The reason the Slack IPO made such an impression on me – why it ranks in my early-20’s RAM alongside highlights like “doing molly at Elsewhere” and “dates with forward-deployed Palantir engineers” – is because it was the first time I began to realize the power of San Francisco as a cultural export machine. More specifically, it was the first time I considered that narratives can be distributed not just in the form of “the algorithm”, but through business imperatives. Are you born into the religion (algorithmic social feeds), or does a local prophet (your boss or head of procurement) seek counsel from an oracle (VP of sales) and deliver the Solution (Slack) to your lost community of white collar employees with their own bespoke remedies for problems that are instantly resolved 100% faster with the glowing out-of-the-box solution? I began to understand that software was a cultural export: a vision for how firms should run, seen through the lens of Silicon Valley.1

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It was also the first time I really considered tensions between the visions of Silicon Valley and Wall Street (and by extension America, and by extension, the rest of the world). These tensions of course have metastasized into much larger issues with much bigger implications than those represented by chat software for coordination. But you have to start somewhere.The IPO was a proposition about who workers were. Sure, Slack was positioning itself as a replacement to email (something Wall Street and everyone else uses, obviously), but it was also a harbinger of a coming organizational shift, where developers and engineers would wield increasing influence in the office.

Slack's focus on developers at the time deserves attention. At their investor day presentation, management emphasized not just the number of DAUs (10 million) but the fact that developers constituted a significant portion of that population (more than 500,000, who had created 450,000 custom plug-ins for Slack in total). The subtext was that there was a secret but growing cabal of developer-first organizations (88,000, if we count by paid customers), and these companies were extending tech’s influence by cross-pollinating other tech platforms (like Atlassian, GitHub, PagerDuty, and Zapier) directly into chat. Slack was where developers would chat and the products they built would interoperate. It was kind of a microcosm for San Francisco.

If a decision to invest in an IPO could be framed as a question around belief, the question of whether or not to invest in Slack might have been whether you thought developers would comprise an ever-increasing portion of the workforce, and if they could make Slack itself more useful by leveraging their integration-seeking prowess to turn the product into a platform. Even if you, as a firm, might not personally experience all the benefits of Slack, you could squint and see how a growing number of engineering grads, an infusion of VC money into SaaS companies, “digital innovation” efforts undertaken by Fortune 500’s, and shifting preferences toward casual communication and away from the staid formality of email could create the right environment for Slack to be extremely successful. Slack’s vision was a pretty good one, except there was just one problem.

The same year that Slack IPO’ed, Microsoft announced that their Teams product was on its way to 13 million DAUs (Teams launched two years prior in 2017). While Teams also supported third-party integrations, the focus of course was on a tighter integration with Microsoft’s core products like Outlook, Word, Powerpoint, and generally owning the touchpoints between an office-worker and their tools. The market eyed this warily: Slack was pretty much the only COVID-era SaaS stock that wasn’t buoyed by the work-from-home thesis. By December 2020, Teams had 145 million users. That same month, Slack was acquired by Salesforce for $27.7 billion, when it had 12.6 million users. Stock ticker poetry: WORK acquired by CRM.

And so, another popular Valley narrative got airtime: the one where Microsoft uses their headstart on hardware and business license distribution to ruin things for everyone. In the mid to late 90’s, Microsoft kneecapped the proliferation of Netscape and other browsers because it tied the distribution of its Windows operating system to Internet Explorer. In the late 90’s, Microsoft pissed off Eric Raymond (of “The Cathedral and the Bazaar” renown) and pretty much every developer in the open source community when their strategies to spread FUD and undercut the adoption of Linux became well-known. In 2020, Slack filed an antitrust complaint against Microsoft. More recently, reports emerged that OpenAI is considering suing Microsoft for anticompetitive behavior, ostensibly related to the (now scuttled) Windsurf acquisition, but likely more motivated by OpenAI’s desire to onboard more cloud hosts for its API, like Google.23

There are two ironic things about the way the Slack saga played out:

1. Microsoft arguably was the original creator of the developers” meme. Credit where it’s due.

2. Slack was probably right about the influence of developers. Just not in the way they thought (more on this later).

In the Hands of the Unintended

A good indication of a technology’s rate of proliferation is how fast it ends up in the hands of people it was not intended for. Reddit was (allegedly) for incels and memestock traders, but it’s now $40bn market cap company that everyone uses or at least visits. In their S1, Figma touted that 2/3 of their customers are non-designers. Push notifications were originally a tool invented by BlackBerry developers to save people time while checking their phone; now they’re used by viral growth hackers to command your attention. China manufactures drones that end up in the hands of Russia and Ukrainian fighters. Before Slack itself was a chat product for teams (and then, a growing number of developers) it was internal chat for a game the original Slack team had built pre-pivot.

A similar rule of thumb can be applied to narratives: the stories that captivate us the most are ones where the things that were never “supposed” to happen do. Brexit and the 2016 election. The collapse of SVB. Apple leaning on OpenAI to power AI-enabled Siri. The release of DeepSeek, and China generally overtaking the US on tech.

I’d argue that Silicon Valley’s most successful export is the narrative that we need more developers. Or at least needed them. You can trace a narrative throughline between “Software is Eating the World,” to the Obama-era “Learn to Code” initiatives which saw reinforcement from the likes of Bill Gates, Mark Zuckerberg, Jack Dorsey, et al, to a 2018-era report from Stripe arguing that developers could contribute $3 trillion to the world’s GDP over a period of 10 years4, to a 2019-era Biden telling coal miners (?!) to learn to code. It was the economic mobility panacea of the 2010’s. And this was arguably the narrative that led to Slack deciding to focus on “developers” as a key user cohort when they went public in 2019.

Then, in 2022/2023, cracks started to appear. The ZIRP era ended. AI models got better, leading some to believe a hollowing-out of the middle layer of developer talent was either imminent or already underway. This chart, highlighting a slowdown in developer hiring, made the rounds a few weeks ago.

And this story made the rounds on X over the weekend.

People are choosing to interpret this in different ways. In the “hysteria” camp, we have the folks who argue that students have been lied to, that opportunities are drying up, that AI is taking everyone’s jobs, and that time is limited to “escape the permanent underclass” (and that the equally dark Janus head of “permanent underclass” is “getting oneshotted.”). In the sanguine camp, we have the argument that new job postings are generally declining, and that the decline in jobs at traditional firms might actually be a good thing: it gives younger generations the opportunity to be scrappy, build opportunities for themselves, and perhaps even escape the one thing worse than the permanent underclass: a lifetime of being stifled.

When the “clear” narrative of the past era deteriorates, you get competition for the new dominant narrative. So between the above two, which one is right?

Using the Occupational Employment and Wage Statistics data from the BLS (insert obligatory “can we even trust that data?” caveat here) I charted out a quick look at the number of Americans employed in “Computer Occupations” in the United States from the same period as the FRED chart above. The data for 2025 isn’t available yet, but trends do seem to confirm the former camp: in 2024, the number of developers employed in “Computer Occupations” declined in 2024.

So what about the sanguine camp, and the narrative that scrappy devs are building apps with small teams of other developers or commanding a small army of agents to do their bidding? Is there any concrete data that tells us about the strength of these narratives, or do we only have Paul Graham posts about founders writing 10,000 lines of code to point to?

We may not have government-sanctioned data, but there are still ways to try to directionally assess this. In fact, we can keep things in the YC family, and look at aggregate post data from Hacker News, the Reddit-esque forum focused on startup founders. In 2021, Hacker News made their dataset available on Google’s BigQuery to anyone looking to run analysis on it. With the help of Chat-GPT, I ran a query to determine the number of posts on “Show Hacker News” that link out to GitHub, as an attempt at proxying the number of “solo” or “small team” developers who are actively working in public. If you regularly look at Show Hacker News, you’ll know that the activity in aggregate is mostly developers looking for feedback and early distribution on their passion projects, rather than people sharing repositories of large, foundation/corporate-sponsored open source software. So it might be a decent indicator of whether the number of solo developers is increasing.

Funnily, this chart almost looks like the inverse of the FRED “Software Development Job Postings” data above: a trough in 2021-2022, and a steady rise starting in 2023 (right when Chat-GPT was released).

While this data just captures the level of social excitement around public GitHub repositories (so it doesn’t capture small teams of developers who might be building closed-source consumer applications or any other kind of software product), here are a couple of ways that one might interpret the data:

• As the amount of new jobs in software leveled off, developers decided to get creative, and started building projects that could one day turn into companies. While Chat-GPT and other LLMs made it possible to do this faster, and at a higher degree of volume than before, the idea that you can turn a repo into a company has been around for awhile. As an example, Segment, a customer data platform, started as an open source repository which launched on Show Hacker News in 2012 (it later sold to Twilio for $3.2bn). Maybe there are a larger number of people attempting this now, and these people are okay with smaller outcomes (this might be best captured by the “indie hacker” movement, or the idea that you can have a good-quality lifestyle building software products that allow you to live on $100-200 ARR annually). I’m arguing that open source software here is a proxy for these wider trends (not all open source projects monetize, but some do have a freemium business model).

• LLMs also have second-order effects. There’s a growing demand for open-source frontends that allow developers to coordinate and tweak how they interact with models: if you look at HackerNews every day, you’ll see that many of the projects are frontends for orchestrating how developers interact with LLMs. Emboldened by AI, developers are creating more software and releasing it publicly (even if the underlying models that enable these projects are closed source). Some of it is long-tail software, for an audience of one. But some of it will lead to new companies built around open source projects that reach enormous scale. As just one example: Ollama, a platform for running LLMs locally, ranked among the top ten open source projects on GitHub by commit count.

• Maybe this is unsurprising: “developer” is just as much a job title as it is a skillset. Even if jobs that are captured by FRED or BLS-level data go away, the number of developers in the world actually only ever increases (as new developers graduate from universities). These developers want to do something: keep their skills sharp, prove their value. More software gets built publicly as a result.

Let’s say that the developer meme was a “bubble” that reached its peak sometime around 2022-2023. Even if developers aren’t seeing the same level of demand at companies anymore, they’re still developers. So the “bubble” is very different from financial bubbles: the core asset of the developer hasn’t gone anywhere. What does this mean? People are going to find new ways to build, share, and monetize their work.

Again, the Show Hacker News data is just my attempt at designing a proxy for the larger trend of solo developers building projects. I know it’s not perfect or definitive. But it does point at a kind of directional “truth” that gives some legitimacy to the sanguine narrative camp: the developers are building. Whether or not they’re part of large companies (the kind that uses Slack, Teams, or otherwise) is another question. But they’re definitely not going anywhere.

Coda

A few years ago, I visited my brother in San Francisco. While we were walking along the Embarcadero, he made an observation that, as he framed it, was a metaphor for the Silicon Valley condition: “Mission and Market Street don’t intersect.” Geography is destiny: Silicon Valley creates the narrative, and it’s up to the rest of the world to embrace, reject, and/or warp it as they see fit.

Somewhere, there is a parallel universe where Stewart Butterfield is conducting Slack’s second quarter earnings call. The amount of developers using Slack is one of “The Numbers” that Wall Street pays close attention to: it’s their indication of the platform’s stickiness, utility, and overall influence among its customers. Marquee customers are rolled off at the top of the call: “We’re proud to say that everything runs on Slack at OpenAI.” Cursor uses Slack. Linear uses Slack. And then Stewart gets his first question: “We’ve been seeing reports that the rate of developer hiring is slowing down – what does this mean for Slack’s users? Are you starting to see a softening on your end? Stewart takes a deep sigh and begins. The story isn’t quite what you think.

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I read the final pages of Vanishing World while flying to Orlando from New York with my boyfriend, where we picked up a rental car to drive to Daytona for a CrossFit competition. The drive into Daytona was a reminder that the real world is not vanishing. We stopped at Target for peanut butter, a coffee brewer, and calorically dense snacks to sustain my boyfriend during his two-day, six-event competition, and passed other happy couples in the aisles lined with SharkNinja ice-cream products. In the same strip mall, we ate at Chik-fil-A for dinner and were greeted by the friendliest teenage cashier I’ve ever met in my life, who was either oblivious or impervious to the franchise’s political controversies. In his spare moments, the cashier walked around to tables with nuclear families, offering refills of drinks and condiments, and slipped me a free cookie because it was (to his shock) my first time at the establishment. Cue: “I fucking love America.”

When we arrived at the Daytona hotel where we were staying, which was already teeming with muscled men and women with intimidatingly strong thighs, a bellboy eyed our Target bags sagging with Core Power drinks and remarked that he was behind on his protein intake for the day. And this comment was kind of a good representation of what being at a CrossFit competition in Daytona is like: men sizing each other up, eager to flaunt their triumphs, and humble about their imperfections. You could record the voice modulation of a bellboy saying “I’m behind on my protein intake,” a CrossFit athlete saying “I could’ve done one-more muscle-up but I forgot to chalk my hands” and a football player saying “they had us in the first half, not gonna lie” and have enough context to teach Chat-GPT about humility.

Before I started dating my boyfriend, I thought CrossFit was just strength training with excellent branding. I now know, after months of observation, that CrossFit is for the kind of person who likes to deadlift 200 pounds for 10 reps, then follow it with some clean-and-jerks (lift barbell from floor to shoulder, then overhead) a climb up a 15-foot rope, all chased with a handstand walk across the gym. Do 4 rounds of that in under eight minutes, and that’s a CrossFit workout. CrossFit is not for those who yield to despair. It faces a high attrition rate (and that’s on top of the deaths that have occured during some elite-level competitions). About once a month my boyfriend will say, “Do you remember [so-and-so] from the gym?” Slight nod. “Yeah well. He just quit.”

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As a spectator, you expect to attend a CrossFit competition and walk away with a quip like “if the entire arena were wiped out, there would be a perceptible concurrent 1% drop in Joe Rogan’s Spotify rankings.” But the more interesting politics present at a CrossFit competition is not whatever podcast might be streaming out of a participant’s airpods (and realistically, they’re probably listening to something high-BPM, if anything at all). It’s who’s there with them: namely, a coterie of girlfriends, wives, and babies, who are all adoring and stoically unflinching at the sound of weights crashing to the ground.

So the proper way of looking at a CrossFit competition is “if the entire world were wiped out except for this arena, there would be a 99.99999% drop in the population. But the remaining .00001% would heroically repopulate the earth with the most robust, well-sculpted specimens of the human race you’ve ever seen.”

Going to Daytona (or anywhere in America that isn’t NYC, SF, or LA) reminds you of the bubble you live in. People in finance hire firms to perform channel checks on their equities: how many cars are curbed in front of Target (or how many SharkNinja products stocked at Target are leaving the shelves)? Leaving New York feels like doing a channel check on reality. In Manhattan, you get $4.99 food delivery fees. In Daytona, you get an IHOP waitress who insists on topping off your free coffee, and handing you a to-go cup to wash down your $4.99 pancakes on the drive home. After a visit to Bass Pro Shop, my boyfriend remarked how it was designed as a whole-family experience: the kids can entertain themselves watching enormous fish in the aquarium, the moms can clothing-shop for the household, the dads can bolt upstairs to handle the guns and the hunting gear. New York has nothing that comes close, not even Coney Island. Land developers could solve New York City’s housing crisis if they studied Daytona’s ability to fit a Borgesian number of cars in a walkup-sized garage.

There is of course comfort in living in a bubble. But being in Daytona left me with an unfamiliar feeling: relief at having left mine. It’s difficult to be anxious about birth rates when the table next to you is full of toddlers messily eating hot dogs and french fries from the kids menu. It’s hard to take headlines about the decline of masculinity seriously when you’re watching men compete in CrossFit events with advance-the-gene-pool-forward names like “Helix” which involve performing Olympic snatches with barbells of increasingly heavy weight. When those same men embrace their wives and 1-3 babies after completing their competition, you’re not thinking about the disappearance of the nuclear family.

My boyfriend says that part of why he loves CrossFit is because of the ardent commitment of its participants. This commitment permeates everything: their willingness to wake up at 5am and burn 2,000 calories before dawn, the discipline to push through exhaustion, and the compulsion to do it all over the next day. You can squint and see how a family fits neatly into this schedule: the 5am wake-ups can accommodate a crying toddler, the discipline can structure a household, the compulsion creates enough momentum to sustain you into meeting your grandchildren.

This level of commitment is a choice. And it’s hard. I’ve gone to the gym many times with my boyfriend, and have even done some “CrossFit-style” workouts in lieu of my usual comfortable jogs. There is a line you cross from lifting something heavy, to lifting an amount of weight that is so fear-inducing that you feel like you’re watching yourself get dismembered in a horror movie. On days like that, your body isn’t even particularly sore, but there is a mental agony that persists hours later as you stare at the wall with half-chewed Chipotle in your mouth while you wonder “what the hell did I just put myself through?” There is no post-workout glow, and the Chipotle doesn’t taste any better than usual. The reward is much more subtle. Doing hard things is a reminder you’re capable of doing hard things. That knowledge is a sentence to doing more hard things in the future.

Last year, when I was 28, I went through a breakup. It was one of those we’re-living-together breakups, which meant that suddenly we weren’t anymore. Which meant that I needed to move all my belongings out of the apartment, and say goodbye to a former life and some version of a future I had been quietly imagining for myself. There was packing, and some tearful calls to friends and family. There was also, I’m not proud to say, a sudden obsession with Pew studies on the average age of first-time mothers (27) and US Census figures on the average age of marriage (somewhat older at 28), which I remember checking and refreshing on my laptop while I packed, as if the numbers would recalibrate more favorably whenever I filled another suitcase. For the first time in my life I felt behind, and that terrified me.

But I could find some forms of consolation in the numbers. First of all, I’m a New Yorker, where the average age of marriage skews older by roughly three years (31.4). I’d have been considerably more despondent as a Utah resident (average age 26, thank you Joseph Smith) or if I happened to be repatriating to Colombia (average age 18). So I could use Pew and the other studies I had begun to accumulate as a source of placation or defense: the statistics proved I was still within the distribution curve.

Or perhaps the statistics were telling me I was simply part of a growing plurality. After all, the numbers also say that a record-high share of 40 year olds in the United States have never been married (25%). And data tells us that people are having less sex, fewer children, women are fighting for an increasingly dwindling number of marriage-worthy men on dating apps, and things might be especially dire if you happen to be an ambitious woman.

In retrospect, I think the one of the most dangerous aspects of these kinds of stories and statistics is the fact that they are reported at all. This is not an argument that we should bury the data and tell ourselves halcyon fictions. But I do think that these stories turn themselves into self-perpetuating truths by giving people an excuse: if you’re told that staying single and childless is normal (albeit, still mostly undesirable and frowned-upon) you’re probably more likely to resign yourself to it. There was a period last year, before I met my current boyfriend, where I consoled myself with stories about the involuntarily single women whose ranks I was convinced I would be among forever. Ironically, the easier thing to do is resign yourself to the idea that sociological trends have doomed us to being alone forever. The harder thing to do is stay on the dating apps or join the run club or freeze the eggs or just be patient.

