It’s fun — and often accurate — to think of tech companies in pairs. Apple and Microsoft defined the PC market; Microsoft and Intel won it. Google and Meta dominate digital advertising; Apple and Google won mobile. That, however, is not the defining pair of the smartphone era, which ran from the introduction of the iPhone in 2007 to the launch of ChatGPT in 2022; rather, the two most important companies of the last two decades of tech were Apple and Amazon, specifically AWS.

The Apple part is easy: the iPhone market created the smartphone paradigm, from its user interface (touch) to its distribution channel (the App Store), and was richly rewarded with a bit under half of the unit marketshare and a bit under all of the total profits. Google did well to control the rest in terms of the Android operating system, and profit from it all thanks to Google Search, but it was Search that remained their north star; the company’s primary error in the era was the few years they let the tail (Android) wave the dog (Google).

The AWS part is maybe less obvious, but no less critical — and the timing is notable. Amazon created AWS in 2006, just 10 months before the iPhone unveiling, and the paradigm they created was equally critical to the smartphone era. I explained the link in 2020’s The End of the Beginning:

This last point gets at why the cloud and mobile, which are often thought of as two distinct paradigm shifts, are very much connected: the cloud meant applications and data could be accessed from anywhere; mobile made the I/O layer available everywhere. The combination of the two make computing continuous.

What is notable is that the current environment appears to be the logical endpoint of all of these changes: from batch-processing to continuous computing, from a terminal in a different room to a phone in your pocket, from a tape drive to data centers all over the globe. In this view the personal computer/on-premises server era was simply a stepping stone between two ends of a clearly defined range.

AWS was not the only public cloud provider, of course — Azure and Google Cloud Platform were both launched in 2008 — but by virtue of being first they both defined the paradigm and also were the the first choice of the universe of applications that ran on smartphones or, more accurately, ran everywhere.

Smartphone Winners and Losers

If Apple and AWS were the definers — and thus winners — of the smartphone era, then it was Microsoft and Nokia that were the losers. The reasons for their failure were myriad, but there was one common thread: neither could shake off the overhang of having won their previous paradigm; indeed, both failed in part because they deluded themselves into thinking that their previous domination was an advantage.

For Microsoft that previous paradigm was the PC and the Windows platform, which the company thought they could extend to mobile; from 2014’s Microsoft’s Mobile Muddle:

Saying “Microsoft missed mobile” is a bit unfair; Windows Mobile came out way back in 2000, and the whole reason Google bought Android was the fear that Microsoft would dominate mobile the way they dominated the PC era. It turned out, though, that mobile devices, with their focus on touch, simplified interfaces, and ARM foundation, were nothing like PCs. Everyone had to start from scratch, and if starting from scratch, by definition Microsoft didn’t have any sort of built-in advantage. They were simply out-executed.

It took Microsoft years — and a new CEO — to realize their mistake, up and to the point where they put their enterprise productivity dominance at risk; from 2015’s Redmond and Reality:

There’s reality, and there’s Redmond, and if one thing marked the last few years of Steve Ballmer’s tenure as the CEO of Microsoft, it was the sense that those were two distinct locales. In reality, Android (plus AOSP in China) and iOS were carving up the world phone market; in Redmond Ballmer doubled-down on the losing Window Phone bet by buying Nokia. In reality Office was losing relevance because of its absence on the mobile platforms that mattered; in Redmond Ballmer personally delayed Office on iOS until the Windows Modern née Metro version was finished. And in reality, all kinds of startups were taking aim at the Microsoft enterprise stack; in Redmond, Microsoft was determined to own it all, just as they had in the PC era.

It’s fitting that Microsoft and Nokia ended up together; perhaps they were able to jointly go to therapy for success-induced obliviousness of market realities. Nokia dominated the phone market for the decade prior to the iPhone, and even once the iPhone was announced, blithely assumed that they could simply lean on their existing advantages to fend off the Silicon Valley usurper. From 2013’s Blackberry — and Nokia’s — Fundamental Failing:

Nokia dominated all the parts of this stack you don’t see: they had, and in some respects, still have, the best supply chain and distribution network. In addition, they had high quality hardware that served every segment imaginable. Notably absent in these strengths is the OS and Apps. By 2009, BlackBerry OS and Symbian were clearly obsolete, and their app ecosystems, such as they were, were eclipsed by iOS and then Android. The problem, as I alluded to above, is that while the OS was ultimately under the control of BlackBerry and Nokia, respectively, and thus could be fixed, the efficacy of their ecosystem wasn’t, and wouldn’t be…

And so, by far the smartest strategic thing either could have done would have been to accept their weakness — they didn’t have an adequate OS or ecosystem — and focus on their strengths…Nokia should have adopted Android-stock, and used their unmatched supply chain and distribution to do to their competitors, well, exactly what Nokia had been doing to their competitors for the last decade (if you think Samsung is running roughshod over everyone today, in 2007 they could only manage 41 million phones compared to Nokia’s 110 million).

Both BlackBerry and Nokia would have gotten a good OS and thriving ecosystem for free and been able to compete and differentiate themselves on the exact same vectors they had previously. To put it another way, RIM and Nokia had never been successful because of their OS or ecosystem, yet both decided their best response to iOS and Android was to build a new OS! In fact, the strategic superiority of the Android option for RIM and Nokia was even then so obvious that I suspect their core failing was not so much strategic as it was all-too-human: pride. Owning an ecosystem seems much more important than owning services or supply chains, even if building said ecosystem completely devalues what you’re actually good at.

