pre-populated environment of what everything in the picture is (call it P(x), and a well-rehearsed strategy for what “questions” you ask of the world. But if you walk into your house and something is amiss - say, the TV is missing - that is a difference that made a difference in what your model of what’s going on, and what questions you now ask of the world in order to figure out what’s going on.

This is why it’s actually incorrect to say “They said something surprising.” They just answered the question. What actually happened is you HEARD something surprising. That’s what information really is. Information is not the baseball (the kid’s answer), it’s the pitch (the impact of the answer on the model that was perceiving it.) It’s not the dancer, it’s the dance.

From Itti & Baldi, 2009: Vision Research

As it turns out, our brains are optimized to care about surprise, and we can measure this scientifically. You can show someone a movie and calculate, for each pixel on the screen, how much uncertainty there is in each pixel over a time series. And you might think “our eyes are probably drawn to the pixels where there is uncertainty”, but that’s not what happens. Maximal uncertainty is actually like the “snow” pattern on an old TV - it’s not visually interesting. What our eyes are actually drawn towards is, guess what, KL Divergence. Our eyes follow the pixels that are surprising. Because that’s where the information is!

In our 20 Questions game, these areas of “surprise” where our eyes are drawn are analogous to whatever bit of information makes us realize, “We have the younger kid, not the older kid.” That little bit of information is probably a small thing, but it’s a small thing that makes all the difference. And that’s where our attention is drawn.

Although Twenty Questions is a great starter example, it’s not very realistic. In real situations, we don’t always get to directly interrogate the Ding an Sich (Kant’s Thing In Itself); we’re dealing with interpretations, representations, unreliable signals, and noisy channels, from which we have to extract and recreate a signal. (The one way in which 20 Questions with kids is realistic is that they are not totally reliable narrators! Their models of the world sometimes differ from ours, and knowing which answers to trust is part of the meta-game.)

Freshly Cut Garden Hose

As it happens, several friends and I have recently gotten into a more grownup version of 20 Questions: blind tasting wine. It adds the dimension of noisy, unreliable signals, and even better illustrates KL Divergence and Surprise.

Blind tasting wine is a fun challenge, and can be absolutely humiliating. It’s a lot like investing - it’s easy to find yourself on a hot streak and think you’re smart, but very hard to actually be skilled at it. But you can work at it and get better. We talk about “training your palate”, and a lot of it is learning to recognize discrete smells and tastes, but it’s just as much a mental training exercise for the general skill of how to sift through and resolve uncertainty.

In moves like Somm, or other romantic representations of wine tasting, you get an impression that candidates are solving an elaborate 20-dimensional puzzle:

• What colour is the wine, and what does that tell you? Is it clear or opaque? A red wine you can look straight through invites several different lines of inquiry (Thin-skinned grape variety? Made it Italy?) and dissuades others (It’s probably not an Aussie shiraz, or an Argentinian Malbec.) A white wine that’s copper coloured might put you in pinot gris territory, but specifically from France, not Italy. But is that really copper, or is it golden?

• What does it smell like? Very perfume-y white wines (Viognier, Gewurtztraminer, Riesling) can be triaged from those that aren’t characteristically aromatic (Gruner, Chablis, White Hermitage) to get you in a narrower zone.

• How hot was it where this wine was grown? Was it grown in a cool place (it’s nervy, tart) or a warm place (jammy, rich, high alcohol)? What about a hot year in a cool place?

• What kind of texture does the wine feel like when you taste it? For red wines, is there tannin (the astringent texture of eating walnut skins), acid (makes your mouth water), tons of both (Italy?) or more one than the other (Pomerol? Beaujolais?) Same for whites - is it acidic, bitter, or both?

• Is there oak on the wine? Is it American oak, which tastes like coconut Lacroix and dill pickle, classically used in Spanish reds? French oak, with vanilla and spices that suggests the wine is expensive? Is the oak new (the taste is obvious) or old (barely any taste, just texture?)

• How old is the wine? Is the fruit dominating, or has it faded and evolved into other tastes like leather? Is there any orange or brown tint to the wine?

• Are there any “giveaways” that immediately hone you in on something? Like black pepper (think Syrah), celery (Gruner), green pepper (Bordeaux varietals), tomato leaves (Sangiovese), cat pee (New Zealand Sauvignon Blanc), gasoline (Riesling), tennis ball can (Australian Riesling), roofing tar (Nebbiolo), peach yogurt (Zin), Icy Hot pain relief cream (Australian anything)…

• And finally, I’m saving this one for last because it’s “the most information” but often the most confusing and least helpful - what does the fruit taste like? Red fruits, black fruits, tree fruits, stone fruits, the flesh of the fruit, the rind of the fruit, the pith? How ripe?

As fun as these “grid tasting” portrayals are, they make it look easier than it is. (It’s a bit like watching poker on TV, when you can see the cards.) In practice, you’re not dealing with clear signals: you’re dealing with broad correlations and faint hints. Any one data point (“raspberry fruit”) could point you in many different directions. Moreover, you aren’t always so sure you’re really tasting something, confusing it for something else, or just imagining it. Taste is notoriously adaptive to the power of suggestion. (Curiously, the one sense I find people chronically under-use is their eyesight - how the wine looks will not fool you, at least not nearly to the same degree as what you think it tastes like from minute to minute.)

Wine can be tasted “double blind”, where you know nothing about it at all, or “single blind”, where you know some information (e.g. what grape, or what region) and you have to figure out the rest of its characteristics. The standard things you’re trying to figure out are, what grape(s), what region, what year, and if you’re particularly ambitious, what producer. While Double Blind tasting looks more impressive, it’s generally thought that Single Blind is the more valuable exercise for learning how to taste - you aren’t floundering around in the dark; you can actually control for some variables while you interrogate others.

Jensen-Shannon Distance

So, when we think back to what we learned in 20 Questions, wine tasting is the same idea: particularly the concept of KL Divergence and “Surprise”. Wine tasting is all about pulling out maps of what you think the wine might be (P(x)), while remaining alert to signals that don’t fit (and suggest you’re looking at Q(x)).

The temptation, particularly for new tasters, is to approach the wines inductively. Let’s do an example! You try a red wine; it’s solidly built with grippy tannins, and old world oaky tobacco-leather taste. Then, like a laser beam, you pick up cassis: black currant fruit. Your mind immediately goes to Bordeaux. Yep, that’s delicious. You try to focus on where in Bordeaux this could be - left vs right bank. Black currant suggests cab; it really reminds you of a Margaux you had last month, so let’s guess that.

The wine is revealed, and it’s not Bordeaux at all. It’s from Rioja, in Spain. Where did your line of questioning go wrong? You can retrace your errors. You were initially on a good path with the oak, tobacco, leather, and black fruit. But that little signal of “cassis” drew away all of your attention, and made you pull out the Bordeaux map. And once you were reading from the Bordeaux map, you missed some pretty big clues.

First, the wine has a screaming taste of dill pickle, which should tell you American oak, and therefore you’re in Spain not France. Second, there’s no trace of green pepper or other pyrazine taste, which doesn’t strictly rule out Bordeaux, but certainly suggests you consider alternatives. And third, tasting it again, that isn’t really black currant, is it. It’s more like black plum. Close, but not the cabernet signal you’d initially thought it was.

(If you like these tasting note differential sheets, they’re from the excellent book Vignette, by Jane Lopes)

The moment you picked up that cassis signal, you were “Surprised” in a particular direction. You interpret it as “Get out the Bordeaux map, that’s the distribution we should be working off.” And then in asking questions about P(x), you got some fuzzy answers that suggested one place or other. But we were wrong about the wine, and we were using the wrong map. The KL Divergence of Tobacco overwhelmed our ability to look around the rest of the picture. We were surprised by one thing, and it blinded us from being surprised by other things.

The failure is that you immediately got into a headspace of “I recognize this.” Inductive reasoning is dangerous, because you’re telling yourself a story about what your senses are telling you, and then you spend all of your energy focusing on that story. So you may miss some big obvious hints.

What we are supposed to do when we blind taste is to resist this temptation to leap to a hypothesis. The right way is to approach it deductively. You look, smell, and taste the wine, and just try and describe it as non-judgementally as possible, gathering information. Then, based on all of your observations, you use logic and theory to rule things out: “What can this wine not be?” Is going to fool you much less of the time than “What do I think it is?”, because you’re not latching onto an inductive hunch. As you do that for a while, you build up for a theoretical case for what this wine is all about - the weather, the winemaking, possible grapes, possible terroirs. Only then are you allowed to float some hypotheses for what the wine is.

Similar to KL Divergence, another term you can take home from this post is “Jensen-Shannon Distance”, which describes how much information you get about the distributions (P(x) vs Q(x); or, “typical Bordeaux vs typical Rioja”) you get from each question you ask, like “Is there any pyrazine?”. The units of JSD, once again, are in bits - how much information you get about the final answer once you know the identity of the “player” (which could mean the grape, the region, or whatever taste profile is in your mind.) For more on JSD (including the important topic of “mutual information”, which I didn’t get to cover here, read the DeDeo paper.)

That’s not to say that a deductive approach leaves you completely invulnerable to false trails. But by analytically working through a grid of characteristics and working through what the wine isn’t, sooner or later you’d encounter signals like “No green pepper” and “Yes dill pickle”, both of which are signals that you are not dealing with distribution P(x), you might be dealing with a different Q(x). And the earlier you can catch these big signals, the more likely you are to see them at all. Because once you get blinders on and you’re going down the path of “Cassis!!”, all of the other signals are just going to get force-fit into your reading of P(x) and you’ll miss what’s actually going on.

It’s never Lupus

There’s a line in medicine, “If you hear hoofbeats, think horses, not zebras” that’s used to illustrate Baysean probabilities. Learning how to appropriately weigh prior probabilities is an important part of learning how to think well. (The classic Bayesian intro question is: “Alice is a quiet person who loves organizing and doesn’t like to be rushed. Do you think Alice is more likely to be a barista or a librarian?” And the right answer is barista, simply because there are many more baristas than librarians out there, and the special information you gained about Jane is small relative to the employment prior.)

What I hope you’ve gotten an appreciation for here it that tiny bits of information, assembled amidst otherwise broad and predictable distributions, have a powerful effect on where we focus our attention. A great example from TV is in House, when tiny details throw him down paths of being absolutely certain he finally has a case of Lupus, but it never is. Now you have a better sense of what’s going on: that one tiny detail that stood out to you seizes a disproportionate amount of your attention, because it tells you “New KL Divergence just dropped, you have work to do”. You then pull out a new map (a new question strategy, in 20 Questions; a new grape or region hypothesis in wine tasting) and from there on, the main thing that matters is interpretation of that new hypothesis. From the little black currant clue that sent us into Bordeaux territory, we then found all kinds of signals took us to Margaux as opposed to St Julien or whatever, even though the broader data set did not actually support Bordeaux all that much.

It reminds me a lot of the book The Magic Mountain by Thomas Mann, where the intellectual spine of the book is an ongoing dialectic between two Italians. One of them is a bourgeois named Settembrini who’s comical, but basically sensible in what he advocates. The other guy, a character named Naptha, is a Jesuit communist warmonger with crazy arguments (among other things, at one point he argues forcefully that heliocentricity is just a passing fad), but with undeniable little grains of truth sprinkled in that are unbelievably compelling, and kind of right. Reading the book, you get fixated on those grains of truth and a whole world of legitimacy gets constructed around them - even if most of his raw content is insane. You can’t help but be fascinated by him, and he somehow wins every argument. He reminds me of RFK a little bit.

It is hard for us to resist these grains of signal, because they are what signal IS. And it’s equally hard for us to step back and sort through what else we’re perceiving, because they’re not signal, unless we really train ourselves to set aside our inductive instinct. What you can do is remember Bateson’s definition of information: a difference that makes a difference. Information is a change in uncertainty-to-be-resolved, and it happens to the receiver.

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But today, we’re at the halfway point through the 2020s, and not to downplay Covid or anything, but we’ve actually returned to something like our old story arc. So let’s check in and see how we’re doing.

For readability, I’m not going to quote the whole text of each prediction - but I encourage you to click through to the original piece, which I have left untouched, as I do for all of my posts.

#1: Enterprise software in the 2020s will replay Softbank’s Capital-as-a-Moat disaster of the late 2010s.
Grade: B-

“The issue is that AI is really expensive. It’s more compute intensive, more data-intensive, more resource-intensive in general. The rewards will be immense, but AI businesses will face heavy fixed and variable costs. So there’ll be a huge advantage to being the market leader; more so than in the previous generation of SaaS businesses.” … “The scale advantages in AI businesses may simply become a new frontier for capital-as-a-moat funding strategies, and there’s a real danger that enterprise software could turn into Softbank 2019 v2.0. That’s life if you’re going horizontal, but it’s dangerous if you’re going vertical.”

So, I made two parallel predictions in the original call - one of which approximately played out, but the other didn’t.

The part I got right was “AI is going to be really compute and capital intensive, and capital-as-a-moat will continue to be a thing.” We absolutely did get this, and we’re now a couple of years into a giant Capex cycle for that exact purpose.

I got two things wrong, though. First, I was imagining this in a context of application companies themselves having to raise and spend all of this money, which isn’t what happened - instead, it’s the frontier models and the cloud giants burning capital, while application companies like Cursor and Replit reap the benefits. Second, I was wrong about this being a vertical market story - what’s actually happened was AI workloads taking over the most horizontal slices possible, like search, writing code, customer support.

And then there’s one last bit that I’m going to cheat and give myself half credit, which was the call about lots of term structure being required to fund all of this. Well, I can’t say I got the call right exactly, but the invention of the GPU-backed loan (or whatever the structure variant) has got to count for something.

#2: There will be no major form-factor that supersedes the smartphone. The phone is it.
Grade: B

“If you went forward in time to 2029, you’d be surprised that the phones are more or less the same. The 2×5 inch glowing glass rectangle will remain more or less similar as our common interface with the internet and with the world. Nicer in some ways, and they’ll have some genuinely cool AR features, but other than that? We figured out the phone. It’s gonna stay put now.

What’s inside the phone will be radically different, for sure – the “re-nationalization” of technology into Western and Eastern tech stacks will place some really interesting pressures on the hardware under the hood. It’ll be especially interesting to see what happens to the Android ecosystem as these pressures intensify.”

Correct, although this was a pretty boring prediction. When I think back to the time of writing this, there was some real hype around AR/VR and new form factors - the Oculus Rift S shipped that year; the rumour mill about Apple shipping smart glasses had kicked into rhythm. And there was a narrative around AirPods being the true gateway into native AR that was a fun thesis. But all in all, the phone was clearly staying put as the device and it’s clearly stayed that way.

Rereading this, I’m a bit disappointed that my hope for cooler vision features out of the iPhone haven’t materialized. Maybe we’ll get them in the back half of the decade, as Apple flexes its on-device computing advantage for making AI useful in the real world. I hope so, that’d be neat.

#3: A tale of two cities: the looming feud between the SF and NYC tech scenes
Grade: F

“In the next decade, watch for this rivalry to take shape. … Each city’s tech scene will come to resent the other’s more and more, and allegiances will be hard to shake: NYC will resent SF for its endless reservoir of technological and tech-cultural capital; SF will resent NYC for being a place people actually want to live. But most importantly, both tech scenes will come to resent each other for being more alike than different with each passing year.”

Our first F. Clearly failed here; this didn’t happen, it’s not going to happen. I will note that there was that little storyline about “Miami vs SF” for vaguely similar reasons, but doesn’t count as redemption. I’ll leave it there; that prediction was a zero.
(If this is happening and I just don’t know about it, please let me know!)

#4: At some point in the 2020s, the following headline will get printed: “Crypto Finds its Killer App: Guns”
Grade: D / B

“Over the next ten years, after our current hangover gets truly washed out, a new one is going to emerge that’s a lot stronger, a lot darker, and a lot more true to the original vision than many people would like to admit: crypto as a part of “Dissident Tech”. … Expect the crypto community to retrench and double down on a core value proposition: crypto as a tool for political and societal dissenters. This means people who don’t really care what the price of Bitcoin is; they actually care about it being uncensorable. That means people who are doing illegal things, it means people on the wrong side of governments, or it could simply mean freedom-oriented people. But the narrative will turn really sharply once the core value proposition embraced genuinely becomes ‘be un-censorable’, as opposed to ‘get rich’.”

Ok so, there were three parts to this prediction and I get some pretty divergent grades when you break them down.

The first guess was, “Crypto winter for quite some time; it’ll come back as Dissident Technology before Number Go Up technology.” F! Or at least, like, D minus. That is not what happened.

The second guess was specifically around ghost guns and payments. There has been some story here; not the literal headline I’d imagined, but the Supreme Court hearing around regulating gun 3d printing under firearm rules did key into crypto payments as part of that grey market ecosystem.

The third part, though, I think I nailed, and that’s on the topic of privacy itself. There is zero doubt that privacy and free speech has come to the forefront as a major global issue for our time. And it’s pretty obvious to anyone paying attention that the origin of this energy came from in and around the crypto community, from the Free Ross movement to the un-cancellable movement. I don’t think it’s a coincidence at all: the social ethos of that community was a critical incubation ground for what’s now a full-blown reactionary movement, and I’m not sure it gets going in the same way had crypto not been around in 2021-2022.

So, I’m going to cheat and take a hybrid midterm grade of “D on the literal call, but B on the broader story.”

#5: Higher ed: undergrad will stay the same, but grad school will get blown up
Grade: C

“I think that the narrative that American higher education is going to collapse in a matter of years is just totally false. There are so many structural forces holding the system in place, and (for most middle or higher income families) so much professional and reputational risk associated with not going to college, that the undergraduate experience isn’t going anywhere.

Grad school, on the other hand, is a totally different story. I’m going to write a whole newsletter about this in the new year but for the time being, I think both “classes” of grad school – professional degrees and research degrees – are going to be disrupted pretty dramatically, and pretty soon.”

Mixed bag here. On the one hand, there was this trend five years ago around “ISAs fully replacing college for most people” that I thought was nonsense, because it completely misunderstands the societal role of college. Yes, it has quietly become more acceptable for specifically motivated and capable people to skip straight to building things, but for the most part, the undergrad education pillar is unchanged and it probably stays that way for a long time. I think that was a pretty easy call so it shouldn’t contribute much to the overall grade on this one.

Grad school, on the other hand, remains ripe for a wave of disruption that we haven’t seen play out yet. Two months following the original post, I wrote down my thoughts on the graduate student industrial complex in Can Twitter Save Science?, and I remain a big fan of that piece as a coherent expression. And the big Trump shakeup of the NIH budget for university overhead, if it goes through, will have a massive impact on how these places actually function.

