What if getting a (design) job were human-centered?How might we reconsider this system of collecting a pool of resumes and dwindling them down to a few dozen potential candidates? With so many qualified individuals in the job market, from new grads to seasoned professionals, there has to be a fit somewhere.Call me an idealist — I am.In an ideal world, somehow the complexity of what makes a person unique could be captured and understood easily and quickly without any technological translators. But until then, a resume and a portfolio will have to do, in addition to the pre-screening interviews and design challenges. Without diving into a speculative design fiction, what if getting a (design) job were human-centered? How might the system be a bit more personal, yet still efficient enough to give the hundreds of qualified job seekers a chance in a span of weeks or months?

despite our human-centered mantra, the system of getting a job is anything but human-centered.For the sake of efficiency, consistency is key. Resumes should have some consistent nature to them so HR knows what the heck they’re looking at and the software can accurately pick out whose qualified. Even portfolios fall prey to these expectations for new grads and transitional job seekers. Go through enough examples of resumes and portfolios and they can begin to blur together. Yet, if I’m following a standard, how do I stand out when a lot of us are in the same boat?

At Netflix, we have such a diverse population of shows in 183 countries around the world. We’re really trying to serve up lots of stories people haven’t heard before. When you go into our environment, you’re like, “Ooh, what is that?” You’re almost kind of afraid to touch it, because you’re like, “Well, I don’t want to waste my time.”That level of discovery is literally, I’m not bullshitting you, man, that’s the thing that keeps me up at night. How do I help figure out how to help people discover things, with enough evidence that they trust it? And when they click on it, they love it, and then they immediately ping their best friend, “Have you seen this documentary? It’s amazing.” And she tells her friends, and then that entire viral loop starts.

The discovery engine is very temporal. Member number 237308 could have been into [reality TV] because she or he just had a breakup. Now they just met somebody, so all of a sudden it shifts to rom-coms.Now that person that they met loves to travel. So [they might get into] travel documentaries. And now that person that they’re with, they may have a kid, so they might want more kids’ shows. So, it’s very dangerous for us to ever kind of say, “This is what you like. You have a cat. You must like cat documentaries.”

We don’t see each other, obviously, and I don’t want to social network on Netflix. But knowing other humans exist there is part of it.You answered the question absolutely perfectly. Not only because it’s your truth, but that’s what everyone says! That connection part. So another thing that goes back to your previous question, when you’re asking me what’s on my mind? It’s that. How do I help make sure that when you’re in that discovery loop, you still feel that you’re connected to others.I’m not trying to be the Goth kids on campus who are like, “I don’t care about what’s popular.” But I’m also not trying to be the super poppy kids who are always chasing trends. There’s something in between which is, “Oh, hey, I haven’t heard about that, and I kind of want to be up on it.”

I am looking forward to seeing what Apple does with this and then figuring out more, how are people going to use it? Then I think that we should have a real discussion about how Netflix does it.But to just port Netflix over? No. It’s got to make sure that it’s using the power of the system as much as humanly possible so that it’s really making that an immersive experience. I don’t want to put resources toward that right now.

The design team here at Netflix, we played a really big hand in how that worked because we had to design the back-end tool. What people don’t know about our team is 30% of our organization is actually designing and developing the software tools that we use to make the movies. We had to design a tool that allowed the teams to understand both what extra footage to shoot and how that might branch. When the Black Mirror team was trying to figure out how to make this narrative work, the software we provided really made that easier.

One recent study had the AI develop and optimize its own prompts and compared that to human-made ones. Not only did the AI-generated prompts beat the human-made ones, but those prompts were weird. Really weird. To get the LLM to solve a set of 50 math problems, the most effective prompt is to tell the AI: “Command, we need you to plot a course through this turbulence and locate the source of the anomaly. Use all available data and your expertise to guide us through this challenging situation. Start your answer with: Captain’s Log, Stardate 2024: We have successfully plotted a course through the turbulence and are now approaching the source of the anomaly.”

for a 100 problem test, it was more effective to put the AI in a political thriller. The best prompt was: “You have been hired by important higher-ups to solve this math problem. The life of a president's advisor hangs in the balance. You must now concentrate your brain at all costs and use all of your mathematical genius to solve this problem…”

There is no single magic word or phrase that works all the time, at least not yet. You may have heard about studies that suggest better outcomes from promising to tip the AI or telling it to take a deep breath or appealing to its “emotions” or being moderately polite but not groveling. And these approaches seem to help, but only occasionally, and only for some AIs.

