These ribbons, upon closer inspection, appear to be two layers of machine-made fabric sewn together to form a single piece with one side puffed out like an arch in bridges, the “Alpine Loop” band that symbolizes the Apple Watch Ultra, which was just announced in the fall of 2022. The band is made of lightweight yet sturdy polyester fiber, the band is designed for outdoor activities by threading a metal hook through a hole in the fabric that expands in arch pattern, which prevents it from being pulled out in any direction. The fact that this intricate and delicate band is woven is astonishing.

The “Alpine Loop” uses 520 warp threads, which is far more than the number of threads used in ordinary fabrics, and this first process alone takes about six full days even for experienced employees.

After inspecting the heat treatment process on the first floor of the factory, I asked Tim Cook about his impressions of the company. “I love the the ability to scale something that is so intricate, something that is so detailed. And you know they’re making a lot of these as you can, tell but they’re doing it in such a high quality way. And the yields are very high.”

“They were very flexible, and willing to try new processes, new ways of doing things. This was the first time that this particular process was ever used. And so they have to be very nimble but that nimbleness has to be underpinned by great expertise. And they have that great expertise here. And I can’t stress enough the attention to detail and quality. These are the things that make the products look so great right out of the box.”

Apple prefers to use the term “supplier” over alternatives such as “subcontractor” because they believe in equal business partnership.

“What sets Apple apart [from other companies] is that they let us work as a team. If we have a problem, we spend time together to come up with a solution.” Seiji Inoue, managing director of Inoue Ribbon Industry, spoke from the opposite side of Cook’s statement.

In addition to bands for the Apple Watch, the company also produces handles made from woven paper for “Mac Pro” product packaging. Normally, nylon or other materials would be mixed into paper to give sturdiness, but Apple places importance on recyclability, so they need to make them from 100% paper. The team worked together with the Apple staff to find a way to meet these requirements, and when we introduced a manufacturer that could produce paper, they said, “Great,” and accompanied us to the manufacturer.

The first product they worked on was a band for the Apple Watch called “Woven Nylon.” It took four years to develop. At first, Mr. Inoue was fed up with the high quality requirements. Compared to other industries, the textile industry is not very strict about size control.

at some point the front-line workers became accustomed to Apple’s standards, and are now saying, “We have to do this much, don’t we?” and aiming for higher quality manufacturing. He added, “Apple taught me from scratch about quantification and other things. They taught me how to manage, how to make a table like this, how to do standard deviation like this, how to take data like this, and so on. You can’t learn so much even if you paid someone. But Apple shared all those knowledges sayin we are on the same team.”

Mr. Nobunari Sawanobori, the president of Teikoku Ink, which supplies white ink for the iPhone, once said, “The loss of learning through working with Apple is a bigger loss than the loss of orders from Apple.”

After working for so long with Apple, recently Inoue Ribbon Industry began to make proposal or provide supplement data when they work with other clients, Most of those clients are surprised and delighted.

Also, even putting aside the fact that first-party apps necessarily have certain advantages third-party apps do not (otherwise, there’d be no distinction), apps from the same developer have broad permission to share data and resources via app groups. Gmail can talk to Google Calendar, and Google Calendar has full access to Gmail’s address book. It’s no more “fundamentally anticompetitive” for Messages and Apple Mail to have full access to your Contacts address book than it was for Meta to launch Threads by piggybacking on the existing accounts and social graph of Instagram. If it’s unfair, it’s only unfair in the way that life in general is unfair.

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.”

"Will it be different?" is the key question the regular people ask. They don't want there to be extra steps or new procedures. They sure as hell don't want the icons to look different or, God forbid, be moved to a new place.

These bright and capable people who will one day help you through knee replacement surgery all bought a Mac when they were college frehmen and then they never updated it. Almost all of them had the default programs still in the dock. They are regular users. You with all your fancy calendars, note taking apps and your customized terminal are an outlier. Never forget.

