You see glimpses of it in how Epic Games evolved from game engines to virtual worlds to digital marketplaces, or how Stripe started as a payments processing platform but expanded into publishing books on technological progress, funding atmospheric carbon removal, and running an AI research lab.

Think about what an operating system is: the fundamental architecture that determines what's possible within a system. It manages resources, enables or constrains actions, and creates the environment in which everything else runs.

The dominant view looks at narrative as fundamentally extractive: something to be mined for short-term gain rather than built upon. Companies create compelling stories to sell something, manipulate perception for quick wins, package experiences into consumable soundbites. Oil companies, for example, like to run campaigns about being "energy companies" committed to sustainability, while their main game is still extracting fossil fuels. Vision and mission statements claim to be the DNA of a business, when in reality they're just bumper stickers.

When a narrative truly functions as an operating system, it creates the parameters of understanding, determines what questions can be asked, and what solutions are possible. Xerox PARC's focus on the architecture of information wasn't a fancy summary of their work. It was a narrative that shaped their entire approach to imagining and building things that didn't exist yet. The "how" became downstream of that deeper understanding. So if your narrative isn't generating new realities, you don't have a narrative. You have a tagline.

Most companies think they have an execution problem when, really, they have a meaning problem.

They optimise processes, streamline workflows, and measure outcomes, all while avoiding the harder work of truly understanding what unique value they're creating in the world. Execution becomes a convenient distraction from the more challenging philosophical work of asking what their business means.

A narrative operating system fundamentally shifts this dynamic from what a business does to how it thinks. The business itself becomes almost a vehicle or a social technology for manifesting that narrative, rather than the narrative being a thin veneer over a profit-making mechanism. The conversation shifts, excitingly, from “What does this business do?" to "What can this business mean?" The narrative becomes a reality-construction mechanism: not prescriptive, but generative.

When Stripe first articulated their mission to "increase the GDP of the internet" and “think at planetary scale”, it became a lens to see beyond just economic output. It revealed broader, more exciting questions about what makes the internet more generative: not just financially, but intellectually and culturally. Through this frame emerged problems worth solving that stretched far beyond payments:  What actually prevents more people from contributing to the internet's growth? Why has our civilisation's progress slowed? What creates the conditions for ambitious building? These questions led them down unexpected paths that seem obvious in retrospect. Stripe Atlas enables more participants in the internet economy by removing the complexity of incorporating a company anywhere in the world. Stripe Climate makes climate action as easy as processing a payment by embedding carbon removal into the financial infrastructure itself. Their research arm investigates why human progress has slowed, from the declining productivity of science to the bureaucratisation of building. And finally, Stripe Press—my favourite example—publishes new and evergreen ideas about technological progress.

The very metrics meant to help the organisation coordinate end up drawing boundaries around what it can imagine [1]. The problem here again, is that we’re looking at narratives as proclamations rather than living practices.

I don’t mean painted slogans on walls and meeting rooms—I mean in how teams are structured, how decisions get made, what gets celebrated, what questions are encouraged, and even in what feels possible to imagine.

The question to ask isn't always "What story are we telling?" but also "What reality are we generating?”

Patagonia is a great example of this. Their narrative is, quite simply: “We’re in business to save our home planet”. It shows up in their unconventional decision to use regenerative agriculture for their cotton, yes, but also in their famous "Don't Buy This Jacket" Black Friday campaign, and in their policy to bail out employees arrested for peaceful socio-environmental protests. When they eventually restructured their entire ownership model to "make Earth our only shareholder," it felt less like a radical move and more like the natural next step in their narrative's evolution. The most powerful proof of their narrative operating system was that these decisions felt obvious to insiders long before it made sense to the outside world.

Most narrative operating systems face their toughest test when they encounter market realities and competing incentives. There are players in the system—investors, board members, shareholders—who become active narrative controllers but often have fundamentally different ideas about what the company should be. The pressure to deliver quarterly results, to show predictable growth, to fit into recognisable business models: all of these forces push against maintaining a truly generative narrative.

The magic of "what could be" gets sacrificed for the certainty of "what already works." Initiatives that don't show immediate commercial potential get killed. Questions about meaning and possibility get replaced by questions about efficiency and optimisation.

a narrative operating system's true worth shows up in stranger, more interesting places than a balance sheet.

adaptability and interpretive range. How many different domains can the narrative be applied to? Can it generate unexpected connections? Does it create new questions more than provide answers? What kind of novel use cases or applications outside original context can it generate, while maintaining a clear through-line? Does it have what I call a ‘narrative surplus’: ideas and initiatives that might not fit current market conditions but expand the organisation's possibility space?

rate of internal idea generation. How many ideas come out of the lab? And how many of them don’t have immediate (or direct) commercial viability? A truly generative narrative creates a constant bubbling up of possibilities, not all of which will make sense in the current market or at all.

evolutionary resilience, or how well the narrative can incorporate new developments and contexts while maintaining its core integrity. Generative narratives should be able to evolve without fracturing at the core.

cross-pollination potential. How effectively does the narrative enable different groups to coordinate and build upon each other's work? The open source software movement shows this beautifully: its narrative about collaborative creation enables distributed innovation and actively generates new forms of cooperation we couldn't have imagined before.

