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.

As furniture and home goods sales have moved online, retail experts told me, more and more stores have sought a piece of the action. But instead of sourcing or creating their own products, many large retailers have relied on overlapping networks of manufacturers, distributors and third-party sellers — creating a baffling (and frankly, shady) shopping environment where many sites sell identical or near-identical items under different names and at wildly different prices.

A few different trends are at play here, and it’s sometimes difficult to know exactly which one you’re witnessing. When I first began looking into this phenomenon two years ago,1 I assumed my lamp and its many, many twins were the obvious product of white-labeling — a popular and growing practice in which competing retailers purchase the same generic product from a single manufacturer, then market it to consumers under different brand names.

This is likely true for many doppelganger products — but certainly not every one. George John, a marketing professor at the University of Minnesota, told me the furniture and home goods industries also have a long-time affection for a technique called “branded variance,” wherein they create slightly different versions of the same item for different retailers.

What we have now is a profusion of well-cast, sleekly produced competence. We have tasteful remakes of familiar titles. We have the evidence of healthy budgets spent on impressive locations. We have good-enough new shows that resemble great old ones.

Put these two forces together — a rising level of talent and production competence on the one hand, the pressure to deliver versions of something viewers already like on the other hand — and what do you get? You get a whole lot of Mid.

MID IS NOT the mediocre TV of the past. It’s more upscale. It is the aesthetic equivalent of an Airbnb “modern farmhouse” renovation, or the identical hipster cafe found in medium-sized cities all over the planet. It’s nice! The furniture is tasteful, they’re playing Khruangbin on the speakers, the shade-grown coffee is an improvement on the steaming mug of motor oil you’d have settled for a few decades ago.

Mid is fine, though. It’s good enough.

Mid TV, on the other hand, almost can’t be bad for some of the same reasons that keep it from being great. It’s often an echo of the last generation of breakthrough TV (so the highs and lows of “Game of Thrones” are succeeded by the faithful adequacy of “House of the Dragon”).

As more people drop cable TV for streaming, their incentives change. With cable you bought a package of channels, many of which you would never watch, but any of which you might.

So where HBO used to boast that it was “not TV,” modern streamers send the message, “We’ll give you a whole lot of TV.” It can seem like their chief goal is less to produce standout shows than to produce a lot of good-looking thumbnails.

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).

what I cheered as an analyst was Altman’s clear articulation of the company’s priorities: lower price first, speed later. You can certainly debate whether that is the right set of priorities (I think it is, because the biggest need now is for increased experimentation, not optimization), but what I appreciated was the clarity.

The fact that Microsoft is benefiting from OpenAI is obvious; what this makes clear is that OpenAI uniquely benefits from Microsoft as well, in a way they would not from another cloud provider: because Microsoft is also a product company investing in the infrastructure to run OpenAI’s models for said products, it can afford to optimize and invest ahead of usage in a way that OpenAI alone, even with the support of another cloud provider, could not. In this case that is paying off in developers needing to pay less, or, ideally, have more latitude to discover use cases that result in them paying far more because usage is exploding.

You can, in effect, program a GPT, with language, just by talking to it. It’s easy to customize the behavior so that it fits what you want. This makes building them very accessible, and it gives agency to everyone.

Stephen Wolfram explained: For decades there’s been a dichotomy in thinking about AI between “statistical approaches” of the kind ChatGPT uses, and “symbolic approaches” that are in effect the starting point for Wolfram|Alpha. But now—thanks to the success of ChatGPT—as well as all the work we’ve done in making Wolfram|Alpha understand natural language—there’s finally the opportunity to combine these to make something much stronger than either could ever achieve on their own.

This new model somewhat alleviates the problem: now, instead of having to select the correct plug-in (and thus restart your chat), you simply go directly to the GPT in question. In other words, if I want to create a poster, I don’t enable the Canva plugin in ChatGPT, I go to Canva GPT in the sidebar. Notice that this doesn’t actually solve the problem of needing to have selected the right tool; what it does do is make the choice more apparent to the user at a more appropriate stage in the process, and that’s no small thing.