And it does look like the shine is starting to really come off of the MBA-to-consulting pipeline, among other job pathways:

On the other hand, the guess I’d made around the big trend being “Companies going all-in on post-undergrad vocational training programs” doesn’t look like it has legs, at least not that I’ve noticed. Still, these are early changes and the system still has a lot of inertia that’s still in place. So overall I’m giving myself a C here: we haven’t seen the rate of change that would be necessary for this prediction to earnestly come through by the end of the 2020s. But some of the trends are right, so we’ll see in five years.

#6: There will be a major speculative bubble in biotech companies.
Grade: Incomplete

"Biotech is really an ideal sector for mass speculation. It impacts everybody, and it’s inside everyone and everything: the TAM of “biology” is infinite. It’s incredibly complex, so it’s easy for novices to grasp onto a story and its importance without understanding the reality of it. And it’s full of high-leverage potential: it’s totally plausible that biological breakthroughs in health, manufacturing, fuel, etc could generate tens or even hundreds of billions of dollars of value.”

OK so, technically there was a speculative bubble in biotech right after I wrote this:

But, like, that doesn’t count and we all know it. That was an everything bubble; biotech just went along for the ride.

However, I absolutely stand by this call some time in the next five years, or maybe ten, because of this guy:

In a couple years, I think we’ll have made enough progress on AI applied to scientific discovery that people’s expectations around “what is possible in life sciences” to reset pretty dramatically. The less comprehensible it is to retail investors, the better. I’m giving myself an “Incomplete” here because it is actually too early to tell for this one.

#7: The only tech giant I’d be seriously worried about is Google.
Grade: B+

“Act One of Google was building the world’s greatest search engine, with the world’s greatest business model. Act Two of Google was colonizing the infrastructure of the internet, like a technological British Empire, in order to protect and fuel its crown jewel capital. The 2020s will be Act Three. As the decade begins, Google may well face the biggest real opponent they’ve ever fought – the United States – at a moment where its internal morale and purpose are crumbling and adrift. They print money, but if you ask me, they’re at risk.”

Welp,

I think I got this one more or less right. At the time I was writing this, everyone was really negative on Amazon and Facebook, who’ve since done fabulously. Google, on the other hand, had a vaguely uneasy feeling about it but was clearly in everyone’s conventionally good graces. Sure enough, the worries I expressed in that prediction have totally played out, culminating in the DOJ calling for Google to forcibly divest Chrome this past November.

The line I regret the most in this piece was “They engage in by far the most actually anticompetitive behaviour of any tech giant”. In hindsight, I wish I’d written instead, “Aside from Apple, who is far worse, but gets away with it.” I’ve learned a lot in the past five years about how critical Google’s work supporting Chrome has been to keeping the web healthy, and in a better state than the dark days of the browser wars.

The thing is, they’re still… doing fine? The stock is up 150% since I wrote this (along with its peers), so it’s hard to argue that the company is “in trouble” by that standard. Moreover, a little paper out of Google called Attention is All You Need went on to redefine the entire field of software and technology, and you hear a lot fewer voices today saying Google wastes their entire R&D budget. But maybe that underscores the point further: Google helped break open what’s now possible with LLMs, and Gemini is quietly pretty good. But it’s clear that Google isn’t disproportionately enjoying the outcomes here: all of its peers have gained as much or more in strategic options and value.

Overall I’m going to give myself a good grade on this even though Google clearly isn’t going to go out of business or anything like it - the trend was correct.

#8: There will be a significant moral panic around microplastics – not due to their environmental effects, but for their health effects.
Grade: A

“At some point in the 2020s we’re going to have another Leaded Gasoline crisis. We’re going to learn that some sort of chemical or microscopic thing that’s inside everything, all around us, actually has some horrible health effect we never knew about before. My pick for what it’ll be? Microplastics – particularly the microplastics inside clothing, like athleisure. The funny thing is, we already know that all of the fancy microplastic stitching and fabric material we put inside athleisure are terrible for the environment. That stretchy, comfy feeling comes at at an affordable price point, but a terrible environmental one – we already know this. But no one cares. We’ll start caring, though, when it turns out that they’re hazardous to us in some way we never understood before.”

Two parts to this one, and I’m gonna go ahead and say I aced them both. First all, there’s the moral panic component to it - which isn’t about ethics exactly, it just means, “This is going to become a popular cause”, and I should’ve originally articulated it that way, but that’s what I meant and I think it reads pretty clearly. Some people who’ve been ahead of the curve on this, like Nat Friedman, have already made a big impact on elites thinking of this as a problem. But as far as popular awareness is concerned, there’s no more obvious sign of “Things we are eating are poisoning us” as a popular meme than our friend here:

Moreover, I’m also going to claim credit for the prediction that the actual source of most microplastics in food - as far as we understand it - is actually from our clothes. For all our focus on processed food preparation, which is important for lots of reasons, the real source of this stuff is external.

Anyway, I’ll claim my first and only A grade for this one. I’ll need it for my GPA though, cause the next one was another clunker on my part:

#9: The 2024 and 2028 elections will be overwhelmed by “Campaign Hacking”: not by foreign governments or enemies, but by opportunistic hustlers and entrepreneurs.

Grade: D-

“We really haven’t yet seen the real extent of what the internet will do to political campaigning. I’m betting that the next creative wave isn’t going to come from the campaigners, but instead from opportunistic hustlers on the outside who figure out how to hijack them. … Hustlers and entrepreneurs, even more than politicians themselves, will figure out the formulas for how to force the media to cover you as a campaign story, almost certainly to the detriment of the primary process itself.”

Nope! Got that one totally wrong, at least so far. This did not remotely happen, as I’d imagined it. I will give myself a D as opposed to an F just because Trump himself has proved willing and able to keep doing all kinds of merchandising schemes as a part of his campaigning and executive style. But I had imagined a situation where the political establishment would actually lose some amount of control over what elections actually are to the reflexive power of social media and entrepreneurial hucksterism. That didn’t happen at all, so, bad grade for me.

#10: A real startup scam is going to seriously spook the Silicon Valley community.

Grade: A-

“I’ve long believed that the real mystery of Silicon Valley isn’t the outsider question, “How is Silicon Valley so wild and crazy”, but actually the insider question: “How is Silicon Valley so stable?” It’s built on speculative finance, it’s full of experiments whose outcome you can’t know for years, and it has to move fast enough and fluidly enough that (at early stage anyway) it effectively works on the honour system despite the FOMO environment. It’s so interesting how, in this environment, there aren’t any scams like this. … One of the important components of Silicon Valley working well is early stage investors willing to write checks without really having to worry about the downside – the worst you can lose is 1x your money. Financially, that’ll be true, scam or not. But reputationally? If a high profile scam becomes the talk of the town, there’s nothing scarier than everyone else knowing that you fell for it.”

I really struggled with what grade to give myself for this one. On the one hand, the prediction clearly came true, hilariously so, at a bigger scale than I could’ve possibly imagined.

That being said, I did describe something somewhat different from this in the prediction: a situation where investors fall in love with a genius founder(s), the founders illegitimately raise lots of money and abscond with it, and investors are left having to answer for “how did you fall for this?” And that is definitively not what happened with FTX. This wasn’t a fundraising scandal; unless you argue (correctly I guess, but pedantically) that FTT token was a fundraising instrument. You can absolutely argue that this prediction did not come perfectly to pass, on those grounds, and I’d accept that.

But the part of the prediction that did go right, in the funniest possible way, was the second-order consequences of “Why were you so captivated by this guy”. Which, in some ways, was the spookiest. It’s not just that everyone loved SBF, it’s that, 1) specifically, Sequoia Capital gave him the highest possible blessing you can get in tech, and 2) we had this amazing storyline of his genius transcending normal human rules. (There was that great story in the Michael Lewis book of SBF at Jane Street betting max-aggressive on every single round of the intern game, which everyone interpreted as “This guy is built different” but in respect may have suggested an alternate reading, “Maybe he’s bad at bet sizing?”)

But even funnier was the narrative of how good SBF was at League of Legends - how he’d be playing during VC pitch meetings, and the magic was working so powerfully that this was taken as a strong vote of confidence.

As it call came crashing down, the absolute funniest part was the revelation that his League of Legends rating wasn’t even that impressive, and anyone could’ve looked and seen that. The scriptwriters get extra points for making AOC deliver the crushing blow.

Nothing actually broke (as predicted), but the chill was very real, in exactly the way we’d thought: he became a pariah not only for how he ran his company, but for breaking a very sacred promise that Silicon Valley collectively makes to itself: that whatever happens, success or failure, we’re not going to spook the magic. Instead, now we’ll forever have the amazing artifact of baffled Matt Levine: “You’re just like, well, I’m in the Ponzi business and it’s pretty good.”

That’s it for now - I’ll check back in another five years for my final report card. How do you think I did? If you wildly disagree with any of these grades, I’d love to hear from you.

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The world of startups and innovation is full of this magic; clear as day for anyone who wants to look. Building new things is hard; not only because of each unit of effort it takes (which is lots), but also from the coordination tax associated with making something undefined. At every level of the game, from the details of product-building to fundraising, it is always going to be hard to coordinate groups of people to create something of unknown value, while asking for an unknown quantity of effort. This is the hard part of the innovation economy, and always has been.

Over the years of watching different parts of this world at work, I’m increasingly convinced that the innovation economy requires some sort of magic to actually work, and that magic is gift culture. Gift culture - which I’ll define loosely here as, “the social traditions within a community around gift exchange and reciprocity” - is load-bearing for creative builders, not just for feel-good reasons. Gift exchange does real economic work by elegantly solving coordination problems that would otherwise be prohibitively challenging.

If you’ve read this newsletter for a while, you’ll recognize that “gift culture helps solve coordination problems” is one of my favourite ideas to work into other stories:

The social subsidy of angel investing | November 2019
Social Capital in Silicon Valley | January 2020
VCs should play bridge | February 2020
Why the Canadian Tech Scene doesn’t work | January 2021

In this post I want to make some progress on the base idea.

One of the gems of the “Silicon Valley Tech Scene” (it’s a mindset, not a place; but the place sure helps) is the hidden social structure we’ve built that coordinates all of its different players to achieve what’s impossible through rational analysis alone. There are certainly other “non-cost-benefit” contributors as well: read Bill Janeway for the best writing on the three-player game of government, financial capital, and company builders; or Joel Spolsky on the transcendent motivation of engineers practicing a craft. But those don’t diminish the remarkable social feat that Silicon Valley has achieved in its building and fundraising culture, which works so durably, and yet is so hard to replicate from scratch.

The specific magic trick we’ve learned how to do is redefining well-worn procedures of the business world - from fundraising to project management - as moments of gift exchange. Startup fundraising, particularly at early stage, can intuitively feel like: “I’m gifting you capital, and in exchange, you’re gifting me shares.” The end result is an equity transaction, but the spirit of the deal is intuitively understood as a carefully measured exchange of gifts. Meanwhile inside of companies, the most important builder’s ritual to align and coordinate work - Demo Day - is again best understood as a gift exchange. “Everybody brings gifts to share at demo day” yields the outcome of aligning and coordinating work, but through a process that’s better understood as gift-giving.

And I’ll bet that, as AI creates so many more fleshed-out ideas at the top of funnel, gift culture will become even more important for exploring and homesteading the innovation frontier.

So, how does this all work?

Firms, Lemons, Cathedrals and Bazaars

To understand the economic magic of gift exchange, we’re going to call on two pieces of conventional economic wisdom: Coase’s Theory of the Firm, and Akerlof’s Market for Lemons problem.

Ronald Coase’s 1937 The Nature of the Firm gave us an compact way to think about what kinds of economic work tend to naturally contract between companies (exchange of goods and services at market prices), versus what kind of work tends to happen within companies. Traditional economics, which loves markets as solutions to any kind of problem, runs up against some classic challenges around coordinating work: the cost of searching for information and communicating with vendors, the cost of bargaining, the risk of losing company secrets, all contribute to doing some work in-house. Overall, Coase gives us a catchall way to think about how the friction associated with a complex transaction will dictate how and where that transaction naturally happens.

Meanwhile, George Akerlof’s 1970 paper, “The Market for Lemons: quality uncertainty and the market mechanism” uses the familiar example of a used car lot to illustrate the problem of repeat games with information asymmetry. In most of the interesting transactions in life, there’s an information mismatch between different parties in a deal: in his example, the seller of a used car knows whether the car is a ‘lemon’ (a clunker) or not, and therefore has a better sense of fair price than the buyer does - who may only know the average value for such a car. His paper articulates why “Bad quality drives out the good”, and why markets become more pessimistic over time about the potential of things.

These two ideas apply in situations where, like a startup out fundraising, we need to make deals and allocate resources around uncertain projects. At the outset, financing a startup means committing to an as-yet-unknown number of fundraises, at as-yet-unknown valuations, before obtaining line of sight to any financial returns. The Market for Lemons paper articulates the nature of the friction (and reputational risk) facing entrepreneurs pitching projects; Coase shows us why non-market mechanisms might have real advantages in coordinating such an exchange. But if not inside large already-resourced firms (who have their own problems with innovating), then where?

Enter Open Source software, a fascinating third alternative to Coase. Open source software demonstrated how Gift Cultures could be a powerful alternative to both companies and markets for building and maintaining complex products. It also gave us some great writing on gift exchange, notably Eric S. Raymond’s The Cathedral and the Bazaar, Homesteading the Noosphere, and other essays that explored the intricate social systems of the early web. In this world of digital abundance where the cost of copying bits was zero, status didn’t come from what you had, but from what you gave away, and the reputation you earned over time from playing repeat cooperative games.

The fascinating thing about open source (formerly “free software”, a much better name) is that it doesn’t just produce a free option for software staples; it often produces the best option in its category; sometimes shockingly better than any paid alternative. The question is, how can a bunch of people on a mailing list giving gifts to each other produce not only high-quality contributions, but high-quality synthesis of individual output into large-scale open source masterpieces? This is the interesting part. You can picture a community of dedicated nerds who take great pride in their craft, and great joy in repeatedly giving gifts to others, as a stable equilibrium. But the more interesting question is why it works collaboratively. What makes the sum greater than the individual parts?

Gift exchange increases communication bandwidth

I think the key to the answer involves the meaning of gifts, and the reputation of gift-givers taking creative risks. Real innovation requires repeated risk-taking, and trying many things that don’t work. Meanwhile, efficient markets discourage people from showing their whole hand, and taking big creative risks, when unsuccessful attempts are interpreted as “lemons.” The trope of the travelling salesman constantly plying new ideas is that after two or three attempts, you either get the door slammed in your face, or move to a new city. Inside of companies, which should be more hospitable to risk-taking, aren’t actually in reality; for the same reasons. If anything, reputation matters more inside of firms than anywhere else - so real, repeated risk-taking is rare.

But all of this changes if everyone agrees that we are in a gift-giving situation. Gift exchange flip the Lemon Market problem upside down, because of the particular social rules around gift-giving and celebrating intent. Gift culture has sacred protocol: if you’re offered a gift, you must accept the gift, and you must reciprocate either in return or by paying it forward. If I offer you a gift, saying, “Here is all this information, promise and potential”, then by the rules of gift exchange, you are obligated to take it seriously, and you are obligated to offer some potential in return, for a window of time.

This frees (actually, compels) everybody in the gift exchange to embrace a measure of creative risk that, in normal circumstances, would signal “I am crazy”. There is a window of time in which different social rules apply, in which you have lots more room to move. You have until that window closes to “figure out what you have here”, at which point the gift exchange concludes, and normal economics reassert themselves. At that point, the deal or plan proposed goes through a conventional assessment of merit. And often it doesn’t pass, but sometimes it does, at way higher levels of ambition than would normally materialize. And that, my friends, is kind of magical. The gift exchange did real work; just like an enzyme does real work catalyzing a reaction.

The best way I can describe the nature of the work done is as information transfer. I have a hunch that gift exchange is actually a higher bandwidth channel for information sharing than market exchange. The giver can assign all sorts of meaning to a gift about its purpose, how it was made, and what they love about it, and the recipient will actually receive that message with much higher fidelity than in other sorts of transactions. That’s Information Theory 101: information isn’t what you say, it’s what they hear. The reciprocal rules of gift exchange compels you to actually hear what other people are telling you about their intent and purpose; which is the single most important bottleneck of creative collaboration. When given a high-bandwidth communication channel for “figuring out what we’ve got here”, we gain a window of opportunity to imagine. For a moment in time, the market for lemons problem is inverted: we are only interested in what could go right. We are blessed with potential.

Gift Capital

So far we’ve been talking about metaphorical gift exchange, but let’s shift gears for a minute and talk about literal gift capital.

We’re all familiar with the two basic ways you can finance something; debt and equity. But it’s worth thinking through where gifts fit on that spectrum. Probably like this:

It’s worth remembering that literal gift capital often plays an irreplaceable role in getting new projects off the ground! It’s funny how gifted seed capital has become kind of taboo in today’s climate; it hits every check box of privilege, luck, unfairness, what have you. But it’s frequently there.

What kind of rights, or claims, does gift capital have? If debt gives the issuer a senior claim on the success of a project, and equity gives you a residual claim, then gifts give you something else: a social claim on the success of a project. That social claim has obvious appeal, and you see it clearly at work in ventures like restaurants, art galleries, show business; where it’s clearly about the social cachet and returns to being able to close a business deal over a meal at your place.

I wrote The Social Subsidy of Angel Investing five years ago about this significant “lift” that this social benefit plays in making Angel Investing in tech startups a worthwhile undertaking:

From the outside, angel investing may look like it’s motivated simply by money. But there’s more to it than that. To insiders, it’s more about your role and reputation within the community than it is about the money. The real motivator isn’t greed, it’s social standing – just like a century ago, with the original Angels who financed Broadway shows. Angel investing is how you stay relevant. It’s how you keep getting invited to things. It’s how you matter. Angel investing isn’t about getting something, it’s about being someone. …

In an environment like this, angel investments are the ultimate flex. They’re the universally permissible bragging format: half “I saw this potential when none of you did” and half “I was invited to this deal and none of you were.” I don’t mean to say here that all angel investment is social posturing – some angel investors are among the kindest, most humble and helpful people I know. But the social returns to angel investment aren’t just a happy side effect. They’re often the main thing people are really after.

It’s also convenient that the investment return profile of angel investing for social status is way more attractive than angel investing for financial return. Angel investors’ money gets locked up for 5+ years (maybe even 10 years), so you face a significant illiquidity punishment relative to the S&P or even real estate. Second of all, your money gets massively diluted by follow-on capital. The more money a startup raises, the more you get washed out as a little guy who can’t defend pro rata.

