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.

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

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

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

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

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

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.

The criticisms I have been seeing about the features of the Pixel 8, however, feel like we are only repeating the kinds of fears of nearly two hundred years. We have not been able to wholly trust photographs pretty much since they were invented. The only things which have changed in that time are the ease with which the manipulations can happen, and their availability.

We all live with a growing sense that everything around us is fraudulent. It is striking to me how these tools have been introduced as confidence in institutions has declined. It feels like a death spiral of trust — not only are we expected to separate facts from their potentially misleading context, we increasingly feel doubtful that any experts are able to help us, yet we keep inventing new ways to distort reality.

The questions that are being asked of the Pixel 8’s image manipulation capabilities are good and necessary because there are real ethical implications. But I think they need to be more fully contextualized. There is a long trail of exactly the same concerns and, to avoid repeating ourselves yet again, we should be asking these questions with that history in mind. This era feels different. I think we should be asking more precisely why that is.

The questions we ask about generative technologies should acknowledge that we already have plenty of ways to lie, and that lots of the information we see is suspect. That does not mean we should not believe anything, but it does mean we ought to be asking questions about what is changed when tools like these become more widespread and easier to use.