Wealthy tech executives spending their fortunes on real estate or more imaginative adventures is a staple of Silicon Valley culture. Some buy islands, others build yachts longer than a football field or fund quixotic flying car projects. Mr. Ive’s fixation on a single city block, by comparison, seems modest.

At LoveFrom, he has tried to trust his instincts. Buying one building led to buying another. A discussion about a new yarn led to his first fashion apparel. Work with one client, Brian Chesky, the chief executive of Airbnb, led to meeting Sam Altman, the chief executive of OpenAI.

He bought it for $8.5 million and discovered its backdoor led to a parking lot encircled by the block’s buildings. He wanted to turn the parking lot into a green space, but learned that he needed to own another building on the block to control the parking lot. So a year later, he bought a neighboring, 33,000-square-foot building for $17 million.

Mr. Weeks cringed. San Francisco’s commercial real estate market would crash during the pandemic, and more than a third of its offices remain vacant. “I don’t really think you need to do that,” Mr. Weeks told Mr. Ive. “I can get you office space.”

worries faded after neighbors met Mr. Ive. He offered to reduce some tenants’ rents, did free design work for others and won over Mr. Peskin, a frequent critic of development in his district, with his plans to preserve the existing buildings.

Over five years, Mr. Ive and Mr. Newson hired architects, graphic designers, writers and a cinematic special effects developer who work across three areas: work for the love of it, which they do without pay; work for clients, which includes Airbnb and Ferrari; and work for themselves, which includes the building renovation.

The project has given Mr. Elkann an appreciation for LoveFrom’s process. In January, he visited the firm’s studio for an hourslong meeting about the car’s steering wheel. He listened as Mr. Ive and others talked about the appropriate steering wheel length and how a driver should hold it. Ferrari’s chief test driver tested an early prototype of the wheel, which borrowed design elements from the company’s sports car and racecar history, to assess how it would perform. “Paying attention to the steering wheel in a car that you want to drive and what the physicality of what that means is something that Jony was very clear about,” Mr. Elkann said. He added that the result is “something really, really different.”

The useful thing about defining good design as a form-context fit is that it tells you where you will find the form. The form is in the context. To find a good relationship, you do not start by saying, “I want a relationship that looks like this”—that would be starting in the wrong end, by defining form. Instead you say, “I’m just going to pay attention to what happens when I hang out with various people and iterate toward something that feels alive”—you start from the context.

The context is smarter than you. It holds more nuance and information than you can fit in your head. Collaborate with it.

If you want to find a good design—be that the design of a house or an essay, a career or a marriage—what you want is some process that allows you to extract information from the context, and bake it into the form. That is what unfolding is.

The opposite of an unfolding is a vision. A vision springs, not from a careful understanding of a context, but from a fantasy

Anything that increases the rate and resolution of information you get from the context will help. And anything that makes it easier for you to act on the context.

A common reason we filter information and become blind to the context is that we bundle things when we think. Thinking about our career, we might think in abstractions like “a job.” But really a career is made up of a bunch of different things like a salary, an identity, relationships, status, a sense of meaning, and so on. It is often easier to find a fit if you unbundle these things, and think about the parts that matter to you individually. Do you actually need more status? Or can you find a better fit if you go low status?

Another common reason the feedback loop of unfolding often works poorly is that people have decided on a solution already. They have turned on their confirmation bias. They have decided that a certain solution is off-limits. Let’s say you are 34 and haven’t found a partner but want kids. If we unbundle this, it is clear that the problem of having a kid and the problem of love are not the same thing, so you could solve your problem by having a kid with your best friend instead. But this feels weird. It is not the vision you have for your life. And it seems dysfunctional. Observe that feeling—it is, perhaps, a part of the context. There is some information there. But to unfold, do not write off any solutions. Leave them all on the table; let them combine and recombine. Many good ideas look bad at first. To increase the rate at which you understand the context, you want to develop a certain detachment. When the context thrashes one of your ideas, you want to say, “Oh, that’s interesting.” It takes practice. But it is worth getting better at. Reality is shy—it only reveals itself to those who, like honest scientists, do not wish it to be something else.

The faster you can collide your ideas against reality, the faster you get feedback.