But from a social returns perspective, you face neither of these problems. Not only are the social returns immediate, they also get reinforced by follow-on capital raises. That $50 million Series B for your favourite portfolio company might have washed you right off the cap table, but it’s an awesome achievement socially. As a bonus, even if the startup ultimately fails or you get recapped out of the picture, you still get a lot of the good will and recognition you were after. Even the downside scenario is pretty good, so long as you’re a good citizen about it.

There’s a very important trick going on here that I want to reemphasize: the social returns to angel investing have superior deal structure dynamics than the actual money part, because the more money gets raised in follow-on rounds, the more your social return compounds - on contrast to your equity stake, which gets diluted down. This solves an important coordination problem for the angel investor, during the window of time when they’re thinking about the social side of the balance sheet. “We don’t know how much money will need to be raised in the future” drops out of the problem statement.

This superposition of financial versus social returns, when considered in one investment, gets everybody in the round thinking in sync: “All right, we all know we’re here for an equity deal, but we’re going to follow social rules as though this were a gift exchange - I gift you capital; you gift me shares. We’re going to follow the rules of gift culture, and its insistence on accepting gifts and reciprocating them; and embrace the high-bandwidth communication channel for ‘figuring out what we’ve got here.’ (Described above.) And then at the end of the gift exchange, we’ll see if we have a path to doing a round that successfully gets us to the next round.”

It’s almost like a Shakespearian “Play within a play” setup: by play-acting one kind of investment, we catalyze another kind. (You know what’s an interesting historical analog? Mike Milken! One of the important things Milken figured out in creating the junk bond market was that “these things need to be sold like equities.” They needed a story to them; even if they were nominally a fixed income product. Milken was a generational master of “create windows of opportunity that didn’t exist before”, and I wonder if this idea would resonate with him.)

Multistage Bagholding Etiquette / the gift of grading on potential

This mixed motive of “investment-as-capital, investment-as-gift” is easier to accept for angel investors. They come in early, invest their personal money, and are allowed to have extracurricular motives. But it also applies to VC firms chasing hot deals, at least a little bit. VCs care about signal just as much as startups or angels do; and some are more disciplined than others on price. But there is an important social grace given to investors who pay arguably-inflated prices that startups need to keep their J-curve going.

Remember that the hard problem of financing innovation is the uncertainty around how many financing tranches you have until companies are graded on results as opposed to on potential. In any given financing, investors have to decide whether this is a “potential” round or a “results” round. The investors leading this year’s round cannot communicate with the people who’ll do next year’s round. If you could know with certainty which kind the next round would be, you could price today’s opportunity more straightforwardly. But we can’t time travel yet (until we beat thermodynamics), so the best we can do is send signals to the future, in the price and term structure we agree to today, about the gift being given today of grading the company on potential.

The gift-culture long game, which has been going on for decades in Silicon Valley, creates confidence that we can pass this “gift of grading on potential” forward, round after round. You can raise a Series A on the story of what the Series B story will be: “The appropriate thing would be for the next round to be priced at 40 at 150 post; can we count on people playing ball?” Everyone is gifting each other permission to not second-guess anything and go for the crazy outcome as opposed to the rationally optimal outcome.

We have to talk about bubbles for a minute here, because speculative bubbles show what this looks like with the mask off. There’s plenty of great writing on the importance of bubbles in creating financial maneuvering room for industries getting off the ground - read Byrne Hobart’s book Boom!, and Bill Janeway’s Doing Capitalism in the Innovation Economy - but I have one thing to add about bubbles that I never hear mentioned. Everyone talks about bubbles as times of lots of optimism, lots of FOMO, lots of irrational exuberance. But you know what else you see a lot of during bubbles? Generosity. This makes sense! Everyone is flush with success, and in good times like those, we are gift-giving people.

Gift Culture rules come to the forefront during bubbles, adding yet another reason why irrationally exciting possibility spaces seem to open everywhere as investment opportunities. During bubbles, there is no coordination challenge whatsoever; not only do you know that the money will be there next time (because number go up), but you also know the money will be there next time because we’re all good for picking up the tab; everyone knows the rules. That’s quietly closer to the thought process behind a lot of follow-on VC financing during good times: it’s not always strictly about number go up; it’s also about signalling to other players in the game, “We are good to pick up the tab.” Sometimes the tab is sickening at the end of the night; like the hangover after 2021 that left us with thousands of stranded zombie unicorns. But, rules are rules! That’s why I’m fond of saying that Silicon Valley is a “controlled bubble.” The social rules of the place keep it behaving reasonably predictably, and why we can reasonably count on multiple rounds of funding coming together to keep gravity turned off for just long enough for startups to grow through it.

Demo Day

Gift culture inside innovative companies matters too. The best illustration is Demo Day, the internal magic trick that makes building the unknown actually work. Not only are “Demo Days” an important component of the startup-investor gift exchange, they’re also an important ritual inside companies where people come together and figure out that essential question, “What is it that we’ve got here?”

You’d be surprised, but even projects with well-defined aims and timelines still have to contend, at the real coal face where the work happens, with the same problem startups face: we don’t know quite yet how this will take shape, or how long it will take. Traditionally, the way you’re supposed to tackle this problem is rigorous Project Management where you try to be as realistic as you can with specs and estimates. But if you want to ship something that’s actually great, you need to discover the best path available; and not just by thinking about it, but actually by building and tinkering towards a goal of some sort.

The answer to this problem is “Demo Day”, which when done correctly, is a very serious ritual that everyone understands is a gift exchange. You show up at demo day, and give gifts of what you’ve worked on to everybody else. And the rules of Demo Day state that everybody has to accept the gifts, and celebrate them. Demo Day is the one reliable time where people actually listen to you when you go into exploratory nuance of possible paths forward. That kind of open-minded curiosity is supposed to happen at brainstorming sessions, alignment meetings, whatever; but no one is actually listening in those meetings. Demo Day is when people actually listen.

The fact that everybody actually listens during Demo Day is why the actual outcome of Demo Day isn’t just good vibes: it’s alignment on work going forward. (Paradoxically, the way you achieve this is by no one ever talking about ‘alignment’ at Demo Day!) The rules of gift culture compel us to accept the gift of possibility, reciprocate it, and pay it forward. And just like in the startup-investor gift exchange, the reason why Demo Days create ongoing lift to projects is because there is another Demo Day next week, and the week after that. Everybody participating knows that we’re in a repeat-game situation, and feels a certain pain of dishonouring the gift if they revert to “the safe, rational, average path forward” by the following Demo Day, rather than digging and pushing to find the actual golden path of progress.

As before, we can think of this as an inversion of the Market for Lemons problem. In a traditional project management regime, you’ll quickly lose the trust of others if you constantly bring forward out-of-band proposals that don’t confirm to “the plan”. The magic of Demo Day is, by putting planning aside for a minute and instead standing up a weekly Gift Exchange, you radically increase the communication bandwidth of the team for discussing and synthesizing, “where should we be heading?” If you can keep up that culture of curiosity, while maintaining a cadence of shipping, you succeed along conventional metrics, and the score takes care of itself.

Gifts versus Slop

If you’re still reading in the hopes that we actually get to AI, thank you for hanging in there; we’re at that part now.

So, I think the obvious thing we’re already seeing is that it’s much, much easier to generate working prototypes. Both inside and outside of companies, we’re just seeing an explosion of top-of-funnel ideas that Replit or Cursor can very quickly stand up and make functional. And that’s great. Like I said last week in Have you ever seen a goth downtown?, I think the first-order effect is that more people can make things more easily, and that’s just straightforwardly a good thing.

There is a misguided belief out there that this is going to somehow reduce the number of funding rounds required to bring startups to maturity. Maybe it’s wishful thinking from seed investors who are tired of being diluted out of their great early stage bets, long for a world of one-and-done seed capital, and see AI as promising discontinuity from the status quo. I don’t understand this thesis at all; I think the more simple-obvious consequence is that this is just like when open source infrastructure (e.g. LAMP stack) and pay-as-you-go cloud hosting meant you didn’t need to raise a million dollars for Oracle before you even started building. That time around, more shots on goal meant a longer glide path for the winners to build distribution and reach maturity; I don’t see why this time would be the opposite. Eric Stromberg has the right idea in this mini-essay.

Funny enough, I think the best finger on the pulse here are the people thinking about building inside of big companies; and the “future of the product manager” discussion. Which goes, look, we can build and test prototype ideas a lot faster now; but that doesn’t help with the main problem of shipping inside big companies, which is the complexity of the beast. Wrangling internal complexity is oddly similar to gaining product distribution in that they’re both merciless work, and you’re on a quest for simplicity (but it’s a messy quest). Finding the the best path, early on, matters.

I think we’re back to gift exchange as the magic trick that gives us room to move, to really find what the group is capable of. I think it’s obvious that AI ought to increase the value and frequency of demo days everywhere, so long as demo days culturally remain gift exchanges. Because if you’re not offering gifts, you’re offering slop. It’s just more stuff that’s shouted but goes unheard. I think it’s pretty obvious that AI-generated software makes the Market for Lemons problem worse: you can ship a lot of shiny looking software that’s absolute garbage underneath the surface. But if you actually think of meaning of what you ship as a gift to others, you are going to care a lot more about its real potential, while recipients are invited to suspend disbelief about what’s below the surface and just think about the blessing of potential.

If you remember one actionable idea from this post, make it “Gifts and slop are opposites.” The world is getting inundated with slop, and what makes it “slop” is the fact that no message is really received by anyone. AI Slop is maybe the lowest-bandwidth communication channel ever made, which isn’t necessarily obvious. Gifts are the opposite of this, because gift exchanges invite us to actually receive information from each other.

I think the people who are going to be big winners in this new age of instant prototyping are the people who are exceptional at gift culture. Because they will understand the scarce resource of this new environment, which guess what, was the same scarce resource before too - the ability to coordinate people, over time, towards promising but unknowable goals. That’s always been the hard part, and it feels magical when we solve it.

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If you’ve made it this far: thanks for reading! I’m back writing here for a limited time, while on parental leave from Shopify. For email updates you can subscribe here on Substack, or find an archived copy on alexdanco.com.

“[The singer] Saint Vincent said something the other day… ‘I am so glad that I moved to New York City and met all the other freaks like me.’ Those aren’t freaks, Saint Vincent, okay? Those are attractive people with heavily vetted idiosyncrasies. Every eccentric fashion choice has been run through a think tank of NYU undergrads that would blow your hair back. You want to see a freak? Go to Albany.”

I chuckle because, first of all, everyone knows someone like this. Whatever their domain, they’ve styled themselves with great flair as “edgy”, or an “outcast”; and have actually convinced themselves that they’re avant-garde. But their actual deal is cookie-cutter counterculture, like it came out of a street punk paint-by-numbers kit. Whatever Albany is for them, they would never be caught there.

There’s nothing wrong with this; it’s more endearing than it is off-putting. But it’s on my mind recently as I try to process everything going on with AI tools, and how quickly they’re getting adopted for creative work. I can’t shake the phrase, “Attractive people with heavily vetted idiosyncrasies” as a description for what AI as a medium wants to produce. (And conversely, the punchline: You want to see a freak? Go to pump.fun.)

To be clear up front, I’m not an AI luddite in any sense. All these new tools let more people make things, and that’s just straightforwardly great. But there’s something a little nauseous about how we express idiosyncrasy, surprise, or character in this new medium. Like the singer in the joke, there’s just a little too much surface confidence in AI output about its self-determination; while the medium keeps vigilance for possible objections.

For plenty of purposes, that presentation is perfect. For example, I absolutely love how matter-of-fact Perplexity is when I’m using it as a reading companion. Perplexity Pro has changed how I read books that assume you know a lot of context; particularly novels. (I’m currently reading The Magic Mountain, which calls on a maze of concepts that battled for supremacy in early 20th century Europe. Having all that context on hand feels like magic.) Similarly, it’s not that AI is incapable of thinking about “style” as a characteristic; quite the opposite. It’s cool that AI ca write code to faithfully render certain syntactical conventions upon request; that’s a real feature.

But there is still a piece that bugs me: the feeling like it’s perpetually checking in with a hundred different NYU think tanks to make sure it hasn’t said anything actually surprising. And that’s what makes me wonder about this scaffolding getting set up everywhere, structuring how we learn and think and make things.

It wasn’t always like that. Early on, particularly with images, the trope about AI was that it compulsively hallucinated extra legs, fingers, and other demented dreamscapes into the medium as a matter of course. And the textures and perspectives it’d imagine up, while not always “correct”, had real character to them on occasion.

It’s mostly gone now, and I kind of miss it. This tweet nailed it, except it’ll be in six years, not twenty:

Getting rid of hallucination was a necessary step towards getting AI ready to do real work. And a lot of the way we’ve done it, beyond just training models harder, is by getting it to “reason”, in many parallel interrogations, about its thought process - “Have I gone down wrong paths here? Was there a more appropriate path I should’ve taken?” And this machine introspection, I suspect, is functionally analogous to the NYU undergrads in the joke. It’s almost as if there’s massively parallel “second-guessing” infrastructure getting put into place everywhere that makes sure, “Whoops! This looks like a little too original thought. Let’s retrace our steps and make sure we’re good with the think tank.” And the artist in me is naturally going to be a bit suspicious.

Country Goths

So, if you’ve made it this far in the essay, you’re probably wondering about the goths and whether they are downtown. Why are we even talking about goths at all?

Goths are a great test case for whether an environment is actually conducive to own-rooted self expression. While there are many kinds of goth (goth taxonomy is beyond the scope of this post), I’ll generalize just to say that generally, goths have a quiet confidence, which manifests externally as shyness. Real goths actually care about authenticity of a self-determined style, as opposed to caring about being perceived as “authentically rebellious”, like the Saint Vincent joke. So I think we can intuit that environments where goths naturally gather might have some creativity-nurturing characteristics, and vice versa.

In fact, you don’t usually see goths downtown. You see them in quiet malls, in the suburbs, farther out. There are actually lots of country goths, which has become a delightful bit of Americana in tiny rural towns today. Not really cities, though. Goths emerge more naturally under conditions of small numbers of people seeing you all the time, rather than conditions of large numbers of people seeing you a little bit.

I don’t think this is a coincidence. Subcultures flourish in places where a smaller number of people see you every day, but they see you more consistently - letting our creative forms of self-expression more successfully burrow into interesting local possibilities. The city, in contrast, means periodically interrogating your style choices from the scrutiny of many different people, which steers you towards “conventionally edgy”.

Let me try and illustrate this. On the left is what happens in the city: you’re getting periodically evaluated by many different observer perspectives, whereas out where there’s space, the evaluation function is a smaller group, but repeatedly over time.

Most of my lived experience with this kind of thing comes from playing in a band, where I saw a similar thing happen in our music scene. The bands from the big cities (Toronto, Montreal) were all clearly inspired by each other, and converged to a consistent style. Meanwhile, there was one band called the Expos from the distant suburb of Newmarket, Ontario, which despite being definitely “in our scene”, sounded like nothing I’ve ever heard before or since. Their sound evolved completely own its own; it followed its own rules, and not ours. They obviously cared about what someone thought (all bands do); but they found their own sound.

Another example that comes to mind is this David Perell’s series of observations about “everything looking the same now”; in this case, company logos all moving to a narrow band of sans serif fonts. There are many possible explanations here, like homogenous design tools, but the Occam’s Razor answer is simply that these brands have global audiences now, who are now vetting these logos for “deficiencies” from many different cultural vantage points. And so the result is something safe and unsurprising, more of the time than before.

I don’t think we have good language for articulating this dichotomy: of “narrow pressure from specific people that forges distinct style” (i.e. goths at the mall) versus “broad pressure to satisfy a perception of authenticity that ultimately minimizes surprise.” (The attractive people with highly vetted idiosyncrasies.) I think it’s worth ruminating on the tradeoffs, though. Particularly if the AI products assisting us now are successfully trained to not hallucinate anymore, but as a result, put infrastructure all around us that guides our creative work into invisible conventions.

A friend of mine had an alternate visual portrayal: “Your persona is a circle on a 2D plane, starting out near the boring origin. Unusual outcomes are far from the origin. Scrutiny by another is a vector that perturbs the circle. In the city the vectors come from many directions, but cancel out and you remain more or less a circle. In the country you get repeated perturbation by a few vectors that deforms you into a spiky, interesting shape.”

Gear

I’m a pretty optimistic person though, so I don’t want to get stuck on a “RIP Creativity” pity blog when there are plenty of reasons to be excited.

First of all, the most encouraging sign is that suddenly everyone is tinkering with computers again. When the initial ChatGPT came out there was this huge burst of people just playing with it, which seemed obvious in the excitement of the moment. But then we didn’t stop; the products kept getting better; and that burst of tinkering became habit-forming for a critical mass of people. That’s why I’m especially dismissive of a storyline that a certain group of people really wants to be true, which goes “AI is making us dumber”, which says a lot more about you than it does about AI or the people using it.

I’m encouraged by people who talk about their AI setups using the same words that musicians use to talk about their guitar amps or pedalboards. This is a good sign. Obsessing over gear - not over purchasing gear exactly, but anything that feels like “how do I get a particular kind of crunch out of this preamp” is a great sign for the medium because it’s a format where you look at your own output serially over time and develop a style that you actually want.

It’s also good when you see people obsessing over other people’s setups: the culture of sharing and comparing prompts on Twitter feels similar to how musicians argue over how to mic a snare drum. This is all directionally very good for the medium.

If there’s a takeaway lesson from this, I think it’s that parallel interrogation of a creative process leads to boring outcomes; serial investigation gives you creative outcomes. AI as a medium itself contains all of this “parallel interrogation” infrastructure that I’m wary about, but how people use AI seems to counterbalance this: it naturally lends to serial “tinkering” behaviour, which offsets that worry for me.

I’m interested to see whether we reach a point like with guitar amps, where the industry understands the “crunch and fuzz” that breathes life into the art, with the craft of using AI well. If you know of great examples where this is already happening, please send them to me, I’d love to see them. Not that there’s anything wrong with “attractive people with heavily vetted idiosyncrasies” as an inherent quality of the medium, but you need a goth dial somewhere too.

If you’ve made it this far: thanks for reading! I’m back writing here for a limited time, while on parental leave from Shopify. For email updates you can subscribe here on Substack, or find an archived copy on alexdanco.com.

It was a vision fair and fortunate.

Your statue spouting blood in many pipes,

In which so many smiling Romans bath’d,

Signifies that from you great Rome shall suck

Reviving blood, and that great men shall press

For tinctures, stains, relics, and cognizance.

This by Calphurnia’s dream is signified.

-Julius Caesar, II, ii, 83-90

The first founding murder in living political memory is complete. Brutus mustered up the willpower to rise up and kill Caesar (November 3, 2020); whom the crowd cast as a monster (November 6, 2020). “Domestic fury and fierce civil strife”, as then covered Italy in the battle of Philippi (and now reached its climax in 2021-2023), was suddenly resolved in the suicide of Brutus: the concluding half of Caesar’s murder (July 13, 2024), and the founding of the Roman Empire.