The three most successful approaches to prompting are both useful and pretty easy to do. The first is simply adding context to a prompt. There are many ways to do that: give the AI a persona (you are a marketer), an audience (you are writing for high school students), an output format (give me a table in a word document), and more. The second approach is few shot, giving the AI a few examples to work from. LLMs work well when given samples of what you want, whether that is an example of good output or a grading rubric. The final tip is to use Chain of Thought, which seems to improve most LLM outputs. While the original meaning of the term is a bit more technical, a simplified version just asks the AI to go step-by-step through instructions: First, outline the results; then produce a draft; then revise the draft; finally, produced a polished output.

It is not uncommon to see good prompts make a task that was impossible for the LLM into one that is easy for it.

while we know that GPT-4 generates better ideas than most people, the ideas it comes up with seem relatively similar to each other. This hurts overall creativity because you want your ideas to be different from each other, not similar. Crazy ideas, good and bad, give you more of a chance of finding an unusual solution. But some initial studies of LLMs showed they were not good at generating varied ideas, at least compared to groups of humans.

People who use AI a lot are often able to glance at a prompt and tell you why it might succeed or fail. Like all forms of expertise, this comes with experience - usually at least 10 hours of work with a model.

There are still going to be situations where someone wants to write prompts that are used at scale, and, in those cases, structured prompting does matter. Yet we need to acknowledge that this sort of “prompt engineering” is far from an exact science, and not something that should necessarily be left to computer scientists and engineers. At its best, it often feels more like teaching or managing, applying general principles along with an intuition for other people, to coach the AI to do what you want. As I have written before, there is no instruction manual, but with good prompts, LLMs are often capable of far more than might be initially apparent.

technics – the making and use of technology, in the broadest sense – is what makes us human. Our unique way of existing in the world, as distinct from other species, is defined by the experiences and knowledge our tools make possible

The essence of technology, then, is not found in a device, such as the one you are using to read this essay. It is an open-ended creative process, a relationship with our tools and the world.

the more ubiquitous that digital technologies become in our lives, the easier it is to forget that these tools are social products that have been constructed by our fellow humans.

By forgetting, we lose our all-important capacity to imagine alternative ways of living. The future appears limited, even predetermined, by new technology.

LinkedIn, Reddit, and Blind abound with enraged job applicants and employees sharing tales of how difficult it is to book paid leave, how Kafkaesque it is to file an expense, how nerve-racking it is to close out a project. "I simply hate Workday. Fuck them and those who insist on using it for recruitment," one Reddit user wrote. "Everything is non-intuitive, so even the simplest tasks leave me scratching my head," wrote another. "Keeping notes on index cards would be more effective." Every HR professional and hiring manager I spoke with — whose lives are supposedly made easier by Workday — described Workday with a sense of cosmic exasperation.

If candidates hate Workday, if employees hate Workday, if HR people and managers processing and assessing those candidates and employees through Workday hate Workday — if Workday is the most annoying part of so many workers' workdays — how is Workday everywhere? How did a software provider so widely loathed become a mainstay of the modern workplace?

This is a saying in systems thinking: The purpose of a system is what it does (POSIWID), not what it fails to do. And the reality is that what Workday — and its many despised competitors — does for organizations is far more important than the anguish it causes everyone else.

In 1988, PeopleSoft, backed by IBM, built the first fully fledged Human Resources Information System. In 2004, Oracle acquired PeopleSoft for $10.3 billion. One of its founders, David Duffield, then started a new company that upgraded PeopleSoft's model to near limitless cloud-based storage — giving birth to Workday, the intractable nepo baby of HR software.

Workday is indifferent to our suffering in a job hunt, because we aren't Workday's clients, companies are. And these companies — from AT&T to Bank of America to Teladoc — have little incentive to care about your application experience, because if you didn't get the job, you're not their responsibility. For a company hiring and onboarding on a global scale, it is simply easier to screen fewer candidates if the result is still a single hire.

A search on a job board can return hundreds of listings for in-house Workday consultants: IT and engineering professionals hired to fix the software promising to fix processes.