The school system is centered around visions, not unfolding. You are asked to make decisions about realities that are five, ten years down the line, and you get no feedback on your decisions.

you’re less torn by anxious attachments when you recognize how something must naturally and necessarily unfold.

Knowledge is freedom from getting mad at facts.

Detachment does not mean you don’t care what happens. It just means you don’t care whether a specific thing happens or not. You want to know the outcome of the coin-toss (you care), but you don’t care whether it is heads or tails even if you’ve bet on heads (you’re not attached to a specific outcome). The important thing is that something happens, which means you’ve successfully kept play going, but without keeping score.

Emotional Self-Management: I like to think of this as accepting the emotions you have instead of having emotions about having emotions in an endless stack.

Fear. Not fear, plus anxiety about fear, plus guilt about anxiety about fear, plus shame about displaying guilt about experiencing anxiety about having fear. This is emotional focus. Instead of retreating from an emotion through layers of additional emotions until you find one you can deal with, you experience the actual emotion for what it is.

I’d posit that a reason why people love CarPlay so much is because the media, communication, and navigation experiences have traditionally been pretty poor. CarPlay supplants those, and it does so with aplomb because people use those same media, communication, and navigation features that are personalized to them with their phones when they’re not in their cars.

No one is walking around with a speedometer and a tachometer on their iPhone that need to have a familiar look and feel, rendered exclusively in San Francisco. As long as automakers supply the existing level of CarPlay support, which isn’t a given, then customers like us would be content with the status quo, or even a slight improvement.

In my humble opinion, Next-Gen CarPlay is dead on arrival. Too late, too complicated, and it doesn’t solve the needs of automakers or customers. Instead of letting the vehicle’s interface peak through, Apple should consider letting CarPlay peak through for the non-critical systems people prefer to use with CarPlay.

Design a CarPlay that can output multiple display streams (which Apple already over-designed) and display that in the cluster. Integrate with the existing controls for managing the interfaces in the vehicle. When the phone isn’t there, the vehicle will still be the same vehicle. When the phone is there, it’s got Apple Maps right in the cluster how you like it without changing the gauges, or the climate controls, or where the seat massage button is.

The everyday irritations people have are mundane, practical, and are not related to how Apple-like their car displays can look.

there are three big categories of barriers. The application barrier, the interview barrier, and the document barrier. And that’s what we spent most of our time iterating on and building a system that could slowly learn about those barriers and then intervene against them.

The application is asking, “Are you convicted of this? Are you convicted of that? Are you convicted of this other thing?” What is that saying to you, as a person, about what the system thinks of you?

Often they’ll call from a blocked number. They’ll send you a notice of when your interview is scheduled for, but this notice will sometimes arrive after the actual date of the interview. Most state agencies are really slammed right now for a bunch of reasons, including Medicaid unwinding. And many of the people assisting on Medicaid are the same workers who process SNAP applications. If you missed your phone interview, you have to call to reschedule it. But in many states, you can’t get through, or you have to call over and over and over again. For a lot of people, if they don’t catch that first interview call, they’re screwed and they’re not going to be approved.

getting to your point about how a website can fix this —  the end result was lowest-burden application form that actually gets a caseworker what they need to efficiently and effectively process it. We did a lot of iteration to figure out that sweet spot.

We didn’t need to do some hard system integration that would potentially take years to develop — we were just using the system as it existed. Another big advantage was that we had to do a lot of built-in data validation because we could not submit anything that was going to fail the county application. We discovered some weird edge cases by doing this.

A lot of times when you want to build a new front end for these programs, it becomes this multiyear, massive project where you’re replacing everything all at once. But if you think about it, there’s a lot of potential in just taking the interfaces you have today, building better ones on top of them, and then using those existing ones as the point of integration.

Government tends to take a more high-modernist approach to the software it builds, which is like “we’re going to plan and know up front how everything is, and that way we’re never going to have to make changes.” In terms of accreting layers — yes, you can get to that point. But I think a lot of the arguments I hear that call for a fundamental transformation suffer from the same high-modernist thinking that is the source of much of the status quo.