It’s so over; we’re so back.

So, now what? Where does the American story go from here?

American exceptionalism, once upon a time

You have to give him credit here. In the cacophony of the first few weeks of a presidency, when campaign themes get absorbed by the demands of governance, it’s hard to keep your foot on the gas pedal of leadership: defining a goal of where we want to go. Trump had no trouble with this the second time around. “We’re going to buy Greenland.” And the Panama Canal; perhaps Canada? And now Gaza.

This is just a remarkable posture. Not because it’s good trolling, but because it reaches way back into the rootstock of American motivation. This idea - “Manifest Destiny” - feels like a theme uniting everything this administration wants to do both domestically and abroad. But it could also be something more audacious: restoring a very old synthesis that energizes and mobilizes the American angst of needing to have our cake and eat it too.

America was founded out of reactionary isolation and opposition to the European balance-of-power system, which drew its legitimacy from Richelieu’s “Raison d’État” principle. If you don’t remember your European history here, Cardinal Richelieu helped create modern Europe in a strategic master-stroke: bringing France (a Catholic monarchy) into the bloody stalemate of the 30 Years War, on the Protestants’ Side; because it was in the national interest for France. Richelieu established the idea that nations, unlike individuals, are judged by results rather than by actions: “Man is immortal, his salvation is hereafter. The state has no immortality; its salvation is now or never.” In contrast to individuals, who are moral beings judged by God for their actions, states don’t have souls. They have a practical obligation to act in their national interest, whatever actions or alliances might be called for in the moment.

The legitimacy of the “national interest” not only survived the French Revolution, it was perfected in the 19th century by European leaders like Metternich, Bismarck, Palmerston and Disraeli who explicitly ran Europe as a pragmatic “balance-of-power” system where alliances shifted all the time, but the overall effect was reasonably peaceful. America, meanwhile, hated this idea. America has always thought of herself in moral terms. We call it “American Exceptionalism”: the idea that American values are in fact universal ones, the American purpose in the world being to spread those values, and our actions subject to judgement on moral grounds.

At the same time, mostly as a privilege of geography, America was really committed to the idea of isolation. We made a big show of refusing to get dragged into in the affairs of Europe. And yet, it proved progressively harder to ignore the temptations that present themselves when you become a powerful country. Robert Tucker & David Hendrickson described the dilemma in their work on Thomas Jefferson: “The great dilemma of Jefferson’s statecraft lay in his apparent renunciation of the means on which states had always ultimately relied to ensure their security and to satisfy their ambitions, and his simultaneous unwillingness to renounce the ambitions that normally led to the use of those means. He wished, in other words, that America could have it both ways - that it could enjoy the fruits of power without falling victim to the normal consequences of its exercise.” In other words, the American energy source in the late 19th and early 20th century, when its growth was most accelerant, was the drive and the unsettled energy of needing to have our cake and eat it too in the international arena. We wanted to throw our weight around and get things, but we didn’t want to compromise our self-image of being morally above European grubbiness.

All of that manic energy found an outlet: the concept of Manifest Destiny, and the related Monroe Doctrine, which resolved that American angst in an audacious way: “everything in the Americas (and anything else we feel like) is ours if we please, and therefore, a domestic matter.” American Exceptionalism remained untainted by Raison d’État. Henry Kissinger’s book Diplomacy paints a remarkable picture of this magic trick, and it resonates just a little too well with what’s unfolding now:

“[19th century] America was at one and the same time turning its back on Europe, and freeing its hands to expand in the Western Hemisphere. Under the umbrella of the Monroe Doctrine, America could pursue policies which were not all that different from the dreams of any European king - expanding its commerce and influence, annexing territory - in short, turning itself into a Great Power without being required to practice power politics. America’s desire for expansion and its belief that it was a more pure and principled country than any in Europe never clashed. Since it did not regard its expansion as foreign policy, the United States could use its power to prevail - over the Indians, over Mexico, in Texas - and to do so in good conscience.”

Of course, we’ve had a whole twentieth century since, where we did in fact assume responsibility for some consequences. Woodrow Wilson recast American Exceptionalism into a higher gear, where “American Values Apply Globally” brought us into two World Wars, sustained the struggle of the Cold War, following which America suffered from crippling anxiety of overthinking everything from the 90s onward. It took a populist revolt president who exudes the idea of selfish success to even suggest self-interest as a valid national theme, but even Trump’s first term wasn’t all that trajectory-changing in the grand scheme of things.

This time is different though. Any kind of organized opposition is in absolute shambles; the real test of Trump and the Republican Party is to what degree they can get themselves aligned. We have a president who absolutely does want to practice power politics, and a national vibe shift explicitly evoking an older time, where throwing power around in bombastic national interest in no way conflicted with the American psyche. “If Canada wants to avoid tariffs, it should simply become the 51st state!” is interpreted as more like the incantation of a magician than as serious diplomacy. But in invokes something important: an earlier, more confident version of America that wasn’t afraid of its own shadow, and that justified its international actions as domestic expressions of self-sovereignty.

The Mar-a-Lago accord

The hardest thing for Trump here is creating the conditions by which he can twist arms internationally, and be “the dealmaker president”, without compromising on the meaning of American power as something greater than Raison d’État. What happens to the American Dollar is the crux of the challenge.

More or less everyone agrees on the premise: the 20th century is over; it isn’t a unipolar American-policed world anymore, and we have to figure out what’s next for the dollar at a time when America has backed itself into a tight fiscal corner. The question is, of course, “What other moves are there?” Scott Bessent has stated “There will be a new Bretton woods”, both before and after starting the job, which suggest he feels pressure to try and solve multiple problems at once.

A recurring theme in Bessent’s interviews is how much he cherishes imagination. I personally appreciate the degree to which he always repeating a message: “The possibility space is so much bigger than you can imagine.” It’s not inconsistent with my assumptions about macro traders, which is that they style themselves as guys who twice a decade hit the biggest swinging dick trade of the year, and then just constantly lose money the rest of the time. Still, just like reading Freud or Graeber, the observations can be really valuable even if the conclusions fall over.

Meanwhile, I get the sense that the “Trade Wars are Class Wars” thesis - which goes, “The exorbitant privilege of the US Dollar is also a burden. It forces America to absorb the world’s excess savings, which is why we have asset bubbles and unfixable trade imbalances” - is taken at least somewhat seriously over there. (Apparently JD Vance likes the book, I can’t remember the source.) At a 30,000 foot level, it’s hard to argue that: 1) the United States exports dollars, in exchange for stuff; 2) this has benefited American consumers and investors at the expense of American manufacturers and certain classes of workers; and 3) this is because the world saves in dollars. The notion that the “exorbitant privilege” of the US dollar is playing out in a less-than-ideal way for America right now is probably right.

But there is no way - absolutely no way - that Trump will accept “de-dollarization” in any form. The understands, correctly, that the “exorbitant privilege” is a privilege, and he’s not going to throw it away. The right question to ask is this: “Today, we advantageously export bonds and import cheap consumer goods. Is there another way we could reap the privilege of the US Dollar?”

Which all brings us to what’s going on with tariffs. I’m not super smart in this department but I am guessing that Bessent and Lutnick’s goal goes quite a ways beyond “we’re gonna use tariffs to protect American manufacturers”; it’s probably more like, “We’re gonna use tariffs as leverage to change how we enjoy the exorbitant privilege of the US dollar; specifically, in a way that gives American companies not only protection, but a genuine competitive advantage in making things over the rest of the world.”

Specifically, could we shift the balance towards something that looks like: “We advantageously export bonds in exchange for importing cheaper inputs?” Oil, metals, commodities, all the stuff that really matters in a balance-of-power world: that’s what Trump really wants; and it’s what he’s going to get. If we could start sucking those things into the country at a massive rate, not only do we flood American business with a cheaper floor on input costs (yes, to the dismay of American resource producers - but Trump has shown little patience for this. He’s not a Texas Oil Man!), we also start accumulating the kind of stuff that could actually back Dollars should a Mar-a-Lago agreement get hammered out. The perception of movement in that direction might be reflexively interesting enough to give them leverage beyond “we’re gonna buy base metals on the global market and stockpile them in order to eventually back the US dollar with them”; that would take a million years and convince nobody, not to mention that we can’t afford it unless Elon fixes the entire deficit and then some. Some real financial alchemy will be required for a new Mar-a-Lago agreement, but this feels directionally promising to me. Bessent understands reflexivity well, and I’ll bet you the goal is approximately right.

“This is about border security”

Everyone expected some sort of tariff announcement by the incoming Trump presidency, but I don’t know how many people foresaw Canada and Mexico - who Trump only recently praised as partners in his North American economic engine - on the receiving end of a 25% slap in the head. The supposed reason given - “border security” - drew howls of are-you-kidding-me exasperation, made worse by the seeming pointlessness of the tariffs themselves.

While the execution of that messaging was certainly clumsy, I believe it accomplishes something very important. Getting Canada and Mexico to do a song-and-dance about border guards in order to postpone tariffs and negotiate concedes the most valuable point in the negotiation right off the bat - that this is somehow an American domestic issue, and will be framed as such.

If they can get tariff negotiations framed as an extension of American domestic authority - first with neighbours but shortly after with Europe and beyond - what this accomplishes is that America gets to negotiate to its trade advantage while maintaining a strange but effective moral clarity in its messaging at home. And, make no mistake here, the primary audience for that message isn’t the people we’re negotiating directly against; it’s Americans and sympathetic allies who actually want to see forward progress on the Trump themes. The overall effect, which is already happening, is that Americans are getting more comfortable with talking about what they want, which is kind of a remarkable achievement to change the vibe of a country so quickly and definitively.

Kissinger remarks later on in Diplomacy: “American foreign policy is rarely approached from the point of view of an overall concept. Ad hoc departmental approaches have more - and more passionate - spokesmen than does an overall strategy, which often has no spokesman at all. It takes an unusually strong and determined president, skilled in the ways of Washington, to break this pattern.” The best chapters in Diplomacy are the ones where Kissinger talks about Nixon and Reagan, contrasting their styles and the different ways in which they effectively led America to think of itself in new ways. Somewhere in the overlap between his open admiration for Nixon (who Kissinger found supernaturally good at diplomatic dealmaking) and his frustration with Reagan (who he thought had an eighth grade reading comprehension at best, yet grudgingly admitted was a seriously great communicator) lies an excellent description of what makes Trump’s leadership style work.

Trump certainly has an overall concept. Someone once gave me a definition of leadership: “Getting a group of people to act along the same vector of force without needing to explicitly tell each person their role.” I think what we’re seeing now with this opening foray of tariffs on America’s allies is best understood as a posture designed to get Team America all pointed in the same direction. Analyzing it from the point of view of the table stakes in question will only take you around in frustrating circles. “The reaction is the content”, the old rule goes. I see no signs this time is any different.

If you’ve made it this far: thanks for reading! I’m back writing here for a limited time, while on parental leave from Shopify. For email updates you can subscribe here on Substack, or find an archived copy on alexdanco.com.

I hope you’ve all had a great start to the year, and please excuse my absence for a good long while. I’m back quickly to say hello, and share what I’ve been working on with my team at Shopify. It’s called Tokengated Commerce, and it’s… exciting, to say the least. I wrote it with one of my dear newsletter pals, Packy McCormick of Not Boring:

Tokengated Commerce | Not Boring, by Packy McCormick, with Alex Danco

As we talk about at length here, I’m a huge believer in collabs as a force multiplier at cultural frontiers, and there’s no one I’d rather collab with for this piece than Packy. I mean, here’s Packy explaining Tokengated Commerce in a single, perfect sentence:

So that’s exactly what we talk about. We cover a lot of things:

• Shopify’s purpose, and finding that purpose in the blockchain world

• What is tokengated commerce and why is it such a big deal

• Tokens as “files” that make composability competitive again

• What making Shopify wallet-aware means

• Oh, and so much more

Our full post is here:

Read the post

Here’s a quick preview of what we talked about:

Let’s open with a question. It’s a very big question. You ready? Here it is:

When you make a bet on crypto, what is it you’re betting on? 

When we bet on crypto “happening”, we’re betting on two things:

1. Wallets change how people behave, 

   and:

2. Over time, this behaviour will win. 

Since you’re my guest here, I’m going to let you go with all the extra “u”’s. Behaviour. Classy. But yes, I like this answer because it incorporates both the social and the technical, and how computers and people interact. “First we shape our interfaces, and then our interfaces shape us.”

I think it covers it kinda nicely, don’t you think? It gives something for everyone. There’s something for the Sound Money folks (our wallets and cryptographic key pairs change the behaviour of how we save and spend, and that behaviour in the long run will outcompete the old behaviour), there’s something for the dapp builders (wallets change how people create and consume software), there’s something for communities (wallets change how people collect and present pieces of their identity). 

I like this starting point because it brings us together on something we can all agree on. Crypto is fundamentally a kind of behaviour, just as much or arguably even more than it is a kind of technology. 

There are many facets to this behaviour, but it starts from cryptography; and it starts in our wallets, when we sign something. And it starts at one moment, and that’s the magic moment where our wallets open a door into a new world and we step inside.

We’re working on some cool stuff inside that world. We call it tokengated commerce. 

Tokengated commerce starts with an observation: that there’s a new kind of internet user out there in the world, called people with wallets. 

I love this framing: people with wallets. Descriptions of what crypto lets people do are common, but I haven’t seen any that describe what crypto lets people become. 

That’s exactly it. Forget what you can do for a second, let’s talk about who you can be. And I’ll tell you the answer: you’ll be someone with a wallet.

Let’s talk about these people with wallets. Who are they? They’re owners. Maybe they own some NFTs that signify community membership, an ENS name, an ETH or USDC balance, maybe a smart contract they’re associated with. They can sign things all over the internet. 

These people behave in an interesting way: they really want to connect their wallets to things. They’re always connecting their wallets to get into discord channels, connecting their wallets to dapps, connecting their wallets to NFT marketplaces. 

How come? Because the things we own through our wallets mean something. When we show up with our wallet, we show up as us. And when we connect our wallets, and feel that magic moment where suddenly the world changes - it sees us, and opens up - that’s a pretty awesome feeling. It feels like this:

What’s going on in this picture? This is from Doodles’ event at SXSW this past march, a pop-up world created by the web3 community Doodles. 

The event was open to everyone, but if you were a Doodle holder, all kinds of experiences in this world - from the entry all the way through to the Shopify-powered gift shop - “opened up” to you in delightful ways. Lights flashed, bubbles blew, special gated merch dropped down on a hand-built roller coaster cart; and your Doodle showed up on a huge screen, announcing to the world: “Welcome, Doodle holder 3125!” And one after another, guests’ faces lit up in the unmistakable joy of being recognized; of successfully entering this new world.

This moment is what tokengated commerce is all about. It’s the moment where you realize, “Ah. I get the relationship between NFTs and commerce now.” NFTs aren’t an output of commerce; they’re an input for commerce. NFTs aren’t a kind of product; they’re a kind of buyer. 

Two things I like there: 

1. The idea that experiences can be open to anyone, but more special for NFT holders. I think music NFTs are really good at this: anyone can listen, a few can be visibly big supporters and get access to special tracks and meet-and-greets, etc…

2. Here it is again. What crypto lets people become.

It’s good to be a non-fungible buyer. You arrive at the storefront, connect your wallet, and suddenly the storefront changes based on an NFT you hold. Maybe a special product gets unlocked, or you get early access to a drop. Maybe you connect your wallet that unlocks a high-quality art print or 3D model of your NFT. Maybe you gain access to a special collab: for this limited-edition drop, your NFT opens a new door into another community. The more you explore, the more doors you find. 

Later on, we talk about something I’m personally very excited about, which takes me all the way back to my days playing in the band: tokengated collabs.

We should ask: how does a community endorse another up-and-coming community on the internet? With collaborative tokengating: when two brands get together, and flip exclusivity into reciprocal inclusivity. 

What are tokengated collabs? When The Hundreds does a collab with Deadfellaz, and you can unlock limited edition merch with an NFT from either of their collections, that’s a tokengated collab. When Superplastic does a collab with Gucci that’s gated to holders of a secret SUPERGUCCI NFT, that’s a tokengated collab. 

Here’s where the magic starts to happen: I belong to my community, you belong to your community, but when we do a collab together, I can invite you in; and you can invite me in. Tokengated collabs flip exclusivity into reciprocal inclusivity. A century-old brand like Gucci gets to endorse a really hot brand like Superplastic, in a way where their fans are both inviting each other into this shared experience, and this shared community they now have together. 

So there’s this obvious question: why does tokengating make this better, why is this different from a loyalty program or a promo code? 

Loyalty programs, promo codes, or any other kind of “account-based” gating system can work for reciprocal inclusivity mechanics - of course they can - but here’s the catch. It’s that your gating rules will exist as dependencies that refer back to accounts and permissions, which get complicated fast if you try to introduce any kind of interesting multiplayer mechanic at all. 

Imagine gating a pop-up storefront across four different brands’ fans, with multiple tiers of access, that get you early access to a merch drop. Where do you “sign in,” and as who? What if you’re a member of multiple communities - do I need codes from each? How do you prevent promo code abuse, which gets more complex the more variables you throw in? I mean, you can do it, but social gating rules built on dependencies break under complexity pretty quickly, and when the fourth wall breaks and the “exclusive community vibe” is spoiled by buggy software, it’s not a good community experience at all.

In contrast, a token is a bearer instrument - (remember the bearer bonds in Die Hard?) - it is much more like a file, or like a physical ticket that you hold in your hand. The NFT does not “link back” to anything, it’s not a reference to a reference to an account entry on one or more merchant databases; it is the ticket itself. 

In, let’s call it “single player mode” - so a single merchant gating to a single list of customers - it’s hard to see the advantage. But as soon as you enter multiplayer mode - multiple merchants gating to multiple sets of fans, through a set of social rules - the ticket being a bearer instrument that does not link back to anything and therefore brings no dependencies with it - but can still be trusted by the gate even though it doesn’t link back to anything (more on this later, this is the “why do you need a blockchain” part) is a radically simplifying mechanic. Because now, the tokengating rules can get super complicated, but they won’t break under the weight of dependencies because there aren’t any.

Reading this is a little lightbulb moment for me. Some of crypto’s killer use cases might be big and wild and impossible-to-imagine, but there’s also part of it that’s just, “This just makes the same thing so much easier that now a lot more of it will happen.” Stablecoin rails are in this camp. 

When we look back ten years from now at 2022 and ask, “What was really obvious and in front of our faces that whole time?” I think one answer will be that collaborative gating will be the mechanic that dramatically simplifies how brands collaborate and endorse each other. Community commerce is about to get a lot more multiplayer. 