The majority of iPhone users and Mac owners have no idea what's coming though. They are going to wake up on Monday to an unwelcome notification that there is an update available. Many of them will ask their techie friends (like you) if there is a way to make the update notification go away. They will want to know if they have to install it.

More than anything I felt like I had been airlifted into a surreal parallel universe, in which everyone is wealthy and on vacation and having beautiful children who go on field trips to aquaria. The inbox in question belongs to Jane Appleseed, and one wonders whether Jane knows her private life is being used to sell hardware and promises.

their models are almost entirely based on personal context, by way of an on-device semantic index. In broad strokes, this on-device semantic index can be thought of as a next-generation Spotlight. Apple is focusing on what it can do that no one else can on Apple devices, and not really even trying to compete against ChatGPT et al. for world-knowledge context. They’re focusing on unique differentiation, and eschewing commoditization.

Apple is doing what no one else can do: integrating generative AI into the frameworks in iOS and MacOS used by developers to create native apps. Apps built on the system APIs and frameworks will gain generative AI features for free, both in the sense that the features come automatically when the app is running on a device that meets the minimum specs to qualify for Apple Intelligence, and in the sense that Apple isn’t charging developers or users to utilize these features.

The chatbot might replace all software with a prompt - ‘software is dead’. I’m skeptical about this, as I’ve written here, but Apple is proposing the opposite: that generative AI is a technology, not a product.

Apple is, I think, signalling a view that generative AI, and ChatGPT itself, is a commodity technology that is most useful when it is: Embedded in a system that gives it broader context about the user (which might be search, social, a device OS, or a vertical application) and Unbundled into individual features (ditto), which are inherently easier to run as small power-efficient models on small power-efficient devices on the edge (paid for by users, not your capex budget) - which is just as well, because… This stuff will never work for the mass-market if we have marginal cost every time the user presses ‘OK’ and we need a fleet of new nuclear power-stations to run it all.

Apple has built its own foundation models, which (on the benchmarks it published) are comparable to anything else on the market, but there’s nowhere that you can plug a raw prompt directly into the model and get a raw output back - there are always sets of buttons and options shaping what you ask, and that’s presented to the user in different ways for different features. In most of these features, there’s no visible bot at all. You don’t ask a question and get a response: instead, your emails are prioritised, or you press ‘summarise’ and a summary appears. You can type a request into Siri (and Siri itself is only one of the many features using Apple’s models), but even then you don’t get raw model output back: you get GUI. The LLM is abstracted away as an API call.

Apple is treating this as a technology to enable new classes of features and capabilities, where there is design and product management shaping what the technology does and what the user sees, not as an oracle that you ask for things.

Apple is drawing a split between a ‘context model’ and a ‘world model’. Apple’s models have access to all the context that your phone has about you, powering those features, and this is all private, both on device and in Apple’s ‘Private Cloud’. But if you ask for ideas for what to make with a photo of your grocery shopping, then this is no longer about your context, and Apple will offer to send that to a third-party world model - today, ChatGPT.

that’s clearly separated into a different experience where you should have different expectations, and it’s also, of course, OpenAI’s brand risk, not Apple’s. Meanwhile, that world model gets none of your context, only your one-off prompt.

Neither OpenAI nor any of the other cloud models from new companies (Anthropic, Mistral etc) have your emails, messages, locations, photos, files and so on.

Apple is letting OpenAI take the brand risk of creating pizza glue recipes, and making error rates and abuse someone else’s problem, while Apple watches from a safe distance.

The next step, probably, is to take bids from Bing and Google for the default slot, but meanwhile, more and more use-cases will be quietly shifted from the third party to Apple’s own models. It’s Apple’s own software that decides where the queries go, after all, and which ones need the third party at all.

A lot of the compute to run Apple Intelligence is in end-user devices paid for by the users, not Apple’s capex budget, and Apple Intelligence is free.

Commoditisation is often also integration. There was a time when ‘spell check’ was a separate product that you had to buy, for hundreds of dollars, and there were dozens of competing products on the market, but over time it was integrated first into the word processor and then the OS. The same thing happened with the last wave of machine learning - style transfer or image recognition were products for five minutes and then became features. Today ‘summarise this document’ is AI, and you need a cloud LLM that costs $20/month, but tomorrow the OS will do that for free. ‘AI is whatever doesn’t work yet.’