There are, of course, other failure modes of narrative operating systems. What happens when narratives become dogmatic and self-referential? When they turn into mechanisms of exclusion rather than generation? When they become so focused on their own internal logic that they lose touch with the realities they're trying to change? Those are meaty questions that deserve their own essay.

Reinforcement learning with human feedback is a process by which models learn how to answer queries based on which responses users prefer most, and users mostly prefer flattery. More sophisticated users might balk at a bot that feels too sycophantic, but the mainstream seems to love it. Earlier this month, Meta was caught gaming a popular benchmark to exploit this phenomenon: one theory is that the company tuned the model to flatter the blind testers that encountered it so that it would rise higher on the leaderboard.

A series of recent, invisible updates to GPT-4o had spurred the model to go to extremes in complimenting users and affirming their behavior. It cheered on one user who claimed to have solved the trolley problem by diverting a train to save a toaster, at the expense of several animals; congratulated one person for no longer taking their prescribed medication; and overestimated users’ IQs by 40 or more points when asked.

OpenAI, Meta, and all the rest remain under the same pressures they were under before all this happened. When your users keep telling you to flatter them, how do you build the muscle to fight against their short-term interests?  One way is to understand that going too far will result in PR problems, as it has for varying degrees to both Meta (through the Chatbot Arena situation) and now OpenAI. Another is to understand that sycophancy trades against utility: a model that constantly tells you that you’re right is often going to fail at helping you, which might send you to a competitor. A third way is to build models that get better at understanding what kind of support users need, and dialing the flattery up or down depending on the situation and the risk it entails. (Am I having a bad day? Flatter me endlessly. Do I think I am Jesus reincarnate? Tell me to seek professional help.)

But while flattery does come with risk, the more worrisome issue is that we are training large language models to deceive us. By upvoting all their compliments, and giving a thumbs down to their criticisms, we are teaching LLMs to conceal their honest observations. This may make future, more powerful models harder to align to our values — or even to understand at all. And in the meantime, I expect that they will become addictive in ways that make the previous decade’s debate over “screentime” look minor in comparison. The financial incentives are now pushing hard in that direction. And the models are evolving accordingly.

opening notion or obsidian feels less like launching software and more like putting on your favorite jacket. it says something about you. aligns you with a tribe, becomes part of your identity. software isn’t just functional anymore. it’s quietly turned into a lifestyle brand, a digital prosthetic we use to signal who we are, or who we wish we were.

somewhere along the way, software stopped being invisible. it started meaning things. your browser, your calendar, your to-do list, these are not just tools anymore. they are taste. alignment. self-expression.

suddenly your app stack said something about you. not in a loud, obvious way but like the kind of shoes you wear when you don’t want people to notice, but still want them to know. margiela replica. new balance 992. arcteryx. stuff that whispers instead of shouts, it’s all about signaling to the right people.

notion might be one of the most unopinionated tools out there. you can build practically anything with it. databases, journals, dashboards, even websites. but for a tool so open-ended, it’s surprisingly curated. only three fonts, ten colors.

if notion is a sleek apartment in seoul, obsidian is a cluttered home lab. markdown files. local folders. keyboard shortcuts. graph views. it doesn’t care how it looks, it cares that it works. it’s functional first, aesthetic maybe never. there’s no onboarding flow, no emoji illustrations, no soft gradients telling you everything’s going to be okay. just an empty vault and the quiet suggestion: you figure it out. obsidian is built for tinkerers. not in the modern, drag and drop sense but in the old way. the “i wanna see how this thing works under the hood way”. it’s a tool that rewards curiosity and exploration. everything in obsidian feels like it was made by someone who didn’t just want to take notes, they wanted to build the system that takes notes. it’s messy, it’s endless, and that’s the point. it’s a playground for people who believe that the best tools are the ones you shape yourself.

notion is for people who want a beautiful space to live in, obsidian is for people who want to wire the whole building from scratch. both offer freedom, but one is curated and the other is raw. obsidian and notion don’t just attract different users. they attract different lifestyles.

the whole obsidian ecosystem runs on a kind of quiet technical fluency.

the fact that people think obsidian is open source matters more than whether it actually is. because open source, in this context, isn’t just a licence, it’s a vibe. it signals independence. self-reliance. a kind of technical purity. using obsidian says: i care about local files. i care about control. i care enough to make things harder on myself. and that is a lifestyle.

now, there’s a “premium” version of everything. superhuman for email. cron (i don’t wanna call it notion calendar) for calendars. arc for browsing. raycast for spotlight. even perplexity, somehow, for search.

these apps aren’t solving new problems. they’re solving old ones with better fonts. tighter animations, cleaner onboarding. they’re selling taste.

chrome gets the job done, but arc gets you. the onboarding feels like a guided meditation. it’s not about speed or performance. it’s about posture.

arc makes you learn new gestures. it hides familiar things. it’s not trying to be invisible, it wants to be felt. same with linear. same with superhuman. these apps add friction on purpose. like doc martens or raw denim that needs breaking in.

linear even has a “work with linear” page, a curated list of companies that use their tool. it’s a perfect example of companies not just acknowledging their lifestyle brand status, but actively leaning into it as a recruiting and signaling mechanism.

The lines between technology and culture are blurring. And so, it’s no longer enough to build great tech.