Finally, we wrap up by talking about my big mission at Shopify: to Make Shopify Wallet-Aware. What does that mean?

We want to make wallets a new input for developers building on top of Shopify’s platform. An NFT in your wallet is much more than “have NFT, or don’t.” That NFT is information; it is a smart contract, and that’s significant. 

Here’s what we mean when we say, “Making Shopify wallet-aware.” 

When a buyer shows up to a storefront, then if they show up with a wallet, all of the stuff that their wallet can sign for, and all of its public, social significance that the buyer wishes to put forward, should be accessible to the storefront and to third-party developers. 

Those developers can take all that relevant state and build all kinds of apps and mechanics that can assume that state as an input. There is going to be a generation of very successful apps built on Shopify that give storefronts new kinds of powers, and today we have four amazing tokengating apps live - PERC Engage, Manifold, Shopthru, and Lit Protocol - powering tokengated commerce for merchants now, with several more behind the scenes that we’ll get to see soon enough.

To read the full piece, head over to Notboring.co and enjoy our conversation in full:

Tokengated Commerce | Not Boring, by Packy McCormick, with Alex Danco

Hope you enjoy it! There’s a lot in there, and I bet some of you will love it and some of you might be still pretty skeptical, which is fine too. It’s all a big adventure we’re in the middle of figuring out.

Until next time,

Alex

Tim Urban at Wait But Why had a little post about this several years back called Horizontal History, which pokes at how our sense of historical time is so poorly developed. For the most part, and I fully count myself among this group, we don't actually have a great sense of the relative timing or relative overlap of different major events and periods of history that happened to take place in different locations, or in different domains.

So, over the past week, I decided to have some fun on Twitter with it. I put out a series of polls, asking simple (but not necessarily easy!) questions about well-known historical events and when they happened.

Here are the results:

Just under 20% correct on our first question: the answer is Don Quixote. Volume 1 was published in 1605, with Volume 2 in 1615, in the time period of Habsburg Spain. That would be twenty years before the Chinese Qing Dynasty was founded on the other side of the world, and a century before the Salem Witch Trials and the South Sea Bubble.

Next question was sort of a trick question, but you mostly fell for the trick:

If you answered any of Suleiman, Luther or Cortez, you get at least partial credit, because all of those events happened within 3 close years of one another. (The correct answer is Suleiman the Magnificent becomes Sultan of the Ottoman Empire). But the one answer that's definitely wrong is actually the one with the most votes - the Forbidden City in Beijing, which was built a whole century earlier.

Round three, a new kind of question:

No clear consensus from the audience here, but 31% of you got the answer right: The Nutcracker (1892) premiered 33 years later than the other three (1859). The first modern oil well was drilled in Pennsylvania, the French seized Saigon (and were subsequently besieged), and Darwin finally published The Origin of Species after sitting on it for many years.

Round four:

Unlike the other questions so far, three out of these four events actually are somewhat sequential: they all have to do with Napoleon. The Rosetta Stone was discovered in 1799 during Napoleon's Egyptian campaign, as he was rising to power. And Beethoven's 9th symphony (1824) was written sort-of-about the end of the Napoleonic Wars, which culminated at Waterloo (1815).

So at the very least, out of the three Napoleon-related questions, you all voted in the right order (barely, though) - but a lot more people voted outside the Napoleon box and went for the Fall of the Shogun & restoration of imperial rule in Japan under Emperor Meiji (1868), which happened a good while later.

Next question, in another new format:

Congrats everyone! This is the first one we got right. The correct answer is indeed Simón Bolívar's life, who helped liberate South America from Spanish colonial rule a whole generation before the other three periods.

Next up:

We also go this one right; almost by outright majority. Close!

1492 was a busy year: Columbus set sail from Spain on his famous voyage, Da Vinci was busy being a Renaissance Man (the Vitruvian Man is this drawing you've certainly seen before; its completion is sometimes dated to 1490 and sometimes to 1492, but I'm going with the latter here), and the Stiegl Brewery - still beloved today for their famous Radlers, among other things - was established.

St Peter's Basilica, on the other hand, was started 14 years later in 1506 but was not actually completed until over a century later, in 1626. Churches take a long time to build.

Our winning streak is about to end, though:

Yeah we got this one really wrong. Exactly wrong, in fact.

First of all, one of our recurring themes is back: churches took a long time to build! Notre Dame construction started in 1163; so yeah, over 850 years ago. Before Genghis Khan, before the freaking Magna Carta which is what I for sure would have guessed was first. The Incas, on the other hand, were a lot more modern than most people think! The Aztecs and Incas were both contemporary to the European conquests, and the Incas (although they had begun to exist sometime in the late 1200s) only really became an empire in a meaningful sense in the 15th century.

Here's a surprising one (it surprised me, anyway):

So, we got this one right, collectively. But I'm willing to bet that more than a few of the people who voted for the Prophet Muhammad's life as the odd one out (the correct choice) thought that his life predated the other three. Nope! I especially would never have guessed that Saint Patrick came so early in history - I had filed him in my mind as a, like, 900-1000 AD type figure in history.

Next up, significantly more recently:

A third of you got this one right: it's Edward Jenner and the smallpox vaccine, 50 years before the other ones - monumental events in immigration, feminism, and religion in the 19th century.

Finally, let's head to the 20th century and see if we're any better at historical order.

This one would've surprised me; I for sure would have guessed the Fresno Drop as happening last, as did a plurality of you. But the correct answer is Yuri Gagarin going to space. Elvis's pelvic censorship, Brown vs. Board of Education, and mythical the founding moment of Credit Cards all came before the first human in orbit.

(If you don't know what the Fresno Drop was, by the way, please go look it up. You won't be disappointed. However, I’m willing to bet that my newsletter readers are more familiar with what happened that day than your average slice of the population.)

Another 20th century question:

I think this is the first question where an outright majority of us got the question right. Kudos! Erwin Schrodinger proposed his riddle of the cat being both alive and dead at the same time, which went down in history as the most famous Quantum Physics thought experiment, back in 1935 - significantly before the other three events, which all took place in 1959.

And finally, here's our last one:

Admittedly, I fudged this one a little bit - as you could pick a couple of different starting points for the patents on SRAM and DRAM, invented a couple years apart and patented over a multi-year process. But even so, the clear outlier is Malcolm X, who died earlier than I'd realized - in 1965. I was also surprised that I'd like to Buy the World a Coke, one of the most famous advertising campaigns of all time, didn't actually begin until 1971.

That's all folks! I hope you enjoyed this, and thanks to everyone who voted in the polls.

Permalink to this post is here: When Did Things Happen? | alexdanco.com

Before we go, I have a quick non-announcement - although I don’t have a date for it yet (my schedule for the next month is pretty full), my next Interintellect Salon is going to be all about Games. These salons are a ton of fun, and if you haven’t joined one yet, I’d love to see you there. If you want to talk about games and play some games with me and 30-40 other friends, reply to this newsletter with a note and I’ll email you once I have a committed date and the Salon posting is up.

Have a great week,

Alex

Two notes: First, I’m hiring for a couple of very special roles, to work closely with me on crypto products at Shopify. See the end of this newsletter to learn more.

Second: Today’s newsletter is a partnership special: I’m filling in for Mario Gabriele at The Generalist, who is off this week and kindly offered me the opportunity to write a guest issue. So today will be something a little different from what I usually write: I’m going to tell you about a specific company I really admire, called Twist Biosciences. This essay will appear in both our newsletters this week.

Two necessary parts here, before we begin: first, this essay’s purpose is to inform, educate, and spark curiosity - not to give investment advice. Second, I personally own shares of Twist, so I am hardly an objective observer. I am a fan here, not an analyst. Don’t treat this essay as anything it isn’t. 

Hope you enjoy it, and that you learn something!

-Alex

Art by ヤツデ, from https://www.pixiv.net/en/artworks/13461444

Last Friday, when Ginkgo Bioworks went public through a SPAC merger, was a joyful day for the synthetic biology community and for an ideological movement that you can best describe as "Solarpunk aesthetic." In contrast to today's parade of software IPOs, which are usually great companies but not particularly radical in the grand scheme of things, Ginkgo Bioworks and the Solarpunks have an actually wild and fascinating vision of the future that could be. It's the idea that we're going to build the future by growing living things. The movement has a name: synthetic biology.

The past thirty years of software have conditioned us to think about value creation through the lens of the infinite leverage of logic. There's no ceiling to how well-crafted the world's information and communication streams can be logically arranged; that's why we say that "Software is eating the world." If the solarpunks have their way, the next thirty years will show us the same thing is true for physical stuff, materials, fuel, and all kinds of work that can one day be bioengineered. Today we catch occasional glimpses, like this bioengineered fireproofing material for airplane wings, reminding us: "There's no reason we can't build a better version of anything, with the right tools and the right founders." And the arms race is on to build those tools.

That's why today, I'm not going to tell you about Ginkgo Bioworks, as awesome a company as they may be. I'm going to tell you about the platform behind them, a factory for building the DNA strands upon which all of life science is subsequently abstracted. That company is called Twist Bioscience. It's one of my favourite companies in the world.

Twist is a DNA factory. They start with inputs - the A, C, T and G chemical letters that make up the base-4 code for life on earth. And they produce outputs - sequences of DNA, called oligonucleotides, whose code gets compiled by cells into molecular products and molecular work. In that sense, they are a manufacturing company.

Twist is also a software company. Led by a brilliant founder, Dr. Emily Leproust, Twist isn't a traditional biotech success story - it's the beginning of a new kind of founder-led, product-oriented bio companies that look much more like tech startups than they do life sciences bets. Today, I'm going to tell you all about them.

Manufacturing DNA 101

In a rather literal sense, Twist is a DNA manufacturing company. So before introducing them properly, I want to take you through a primer (pun absolutely intended, IYKYK) of how we create this stuff.

How do you make DNA? The answer is, it depends. Specifically, it depends on whether you have some already. We're very good at Copy-Pasting DNA, but actually writing brand new DNA sequences is harder.

Copy-pasting existing DNA sequences that we already have is something we've more or less figured out: cells already know how to do it, and we know how to hijack cell machinery to do that work for us. It's a broad theme you'll find in biology generally: we're much better at borrowing life's tools that already exist than we are at building brand new ones.

There's one company in particular whose origin story is important to know: Genentech. If you don't know anything about biotech whatsoever, but you do know your Silicon Valley history, there's a good chance you've already heard of Genentech as the "original biotech home run", and helped cement KPCB as an elite VC fund in the 1970s.

Genentech showed that you can hijack existing cellular machinery called Restriction Enzymes (essentially, molecular scissors) to selectively cut out a gene from its genome, isolate it, and stick that gene in another cell, where it can operate and replicate. This technique, called "Cloning", was a huge step forward for molecular biology, and gave us products like synthetic insulin. The subsequent decades of progress in molecular biology largely built on that basic technique of Copy-Paste to do increasingly sophisticated things. 

But what if you want to make a new DNA sequence? We can do Copy-Paste, but what about Write? Bad news: writing new DNA sequences from scratch is substantially harder.

The challenge here, fundamentally, is an assembly problem. Our inputs are a bunch of DNA bases - As, C, Ts and Gs, which we can chemically produce in bulk. Our output needs to be a DNA strand of those letters, assembled in exactly  the right sequence that we want, without a template strand to copy off of. The basic process that we use to do this assembly, which we've known for decades, is called Phosphoramidite Oligonucleotide Synthesis. It's an intimidating name, but actually a pretty simple assembly process. I'll take you through it:

We start with our first letter, attached to a solid support like the bottom of a chemical test tube. Once we have our first letter, we're going to cycle through a series of chemical reactions that sequentially attaches letters, one by one:

First, we have "uncapping": we chemically remove a protecting cap on the top of the sequence. Then, the second chemical reaction is "coupling": we add our second letter through a chemical process that bonds it to the first letter. Then the third chemical reaction is "capping": we oxidize and protect the top of the sequence, to preserve the integrity of our strand, then wash everything out. Finally, we start again with the next letter.

That's it! You now understand the basic assembly reaction of how we make DNA. It's pretty cool. But it's also hard, in two pretty predictable ways: cost, and quality control.

Our first challenge is cost. Remember that every time you're adding a letter to a sequence, that's three (really, four) chemical steps that you have to do in sequence. So when we think about costs, we have the cost of running a process to assemble them in the right order (lab equipment, and highly trained human labour), and then cost of the inputs themselves (the ACTGs plus other reagents that make the reactions go). And remember, every time you do a reaction to add a letter, you have to wash everything away following that step, so you can move onto the next step. So most of your inputs are getting wasted; literally washed down the drain.

With our traditional setup, if you're doing good chemistry and you're wasting as little as possible, and you have the whole setup pretty automated, you could get your all-in costs down to somewhere around a dollar per letter for a starting quantity of a gene. Sounds pretty cheap, right? Well, a typical gene might be somewhere around a few thousand letters. And in a typical design-build-test-repeat sequence of actually doing stuff with these DNA sequences, you'll go through hundreds if not thousands of them. Most of the work of biology doesn't deal with single sequences; it works with libraries of sequences upon which you do work in parallel. So you can see how this will get expensive quickly.

The second challenge is error rate. No chemical reaction is perfect; there is always some error just from the fussiness of thermodynamics. Again, if you're doing a really good job and your error rate each time you add a letter is, say, 1/10th of a percent error (really good!), that error adds up if you're trying to synthesize strands of DNA that are 100 base pairs long or more. Generally, the longer the length of sequences you're trying to make, the more error rate you're going to get; and that's a big problem: error-checking costs money, and losing yield costs money.

So, for a while, DNA synthesis was stuck being too expensive. It's not like it was especially hard; it just did not scale. You had to be an established academic lab or pharmaceutical company to be able to do it in sizeable quantities, and even then, it was still scarce enough that you had to ration out what you made. You couldn't waste any, and it's hard to innovate on something  where you can't afford to waste the input materials.

But we understood, intuitively, how to get out of the cost trap: what if we could speed up the chemistry, not by making one oligo strand synthesized cheaper and faster, but instead by synthesizing huge arrays of DNA strands all at once? We tried a few fancy ways of making this work, like light-activated chemistry where you take a whole array of DNA strands and then selectively activate chemical reactions by shining light on and off in specific spots and sequences. But then we found inspiration from another technology that's much more everyday familiar: the inkjet printer.

Imagine if, instead of a sheet of paper that runs through your printer, you have a two-dimensional array of strand supports, with DNA sequences growing off of each one. You can imagine an inkjet printer head, filled up with A, C, T and Gs, then darting around the page dropping its cargo in tiny, targeted chemical reactions across the entire array, in tiny quantities.

This is appealing for two obvious reasons: first, if you could inkjet-print huge arrays of DNA strands, you can multiplex them: you can ask for 100 unique strands, or 50 of one and 50 variants, or anything you like. The printer will just print whatever you tell it. Second, if you get the engineering right, your chemical reactions can take place with fewer reagents per letter addition - one to two orders of magnitude less than before. So you have a lot less waste, and so a lot less input cost of raw materials. This method promised much lower costs: paying cents per base pair, not dollars.

What's the catch? There's always a tradeoff, and in this case, the tradeoff was error rate. The inkjet printer method was simply not as reliable as the old school way. But a few businesses got to work improving the process, notably a company called Agilent, where one of their scientists, Dr. Emily LeProust, was cracking the puzzle of how to get it all: large custom arrays, long sequences, high quality, and low price.

Twist: The DNA API Business

In 2013, LeProust left Agilent and started a new company, along with cofounders Bill Banyai and Bill Peck, called Twist Biosciences. (The dispute of whether or not she took any proprietary knowhow with her was not settled until years later.) They've had quite a ride since.

Twist is conceptually easy to understand: it's DNA manufacturing as a service. And Twist is good at it: they took the inkjet printer metaphor to its logical extreme, which is how silicon chips are made at fabs. They print thousands of DNA oligo sequences at a time, on silicon chips, with very low error rates. Then they package them up and send it all to you in the mail. The whole process takes a few days, from order sequence in to high-quality DNA out. They don't have the business all to themselves; there are competitors like IDT, GenScript, GeneArt and Genewiz. 

It’s still a “small” business, as far as big platform companies go, but it’s accelerating: they made $35 million of revenue in Q2 2021, at 44% gross margin, up from $21 million at 22% gross margin the year prior. They’re also still definitely operating as a startup; they’re burning $42 million a quarter, with $400 million available from a recent equity raise. So this is a startup that is absolutely shoveling money into growth, especially as they establish their new “Factory of the Future” near Portland that will dramatically increase their DNA synthesis throughput. 

So this is well outside “Rule of 40” territory (The rule of thumb for growth tech companies, suggesting y/y growth rate plus profit margin should equal at least 40%). But the fact that they invite the comparison at all is what matters here: this is not a software company selling SaaS seats; this is a company that will have to spend a whole lot of money to make money. In all likelihood, you could only build Twist in a discount rate environment like today. 

But we’ll take it: enough shareholders believe that this company should trade like a software company, not a biotech company. (It currently trades north of 30x sales). Part of it is real belief in the possibility of a synthetic biology inflection point coming soon, with Twist as a call option on the story that will come. Part of it is the stickiness of the revenue, which isn’t recurring revenue strictly speaking, but isn’t far off.

But even today, there’s another meaningful way that Twist is more comparable to a growth-stage tech company than to a manufacturing company: its product. Because Twist is an API business.

Although there are plenty of customers ordering DNA by hand, where a physical human in a lab somewhere is ordering sequences on Twist's website, that's not the real business at scale. The real business starts when it all automates. Twist's best customers, like Ginkgo Bioworks, are building modern biology factories: and that means machines making decisions, and communicating with other machines.

Here's where it's important to understand one way that bioengineering and software engineering are different: biologists get things done through massive parallel attempts much more explicitly than software engineers do. When bioengineers program a new cell line to do molecular work, that kicks off a process that likely loads thousands of "build cycles" in parallel, which we try to automate as much as possible. Here's why it's so important that Twist is an API company: this only actually works at scale if machines can take over the process below a certain level of abstraction, and talk directly to each other.

"Level One" of Twist's business, if you want to call it that, is the DNA ordering through API call product. It's a good product. But it's just the beginning; it's like EC2 from AWS. And just like AWS, which many people did not understand and value correctly at first ("It's just charging me for generic CPU cycles on someone else's computer? Who would pay for this? We've got on-premise machines 1000x times as powerful!"), a lot of incumbent biotech people did not see Twist as all that interesting when they launched. Here's a representative quote from 2015:

"The promise of mass-produced DNA doesn’t impress Rob Carlson, a biotech consultant and managing director of the BioEconomy Capital venture fund. “I don’t understand the business model,” he says. Carlson is skeptical that cheap DNA assembly will lead to a proliferation of startups with ideas for profitable microbes. “So you can make and test a whole bunch more DNA—but that’s not the hard part,” he argues. “Going from test tube to bench scale to commercial scale, that’s 90 percent of cost.” For a startup to build a business around a yeast that cranks out a pharmaceutical, for example, it must manage massive tanks full of microbes. Reducing the cost of the initial DNA manufacturing would only give the company pocket money, Carlson says: “Hooray, they get to buy beer, or more pizza on Friday.”