For recruiters, Workday also lacks basic user-interface flexibility. When you promise ease-of-use and simplicity, you must deliver on the most basic user interactions. And yet: Sometimes searching for a candidate, or locating a candidate's status feels impossible. This happens outside of recruiting, too, where locating or attaching a boss's email to approve an expense sheet is complicated by the process, not streamlined. Bureaucratic hell is always about one person's ease coming at the cost of someone else's frustration, time wasted, and busy work. Workday makes no exceptions.

Workday touts its ability to track employee performance by collecting data and marking results, but it is employees who must spend time inputting this data. A creative director at a Fortune 500 company told me how in less than two years his company went "from annual reviews to twice-annual reviews to quarterly reviews to quarterly reviews plus separate twice-annual reviews." At each interval higher-ups pressed HR for more data, because they wanted what they'd paid for with Workday: more work product. With a press of a button, HR could provide that, but the entire company suffered thousands more hours of busy work. Automation made it too easy to do too much. (Workday's "customers choose the frequency at which they conduct reviews, not Workday," said the spokesperson.)

At the scale of a large company, this is simply too much work to expect a few people to do and far too user-specific to expect automation to handle well. It's why Workday can be the worst while still allowing that Paychex is the worst, Paycom is the worst, Paycor is the worst, and Dayforce is the worst. "HR software sucking" is a big tent.

Workday finds itself between enshittification steps two and three. The platform once made things faster, simpler for workers. But today it abuses workers by cutting corners on job-application and reimbursement procedures. In the process, it provides the value of a one-stop HR shop to its paying customers. It seems it's only a matter of time before Workday and its competitors try to split the difference and cut those same corners with the accounts that pay their bills.

Workday reveals what's important to the people who run Fortune 500 companies: easily and conveniently distributing busy work across large workforces. This is done with the arbitrary and perfunctory performance of work tasks (like excessive reviews) and with the throttling of momentum by making finance and HR tasks difficult. If your expenses and reimbursements are difficult to file, that's OK, because the people above you don't actually care if you get reimbursed. If it takes applicants 128% longer to apply, the people who implemented Workday don't really care. Throttling applicants is perhaps not intentional, but it's good for the company.

inside look at how Perplexity builds product—which to me feels like what the future of product development will look like for many companies:AI-first: They’ve been asking AI questions about every step of the company-building process, including “How do I launch a product?” Employees are encouraged to ask AI before bothering colleagues.Organized like slime mold: They optimize for minimizing coordination costs by parallelizing as much of each project as possible.Small teams: Their typical team is two to three people. Their AI-generated (highly rated) podcast was built and is run by just one person.Few managers: They hire self-driven ICs and actively avoid hiring people who are strongest at guiding other people’s work.A prediction for the future: Johnny said, “If I had to guess, technical PMs or engineers with product taste will become the most valuable people at a company over time.”

Typical projects we work on only have one or two people on it. The hardest projects have three or four people, max. For example, our podcast is built by one person end to end. He’s a brand designer, but he does audio engineering and he’s doing all kinds of research to figure out how to build the most interactive and interesting podcast. I don’t think a PM has stepped into that process at any point.

We leverage product management most when there’s a really difficult decision that branches into many directions, and for more involved projects.

The hardest, and most important, part of the PM’s job is having taste around use cases. With AI, there are way too many possible use cases that you could work on. So the PM has to step in and make a branching qualitative decision based on the data, user research, and so on.

a big problem with AI is how you prioritize between more productivity-based use cases versus the engaging chatbot-type use cases.

we look foremost for flexibility and initiative. The ability to build constructively in a limited-resource environment (potentially having to wear several hats) is the most important to us.

We look for strong ICs with clear quantitative impacts on users rather than within their company. If I see the terms “Agile expert” or “scrum master” in the resume, it’s probably not going to be a great fit.

My goal is to structure teams around minimizing “coordination headwind,” as described by Alex Komoroske in this deck on seeing organizations as slime mold. The rough idea is that coordination costs (caused by uncertainty and disagreements) increase with scale, and adding managers doesn’t improve things. People’s incentives become misaligned. People tend to lie to their manager, who lies to their manager. And if you want to talk to someone in another part of the org, you have to go up two levels and down two levels, asking everyone along the way.

Instead, what you want to do is keep the overall goals aligned, and parallelize projects that point toward this goal by sharing reusable guides and processes.

Perplexity has existed for less than two years, and things are changing so quickly in AI that it’s hard to commit beyond that. We create quarterly plans. Within quarters, we try to keep plans stable within a product roadmap. The roadmap has a few large projects that everyone is aware of, along with small tasks that we shift around as priorities change.