ChatGPT will seamlessly switch between text generation, image generation, and web browsing, without the user needing to change context. What is necessary for the plug-in/GPT idea to ultimately take root is for the same capabilities to be extended broadly: if my conversation involved math, ChatGPT should know to use Wolfram|Alpha on its own, without me adding the plug-in or going to a specialized GPT.

the obvious technical challenges of properly exposing capabilities and training the model to know when to invoke those capabilities are a textbook example of Professor Clayton Christensen’s theory of integration and modularity, wherein integration works better when a product isn’t good enough; it is only when a product exceeds expectation that there is room for standardization and modularity.

To summarize the argument, consumers care about things in ways that are inconsistent with whatever price you might attach to their utility, they prioritize ease-of-use, and they care about the quality of the user experience and are thus especially bothered by the seams inherent in a modular solution. This means that integrated solutions win because nothing is ever “good enough”

the fact of the matter is that a lot of people use ChatGPT for information despite the fact it has a well-documented flaw when it comes to the truth; that flaw is acceptable, because to the customer ease-of-use is worth the loss of accuracy. Or look at plug-ins: the concept as originally implemented has already been abandoned, because the complexity in the user interface was more detrimental than whatever utility might have been possible. It seems likely this pattern will continue: of course customers will say that they want accuracy and 3rd-party tools; their actions will continue to demonstrate that convenience and ease-of-use matter most.

Maybe we’ll get to a point where the novelty will be that a human being wrote something: This person proved that they were in a box away from any A.I. when they wrote this thing

as writers and creators, you want people to watch your show, so if you can make something look and sound like something else that people already want to watch, then you might be able to convince a producer that it will have legs. But I discovered through doing “Fleabag” that you have to write something that is more dangerous, more honest, more unusual and more provocative — especially if it’s going to go into a pool with a million other things. Honing the uniqueness of whatever you do is your best chance. I know I’m saying that having just signed up to do “Tomb Raider,” which has an audience already, and I know that’s what Amazon wants, and Amazon made this unbelievable deal8 8 In 2019, Variety reported about Waller-Bridge’s development deal with Amazon Studios, that “sources say the deal is worth around $20 million a year.” with me. I care so much about delivering for them. Being able to do that dangerous, naughty, transgressive stuff in the heart of something that is very valuable to them in terms of I.P. satisfies both of those things, but the discipline for me is to not just give them the “Tomb Raider” they think they want, but to give them something else.

People are going to interpret everything I do as my feelings on contemporary womanhood because I’m a contemporary woman. I don’t want to escape that part of me. I can see how I’ve gone into masculine roles with Bond and “Indiana Jones,” but those worlds are the ones that always intrigued me. The high-stakes action world appeals to me, whether it’s masculine or feminine. I like the urgency of it and the idea of being able to write a female character in a world like that.

It’s a window into your psychology: You want to be a pleaser and do the assignment well, but what you actually want to do is something riskier. Oh, my gosh. That’s exactly what it is. But the best thing is when you satisfy both. The journey there can be quite [laughs] — I love the feeling of having done what’s been asked, but I hate the feeling of pleasing.

I think that with Bond there is something dangerous, transgressive and incendiary about that character, and it’s the same with Indy. He completely revolutionized the action hero, which Harrison1 1 Harrison Ford, of course, who has said “Indiana Jones and the Dial of Destiny” will be his last go-round with Indy’s famous whip and fedora. is dead set against him being described as, but there was something that broke the form. We accept them now as the biggest franchises, but in the kernel of these characters is something naughty and dangerous. They were the rascals of their time, and I feel like Villanelle and Fleabag are rascals.2 2 Villanelle is the name of the assassin character, played by Jodie Comer, in “Killing Eve.” “Fleabag,” for those who haven’t seen it, is that show’s title character, played by Waller-Bridge. The show earned six Emmy Awards and 11 nominations for its second season. So it was less like, “I want to go do this big movie,” and more, “I want to play in the sand pit with these rascals.” That’s one way of looking at it.