Of course, anyone who comes from the software world can see where this is going. The cloud isn’t, in fact, just “someone else’s machine”; it’s a different build mindset. The cloud isn’t something you use; it’s something you do. 

But even before we get to Twist's really disruptive impact, it's worth mentioning the "Level 2" businesses they're building on top of their oligo product, which are pretty impressive in their own right. Remember how much of the work of bio-building is through massive parallel attempts? Twist doesn't just want to sell you the DNA for those attempts, they want to sell you the input libraries of variants that go into those parallel build cycles.

If you go to Twist's product page now, you'll see products like Oligo Pools and Variant Libraries, which are basically input products to The Massive Parallel Build, as opposed to fulfilling individual commodity oligo orders. They're steadily moving up to higher and higher levels of abstraction, as they start going after some of commercial big guns: antibodies, immunotherapy, and other next-generation therapeutics that achieve outcomes through programming cells, rather than old-school therapeutics development. That's why ten years from now, we may well consume a whole host of products with Twist DNA quietly inside of them, just like today consume the internet without knowing which cloud providers are behind what.

But Twist's real potential isn't to improve the biotech back end. It's changing the biotech game entirely.

The Bio Founder, and the beginning of Product-Market Fit in Biotech

So what's the real point of all of Twist, in the grand scheme of things? It's not just DNA-as-a-service, or their library of libraries business. The opportunity is to catalyze a generational transformation in how life sciences companies are started and grown.

Every complex ecosystem, and every organizational paradigm, started from an origin crystal. And the particulars of that origin crystal - exactly how it started, what it looked like, what worked, and why - become a template that gets copied for generations - at some point, without anyone knowing what they're even copying.

Our modern software industry began this way, with the "Traitorous Eight" who left Shockley Semiconductor to found Fairchild Semiconductor, and subsequently Intel. These companies were born out of an understanding -  between two founders, Bob Noyce and Gordon Moore, and one investor, Arthur Rock - that the founders were the capital of the business. This was a more radical idea at the time than people today can really understand, and it established a model for how to build founder-led, venture backable companies. Today, the entire startup industry is geared around the founder as the scarce resource.

But not biotech. Biotech has a different origin story, which we mentioned earlier: Genentech. Genentech was founded as a partnership between a scientist at a lab (Herbert Boyer) whose job was science, and a Venture Capitalist (Bob Swanson) whose job was commercialization. Nearly every biotech startup since has followed a similar model. Biotech does not really have the singular concept of "Founders" the way that software does: there are the Scientists, led by a Principal Investigator, whose job is mitigating technical risk; and there is Management, whose job is commercial development.

There are many reasons why the Biotech Scientists + Management setup evolved the way it did: a different risk profile, different costs, the cultural attitudes around the "purity" of scientific research, and just generally the "We shape our tools, and they shape us" mechanic. Once biotech VCs learned how to finance bets that way, then that's what the bets became. But this separation came at a huge hidden cost: it virtually precludes, right from the beginning, the potential for founders to forge product-market fit that doesn't yet exist.

That's why the only biotech companies you've ever heard of, in all likelihood, make therapeutics. You don't need to craft product-market fit if you're making an Alzheimer's drug; it either passes trials or doesn't. You don't need a founder to develop drugs. But you do need a founder if you want to build entirely new things. 

The problem with the biotech industry, up until recently, was that there was no real path to becoming a founder. Your startup costs were high enough that the only path in was to start with a lab and VC funding, and follow the tried-and-true commercial spinoff model that biotech knows how to do.

This is why companies like Twist are so important. They make it easier to become a founder, and they open the funnel for builders to build. Tony Kulesa wrote a fantastic post a few weeks ago called The Future of Biotech is Founder-Led, which highlights what's going on perfectly: biotech is finally reorienting around a new way to build companies, pioneered by the software world. This world isn't run by scientists and management teams: it's run by founders, and by developers.

You may remember a few years back, Twilio had an advertising campaign: "Ask your developer!" If you look at companies like Twilio, Stripe, Plaid, and all these other amazing API companies, the right way to understand them from the beginning was: "They make founders and developers more productive."  They can try things faster, and they can forge product-market fit more quickly.

That's what Twist is doing for bio founders, bio engineers, and the whole biology startup community. They're your DNA cloud infrastructure where you get started, the silent partner behind your quest for product-market fit, and the robot factory that scales with you, as you grow like Ginkgo. Twist is becoming one of a handful of companies that opens the door for biologists to build. And in the 21st century, so many of our biggest problems can't be solved digitally; they must be solved through physical matter, and finding new ceilings for how well we can build the world around us. I can't really think of too many companies with a more important mission than that.

Permalink to this post is here: Twist Biosciences, the DNA API | alexdanco.com

One more thing, before we go: I’m hiring for a couple of special roles on the blockchain team at Shopify.

If you’ve read my newsletter for a little while, I think you have a sense for how I like to think about the world: I love to think about systems, I love thinking about complexity, about change, and especially about the people inside those changing systems. There’s nothing more professionally rewarding in the world than getting a small, special group of people in one place, with a shared purpose, to go explore and build together.

There’s no doubt, in my mind, that we are currently at the beginning of Crypto’s Eternal September. There is no more “crypto community” versus “everybody else” anymore; not for long, anyway. And Shopify could have a really interesting role to play in what happens next.

Over the past few months, we’ve gathered together a small and awesome team inside of Shopify to go figure this out. And I want to extend an invitation to all of you (well, the devs among you anyway, for the time being) to join us. 

Curious? For a description of what we’re looking for, and how to get in touch, head here.

Have a great week,

Alex

Hello everyone!

This week, I’m back with another round of ten books to recommend. I did one of these the first time a few months ago, and I got a lot of happy feedback (and new book suggestions!) from all of you, so thank you very much for that. It also seems like it had some real impact. See this supporting evidence, kindly grabbed by David Perell:

However, this time around I want to do something a little different: I’m going to recommend ten books from my bookshelf that I haven’t read yet, but want to. Here goes:

1: The Verge: Reformation, Renaissance, and 40 Years that Shook the World, by Patrick Wyman. 

I’ve been recently reading a lot of European history, particularly around what happened during and after the 30 Years War (which was so interesting I might actually do a newsletter post on it sometime soon. Stay tuned.) Wyman’s book, which came highly recommended (and was just released recently) looks like one of my favourite kind of historical books: “Hey, you know those four separate things that you learned about at some point or another, but never connected the dots that they all happened at the same time, and for synchronous reasons? Here’s that chronology, retold in a way that helps make sense of that progression.” 

2: John Law: A Scottish Adventurer of the Eighteenth Century, by James Bunchan

Speaking of European History (although 150 years later), this is another book actually bought like a year and a half ago but haven’t gotten around to starting - it’s massive, first of all, but that shouldn’t be a deterrent because I’m looking forward to what’s in there. 

If you’re not familiar with who John Law is, he was the architect of what turned out to be the greatest financial bubble (and collapse) of all time: the Mississippi Scheme, which was the French Government trying to bail themselves out through promoting stock in the trade rights to the North American colonies (a pretty similar story to the South Sea Bubble - but even worse). You know that bubbles are one of my absolute favourite topics for so many reasons, and the Mississippi Scheme was the greatest of them all. 

3: Cotton Mathis: Journey to Memphis, by Cecilia Croft Clanton

I’ve only ever been to Memphis once, which was for an ag-tech conference back when I was on the Social Capital Discover Team a few years ago, and it was a fascinating experience. I had not really appreciated the degree to which Memphis was really the capital of the South for a long time, or what had happened since then. This book was recommended to me by the organizer of the conference, who was a Memphis old-timer that had helped rebuild a lot of the civic infrastructure and local business community (not to mention the Peabody Hotel, the Grand Old Southern Hotel where we were all staying, which had been shuttered to decades but recently restored back to its former opulence.) I meant to read it right then, but lost track of it shortly after. I found it again recently in an old box of books, and now it’s back in my “read soon” list. 

4: Private Truths, Public Lies: The Social Consequences of Preference Falsification, by Timur Kuran

This was an Antonio García Martínez rec from Twitter, his last rec that I read (Escape From Freedom by Erich Fromm) was fantastic and so I’m looking forward to this one as well. If you know Antonio, you probably have some idea of what you’re getting into with this; this is already a topic I spend a fair amount of time thinking about so we’ll see what comes of it. 

5: The Age of the Unthinkable, by Joshua Cooper Ramo

Probably topical right now, given the unfolding tragedy of the American exit from Afghanistan. There’s no point in denying, at this point, that the biggest and hardest problems today are Complexity Problems that don’t fit any of our habitual or historic problem-solving approaches. There’s a familiar judo quality to them, where our own effort solving the problem isn’t merely useless; it reliably makes the problem worse. Seems like a good general phenomenon to appreciate. 

6: Klara and the Sun, by Kazuo Ishiguro

This only half-counts, since I’ve already started reading the book (but I haven’t finished it yet so I think it’s ok.) Kazuo Ishiguro is just an unbelievably talented writer, and one of the best contemporary authors for illustrating not what factually happened, but what people convince themselves happened. Which, as you know, may as well be what happened. 

7: 100 Years of Solitude, by Gabriel Garcia Marquez

This is one of the few books that I’m actually embarrassed to admit I haven’t read. Because everyone who’s well-read, well, they already have. Often more than once, in this case. I’ve read Love in the Time of Cholera and enjoyed it very much, although I read it many years ago and I honestly barely remember what happens except for the end, so maybe I ought to go back and read that too, but this one seems like the more important one to prioritize given its near-universal status as one of the must-read books. 

8: The Gene: an Intimate History, by Siddhartha Mukherjee

I’ve been getting back on a bit of a biology kick lately. With any luck, I’m going to write a few bio-focused newsletter episodes soon, so that’ll be fun. But in the meantime, this is a book that’s been recommended to me so many times, I feel like I have no choice but to go pick it up. The Emperor of All Maladies was also one of those universally-recommended books too, by the same author, although I’m actually more interested in this one (seems less depressing, too). 

9: Notes from the Underground, by Fyodor Dostoevsky

Although I’ve read and loved both Crime and Punishment and The Brothers Karamazov (the latter being arguably one of the finest works of fiction ever), I’ve never read this shorter one. And it’s actually on my list here for a special reason: because of the translators. Richard Pevear and Larissa Volokhonsky (who are married, also) are unbelievably talented translators; they also have a translation of War and Peace that’s phenomenal. It’s really hard to overstate how much the translation of a classic novel will shape how you experience it; but I still know people who buy whichever copy comes up first in a search rather than reading and choosing which version they’ll get. Please please please: choose your translations wisely, they’ll really have a huge impact on what you read and what you take from it. 

10: Open Secrets, by Alice Munro

Finally, I’ll end with an Alice Munro, which is another author that I am mortally embarrassed (especially as a Canadian) that I haven’t actually read yet. I feel like most non-Canadians have heard of Margaret Atwood as Canada’s most famous female author, but Munro is actually the more decorated one (she recently won the Nobel Prize for literature) and I think it’s not so great that I haven’t actually read any of her fiction. Open Secrets (a collection of stories; not a single novel) seems like a good place to start. 

As one last minder, here’s a summary of the InterIntellect Salon I’ll be hosting next Wednesday:

We’ll start by covering Claude Shannon’s Information Theory – really wrap our heads around what it means when we say, “Information isn’t what you say, it’s what’s understood.” It’s not a noun, it’s a verb; it’s not the baseball, it’s the pitch. 

We’ll cover Information as Resolution of Uncertainty, which is the right way to think about what information really is, and why that matters. And we’ll try our best to understand one of the hardest, but most important ideas of the information age: What is Entropy?

We’ll spend some time talking about human perception, how we process incoming information and how we resolve uncertainty about the world around us. We’ll use this understanding as a foothold to tackle a little bit of Marshall McLuhan, and really understand, from a neuroscience perspective, what he meant by “Hot” and “Cool” media. It does not mean what most people think it does.

Then we’ll end on the ideas of Information as Capital, and Information as Life: what it means for information (or low-entropy) to propagate forward in time.

I’d love to see you there. I’m going to keep it limited to a small number of spots, and the last few I’ve hosted have filled quickly. You can get tickets here.

Have a great week, 

Alex

Welcome back to Part 5 of Dancoland. If you’ve missed any of parts 1-4, you can catch up here.

Dancoland, Part 1: The Dream

Dancoland, Part 2: Just-So Stories

Dancoland, Part 3: Thinking in Layers

Dancoland, Part 4: Everything Loops

This chapter will be the last one of this series.

If you like, you can find the whole series in its permanent home here:

Dancoland | alexdanco.com

Here we go:

Student: I don’t really know how to ask this, but why did you come here? You never actually told me why you showed up in my dream. 

Teacher: So… this is a difficult question for me. But I’m glad you asked. It’s about time I told you why I’m here. 

Over this past hour, it’s been so wonderful and inspiring to see you figure out Systems Thinking. At first, you didn’t really get it; but then you worked on it. You kept at it, and you figured it out so well that you’ve surpassed me. I’m so proud. 

But, here’s the thing. This doesn’t come naturally to many people your age. Not because people are inherently bad at systems thinking! But it’s hard for people to make that first step, around unlearning cause-and-effect thinking. And I’ve been part of the problem. 

Student: How were you part of the problem? You were a great teacher. 

Teacher: I really tried my best, and I love teaching. But there’s something about school itself that does people a disservice, I think, when they go off into the real world and have to figure out the systems they encounter. I’ve traveled around, visiting former students, trying to figure out how to teach systems thinking. I never learned how. But then watching you, over this past hour, I realized something important. 

Student: Watching me? What did I do that was so special?

Teacher: The big breakthrough, for me, was: you were able to learn systems thinking within the context of a system you already knew. 

You see, my whole life I’ve taught kids who have all these different interests. Some of them love the woods; and know so many things about the forest as a system. Some kids love computers; and they understand it as a system too. Some kids love learning languages; some kids just love playing soccer. 

What they all have in common is, when they’re inside a system they already know and love, they spend so much time looking around and so much time making things. They spend all of their time thinking about purpose, one way or another: either looking at things and wondering about their purpose, or making things with their own purpose. 

Student: Student: You’re right. I’d never thought about it before, but kids are naturally great systems thinkers. 

Kids don’t naturally think in terms of cause-and-effect. They think in terms of purpose. They look at the world around them, and say, “The flower’s purpose is to bloom, and the grass’s purpose is to grow, and the sun’s purpose is to shine, and the garbage truck’s purpose is to pick up garbage, and the fire truck’s purpose is to put out fires.” 

It’s so simple, and to some people it appears young or juvenile but it’s actually so wise. You and I can see why: “The garbage truck’s purpose is to pick up garbage” is actually much smarter than, “because we put out the garbage, therefore the garbage truck picks it up.” Same for “The flower’s purpose is to bloom.” Or, really, anything: it seems too simple, but when you actually understand the whole system around the flower, you realize, that’s exactly right. The flower’s purpose is to bloom. 

Teacher: That’s a great way to put it. 

Student: You know when this first gets challenged, though? I don’t think it’s at school; it starts earlier than that. 

Let me ask you something: what’s the most famous question that kids ask? All the time?

Teacher: That’s an easy answer. It’s “Why?” Kids ask Why all the time. It’s a well-known thing kids do.

Student: That’s right. Kids naturally ask, “Why?” about everything out there in the world. 

But here’s the thing: when kids ask “Why”, they mean one thing; but parents hear something else. 

When kids ask Why?, they’re asking, what is the purpose of something. But parents hear: what caused something. 

Teacher: Oh wow. You’re right. Kids see the world as made of purpose, but grown-ups see the world as made of cause-and-effect. So the word “Why” means completely different things. Actually, opposite things. 

Student: Yeah. I think that’s part of why grown-ups get frustrated answering the endless “Why” questions. Because they get harder and harder to answer, if you’re thinking about them in terms of chains of cause-and-effect. 

For grown-ups, “Why” is like a tightrope. The longer the chain of questions, the harder it is to stay balanced on it. It’s stressful. 

But kids don’t see it that way at all. To them, Why isn’t a tightrope at all. It’s like a spider web, or a jungle gym. They keep asking why in order to learn, what’s the purpose of that? And that? And that? And each answer makes the web stronger, and more stable, and more fun to climb on and explore. 

Teacher: That’s exactly right. But here’s what I was getting at before: kids are naturally such great systems thinkers, and are so naturally curious about the world. They spend all of their time looking around, and making things, in environments that they already know.

But then they go to school. School doesn’t work like that.

Student: It sure doesn’t. In a classroom there can only really be one textbook; one set of lessons to learn, and one problem set to solve. So in order to make that work, you have to take all of the kids out of the environments they already know, and are already interested in, and put them into some neutral context that’s the same for everyone.

Teacher: And so, what do we do in school? We solve problems. We learn there’s a formula for how to do a problem, and then a set of problems with definite answers, and a cause-and-effect relationship between the questions and the answers. 

When you initially told me that just-so story about your walking speed and village size, that was familiar to me; it’s the kind of thinking you learn in school. It’s sensible; it proceeds from X to Y to Z in an orderly manner. But it’s not how your world works; or how any world works. It’s tightrope thinking. 

Student: Yeah. Whereas if you look at kids in environments where they’re thriving - so could be sports, or clubs, hobbies, their neighbourhoods, or to be honest sometimes it’s even in school, in subjects they’ve really fallen in love with - what they all have in common is they aren’t problem solving. There’s lots of looking, and lots of making, but no trying to “solve” anything. 

Teacher: Well, I do know something at the end of all of this. And it’s that I want to thank you for sharing your dream with me. I know I learned something. 

Student: I’m really glad you came here. I did too. 

Teacher: And on that note, it’s nearly morning. I need to get going soon, before you wake up. 

So in our last few minutes together, I have one last question for you: how do we preserve, and regain, that Childhood systems-thinking mode? Where instead of heading down the path of tightrope knowledge and cause-and-effect thinking, we can joyfully play in our purposeful, systems-thinking mindset? 

Student: That’s a great question to end on. And I think I have something for you. Two things, actually. One parable, and one piece of advice I heard once. I heard both of them a long time ago, and they didn’t resonate with me at all back then. I never thought about them much before. But now I think they make sense. 

The first one is the story of the frog and the scorpion. Do you know it?