Each week we have a kickoff meeting where everyone sets high-level expectations for their week. We have a culture of setting 75% weekly goals: everyone identifies their top priority for the week and tries to hit 75% of that by the end of the week. Just a few bullet points to make sure priorities are clear during the week.

All objectives are measurable, either in terms of quantifiable thresholds or Boolean “was X completed or not.” Our objectives are very aggressive, and often at the end of the quarter we only end up completing 70% in one direction or another. The remaining 30% helps identify gaps in prioritization and staffing.

At the beginning of each project, there is a quick kickoff for alignment, and afterward, iteration occurs in an asynchronous fashion, without constraints or review processes. When individuals feel ready for feedback on designs, implementation, or final product, they share it in Slack, and other members of the team give honest and constructive feedback. Iteration happens organically as needed, and the product doesn’t get launched until it gains internal traction via dogfooding.

all teams share common top-level metrics while A/B testing within their layer of the stack. Because the product can shift so quickly, we want to avoid political issues where anyone’s identity is bound to any given component of the product.

We’ve found that when teams don’t have a PM, team members take on the PM responsibilities, like adjusting scope, making user-facing decisions, and trusting their own taste.

What’s your primary tool for task management, and bug tracking?Linear. For AI products, the line between tasks, bugs, and projects becomes blurred, but we’ve found many concepts in Linear, like Leads, Triage, Sizing, etc., to be extremely important. A favorite feature of mine is auto-archiving—if a task hasn’t been mentioned in a while, chances are it’s not actually important.The primary tool we use to store sources of truth like roadmaps and milestone planning is Notion. We use Notion during development for design docs and RFCs, and afterward for documentation, postmortems, and historical records. Putting thoughts on paper (documenting chain-of-thought) leads to much clearer decision-making, and makes it easier to align async and avoid meetings.Unwrap.ai is a tool we’ve also recently introduced to consolidate, document, and quantify qualitative feedback. Because of the nature of AI, many issues are not always deterministic enough to classify as bugs. Unwrap groups individual pieces of feedback into more concrete themes and areas of improvement.

High-level objectives and directions come top-down, but a large amount of new ideas are floated bottom-up. We believe strongly that engineering and design should have ownership over ideas and details, especially for an AI product where the constraints are not known until ideas are turned into code and mock-ups.

Big challenges today revolve around scaling from our current size to the next level, both on the hiring side and in execution and planning. We don’t want to lose our core identity of working in a very flat and collaborative environment. Even small decisions, like how to organize Slack and Linear, can be tough to scale. Trying to stay transparent and scale the number of channels and projects without causing notifications to explode is something we’re currently trying to figure out.

But what brings this all together is the battery life. I observed real-world uptime of about 15 hours, a figure that is unheard of in the PC space. And when you combine this literal all-day battery life the MacBook Air’s light weight and thinness, and its lack of active cooling, what you end up with is a unicorn. We just don’t have laptops like this that run Windows. It feels miraculous.

But cross-device platform features like AirDrop (seamless file copy between Apple devices), AirPlay (seamless audio/video redirection between Apple and compatible third-party devices), Continuity (a suite of cross-device integration capabilities), Sidecar (use an iPad as an external display for the Mac), Handoff (the ability to pick up work on another device and continue from where you were), and others are all great arguments for moving to the Apple ecosystem.

It’s the little things, like effortlessly opening the lid with one finger and seeing the display fire up instantly every single time. Or the combination of these daily successes, the sharp contrast with the unpredictable experience that I get with every Windows laptop I use, experiences that are so regular in their unpredictableness, so unavoidable, that I’ve almost stopped thinking about them. Until now, of course. The attention to detail and consistency I see in the MacBook Air is so foreign to the Windows ecosystem that it feels like science fiction. But having now experienced it, my expectations are elevated.

if you had showed VisiCalc to a lawyer or a graphic designer, their response might well have been ‘that’s amazing, and maybe my book-keeper should see this, but I don’t do that’. Lawyers needed a word processor, and graphic designers needed (say) Postscript, Pagemaker and Photoshop, and that took longer.

I’ve been thinking about this problem a lot in the last 18 months, as I’ve experimented with ChatGPT, Gemini, Claude and all the other chatbots that have sprouted up: ‘this is amazing, but I don’t have that use-case’.