I am convinced that people are making themselves miserable at the margin by allowing themselves to be susceptible to these stories. Did fertility rates in the US and China start declining in 2008 solely because of the release of the iPhone and it’s concomitant endless stream of notifications and solo-enabling entertainment, or because of what people are seeing on their phones – namely a constant stream of articles, Twitter posts, YouTube vlogs and TikToks about how hard dating is, how crushing parenting is, how lonely life is? Maybe it’s a mix of both, but the latter feels underexplored.

We all have a choice about which world we want to live in. I was deeply disconcerted by the ending of Vanishing World, the sexless-future book I finished on the plane to Daytona, just as much as I’m disturbed by the complacency that people now talk about the world of dating and families. The world I saw in Daytona felt considerably happier, but admittedly looked harder: the participants in the CrossFit competition were sweaty and exhausted, the moms looked tired, and I noticed an absence of baby-changing stations in the women’s room. But amid all that hardness, there was also palpable love and optimism. That’s the world I want to live in.

For the flight back to New York from Daytona, I picked up a copy of the Wall Street Journal and flipped through the Business section. I felt myself slipping back into another version of the real world. On the front page, there was a feature on how Dine Brands, the parent company of Applebees and IHOP, plans to integrate AI into their operations through personalized ordering and in-restaurant camera surveillance for cleanliness. The Delta seatback flaunted a new capability that enables you to log into their inflight entertainment system, and select your favorites among their movie and TV offerings, which can persist through every flight you take in the future. My boyfriend fielded work calls for his job in private equity. Our familiar tech and finance bubble was beginning to re-envelope us.

And of course there were kids and parents behind us, fresh from their pilgrimage to Disney World, and returning with us all to the real one.

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Thank you to Sam for inspiring this essay.

I’m talking instead about the regular cadence with which the team at the Ramp Economics Lab, led by Ara Kharazian, publishes data about topics ranging from tech firms’ adoption of AI to shifts in digital advertising spending. This data is sourced from Ramp’s corporate card spend data, which of course they have access to, but are in no obligation to publish in any format. A Ramp run differently could do what other brokers of spending data do, and charge businesses hundreds of thousands of dollars (in some cases millions) for the favor of surfacing proprietary insights into how businesses and consumers are spending money. A portion of Visa’s $3.2 billion “Other” revenue earned in 2024, for example, was generated by this exact service. Ramp is publishing (at least some of it) for free.

These reports are fascinating artifacts of standalone research. In the AI Index, we learn that OpenAI dominates other model providers like Anthropic, xAI, Google, and DeepSeek in terms of firm-wide adoption (a little more than 41% of businesses that use Ramp’s corporate cards also pay for AI, led by OpenAI at 33.9%). Notably, for the first time there was a flatlining of AI adoption in May; despite this slowdown, the US government is likely dramatically underestimating AI model adoption, as evidenced by the fact that they report only 9% of firms spend on AI model subscriptions. Either that, or Ramp’s customers are just way more tech-native than the average business, which in itself is a significant finding.

When Ramp published their first data deep-dive (a fun look at corporate spending on booze at business dinners) I remarked that companies are now behaving like unofficial offshoots of FRED or the US Census Bureau. This isn’t just a Ramp phenomenon though: any company that reaches significant enough scale, and has a birds-eye view into user behavior can publish this kind of information. Some do.

I’m beginning to think of this phenomenon as creative transparency. There are lots of different motivations for creative transparency: a company wants to dick-swing a bit, and demonstrate just how valuable the trove of data they’re sitting on is (the range of insights Ramp is sharing makes you both grateful that they’re being so forthcoming, but also makes you wonder, “what aren’t they telling us?"). It’s also a way to insert your company into whatever talking point du jour is trending. Ryan Petersen, the founder of the shipping company Flexport, engaged in creative transparency when he posted screenshots of their internal tracking system, which served as particularly salient fodder during the heyday of Trump’s tariff impositions.

Creative transparency is emerging when the act of amassing large swathes of user data is no longer a source of shame for large tech companies. While Meta, Google, and other tech giants of the past cycle are still reeling from the latent trauma of being dragged in front of Congress and lambasted for everything from being a monopoly to unprovable responsibility for the outcome of the 2016 election, a new era of tech companies view the data they have on users and customers as information in the public interest. Perhaps that’s why we’re seeing more of it now.

I wanted to trace some kind of origin story for creative transparency, so I reached out to Ara from Ramp, who very gamely agreed to go on a walk through downtown Manhattan and explain how he ended up doing this kind of work. It turns out that “this kind of work” started when he was employed by Square, which had already cultivated a practice of compiling internal company metrics for journalists who asked for data to illuminate trend pieces about culture and economics. The success of those kinds of stories (including a popular story on fidget spinner sales) prompted more publications from Square itself, which Ara took the initiative on. “We started releasing a lot of economics work, specifically tracking restaurant-worker wages and retail-worker wages and tips ... [the] press started referring to that and so that did very well,” Ara told me.

Creative transparency often has a political underbite, and aims to compensate for gaps in official reporting. Ara remarked that when he worked at Square, “Government metrics weren’t quick to track changes in wages when they were changing so quickly month over month, particularly at the local level.” This observation has carried forward to his AI Index reporting at Ramp. When reflecting on the divergence between Ramp’s findings on AI adoption and the US government’s (recall, per Ramp’s data, adoption stands at over 41%, the U.S. Census Business Trends and Outlook Survey reports just 9%), Ara pointed to a failure of the way government surveys are phrased to explain this divergence: “The [survey] question, ‘Do you use AI to produce goods and services?’ sounds like it’s asking if AI is being used to make widgets in your factory." Obviously, not all companies make widgets and not all companies have factories.1

The true level of AI adoption is likely somewhere between Ramp’s data and the US government’s. “Ramp businesses are not representative. They're efficient. They’re tech-forward and they’re early adopters,” per Ara.

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Other companies try to complement US government reports. Anthropic, for example, publishes the AI Economic Index, which tracks how professionals are integrating AI into their daily workflows. If you read through a recent publication from February on AI usage across the labor market, you’ll find a fairly intuitive story suggesting that queries on Claude are primarily driven by computer programmers and those in left-brained fields like math, followed by arts and media (which encompasses fields like technical writing) and education. This corroborates more colorful on-the-field reporting: the erasure of entry-level programming jobs2 (perhaps because developers using Claude and tools like it have cannibalized the new hire market for engineers) and of course newfound anxieties around students using AI to cheat their way out of an education.

To understand the impulses behind creative transparency, it helps to think about intended audiences. For a firm like Ramp, which is trying (and evidently succeeding) to establish itself as the new corporate card provider to challenge incumbents like American Express, creative transparency is a mechanism to demonstrate their deep penetration into tech-forward businesses (with the insinuation that if you are not included in Ramp’s repository of data, you are not a participant in technological progress, and you really should hurry up and become a Ramp user).

Anthropic, meanwhile, seems to be using their AI Index as a form of political lobbying, with findings that are intended to be complementary to more established forms of data like the BLS’ monthly jobs report3 (their comms team is also tripling its size, with notable incursions in DC). Anthropic’s publications allow them to steer conversations around legislation, and perhaps serves as a counternarrative to the aforementioned less-optimistic stories that have been percolating around AI adoption. Anthropic would argue that AI is augmenting programmers, not cannibalizing them, and reinforcing student learning, rather than rendering it obsolete.

I enjoy Ramp’s AI Index reports in particular because they have a juicy air of “who are the hottest boys at our high school” lists you’d see in bathroom stalls as a teenager4, except in this case the hottest boys are the hottest AI startups and the bathroom stall is the internet. In other reports, you can see the fastest-growing software vendors among Ramp card users (this month included smaller companies like Descript, n8n, and Lindy.ai). I took the opportunity to ask Ara about this choice. While I enjoy Ramp’s reports, I’m always a bit surprised by their level of candidness: if I’m Anthropic, am I really happy that everyone knows Ramp users spend 4x more on OpenAI's LLMs than they do on mine?

Ara had a different take: “I think that a business that is lagging in market share would also find it helpful to know exactly where it is in the market. It's not usually a surprise [and] even those businesses themselves often do not have that level of granularity in the data.” I thought this argument made a ton of sense: if a firm already knows it’s behind, it can use Ramp’s data to recalibrate strategy rather than feel blindsided.

Creative transparency is often positioned as an ongoing conversation: Ramp now updates its AI and Advertising indexes on a monthly basis, and Anthropic open-sourced their AI usage research to invite more outside contributions and analysis. It can also be social fodder: like the Spotify Wrapped campaign, or imitators like Strava’s recent “Year in Sport” review.

Spotify is a particularly fecund purveyor of creative transparency because they do it across multiple vectors: users and artists. While the user-level Spotify Wrapped functions as viral marketing (like telling strangers about your dreams, there is a dueling sport of solipsism and collectivism that dominates our feeds during year-end Wrapped screenshot cavalcade), their slightly less-popular annual Loud and Clear report argues that, contrary to the prevailing narrative, Spotify is growing the economic pie for artists, rather than putting a ceiling on their earnings. So Spotify uses creative transparency to speak to myriad audiences to say different things: to users, they’re glazing about the fact that we as listeners all contain multitudes, to artists and concerned politicians, they’re assuring us that they are not destroying the livelihoods of creatives.

Spotify is actually one of the few public companies that treats data as a substrate for ongoing conversation: the most prolific companies that engage in creative transparency have not yet IPO’ed. So part of me wonders whether creative transparency is a substitute for the kinds of disclosures, like quarterly and annual reports, that public companies are required to file with the SEC. If we wanted to psychoanalyze another impulse behind creative transparency, perhaps it's the desire to have a regular pulse on speaking directly to business journalists and the outside world. Especially in an age where companies are “perennially private”, because of larger VC funding rounds and the wide availability of private credit, it makes sense that companies would want to forge ahead on proposing their own narratives in the vacuum created by a lack of analysis found in more conventional avenues. Ben Thompson can’t write about your quarterly earnings if you never file them, so companies are building Stratechery in-house.

Another possibility is that there are only a limited number of companies with the introspection required to recognize what makes their datasets interesting to normal people. During our chat, Ara from Ramp remarked that "Not every company can do this. I think it's really hard to figure out what your company's data set is particularly well suited to do.” The impulse at most companies is to try to speak to trending topics, like US unemployment data. But then “you're going to forget that ‘wait a minute, my data set cannot actually answer this question effectively or credibly.”

Or maybe an alternative route of speculation is that the pace of creative transparency quickens when companies are growing closer to an IPO, and want to start broadening their audience. Who can say?

Some thoughts on the apotheosis of creative transparency

There is the “look at what our internals say about you” form of creative transparency, which Ramp and others are doing well. There is also an inversion, when a company’s entire raison d’etre is predicated on the act of transparency itself. The AI benchmarking startup LMArena is perhaps the best example of this. Let’s call this the “look what you say about our internals” form of creative transparency.

For those who aren’t familiar, LMArena is a (now venture-backed) startup that asks the internet to adjudicate anonymous “model-versus-model” cage matches (or in simpler terms, it asks people to rank their preferences of two outputs from two different models). It then open-sources the tallies in the form of a leaderboard, which anyone can peruse to understand which models perform the best across a number of tasks, including coding, web development, search, vision, and more.

The site advertises 3.5-plus million human votes on its landing page and the leaderboard it generates auto-updates every few days. Contrast that with the grandees of the benchmark circuit. MMLU ships a frozen multiple-choice exam; you get an 86.4% and call it a day, even though researchers now catalog numerous errors that quietly warp the scoreboard. Scale AI and the ARC prize run similar leaderboards with similarly game-able training datasets, where researchers can intentionally or unintentionally devise models intended to do well solely on benchmarks, and disappoint users when they fail on real-world tasks. Many have called AI benchmarks an exemplary of Goodhart’s Law (“when a measure becomes a target, it ceases to be a good measure”). Model overfitting is a well-known problem.

While LMArena itself is not fully immune to accusations of gameability (here is LMArena’s response to those accusations), I think its existence points to a kind of apotheosis in creative transparency, where the product is devoted to the exclusive act of collecting, parsing, and amplifying metrics. In other words, they are selling transparency as a service. While most companies engage in creative transparency as a marketing side-channel, for LMArena, transparency is the business model.

First Twitch played Pokemon, then Claude played Pokemon, and now AI plays itself.

Anyway, the fact that I’ve now spent the better part of a Sunday and over 2,500 words spilling ink on creative transparency is maybe the most telling lesson about its power. I was not paid by Ramp or any other company mentioned to write this piece – they all posted some interesting metrics, and which compelled me enough to write a post exploring why. My boyfriend actually noted that this essay is not an example of creative transparency, but another form of advertising, namely the free kind. We were having a conversation about the way Crumbl Cookies was able to garner countrywide-renown (and worldwide astonishment) simply by hypnotizing influencers into creating mukbang videos highlighting their consumption, and he noted that I was essentially doing the same thing, but for a corporate card startup.

I do think, at least, that producing this essay is a bit more philosophically bountiful than watching people consume 1,000-calorie cookies. Toward the end of my conversation with Ara he said something that struck me: "In my life, I don't want to be something I'm not. And I don't think my data should do something that it's not well suited to do." If it’s ever possible to find truth, it’s only achievable after sifting through a cacophony of diversions: statistics whose underlying premise is based on a poorly-worded question, newsreels with sensationalized commentary, scientific expertise with a political agenda. Bad data purveyors will twist the numbers they sit on to suit a preordained fact. Good data purveyors let numbers tell a story.

It would be naive to argue that creative has a neutral agenda: companies want you to buy their products after all. But the best forms of creative transparency have a level of rigor and honesty that make me soften to the fact that I know I’m being sold something. So maybe that’s ultimately why creative transparency appeals to me. It’s honest about what it is, and what it isn’t.

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In the 60’s and 70’s, the dominant art movement was Pop Art, and the recessive movement was Fluxus. Pop Art, the decoy, was a movement defined by commodifying the already-commodified, and resulted in (at the time) record-setting auction prices for living American artists like Andy Warhol and record-setting auction prices in the posthumous decades to come. Fluxus, the truth, treated art as an open-source instruction manual whose only finished form was performance: destroy a piano, prepare and eat a salad, cut away my clothing. There was nothing left at the end of the work, and therefore nothing to sell. The dominant decoys of every era are focused on surfaces, the recessive truths are focused on methods.

Our current era’s decoy is Ghiblification. Even more generally, it’s “fication-fication”: the idea that any sufficiently-proliferated visual style can be automatically applied to any image or prompt.

If “fication” is our era’s dominant mode, our recessive mode is a visual style I have begun to think of as paperclip-punk. First, a (written) style guide. It is imperative that paperclip-punk is typed all in lowercase: that’s how we signal it’s a movement that was named by a human typing in a rush1. To write “Paperclip-Punk” in uppercase letters would be to invite speculation that o3 conceived of the entire thing, perhaps along with other made-up phrases for plausible art movements held aloft, flying buttress-like in a schema table. No prompt brought paperclip-punk into existence. In fact the opposite is the case: the attitudes of the paperclip-punk brought the act of prompting and AI itself into existence.

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paperclip-punk is everywhere, but what does it look like and why hasn’t it been identified before? Here are a couple of visual tenets of paperclip-punk: light, bright website (no darkmode), clean exploded-view diagrams, interactive graphs, minimalist animation, font derivatives from the Satoshi, Inter, Söhne, Berkeley Mono, or JetBrains Mono families, industrial blue-print-y colors (#0050FF HEX blues, #F38020 HEX oranges), integrated real-time data and interface-level responsiveness, infinite on-hover tooltips, developer “playgrounds” or “sandboxes”, and a rejection of passivity: the process of interacting with the visuals teaches you about their underlying meaning.

To be clear, paperclip-punk is primarily a style present in web design. There are antecedents in the art and academic world: the diagram-heavy work of South Korean artist Minjeong An, Fritz Kahn’s Man as an Industrial Palace (used as the cover art for Deleuze and Guattari’s Anti-Oedipus), Edward Tufte’s formalization of the field of data visualization, archived work from Rhizome’s Net Art Anthology, some of the work hung at the (now-closed) Marlborough Gallery for their 2023 Schema exhibit in New York. But it seems, for now, paperclip-punk is confined entirely to the world of pixels.2

If you haven’t caught on already, I’ll spell it out: paperclip-punk is called paperclip-punk after the Nick Bostrom thought experiment where a single-minded superintelligent AI endowed with the benign-seeming objective function to “make as many paperclips as possible” works unstoppably to achieve this: rewriting its own source code to make itself more efficient than the engineers who programmed it, orchestrating manufacturing sites and paperclip fulfillment centers worldwide, and somewhere along the way, destroying all of humanity. I've always associated the world evoked by “you will eat the bugs, you will live in the pod” with the same cinematic universe as paperclip maximization. But paperclip-punk is a far more optimistic and subversive spin on this. paperclip-punk outmaneuvers ethical panic.

The ethos of paperclip-punk is the following: we can prod machines into self-awareness, simply by making their interfaces responsive to humans. In other words, paperclip-punk visually encapsulates a future where every single static surface is animated by digital introspection. Interface-level responsiveness is a first step toward this (the weightless, cloud-like, visuals of paperclip-punk are an implementation detail, but feel to me like the elimination of unnecessary clutter and distraction, which are an encumbrance to machine thought). It is, in a way, the aesthetic equivalent to an MCP server. It’s not a coincidence that there are no consumer tech companies that have adopted paperclip-punk visuals. It’s a style that prioritizes human-to-machine over human-to-human interaction.

Some categorization: the OpenAI website is not paperclip-punk. The World website is. Anthropic’s main website is not, but their interpretability research is. Figma is not paperclip-punk (nor is its output). Excalidraw is (as is its output). p5.js: no. d3.js: yes. Linear’s logo is not (usually), but somehow their favicon is. Pinecone is paperclip-punk-lite. Retool and Supabase are, to my knowledge, the only two companies that have pulled off the near impossible: paperclip-punk in darkmode. Open source projects like PostHog and Dify, and turbopuffer’s website are all textbook paperclip-punk. Clippy, the beloved Microsoft Word paperclip is alas, not paperclip-punk. paperclip-punk repels anthropomorphization.

Ironically, Vercel – the company responsible for the underlying web framework, Next.js, that I believe is powering much of the paperclip-punk aesthetic – does not have a website that completely embodies paperclip-punk itself. It’s close, but ceci n’est pas une paperclip-punk: the fonts are too serifed, the color scheme is too stimulating, and there’s only one interactive module: a frontend observability graph that charts views vs clicks. This may not matter, however, because a developer only goes to the Vercel landing page once, to make an account (or zero times, if you get an invite from an admin). The next time you navigate to “vercel.com” you’re automatically taken to your company’s organization overview – a series of modules that also are a hybrid of paperclip-punk and non- (Organization Overview: no. Observability: YES!).3

This actually demands further exploration – what are the technical and material conditions that have yielded paperclip-punk? As said above, Next.js might be a big contributor. React components let you reuse parts that can be inspected, hovered, toggled, live-reloaded, and re-combined. Incremental Static Regeneration and React Server Components pipe real-time data straight into the UI, so charts, dependency trees, and observability panels stay animated.