If you slowly do this kind of stuff, you can build resilient and durable interventions in the system without knocking it over wholesale. For example, I mentioned procedural denials. It would be adding regulations, it would be making technology systems changes, blah, blah, blah, to have every state report why people are denied, at what rate, across every state up to the federal government. It would take years to do that, but that would be a really, really powerful change in terms of guiding feedback loops that the program has.

when you start to read about civic technology, it very, very quickly becomes clear that things that look like they are tech problems are actually about institutional culture, or about policy, or about regulatory requirements.

If you have an application where you think people are struggling, you can measure how much time people take on each page. A lot of what technology provides is more rigorous measurement of the burdens themselves. A lot of these technologies have been developed in commercial software because there’s such a massive incentive to get people who start a transaction to finish it. But we can transplant a lot of those into government services and have orders of magnitude better situational awareness.

There’s this starting point thesis: Tech can solve these government problems, right? There’s healthcare.gov and the call to bring techies into government, blah, blah, blah. Then there’s the antithesis, where all these people say, well, no, it’s institutional problems. It’s legal problems. It’s political problems. I think either is sort of an extreme distortion of reality. I see a lot of more oblique levers that technology can pull in this area.

LLMs seem to be a fundamental breakthrough in manipulating words, and at the end of the day, a lot of government is words. I’ve been doing some active experimentation with this because I find it very promising. One common question people have is, “Who’s in my household for the purposes of SNAP?” That’s actually really complicated when you think about people who are living in poverty — they might be staying with a neighbor some of the time, or have roommates but don’t share food, or had to move back home because they lost their job.

I’ve been taking verbatim posts from Reddit that are related to the household question and inputting them into LLMs with some custom prompts that I’ve been iterating on, as well as with the full verbatim federal regulations about household definition. And these models do seem pretty capable at doing some base-level reasoning over complex, convoluted policy words in a way that I think could be really promising.

caseworkers are spending a lot of their time figuring out, wait, what rule in this 200-page policy manual is actually relevant in this specific circumstance? I think LLMS are going to be really impactful there.

It is certainly the case that I’ve seen some productive tensions in counties where there’s more of a mix of that and what you might consider California-style Republicans who are like, “We want to run this like a business, we want to be efficient.” That tension between efficiency and big, ambitious policies can be a healthy, productive one. I don’t know to what extent that exists at the state level, and I think there’s hints of more of an interest in focusing on state-level government working better and getting those fundamentals right, and then doing the more ambitious things on a more steady foundation.

California seemed to really try to take every ambitious option that the feds give us on a whole lot of fronts. I think the corollary of that is that we don’t necessarily get the fundamental operational execution of these programs to a strong place, and we then go and start adding tons and tons of additional complexity on top of them.

Having an opinionated set of tradeoffs exposes your approach to a set of weaknesses. The more you tip the scale on one side, the weaker something else will be. That’s okay! Making those difficult choices is what people pay you for. You should be proud of your compromises. My favorite products are opinionated. They make a clear statement about what they are not good at, in favor of being much better at something else.

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?

The cover immediately sets Ways of Seeing apart from its contemporaries, the book itself begins on the cover. Rather than creating a conventionally appealing cover, Hollis chose to bypass this tradition entirely, instead placing the text and an image from the start of the first chapter straight onto the front, just beneath the title and authors name. This directness has a link with the television series, mimicking how the first episode began with no preamble or title sequence, Berger got started immediately, drawing the audience in with his message rather than any distractions.

Another link to Berger’s presenting style is Hollis’ choice of typeface, bold Univers 65 is used for the body copy throughout, in an attempt to achieve something of the captivating quality of Berger’s voice.

The layout also employs large indents rather than paragraph breaks, something of a Hollis trademark. But this mirrors how Berger had presented on television, there was little time wasted with atmospheric filler shots or long gaps in speech, the message was key and continuous.

The key reason that Ways of Seeing has become iconic as a piece of book design is how it dealt with text and image: the two are integrated, where an image is mentioned in the text it also appears there. Captions are avoided where possible. When unavoidable they are in a lighter weight of type and run horizontally, so as not to disrupt the text. Images are often set at the same width as the lines of text, or indented by the same amount, this democratises the text and image relationship. Occasionally works of art are cropped to show only the pertinent details. All of these features are a big departure from the art books of the time which usually featured glorified full page colour images, often in a glossy ‘colour plate’ section in the middle, completely distanced from where the text refers to them.