I couldn’t write anything that I felt didn’t have that deeper element sincerely at the heart of it, and that writer is with me everywhere I go. It’s ever-present: What does this mean? Because I’m obsessed with having an audience be moved.6 6 Waller-Bridge said the most recent things that moved her were the TV series “Dead Ringers,” a concert by the singer Christine Bovill in which she performed Edith Piaf songs and a revival of “Guys and Dolls” at the Bridge Theater in London. I was moved when I read the script, and I was moved when I heard Jim7 7 James Mangold, the director of “Dial of Destiny.” He is the first director other than Steven Spielberg to direct an “Indiana Jones” movie. and Harrison and Kathy talk about it. I mean, I wasn’t in tears on the floor, but I felt that tingle of, this has got some human stuff going on. But the day-to-day? Some of the days were superfun, and we did look really cool. But the proper actors don’t want to just look cool. They want to make you cry while looking cool.

Among the most eye-opening details in this article, about one-fifth of the value of U.S. online orders are returned every year. That is an enormous amount of waste — potential and realized.

I sometimes think about the fact that Amazon S3 effectively has to exist until the heat death of the universe. Many millennia from now, our highly-evolved descendants will probably be making use of an equally highly evolved descendant of S3. It is fun to think about how this would be portrayed in science fiction form, where developers pore through change logs and design documents that predate their great-great-great-great grandparents, and users inherit ancient (yet still useful) S3 buckets, curate the content with great care, and then ensure that their progeny will be equally good stewards for all of the precious data stored within

when a customer wants to buy a product, they usually go straight to Amazon.com and enter what they’re looking for. But there’s also a large segment of customers who begin their search on google, and ends up at Amazon. Well guess what. When that type of search to purchase experience happens, Amazon has to pay google. Internally, Amazon thought that if they could force users to go straight to Amazon, offer a small but obviously less amount of money to charity from each customer than would have been paid to google, it would help kill customers going to google, save Amazon more money than paying google, and be good overall for the brand value of Amazon.

There is no way for a customer to go through the traditional shopping experience, and then during checkout decide they want to give a portion of their purchase to charity, because giving to charity isn't the point of the overall program. Amazon Smile was developed by the Traffic Optimization team, whose entire purpose is increasing efficiency and lowering costs of getting customers to Amazon. A team of Amazon employees whose sole purpose is doing good in the world doesn't exist, despite employees repeatedly asking for such a team to be built in pretty much every single all-hands meeting.

Literally everything the company does is about profits, and extended customer lifetime value. Everything. Even the charity programs are just designed to save Amazon money.

numerous sources say they cannot discern what kind of material Salke and head of television Vernon Sanders want to make. A showrunner with ample experience at the studio says, “There’s no vision for what an Amazon Prime show is. You can’t say, ‘They stand for this kind of storytelling.’ It’s completely random what they make and how they make it.” Another showrunner with multiple series at Amazon finds it baffling that the streamer hasn’t had more success: Amazon has “more money than God,” this person says. “If they wanted to produce unbelievable television, they certainly have the resources to do it.”

Although Amazon has clocked staggering growth, it generates meager profits, choosing to price below-cost and expand widely instead. Through this strategy, the company has positioned itself at the center of e-commerce and now serves as essential infrastructure for a host of other businesses that depend upon it. Elements of the firm’s structure and conduct pose anticompetitive concerns—yet it has escaped antitrust scrutiny.

This Note argues that the current framework in antitrust—specifically its pegging competition to “consumer welfare,” defined as short-term price effects—is unequipped to capture the architecture of market power in the modern economy. We cannot cognize the potential harms to competition posed by Amazon’s dominance if we measure competition primarily through price and output. Specifically, current doctrine underappreciates the risk of predatory pricing and how integration across distinct business lines may prove anticompetitive.

These concerns are heightened in the context of online platforms for two reasons. First, the economics of platform markets create incentives for a company to pursue growth over profits, a strategy that investors have rewarded. Under these conditions, predatory pricing becomes highly rational—even as existing doctrine treats it as irrational and therefore implausible.

Second, because online platforms serve as critical intermediaries, integrating across business lines positions these platforms to control the essential infrastructure on which their rivals depend. This dual role also enables a platform to exploit information collected on companies using its services to undermine them as competitors.