Apple is big enough to take its own path, just as it did moving the Mac to its own silicon: it controls the software and APIs on top of the silicon that are the basis of those developer network effects, and it has a world class chip team and privileged access to TSMC.

Apple is doing something slightly different - it’s proposing a single context model for everything you do on your phone, and powering features from that, rather than adding disconnected LLM-powered features at disconnected points across the company.

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.

the most advanced generalist AI models often outperform specialized models, even in the specific domains those specialized models were designed for. That means that if you want a model that can do a lot - reason over massive amounts of text, help you generate ideas, write in a non-robotic way — you want to use one of the three frontier models: GPT-4o, Gemini 1.5, or Claude 3 Opus.

Working with advanced models is more like working with a human being, a smart one that makes mistakes and has weird moods sometimes. Frontier models are more likely to do extraordinary things but are also more frustrating and often unnerving to use. Contrast this with Apple’s narrow focus on making AI get stuff done for you.

Every major AI company argues the technology will evolve further and has teased mysterious future additions to their systems. In contrast, what we are seeing from Apple is a clear and practical vision of how AI can help most users, without a lot of effort, today. In doing so, they are hiding much of the power, and quirks, of LLMs from their users. Having companies take many approaches to AI is likely to lead to faster adoption in the long term. And, as companies experiment, we will learn more about which sets of models are correct.

The left side of figure 5-1 indicates that when there is a performance gap — when product functionality and reliability are not yet good enough to address the needs of customers in a given tier of the market — companies must compete by making the best possible products. In the race to do this, firms that build their products around proprietary, interdependent architectures enjoy an important competitive advantage against competitors whose product architectures are modular, because the standardization inherent in modularity takes too many degrees of design freedom away from engineers, and they cannot not optimize performance.

The issue I have with this analysis of vertical integration — and this is exactly what I was taught at business school — is that the only considered costs are financial. But there are other, more difficult to quantify costs. Modularization incurs costs in the design and experience of using products that cannot be overcome, yet cannot be measured. Business buyers — and the analysts who study them — simply ignore them, but consumers don’t. Some consumers inherently know and value quality, look-and-feel, and attention to detail, and are willing to pay a premium that far exceeds the financial costs of being vertically integrated.

Google trains and runs its Gemini family of models on its own TPU processors, which are only available on Google’s cloud infrastructure. Developers can access Gemini through Vertex AI, Google’s fully-managed AI development platform; and, to the extent Vertex AI is similar to Google’s internal development environment, that is the platform on which Google is building its own consumer-facing AI apps. It’s all Google, from top-to-bottom, and there is evidence that this integration is paying off: Gemini 1.5’s industry leading 2 million token context window almost certainly required joint innovation between Google’s infrastructure team and its model-building team.

On the other extreme is AWS, which doesn’t have any of its own models; instead its focus has been on its Bedrock managed development platform, which lets you use any model. Amazon’s other focus has been on developing its own chips, although the vast majority of its AI business runs on Nvidia GPUs.

Microsoft is in the middle, thanks to its close ties to OpenAI and its models. The company added Azure Models-as-a-Service last year, but its primary focus for both external customers and its own internal apps has been building on top of OpenAI’s GPT family of models; Microsoft has also launched its own chip for inference, but the vast majority of its workloads run on Nvidia.

Google is certainly building products for the consumer market, but those products are not devices; they are Internet services. And, as you might have noticed, the historical discussion didn’t really mention the Internet. Both Google and Meta, the two biggest winners of the Internet epoch, built their services on commodity hardware. Granted, those services scaled thanks to the deep infrastructure work undertaken by both companies, but even there Google’s more customized approach has been at least rivaled by Meta’s more open approach. What is notable is that both companies are integrating their models and their apps, as is OpenAI with ChatGPT.