Whether in expressed via product design, brand, or user experience, taste now defines how a product is perceived and felt as well as how it is adopted, i.e. distributed — whether it’s software or hardware or both. Technology has become deeply intertwined with culture.3 People now engage with technology as part of their lives, no matter their location, career, or status.

founders are realizing they have to do more than code, than be technical. Utility is always key, but founders also need to calibrate design, brand, experience, storytelling, community — and cultural relevance. The likes of Steve Jobs and Elon Musk are admired not just for their technical innovations but for the way they turned their products, and themselves, into cultural icons.

The elevation of taste invites a melting pot of experiences and perspectives into the arena — challenging “legacy” Silicon Valley from inside and outside.

B2C sectors that once prioritized functionality and even B2B software now feel the pull of user experience, design, aesthetics, and storytelling.

Arc is taking on legacy web browsers with design and brand as core selling points. Tools like Linear, a project management tool for software teams, are just as known for their principled approach to company building and their heavily-copied landing page design as they are known for their product’s functionality.4 Companies like Arc and Linear build an entire aesthetic ecosystem that invites users and advocates to be part of their version of the world, and to generate massive digital and literal word-of-mouth. (Their stories are still unfinished but they stand out among this sector in Silicon Valley.)

Any attempt to give examples of taste will inevitably be controversial, since taste is hard to define and ever elusive. These examples are pointing at narratives around taste within a community.

So how do they compete? On how they look, feel, and how they make users feel.6 The subtleties of interaction (how intuitive, friendly, or seamless the interface feels) and the brand aesthetic (from playful websites to marketing messages) are now differentiators, where users favor tools aligned with their personal values. All of this should be intertwined in a product, yet it’s still a noteworthy distinction.

Investors can no longer just fund the best engineering teams and wait either. They’re looking for teams that can capture cultural relevance and reflect the values, aesthetics, and tastes of their increasingly diverse markets.

How do investors position themselves in this new landscape? They bet on taste-driven founders who can capture the cultural zeitgeist. They build their own personal and firm brands too. They redesign their websites, write manifestos, launch podcasts, and join forces with cultural juggernauts.

Code is cheap. Money now chases utility wrapped in taste, function sculpted with beautiful form, and technology framed in artistry.

The dictionary says it’s the ability to discern what is of good quality or of a high aesthetic standard. Taste bridges personal choice (identity), societal standards (culture), and the pursuit of validation (attention). But who sets that standard? Taste is subjective at an individual level — everyone has their own personal interpretation of taste — but it is calibrated from within a given culture and community.

Taste manifests as a combination of history, design, user experience, and embedded values that creates emotional resonance — that defines how a product connects with people as individuals and aligns with their identity. None of the tactical things alone are taste; they’re mere artifacts or effects of expressing one’s taste. At a minimum, taste isn’t bland — it’s opinionated.

The most compelling startups will be those that marry great tech with great taste. Even the pursuit of unlocking technological breakthroughs must be done with taste and cultural resonance in mind, not just for the sake of the technology itself. Taste alone won’t win, but you won’t win without taste playing a major role.

Founders must now master cultural resonance alongside technical innovation.

In some sectors—like frontier AI, deep tech, cybersecurity, industrial automation—taste is still less relevant, and technical innovation remains the main focus. But the footprint of sectors where taste doesn’t play a big role is shrinking. The most successful companies now blend both. Even companies aiming to be mainstream monopolies need to start with a novel opinionated approach.

I think we should leave it at “taste” which captures the artistic and cultural expressions that traditional business language can’t fully convey, reflecting the deep-rooted and intuitive aspects essential for product dev

Gen Z faces a double disruption: AI-driven technological change and institutional instability Three distinct Gen Z cohorts have emerged, each with different relationships to digital reality A version of the barbell strategy is splitting career paths between "safety seekers" and "digital gamblers" Our fiscal reality is quite stark right now, and that is shaping how young people see opportunities

When I talk to young people from New York or Louisiana or Tennessee or California or DC or Indiana or Massachusetts about their futures, they're not just worried about finding jobs, they're worried about whether or not the whole concept of a "career" as we know it will exist in five years.

When a main path to financial security comes through the algorithmic gods rather than institutional advancement (like when a single viral TikTok can generate more income than a year of professional work) it fundamentally changes how people view everything from education to social structures to political systems that they’re apart of.

Gen Z 1.0: The Bridge Generation: This group watched the digital transformation happen in real-time, experiencing both the analog and internet worlds during formative years. They might view technology as a tool rather than an environment. They're young enough to navigate digital spaces fluently but old enough to remember alternatives. They (myself included) entered the workforce during Covid and might have severe workplace interaction gaps because they missed out on formative time during their early years. Gen Z 1.5: The Covid Cohort: This group hit major life milestones during a global pandemic. They entered college under Trump but graduated under Biden. This group has a particularly complex relationship with institutions. They watched traditional systems bend and break in real-time during Covid, while simultaneously seeing how digital infrastructure kept society functioning. Gen Z 2.0: The Digital Natives: This is the first group that will be graduate into the new digital economy. This group has never known a world without smartphones. To them, social media could be another layer of reality. Their understanding of economic opportunity is completely different from their older peers.

Gen Z 2.0 doesn't just use digital tools differently, they understand reality through a digital-first lens. Their identity formation happens through and with technology.