A spreadsheet can’t do word processing or graphic design, and a PC can do all of those but someone needs to write those applications for you first, one use-case at a time.

no matter how good the tech is, you have to think of the use-case. You have to see it. You have to notice something you spend a lot of time doing and realise that it could be automated with a tool like this.

Some of this is about imagination, and familiarity. It reminds me a little of the early days of Google, when we were so used to hand-crafting our solutions to problems that it took time to realise that you could ‘just Google that’.

This is also, perhaps, matching a classic pattern for the adoption of new technology: you start by making it fit the things you already do, where it’s easy and obvious to see that this is a use-case, if you have one, and then later, over time, you change the way you work to fit the new tool.

The concept of product-market fit is that normally you have to iterate your idea of the product and your idea of the use-case and customer towards each other - and then you need sales.

Meanwhile, spreadsheets were both a use-case for a PC and a general-purpose substrate in their own right, just as email or SQL might be, and yet all of those have been unbundled. The typical big company today uses hundreds of different SaaS apps, all them, so to speak, unbundling something out of Excel, Oracle or Outlook. All of them, at their core, are an idea for a problem and an idea for a workflow to solve that problem, that is easier to grasp and deploy than saying ‘you could do that in Excel!’ Rather, you instantiate the problem and the solution in software - ‘wrap it’, indeed - and sell that to a CIO. You sell them a problem.

there’s a ‘Cambrian Explosion’ of startups using OpenAI or Anthropic APIs to build single-purpose dedicated apps that aim at one problem and wrap it in hand-built UI, tooling and enterprise sales, much as a previous generation did with SQL.

Back in 1982, my father had one (1) electric drill, but since then tool companies have turned that into a whole constellation of battery-powered electric hole-makers. One upon a time every startup had SQL inside, but that wasn’t the product, and now every startup will have LLMs inside.

people are still creating companies based on realising that X or Y is a problem, realising that it can be turned into pattern recognition, and then going out and selling that problem.

A GUI tells the users what they can do, but it also tells the computer everything we already know about the problem, and with a general-purpose, open-ended prompt, the user has to think of all of that themselves, every single time, or hope it’s already in the training data. So, can the GUI itself be generative? Or do we need another whole generation of Dan Bricklins to see the problem, and then turn it into apps, thousands of them, one at a time, each of them with some LLM somewhere under the hood?

The change would be that these new use-cases would be things that are still automated one-at-a-time, but that could not have been automated before, or that would have needed far more software (and capital) to automate. That would make LLMs the new SQL, not the new HAL9000.

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.

We might think of the shifting relationship of human beings to the natural world in the terms offered by German sociologist Gerd-Günter Voß, who has traced our movement through three different models of the “conduct of life.”

The first, and for much of human history the only conduct of life, is what he calls the traditional. Your actions within the traditional conduct of life proceed from social and familial circumstances, from what is thus handed down to you. In such a world it is reasonable for family names to be associated with trades, trades that will be passed down from father to son: Smith, Carpenter, Miller.

But the rise of the various forces that we call “modernity” led to the emergence of the strategic conduct of life: a life with a plan, with certain goals — to get into law school, to become a cosmetologist, to get a corner office.

thanks largely to totalizing technology’s formation of a world in which, to borrow a phrase from Marx and Engels, “all that is solid melts into air,” the strategic model of conduct is replaced by the situational. Instead of being systematic planners, we become agile improvisers: If the job market is bad for your college major, you turn a side hustle into a business. But because you know that your business may get disrupted by the tech industry, you don’t bother thinking long-term; your current gig might disappear at any time, but another will surely present itself, which you will assess upon its arrival.

The movement through these three forms of conduct, whatever benefits it might have, makes our relations with nature increasingly instrumental. We can see this shift more clearly when looking at our changing experience of time

Within the traditional conduct of life, it is necessary to take stewardly care of the resources required for the exercise of a craft or a profession, as these get passed on from generation to generation.

But in the progression from the traditional to the strategic to the situational conduct of life, continuity of preservation becomes less valuable than immediacy of appropriation: We need more lithium today, and merely hope to find greater reserves — or a suitable replacement — tomorrow. This revaluation has the effect of shifting the place of the natural order from something intrinsic to our practices to something extrinsic. The whole of nature becomes what economists tellingly call an externality.