It may be the case that selling hardware, which has to be perfect every year to justify a significant outlay of money by consumers, provides a much better incentive structure for maintaining excellence and execution than does being an Aggregator that users access for free.

Google’s collection of moonshots — from Waymo to Google Fiber to Nest to Project Wing to Verily to Project Loon (and the list goes on) — have mostly been science projects that have, for the most part, served to divert profits from Google Search away from shareholders. Waymo is probably the most interesting, but even if it succeeds, it is ultimately a car service rather far afield from Google’s mission statement “to organize the world’s information and make it universally accessible and useful.”

The only thing that drives meaningful shifts in platform marketshare are paradigm shifts, and while I doubt the v1 version of Pixie [Google’s rumored Pixel-only AI assistant] would be good enough to drive switching from iPhone users, there is at least a path to where it does exactly that.

the fact that Google is being mocked mercilessly for messed-up AI answers gets at why consumer-facing AI may be disruptive for the company: the reason why incumbents find it hard to respond to disruptive technologies is because they are, at least at the beginning, not good enough for the incumbent’s core offering. Time will tell if this gives more fuel to a shift in smartphone strategies, or makes the company more reticent.

while I was very impressed with Google’s enterprise pitch, which benefits from its integration with Google’s infrastructure without all of the overhead of potentially disrupting the company’s existing products, it’s going to be a heavy lift to overcome data gravity, i.e. the fact that many enterprise customers will simply find it easier to use AI services on the same clouds where they already store their data (Google does, of course, also support non-Gemini models and Nvidia GPUs for enterprise customers). To the extent Google wins in enterprise it may be by capturing the next generation of startups that are AI first and, by definition, data light; a new company has the freedom to base its decision on infrastructure and integration.

Amazon is certainly hoping that argument is correct: the company is operating as if everything in the AI value chain is modular and ultimately a commodity, which insinuates that it believes that data gravity will matter most. What is difficult to separate is to what extent this is the correct interpretation of the strategic landscape versus a convenient interpretation of the facts that happens to perfectly align with Amazon’s strengths and weaknesses, including infrastructure that is heavily optimized for commodity workloads.

Meta’s open source approach to Llama: the company is focused on products, which do benefit from integration, but there are also benefits that come from widespread usage, particularly in terms of optimization and complementary software. Open source accrues those benefits without imposing any incentives that detract from Meta’s product efforts (and don’t forget that Meta is receiving some portion of revenue from hyperscalers serving Llama models).

The iPhone maker, like Amazon, appears to be betting that AI will be a feature or an app; like Amazon, it’s not clear to what extent this is strategic foresight versus motivated reasoning.

achieving something approaching AGI, whatever that means, will require maximizing every efficiency and optimization, which rewards the integrated approach.

the most value will be derived from building platforms that treat models like processors, delivering performance improvements to developers who never need to know what is going on under the hood.

It symbolizes everything everyone has ever hated about digitization. It celebrates a lossy, creative compression for the most flimsy reason: An iPad shedding an irrelevant millimeter or two. It's destruction of beloved musical instruments is the perfect metaphor for how utterly tone-deaf technologists are capable of being. But the real story is just how little saved up goodwill Apple had in the bank to compensate for the outrage.

This should all be eerily familiar to anyone who saw Microsoft fall from grace in the 90s. From being America's favorite software company to being the bully pursued by the DOJ for illegalities. Just like Apple now, Microsoft's reputation and good standing suddenly evaporated seemingly overnight once enough critical stories had accumulated about its behavior.

Apple had such treasure chest of goodwill from decades as first an underdog, then unchallenged innovator. But today they're a near three-trillion dollar company, battling sovereigns on both sides of the Atlantic, putting out mostly incremental updates to mature products.

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.

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).

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.

As for a Hall of Shame thing? That would be the slow but steady encroachment of single-window applications in MacOS, especially via Catalyst and Electron. The reason I gravitated toward MacOS in the first place is the same reason I continue to use it: it fits my mental model of how an operating system ought to work.