Teacher: No. Tell me. 

Student: A long time ago, a frog and a scorpion were friends. One day, the scorpion needed to cross a river. He asked the frog to carry him across, and the frog agreed. But then halfway across the river, the scorpion stung the frog. 

Frog cried out: “Why did you do that? Now I will die, and then you will drown.” 

And the scorpion replied: “I’m sorry. It’s just in my nature.” 

Teacher: That’s a depressing story.

Student: It is; or, I guess, I thought it was. The scorpion had a purpose, and he carried out his purpose. There’s no cause-and-effect explanation for why this happened. You won’t find any answers there, or any comfort. 

But here’s the second piece of advice, and once you hear it, I think you’ll understand the story in a different light. It goes:

We become the stories that we tell about ourselves. 

Teacher: We become the stories that we tell about ourselves? 

Student: That’s right. Because the stories that we tell about ourselves become our purpose. And then our purpose carries itself out. If we become the scorpion, it’s because we told a story about ourselves: “I am the scorpion!” And then that becomes our purpose. 

Purpose is something that we choose. So it’s important to go find the right one, because the purpose you choose will determine how you spend it, and how you recharge it. And that will determine who you are, in this world. 

Let me leave you with a simple system to think about. It’s a system with three parts: a parent, a child, and their toys. 

Teacher: Sounds promising. I’m ready.

Student: Let’s begin with the toys. Through the eyes of the child, the toys have a clear purpose. They’re to be played with! That’s the point of toys; to play with them joyfully. By playing with the toys, their purpose is spent. They end the afternoon strewn all over the floor, in total disorder. 

Meanwhile, through the eyes of the parent, there’s another purpose at work: the purpose of their child’s happiness. At the end of the day, the parent spends their purpose putting the toys away for another day of play. At the end, the parent is tired. They feel spent. 

If those systems existed in isolation from one another, they’d reach equilibrium after one cycle. We’d end with the toys in disarray, and the parent exhausted. We’d achieve disorder, permanently. 

But these two systems restore one another’s purpose, and that’s why they persist. The parent’s effort restores the purpose of the toys, and the child’s joy restores the purpose of the parent. 

Teacher: I think I see where you’re going here. What makes this system work is the parent choosing: “My purpose is my child’s happiness”, as opposed to something like, “My purpose is cleaning up.” One recharges the system; and makes it loop. The other doesn’t. It dead ends.

Student: Exactly. If the parent tells a story about themselves that goes, “I spend all day cleaning up after my kid”, then they’re going to believe it. Not only are they going to get repeatedly tired and cranky, they’ll also be failing to participate in this system, in a meaningful way. The system will find a different, much worse steady state, with a tired parent and a resentful kid. 

But when the parent sees their purpose as “I am the parent of a happy child”, then not only will helping out feel like recharging, it’ll also help this system enter new states, like the parent and child playing together, and making things together. The parent will get to participate in the child’s purpose, in the order and chaos of play. 

Teacher: I bet this is where understanding “Order and Chaos being the same thing” can really help. 

Student: Exactly! That’s one of the best things about being a parent, is appreciating how the child’s purposeful play is simultaneously so orderly (when you look at from the outside in), but also so chaotic (from the inside out) in a special sense: tiny discoveries made while playing can cascade into positive feedback cycles of new interests, new understanding, and a new way of interacting with the world that really changes the world around us. 

Teacher: That’s right. Some small detail that a kid notices while playing, or some tiny event that shapes the course of a playful afternoon, could massively change how that kid goes on to experience and appreciate the world. It’s the best kind of chaos. Not only is it Purpose that recharges Purpose (the parent’s), it’s also Purpose that creates new Purpose (the child’s). 

Wow, all of those lessons from our time together just came together all at once there. 

Student: Kids show us the way, don’t they. 

Teacher: They sure do. 

Student: All right, I guess we’d better go. But I’m thinking we should probably leave something behind. 

Teacher: You read my mind. Do you have a pen? 

Student: Sure do.

The teacher and the student both smiled. They knew their time was up; it was morning. The student started to wake up. But before he did, there was one last thing he had to do, which was pack up this whole entire dream - the farm, the furnace, the bugs, the layers, and everything else - and put it into his Backpack of Knowledge. He had a feeling he might need that backpack another night, for another dream. Some day. 

And then he put on the backpack, and woke up.

Thanks for reading along. You can find a permanent link here:

Dancoland | alexdanco.com

The ideas in this series are based on the book Order out of Chaos by Ilya Prigogine, who won the Nobel Prize for Chemistry in 1977. It’s been fun trying to translate these ideas into a more approachable narrative and setting.

This story first saw the light of day (in early draft form) in an Interintellect Salon I hosted back in May. It was a ton of fun, and I think going forward this is how I’ll work through these longer-form ideas: first in a semi-private salon, with a friendly audience, so we can work the bugs out, and then in the newsletter later.

The next one of these Dancoland series is going to be all about Information. It’s going to be good. I’ll be hosting a salon on September 1st, from 1 to 3:30 EST, to share it with any of you who’d like to join. I’m going to cap it at 30 or so attendees, like the previous ones, which seems to be a really nice group size for these events.

You can register here:

The Medium, The Message and the Mind | September 1st, at the Interintellect

Have a great week,

Alex

Welcome back to Part 4 of Dancoland. If you’ve missed any previous chapters, you’ll need to read those first.

Dancoland, Part 1: The Dream

Dancoland, Part 2: Just-So Stories

Dancoland, Part 3: Thinking in Layers

Today’s Chapter will be the second-to-last one. Next week we’ll conclude.

Student: Okay, I think I’m ready. I’m going to try to get all the way through the logic of how Dancoland works. 

Teacher: I can’t wait. Let’s hear it. 

Student: So, over the course of our journey together, you’ve shown me different systems that have found steady states. Before we met, I’d believed that this idea of “steady state” was somehow synonymous with the idea of “equilibrium”. But that was wrong: systems find their orderly steady state when they’re out of equilibrium, because they flow. They’re in motion. They have a purpose. 

That makes sense to me now. But there’s one adjustment I want to make to this idea. And that’s to clarify something important: steady state doesn’t mean static, either. These systems aren’t flowing monotonously; it’s more like they’re breathing. They’re looping. 

Think about every system we saw together: the farm, the nomads, the market, the bugs. All of these orderly systems are actually looping. The farm loops each year through growing, harvesting, regenerating, and planting seasons. The market loops each day through setting up, trading bustle, cleaning up, and preparing for the next day. 

Teacher: I’m listening. This feels promising. 

Student: Looping systems have one big rule. At the end of the loop, you have to end up back where you started. Something gets accomplished in between - things move through the system, like nutrients move through plants and into harvests. But at the end of the loop, the farm is back to where it started. 

Year after year, the farm flows consistently, and it loops persistently. That’s why we say the farm has a consistent purpose. It loops in a way that accomplishes something, while repeatedly returning to where it started. 

This idea got harder to follow when we started talking about the bugs. The bugs reproduce and evolve through positive feedback. I really understood how the bugs had purpose, and how that purpose coexisted with - and often, challenged - the other systems in the environment around the bugs. 

But those other flowing systems in the environment around the bugs are trying to loop too. By purposefully making more and more of themselves, the bugs challenged and sometimes dominated the other loops, and threw those loops off into new directions.

That’s when, if you remember, I said that one sentence where you interrupted me; you said stop, this is really important to get right. 

Teacher: I do remember that. 

Student: I said, “The fact that bugs can perpetually make more of themselves in a predictable way - feels inseparable from the environment around them being unpredictable.”

Teacher: Correct.

Student: Remember how much I struggled here? I was having a hard time superimposing the idea of order - (“like causes like, predictably perpetuated, forward in time”) and chaos (“small inputs unpredictably cause massive consequences”). It’s like, are the bugs making the world more predictable, or unpredictable?

But then I figured it out, and the breakthrough came from a surprising place. It’s such a simple answer, it was hiding right there, in plain sight. And now I know how to show it to you. 

Teacher: How so?

Student: Come walk around with me. 

The teacher came over, and they resumed walking together. 

Student: You see, this whole time, we’ve been walking around. At first I was just enjoying the exercise, but I began to enjoy seeing what we’ve drawn together, and what we’ve learned, from all of these different vantage points. But then just now, I realized something. Come over here, step into this middle loop with me. From the point of view of the purposeful bugs, what do you see when you look out? 

Teacher: I see chaos. Everything around me is changing all the time, in unpredictable ways. My purpose is chaotic. 

Student: Okay, now let’s walk out of the loop, and look back in. Now what do you see, when you look at where you just were?

Teacher: I see order. I see the relentless, predictable drive of the bugs, who keep looping and looping with consistent purpose. The purpose is orderly. 

Student: And what does that tell you?

Teacher: It tells me that… oh hey! I see it now! Order and Chaos are just two different perspectives on the same thing! 

When we say “Order” that means Purpose, from the Outside Looking In. 

And when we say “Chaos”, that means Purpose, from the Inside Looking Out. 

Student: I’m thrilled this is resonating. You never know if something actually makes sense until you can get someone else to explain it back to you. 

Here’s how I understand it: 

The ideas of “Order” and “Chaos” aren’t opposites at all. They’re both describing the same thing, but from mirror points of view. They’re describing purpose - the drive of a positive feedback loop in a world of other driven loops.

From the inside perspective, purpose feels like chaos - the more purposeful the loop, and the harder you drive the loop, the more unpredictable the world around you will feel. Everything changes around you, in ways you can never anticipate or even control. 

But from the outside perspective, looking in, purpose feels like order - the more purposeful the loop, the more orderly the universe feels, because you can look at the positive feedback loop driving its flow forward, and driving its purpose forward. From the outside, you recognize purpose as order: the steady state of a non-equilibrium system. 

I’m sure you’ve had experiences like this - where from the inside it felt so chaotic, but from the outside, it appeared so inevitable. That’s what purpose is. 

Teacher: I think I’m going to call it. We’ve figured out the first rule. 

Student: That’s right. The first rule of Dancoland: Order is Chaos. I think we can check that one off: Order is Chaos, because they both mean purpose - but from two different perspectives. 

And you know what? I feel like a weight has been lifted off of my shoulders a little bit. I feel like “cause” and “effect” just became a little less important. Loops don’t have a start or an end; they don’t have a cause or an effect. They have a purpose.

I think I’m finally letting go.

Teacher: Nice! Feels good. 

Student: Sure does. I feel like getting the first rule is the hardest step; and now we’re in good shape to work through the other three. 

In fact, you know what, the second rule is actually just a stone’s throw away. “Chaos is Order”. I bet you that isn’t just a carbon copy of the first rule; it’s there for a reason. 

Teacher: I bet you this has to do with where loops come from. 

Student: You’re right. In fact, we already basically spelled this out, when we talked about layers emerging. Remember when we said, At some point, some aspect of the bug’s metabolism started making more of itself, and more and more, and at some point it made sense to call that a “cell”. And then as the cells made more of themselves, they became organ systems and then bugs and then bug communities. 

These looping layers, with their own purpose, had to emerge out of somewhere. They had to “escape” the world that existed before them. For a new layer to emerge that’s actually new, it has to have come from a starting point that has never existed before. 

That’s why chaos is so useful. Positive feedback loops are where “new” comes from. The drive of the purposeful loops in the existing world - which seem like such a strong barrier to change, if you’re looking from one perspective - is also what gives birth to change, if you look from another perspective. 

Teacher: You have to break out of loops in order to enter new spaces. 

Student: That’s right. That’s why chaos is such an important part of what’s going on. Nothing is permanent, because purpose creates the conditions for new purpose. 

Teacher: I like that. Purpose creates the conditions for new purpose.

Student: Chaos is order. There’s number two. Let’s check it off.

Teacher: Done. But that leaves three and four. They seem trickier. 

“Order without chaos is disorder.” And

“Order without disorder is disorder.” 

Student: You know what? I think I know what these are all about, too. But to answer them, we have to talk about disorder, and we have to finally address the meaning of the Furnace of the Universe. 

Teacher: Let’s do it. 

Student: So here’s the thing. As we’ve been talking about Order and Purpose, it’s been bugging me that we’d never actually talked much about Disorder. What even is it? 

My instinct was to say, “Disorder must be the opposite of order.” It seems sensible and obvious. But then I thought back to how I used to think that Chaos was the opposite of order, too. 

And that sure wasn’t true. Order and Chaos both mean Purpose, just from mirror points of view. So then what is Disorder? Is it Anti-purpose? Un-Purpose? Lack of purpose?

It’s none of those things. Disorder is SPENT Purpose. 

Teacher: Disorder is Purpose that’s been consumed?

Student: That’s right. 

Let’s go back to our loops. The farm, the nomads, the market, the bugs. All of these loops are productive: they do something useful in the world. And they’re all perpetuating: they end up back where they started. That’s what makes them loops. 

But they also consume something. They’re not perpetual motion machines. Each time the farm goes through the loop, it consumes the energy of the sunlight, the nourishment of the rainfall, and the effort of the villagers. Each time the market goes through the loop, it consumes the demand of the buyers, the arrangement and preparation of the vendors, and on a long enough scale, even the durability and structural integrity of the building. Every loop produces disorder. Every loop consumes purpose. 

Teacher: Disorder is the remnant of consumed purpose. 

Student: That’s the meaning of the furnace of the universe. The universe continually spends its purpose. I remember when you showed me the furnace at the very beginning of our time together, I could only see it as a bad thing. You told me:

We see the world as structured and orderly, but that structure is temporary. Over the long run, all of the organized structure in the world slowly but irreversibly breaks down into perfectly uniform disorder. Mountains eventually grind down into dirt; plants and animals die. Friendships end, social contracts dissolve. Kingdoms eventually fall away and are forgotten. Even the sun will run out one day. It’s like that riddle from The Hobbit. Nothing survives time, in the long run. 

Maybe that’s how we should think about time. Time is the forward motion of purpose. Purpose is created, and purpose is spent. It moves forward, in one direction: the direction of time. 

Teacher: Time is the forward motion of purpose. I guess that’s the final nail in the coffin for cause-and-effect thinking, isn’t it. 

Student: That’s right. Cause and effect will fool you: in a world where everything is looping, there is no cause or effect. But there is purpose, and purpose moves in one direction: it is created, and it is spent. 

Teacher: And it’s renewed, too. 

Student: Well, it can be. That’s up to us. 

The systems that persist, like the villagers and the nomads, persist because they’ve found an arrangement of stocks and flows and surrounding loops that both consumes and renews its purpose. 

Think about the difference between the villagers and the nomads: they are similar systems with similar purposes. They’ve both found different ways of feeding themselves, with their own looping balances of stocks and flows that renew themselves season after season, and re-consume their renewed purpose. 

Persistent loops must renew their purpose. They’re like muscles: the purpose of your muscles is for you to use them. How you use that purpose is up to you. You can spend them in a way that renews their purpose, by using them purposefully, or spend it in a way that doesn’t, by doing nothing. 

The same goes with each of the loops we saw on our journey. The farm ends its growing season exhausted; in disorder. The market ends its day messy; spent; disordered. But they loop back again, the next day and the next year, not because they’re just passively orbiting around and around, but because their purpose is continuously renewed by the participants around them, as they carry out their own loops and their own purposes.

Furthermore, sometimes we act with enough purpose that we chaotically rearrange the participants and loops around us, until a new loop gets established, which finds its own purpose. Like the market found its purpose on top of the farm, or how the many layers of the bugs evolved on top of one another. 

In consuming their own purpose, loops regenerate other purpose, and sometimes even create new purpose that has never existed before.

Teacher: Loops carry out their work by spending purpose. They regenerate through other loops spending their purpose. 

Student: That’s exactly right. 

Hey, I think it’s time to finish off our four rules. I think we’re ready. 

Teacher: So what did we have left? Order without Chaos is Disorder, and Order without Disorder is Disorder. 

Student: Let’s try the first one.

Order without Chaos is Disorder - so that means:

Purpose -  if it’s never directed outwards - is Spent Purpose. 

That makes sense to me. If purpose is not directed outward onto the surrounding world, it is simply spent, and nothing more. It’s never renewed, because there’s never any reason to do so. If you do things but don’t tell anyone, then your purpose can only really be consumed once. But if you do things and tell people, your purpose can self-renew. 

And how about the second one? 

Teacher:

Order without Disorder is Disorder - so that means:

Purpose - if you don’t spend it - is spent. 

Yeah, that’s another way of saying, “Use it or lose it”, just even more clearly this time. If you never spend your purpose, then it goes away all the same. 

Student: So there we have our four rules:

Order and Chaos are mirror perspectives of Purpose.

Purpose creates the conditions for more Purpose.

Purpose, if you don’t direct it outwards, is Spent Purpose.

Purpose, if you don’t spend it in the first place, is Spent Purpose.

Teacher: Looks great. I love it. 

Student: And you know what? Now that I look around at all these boxes around us, which came in my backpack of knowledge, I finally realize what they are. 

All of these boxes, together, represent everything I know, and everything I’ve learned and experienced over my lifetime. And each one of them - if you open them up - has a purpose. 

There aren’t any just-so-stories in here at all. Nothing here is a cause or effect. There are just boxes and boxes full of everything I know, and why it’s there, and what’s their purpose in the world. 

That’s what I’ve been accumulating my whole life. I just didn’t realize it until now, until you brought me here. 

He looked over at his teacher, who was beaming with pride. She was clearly so happy for him. But at the same time, he couldn’t help but notice some sadness in her eyes, too. 

Student: I don’t really know how to ask this, but why did you come here? You never actually told me why you showed up in my dream. 

Next week’s episode will be the last in this series. See you then.

Before we go, here’s this week’s Tweet Caption Contest:

First, congratulations to our winner, Friedrich from Toronto, for last week’s winning caption:

“This return to the office policy is a real drag. See you tonight, darling!” -Friedrich, from Toronto

Our current finalists, for voting consideration:

“My mom (left) vs. my wife (right) when I crack a joke at Thanksgiving dinner.” - Sean, from Berlin

“Jokes aside, folks, these tubes will be used to transport everything *except* the internet.” - Daav

“We’ve reached a compromise for the cryptocurrency amendment: we’re going to tax whatever NFTs are instead.” - Jill

Vote here

And here’s this week’s tweet, for your submission:

Submit

You can vote and submit your responses here.

Have a great week,

Alex

Welcome back to Dancoland. If you aren’t caught up, head here to start at the beginning.

Last week, we’d learned how different systems can find their own steady states, and how order emerges when systems flow. But hadn’t really gone below the surface in search of real secret of Dancoland. For instance, we haven’t yet talked about the bugs.

Student: There’s something I don’t understand yet. And that’s why you showed me the bugs. 

Teacher: The bugs certainly don’t seem like they’re in a steady state, do they. 