Design is not used for prettifying, or to create appeal, rather it is used for elucidating, to spread his message or get his point across as clearly as possible. Be it a point about art and politics, art and gender, the ethics of advertising, the human experiences of a rural GP, or economic migrants in Germany — the design is always appropriate to what Berger wants to say, but does so economically without redundancy.

Even in Portraits: John Berger on Artists published by Verso in 2015, Berger insisted on black and white reproductions, arguing that: “glossy colour reproductions in the consumerist world of today tend to reduce what they show to items in a luxury brochure for millionaires. Whereas black-and-white reproductions are simple memoranda.”

the images in the book “illustrate the essentially dialectical relationship between text and image in Berger’s work: the pattern in which an image shapes a text, which then goes on to shape how we understand that image.”

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.

LLMs will represent a step change in tool support for end-user programming: the ability of normal people to fully harness the general power of computers without resorting to the complexity of normal programming. Until now, that vision has been bottlenecked on turning fuzzy informal intent into formal, executable code; now that bottleneck is rapidly opening up thanks to LLMs.

If this hypothesis indeed comes true, we might start to see some surprising changes in the way people use software: One-off scripts: Normal computer users have their AI create and execute scripts dozens of times a day, to perform tasks like data analysis, video editing, or automating tedious tasks. One-off GUIs: People use AI to create entire GUI applications just for performing a single specific task—containing just the features they need, no bloat. Build don’t buy: Businesses develop more software in-house that meets their custom needs, rather than buying SaaS off the shelf, since it’s now cheaper to get software tailored to the use case. Modding/extensions: Consumers and businesses demand the ability to extend and mod their existing software, since it’s now easier to specify a new feature or a tweak to match a user’s workflow. Recombination: Take the best parts of the different applications you like best, and create a new hybrid that composes them together.

Chat will never feel like driving a car, no matter how good the bot is. In their 1986 book Understanding Computers and Cognition, Terry Winograd and Fernando Flores elaborate on this point: In driving a car, the control interaction is normally transparent. You do not think “How far should I turn the steering wheel to go around that curve?” In fact, you are not even aware (unless something intrudes) of using a steering wheel…The long evolution of the design of automobiles has led to this readiness-to-hand. It is not achieved by having a car communicate like a person, but by providing the right coupling between the driver and action in the relevant domain (motion down the road).

Think about how a spreadsheet works. If you have a financial model in a spreadsheet, you can try changing a number in a cell to assess a scenario—this is the inner loop of direct manipulation at work. But, you can also edit the formulas! A spreadsheet isn’t just an “app” focused on a specific task; it’s closer to a general computational medium which lets you flexibly express many kinds of tasks. The “platform developers"—the creators of the spreadsheet—have given you a set of general primitives that can be used to make many tools. We might draw the double loop of the spreadsheet interaction like this. You can edit numbers in the spreadsheet, but you can also edit formulas, which edits the tool

what if you had an LLM play the role of the local developer? That is, the user mainly drives the creation of the spreadsheet, but asks for technical help with some of the formulas when needed? The LLM wouldn’t just create an entire solution, it would also teach the user how to create the solution themselves next time.

This picture shows a world that I find pretty compelling. There’s an inner interaction loop that takes advantage of the full power of direct manipulation. There’s an outer loop where the user can also more deeply edit their tools within an open-ended medium. They can get AI support for making tool edits, and grow their own capacity to work in the medium. Over time, they can learn things like the basics of formulas, or how a VLOOKUP works. This structural knowledge helps the user think of possible use cases for the tool, and also helps them audit the output from the LLMs. In a ChatGPT world, the user is left entirely dependent on the AI, without any understanding of its inner mechanism. In a computational medium with AI as assistant, the user’s reliance on the AI gently decreases over time as they become more comfortable in the medium.

our truly quirky dimensions are never really grasped by these recommendation algorithms. They have all the dullness of a Demographics 101 curriculum; they sketch our personalities with the crudity of crime-scene chalk-outlines. They’re not wrong about us; but they’re woefully incomplete.