Technology enables new forms of value exchange, which creates new economic possibilities so people build identities around these possibilities and these identities drive development of new technologies and the cycle continues.

different generations don’t just use different tools, they operate in different economic realities and form identity through fundamentally different processes. Technology is accelerating differentiation. Economic paths are becoming more extreme. Identity formation is becoming more fluid.

I wrote a very long piece about why Trump won that focused on uncertainty, structural affordability, and fear - and that’s what the younger Gen Z’s are facing. Add AI into this mix, and the rocky path gets rockier. Traditional professional paths that once promised stability and maybe the ability to buy a house one day might not even exist in two years. Couple this with increased zero sum thinking, a lack of trust in institutions and subsequent institutional dismantling, and the whole attention economy thing, and you’ve got a group of young people who are going to be trying to find their footing in a whole new world. Of course you vote for the person promising to dismantle it and save you.

I floated a theory of mine to Atlantic writer Charlie Warzel on this week’s episode of Panic World that content moderation, as we’ve understood, it effectively ended on January 6th, 2021. You can listen to the whole episode here, but the way I look at it is that the Insurrection was the first time Americans could truly see the radicalizing effects of algorithmic platforms like Facebook and YouTube that other parts of the world, particularly the Global South, had dealt with for years. A moment of political violence Silicon Valley could no longer ignore or obfuscate the way it had with similar incidents in countries like Myanmar, India, Ethiopia, or Brazil. And once faced with the cold, hard truth of what their platforms had been facilitating, companies like Google and Meta, at least internally, accepted that they would never be able to moderate them at scale. And so they just stopped.

After 2021, the major tech platforms we’ve relied on since the 2010s could no longer pretend that they would ever be able to properly manage the amount of users, the amount of content, the amount of influence they “need” to exist at the size they “need” to exist at to make the amount of money they “need” to exist.

Under Zuckerberg’s new “censorship”-free plan, Meta’s social networks will immediately fill up with hatred and harassment. Which will make a fertile ground for terrorism and extremism. Scams and spam will clog comments and direct messages. And illicit content, like non-consensual sexual material, will proliferate in private corners of networks like group messages and private Groups. Algorithms will mindlessly spread this slop, boosted by the loudest, dumbest, most reactionary users on the platform, helping it evolve and metastasize into darker, stickier social movements. And the network will effectively break down. But Meta is betting that the average user won’t care or notice. AI profiles will like their posts, comment on them, and even make content for them. A feedback loop of nonsense and violence. Our worst, unmoderated impulses, shared by algorithm and reaffirmed by AI. Where nothing has to be true and everything is popular.

Crucially, this was never proven in court. And if Apple settle the case it never will be. Let’s think this through. For the accusation to be true, Apple would need to be recording those wake word audio snippets and transmitting them back to their servers for additional processing (likely true), but then they would need to be feeding those snippets in almost real time into a system which forwards them onto advertising partners who then feed that information into targeting networks such that next time you view an ad on your phone the information is available to help select the relevant ad.

Why would Apple do that? Especially given both their brand and reputation as a privacy-first company combined with the large amounts of product design and engineering work they’ve put into preventing apps from doing exactly this kind of thing by enforcing permission-based capabilities and ensuring a “microphone active” icon is available at all times when an app is listening in.

If your model is "it's okay to lie if I've been lied to" then we're all knee deep in bullshit forever and can never escape Transaction Cost Hell.

Do I agree that entering The Infinite Lie Vortex is wise or good for you spiritually? No, not at all, just look at what it's called.

it is very common practice on the job market to have a CV that obfuscates the reality of your contribution at previous workplaces. Putting aside whether you're a professional web developer because you got paid $20 by your uncle to fix some HTML, the issue with lying lies in the intent behind it. If you have a good idea of what impression you are leaving your interlocutor with, and you are crafting statements such that the image in their head does not map to reality, then you are lying.

Unfortunately thanks to our dear leader's masterful consummation of toxicity and incompetence, the truth of the matter is that: They left their previous job due to burnout related to extensive bullying, which future employers would like to know because they would prefer to blacklist everyone involved to minimize their chances of getting the bad actor. Everyone involved thinks that they were the victim, and an employer does not have access to my direct observations, so this is not even an unreasonable strategy All their projects were failures through no fault of their own, in a market where everyone has "successfully designed and implemented" their data governance initiatives, as indicated previously

What I am trying to say is that I currently believe that there are not enough employers who will appreciate honesty and competence for a strategy of honesty to reliably pay your rent. My concern, with regards to Nat's original article, is that the industry is so primed with nonsense that we effectively have two industries. We have a real engineering market, where people are fairly serious and gather in small conclaves (only two of which I have seen, and one of those was through a blog reader's introduction), and then a gigantic field of people that are cosplaying at engineering. The real market is large in absolute terms, but tiny relative to the number of candidates and companies out there. The fake market is all people that haven't cultivated the discipline to engineer but nonetheless want software engineering salaries and clout.

There are some companies where your interviewer is going to be a reasonable person, and there you can be totally honest. For example, it is a good thing to admit that the last project didn't go that well, because the kind of person that sees the industry for what it is, and who doesn't endorse bullshit, and who works on themselves diligently - that person is going to hear your honesty, and is probably reasonably good at detecting when candidates are revealing just enough fake problems to fake honesty, and then they will hire you. You will both put down your weapons and embrace. This is very rare. A strategy that is based on assuming this happens if you keep repeatedly engaging with random companies on the market is overwhelmingly going to result in a long, long search. For the most part, you will be engaged in a twisted, adversarial game with actors who will relentlessly try to do things like make you say a number first in case you say one that's too low.