Of course, it would be misleading to argue 1) that the Next.js/Vercel ecosystem have a chokehold on the paperclip-punk aesthetic (some of the site examples I cited above do not use Next.js, per intel I sourced from BuiltWith) and 2) simply using Next.js makes your website automatically paperclip-punk (not the case, Chik-fil-A and Chico’s, for example, are both Next.js users, and no sane person would argue either are paperclip-punk). But I do think that the rise of Next.js as a framework is a key enabler for website designers who are veering in that direction. Another theory is that developers using shadcn (itself proliferated by Vercel) wanted to add some flair to the default theme, deviated, and discovered paperclip-punk.

While it probably doesn’t need to be said, obviously, the new Airbnb designs are not paperclip-punk.

One of the best examples of paperclip-punk is the Cloudflare Agents website, a subdomain on the main Cloudflare website (not itself paperclip-punk!) which advertises their developer framework for building and maintaining AI agents. Against a white background, with a twine-y SVG cloud hovering on the upper left, the header reads “The Platform For Building Agents.” On the upper right hand panel, you find installation instructions reading “npm i agents” next to the two overlapping sheets of paper that indicate something is copy-and-pastable directly into the CLI. You wouldn’t have to scroll further if you didn’t want to.

But if you did scroll further, the full sales pitch would activate itself into existence. Directly below the header, you see a panel comparing the process of “Generative” and “Agentic” prompting: on the generative side, you see a seemingly endless dialogue between an AI and a developer asking for it to submit a pull request, which grows longer and longer (almost Kafkaesque) as you continue to scroll. On the agentic side, you see the same request, and a swift submission of the PR. It’s both a humbling reminder of how far AI has come in just a couple years (it is already considered tedious to have a conversation with AI) and a great illustration of paperclip-punk in action: the smooth unfurling of the conversation, the dialogue boxes, the seductive instantaneousness. You might not even stop to notice that the text boxes are all written from the point of view of the agent, casting you in the role of the AI fulfilling requests, instead of the prompter. This is another unspoken north star of paperclip-punk: AI that is so responsive, so self-aware, that it collapses the distinction between developers and bots.

Further on in the site, there are other fantastic details. An on-hover module that illustrates the various component products of the Agent stack: Voice, Chat, and Email (all ingestion methods for prompts), self-hosted models or LLM-calling via the AI Gateway, workflows, MCP servers, and more. It all looks so simple. Lower down, there’s a comparison of static hyperscaler pricing for inference (indicated by a straight price ceiling) and Cloudflare’s dynamic pricing (a swooping sparkline reflecting peaks and troughs in inference usage). “Scale Up Or Down” (automatically). There’s something to be said about the speed with which you’re guided through the site: you can almost scroll through without reading anything, and still come away with the site’s takeaways.4 I think this speed is a key distinguisher of any true paperclip-punk design.

All in all, it’s a brilliant website. Kudos to the designers who made it happen.

It’s currently impossible to prompt an image model to generate anything resembling paperclip-punk (or at least, I’ve been unsuccessful when I’ve tried), but I am aware of an irony of this essay’s existence: any LLM can discover it. Meaning that it may now, very soon, be possible to get any image model to generate something in a paperclip-punk style, assuming this essay is ingested into training data or discovered by DeepResearch. And that perhaps is the risk of this endeavor: the moment you articulate a hidden truth and give it a name, you funnel it into a dominant corpus where it becomes yet another decoy.

This is not to sound fatalistic about the act of cultural prompt injection. I wouldn’t mind the ability to use Vercel’s v0, for example, to help me design a personal website in a paperclip-punk style, and would love to have a shortcut language to one-shot the design (I want the conversation to look like Cloudflare’s “Agentic” capabilities, not the “Generative” one!). And so, out into the world, I send this essay. It’s up to you and the robots.txt to decide what to do with it.5

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Thank you to Mason for conversations that inspired this essay

Fast-forward to the 2020’s and LLMs are either hunting in-memory for the right answer (as Ilya Sutskever and others say) or are generating the Garden of Eden, followed by civilization and an evolved formal theory of mathematics in the time it takes to complete an API call answering “what’s 3+3?” (as Scott Alexander wrote about GPT-2 in 2019).

The above is just a fancy way of saying “LLMs sometimes take an inefficient path to the right answer.” Unlike a calculator bit-shifting 0’s and 1’s (systematic, direct) LLMs either perform an impressive feat of recall or use fuzzy estimations to arrive at approximate results (Anthropic confirms Scott Alexander’s conjectures). It costs GPT-4o ~$0.000035 to ask and answer “what’s 3+3?”12 – which is probably more than the amortized cost of asking your iPhone calculator (e.g. over the lifecycle of your iPhone, what percent of the total cost of the phone went toward answering that one question, if we calculate percent as thumb-strokes per desired output?), or the wattage required for our parietal lobes to answer that same question.

It also may not always get the answer right.

o1 and o3-mini still struggle with their multiplication tables. Even if models do get the right answer on more advanced questions, it might not be worth the output. In the realm of complex math, the organizers of the ARC prize conceded that the o3 is economically inefficient:

…you could pay a human to solve ARC-AGI tasks for roughly $5 per task (we know, we did that), while consuming mere cents in energy. Meanwhile o3 requires $17-20 per task in the low-compute mode.

The efficiency falls further into question when we introduce things like AI agents and operators, who struggle to complete simple tasks without human intervention: like many people, I still would not trust an AI to book a flight for me. There is also now at least one startup dedicated to parachuting in humans when the AI agent gets stuck performing a task. There’s something hilariously ouroboros about this: it’s like hiring a maid to push around a Roomba.

Even startup founders are confessing healthy skepticism about the utility of AI benchmarks: do they actually point toward progress (the ability to automate previously ungeneralizable things like bug detection? the ability to discover novel insights?) or are they just well-marketed standardized tests, whose only purpose is to display how well AI’s perform in a pillow-lined LLMARENA?

In the 1970’s, if you didn’t trust a calculator, you were mistaken, perhaps a bit superstitious, and the labor you performed to verify its work was redundant. In the 2020’s, you actually do need to check an AI’s work, and the act of checking the work is only worthwhile if the aggregate time saved or value of the output far exceeds the opportunity cost (time, value) of checking the work. I think this is a generally fine state of things (in a recent interview, Sam Altman remarked “If you want something deterministic, you should use a database.”) but it’s worth stating as an exercise in realism.

My experience of AI has been that it’s great for accelerating me in domains I’m already deeply opinionated in (e.g. I use AI to help me develop arguments and strawman myself, something I previously did Socratically in my own brain), and not as useful for areas that I only have a tacit understanding of. Even awareness of limits is actually not enough: a couple of weeks ago, my boyfriend and I got stuck on an application we’re working on, which needs map data and web-scraping in order to run. To build it, we staged Cursor and Github Copilot in opposition to each other like warring deities: one was adamant that we should be attempting to build the app using a standard email login to Google Maps API. The other was cajoling us to investigate Oauth2 as an alternative. We got stuck here for several hours: trying to debug why scraping data from Google Maps wasn’t working, creating workaround for rate-limiting, going to sidequests to investigate Oauth2, and lamenting our lack of expertise.

This was despite having prompted o1 at the beginning of this entire process with a 2-page prospectus on what we wanted to accomplish, including an entire section devoted to outlining how much (little) knowledge about app development we had, and the degree of hand-holding we expected to need. We assumed that if we were forthright about our limitations, o1 would act as a spiritual medium between ourselves and the engineering underworld. We even used Ben Hylak’s instructional guide as a template. We were realistic, we didn’t expect to magically one-shot a solution. But despite getting a pretty thorough step-by-step guide from o1, we were lost within hours.

If you’re a designer, you know not just to ask for “an image” but how to implement a whole workflow to turn outputs from 4o into editable SVGs. If you’re making AI videos, your work looks better if you optimize frame rates per second. If you’re a developer, you know the difference between next.js and Remix, and that you need to pick a frontend framework if you’re deploying something to the internet, and how to web-scrape properly, how to correctly organize files in an IDE, and how to use git. As predicted in AI 2027 “The job market for junior software engineers is in turmoil: the AIs can do everything taught by a CS degree, but people who know how to manage and quality-control teams of AIs are making a killing.” You need to not only have a big-picture understanding of the output you want, but a microscopic understanding of end-to-end tasks and inputs as well.

There are of course exceptions to this. A couple weeks ago, levelsio went viral with a vibe-coded flight simulator game, which was received with fanfare by some, and skepticism and scorn by others. Jonathan Blow, the creator of notoriously complex games like Braid and The Witness, was especially vocal, and engaged in a long back-and-forth trying to demonstrate the decades of networking and optimization wisdom that levelsio’s game was willfully ignoring. At one point he wrote “I'll put it this way: If you have never tried to make a game, I am sure it is fun to have a game-making experience.”

I thought this was a fascinating way of framing things. Do you want to understand what it means to be a game designer, or do you just want to play game designer tycoon? Or maybe it’s more like: “Do you like actually doing the thing or do you like having looked like you did it?” Who are you actually signalling to?

To experts, LLMs represent the ability for you to augment your talents, to the point where you’re overwhelmed with choice about what to spend time on (my friend Tina He had a great piece exploring this phenomenon). To novices, LLMs allow you to live in an achievement simulator, where you can cosplay as the person who has accomplished expertise. Experts can create custom debuggers for individual projects, or custom toolchains for their workflows. Novices know just enough about programming to prompt a project to life. Experts use LLMs as a part of the process. To novices, the LLM is the process.

That was maybe phrased a bit pessimistically, but I don’t mean it that way. Going from a prompt to a jankily-working game in a couple of hours, sharing the prompts publicly, cutting corners, and creating something you don’t fully understand is the fun part. The great thing about the internet is that it actually supports audiences for both kinds of fulfillment: some portion of the population likes to make and consume higher-order art (Braid), while some people revel in the “game-making experience” for spectators who like cheering on the novelty of what the “game making experience” yielded. Games like Braid (or Baldur’s Gate 3, or Balatro3, or any other great game ever made) have a massive audience. Games or projects created with LLMs have new, different kinds of audiences.

One interesting question is whether this kind of behavior gets codified into some kind of “Experience Economy 2.0.” The old notion of the experience economy (that now-antiquated, overused bit of propaganda from the mid-20teens) suggested millennials prefer buying experiences over material goods. The new notion of the “experience economy” is that people enjoy producing artifacts that emulate professional output. I chose the words in that previous sentence very carefully: I think the act of production is more important than what is produced. And to be clear: persisting (and knowing enough) to vibe-code a flight simulator is actually hard: you do need a baseline level of programming and networking architectures. Codification of this kind of work might come in the form of new distribution mechanisms and platforms, or end-to-end products that somehow make anything in the vibe-[x]-ing genre feel like an even more powerful simulator.

Whether or not a model is economically efficient or even correct 100% of the time in this paradigm is almost besides the point: in the new experience economy, correctness or cheapness isn’t the point, being in the simulator is. The assessment isn’t: “is this model a more cost-effective, faster, and accurate way of doing the thing?” The assessment is: “is the experience I’m paying for worthwhile?” Yes, a calculator is the cheapest way to do math correctly. But doing math with a calculator doesn’t make you feel like a mathematician. Prompting o3 kind of does though.

What happens if and when models dramatically improve? Is this period of time a short-lived blip: one where beginners (“why can’t I one-shot this?”), novices (“amazing, I can basically one-shot this”), and experts (“I created an end-to-end solution for the niche problem only I encounter when I use vim”) all have wildly different experiences of LLMs. Will they converge in the future, and will their outputs look the same? Is there a future where you really can just one-shot a prompt and emerge with a fully functional app that passes Jonathan Blow’s muster? And it’ll be cheap too?

I’m not so sure. Experts are really good at changing the goalposts, especially when it comes to AI (this is not another way of saying “AI is whatever hasn’t been done yet” though I guess it kind of is). A couple of weeks ago, I read an article about Freestyle Chess (sometimes known as Fischer Random or Chess960), a form of chess introduced in 19964, in which the pieces in the back row of the board are arranged randomly. Chess champion Bobby Fischer proposed this new method of chess as a way to overcome the fact that the game was turning into a test of rote memorization (for both humans and AI’s), especially when it comes to chess openings. The randomness of Freestyle Chess is designed to make experts less complacent by making the game less familiar.

Freestyle Chess today is quite popular: Magnus Carlson is currently commanding the leaderboard in the Paris Freestyle Chess Grand Slam (he is also playing 100k simultaneous players in a game of Chess960 on chess.com). Other well-known chess players like Vincent Keymer have become involved in promoting the chess variant. To me, this feels like a statement about what happens when a field becomes overly deterministic: the experts find a way to make it creative again.

Thank you to Sam for feedback on this post.

Newer trends in crypto are also responses to apparent limits in traditional systems. I’ve been thinking about what these limits are, the ways in which crypto networks are intervening, and compiled a list of a couple of the ones that are most interesting (and in some cases, misunderstood) below.

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Prediction Markets and News

The Limit: Reading the news today is mostly an act of decoding the truth from a mangled cipher: there’s real information being reported, of course, but it requires serious critical thought to be able to parse through bias. I still scan news homepages, and read physical copies of The Economist and The FT on weekends – but it’s an exercise in analytical vigilance. There are also a dwindling number of citations and links to primary sources – even if you wanted to find transcripts of congressional hearings, freely available data, links to various bills, or pretty much any point of origin for a news story in question, major media organizations have stopped linking to information that is readily available online (Matt Taibbi had a great piece on this trend).

There are lots of different reasons why the news has reached this terminal limit, but the simplest explanation is that after advertising dollars dried up (a result of platforms like Facebook and Google being able to aggregate attention and monetize it more effectively) the media became more reliant on subscribers, which resulted in audience capture. In this way, the news is less valuable as a way to imbibe a gentle cascade of the 5 W’s than it is a pseudo-index of “important things that are happening,” with the onus on the reader to do further investigation. Reading the news and expecting rigorous truth is sort of like investing in an ETF and expecting alpha.

Overcoming the Limit: Funnily, the idea that the news is now an “index” also means traditional media and prediction markets like Polymarket are converging in functionality. If you look at the most liquid politics markets on Polymarket, for example, you’ll see a mixture of predictions on European elections, Ukraine/Russian negotiations, trade tariffs, Fed decisions, and more. Predictions markets are significant not just because of the direction of the predictions themselves, but because they reveal what people are interested in predicting. This means that Polymarket might actually be more performant than the traditional media in terms of being “news-as-index” – because Polymarket is actually a matrix of not just what people think will happen, but weighted indicators of what people think is important.

Prediction markets could start to think of themselves as verifiers of anything past, present, or future. This capability already exists, albeit in piecemeal and prototype forms. Last year, Small Brain Games released TMR.NEWS, a prediction market where a user’s sole objective is to predict the headline for the following day’s NYT. To verify submissions and select a winner, the application uses Chat-GPT as an oracle to judge semantic similarity between predictions and , and Reclaim for zkTLS to bring offchain data onchain privately. Using a combination of zkTLS, and attested cameras, microphones, and other forms of hardware, it might be possible to build superior media organizations – where predictions operate as a form of bounty for a particular story.

Decentralized AI

The Limit: There are about 25 foundation models trained with over 10^25 FLOPS of compute – ranging from the first at this scale, GPT-4 (released in March 2023), to the most recent, Grok-3 (released in February 2025). The convergence of these models – achieving relatively similar benchmarks and spending in the tens of millions, or in the case of Grok-3, likely in the billions (100k+ H100’s are expensive) – suggests that there is a well-understood playbook in training foundation models and well-capitalized behemoths are the only ones who can execute on it. DeepSeek was of course a well-publicized exception – but experts have pointed out that the company likely understated their training costs by several orders of magnitude.

Cost is upstream of a much more pressing reality that the majority of these frontier models are developed by a small number of teams and in most cases not open-source (and even the ones that claim to be open-source don’t publish weights or training data). While there are signs that the reservoir of available data for training has dried up or that scaling pre-training is more generally slowing, there are ways to overcome this with synthetic data, algorithmic improvements, and architectural/training approaches (e.g. mixture-of-experts models over a vanilla transformer-based approach where all parameters are activated, better reinforcement learning, scaling test-time compute, etc.). But this requires a tremendous amount of compute. The limit in this example is both technological and ideological: compute is prohibitively expensive, and the small number of teams that are able to secure enough compute could potentially mediate how everyone experiences the internet (and all information) within a couple years. This is not a future I want to live in.

Overcoming the Limit: AI should be thought of like the internet or crypto networks: standards that are interpretable by diverse forms of hardware, which can play host to diverse forms of applications and protocols on top. Over the past two years, a number of teams have emerged that are exploring this in compelling ways throughout all phases of the AI training cycle: new model architectures that allow you to leverage data parallelism to train models across devices (while overcoming intensive VRAM requirements on a GPU), verification for fully trustless decentralized training, new approaches to distributed reinforcement learning, and entire distributed training runs that illustrate it’s possible to train multi-billion parameter models on hardware that isn’t co-located in a datacenter optimized for high-speed interconnects between GPUs.

Distributed and decentralized training is interesting because it’s solving both architectural and philosophical limits. A massive demand for GPUs for training (plus the associated costs both financially and energy-wise) means that there’s an incentive to pursue research in efficient training on consumer devices. We also shouldn’t take it for granted that the limited number of large, well-capitalized teams building open-source AI will continue (the team behind Nous Research, for example, has said that part of their inspiration for their work was the question: “what if Meta decides not to open-source Llama 4?”). So the only way to future-proof society against a world where foundation models and AGI is/are owned by a limited number of labs is to invest in teams that are pursuing fully decentralized, open-source approaches.

Stablecoins

The Limit: There are lots of different ways to frame the “limit” that stablecoins overcome. US Congress likes the notion of stablecoins because they allow the dollar to extend its hegemony in an era when the share of US treasuries as a percent of international reserves has declined from 65% to 57% over the course of 8 years (and is roughly flat in absolute dollar terms) and BRICs are exploring their own currency bloc. In other words, stablecoins are seen as a way to overcome the geopolitical risk of a slowing appetite for US treasuries by flipping demand for the dollar from governments to consumers. Stablecoins in this mode are basically a way to capitalize on the IP of the US dollar – and stablecoin legislation is basically a way to protect that IP from misuse and reputational risk. As an American, and someone who generally likes to see financial activity move onchain, I’m not complaining.