Student: Not at all. They’re growing, evolving, and changing their environment all the time. It looks pretty chaotic and unpredictable. Maybe we can illustrate this with another system diagram. I’m not sure where to start though. 

Teacher: Well, what is the main thing flowing through the system?

Student: I think it’s also nutrients. What’s a bug, after all, other than a sophisticated system for turning nutrients into more bugs? 

Ok so let’s draw this out. Nutrients start their lives out in the environment just like before; and they end their lives consumed by the bugs’ metabolism. In between we’ve got a couple of stocks where it’ll accumulate along the way. 

And let’s also put a large stock of bugs called Bug Population, which affects how quickly the nutrients get eaten.

Teacher: Anything else we know about that affects this stock-and-flow system?

Student: Sure. So the bugs live in a complex environment; they don’t have it easy. There are predators who eat the bugs. And there are competitors, who eat the bug food. The larger either of those stocks get, the more pressure it puts on the bug population.

Teacher: So far this looks good. But let’s get to the important part here, which is the fact that the bugs reproduce and evolve. There’s an important concept we haven’t talked about yet, called positive feedback. 

Student: Ok, let’s draw this out carefully. Bugs reproduce by laying eggs. When they’re happy and eating lots, they lay a lot of eggs. Then, those eggs hatch into baby bugs at a certain rate, and they flow into the mature bug population. 

That’s a reinforcing cycle. More of the same makes more of the same. If it goes on forever, you get infinite bugs. So clearly that must stop somewhere; either because they run out of nutrients, or the predators, or too much competition, or something. 

Teacher:  Now remember, the bugs don’t just reproduce; they also learn and evolve. They’re actively learning how to change their environment to their advantage, aren’t they. 

Student: You’re right. Not only do the bugs make more bugs; they make more evolved bugs. And those evolved bugs are making tools and technology to alter their environment. Some of their tools help defend them against predators; some of their tools help them grow bug food faster. 

The bug ecosystem is totally thriving. Lots of food means lots of eggs, lots of new bugs, and more evolved bugs. This system seems like it could totally go on for a long time; now that the bugs have learned how to build technology and alter their environment for their benefit. I can’t say for certain that any of the environment around them will survive intact, mind you.

So I guess I’d say that the long-term order and stability of the bugs - so, in other words, the fact that bugs can perpetually make more of themselves in a predictable way - feels inseparable from the environment around them being unpredictable. 

Teacher: Hold up. There’s a lot in what you said just there. I want to make sure this doesn’t fly by without us noticing. Maybe we’d better critically examine what you mean by “predictable” and “unpredictable”. 

Student: Right. So, when I’m saying that the bugs are “predictable”, I mean that what I see now - bugs making more bugs - is a pretty good prediction of what tomorrow’s or next year’s bugs will look like. Just like how our farm was orderly - our farm this year is pretty representative of our farm next year. Or the building that hosts the market is predictable - the building today accurately portrays what the building is likely to be tomorrow, with maybe only small changes. 

The order comes from self-perpetuation. There’s always something driving the system forward, so that like makes like. 

But the environment around the bugs is not predictable. Tiny changes today might cascade into massive changes in the future, because positive feedback can amply things so powerfully. Look, we can see the bugs right in front of us; they’re radically altering the ecosystem around them, with all these interesting second-order effects. 

This must be what they mean by “chaos”. Tiny movements on one side of our system drive really big changes on the other, and those big changes drive other changes in our system somewhere else. 

Teacher: How would you describe the difference between order and chaos?

Student: Hmm. This feels like an important question. I want to say, based on my experience, that systems are either orderly or they’re chaotic. But given our conversation so far, I’m getting this nagging feeling like that’s not true. It feels suspiciously like calling systems orderly or chaotic is actually a symptom of cause-and-effect thinking that I need to move past.

Teacher: Go on. 

Student: Just now, when we went back to look at the bugs, the big idea we introduced was positive feedback. Like makes like. Bugs make more bugs. It’s a driving force, it’s like a sense of purpose for this corner of our system. 

When we introduced this idea, I’d initially thought of positive feedback as this driver of order; like it drives alignment in a system. When I see bugs reproducing today, I know that I will see bugs reproducing tomorrow, and the day after that. When a group of people align around a purpose, that’s a kind of order.

But at the same time, positive feedback seems so dangerous and chaotic. When something in a system makes more and more of itself, that can rip the system apart, in some way you’ll never ever be able to predict. Positive feedback is clearly the driver behind systems that tip into chaos; like crowds that panic, or non-native species that invade new ecosystems. When a group of people all suddenly align around an idea or a purpose, that can be really unpredictable. 

I’m not sure how to resolve these two ideas. But I know they both have something to do with growth, and with evolution. The bugs are a positive feedback system that makes the future both more predictable and more unpredictable. Depending on where you’re looking. Again, this feels like a cause-and-effect symptom that’s trying to fight for its life, as I’m trying to shake it off.

Teacher: I’m glad you mentioned that word, evolution. You’re on the right track. Is there anything we saw, in our travels together, that might resolve this paradox for us?

Student: Maybe. You know what, this actually reminds me of when we saw the city by the sea, that seemed so strange and advanced to me. 

That city, and the market, and the whole system around it evolved, just like the bugs evolved. That market evolved because people need to eat; and it keeps existing because people continue eating. It has an ongoing purpose.

But that’s true for farming too. The market didn’t displace farming. It emerged as a new layer on top of farming. 

Student: I’m not going to try to work out the new steady states it might find right now. But I’m sure that as the market evolves and settles into its own steady state layer, the Farming Layer next door responds by finding a new steady state of its own, and adjusting its purpose. That must have been an interesting transition. 

Teacher: You’re on the verge of completing this breakthrough, and I want to push you here. I want you to think about that phrase from before: Order emerges in the steady-state of non-equilibrium systems. Remember that? It was a big concept. 

Now I want you to take that concept and tell me about the bugs, and how the bugs evolve.

Student: Ok, the bugs are a layered system, just like the farm and the market. Their metabolism, cells, tissues, organs, and individual selves are all layers with their own systems in steady state. And beyond the individual bugs, they organize into communities, they make tools, they invent technologies. Each of these layers is its own system with its own purpose, but they all exist in context with all the other layers.

So maybe we should attach an addendum to our motto: if order emerges in the steady-state of non-equilibrium systems, then evolution is about emerging, multi-layered steady-states of non-equilibrium systems. 

Teacher: I couldn’t have said it any better myself. 

Student: I think I suddenly see my problem. It’s a problem with the way I’ve been using language, and with the words “predictable” and “unpredictable”. I’ve been using the terms in my old cause-and-effect way, like A predictably causes B and then C, or A unpredictably causes “?”. The problem was the word cause!

I see now. That’s why you asked me to critically examine the words “predictable” and “unpredictable”. I guess I didn’t totally pass that test; I still had some lingering cause-and-effect symptoms. I’m not 100% sure how to recharacterize them, though. 

Teacher: Maybe we can find a good definition together. Tell me: where do layers come from? I think you already know everything you need to answer this.

Student: When I think about the market as a layer that emerged; it came from positive feedback. The market is a great idea; so it perpetuates itself, and makes more of itself; and copies itself across many different cities and use cases. So at some point it made sense to think of it as its own layer, distinct from farming; and to think of it as having found its own steady state. 

And when I think about the bugs, I guess the lowest “layer” I can think of is probably the bug’s metabolism; which is basically the part of the bug that eats, in a really primitive sense. But at some point, some aspect of the bug’s metabolism started making more of itself, and more and more, and at some point it made sense to call that a “cell”. And then as the cells made more of themselves, they became organ systems and then bugs and then bug communities. 

So I guess where you were probably hoping I’d get to is, new layers emerge from positive feedback cycles. When something persists, and makes more and more of itself, and starts to significantly change the system around it, it starts making sense to call it its own layer. And that’s useful for us, because we can try to understand that layer as its own little system that’s found a steady state, or is on its way to finding one. 

Teacher: So, when do you think a layer officially becomes a layer? What gives a new layer its identity, as it comes into its own?

Student: I mean, I’m not sure you can formally draw a line and say, before this line it’s not a layer and after the line it is. But I can say this: before crossing the line, there’s no way to know what the layer will be. And after the line, we know what the layer is, because the layer has a purpose. 

We can say, the purpose of the farming layer is getting nutrients into plants and plants into harvests. The purpose of the market is getting harvests into baskets. The purpose of the bugs’ metabolism is consuming calories. The bugs’ tools have purpose, and their community has purpose. Every layer has a purpose.

And we know these layers have continuous purpose, because they’re not in equilibrium. They flow. That is their purpose. And we know they’re out of equilibrium because the layers around them are out of equilibrium. All the way down to the bug’s metabolism; or the plants’ nutrients; and then if you go down further than that, all the way down to the furnace of the universe. 

Huh. 

Oh wow. 

The student fell silent for a minute. 

Student: Okay. Let me collect my thoughts for a second. But I think things fell into place. I think I understand the Four Rules now. 

Taking next week off, but when we return, we’ll open the door and see how Dancoland works.

Before we head out, here’s this week’s Tweet Caption Contest:

Congratulations to Reid C., from Palo Alto, for submitting the winning caption:

“Fish stocks only go up.”

Our current finalists, for voting consideration:

“This return to the office policy is a real drag. See you tonight, darling!” -Friedrich, from Toronto

“Hurry up and fix it, we need to beat Bezos!” -Ahan, from SF

“Hey there! We were just reading your tweets.” -Claire T, from USA

Vote here

And finally, this week’s Tweet:

Submit Caption

You can vote and submit your responses here.

Have a great week,

Alex

Welcome back to Dancoland. If you missed part one last week, read that first.

Teacher: Ok, let’s examine some of those things you told me about your village. I’d love to see what assumptions are behind them. 

Student: Okay, so like I said. When I look at all of these building blocks of what I know - how fast we walk, how we farm our food, and how big our village is - I see some pretty clear cause-and-effect relationships. A) this is how fast we walk, leads to B) how far out we can reach each day, leads to C) how large a harvest we can collect, leads to D) how large a village we can support. If it gets any bigger, we go hungry. Pretty clear to me. 

Teacher: Thank you for helping me see your thought process. You’re certainly more familiar with your village and your farming than I am. But I’m skeptical that this system actually works the way you’ve presented it. I have a strong suspicion that you’ve told me a just-so story. 

Student: What’s a just-so story? 

Teacher: Whenever you hear an explanation for why a system works the way it does, with a neat, tidy conclusion, and you hear an answer that goes, “because it is just so.” 

As an example, you may have wondered: why do Giraffes have such long necks? If someone told you, “because that’s precisely how high the leaves on the savannah trees are”, that is in fact a correct observation. But you ought to be suspicious of any kind of cause-and-effect conclusion you draw from that. Both the giraffes and the trees live inside a system. Same with your village: your crops, your harvest, and your villagers all exist inside a system. 

Student: I’ll certainly agree with that. There are lots of factors in my village that go into the details of how we farm. I’ve learned about them over the course of my life. But I don’t see what’s in conflict with my cause-and-effect conclusion I laid out for you. 

Teacher: Maybe I can show you something. How about we redraw our food system, except instead of focusing on causes and effects, first we start with something simple: what’s flowing through the system. 

Let’s zoom out and ask: what is the basic thing that flows all the way through the system, from the beginning to the end?

Student: I’d say it’s nutrients. At the very beginning, energy and nutrients exist out in the world. And at the very end, they’re consumed by our metabolism. That’s the biggest picture I can think of, anyway. 

Teacher: Very good. I think that’s correct. We don’t need to worry about how the nutrients got into our environment; and we don’t really need to worry about what happens after we burn them as calories. So let’s draw those as clouds at either end of our system, with a pipe that flows from one end to the other. This is our starting point.

Now, what’s the next thing we can add to our system? Do these nutrients accumulate anywhere in the middle? 

Student: Sure they do. I can think of at least two places. First, they accumulate in plants, in our crop yield that grows every season. And then after we harvest them, they sit around, before we steadily consume them. 

Teacher: Looks good to me. These two components you’ve drawn here have names - flows, which flow at a certain rate, and stocks, which accumulate in a certain size. Stocks and flows are the basis for good system thinking. So we’re making progress.

Now why don’t we add in those variables we talked about before - our walking speed, and our village size - and let’s see where they land. 

Student: OK. So, as I said before, our walking speed influences how far out from our village we can cover. So let’s draw that in. And our area covered matters, because our area times our crop yield equals our potential harvest.

The other thing we’ve got to include here is our village size. Our village size affects the rate at which we can harvest our crops, and the rate at which we eat them. 

And now we’re back to my point I was making earlier. Our walking speed affects our coverage area. That affects our harvest. And our harvest size affects our villager population. 

Teacher: Again, I’m not challenging your claim that these factors are related to each other in some way. But what I am challenging is the idea of it being a fixed cause-and-effect relationship. 

How about I ask you a different question. If your walking speed and coverage area is so critical to your well-being, why don’t you do something about it? For example, why don’t you have horses in your village that help you move around faster?

Student: That’s a great question. We’ve actually thought about this a lot. Let me show you by drawing it out in our system. 

If we added horses to our village, they would increase our coverage area. But they also eat food; way more than a person does. We’ve done the math on this: adding horses to our village, even if it increases our crop coverage area, wouldn’t actually increase the population size we could support. Without enough food, it puts pressure on our village population, or our horses, or both.

Teacher: I’d like to highlight something important you’ve drawn here, which are these dotted lines looping back that keep the villager and horse populations in check. I’m glad to see you include this: this is negative feedback. It’s how systems find a steady state, and remain there consistently. 

In this case, the negative feedback loops keep the horse population at zero. And that matches with your real life experience, which is that the village doesn’t have any horses - and from what you’re telling me, has no interest in getting any. 

Student: That’s right. Horses consume more net harvest than they create, so they don’t make sense. 

Teacher: Let me challenge you here. Remember when we met the nomads? They had a lot of horses. Any idea why?

Student: They did, that’s true. I guess they live in a different environment than we do. We’re in the fertile valley, whereas they’re up on the steppe where it’s cold and plants don’t grow very fast. So the rate of nutrients flowing into crops is much slower. Let’s draw that in. 

Aha, I see it now. Since the nomads are in a harsh environment, their crop yield per acre is lower than ours, so they need a bigger coverage area to get any harvest at all. So they have to have horses. And although that doesn’t leave much food left over for a large village, they at least get to eat something. Their system is different than ours; and they’ve found a different steady state. 

Teacher: So what does this tell you about the relationship between your walking speed and your village size?

Student: I see what you meant before about it being a “just-so story”. It’s not a cause-and-effect relationship like I’d believed before. The system is circular; it doesn’t start or end anywhere. But what matters is that we’ve found a steady state for our food system, and the nomads have found a totally different steady state for theirs. And when we saw the big bustling city, they had their own steady state too; that supported way more people, and I’m sure worked quite differently than either of ours. 

Teacher: Let me ask you something. You’re using this word, “steady state”, that’s an important concept. Why are these systems stable? 

Student: Well… at least for us, our system is stable because we’ve always got to eat. If we stopped eating, then this whole system would go away and eventually be forgotten. Our food system persists because it’s never at rest. There’s always flow going through the system, because we’re always hungry. Same goes for the nomads, or anyone. 

Teacher: You’re a quick learner. That’s exactly right. These systems are stable and orderly, year after year, not because they’re at rest - but because they’re continuously flowing. This is an important foundational concept: order emerges in the steady-states of non-equilibrium systems. 

Student: Order emerges... in the steady-states... of non-equilibrium systems. I think I get it. The driving purpose of our orderly food system, with its predictable flows and stocks, is that we always have to keep eating. The fact that our system is out of equilibrium is why it’s stable. That’s counterintuitive. But it makes deep sense to me. 

Teacher: That’s why it’s so important we started our system diagram with those two clouds on either end - the nutrients on one side, and the metabolism on the other side. If those didn’t exist, and our system were completely closed, then eventually it would find some equilibrium point, and stop flowing. And, especially when it comes to metabolism, you don’t want to be at equilibrium. That means you’re dead.

Student: But hold on. Some things must be stable and at rest, though. Like, what about this big building that the city market is in? That’s pretty inert. It’s not part of some living system; it’s just a building made of bricks. It sits there. 

Teacher: Are you sure? Why did it get built? Who takes care of it? Why has its identity remained consistent over time? Why do people keep coming back to it every day to market their food, with consistent purpose, for so many years?

Student: Got it. So if the building were actually at rest, it would be ignored, and would eventually crumble into dirt. But it’s not at rest. The building is stable because people actively maintain it; because it has a purpose. It’s part of a system that keeps flowing, called “the food market”, which is the people who are motivated to keep going there, and keep rebuilding it, and keep it in a consistent steady state and consistent identity for years and years. 

And when we look at really big systems that seem so consistent, like the sun always rises every morning; that’s pretty consistent everywhere. But that’s just a big, old system we’re all inside, that’s in a really reliable steady state. That’s why it’s so predictable to us. 

Teacher: You’re absolutely right. 

Student: I just realized something. The first time you showed me the great big world, I thought you were teaching me that I was thinking small and I needed to think big. I felt like I was small and silly for only seeing my little boxes, and that by learning the laws of the universe I became wiser and enlightened. Now I realize that this wasn’t really your lesson at all, was it. 

Teacher: That’s right. 

Student: What you were actually showing me, by revealing all of the different people and places and practices out in the world, is how many different systems there are, and how differently systems can organize themselves, yet still be in equally valid and happy steady states. 

So it’s not like ‘inside the box you’re dumb and outside the box you’re smart’. There are actually an infinite number of different boxes and arrangements and possible systems that could emerge in the world. 

The point of exploring them is to understand their diversity, and the infinite number of different ways they can find steady states, not some deterministic laws of cause and effect. And when you bring along all of your backpack of knowledge, full of experience from systems you already know, and then explore a new one, you gain some remarkable new perspectives. 

Teacher: It’s a real gift, isn’t it. Once you see it that way, the whole world opens up as a place of infinite possibility, rather than defined boundaries.

Student: Yep. But there’s something I don’t understand yet. And that’s why you showed me the bugs. 

Come back next week, when we’re going to talk about the bugs.

Before we go, here’s our Tweet Caption Contest for this week:

Congrats to Max T. from Seattle, for his winning submission:

“I am the Lorax, and I speak for the trees.”

Our current finalists, for your voting consideration:

“This is a finally balanced, self-regulating and adaptive ecosystem.” -Beevis, from North America

“Fish stocks only go up.” - Reid C., from Palo Alto

“I told you to wear a mask!” -sanc909, from the UK

Vote here

And finally, here’s this weeks’s tweet, awaiting your submissions:

Submit Caption

You can vote and submit your responses here.

Have a great week,

Alex