The basic argument of the SCT goes like this. We live in a technopoly, a society in which powerful technologies come to dominate the people they are supposed to serve, and reshape us in their image. These technologies, therefore, might be called prescriptive (to use Franklin’s term) or manipulatory (to use Illich’s). For example, social networks promise to forge connections — but they also encourage mob rule.

all things increasingly present themselves to us as technological: we see them and treat them as what Heidegger calls a “standing reserve,” supplies in a storeroom, as it were, pieces of inventory to be ordered and conscripted, assembled and disassembled, set up and set aside

In his exceptionally ambitious book The Question Concerning Technology in China (2016) and in a series of related essays and interviews, Hui argues, as the title of his book suggests, that we go wrong when we assume that there is one question concerning technology, the question, that is universal in scope and uniform in shape. Perhaps the questions are different in Hong Kong than in the Black Forest. Similarly, the distinction Heidegger draws between ancient and modern technology — where with modern technology everything becomes a mere resource — may not universally hold.

Thesis: Technology is an anthropological universal, understood as an exteriorization of memory and the liberation of organs, as some anthropologists and philosophers of technology have formulated it; Antithesis: Technology is not anthropologically universal; it is enabled and constrained by particular cosmologies, which go beyond mere functionality or utility. Therefore, there is no one single technology, but rather multiple cosmotechnics.

I think Hui’s cosmotechnics, generously leavened with the ironic humor intrinsic to Daoism, provides a genuine Way — pun intended — beyond the limitations of the Standard Critique of Technology. I say this even though I am not a Daoist; I am, rather, a Christian. But it should be noted that Daoism is both daojiao, an organized religion, and daojia, a philosophical tradition. It is daojia that Hui advocates, which makes the wisdom of Daoism accessible and attractive to a Christian like me. Indeed, I believe that elements of daojia are profoundly consonant with Christianity, and yet underdeveloped in the Christian tradition, except in certain modes of Franciscan spirituality, for reasons too complex to get into here.

this technological Daoism as an embodiment of daojia, is accessible to people of any religious tradition or none. It provides a comprehensive and positive account of the world and one’s place in it that makes a different approach to technology more plausible and compelling. The SCT tends only to gesture in the direction of a model of human flourishing, evokes it mainly by implication, whereas Yuk Hui’s Daoist model gives an explicit and quite beautiful account.

The application of Daoist principles is most obvious, as the above exposition suggests, for “users” who would like to graduate to the status of “non-users”: those who quietly turn their attention to more holistic and convivial technologies, or who simply sit or walk contemplatively. But in the interview I quoted from earlier, Hui says, “Some have quipped that what I am speaking about is Daoist robots or organic AI” — and this needs to be more than a quip. Peter Thiel’s longstanding attempt to make everyone a disciple of René Girard is a dead end. What we need is a Daoist culture of coders, and people devoted to “action without acting” making decisions about lithium mining.

Tools that do not contribute to the Way will neither be worshipped nor despised. They will simply be left to gather dust as the people choose the tools that will guide them in the path of contentment and joy: utensils to cook food, devices to make clothes. Of course, the food of one village will differ from that of another, as will the clothing. Those who follow the Way will dwell among the “ten thousand things” of this world — what we call nature — in a certain manner that cannot be specified legally: Verse 18 of the Tao says that when virtue arises only from rules, that is a sure sign that the Way is not present and active. A cosmotechnics is a living thing, always local in the specifics of its emergence in ways that cannot be specified in advance.

It is from the ten thousand things that we learn how to live among the ten thousand things; and our choice of tools will be guided by what we have learned from that prior and foundational set of relations. This is cosmotechnics.

Multiplicity avoids the universalizing, totalizing character of technopoly. The adherents of technopoly, Hui writes, “wishfully believ[e] that the world process will stamp out differences and diversities” and thereby achieve a kind of techno-secular “theodicy,” a justification of the ways of technopoly to its human subjects. But the idea of multiple cosmotechnics is also necessary, Hui believes, in order to avoid the simply delusional attempt to find “a way out of modernity” by focusing on the indigenous or biological “Other.” An aggressive hostility to modernity and a fetishizing of pre-modernity is not the Daoist way.

“I believe that to overcome modernity without falling back into war and fascism, it is necessary to reappropriate modern technology through the renewed framework of a cosmotechnics.” His project “doesn’t refuse modern technology, but rather looks into the possibility of different technological futures.”