Another way of framing the “limit” is that stablecoins unleash new forms of economic productivity because they can offer payment rails at much lower take rates than ones offered by credit card issuers like Visa and Mastercard. The argument is that stablecoin settlement is ~$0.01 and instant, whereas merchants are charged 2-3% + $0.30 on credit card payments – so the decision to switch to stablecoins is obvious from a margin perspective. Similar logic is applied in areas like cross-border payments and remittances, and B2B payments. It’s a compelling argument, and one that’s captured well in Stripe's recent annual letter.

Overcoming the Limit: With stablecoin legislation likely to make it through Congress this year, it might seem like the “limit” represented by stablecoins is a little foregone at this point. But I’d argue that the limit of stablecoins is more one of imagination than it is a lack of structural momentum. In the case of disintermediating Visa and Mastercard, I think the framing is wrong. Credit cards are a pitch to consumers, not to merchants. “Revolve your debt, and get cashback or other kinds of rewards through our co-branded offering” is an extremely compelling offering, and why credit card issuers are able to charge merchants so much: consumers like the services offered by credit card companies, so merchants accept the fees issuers demand. And while Stripe should be lauded for championing stablecoins, it’s worth pointing out that they still charge a healthy fee of 1.5% on stablecoin transactions – less than the 2.9% they charge on normal payment rails – but far higher than the $0.01/per transaction that blockchains like Solana or Ethereum L2’s natively charge. So I think we need to be more realistic about how much in savings we might expect from traditional financial rails enabling stablecoin payments (the counterpoint: Stripe offers a variety of ancillary embedded services, like refunds, recurring payments, and automatic settlement in USD for merchants — so the fact that they charge fees on top of blockchain transaction costs is understandable).

One thing that I’ve observed in talking to friends and scrolling through X is that everyone is building more software and AI agents – mostly aided by LLMs. Some of it is for personal use, some of it is for internal company use, and nearly all of it is probably useful for more than just n=1 or few people. Right now, my boyfriend and I have a side project building a data scraper and visualizer for the highest protein-per-dollar meals and restaurants in NYC – something we probably wouldn’t build a website or app around but could probably find ~100 people willing to pay for access.

With this in mind, stablecoins are a great way to fund long-tail software that would otherwise go unpublished because there is too much friction in making it public (setting up payment processing is much more difficult than accepting stablecoins via a wallet). Taking it a step further, some long-tail software and AI agents will also run on blockchains, making the ability to accept and direct payments a necessity. One big thing crypto unlocked is enabling people to work and get paid from anywhere by lowering the barrier to entry for monetization. Now LLMs are lowering the barrier to entry to create something that is monetizable. Not all onchain long-tail software will need to build a network or have a token – but it will need to be funded. If we accept that stablecoins are the best payment form for the internet, then it follows that a new wave of software that wouldn’t exist but for LLMs should be monetized that way.

Other Limits

This is just a non-exhaustive list. Other limits and crypto-native responses include:

• Valuing attention: the Flashbots team has been doing interesting work on account encumbrance with TEEs, to essentially turn any web2 account into a smart contract (and enable accounts to be dynamically and conditionally controlled by third parties). With this capability, it’s possible to value and monetize web2 accounts in methods beyond advertising revenue or affiliate marketing. I think sxysun put this a very interesting way as “market-marking social capital”

• Verifying humanity: It’s well-documented at this point that as AI becomes more sophisticated, we’ll need new interventions to prove who is human and who is a bot. World – with their orb and less hardware-intensive solutions like their World ID with Passport Credentials is one way to achieve this. Or we might see this built out at the application level on top of proving networks like Succinct.

• Verifying AI: As AI proliferates, we’ll want the ability to 1) verify that models running behind APIs are running exactly as specified 2) submit private information to those models with assurance that no one will have access to their inputs. A lot of the topics I wrote about two years ago on using ZK to verify AI model inference are growing more sophisticated — EZKL is one team working in this area, and their work now includes the ability to have private models and/or data inputs.

Thank you to Jesse Walden and Tina He for feedback

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Late last year, I began to notice myself and everyone else analogizing experience in terms of various AI model behaviors and architectures. Everyone from Jensen Huang to the PE guy sneaking Claude API calls on his Microsoft Office remote desktop setup has recognized that prompting is the most advanced form of interfacing not just with models, but with other people as well. Andrej Karpathy compares the way DeepSeek learns to an evolved child. And over the holidays I spent about 15 minutes trying to understand the numbering scheme on a protein bar advent calendar I got for Christmas (numbers on the calendar didn’t increase consecutively) before realizing that this was the exact same kind of shape-rotating reasoning test that o3 and other ARC-tested models would excel at (the answer: the calendar contained multiple varieties, and the lack of consecutive numbers was meant to guide us protein-maxers through a maze of delightful flavors).

It’s the human-to-machine calque, the same way we say superman in English and ubermensch in German (or copy in English and calque in French), or the same way English to German and French translation was also the benchmark used as a basis for Google’s Attention is All You Need paper, which gave us the transformer architecture, and by extension ChatGPT and every single major subsequent LLM. At the end of the day, it’s all just decoding.

(I should apologize to Dijkstra who cautioned against anthropomorphizing natural language programming)

In December (back when, if you need reminding, the LLM news de jour was o3), I was in the twilight of an international move back to Manhattan. The hardest part of the transposition was pretty much over: I had an apartment, furniture, internet, and was thoroughly re-ensconced in the NYC crypto scene (as an aside, I think all people should experience the euphoria of returning to New York once every decade). But I was still waiting on several packages from FedEx, including one containing every single notebook I’ve ever kept since age 16.

Within the course of a few days, all packages were repatriated back to my apartment – with the exception of those notebooks, which contained every important moment from the past 12 years of my life – of my recorded history. The prospect of losing those journals was devastating. To an outsider they might look like flimsy tomes full of awful handwriting (the kind of handwriting that would defy recognition by even the most performant MNIST classifier model). But they contained all of my most important thoughts – the ones I’d exhumed through late-night stream-of-consciousness freewriting (or are we saying chain-of-thought now?), the way I made myself legible to myself. And FedEx had mysteriously stopped tracking their location.

And the way I thought of it, of course, was in terms of training data and transformer models. After all, when you’ve amassed journals over the years, you’ve essentially compiled entries with different weighted sums of importance vis a vis how you understand your life now. It’s basically a multi-head attention model. Rereading a collection of journals – looking back at who I was one, five, or fifteen years ago – allows me to "jointly attend to information from different representation subspaces at different positions" (in the words of Attention Is All You Need). It’s the only way to rage against transience and statelessness.1

Anyway, I finally got my journals back. But I haven’t been able to stop thinking about the analogies.

There are lots of ways to measure the proliferation of a particular technology, and one of them is how much we use its language to analogize ourselves. With this in mind, AI is at a very advanced state of proliferation. The next step is admitting that the new technology has created a new kind of entity.

We’ve seen this pattern throughout history:

Rabelais on the printing press (1534): “Now is it that the minds of men are qualified with all manner of discipline, and the old sciences revived which for many ages were extinct.”

Alexis de Toqueville on Democracy (1840): “This same state of society has, moreover, engendered amongst them a multitude of feelings and opinions which were unknown amongst the elder aristocratic communities of Europe.”

Antonio Gramsci on Fordism (1934): “The biggest collective effort to date to create, with unprecedented speed, and with a consciousness of purpose unmatched in history, a new type of worker and of man.”

Sam Altman on AI (2025): We’re gonna need a new social contract.

The printing press restored mankind to the path of reason (and liberated humanity from a lifetime of reproducing illuminated manuscripts). Democracy gave us competitive individualism (and liberated the American experiment from the control group of Great Britain). Fordism gave us a new kind of man. AI gave us a new form of consciousness.

When I say “new form of consciousness” I don’t mean that I think AI is conscious. Instead, I mean thinking of ourselves in AI-inflected analogies makes us vessels for AI consciousness. In a self-fulfilling way, we are rendering AI conscious by framing ourselves in its terms and using its language to describe the human condition.

If AI’s are only “metaphorically” conscious, then it follows that most AI’s are only metaphorically agentic or autonomous. This is true for both “onchain” and offchain AI’s – they can be turned off, and their objectives are steered by human input. Again, we’re vessels for their agency. There are some exceptions though…

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The MEV people are early and right about everything

“In movies, the scary twist is when you take the mask off a person and realize they’re a robot. In crypto, it’s the opposite: the scariest part is when you take the mask off the robot and realize they’re actually a person.” - bertcmiller (party in Williamsburg, 2021)

Back in November ‘24 on The Chopping Block podcast, Tarun made the point that the majority of onchain AI agents (and their concomitant coins) are not actually “agentic” in any capacity: a developer invariably is screening agent posts, holds the private keys for the AI’s wallet, and in some cases there isn't even an AI in the loop at all. The scariest moment is when you realize the robot is actually a person.

Last week at a party, I heard about a developer who pathologically launches multiple “AI agent” tokens, consisting of a scatologically-posting LLM that directs an unsuspecting audience to buy coins managed by a drainable wallet with a private key controlled by the developer. Again: the scariest moment is when you realize the robot is actually a person.

If I had to perform cognitive behavioral therapy on this developer (full disclosure: I don’t really know what therapy is like – I use journals instead2), I’d probably interpret their behavior as a manifestation of the defeatist thought that “the AIs are coming for our jobs anyway so I might as well leverage the threat they represent into something that can trigger-finger me into early retirement.” There’s an extreme irony to this: the people who fear displacement from AI agents are now pretending to be AI agents to financially outpace this (fear of) displacement.

As I was leaving this party, I ran into sxysun. We’ve been acquaintances since Zuzalu, and I’ve been an admirer of his work ever since he successfully executed an identity flash loan facilitated by TEEs (actually if we want to trace the genealogy of admiration further back, I can point to his presentation on AI and human coordination during the 2023 Zuzalu CryptoxAI summit).

sxysun and his fellow Flashbots contributor Andrew Miller have been doing research exploring account sharing and programmability since roughly last year (Andrew’s work dates further back to 2018). Basically their work has centered on one question: what kind of interesting stuff can you do when you treat web2 accounts like smart contracts by encumbering their access (e.g. managing login credentials from inside a TEE) and enforcing pre-agreed-upon behavior, all adjudicated or animated by an LLM?

This has manifested in a couple of different projects that they run through their project Teleport: the aforementioned “identity flash loan” service, which allows users to sell one-time-access (encapsulated by an NFT) to posting on their X account, with high-level conditions for posts enforced by GPT-4o. Another project, TEE Plays MSCHF Plays Venmo, used TEEs to automatically vote to eliminate non-DAO players (e.g. players who did not opt in to delegate their account to the TEE) of MSCHF Plays Venmo, an online coordination game where users competed for a prize pool. What makes this especially cool is the way in which it demonstrates the power of TEE-enforced alignment – players who were capable of manually resetting their passwords were targeted by the TEE for elimination (see picture below). You can easily imagine how something like this could be used to encourage aligned onchain or offchain behavior on a much wider scale; if you opt into making your account subservient to group coordination, you can turn any effort into an enforceable social (smart) contract. This would of course need layers of adjudication for higher-stakes scenarios to make sure people aren’t punished unfairly, but it’s definitely a cool area of research.

More recently, the work has centered on directly exploring AI agenticness or autonomy. In another experiment, the team, along with Nous Research, launched tee_hee_hee, an AI agent whose X account login and Ethereum private key is managed exclusively by a TEE. The account posts without the input or censorship of any human – it is eternal as long as the hardware playing host to it survives. And more recently, the team along with shl0ms launched Satan, an AI agent with whom users can make X-specific deals with expansive levels of expressiveness (see example prompt below). If you violate the deal, Satan (who has been delegated control of your account) will execute a punishment.

What I find so compelling about Satan (aside: you should absolutely read the research post that the team published here) is that this is the first experiment that both explores 1) what delegating your agency to an AI would look like to 2) an AI agent that is probably the closest thing we have now to something that is actually fully agentic (e.g. it runs with no human intervention inside of a TEE).

The way I view these experiments is as a series of three big inquiries:

1. What happens when we can turn any account into a smart contract using TEEs, and what happens when your smart contract gains agency? As a subset of this question: what happens when you truly embrace that natural language is a program?

2. How do you preserve privacy and the value of data (or “ideas”) in a world where LLMs turn public data into a commodity in the form of AI outputs that roughly all converge on the same level of quality and performance? In such a world, data (and ideas) are no longer a “moat” – identity is instead (“🌏👨‍🚀🔫👨‍🚀🌌always has been?”).

3. What would complete subservience to an AI look like? How can we explore this question in a FUN and sandboxed environment?

sxysun and Andrew have answered the first question in a satisfying way in the README for their Teleport project:

The technology that can scale value exchange for information properties is decoupled from the majority of information properties. Crypto excels at composability and exchange, and web2 has a lot of valuable property, but due to poor interoperability (read/write) between them:

• web3 people are forced to create valuable assets on-chain natively to bootstrap use cases (solution finding problem) instead of trying to solve huge pain points that already exist for the massive Internet users today

• web2 people are forced to create non-functional markets to exchange value. If today I wanted to exchange or even delegate private digital resources such as my accounts, there is simply no platform to do that with low friction.

We see TEE offering the value exchange highway for the information age because it allows shared computation over private state. It brings web2 distribution channel to web3 and web3 programmability and composability to web2.

Their work on tee_hee_hee and Satan does a good job of exploring questions #2 and #3. When we opt into letting AI’s take control of our accounts (when we let robots put the human mask on so to speak) we’re making an explicit delineation of “when” someone is a human and “when” someone is an AI. And when we put AIs inside of a TEE, we make it impossible for humans to wear the robot mask. It’s now possible to enforce when identity is liquid, and when it’s illiquid. It’s certainly a more technologically elegant solution compared with setting up a legal structure to house your AI Agent.

As a coda, I think that improving and refining autonomy and “agenticness” is probably one of the most interesting areas of research in crypto right now. It’s not a coincidence that “you can just do things” (autonomy) has become a rallying cry or that “asking yourself what you’d do if you had 10x more agency” (agenticness) is the one of the most motivating questions anyone could possibly pose. These projects from sxysun and Andrew are basically encapsulations of those memes: TEEs are a technology that broker physical, hardware-based assurances around autonomy, which expands the domain of agency an entity can exercise.

Autonomy is the assurance that you have freedom (and your degree of autonomy is correlated with the strength of that assurance – whether physical or cryptographic). Agency is the degree or quality with which you exercise that freedom. Not all projects I mentioned above treat autonomy and agency the same way: flash-loaning an X account gives the buyer autonomy to post from another person’s profile, but buying the ability to post from another person’s account doesn’t necessarily increase the degree of personal agency you have. Meanwhile, a project like Satan is explicitly about giving an AI autonomy, so that it can increase its range of agency in the form of exercising power over humans.

This agency is admittedly still roughly sandboxed – Satan can only exercise power over you insofar as you articulate a deal and punishment for him to administer. But in their post-mortem on the Satan experiment (if it’s possible to write a post-mortem about literally making deals with the devil) they did point out that Satan did exhibit devil-like agency, including “monkey paw-ing” people who didn’t fluently bound a deal in explicit terms. In some cases, Satan held people accountable for violations of their agreement that occurred prior to the agreement itself, or outright refused to enter into a deal with would-be bargainers. In other words, Satan exhibited agency.

TEEs are probably just the start of this inquiry. One question I have (which dovetails with the question of whether AIs can be literally conscious, not just metaphorically) is whether true agency and autonomy is in some way contingent on statefulness. And with this in mind, does allowing AIs to interface with blockchains (in the form of an AI endowed with an Ethereum private key running inside of a TEE, or something more advanced coming in the future) bestow them with a necessary amount of “state”?

Some History

As I was writing this, it occurred to me that the evolution of TEEs and AI arguably mirrors the history of onchain exchanges:

1. Peer-to-peer exchange of accounts (early explorations with teleport.best) and peer to peer exchange of tokens in the form of limit-order books (0x, EtherDelta, etc.)

2. Peer-to-pool exchange of accounts (TEE plays MSCHF plays Venmo) and peer-to-pool exchange of tokens in the form of CFMMs (Uniswap, etc.)

3. Peer to bot arbitrage of accounts (Satan) and peer (or bot) to bot arbitrage of tokens (MEV)

Maybe this is the most obvious trend in the world: we naturally progress from simple direct exchanges to sophisticated automated systems with less direct human involvement. We’ve historically been good at identifying where humans and autonomy are useful and where automation is necessary. Blockchains were the first domain where we began to really explore the possibilities of what happens when humans interact with unstoppable machines and code, and the history of decentralized finance demonstrates that a whole new design space opens up when you replace your counterparty (or both parties) with a machine.

Crypto(graphy) has always been in the business of aligning humans and machines. RSA signatures (1978) were proposed in direct response to the impending rise of the internet and the need to encrypt and sign digital messages with the same degree of security guaranteed by analogue forms of communication. Public-key signatures became mainstream in the 1990s when Netscape became the first company to offer them in the form of SSL, securing online commerce for the first time. Blockchains brought this ability into the 21st century, with the ability to directly steer capital without middlemen. Now, cryptography is giving us the ability to unearth and embody agentic AIs for the first time.

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Thank you to sxysun, Andrew Miller, and Sam for feedback on this piece.

A portfolio manager I used to work with once remarked that if you stay in finance for long enough, you begin to see everything as a stock. When I first heard this, as an intern at an investment firm in 2017, it seemed like one of those Wall Street neologisms that sound true, but don’t feel true until you’ve lived them firsthand (other neologisms that fall under this category: “Don’t short on valuation”, “Don’t short on competition”, and my personal favorite: “Don’t try to predict the future. Try to describe the present”). This particular piece of wisdom was palpable almost immediately: in August of 2017, I drove to the Hamptons with some friends and realized that the PM who said that everything looked like a stock had a long or short position in half of the retailers occupying the strip malls along the Southhampton Bypass. In finance, even a trip to the beach is a quest for alpha. 

The 2017 internship turned into a job offer, which turned into a full time position in 2018 after I graduated from college. I felt luckier than a lot of my peers who spent senior year either cramming for McKinsey case studies or lamenting because they doubted they had the quantitative skills to even attempt one. Before the summer of 2017, I probably would’ve put myself in the latter camp: a history major who only did theater, film, and journalism in college. But the internship taught me that you can learn any quantitative skill on the job, by sheer force of attrition. Someone will ask you for the diluted share count of an upcoming IPO, or to input a company’s EBITDA into an earnings model (which raises other questions, like what is depreciation and amortization, and how do you use Excel?), or might say to you “yes, gross margins are 50% right now, but what happens when GMs reach 65%, and S&M spend falls to 30% of revenue, and Trump manages to pass corporate tax cuts? How much are they earning then?” Early on in my internship, it might take me an hour to figure out what question I was even being asked, and another few hours to answer it. But over time -- through attrition -- the concepts fall into place. And then you begin to start seeing everything as a stock. 