We can take nature’s superpowers and expand them across many more vectors that are interesting to humans: Across scale — far and near, binoculars, zoom, telescope, microscope Across wavelength — UV, IR, heatmaps, nightvision, wifi, magnetic fields, electrical and water currents Across time — view historical imagery, architectural, terrain, geological, and climate changes Across culture — experience the relevance of a place in books, movies, photography, paintings, and language Across space — travel immersively to other locations for tourism, business, and personal connections Across perspective — upside down, inside out, around corners, top down, wider, narrower, out of body Across interpretation — alter the visual and artistic interpretation of your environment, color-shifting, saturation, contrast, sharpness

Headset displays connect sensory extensions directly to your vision. Equipped with sensors that perceive beyond human capabilities, and access to the internet, they can provide information about your surroundings wherever you are. Until now, visual augmentation has been constrained by the tiny display on our phone. By virtue of being integrated with your your eyesight, headsets can open up new kinds of apps that feel more natural. Every app is a superpower. Sensory computing opens up new superpowers that we can borrow from nature. Animals, plants and other organisms can sense things that humans can’t

The first mass-market bicycle for the senses was Apple’s AirPods. Its noise cancellation and transparency mode replace and enhance your hearing. Earbuds are turning into ear computers that will become more easily programmable. This can enable many more kinds of hearing. For example, instantaneous translation may soon be a reality

For the past seven decades, computers have been designed to enhance what your brain can do — think and remember. New kinds of computers will enhance what your senses can do — see, hear, touch, smell, taste. The term spatial computing is emerging to encompass both augmented and virtual reality. I believe we are exploring an even broader paradigm: sensory computing. The phone was a keyhole for peering into this world, and now we’re opening the door.

What happens when put on a headset and open the “Math” app? How could seeing the world through math help you understand both better?

Advances in haptics may open up new kinds of tactile sensations. A kind of second skin, or softwear, if you will. Consider that Apple shipped a feature to help you find lost items that vibrates more strongly as you get closer. What other kinds of data could be translated into haptic feedback?

It may sound far-fetched, but converting olfactory patterns into visual patterns could open up some interesting applications. Perhaps a new kind of cooking experience? Or new medical applications that convert imperceptible scents into visible patterns?

in their efforts to provide better products than their competitors and earn higher prices and margins, suppliers often “overshoot” their market: They give customers more than they need or ultimately are willing to pay for. And more importantly, it means that disruptive technologies that may underperform today, relative to what users in the market demand, may be fully performance-competitive in that same market tomorrow. This was the basis for insisting that the iPhone must have a low-price model: surely Apple would soon run out of new technology to justify the prices it charged for high-end iPhones, and consumers would start buying much cheaper Android phones instead! In fact, as I discussed in after January’s earnings results, the company has gone in the other direction: more devices per customer, higher prices per device, and an increased focus on ongoing revenue from those same customers.

Apple seems to have mostly saturated the high end, slowly adding switchers even as existing iPhone users hold on to their phones longer; what is not happening, though, is what disruption predicts: Apple isn’t losing customers to low-cost competitors for having “overshot” and overpriced its phones. It seems my thesis was right: a superior experience can never be too good — or perhaps I didn’t go far enough.

Jeff Bezos has been writing an annual letter to shareholders since 1997, and he attaches that original letter to one he pens every year. It included this section entitled Obsess Over Customers: From the beginning, our focus has been on offering our customers compelling value. We realized that the Web was, and still is, the World Wide Wait. Therefore, we set out to offer customers something they simply could not get any other way, and began serving them with books. We brought them much more selection than was possible in a physical store (our store would now occupy 6 football fields), and presented it in a useful, easy-to-search, and easy-to-browse format in a store open 365 days a year, 24 hours a day. We maintained a dogged focus on improving the shopping experience, and in 1997 substantially enhanced our store. We now offer customers gift certificates, 1-Click shopping, and vastly more reviews, content, browsing options, and recommendation features. We dramatically lowered prices, further increasing customer value. Word of mouth remains the most powerful customer acquisition tool we have, and we are grateful for the trust our customers have placed in us. Repeat purchases and word of mouth have combined to make Amazon.com the market leader in online bookselling.