The metaphor suggests precisely what to do: If you want to have wilder, curiouser thoughts, you have to avoid the industrial monocropping of big-tech feeds. You want an intellectual forest, overgrown with mushrooms and towering weeds and a massive dead log where a family of raccoons has taken up residence.

For me, it’s meant slowly — over the last few years — building up a big, rangy collection of RSS feeds, that let me check up on hundreds of electic blogs and publications and people. (I use Feedly.) I’ve also started using Fraidycat, a niftily quixotic feed-reader that lets you sort sources into buckets by “how often should I check this source”, which is a cool heuristic; some people/sites you want to check every day, and others, twice a year.

Other times I spend an hour or two simply prospecting — I pick a subject almost at random, then check to see if there’s a hobbyist or interest-group discussion-board devoted to it. (There usually is, running on free warez like phpBB). Then I’ll just trawl through the forum, to find out what does this community care about?

Here are the adoption curves for a handful of major technologies in the United States. There are big differences in the speeds at which these technologies were absorbed. Landline telephones took about 86 years to hit 80% adoption.Flush toilets took 96 years to hit 80% adoption.Refrigerators took about 25 years.Microwaves took 17 years.Smartphones took just 12 years.Why these wide differences in adoption speed? Conformability with existing infrastructure. Flush toilets required the build-out of water and sewage utility systems. They also meant adding a new room to the house—the bathroom—and running new water and sewage lines underneath and throughout the house. That’s a lot of systems to line up. By contrast, refrigerators replaced iceboxes, and could fit into existing kitchens without much work. Microwaves could sit on a countertop. Smartphones could slip into your pocket.

One of the most impactful uses of LLM technology lies in content rewriting, which naturally capitalizes on these systems’ robust capabilities for generating and refining text. This application is a logical fit, helping users enhance their content while engaging with a service.

Similar to summarization but incorporating an element of judgment, features like Microsoft Team CoPilot’s call transcript summaries distill extensive discussions into essential bullet points, spotlighting pivotal moments or insights.

The ability to ‘understand’ nuanced language through summarization extends naturally into advanced search functionalities. ServiceNow does this by enabling customer service agents to search tickets for recommended solutions and to dispel jargon used by different agents.

Rather than merely focusing on content creation or manipulation, emerging applications of these systems provide new perspectives and predict outcomes based on accumulated human experiences. The actual value of these applications lies not merely in enhancing efficiency but in augmenting effectiveness, enabling users to make more informed decisions.

Tashi pairs her Loewe cotton shirtdress with Chanel espadrilles when she’s older, and wears Cartier jewelry exclusively (despite her real-life Bulgari ambassadorship). She applies Augustinus Bader cream on her body and wears lots of camel cashmere. This is Tashi attempting to exert her dominance over everyone else: She is a better tennis player, she is wealthier, she is more mature, and she would like you to know it. When she is young, she’s in Adidas campaigns, which our culture has come to know as a signifier for the sports prodigy, the role model.

Art is a good boy and wants to be told so. He listens to Tashi and does what she wants. He wears good-boy clothes: crisp white Uniqlo polo shirts on the court and navy quarter zips off-duty. Patrick, however, is sleazy, and what he wants from Tashi is both a challenger and someone who will put him in his place. He evokes a Peter Pan “I’ll never grow up” energy in mismatched athleisure, and drives a rundown car despite coming from money.

Haptic sensations feel rewarding, so users feel compelled to repeat these actions.What's the optimal length for vibrations? Try 400ms

But what if you sell products on desktop?Instead of using vibrations, try animating products upon selection. Move items into the basketShake from side to sideGrow and shrink

In an online grocery store, customers bought more items when they felt vibrations while adding items to their cart (Hampton & Hildebrand, 2021).

Classical Conditioning. Vibrations often co-occur with social messages. Therefore, vibrations feel good because these sensations have been frequently paired with hits of dopamine (Hampton & Hildebrand, 2021).Perceived Ownership. Vibrations mimic touch, as if you are physically touching an item on your device. And touch is key to ownership (Li, Cowan, Yazdanparast, & Ansell, 2024; Peck, Barger, & Webb, 2013).