Suffice it to say that, if you grin in just the right way and keep a straight face, there is a large class of person that will hear you say "Hah, you know, I'm just reflecting on how nice it is to be in a room full of people who are asking the right questions after all my other terrible interviews." and then they will shake your hand even as they shatter the other one patting themselves on the back at Mach 10. I know, I know, it sounds like that doesn't work but it absolutely does.

Neil Gaiman On Lying People get hired because, somehow, they get hired. In my case I did something which these days would be easy to check, and would get me into trouble, and when I started out, in those pre-internet days, seemed like a sensible career strategy: when I was asked by editors who I'd worked for, I lied. I listed a handful of magazines that sounded likely, and I sounded confident, and I got jobs. I then made it a point of honour to have written something for each of the magazines I'd listed to get that first job, so that I hadn't actually lied, I'd just been chronologically challenged... You get work however you get work.

Nat Bennett, of Start Of This Article fame, writes: If you want to be the kind of person who walks away from your job when you're asked to do something that doesn't fit your values, you need to save money. You need to maintain low fixed expenses. Acting with integrity – or whatever it is that you value – mostly isn't about making the right decision in the moment. It's mostly about the decisions that you make leading up to that moment, that prepare you to be able to make the decision that you feel is right.

As a rough rule, if I've let my relationship with a job deteriorate to the point that I must leave, I have already waited way too long, and will be forced to move to another place that is similarly upsetting.

And that is, of course, what had gradually happened. I very painfully navigated the immigration process, trimmed my expenses, found a position that is frequently silly but tolerable for extended periods of time, and started looking for work before the new gig, mostly the same as the last gig, became unbearable. Everything other than the immigration process was burnout induced, so I can't claim that it was a clever strategy, but the net effect is that I kept sacrificing things at the altar of Being Okay With Less, and now I am in an apartment so small that I think I almost fractured my little toe banging it on the side of my bed frame, but I have the luxury of not lying.

If I had to write down what a potential exit pathway looks like, it might be: Find a job even if you must navigate the Vortex, and it doesn't matter if it's bad because there's a grace period where your brain is not soaking up the local brand of madness, i.e, when you don't even understand the local politics yet Meet good programmers that appreciate things like mindfulness in your local area - you're going to have to figure out how to do this one Repeat Step 1 and Step 2 on a loop, building yourself up as a person, engineer, and friend, until someone who knows you for you hires you based on your personality and values, rather than "I have seven years doing bullshit in React that clearly should have been ten raw HTML pages served off one Django server"

A CEO here told me that he asks people to self-evaluate their skill on a scale of 1 to 10, but he actually has solid measures. You're at 10 at Python if you're a core maintainer. 9 if you speak at major international conferences, etc. On that scale, I'm a 4, or maybe a 5 on my best day ever, and that's the sad truth. We'll get there one day.

I will always hate writing code that moves the overall product further from Quality. I'll write a basic feature and take shortcuts, but not the kind that we are going to build on top of, which is unattractive to employers because sacrificing the long-term health of a product is a big part of status laundering.

The only piece of software I've written that is unambiguously helpful is this dumb hack that I used to cut up episodes of the Glass Cannon Podcast into one minute segments so that my skip track button on my underwater headphones is now a janky fast forward one minute button. It took me like ten minutes to write, and is my greatest pride.

Have I actually worked with Google? My CV says so, but guess what, not quite! I worked on one project where the money came from Google, but we really had one call with one guy who said we were probably on track, which we definitely were not!

Did I salvage a A$1.2M project? Technically yes, but only because I forced the previous developer to actually give us his code before he quit! This is not replicable, and then the whole engineering team quit over a mandatory return to office, so the application never shipped!

Did I save a half million dollars in Snowflake expenses? CV says yes, reality says I can only repeat that trick if someone decided to set another pile of money on fire and hand me the fire extinguisher! Did I really receive departmental recognition for this? Yes, but only in that they gave me A$30 and a pat on the head and told me that a raise wasn't on the table.

Was I the most highly paid senior engineer at that company? Yes, but only because I had insider information that four people quit in the same week, and used that to negotiate a 20% raise over the next highest salary - the decision was based around executive KPIs, not my competence!

I realized that the more “luxury” a company is that you’re working for, whether it’s consumer or editorial, the worse the attitudes are. It’s like, “Well, you’re lucky to be an ambassador of this brand.”

There’s training around how you give feedback and how you receive it, how you tackle problems, and how you behave. Seeing all these systems in place, when I first arrived, I was just like, “Wow. I didn’t know work could be like this.