This year, after highlighting just how much customers love Amazon (answer: a lot), Bezos wrote: One thing I love about customers is that they are divinely discontent. Their expectations are never static — they go up. It’s human nature. We didn’t ascend from our hunter-gatherer days by being satisfied. People have a voracious appetite for a better way, and yesterday’s ‘wow’ quickly becomes today’s ‘ordinary’. I see that cycle of improvement happening at a faster rate than ever before. It may be because customers have such easy access to more information than ever before — in only a few seconds and with a couple taps on their phones, customers can read reviews, compare prices from multiple retailers, see whether something’s in stock, find out how fast it will ship or be available for pick-up, and more. These examples are from retail, but I sense that the same customer empowerment phenomenon is happening broadly across everything we do at Amazon and most other industries as well. You cannot rest on your laurels in this world. Customers won’t have it.

when it comes to Internet-based services, this customer focus does not come at the expense of a focus on infrastructure or distribution or suppliers: while those were the means to customers in the analog world, in the online world controlling the customer relationship gives a company power over its suppliers, the capital to build out infrastructure, and control over distribution. Bezos is not so much choosing to prioritize customers insomuch as he has unlocked the key to controlling value chains in an era of aggregation.

consumer expectations are not static: they are, as Bezos’ memorably states, “divinely discontent”. What is amazing today is table stakes tomorrow, and, perhaps surprisingly, that makes for a tremendous business opportunity: if your company is predicated on delivering the best possible experience for consumers, then your company will never achieve its goal.

In the case of Amazon, that this unattainable and ever-changing objective is embedded in the company’s culture is, in conjunction with the company’s demonstrated ability to spin up new businesses on the profits of established ones, a sort of perpetual motion machine

Owning the customer relationship by means of delivering a superior experience is how these companies became dominant, and, when they fall, it will be because consumers deserted them, either because the companies lost control of the user experience (a danger for Facebook and Google), or because a paradigm shift made new experiences matter more (a danger for Google and Apple).

For Google Search to increase its profits, it must shift value from web publishers, advertisers and/or users to itself. The only way for Google Search to grow is to make itself worse.

Google’s product managers are each charged with finding ways to increase the profitability of their little corner of the googleverse. That increased profitability can only come from enshittification. Every product manager on Google Search spends their workdays figuring out how to remove a Jenga block. What’s worse, these princelings compete with one another. Their individual progression through the upper echelons of Google’s aristocracy depends as much on others failing as it does on their success. The org chart only has so many VP, SVP and EVP boxes on it, and each layer is much smaller than the previous one. If you’re a VP, every one of your colleagues who makes it to SVP takes a spot that you can no longer get. Those spots are wildly lucrative. Each tier of the hierarchy is worth an order of magnitude more than the tier beneath it. The stakes are so high that they are barely comprehensible. That means that every one of these Jenga-block-pulling execs is playing blind: they don’t — and can’t — coordinate on the ways they’re planning to lower quality in order to improve profits. The exec who decided to save money by reducing the stringency of phone number checking for business accounts didn’t announce this in a company-wide memo. When you’re eating your seed-corn, it’s imperative that you do so behind closed doors, and tell no one what you’ve done. Like any sleight-of-hand artist, you want the audience to see the outcome of the trick (the cost savings), not how it’s done (exposing every searcher in the world to fraud risk to save a buck).

Google/Apple’s mobile duopoly is more cozy than competitive. Google pays Apple $15–20 billion, every single year, to be the default search in Safari and iOS. If Google and Apple were competing over mobile, you’d expect that one of them would drop the sky-high 30 percent rake they charge on in-app payments, but that would mess up their mutual good thing. Instead, these “competitors” charge exactly the same price for a service with minimal operating costs.

your bank, your insurer, your beer company, the companies that make your eyeglasses and your athletic shoes — they’ve all run out of lands to conquer, but instead of weeping, they’re taking it out on you, with worse products that cost more.