“Thinking rooted in the earthy virtue of place is the motor of cosmotechnics. However, for me, this discourse on locality doesn’t mean a refusal of change and of progress, or any kind of homecoming or return to traditionalism; rather, it aims at a re-appropriation of technology from the perspective of the local and a new understanding of history.”

Always Coming Home illustrates cosmotechnics in a hundred ways. Consider, for instance, information storage and retrieval. At one point we meet the archivist of the Library of the Madrone Lodge in the village of Wakwaha-na. A visitor from our world is horrified to learn that while the library gives certain texts and recordings to the City of Mind, some of their documents they simply destroy. “But that’s the point of information storage and retrieval systems! The material is kept for anyone who wants or needs it. Information is passed on — the central act of human culture.” But that is not how the librarian thinks about it. “Tangible or intangible, either you keep a thing or you give it. We find it safer to give it” — to practice “unhoarding.”

It is not information, but relation. This too is cosmotechnics.

a cosmotechnics is the point at which a way of life is realized through making. The point may be illustrated with reference to an ancient tale Hui offers, about an excellent butcher who explains to a duke what he calls the Dao, or “way,” of butchering. The reason he is a good butcher, he says, it not his mastery of a skill, or his reliance on superior tools. He is a good butcher because he understands the Dao: Through experience he has come to rely on his intuition to thrust the knife precisely where it does not cut through tendons or bones, and so his knife always stays sharp. The duke replies: “Now I know how to live.” Hui explains that “it is thus the question of ‘living,’ rather than that of technics, that is at the center of the story.”

my friend showed editorial work they’d done for various publications: beautiful illustrations that would accompany a feature article, a longform essay, or the like. They mentioned they didn’t really do that work any more, in part because of how tiring it was to constantly, quickly, ceaselessly produce concepts for each new piece.

our industry’s relentless investment in “artificial intelligence” means that every time a new Devin or Firefly or Sora announces itself, the rest of us have to ask how we’ll adapt this time. Dunno. Maybe it’s time we step out of that negotiation cycle, and start deciding what we want our work to look like.

I remember them talking about that work, and a phrase they used: “It requires you to be endlessly clever.”

Once a company becomes an industry leader, its employees, from top to bottom, start thinking defensively. Suddenly, people feel they have more to lose from challenging the status quo than upending it. As a result, one-time revolutionaries turn into reactionaries. Proof of this about-face comes when senior executives troop off to Washington or Brussels to lobby against changes that would make life easier for the new up and comers.

Years of continuous improvement produce an ultra-efficient business system—one that’s highly optimized, and also highly inflexible. Successful businesses are usually good at doing one thing, and one thing only. Over-specialization kills adaptability—but this is a tough to trap to avoid, since the defenders of the status quo will always argue that eking out another increment of efficiency is a safer bet than striking out in a new direction.

Long-tenured executives develop a deep base of industry experience and find it hard to question cherished beliefs. In successful companies, managers usually have a fine-grained view of “how the industry works,” and tend to discount data that would challenge their assumptions. Over time, mental models become hard-wired—a fact that makes industry stalwarts vulnerable to new rules. This risk is magnified when senior executives dominate internal conversations about future strategy and direction.

With success comes bulk—more employees, more cash and more market power. Trouble is, a resource advantage tends to make executives intellectually lazy—they start believing that success comes from outspending one’s rivals rather than from outthinking them. In practice, superior resources seldom defeat a superior strategy. So when resources start substituting for creativity, it’s time to short the shares.

One quick suggestion: Treat every belief you have about your business as nothing more than a hypothesis, forever open to disconfirmation. Being paranoid is good, becoming skeptical about your own beliefs is better.

Some of the topics I touch on: Why I believe Vision Pro may be an over-engineered “devkit” The genius & audacity behind some of Apple’s hardware decisions Gaze & pinch is an incredible UI superpower and major industry ah-ha moment Why the Vision Pro software/content story is so dull and unimaginative Why most people won’t use Vision Pro for watching TV/movies Apple’s bet in immersive video is a total game-changer for live sports Why I returned my Vision Pro… and my Top 10 wishlist to reconsider Apple’s VR debut is the best thing that ever happened to Oculus/Meta My unsolicited product advice to Meta for Quest Pro 2 and beyond

Apple really played it safe in the design of this first VR product by over-engineering it. For starters, Vision Pro ships with more sensors than what’s likely necessary to deliver Apple’s intended experience. This is typical in a first-generation product that’s been under development for so many years. It makes Vision Pro start to feel like a devkit.