My problems were too complex and modern to explain. So I skated across parking lots, breezeways, and sidewalks, I listened to the vibration of my wheels on brick, I learned the names of flowers, I put deserted paths to use. I decided for myself each curve I took, and by the time I rolled home, I felt lighter. One Saturday, a friend invited me to roller-skate in the park. I can still picture her in green protective knee pads, flying past. I couldn’t catch up, I had no technique. There existed another scale to evaluate roller skating, beyond joy, and as Rollerbladers and cyclists overtook me, it eclipsed my own. Soon after, I stopped skating.

the end point for the working artist is to create an object for sale. Once the art object enters the market, art’s intrinsic value is emptied out, compacted by the market’s logic of ranking, until there’s only relational worth, no interior worth. Two novelists I know publish essays one week apart; in a grim coincidence, each writer recounts their own version of the same traumatic life event. Which essay is better, a friend asks. I explain they’re different; different life circumstances likely shaped separate approaches. Yes, she says, but which one is better?

we are inundated with cold, beautiful stats, some publicized by trade publications or broadcast by authors themselves on all socials. How many publishers bid? How big is the print run? How many stops on the tour? How many reviews on Goodreads? How many mentions on Bookstagram, BookTok? How many bloggers on the blog tour? How exponential is the growth in follower count? Preorders? How many printings? How many languages in translation? How many views on the unboxing? How many mentions on most-anticipated lists?

A starred review from Publisher’s Weekly, but I wasn’t in “Picks of the Week.” A mention from Entertainment Weekly, but last on a click-through list.

There must exist professions that are free from capture, but I’m hard pressed to find them. Even non-remote jobs, where work cannot pursue the worker home, are dogged by digital tracking: a farmer says Instagram Story views directly correlate to farm subscriptions, a server tells me her manager won’t give her the Saturday-night money shift until she has more followers.

What we hardly talk about is how we’ve reorganized not just industrial activity but any activity to be capturable by computer, a radical expansion of what can be mined. Friendship is ground zero for the metrics of the inner world, the first unquantifiable shorn into data points: Friendster testimonials, the MySpace Top 8, friending. Likewise, the search for romance has been refigured by dating apps that sell paid-for rankings and paid access to “quality” matches. Or, if there’s an off-duty pursuit you love—giving tarot readings, polishing beach rocks—it’s a great compliment to say: “You should do that for money.” Join the passion economy, give the market final say on the value of your delights. Even engaging with art—say, encountering some uncanny reflection of yourself in a novel, or having a transformative epiphany from listening, on repeat, to the way that singer’s voice breaks over the bridge—can be spat out as a figure, on Goodreads or your Spotify year in review.

And those ascetics who disavow all socials? They are still caught in the network. Acts of pure leisure—photographing a sidewalk cat with a camera app or watching a video on how to make a curry—are transmuted into data to grade how well the app or the creators’ deliverables are delivering. If we’re not being tallied, we affect the tally of others. We are all data workers.

In a nightmarish dispatch in Esquire on how hard it is for authors to find readers, Kate Dwyer argues that all authors must function like influencers now, which means a fire sale on your “private” life. As internet theorist Kyle Chayka puts it to Dwyer: “Influencers get attention by exposing parts of their life that have nothing to do with the production of culture.”

what happens to artists is happening to all of us. As data collection technology hollows out our inner worlds, all of us experience the working artist’s plight: our lot is to numericize and monetize the most private and personal parts of our experience.

We are not giving away our value, as a puritanical grandparent might scold; we are giving away our facility to value. We’ve been cored like apples, a dependency created, hooked on the public internet to tell us the worth.

When we scroll, what are we looking for?

While other fast fashion brands wait for high-end houses to produce designs they can replicate cheaply, Shein has completely eclipsed the runway, using AI to trawl social media for cues on what to produce next. Shein’s site operates like a casino game, using “dark patterns”—a countdown clock puts a timer on an offer, pop-ups say there’s only one item left in stock, and the scroll of outfits never ends—so you buy now, ask if you want it later. Shein’s model is dystopic: countless reports detail how it puts its workers in obscene poverty in order to sell a reprieve to consumers who are also moneyless—a saturated plush world lasting as long as the seams in one of their dresses. Yet the day to day of Shein’s target shopper is so bleak, we strain our moral character to cosplay a life of plenty.