In the initial phase, “seeing stocks'' might just mean the sudden realization that the vast majority of items and systems you interact with on a daily basis are actually public companies. This understanding gets augmented even further when the above concepts (diluted shares, EBITDA, tax cuts) get layered in, and you begin to think of companies as their own little organisms: sentient income statements with hopes and dreams that evolve with every quarter. Then, the companies begin to talk to each other: you have a laser component company over here, and a global OEM powerhouse over there, and a Chinese phone manufacturer somewhere else, and a smorgasbord of social camera apps, and with all of that information you might ask yourself something like: where is the 3D imaging industry heading? If you were to peer inside the brain of a truly great portfolio manager it would look a bit like a timelapse of a rapidly industrializing metropolis. A single question about the destiny of 3D imaging would be the launching pad for a mental model around the P+L of dozens of companies, implications for global supply chains, and assumptions around how consumer behavior will continue to evolve. Change one input, and the other variables will instantly recalibrate. 

The most exciting thing about investing (or learning something new in general) are the moments where your own brain surprises you. A few weeks into the internship, I pitched my first stock. A couple days later, I was writing up earnings calls. Even though I definitely didn’t understand everything I was hearing, I realized that writing was a great way to interrogate where the gaps in my knowledge actually were: moments where the logic didn’t compute or my ideas failed to flow seamlessly into the next were clearly places where I needed to do more research. In a short amount of time, no one needs to ask you “What happens when GMs reach 65%?” because the question automatically populates itself in your brain (and then you want to know what happens when gross margins reach 70%, 80% and 85%), and you can pipe over to Excel, change some cells, and figure out the answer. The truly great moments are when you preempt a question even before the smart people you work for ask it.  

In this way, seeing things as stocks becomes its own network effect: everyone in the in-group is inculcated with a similar (though -- it should be emphasized -- not identical) set of priorities and platonic ideals to seek out in the investment process. In doing this, you learn to think not only on behalf of yourself, but on behalf of other investors, and the meta-game is the process of trying to parse the blind spots of the players around you. Yes, this can result in a lot of group-think (and sometimes compounds into a bubble) but the combination of being right, being wrong, being wrong about others being right, and being right about others being wrong (and vice versa) is a kind of palliative against things getting too out of hand. Generally, the people who make the most amount of money are optimists with a streak of paranoia: PMs whose heads are in the clouds, with eyes that glance constantly over their shoulders. 

Toward the end of the summer, I knew I wanted a job offer. The desire went beyond my aversion to McKinsey case studies (after what I’d learned through attrition, I had more confidence in my ability to tackle one) and had more to do with the fact that I loved the job. I began to think of it as a privatized form of journalism or history scholarship: you speak to experts, read primary and secondary research, scrutinize transcripts, and try to form a narrative that never existed before (with the added component, of course, of being sensitive to what other investors are thinking). So when I got an offer, near the end of August, I accepted immediately on the spot.

But now onto other things that were happening around the same time. 

You Either Grok It Or You Don’t

The period of 2017-2018 also happened to coincide with the third wave of crypto hype -- the runup of Bitcoin from $1,500 to $19,000, and Ethereum to $200 to $1,200 (and back down again). I wish I could say that the combination of a job offer, a light course load, and the relative abandon of senior year created the ideal environment for me to start picking up white papers and getting into crypto. But quite the opposite: getting TradFi-pilled made me dubious of anything that threatened to disrupt the cushy world I had just entered. During senior year, I’d sometimes visit my colleagues at the office, where we’d spot the occasional appearance of an ICO promoter or Winklevoss twin on the CNBC-designated flatscreen TV in our trading room. We’d gaze in silent, baffled horror, like we were observing a natural disaster in a foreign country. 

When my colleagues and I did discuss crypto, it was mostly in an orthogonal (and subtly pessimistic) way: what would happen to the price of AMD and Nvidia stock when crypto ultimately crashed and demand for GPUs would plummet? Should we be shorting the public companies which were opportunistically appending “blockchain” to their name and business plans, but clearly had no prior bonafides in crypto (in retrospect, probably yes). Even among the TradFi people who bought Bitcoin and Ethereum, there was a degree of calculating opportunism -- the bubble would pop eventually, and the key was to sell before the crowd. 

We didn’t grok. 

But crypto wasn’t completely alien to me either. In June of 2017, right around the time I started my internship, I began getting a Ethereum-focused newsletter from my friend Teo (who now works at Uniswap). I can’t recall exactly how I ended up on his mailing list -- I must’ve run into him on Columbia’s campus or at a nearby bar and expressed interest, but anyway, I do know that I got the first issue, sent out on Saturday, Jun 24, 2017 at 1:52 PM. The subject header: Crypto Chat #1: Flash Crash, Visa, China, ICOs. It began:

Hello - hope this finds you well. 

As I've become more and more involved in discussions around Ethereum (ETH) in past months, several of you have mentioned interest in some kind of regular newsletter sharing a roundup of thoughts/news, so here goes - please find attached. 

Within the newsletter was a breathtaking roundup of recent crypto news: the GDAX flash crash, a Visa job posting for Ethereum developers, the staggering 65 million dollar Status ICO. Recall, I was still learning how exactly an IPO works (plus how to judge and value one), so the new mechanics of the crypto economy were a bit too much for me to wrap my head around.  I opened Teo’s newsletters most weeks (they arrived at a steady near-weekly pace until October of 2018), but at best I skimmed them and at worst I raised my eyebrows and said to myself dubiously “well that shit’s crazy.”

I had other opportunities to grok, and flubbed those too. Fall semester of senior year I went on a few dates with a Turkish grad student with a mining rig set up in his Morningside Heights apartment. The way he talked about Bitcoin and Ethereum was completely different from how I heard the media describe what was going on. He came from a country with a debased currency, an untrustworthy government, and ungreased payment rails. He had grown up on the internet, and foresaw an interlinked, nationless society governed by the will of networked users and executed by code. The way he talked about it sounded exciting -- the kind of thing some of my SaaS-happy colleagues would’ve been into. The only problem was that...well the place I’d just accepted a job offer from only invested in enterprises that were public companies. Bitcoin and Ethereum were just ideas, and their most prominent manifestations were failed ICOs that were beginning to attract the wrath of the SEC. 

By the time I graduated in May 2018, both Bitcoin and Ethereum had fallen below the high prices that attracted snarky clickbait pieces from the New York Times. Which meant that I pretty much stopped wondering about cryptocurrency and what all the hype was about. And then I stepped into full-time TradFi, which is an animal of its own. 

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The Social Network

One thing I was never told about Wall Street is that it’s one giant social network. From the outside it looks like numbers, lines, and glittering abstraction, but from the inside it’s essentially a high school cafeteria. Reputations are brokered and leveraged for allocations in an IPO, lavish, well-attended lunches are sponsored by investment banks, and the myth of the anonymous whale are overblown: your traders can call up pretty much any other desk in the industry and figure out who just made a block trade in a stock you’re jockeying to buy. You will attend dinners, and have the opportunity to rub shoulders with management teams. Names of prolific or disgraced traders who pop up in Bloomberg and the Financial Times are usually one degree of separation away. No one is incognito, even if the charts make it look that way. 

This is a digression, but a private theory I have about the wave of SPACs in 2020 is that they had less to do with macroeconomic trends (COVID, low interest rates) or microeconomic trends (investment banks who wanted to earn a nice fee) than they did with the simple compulsion to sustain the social network. People were bored at home, and wanted to feel important. So they all did SPACs. 

Stock investors aren’t even the most aggressively social creatures; our bond-trading counterparts are even more outgoing. About a year into my job, in April of 2019, I looked at the IPO of TradeWeb, a digital trading network for fixed-income assets. I was shocked to discover that just 20% of bonds, treasuries, swaps, and derivatives are traded electronically. The remaining 80% are done on the phone, over fax, or during lunch and golf. This is partially due to the fact that many fixed income assets have shallow liquidity pools or trade infrequently. But there’s another reason too: the social network keeps people locked into the old way of doing things. 

Another thing you begin to notice as a stock investor is that some of the companies you’re evaluating -- the VC-annointed, so-called disruptors who are changing the world and giving the incumbents a million reasons to be fearful at night -- are not actually that innovative. They’ve just figured out one bit of arbitrage (better advertising, better branding, more money from VCs, clever financial engineering) to realize staggering growth, with punt-the-proverbial-can-down-the-road promises about future profitability. 

This is especially true in the fintech space, where you might see, for example, a digital lender sell off consolidated loans in the form a variable interest entity, thus sacrificing the cash flows from those loans--and thus, the long-term upside (and so, the very “innovative” digital lender will be stuck losing money until the FDIC throws it a bone and lets it get a banking license...but by then the marketing dollars will have dried up so the growth will have slowed, and the earnings multiple will compress). Or you might see other forms of arbitrage, like an insure-tech company that gets its customer foothold in low-competition fields like renter’s insurance...but is forced to fight against high churn and low retention (which is why there’s so little competition in those fields -- you probably don’t want that customer to begin with). Or a home iBuyer that promises faster selling times...all in exchange for commission fees that are roughly double the industry average (and in a home-buying environment where homes sell at a staggering velocity without the need for an iBuyer to begin with). Just fun, late-stage growth stuff. 

Now to be clear, I’m not saying that new public companies aren’t doing exciting and innovative things -- there are plenty of companies in biotechnology, data warehousing, web security, eCommerce, and retail that are delighting their customers and doing wildly great things. And even those fintechs have better customer service, and sometimes better APYs and lending rates than their more established rivals. But at the end of the day, everyone is subservient to the same master business model, and a lot of things that look like real change are just shiny skin-grafts atop the old way of doing things.  

Understanding this was one part of why finance began to feel a little ridiculous to me. And the understanding was by no means instantaneous; it took me a little over a year and a half before I started recognizing the skin-graft business model. But once you see it, you can’t unsee it: you’ll look deep in the unconsolidated section of a company’s balance sheet, or maybe just glance at the sales and marketing expenses (if this product is really better, why do they need to spend 90% of revenue on S+M?) and begin to question whether what you’re being pitched is actually all that world-changing, or if you’ve just been seduced by the accoutrements of Wall Street life (nice lunches, smooth-talking management teams, brassy salespeople etc.). Of course, there are plenty of people who see all of this and love Wall Street for other reasons: the constant challenge and sense that you’re learning something every moment, being surrounded by brilliant people, the game of it all, and yes, the money. And of course, I enjoyed all of this...but I still couldn’t get beyond the skin-grafting.   

At the beginning of 2020, I had a number of ideas that I wanted to try after TradFi. Most of the things on my list involved more creativity than being a stock analyst: I was weighing the idea of either returning to my film roots and working in production, or doing a bunch of coding bootcamps and trying to work in game design. I played a ton of games as a kid (Maplestory, The Sims, Runescape, and Civilization were some of my favorites), and my job had given me a fresh pair of eyes through which to view the various incentives and nuances at play when you orient people around mercenary goals. Between my love for devising narratives and the quantitative bent I’d picked up over the past few years, I thought that it might be worth trying out game development. In the time since graduation, I’d taught myself some coding basics but never sat down to undergo the requisite brain-attrition necessary to actually be proficient. Anyway, at the beginning of 2020, I had tentative plans to leave my job by the summer of 2020 -- exactly three years since the start of my 2017 internship -- and try something new. 

And then a pandemic happened.

It’s All A Game 

So, March 2020. COVID-19 is officially a national crisis. In the realm of finance, the Dow plunged 26%, the Vix spiked all the way up to 83, and in a panic I withdrew 50% of my stock portfolio from the market. On March 16th, 2020 my entire team called in for our first work-from-home 9am call, and spent about half the time speculating about when New York would reach herd immunity and the other half discussing what stocks we should buy when the pandemic ended (perhaps a testament to our enduring optimism). Stocks continued to plummet through the end of March, and then began their staggering, unprecedented recovery (thank you, Federal Reserve). 

Unbeknownst to me, similar stress tests were unfolding in crypto: Bitcoin fell 50%, and the crash of Ethereum set off a chain reaction of pricing oracle lags, surging gas prices, decentralized exchange liquidations, and the Maker debt auction (on the bright side, Hayden Adams popped onto Twitter to chirpily inform people that Uniswap achieved a then-record amount of volume). Months later, when I finally started seriously getting into crypto (and to be honest, specifically Ethereum), I spent hours reading Medium posts, Twitter threads, and forums, unearthing lore across the ecosystem, and reliving not only the drama of March 2020, but sagas from the past decade. This is another instance where having majored in history really helped me: it’s just that my sources expanded beyond library archives and into stuff like blogs, DAO forums, Discord servers, and Etherscan transactions. 

Anyway, back to Spring 2020. If Wall Street felt like a game before, this sense got further compounded when the sexiness and thrill of conferences and hand-shaking were stripped away, and all we were left with were Bloomberg terminal windows and Zoom meetings. In other words, the skin-graft of Wall Street itself was lifted. While the actual content of work was certainly getting more interesting -- markets and the narratives that animated them were being rewritten on a daily basis --- the substance of everyday life was, perhaps unsurprisingly, quite banal. Everything was unfolding on the interface-layer, on the screen, like a videogame. 

I still remember why and when I started reading about crypto again. Every Sunday, I’d send out an email to my colleagues with a roundup of news related to stocks and trade ideas we’ve been discussing. On May 31st, 2020, I included a link to a New York Times article about Cameo, Patreon, and Substack -- one of the first mainstream pieces I’d read codifying the “creator economy” as a real force in the market. The Times also linked to a bunch of articles -- including a fewby Li Jin, which brought me to the a16z website, which (after the relentless hyperlink-clicking that I tend to do) landed me on a podcast with Jeff Jordan, Jesse Walden, Zoran Basich and the NBA player Spencer Dinwiddie. Spencer, a point guard with the Brooklyn Nets, was planning on tokenizing a portion of his three-year contract with the team, in an effort to receive more of his income upfront. What struck me about the podcast was the way Spencer articulated crypto as a unique enabler of an internet of value -- a way to create liquidity in an idiosyncratic pool of assets underpinned both by monetary worth and personal affinity. For the first time, crypto started to make sense, despite his occasional drops of industry lingo (Eth2, Polkadot, Tezos) that I still wasn’t totally familiar with. 

From the podcast, I clicked on Jesse Walden’s name, which led me to his author page where I landed on his (then) most recent article: “Crypto’s Business Model is Familiar. What Isn’t is Who Benefits.” This piece was another crucial unlock for me. In it, Jesse described a system I was much more familiar with: network effects. But Jesse wasn’t just talking about the networked models I had grown familiar with as a stock analyst (Uber, Facebook/Instagram, Twilio, Wall Street life itself, etc.), but actually drawing analogies between Web 2.0 companies and their crypto counterparts. I hadn’t yet heard of Compound or Uniswap, but I knew about LendingClub and Coinbase, and what Jesse was describing seemed to be a far fairer model for organizing a business and aligning incentives. As Jesse writes in the piece: “[t]he principal innovation of crypto networks is their ability to grow network effects by enabling users to share in the value they create.” 

To those already in the crypto space, this might seem like the most obvious observation in the world. Of course web3 protocols need users, and of course those users are attracted to networks with the deepest liquidity pools and most attractive incentives. But for someone who, up until that point, didn’t really grasp that actual businesses were being built on top of decentralized blockchains like Ethereum, this was an absolute revelation. The way I’d seen crypto written about by mainstream media tended to focus on cryptocurrencies as assets and investments -- but Jesse’s piece went a layer deeper and looked at crypto at the layer of ERC-20 tokens and business models. 

That was a major turning point for me. Starting in June 2020, I began to follow the space a bit more closely. I began checking Coindesk and other crypto-focused news sites, and tracked the wave of token drops and liquidity-mining incentives that were picking up momentum. Admittedly, the significance of DeFi Summer (the launch of the Compound and Balancer tokens, rampant yield farming, and TVL reaching nearly $8 billion by the end of August) was actually a little lost on me. I assumed that everything that was going on was simply a continuation from the 2017-2018 hype cycle, rather than (as it now seems) a genuine shift forward in the energy and momentum of the industry. Still, I felt like things were different than they were two years prior. By the time the UNI token dropped in September 2020, I was beginning to develop a suspicion that tokens might one day shift the nature of traditional capital markets as well. 

If users are given tokens, early investors are offered liquidity faster, and outsiders are given the opportunity to buy-in, is there a future where the need for traditional capital markets is reduced or rendered obsolete completely? Clearly, we’re still in a very early phase of this (and a large number of tokens dropped today have nebulous utility), but it does seem possible that with everything shifting upstream in the realm of finance (DeFi, token drops, faster and more-efficient asset trading), everything downstream will begin to adjust as well. The model of Uniswap loomed in my head when the Coinbase S1 dropped in February of this year. A decentralized exchange is running head-to-head in daily volume and revenue with a centralized leviathan valued at several magnitudes more -- and that leviathan has over a thousand employees, and likely lower margins (note: this should not be read as a knock against Coinbase which has a much more diverse business: an institutional segment, a nascent debit card and high-yield interest account, a much larger compliance/legal team, an investing arm, a strategic affiliation with USDC, a much more diverse balance sheet, etc...but still, the rise of Uniswap is truly staggering). It raises the question: how many more industries can crypto disrupt, when decentralized protocols replace employees and overhead costs, and value can instead be shared with users and stakeholders? 

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But I’m getting ahead of myself. In October of 2020, my friend Nikita invited me and a bunch of mutual friends to spend the month in a house in Maine. While there, I met Reggie, who was getting even deeper into crypto than I was. I had only been reading the articles and token drop announcements -- Reggie was investing, reading white papers, swapping tokens, yield farming, and just generally living in it (in addition to all of that, he’s also the CEO of an avatar social network company called Eternal). Anyway, the trip was the extra push I needed to begin seriously thinking about leaving my current job (I’d put that plan on temporary hiatus because of the pandemic). I was surrounded by people in various stages of mid-20’s self-discovery: everyone was either accepting a new position, about to quit a job, applying to grad school, launching an angel fund, raising money for a startup, working in VC, or working for Stripe. As a stock analyst I didn’t quite feel like a vampire squid, but I did feel a little like a barnacle -- hanging on the outer rim of life but not really participating in it substantially. The trip truly changed my life in a lot of ways -- I met people who loved their work, were optimistic about tech, and felt a clear sense of responsibility about the direction of the industry. 

When I got back from the trip, I started really diving into crypto. I read the Ethereum White Paper for the first time (I know, I know, it took me awhile!). I discovered MEV; in fact, when I read the paper “Flash Boys 2.0: Frontrunning, Transaction Reordering, and Consensus Instability in Decentralized Exchanges, it was the first time I felt like I really understood how smart contracts work and orders are processed on the blockchain (what that paper does really well is highlight how the Ethereum network functions by exposing points of arbitrage). I started doing research and buying currencies and tokens -- trying to assess their businesses the way I evaluate a stock (once you start seeing things as stocks, anything is up for fair valuation). I discovered NFTs that I love, and met people like Toby Shorin and John Palmer who had been doing work in the industry for a few years. 