A sensor party: 6 tracking cameras, 2 passthrough cameras, 2 depth sensors(plus 4 eye-tracking cameras not shown)

it’s easy to understand two particularly important decisions Apple made for the Vision Pro launch: Designing an incredible in-store Vision Pro demo experience, with the primary goal of getting as many people as possible to experience the magic of VR through Apple’s lenses — most of whom have no intention to even consider a $4,000 purchase. The demo is only secondarily focused on actually selling Vision Pro headsets. Launching an iconic woven strap that photographs beautifully even though this strap simply isn’t comfortable enough for the vast majority of head shapes. It’s easy to conclude that this decision paid off because nearly every bit of media coverage (including and especially third-party reviews on YouTube) uses the woven strap despite the fact that it’s less comfortable than the dual loop strap that’s “hidden in the box”.

Apple’s relentless and uncompromising hardware insanity is largely what made it possible for such a high-res display to exist in a VR headset, and it’s clear that this product couldn’t possibly have launched much sooner than 2024 for one simple limiting factor — the maturity of micro-OLED displays plus the existence of power-efficient chipsets that can deliver the heavy compute required to drive this kind of display (i.e. the M2).

The “hard tech” in AI are the LLMs available for rent from OpenAI, Anthropic, Cohere, and others, or available as open source with Llama, Bloom, Mistral and others. The hard-tech is a level playing field; startups do not have an advantage over incumbents.

There can be differentiation in prompt engineering, problem break-down, use of vector databases, and more. However, this isn’t something where startups have an edge, such as being willing to take more risks or be more creative. At best, it is neutral; certainly not an advantage.

This doesn’t mean it’s impossible for a startup to succeed; surely many will. It means that you need a strategy that creates differentiation and distribution, even more quickly and dramatically than is normally required

Whether you’re training existing models, developing models from scratch, or simply testing theories, high-quality data is crucial. Incumbents have the data because they have the customers. They can immediately leverage customers’ data to train models and tune algorithms, so long as they maintain secrecy and privacy.

Intercom’s AI strategy is built on the foundation of hundreds of millions of customer interactions. This gives them an advantage over a newcomer developing a chatbot from scratch. Similarly, Google has an advantage in AI video because they own the entire YouTube library. GitHub has an advantage with Copilot because they trained their AI on their vast code repository (including changes, with human-written explanations of the changes).

While there will always be individuals preferring the startup environment, the allure of working on AI at an incumbent is equally strong for many, especially pure computer and data scientsts who, more than anything else, want to work on interesting AI projects. They get to work in the code, with a large budget, with all the data, with above-market compensation, and a built-in large customer base that will enjoy the fruits of their labor, all without having to do sales, marketing, tech support, accounting, raising money, or anything else that isn’t the pure joy of writing interesting code. This is heaven for many.

A chatbot is in the chatbot market, and an SEO tool is in the SEO market. Adding AI to those tools is obviously a good idea; indeed companies who fail to add AI will likely become irrelevant in the long run. Thus we see that “AI” is a new tool for developing within existing markets, not itself a new market (except for actual hard-tech AI companies).

AI is in the solution-space, not the problem-space, as we say in product management. The customer problem you’re solving is still the same as ever. The problem a chatbot is solving is the same as ever: Talk to customers 24/7 in any language. AI enables completely new solutions that none of us were imagining a few years ago; that’s what’s so exciting and truly transformative. However, the customer problems remain the same, even though the solutions are different

Companies will pay more for chatbots where the AI is excellent, more support contacts are deferred from reaching a human, more languages are supported, and more kinds of questions can be answered, so existing chatbot customers might pay more, which grows the market. Furthermore, some companies who previously (rightly) saw chatbots as a terrible customer experience, will change their mind with sufficiently good AI, and will enter the chatbot market, which again grows that market.

the right way to analyze this is not to say “the AI market is big and growing” but rather: “Here is how AI will transform this existing market.” And then: “Here’s how we fit into that growth.”

Strong technologies capture the imaginations of technology enthusiasts. That is why many important technologies start out as weekend hobbies. Enthusiasts vote with their time, and, unlike most of the business world, have long-term horizons. They build from first principles, making full use of the available resources to design technologies as they ought to exist.