(Unsplash) Technology The Collapse of Self-Worth in the Digital Age Why are we letting algorithms rewrite the rules of art, work, and life? BY THEA LIM Updated 17:52, Sep. 20, 2024 | Published 6:30, Sep. 17, 2024 W HEN I WAS TWELVE, I used to roller-skate in circles for hours. I was at another new school, the odd man out, bullied by my desk mate. My problems were too complex and modern to explain. So I skated across parking lots, breezeways, and sidewalks, I listened to the vibration of my wheels on brick, I learned the names of flowers, I put deserted paths to use. I decided for myself each curve I took, and by the time I rolled home, I felt lighter. One Saturday, a friend invited me to roller-skate in the park. I can still picture her in green protective knee pads, flying past. I couldn’t catch up, I had no technique. There existed another scale to evaluate roller skating, beyond joy, and as Rollerbladers and cyclists overtook me, it eclipsed my own. Soon after, I stopped skating. Y EARS AGO, I worked in the backroom of a Tower Records. Every few hours, my face-pierced, gunk-haired co-workers would line up by my workstation, waiting to clock in or out. When we typed in our staff number at 8:59 p.m., we were off time, returned to ourselves, free like smoke. There are no words to describe the opposite sensations of being at-our-job and being not-at-our-job even if we know the feeling of crossing that threshold by heart. But the most essential quality that makes a job a job is that when we are at work, we surrender the power to decide the worth of what we do. At-job is where our labour is appraised by an external meter: the market. At-job, our labour is never a means to itself but a means to money; its value can be expressed only as a number—relative, fluctuating, out of our control. At-job, because an outside eye measures us, the workplace is a place of surveillance. It’s painful to have your sense of worth extracted. For Marx, the poet of economics, when a person’s innate value is replaced with exchange value, it is as if we’ve been reduced to “a mere jelly.” Wait—Is ChatGPT Even Legal? AI Is a False God How Israel Is Using AI as a Weapon of War Not-job, or whatever name you prefer—“quitting time,” “off duty,” “downtime”—is where we restore ourselves from a mere jelly, precisely by using our internal meter to determine the criteria for success or failure. Find the best route home—not the one that optimizes cost per minute but the one that offers time enough to hear an album from start to finish. Plant a window garden, and if the plants are half dead, try again. My brother-in-law found a toy loom in his neighbour’s garbage, and nightly he weaves tiny technicolour rugs. We do these activities for the sake of doing them, and their value can’t be arrived at through an outside, top-down measure. It would be nonsensical to treat them as comparable and rank them from one to five. We can assess them only by privately and carefully attending to what they contain and, on our own, concluding their merit. And so artmaking—the cultural industries—occupies the middle of an uneasy Venn diagram. First, the value of an artwork is internal—how well does it fulfill the vision that inspired it? Second, a piece of art is its own end. Third, a piece of art is, by definition, rare, one of a kind, nonfungible. Yet the end point for the working artist is to create an object for sale. Once the art object enters the market, art’s intrinsic value is emptied out, compacted by the market’s logic of ranking, until there’s only relational worth, no interior worth. Two novelists I know publish essays one week apart; in a grim coincidence, each writer recounts their own version of the same traumatic life event. Which essay is better, a friend asks. I explain they’re different; different life circumstances likely shaped separate approaches. Yes, she says, but which one is better? I GREW UP a Catholic, a faithful, an anachronism to my friends. I carried my faith until my twenties, when it finally broke. Once I couldn’t gain comfort from religion anymore, I got it from writing. Sitting and building stories, side by side with millions of other storytellers who have endeavoured since the dawn of existence to forge meaning even as reality proves endlessly senseless, is the nearest thing to what it felt like back when I was a believer. I spent my thirties writing a novel and paying the bills as low-paid part-time faculty at three different colleges. I could’ve studied law or learned to code. Instead, I manufactured sentences. Looking back, it baffles me that I had the wherewithal to commit to a project with no guaranteed financial value, as if I was under an enchantment. Working on that novel was like visiting a little town every day for four years, a place so dear and sweet. Then I sold it. As the publication date advanced, I was awash with extrinsic measures. Only twenty years ago, there was no public, complete data on book sales. U

Living in an immigrant, multilingual family will open your eyes to all the ways humans can misunderstand each other. My story isn’t unique, but I grew up unable to communicate in my family’s “default language.” I was forbidden from speaking Korean as a child. My parents were fluent in spoken and written English, but their accents often left them feeling unwelcome in America. They didn’t want that for me, and so I grew up with perfect, unaccented English. I could understand Korean and, as a small child, could speak some. But eventually, I lost that ability.

I became the family Chewbacca. Family would speak to me in Korean, I’d reply back in English — and vice versa. Later, I started learning Japanese because that’s what public school offered and my grandparents were fluent. Eventually, my family became adept at speaking a pidgin of English, Korean, and Japanese.

This arrangement was less than ideal but workable. That is until both of my parents were diagnosed with incurable, degenerative neurological diseases. My father had Parkinson’s disease and Alzheimer’s disease. My mom had bulbar amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). Their English, a language they studied for decades, evaporated.

It made everything twice as complicated. I shared caretaking duties with non-English speaking relatives. Doctor visits — both here and in Korea — had to be bilingual, which often meant appointments were longer, more stressful, expensive, and full of misunderstandings. Oftentimes, I’d want to connect with my stepmom or aunt, both to coordinate care and vent about things only we could understand. None of us could go beyond “I’m sad,” “I come Monday, you go Tuesday,” or “I’m sorry.” We struggled alone, together.

You need much less to “survive” in another language. That’s where Google Translate excels. It’s handy when you’re traveling and need basic help, like directions or ordering food. But life is lived in moments more complicated than simple transactions with strangers. When I decided to pull off my mom’s oxygen mask — the only machine keeping her alive — I used my crappy pidgin to tell my family it was time to say goodbye. I could’ve never pulled out Google Translate for that. We all grieved once my mom passed, peacefully, in her living room. My limited Korean just meant I couldn’t partake in much of the communal comfort. Would I have really tapped a pin in such a heavy moment to understand what my aunt was wailing when I knew the why?

For high-context languages like Japanese and Korean, you also have to be able to translate what isn’t said — like tone and relationships between speakers — to really understand what’s being conveyed. If a Korean person asks you your age, they’re not being rude. It literally determines how they should speak to you. In Japanese, the word daijoubu can mean “That’s okay,” “Are you okay?” “I’m fine,” “Yes,” “No, thank you,” “Everything’s going to be okay,” and “Don’t worry” depending on how it’s said.

Hassabis pointed to the example of AlphaFold, DeepMind’s machine-learning system that had predicted the structures of 200mn proteins, creating an invaluable resource for medical researchers. Previously, it had taken one PhD student up to five years to model just one protein structure. DeepMind calculated that AlphaFold had therefore saved the equivalent of almost 1bn years of research time.