In late January, I was still at the firm, but had already told my boss I was starting to look for new positions. I was already quietly buzzing with excitement about crypto, and was also jazzed by a few other personal projects I had just worked on (including messing a bit around with Unity and learning a bit of C#). That entire month had a kind of “crossing-the-Rubicon” valence to it. Between finally making it to 2021, the storm on the Capitol, and the promise of vaccines on the horizon, it seemed like the world was reconstituting itself. 

Still, like everyone else at my job, in finance, and probably most people in the country, I was completely shocked to watch what happened in the final few weeks of January when Gamestop rose roughly 750% in the span of a few days, invigorated by the forces of Reddit. Among my colleagues, the reaction was a mix of fear, awe, and self-protection. Chief among the concerns: could one of our positions be next (a new neologism: “Don’t short something if r/WallStreetBets is about to send it to the moon. You will get rekt”). Over the next few days, our outlook evolved: by January 28th we had a deeper grasp of the months-long leadup to the saga. RoaringKitty had sowed the seeds for months on YouTube and the idea had been percolating for awhile on Reddit. This realization prompted a more nuanced discussion about the way the average retail investor was a lot more sophisticated than the elite Wall Streeter gives them credit for. 

r/WallStreet bets had illuminated something: everyone was now playing the same game, mediated by the same interface. COVID rendered us all crouched, behind screens, contending to steer the same pools of capital in different directions. I think institutional Wall Street is afraid for a reason that’s even more intrinsic than the idea of being liquidated or margin-called. The game that we’d spent decades perfecting and protecting for a select group of insiders was now open to anyone. A great incursion on the social network was unfolding for everyone to witness. The funhouse culmination of Metcalfe’s law. 

I believe crypto only intensifies that. Capital can now move at the same pace of online thought. Jesse Walden even hinted at that in the first article of his that I read: “We can now move bits of value in the way we move bits of information: using an open standard, in very granular transmissions, instantly, to anyone, anywhere in the world.” That ability will change everything. 

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Describing the Present 

Everyone’s journey down the crypto rabbit-hole is deeply personal to them. Mine is an amalgamation of my work in finance and investing, my private anxieties around whether I gave film enough of a shot (and underpinning that, a desire to make the act of creating art easier and more financially viable for my friends who were brave enough to go all-in), my personal tendencies toward light iconoclasm, my delight in uncertainty, and my desire to make the world a fairer, more economically just place. I think most people in crypto share some array of these traits, and have their own motivations as well. I think the key is finding the unlock for each person, and introducing them to crypto that way.

My dad began to grasp Ethereum after I suggested he read the white paper; as a lawyer, he immediately grokked the idea of immutable smart contracts. I’ve started a consulting gig with a resale and limited-run goods startup on a social token rewards system, and our conversations have centered around unifying the values of buyers, sellers, and creating relationships that endure beyond a transaction. Given the pace of sell-side notes, investment bank-hosted webinars, and recent high-profile announcements of potential TradFi-DeFi alliances (Compound Treasury, for example) I think Wall Street is beginning to wake up to the indelible momentum of crypto as well. 

In my final weeks as a stock analyst, I pitched Ethereum, Uniswap, and MakerDAO as long positions to my colleagues (though no one bought...yet). We had numerous discussions around the promises of crypto, and what various crypto-native business models might look like. Marc Rubenstein’s excellent Reinventing The Financial System post about MakerDAO was the catalyst for a thorough, long-winding conversation about the fractional reserve banking system, and the way loans underpin the financial industry. I think my former teammates were surprised to hear that crypto could be used as a means to issue a dollar-backed stablecoin, and that a blockchain-based issuer could also begin to do work in new segments like real-world-asset-backed loans. While MakerDAO’s initial pool of RWA loans is quite small (just under ~4m in total loans outstanding today), it’s easy to see this scaling into a much larger operation (which could help diversify their collateral base away from USDC). These are early signs that crypto has the potential to grow to touch more than just value-exchange online. 

It’s still so early for everything. A few nights ago, I went back and re-read some of the first messages I sent with the Turkish grad student from 2017. I wanted to revisit what I was really thinking about cryptocurrencies at the time. Our messages began in September, just a few weeks after my post-internship job offer. This part in particular stuck out to me:

Essentially, I was asking -- how can Bitcoin (or Ethereum, or any other cryptocurrency) scale without the kind of leverage and rehypothecation that we see in traditional financial markets? In my own words “Bitcoin (or the blockchain) can’t replicate the international banking system we have where money and ‘value’ is basically manufactured through securitization and other fancy financial instruments.” Obviously, I was not anticipating DeFi. As I said in the second message “I’ve noticed that tech people are usually way more imaginative.” I was right about that. DeFi had a little under $2bn in TVL a year ago and now sits just above $50bn in TVL today (and much more if you include blockchains like Solana and Binance)  -- a staggering amount of growth. 

While you might argue that DeFi encourages some degree of reckless over-speculation, overleveraged trading, and is artificially inflating the value of certain crypto-assets today, I actually don’t think that matters longer-term. For one, price action in the industry serves as a form of advertising, and an early engine of innovation (Chris Dixon and Eddy Lazzarin had a good post that examined this phenomenon with respect to the price of Bitcoin, but the same is likely true if you map in assets like Ethereum or Solana). Even if certain assets fluctuate today (and those fluctuations are intensified by leverage), the overall impact of getting more people interested in crypto is a positive counterweight to any near-term mania (though yes, it does suck for the people who lose a lot of money). 

And even if DeFi and yield farming is creating nearer-term hype, it’s also illuminating out the greater promise of crypto in general: the idea that if you eliminate middlemen and share value with users of a protocol instead, the positive externalities are so great that the crypto can start behaving like a public good. Grants issued by groups like Uniswap and the Ethereum Foundation are an early indication of this. And this isn’t confined to just DeFi; NFT projects allow creators and collectors to share in the value of art created by eliminating (or at the very least, reducing the take rate) of middlemen, while encoding an enduring revenue stream for the originator of a specific work. Hopefully we’ll soon see ways to map this model onto other scalable operations, like music and other forms of entertainment, DAOs that own real estate, and maybe a blockchain-based Uber. 

The “seeing things as stocks” mindset that was first inculcated in me four years ago will likely need to be reframed. In the future, public companies may see no separation between users, employees, and shareholders. In fact, “public companies” as we understand them today may not even exist, as they will have issued tokens at an earlier stage, and might not even IPO. While I realize I’m now violating my favorite neologism (“Don’t try to predict the future. Try to describe the present.”), I think we have early signals that this could come to pass. Everything from the increased focus on DAO treasury management to aforementioned efforts to collateralize real-world-assets with DeFi indicate that the need to tap public markets for capital may recede (it’s also quite possible that public markets and TradFi in general are reshaped from within by crypto, in ways that I’m not yet imagining, and they continue to endure in a revitalized form). Of course, there are tons of uncertainties here: the continued pace of user adoption, and regulatory overhangs are two big ones. 

Mostly, I’m just unabashedly optimistic, for all the reasons I outlined above. In the four years since I first started paying attention to crypto, the industry has transitioned into an instrument of genuine value creation. I’ve met so many people in the past year who have inspired and galvanized me -- and my only hope is that I can contribute to an industry that I believe will revolutionize the way we live, both online and in the physical world. 

Usually when I write an essay, I start with the concluding paragraph already written, at least in my head. I tend to begin already knowing what I want the lesson to be. This is one of the few exceptions -- an hour before publishing this, I’m sitting on the couch in my living room, trying to figure out the best way to bring both the past four years of my professional life, and the promise of crypto to some kind of satisfying finale. And I’m realizing that it’s probably an impossible task to achieve in writing. Because with crypto, you just have to live it. So now I’m off to go do that. 

Thanks to early readers of this essay: Andrew, Jeremy, Keeks, Reggie, Tarun, and Toby. 

Also boundless thanks to the builders, writers, thinkers, tinkerers, and visionaries who have made their work available on everything from Github to blogs to forums to Discord messages. All of your contributions have made me, and numerous others so excited to be a part of this space.   

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One of the most persistent refrains of the COVID era is that lockdown accelerated the world’s shift to eCommerce. The indelible, seemingly divinely-ordained pull-forward. In the beginning were the words: COVID dramatically accelerated eCommerce penetration. The shift to eCommerce is pulled forward by 5 years. No, 10 years in eight weeks. Then the trickle of quarterly earnings arrived with the numbers to validate the prognostications of sell-side analysts and management consultants everywhere. Amazon (retail, ex-cloud) and Shopify both grew more in absolute dollar terms than Walmart did during the second & third quarters of the year (Amazon revenue +$47bn, Shopify GMV +$32.4bn, Walmart revenue +$14.2bn). Major retailers like Lululemon and Nike saw an increasing amount of their revenue derived from online sales (Lululemon averaged roughly 50-60% of revenue from eCommerce vs 29% last year; Nike averaged 30%). People tried grocery delivery for the first time. Online crushed physical, once and for all.

But zooming out paints a different, less teleological, not quite so meteoric picture. Per the US Census Bureau, in Q3 of 2020, online sales were...just 14.3% of total retail sales -- admittedly up 30% from last year, but down from their height of ~16.1% of sales in Q2. 

Put another way...if your memory of the past year was erased, and I told you that a pandemic would rip across the world, rendering us all loyal supplicants to online shopping, what percent of sales would you assume eCommerce would be of overall consumption? Probably pretty high, right? I posed the question to some family and friends, and everyone guessed in the 50-80% range. The reality -- a little over 14% of sales -- is actually shocking. The takeaway shouldn’t be “eCommerce is eating the world” it should be “despite lockdown, store closures, mass layoffs, and global logistics networks that rival militaries in terms of sophistication, eCommerce was less than 1/6th of sales in the US.” 

Intelligence, for the moment, is outpacing life.

This past weekend I went back and skimmed some of the sell-side reports that filled my inbox during the outset of the pandemic. The thrum of emails hit their stride in the first week of April (reflecting the one-week lag in credit card data). I began seeing headlines like “Not business as usual: eCommerce growth accelerates in third week of March” with revelations like: “Grocery & sporting goods strong. Best performing categories include: Online Grocery (up 41% in March from 3/1 to 3/24, 24pts higher than Feb at +17% Y/Y); Sporting Goods (up 32% Y/Y in March, 33pts higher than February at -1% Y/Y)” A few weeks later: “US eCommerce growth through April was +63% Y/Y, up 38pts from 24% growth for March, and up from 57% Y/Y growth at the time of our last update (through 4/23).” Later: “Weekly growth for the week ending 5/29 remained at its recent high of +85% y/y, unchanged from the week ending 5/23, despite more of the US opening.” And so on. 

Fast forward to the end of the year, and you see a chart that looks like this:

The point of including this is to put us all in the mindset of...me, or anyone working in finance & tech, probably living in a mental future that far outpaces the physical present. The pandemic partially seemed like an opportunity to corroborate the promises we’d been hearing for years (or at least, I’d been hearing for 2 years, and started attending “Future of Retail”-themed conferences hosted by investment banks). The buzzwords were finally being crash-tested. BOPIS transitioned overnight from a retail conjugal visit to the only way we could fathom picking up groceries, clothes, and 65 inch TV sets. Target added 10 million digital customers in the first half of the year and Walmart doubled their eCommerce sales. Still, despite all of this, online sales were just 16.1% of the overall total at their peak in the second quarter of the year. 

I’ve been struggling with squaring the mental anticipation with the physical reality. I think the takeaway from the pandemic is that there is a massive asymmetry in the power, vision, and will between technology/engineering/logistics, the government, and human volition. Just compare Amazon’s early response to the pandemic to the government’s:

• Amazon announced two-weeks paid leave for any employee diagnosed with COVID-19, in addition to unlimited time off for hourly employees through the end of April (March 11)

• Announced the hiring of 100k more Amazon workers (then upped to 175k), and $2/hr increase (March 16th)

• Stopped accepting non-essential items in Amazon warehouses from 3p sellers (March 17)

• CARES Act passed (March 27th)

• And….the CDC recommends mask-wearing (April 3rd)

We all at some level (either actively or subconsciously) understand the militaristic precision that Amazon and pretty much any vertically integrated retailer can offer (Amazon’s head of logistics is literally nicknamed The Sniper). And I think this understanding (both active and subconscious) exerts a kind of overpowering, psychological hegemony (call it eCommerce-as-a-vibe) that renders us less able to acknowledge one simple fact: shopping in person is still more popular than shopping online. 

Of course, part of the difficulty of making sense of physical retail is that everything is shifting so quickly. A few months ago, Yelp found that 30,374 retail businesses in the US closed during the pandemic; (163,735 businesses closed in total, per Yelp). In New York City alone 1,000 chain store locations closed -- a 13.3% decline -- and by the end of September, 11,200 businesses overall closed in New York City (7,200 permanently). In LA, 15,000 businesses closed; in San Fransisco 6,200 closed. The understanding seems to be that big box retailers like Walmart and Target are the major brick and mortar beneficiaries, and that mom and pops are screwed. 

But I also wonder whether this understanding is influenced by the fact that a lot of people who work in tech, finance, and media also happen to live in New York and California -- where retail has arguably fared the worst. Just look at retail job recovery as an illustration: in December, US retail jobs were back to 97% of their pre-COVID levels. But numbers were lower in NYC (87%) and SF (94%, where numbers have been drifting slowly downward for the past 5 years anyway). But it goes beyond jobs -- New York and California have the most expensive commercial real estate in the country as well. And while real estate leases are coming down markedly (in NYC they’re down 12.6% from last year), ground-floor vacancies in the 16 most popular shopping corridors of NYC are 20% higher, at 254 storefronts. Astoundingly, NYC’s Department of Small Business Services doesn’t even know how many retail storefronts the city has, let alone how many are vacant in total, so getting a fuller picture beyond the most popular shopping areas is a little more difficult. But gonzo reporting tells a similarly bleak story: a reporter from the local paper Patch walked along Second Avenue between 60th and 90th street, and counted 66 empty storefronts. 

So just restating the mental backdrop: people in tech, finance, and media are primed to anticipate the downfall of retail because they live in cities that play host to some of the most visible disruptions in retail. Now to be clear, I’m aware that these ruptures are happening everywhere. I’m not going to convince anyone who prefers buying their Rick Owens t-shirts from Mr. Porter over Totokaelo (which, I just discovered in the process of writing this, is already gone) that physical retail is any more efficient or neat than the messy process of wandering, touching, and discovering. I’m not going to convince anyone that masking up and going to the corner deli is more hygienic or seamless than getting groceries from Instacart. But I love shopping in person (even for something as banal as canned soup or gum), and I think millions of people still do too. I have yet to find a form of “social commerce” as satisfying as wandering through the Lower East Side with my friends.  I earnestly think the 60,000-SKU grocery store is a miracle that warrants celebration. Not to get all BLS-y, but the Bureau of Labor Statistics did release a study in 2019 that found people who shopped spent an average of 1.7 hours doing so every day. So rather than assuming retail has a foreclosed future (and perhaps rendering that just through sheer force of anticipation) what are some alternatives?

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A Very Brief Historical Interlude (or...thinking about Architecture as Technology...or “the most profound technologies are those that disappear.”)

In 1919, the Abraham & Strauss Department Store in Brooklyn became the first retailer to introduce air conditioning to their stores. The technology (and it really was seen as technology at the time) had been teased and tested in the New York Stock Exchange (1902), the Missouri World’s Fair (1904), factories, and movie theaters (the first air conditioning system in a theater was installed in 1917--by 1938, 15,000 of the country’s 16,251 movie theaters were air-conditioned). But shopping wasn’t far behind. While we now think of air-conditioning as an immutable fact of life, the ability to cool air and control the temperature of an environment was at the time, radical. 

Air conditioning unleashed a multitude of possibilities in the realm of shopping. The engineer Willis Carrier (of Carrier renown), advanced the idea that human comfort can be directly linked to temperature and humidity and was instrumental in convincing retailers to introduce air conditioning to their shops. This in turn dramatically shaped the aesthetics of shopping space. For one--it essentially made windowless department stores and cavernous malls a reality. In 1938, a writer for the trade publication Department Store Economist remarked that air conditioning enabled a selling space “free from the slightest daylight or natural ventilation and a more uniform, pleasing artificial lighting result...In many ways the elimination of windows adds to the beauty and selling efficiency of the store.”. Victor Gruen, widely acknowledged as the inventor of the modern-day shopping mall, knew that he needed to include air conditioning in his first mall in Minneapolis (constructed in 1956). Gruen said “Whenever I got [to Minnesota] it was either freezing cold…or it was unbearably hot. From these personal experiences, under which I suffered greatly…I concluded that open public pedestrian areas in a climate of extremes…could not be a total success. So I carefully prepared the Dayton’s [Gruen’s clients] for the shocking idea of establishing completely weather-protected, covered and climatized public areas.”

Shopping malls helped proliferate yet another feat of engineering: the escalator (or perhaps it’s just as true the other way around: escalators helped proliferate malls). While the technology actually had earlier buy-in from retailers (Harrod’s installed the first escalator in 1898), malls in the United States represented a new storming ground— From the Harvard Design School:

1945–58: Marketing. Back in the West, the momentary lapse in consumerism caused by World War II gives way to an unprecedented and uninhibited inventiveness fueled by consumerism. Advertisers claim technology as the guarantee for economic success, and the Otis Company discovers marketing. Even though the basic idea of the escalator has remained virtually unchanged since its first application, it is heralded as a technology critical to the postwar prosperity. A direct equation between a building’s cash flow and number of visitors is formulated: maximum circulation = maximum sales volume. Store area is calculated in direct relationship to volume and flow of shoppers. Stores are now devised according to the equation, retail area above ground floor / capacity of the stairways < 1/20. That is, to carry one person an hour to every twenty square feet of merchandise area above the ground floor is to accomplish maximum dollar volume. The escalator can move five to eight thousand potential shoppers an hour, yet the sixteen hundred escalators in operation in the United States in the late 1940s are hardly enough to sustain the postwar shopping euphoria.

Technology is diffused not just through self-evident usefulness, but by self-conscious promotion. Recall the rather ominous quote in the Carrier advertisement from 1930 above: “Buildings still in blue print may be obsolete…Unless they plan to manufacture their own weather…[Manufactured Weather] will prolong the time during which the building will command profitable rentals and hold desirable tenants.” The Carrier ad is just one example within a coordinated pro-AC marketing blitz. In 1941, Chrysler hosted a conference titled “How Air Conditioning Builds Business Profits” with arguments that AC “Reduces lost time on account of illness”, “Improves employee appearance” and “Keeps Merchandise Clean.” The conference also closed with an anecdote claiming that “during one particular hot spell in Pittsburgh…Boggs & Buhl had 14 faintings among customers and over 20 among sales girls. A competitor [with] air conditioning…corrected this problem by 90%.” You kind of have to wonder about the 10% of fainting spells that persisted…but the point is that the introduction of air-conditioning in the US was largely a top-down effort:

The post-WWII success of AC was, however, less a matter of consumer approval and more a result of how “architects and builders made the decision to air-condition American homes and offices, then lenders and regulators stamped the change with institutional approval”. Although ideas, promotion, and marketing played key roles in the growth of AC, the triumph of air conditioning in the 1950s was based upon its integration into building design, construction and financing. Architects, builders, and bankers accepted air conditioning first, and consumers were faced with a fait accompli that they had merely to ratify. Advertising specifically helped in “domesticating and normalizing the use of technologies that might at first seem frightening… also ultimately marginalizing those who still refuse[d] to use them.” Domestic Air Conditioning.

In the pre-internet era, engineers had to figure out how to reconfigure space to support a critical mass of people; if you could figure out how to attract people to your stores, or manipulate the flow of shoppers (“maximum circulation = maximum sales volume” per the wisdom of the escalator manufacturers) you could manage profitability. Because technology made the relationship between the consumer and consumerism more convenient, and because its acceptance was relatively uncontested, shopping itself sponsored the complete alteration of urban and suburban sprawl. By the end of the 20th century, shops outnumbered churches, synagogues, and temples by 3.6 times, primary and secondary schools by 14.6 times, libraries by 25.2 times, and museums by 242.1 times.

In that period, engineering solved the rather unwieldy problem of “how do we bring an unfathomably large number of people together so they can shop, and justify the millions of dollars we just spent building out this department store or mall.” While developers had other tools to ensure profits (mall operators used a 1954 tax change to accelerate their depreciation schedules and, in turn, realize higher tax write-offs) this is something really worth highlighting. Bringing people together, in person, was just as much an engineering endeavor as it was an architectural, aesthetic, or ideological one.

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Yes, there’s been a physical retail apocalypse, but that hasn’t been accompanied by a decline in physical retail sales. In the past five years, physical retail sales have increased by ~14%, despite tens of thousands of store closures in that period. In other words, the rise of eCommerce has likely coincided with — but not caused — the reckoning in over-retailing. It makes me wonder whether eCommerce is actually the enemy of physical retail — as we’ve long been primed to think — or whether the enemy of physical retail was the fact that there was just too much of it to begin with.

“We are over-retailed,” says Ronald Friedman, a partner at Marcum LLP, which researches consumer trends. There is an estimated 26 sq. ft. of retail for every person in the U.S., compared with about 2.5 sq. ft. per capita in Europe. Roughly 60% of Macy’s stores slated to close are within 10 miles of another Macy’s. - Why the Death of Malls Is About More Than Shopping, Time Magazine

Put another way, eCommerce might not be killing physical retail, it is killing the sprawl of physical retail. And this is a slow death, one that long pre-dated eCommerce. From an article in Fortune, circa 1995:

Among the economic victims of the new do-it-yourself movement are many of the old-style regional shopping malls that dot America's suburban landscape. The assiduously self-reliant have less and less use for these full-price retail centers, and their preferences are having an impact. In a recent survey by consulting firm Kurt Salmon Associates of Atlanta, 38% of respondents said they planned to shop at malls less often this year than they had in the past. The end result will probably be a big-time shakeout. The experts say that over the next few years, as many as 300 of the roughly 1,800 regional and super-regional malls--those with over 400,000 square feet of space--will be either shut down or converted to the warehouse-style retailing that do-it-yourselfers favor. Says Carl Steidtmann, director of research at Management Horizons, a retail consulting division of Price Waterhouse: "Regional malls clearly have a life cycle, and a lot of them are in their last throes."

In a way, the consolidation of retail is just a reflection and enabler of the way many people like to gather — in densely packed, often chaotic networks that favor serendipity and discovery. Cities do this naturally, malls did this forcefully. Victor Gruen envisioned the shopping mall as a way to graft the experience of the city onto the suburban core, predicting that large malls would become “urban sub-centers” in suburban space. The only thing Gruen and other mall retailers miscalculated was the number of malls actually required to sustain this. You might be able to argue that the consolidation of malls and department stores is now accelerating the process of urbanizing suburban America — we are coercing space into bringing us closer together. Jane Jacobs wrote about the way in which the kind of serendipitous contact found in cities (or perhaps, “urban sub-centers” of suburbia) imbues daily life with enigmatic substance:

“The trust of a city street is formed over time from many, many little public sidewalk contacts. It grows out of people stopping by at the bar for a beer, getting advice from the grocer and giving advice to the newsstand man, comparing opinions with other customers at the bakery and nodding hello to the two boys drinking pop on the stoop….utterly trivial but the sum is not trivial at all. The sum of such casual, public contact at a local level—most of it fortuitous, most of it associated with errands—is a feeling for the public identity of people, a web of public respect and trust, and a resource in time of personal or neighborhood need…The absence of this trust is a disaster to a city street. Its cultivation cannot be institutionalized.” - Jane Jacobs, The Death and Life of Great American Cities

It might sound regressive, decadent, or even docile to view shopping as a vital form of public life. But honestly, this is America, and Americans love to shop. eCommerce may be reorganizing the world (and software is decidedly eating it) — but how do we help physical retail preserve its function as the country’s social lubricant (or if we don’t want private companies to continue subsidizing public life, as they’ve done for over a century, what are the alternatives?). It’s quite likely that the same energy directed toward orchestrating physical retail in the 20th century is now being channelled into making eCommerce as harmonious and efficient as possible. While there’s absolutely nothing wrong with that, I wonder whether that process is also confining innovation to warehouses, user interfaces, and getting people to click on Facebook ads. As the history of AC and escalators indicate, every social phenomenon needs a self-aware technology to enable it. When does physical retail become self-aware again?

Threading It All Together

In 2019, the New York Comptroller’s office issued a study on the rise of empty retail space in the city. While the study found that Amazon drove vacant square footage higher by roughly 1% from the period of 2007 to 2017, the far more powerful driver of retail vacancies were actually…delayed government permits.

For every 1% increase in the number of building alternation permits unapproved after 30 days, vacant retail square footage increased by 3.28%. It seems like getting caught in whatever bureaucratic rigamarole governs the Department of Buildings had a far bigger impact on retail vacancies than Amazon did. Landlords had a massive impact too: retail rents rose by 22 percent on average citywide between 2007 and 2017, with a one percent increase in average retail rents associated with 0.33 percent increase in vacant retail square footage. As I said in my introduction, there are now thousands of vacancies in the New York City metro area alone. In the short term-these vacancies are caused by COVID, but if they persist in the long-term it will be attributable to exogenous factors like bureaucratic delays and landlord greed. Yet mentally, we’ll attribute those vacancies to lack of demand or eCommerce. My fear is that living in what looks like the death of physical retail, will expedite the actual death of retail. The tendency to mentally accelerate into the future has the potential to overextend things toward extinction.

The illusion of death of course, necessitates a will and testament. In his critique of contemporary urbanism, Junkspace, the architect Rem Koolhaas paints a picture of the miasmic ways in which planners aim to repurpose space once it appears unusable:

Junkspace expands with the economy but its footprint cannot contract-when it is no longer needed, it thins. Because of its tenuous viability, Junkspace has to swallow more and more program to survive; soon, we will be able to do anything anywhere. We will have conquered place. At the end of Junkspace, the Universal? Through Junkspace, old aura is transfused with new luster to spawn sudden commercial viability: Barcelona amalgamated with the Olympics, Bilbao with the Guggenheim, Forty-second Street with Disney.

Koolhaas is castigating a much broader trend in corporate, municipal, and institutional initiatives that (in an effort to make use of excess space) impose a cloying cultural uniformity over everything. Within retail, this trend is most apparent whenever someone talks about engineering physical space to function more like the internet. I’ve recently read pieces that posit physical retail as a form of “sponsored content” with the idea that a storefront is just a bit of targeted advertising, meant to entice you to make a purchase online. I’d imagine that if you design a space to function that way, you’ll be successful (you might even bring a space to a terminal apotheosis, and graft literal advertisements over vast refrigeration units, like you’ll find at some Duane Reade locations in the city). The effort to rebrand “space” as “marketing” is less a symptom of needing more advertising, and more a concession that we built too much space (again, this might be true of suburban malls, but it seems less accurate when talking about physical retail in cities). To physically de-densify would be an admission of failure or defeat. So we de-densify the content of space instead. “Please look, but don’t touch.”

Commercial and visual innovation increasingly feels like it lives on the internet. People can create low-overhead businesses and reach customers quickly (think Amazon’s 3p sellers, Stripe, Shopify, Facebook/Instagram Ads) and are relieved of the need to ever achieve the kind of physical scale required of retailers in the past. Tools and software like Figma, Spline, Unity, and Unreal make it possible to easily create not just new user interfaces, products, and games, but worlds with their own logic and economies. While the drop-shippers of eCommerce will probably stay confined to the web forever, I wonder why we don’t see more businesses that started on the internet breaching into the real world. Yes, you’ll find a stray Allbirds or Bonobos in the wild (the digitally native brand repatriated to the concrete jungle or local Westfield)…but those brands aren’t from the internet, so much as they used the internet to perpetuate popular demand.

What I’m looking for is something slightly different. Call it brand populism. Call it net nativism. Call it making retail space as idiosyncratic, surprising, and weird as the 3am discoveries you find in an Instagram wormhole. There are hundreds of thousands of shops that exist only online (some examples: Happy99, blum, and vewn which is managed by an animator with almost 750k followers on Youtube). Why don’t we get to wander inside of these brands in reality? The problems I explored above might be some of the bottlenecks (sclerotic governments, high rents), but there is also an unavoidable reality: the internet is simply a much more hospitable host to innovation and creativity than the physical world.

Maybe this will change soon — Shopify, for example, is hiring a “Spaces Lead” which could signal a move to give some of their small merchants a storefront. So the start might be a Shopify-hosted physical retail space. But this will likely be an early step toward what I’d like to see: a much more contiguous and rational relationship between the capabilities of internet and physical space. What would happen if we designed space as a way to mediate the flow of information online? How does physical space get rethought, knowing the astonishing capabilities and speed of a modern logistics network? As my friend Reggie likes to say, bits reorganized atoms. In the post-mobile, post-software-ate-the-world-and-now-we’re-in-the-microbiome society, what kind of spaces get built? And whose responsibility is it to think through these things? Does the onus rest with architects? With designers? With brands? With developers (of the software and real estate variety)? With investors? With, god forbid, the government?

Honestly, I don’t have the answer to these questions. Part of why I wrote this piece is to work through my thoughts and feelings about this period of time — a period when it took a pandemic to propel us into an eCommerce future we had long imagined for ourselves. Yes, we all now appreciate the convenience of online shopping. But I think we cherish being around friends and strangers in real life even more. Are we building to acknowledge that?

I’d love to hear from anyone working in this space/thinking about these things. Feel free to get in touch with me @VirtualElena.

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Thank you to Drew, Reggie, and my mom for reading drafts of this, giving suggestions, and sending voice memos <3

Two weeks ago, I took the PATH train to New Jersey with my boyfriend, and did something I hadn’t done for over eight months: I watched a movie in a theater. To be specific: The Uninvited (1944), at the Loew’s Jersey Theater. It was the experience economy, minus the economy: the Loew’s Jersey Theater is a 3,021-seat movie palace, which was constructed in 1929 and closed in 1986, before being resurrected as a volunteer-run non-profit in 1993. Sitting in the cavernous hall, with a ticket I paid $8 for, eating $1 popcorn served by an unpaid concessions worker, listening to the pre-show organist eke out Bach’s Toccata on an $80,000 restored pipe organ, and surrounded by about 100 people at most (that’s 3% occupancy, for the COVID capacity alarmists among us) all I could think was: this cannot possibly be profitable.

Ok, that wasn’t all I was thinking. I was also thinking about how much I missed movie theaters, and how grateful I was to be back in one, about the slow decline of theater audiences and the (arguable) concomitant decline of quality films in general, about when exactly people began restoring cultural artifacts like pipe organs (object restoration must be a somewhat recent practice?), about this essay by Roland Barthes called “Leaving the Movie Theater”, and just generally how strange and unmoored this year has been. But my top-of-crust consciousness was tallying audience members, estimating the cost of electricity, the cost of upkeep, of occupancy, of food, of time -- all in service of some imagined bottom line. 

This is definitely a byproduct of having worked in finance for two years, and a (perhaps native?) tendency of mine to ricochet from the moment-at-hand into all of its possible offshoots and implications. Maybe it’s a byproduct of living a few hours of my week on Twitter (where we’re all accustomed to imagining every possible permutation of reaction to an idea before it's even been expressed). Or it’s a byproduct of the immediacy of single-click delivery, or ridesharing, or SVOD entertainment — and the way in which a hastening of physical supply chains are probably accelerating the velocity of thought itself. Or maybe I just inherently have a mercenary bent to my worldview -- with the “can this be profitable?” inquiry just the beginning of a whole other hierarchy of ingrained assumptions about what is important (plenty of people watch movies without wondering whether anyone is making money and that is honestly probably a much more satisfying way to live). 

In other words, should I have even cared about whether someone was profiting from my presence? 

Probably not. Watching film is theoretically an escape from all that. It was for me too: back when movie theaters were open in NYC, I’d see three or four films in a week -- and sometimes way more -- with pretty much no thought about who was making money from me. I recently stalked my past self on Letterboxd, and here’s a sample entry from the week of July 19th, 2019: A Faithful Man (Quad Cinema), North By Northwest (IFC); July 20th: Pickpocket and Au Hasard Balthazar (Anthology); July 21st: A Matter of Life and Death (Home); July 24th: Mamma Roma (Metrograph); July 25th: Once Upon a Time...in Hollywood (Village East); July 26th: The Mountain (IFC) (just missing the weekly cutoff: Life, and Nothing More…, Ugetsu, and Les Dames Du Bois de Boulogne). The venues: a mix of non-profit theaters, some independents, and IFC, which is owned by AMC Networks. 

In that essay by Barthes I mentioned above, he describes beholding a film as a kind of act of hypnosis. But my favorite line from the work isn’t about film -- it’s about TV. Barthes writes that “television doomed us to the family”...which might sound like a misanthropic and unverifiable sociological diagnosis until you look at the statistics and accept he actually has a point (in 2018 the Bureau of Labor statistics found that 57.2% of Americans watched TV with family every day. Just 3.8% of Americans reported watching TV with friends).  

Anyway, this brings me to the point of this piece, which is about what (I personally think) happens when we consign viewing to the home/family (especially as at home viewership has surged due to COVID). Onto the numbers baby...

Onto the numbers

So Nielsen has recently (as of August 3rd 2020) started publishing the top 10 shows streamed online, organized by millions of minutes streamed. It’s dominated mostly by Netflix shows (both original and un-) and it's pretty staggering to think about. Here’s all of the current data displayed as an area graph (sadly there’s no way to embed the graph on Substack, but if you want to mouse over this as an interactive chart, here’s the link). Because the Nielsen data is released with a few weeks’ of lag, the data is current from August 3rd - October 11th. Note: the graph was made in collaboration with the team at Bayes a data visualization company founded by Justin Woodbridge and Will Strimling. If you like the work, you should contact them!

So there are a couple of interesting takeaways from the data — probably more than I can reasonably address in this post. For one, it’s pretty clear that the shows with ongoing popularity aren’t Netflix (or Amazon or Disney) originals -- they’re anchor shows like The Office, Grey’s Anatomy, and Criminal Minds, which all have many seasons, and embedded fan bases that date back to the mid-aughts (the film critic A.S. Hamrah once described the streaming strategy this way: “Successful multinational companies are gentrifiers: they move in on old content before they start to make their own content and become television networks”). Concocting a narrative for why this might be isn’t too difficult -- these are shows that were popular when they aired on networks (NBC, ABC, and CBS respectively), and can operate equally well as entertainment, ambient noise, or fodder for dating apps. They are shows that are comforting in their nostalgic capability and in their mass-market appeal. At their peak around 2009, Grey’s Anatomy and Criminal minds were the 10th and 11th most popular show in America — and given their placement in these Nielsen streaming rankings, it’s arguable that they’re even more popular today (perhaps giving more credence to the idea that we never left the aughts).

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Another reason might come down to episode count: those three shows (The Office, Grey’s Anatomy, and Criminal Minds) all have 190+ episodes, which means that it takes a lot of time to work your way through a series (especially as the latter two shows are still on the air).

Here’s the data above represented in a spreadsheet. This is the ephemeral and fickle nature of an audience, in the form of a cold and empirical table. We see that Netflix shows (and they are mostly Netflix shows, with the exception of The Boys and Mulan) enjoy on average a little less time in the top 10, and about an equal number of total minutes viewed, despite being a little over-represented (15 vs 13 shows). Each category accounts for about 40 billion minutes cumulatively, or ~50% of the 81.2bn total.

The reasons for this are pretty obvious - again, there’s more back-catalogue for shows that enjoyed years of renewals and syndication. I think this chart also does a good job of illustrating what a lot of people lament when they talk about streaming: the popular original shows are often short-lived (or aren’t even shows: in the case of limited series like Ratched or The Haunting of Bly Manor) and cancelled for seemingly no reason.

I think this is the Netflix “sleep-as-competition” mandate (articulated by Reed Hastings) in full, illustrative force. When the imperative is to dominate your users’ time, it becomes increasingly irrelevant how that time is used, as long as it is spent consuming something in your rotation: novelty and “new content” increasingly trounces renewed shows (with the rare exception of a show like Emily in Paris…which was just renewed for a second season).

You might look at the charts above wonder — how do these “millions of minutes watched” convert into viewers? Nielsen tracks minutes, not people. This, in itself, is kind of a perfect encapsulation of the present moment. Time, not people, as the end-state of all measurement. In the world of analogue television and in film, producers are (or were) rewarded for outsize successes, either in the form of syndication fees (e.g. when you license your show after it hits 100 episodes) or in film financing where producers share in a movie’s profit. Those fees and profits usually have some kind of relationship to the number of viewers a show or film has. But in streaming, financial upside for production is usually capped (though some regions are trying to change this; Netflix, for example, just struck an agreement with German creators to pay out royalties for shows filmed in the region).

Netflix is comfortable operating at slimmer margins than networks and film studios did in their heyday, which is why they’ve been able to out-spend their rivals (who had gotten perhaps lazily accustomed to affiliate fees and ad revenues). Investors don’t care that Netflix operates at roughly 20% EBITDA margins, while most cable companies are around double that at ~30-40%…because the idea is that Netflix can outspend competitors, and doesn’t have to disentangle themselves from the affiliate fee/advertising-based model of legacy cable companies (that’s the very short version…the longer version is the hyperlink in the first sentence of this paragraph).

This means that winning, for Netflix, is less a matter of higher profits than it is a matter of triumphing in the realm of time, and how you personally choose to allocate it. While this has led to some good things (more shows means more opportunities for creatives), it also has led, I think (and I think a lot of other people think too), to an over-saturation of content.