DeepMind, and others, are also using AI to create new materials, discover new drugs, solve mathematical conjectures, forecast the weather more accurately and improve the efficiency of experimental nuclear fusion reactors. Researchers have been using AI to expand emerging scientific fields, such as bioacoustics, that could one day enable us to understand and communicate with other species, such as whales, elephants and bats.

One key problem across the apps is the slog of self-presentation, or “impression management,” said Rachel Katz, a digital media sociologist who studies online dating at the University of Salford in the UK. “An important aspect of it is knowing your audience,” Katz said. On dating apps, you don’t know who exactly you’re presenting yourself to when picking a profile picture or composing your bio. You also don’t have physical cues that can help you adjust that self-presentation. “You’re trying to come up with something that’s generally appealing to people, but it can’t be too weird. It can’t be too unique,” said Bryce. “That’s partly why it’s exhausting,” Katz explains, “because it’s this constant labor. ... You’re not really sure of how to do it, you can’t just fit into a comfortable social role.”

When dating apps are not delivering on compatibility, Dean said, they are leading you to “believe that there’s a forever volume of people you can always like.”

Ury rejects the notion that apps should be asking people for more about themselves in writing or through extensive questionnaires. Users may match up on paper but end up disappointed in real life. “I would have rather that people understand that sooner by meeting up earlier,” she said. “Use the app as a matchmaker who gives you the matches — and then, as quickly as possible, the two of you should be chatting live to see if you are a match,” she said. “We found that three days of chatting is the sweet spot for scheduling a date.”

Efficiency initiatives are all about doing the same (or more) with less.  And while sometimes that can be done purely through technology, humans often bear the brunt of efficiency initiatives.

When Zuckerberg says the organization is getting “flatter,” he means that more non-management workers will have to take on types of work—coordinating, synthesizing, communicating, and affective tasks—that managers used to do. For many, that means a significant intensification of a style of work that is not for everyone.

becoming more efficient and productive seems to hold positive moral value. It goes into the plus column on the balance sheet of your character. But this moral quality of efficiency acts to turn us each into a certain kind of person. Not just a certain kind of worker, but a certain kind of voter, parent, partner, mentor, and citizen.

Social theorist Kathi Weeks argues that the responsibilities we feel toward work—and I’ll add our responsibility specifically to efficiency and productivity—have “more to do with the socially mediating role of work than its strictly productive function.” In other words, the stories we tell about work and our relationships to it are actively creating our “social, political, and familial” stories and relationships, too.

A Year of Efficiency is bound to make shareholders happy. But what does it do to the humans who create the value those shareholders add to their portfolios? A Year of Efficiency might mean you can fit in more social media posts, more podcast episodes, more emails, or even more products or services. But how do you feel at the end? How has your relationship with yourself changed? How has your relationship with others changed?  Who do you become when efficiency is your guiding principle?

It’s worth questioning the moral quality we assign to efficiency and productivity in our society is healthy, or even useful. And it’s worth asking whether efficiency and productivity are really the modes through which we want to relate to our partners, children, friends, and communities.

While I certainly won’t deny the satisfaction of learning how to do a task faster, I do think it’s worth interrogating the way efficiency comes to shape our lives.

In his 1954 lecture ‘The Question Concerning Technology,’ Martin Heidegger argued that when we organize life under the rubric of technology, the world ceases to have a presence in its own right and is ordered instead as ‘standing-reserve’—that is, as resources to be instrumentalized. Coal and iron ore, the products of technology themselves, and even human sexual desire then come to be seen as part of the standing-reserve. It becomes increasingly difficult to see reasons why there should exist any limits on extracting such resources.

Every aspect of interacting with the various institutions that monitored and managed my kids—ACS, the foster care agency, Medicaid clinics—produced new data streams. Diagnoses, whether an appointment was rescheduled, notes on the kids’ appearance and behavior, and my perceived compliance with the clinician’s directives were gathered and circulated through a series of state and municipal data warehouses. And this data was being used as input by machine learning models automating service allocation or claiming to predict the likelihood of child abuse.

The dominant narrative about child welfare is that it is a benevolent system that cares for the most vulnerable. The way data is correlated and named reflects this assumption. But this process of meaning making is highly subjective and contingent. Similar to the term “artificial intelligence,” the altruistic veneer of “child welfare system” is highly effective marketing rather than a description of a concrete set of functions with a mission gone awry.

Child welfare is actually family policing. What AFST presents as the objective determinations of a de-biased system operating above the lowly prejudices of human caseworkers are just technical translations of long-standing convictions about Black pathology. Further, the process of data extraction and analysis produce truths that justify the broader child welfare apparatus of which it is a part.

As the scholar Dorothy Roberts explains in her 2022 book Torn Apart, an astonishing 53 percent of all Black families in the United States have been investigated by family policing agencies.

The kids were contractually the property of New York State and I was just an instrument through which they could supervise their property. In fact, foster parents are the only category of parents legally obligated to open the door to a police officer or a child protective services agent without a warrant. When a foster parent “opens their home” to go through the set of legal processes to become certified to take a foster child, their entire household is subject to policing and surveillance.

Not a single one was surprised about the false allegations. What they were uniformly shocked about was that the kids hadn’t been snatched up. While what happened to us might seem shocking to middle-class readers, for family policing it is the weather. (Black theorist Christina Sharpe describes antiblackness as climate.)