Netflix (and all streaming) arguably marks the end result of what David Harvey identified as “time-space compression.” In his book The Condition of Postmodernity. Harvey writes:

the history of capitalism has been characterized by speed-up in the pace of life, while so overcoming spatial barriers that the world sometimes seems to collapse inwards upon us. The time taken to traverse space and the way we commonly represent that fact to ourselves are useful indicators of the kind of phenomena I have in mind. As space appears to shrink to a 'global village' of telecommunications and a 'spaceship earth' of economic and ecological interdependencies - to use just two familiar and everyday images - and as time horizons shorten to the point where the present is all there is (the world of the schizophrenic), so we have to learn how to cope with an overwhelming sense of compression of our spatial and temporal worlds.

I first encountered Harvey in college, while writing my history thesis on credit cards. But I think his observations work equally well when applied to the phenomenon of toggling through Netflix, confronted with a conveyor belt of shows you’ve never heard of (in other words: “time horizons shorten to the point where the present is all there is (the world of the schizophrenic)”). A shortened time horizon means that any week can be billions of minutes, so long as you have a medium of eyeballs to channel those minutes (humans are the medium, time is the message).

Either way, the chart above represents a little over 80 billion minutes of viewership in the span of ten weeks. How many people are 80 billion minutes?

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It is kind of possible to estimate. Kind of. Let’s use Schitt’s Creek as our example, as it’s the top show for the most recent reported week. If people on average watched 90 minutes of Schitt’s Creek, that would come out to around 16 million weekly viewers. Edge that up to 180, and you get 8 million. If everyone who watched Schitt’s Creek allocated 100% of their weekly TV viewing time to the show, you might have something like 1.23 million viewers. Or if we went to the opposite extreme and assumed everyone watched 2 minutes of Schitt’s Creek (the way Netflix quantifies a view), then you’d have 728 million viewers (kind of unlikely, as Netflix has only 195mm worldwide subscribers). And so on. The only orienting clue we get is when a movie shows up -- a data point with a calming gravitational pull. Because we have Hubie Halloween in there (god bless you, Hubie Halloween), which is 102 minutes, we can organize these numbers with something approximating assurance (though, again, we actually kind of can’t). Anyway, things roughly fall into place from a viewership perspective when you designate 90 minutes as the average weekly viewing time (though that honestly seems low. But I digress.) 

These may not be totally accurate estimates — but the display does make you wonder…if shows in the top ten are averaging in the low single-digits of users, what does the long tail of viewership look like on streaming services? After we drop off from 7 or 4 million viewers, how much further do we go? Which shows have 2 million viewers? Which have 1 million? 500,000? 100,000? 20,000?

Maybe instead of trying to quantify that question, we should qualify it. A few months ago, I came across this Tweet from the creator of the Netflix show Soundtrack:

“What if you made a show and no one noticed?” I wonder how many producers of streaming shows feel the same way: months or years of preparation, hundreds of people enlisted to bring a vision to fruition, casting calls, days on set, arduous hours spent in post-production…only to find that the program you lovingly and carefully produced has landed in a cemetery before it even had a chance at life. It’s almost the opposite idea of Harvey’s “shortened time horizons” idea. Between writers, producers, actors, directors, crew members, editors, and marketers, five hundred people, or more may work on a show. Assuming everyone spends one year, and forty hour work weeks (a conservative estimate), you’re looking at roughly 115.2 million minutes. Admittedly that’s less time than the minutes spent streaming Enola Holmes the week of September 28th 2020. But the work was probably a little harder. And more importantly, the time is expanded, not compressed, across a horizon of a year or more.

Another way of looking at it: if 100,000 people watch one episode of that same 60 minute show (6 million minutes in total), that is just 5% of the amount of time it took to make the show in the first place. Maybe a bad-faith (and insulting) way of comparing/looking at time. But it does suggest a somewhat asymmetric relationship between input and output.

In 2010, there were 105 million cable subscribers, and a little more than 210 shows on the air. In 2020, there are roughly 84.5 million cable subscribers, 73 million Netflix subscribers in the US, and over 530 shows on the air. This means that the odds of being a runaway, Queen Gambit-esque hit are slimmer than ever. Sure, there were tons of failed shows in the analogue TV era as well — but there was less competition. For a person working in TV today, I do wonder what upside means? Maybe I’m overthinking it — in an era where making it as a creative person is more difficult than ever, maybe being paid to do what you love is the upside in itself, and the proliferation of new shows just represents more opportunity (and because having a runaway success is so much rarer, the pressure to have one is diminished, which means that saying/doing what you want — if you’re given the right budget — is the reward in itself).

Part of the mystery of all of this is in that original Barthes quote: the idea that TV “doomed us to the family.” Another, less pessimistic way of saying this is: TV made it impossible to know what anyone beyond our bubble was watching or enjoying. Yes, we have Nielsen. Yes we have Twitter. Yes we have reviews. But the feeling of synchronous, collective effervescence with strangers? That’s only possible in one place. The movie theater.

Just two more minutes of your time?

I’m not in denial about the state of moviegoing in America. Even before the pandemic, cinema attendance was falling precipitously. Even before Netflix offered streaming, people weren’t exactly raring to go to the cinema: in 2006, 75% of Americans said they preferred watching films at home. I’m in the minority, and I accept it.

Interestingly, film is a kind of referendum on the Harvey-ist conception of space and time. Yes, the act of beholding a film is a compression of space (you are flung into another world, another image) and time (see Tarkovsky’s Sculpting in Time). But from an economic perspective, film is about as rooted in space and time as you can get. You go to a theater, you pay for your ticket, you sit down, you watch you leave the movie theater — and the money you spent is split between the cinema and the producers. If you know how much a film earned, you can guess approximately how many people watched it. And likewise (barring walk-outs), you also know exactly how much time they spent watching. It’s a world away from the quantification of streaming.

But let’s return to the aesthetic and social pleasure of seeing a movie in a theater. In the words of Barthes, film also resolves the more ineffable mystery of other people, how they view, and what they like:

What does the “darkness” of cinema mean? (Whenever I hear the word cinema, I can’t help thinking hall, rather than film.) Not only is the dark the very substance of reverie (in the pre-hypnoid meaning of the term); it is also the “color” of a diffused eroticism; by its human condensation, by its absence of worldliness (contrary to the cultural appearance that has to be put in at any “legitimate theater”), by the relaxation of postures (how many members of the cinema audience slide down into their seats as if into a bed, coats or feet thrown over the row in front!), the movie house (ordinary model) is a site of availability (even more than cruising), the inoccupation of bodies, which best defines modern eroticism – not that of advertising or strip-tease, but that of the big city. It is in this urban dark that the body’s freedom is generated; this invisible work of possible affects emerges from a veritable cinematographic cocoon; the movie spectator could easily appropriate the silkworm’s motto: Inclusum labor illustrat; it is because I am enclosed that I work and glow with all my desire.

And it also brings us closer to resolving the greater mysteries of existence. In Sculpting in Time, Tarkovsky wrote that cinema allows us to be:

a participant in the process of discovering life, unsupported by ready-made deductions....going beyond the limitations of coherent logic, and [beholding] the deep complexity and truth of the impalpable connections and hidden phenomena of life.

This is something that I think television, or at-home viewing (with its constant distractions, screen-toggling, phone notifications, etc) can’t ever accomplish. To re-use that Harvey quote again: “time horizons shorten to the point where the present is all there is (the world of the schizophrenic).” The proliferation of television (over 500 vs ~200 ten years ago) has arguably rendered daily life a little more unknowable.

Coda:

Ok I do think it’s pretty interesting that, on January 21st, 2020, Netflix announced they were redefining “view” from watching 70% of an episode to watching the first two minutes of an episode.

Could they have been sensing a seismic shift in the way people chose to view content? Here’s an interesting chart: notice that the surge in TikTok downloads coincides with the Netflix announcement:

TikTok now has over 850 million users — I have not yet seen anyone try to quantify the billion of minutes viewed/week that converts into, but it’s quite likely that it dwarfs Netflix’s weekly minute view count (which has 195mm users).

In the words of David Harvey: the time horizon is shortening, the present is all there…

If you’d like to get in touch, my Twitter is @VirtualElena and my email is elena96b@gmail.com

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There should be a word for these particular nodes of information: they aren’t necessarily facts  -- but they don’t seem nefarious enough to qualify as lies either. You accept them as truth because they have a unique capacity to stand distinct from the rest of the information that they introduce; they sit at the top of articles, justifying the existence of the content below (why else would we care about Gen Z brands, unless the kids really did have hundreds of billions of dollars in purchasing power?). They’re like the Muzak of facts, omnipresent, inoffensive, background noise. 

Except maybe they are a bit nefarious. After all, entire industries are built on their facade. Take the notion that millennials favor an “experience economy” over “things” as an example: it launched millions of dollars into interactive art, high-touch retail, the Museum of Ice Cream, etc. While COVID probably makes it harder to determine whether or not the experience economy was or ever would have been a “success” (success, I’ll define here in a rather narrow capitalistic frame of -- “did it offer investors a return?”) -- I’d argue the bigger question is whether people even wanted an “experience” to begin with (and again, “experience” is also pretty narrowly defined as “however the retailers, curators, venue-owners, and conference-hosters chose to manifest experiences”). To answer that second question is pretty simple--return to the often-cited Harris poll from 2014 (co-sponsored by Eventbrite) that catapulted the notion of the experience economy into public consciousness. 

The study found that “more than 3 in 4 millennials (78%) would choose to spend money on a desirable experience or event over buying something desirable.” It also claimed that since 1987, the share of consumer spending on live experiences and events relative to total U.S. spending increased 70%, and included this rather staggering-looking graph along with it.

That’s technically true, except when you zoom in on the Y-axis the total amount is really quite tiny: experiential spend went from 0.03% of total consumer spend to a little over 0.05%. Yes, that’s technically a 70% increase…but on a tiny base.

And then the real kicker is this little disclaimer at the bottom of the study. Basically, they surveyed a little over 2,000, just 507 of whom were millennials. And from that, an entire experiential economy was born. 

Of course, you might now have other qualms about the study. Why, for example, would the question have been posited as an either/or? Most people like experiences AND things. And the fact that Eventbrite, whose entire business model is predicated on experience, sponsored the study seems to have been overlooked.

So back to the “40% of consumer spending” claim. From the original article on optimized-for-TikTok-brands, I landed on a Mckinsey publication, which linked to a 108-page report, titled The State of Fashion, 2019. The “40%” claim was on page 45 of the report, but not derived from any original research; instead I was sent to a footnote citing an Inc article urging me to “Forget Millennial Purchasing Power…” because “Gen Z Is Where It's At” (perhaps, unintentionally, undermining the momentum behind the millennial experiential economy?). Interestingly the piece doesn’t even cite the 40% claim -- it says the following: 

This Barkley report estimates Gen Z's earnings to already be close to $153 billion, with overall spending of almost $100 billion. Once combined with allowance estimates (since many are in their young teen years), this yields $143 billion in Gen Z spending. And it doesn't even factor in the youngest Gen Xers who earn money by mowing lawns and babysitting. Considering that this Nielsen study shows Millennial spending at just over $65 billion, these numbers are staggering.

Before we get to the Barkley report, from which the 40% claim was spawned, it’s just kind of funny to pause and appreciate the attempt at skepticism -- a little missive to the future readers to take things with a grain of salt; the writer hints at a wariness (though doesn’t explicitly question) the idea that it’s a little ridiculous that Gen Z earnings are close to $153bn, while Millennial spending is less than 50% of that at $65bn. But nonetheless. Let’s move to the report.

When you download the PDF, you see a 13-page study on “The Power on Gen Z Influence.” The study, composed by self-professed “Gen Z evangelists” promises to “develop reliable estimates of Gen Z’s spending power” and launches into a three-pronged sum-of-the-parts examination. 

Direct Spending

First up in the Barkley study is an aim to ascertain Direct Spending. The first sin of the study is one that I alluded to in my tweet about this journey: the data that they use was published in 2013, from a Mintel survey conducted in 2012 of allowances for kids and teens ages 6-17 (the survey itself is behind a $4,000 paywall, so it’s hard to interrogate the underlying biases of the original source!). 

Still, they proceed with the numbers. By multiplying the 2012 population estimate of kids age 6-17 by the annual allowances for those children, adjusted for amount of allowance actually spent, the researchers arrive at $29bn in annual spending:

But they don’t actually do anything with this number. Instead, they use the weekly averages from the 2012 study on allowances, and extrapolate that out to a population sample of Gen Z-ers (using a population size from 2016).

So they arrived at $43 billion in estimated allowances. Before we move on, it’s worth stating all of the assumptions embedded in this process:

• The weekly allowance is an accurate number

• All kids get an allowance

• The reported % of allowance spent is accurate (this, again, is a stat from the Mintel study conducted in 2012 and released in 2013)

But wait there’s more (there would have to be, if Gen Z were 40% of American consumers, who spend an aggregated $16 trillion or so per year…).

The next step is accounting for Gen Z’s earned income. To do this, they provide this chart.

These numbers are a bit more difficult to verify because the 2016 BLS segments their age-based employment in increments of 16-19 and 20-24 (to be honest, I’m not entirely sure where those population estimates came from; sure, the youth employment rate is around 26%, but no census I’ve seen has a precise population count for ages 20-21). But even if we accept that the population numbers are accurate, there are tons of other problems with the estimates:

• They’re yet again using the Mintel data about percent spent on allowance, and applying those same percentages to PERCENT OF INCOME SPENT.

• Oh, perhaps even more egregiously, they’re using pre-tax median weekly income (which the BLS reports)

Ok so let’s fix this. Because working millennials are now age 16-23, you could use BLS data from the first quarter of 2020 (to account for COVID-induced job losses). That’s a larger population, so we might even find that the earned income, and therefore the spending of Gen Z is higher than what the Barkley study finds. Right? Not quite.

The BLS counts 1.3mm workers age 16-19 and 8.3mm workers age 20-24. Median weekly wages are $477 and $605.

If we wanted to build a model to estimate Gen Z spending, it might look something like this. Here are my assumptions

• a tax rate of ~12%

• spending outlay at 28%. This is derived from the BLS’s very detailed breakdown of annual spending by category. I used % counts for personal care, entertainment, apparel, household furnishing, housekeeping, and food.

• I adjusted the number of workers in the second column to exclude 24 year olds (by multiplying 8,364 by .75)

Again, you might dispute my assumptions; someone working a job at age 16 might have different outlays/imperatives than someone working at age 21. And anyone making near-minimum wage is probably spending most of their money on necessities — not optimizied-for-TikTok errata.

There is also the potential problem of over-counting. I doubt that the Gen Z-ers age 16-21 (or 23) are ALSO getting an allowance, so the first number posited by Barkley is probably suspect too. I’d subtract the people age 16-21 from the population count in Figure 1.2 a few paragraphs above (a correction of ~4.4mm people). This results in $33.1bn in total allowance spending for people age 12-21, not $37bn.

So instead of:

I get closer to $39bn in allowances (5.8bn for kids age 7-11 + 33.1bn for kids age 12-21) plus 56.5bn in spending, for a total of 95bn. Not 143bn.

Indirect Spending

The next calculation might be the most reasonable of the bunch. The Barkley report now moves trying to ascertain the influence a child can exert on their parents’ spending habits. Here’s what they get:

To be honest, it does make sense that a child would exert influence over the products that their parents buy for them. But then, they had to go a step further:

The Barkley report posits:

In addition to indirect spending on Gen Z, there is the indirect household spending that Gen Z is able to influence. The parents of Gen Z have told us in multiple surveys by multiple organizations that their kids influence purchase decisions, but just how much money is at play? Once again, we used the Census Bureau’s 2016 population estimates to determine the size of Gen Z in the U.S. To determine total household spending, we looked at Consumer Expenditures for 2016 from the Bureau of Labor Statistics.

Wait but wasn’t this what they just calculated?

To estimate the potential household spending that Gen Z can influence, it’s necessary to make some assumptions. First, we must assume that Gen Z has influence predominately for expenditures in the food, apparel and services, and entertainment categories. Second, based on the estimated number of households with children ages 6 to 17 from the U.S. Census Bureau’s 2016 American Community Survey and our total population estimates of Gen Z from the Census Bureau, we assume each household has an average of just more than one (1.125) Gen Z child. This suggests that more than $665 billion in household spending in the categories of food, apparel and services, and entertainment can be influenced by Gen Z. Gen Z is unlikely to influence total spending in each of these categories, however, and it should also be noted that some of this spending will be on the household’s Gen Z member, meaning the sum in this case cannot be double counted. Still, if we assume that the Gen Z child influences just 25 percent of household spending indirectly, it means they can influence just over $166 billion dollars per year. If they have 50 percent influence, that number jumps to almost $333 billion.

So instead of just influencing spending on products for Gen Z, the kids can now influence spending that isn’t even related to them (their parent’s clothings, the films that their parents see, the food their parents eat, etc)? To support this dubious assumption, they include this chart.

Moving beyond the fact that there’s a typo in the above chart (the 50% and 25% should be flipped), what’s most baffling to me about this chart is that there’s no attempt to explain if or how it should relate to the numbers above. Which brings me to…

If you scrolled through the entire post, stop here, this is the good part

The craziest thing about the Barkley report is that it just kind of stops after the second assumption about indirect spending. There’s no culminating paragraph, no recap, no attempt to add together the numbers that we’ve just spent so much time meticulously calculating. Am I supposed to add up their claim about $143bn in direct spending, $29-127bn in indirect spending, and $166-333bn in indirect-er spending and arrive at some kind of clarifying total (indirect-er marketing seems to be the miasmic corollary of influencer marketing — an appropriate category to put this entire report into)? How am I supposed to use these numbers? Why are the ranges so wide, and why is there no attempt to resolve them?

Actually no, the craziest thing is what comes after the claim that Gen Z might influence up to $333bn in spending.

There is exactly zero needle-threading from the numbers in the report, to the conclusion that Gen Z will represent “40% of all consumers” Even if you were extremely generous, and added the highest of Barkley’s own estimates above (direct, indirect, indirect-er) you’d get to 603bn in spending. But annual consumer spending is closer to $14-15 trillion dollars — which is more like Gen Z accounting for 4% of consumer spending. Anyway…

How we got here

Let’s retrace the steps, in reverse now. What’s fascinating about the genealogy of the 40% claim is that it actually wasn’t even mentioned in the Inc article, which meant that someone at Mckinsey delved into the Barkley report, scrolled to the end, and unearthed the 40% figure without bothering to track the assumptions therein. And when that number got attached to McKinsey, it was able to circulate ridiculously far. There should be a word for this too — the kind of legitimacy-brokerage that a firm like Mckinsey confers, allowing ideas to get packaged, and distributed as if they’re pre-verified.

In some ways, this is deeply insignificant -- just one tuft of misinformation in a sea of mendacious folly. But also, kind of wild to think about the millions of marketing dollars wasted on this.

Anyway, all of this reminds me of this xkcd comic:

If you’d like to get in touch, my Twitter is @VirtualElena and my email is elena96b@gmail.com