If the first commonality in Microsoft and Nokia’s failure is the assumption that dominance in one paradigm would seamlessly translate into dominance in the next, then the second was in not making the strategically obvious choice — embracing iOS and Android for Windows, and Android for Nokia — for fear of losing control and long-term relevance. What separates the two companies is that Microsoft, under CEO Satya Nadella, rectified their mistake, while Nokia doubled-down with Windows Phone; that is why Microsoft still matters today — more than ever, in fact — while Nokia phones no longer exist.

The two companies that stood in constrast to Microsoft and Nokia were Google and Samsung; while their dominance of the non-iPhone market seems obvious in retrospect, it wasn’t at all pre-ordained. What is impressive about both companies is that they had the opposite of pride: both were quite shameless, in fact. From 2013’s Shameless Samsung:

Every pre-iPhone phone maker is irrelevant, if they even exist, except for Samsung, who is thriving. Samsung the copycat was smart enough to realize they needed to change, and quickly, and so they did.

Or maybe it wasn’t being smart. Maybe it was simply not caring what anyone else thought about them, their strategy, or their inspiration. Most successful companies, including Apple, including Google, seem remarkably capable of ignoring the naysayers and simply doing what is right for their company. In the case of smartphones, why wouldn’t you copy the iPhone? Nokia refused and look where that got them!

We, especially in the West, have a powerful sense of justice and fairness when it comes to product features and being first. Business, though, is not fair, even if it is more just than we care to admit.

Just as Samsung blatantly copied Apple hardware, Android blatantly copied the iOS interface:

Plenty of people mocked Google for this shift, but not me: Apple figured out what worked; it would have been foolish to not copy them.

Foolish like Microsoft and Nokia.

Apple, Amazon, and AI

There were striking resemblances in last week’s earnings calls from Apple and Amazon, not just to each other, but to this early smartphone era that I have just recounted. Both companies are facing questions about their AI strategies — Apple for its failure to invest in a large language model of its own, or deeply partner with a model builder, and Amazon for prioritizing its own custom architectures and under-deploying leading edge Nvidia solutions — and both had similar responses:

It’s Early

Tim Cook (from a post-earnings all-hands meeting):

Cook struck an optimistic tone, noting that Apple is typically late to promising new technologies. “We’ve rarely been first,” the executive told staffers. “There was a PC before the Mac; there was a smartphone before the iPhone; there were many tablets before the iPad; there was an MP3 player before iPod.” But Apple invented the “modern” versions of those product categories, he said. “This is how I feel about AI.”

Andy Jassy:

The first thing I would say is that I think it is so early right now in AI. If you look at what’s really happening in the space, it’s very top heavy. So you have a small number of very large frontier models that are being trained that spend a lot on computing, a couple of which are being trained on top of AWS and others are being trained elsewhere. And then you also have, I would say, a relatively small number of very large-scale generative AI applications.

We Will Serve Actual Use Cases

Tim Cook:

We see AI as one of the most profound technologies of our lifetime. We are embedding it across our devices and platforms and across the company. We are also significantly growing our investments. Apple has always been about taking the most advanced technologies and making them easy to use and accessible for everyone, and that’s at the heart of our AI strategy. With Apple Intelligence, we’re integrating AI features across our platforms in a way that is deeply personal, private, and seamless, right where users need them.

Andy Jassy:

We have a very significant number of enterprises and startups who are running applications on top of AWS’ AI services and but, like the amount of usage and the expansiveness of the use cases and how much people are putting them into production and the number of agents that are going to exist, it’s still just earlier stage than it’s going to be, and so then when you think about what’s going to matter in AI, what are customers going to care about when they’re thinking about what infrastructure use, I think you kind of have to look at the different layers of the stack. And I think…if you look at where the real costs are, they’re going to ultimately be an inference today, so much of the cost in training because customers are really training their models and trying to figure out to get the applications into production.

Our Chips Are Best

Tim Cook:

Apple Silicon is at the heart of all of these experiences, enabling powerful Apple Intelligence features to run directly on device. For more advanced tasks, our servers, also powered by Apple Silicon, deliver even greater capabilities while preserving user privacy through our Private Cloud Compute architecture. We believe our platforms offer the best way for users to experience the full potential of generative AI. Thanks to the exceptional performance of our systems, our users are able to run generative AI models right on their Mac, iPad, and iPhone. We’re excited about the work we’re doing in this space, and it’s incredibly rewarding to see the strong momentum building.

Andy Jassy:

At scale, 80% to 90% of the cost will be an inference because you only train periodically, but you’re spinning out predictions and inferences all the time, and so what they’re going to care a lot about is they’re going to care about the compute and the hardware they’re using. We have a very deep partnership with Nvidia and will for as long as I can foresee, but we saw this movie in the CPU space with Intel, where customers are anchoring for better price performance. And so we built just like in the CPU space, where we built our own custom silicon and building Graviton which is about 40% more price performance than the other leading x86 processors, we’ve done the same thing on the custom silicon side in AI with Trainium and our second version of Trainium2…it’s about 30% and 40% better price performance than the other GPU providers out there right now, and we’re already working on our third version of Trainium as well. So I think a lot of the compute and the inference is going to ultimately be run on top of Trainium2.

We Have the Data

Tim Cook:

We’re making good progress on a more personalized Siri, and we do expect to release the features next year, as we had said earlier. Our focus from an AI point of view is on putting AI features across the platform that are deeply personal, private, and seamlessly integrated, and, of course, we’ve done that with more than 20 Apple Intelligence features so far, from Visual Intelligence to Clean Up to Writing Tools and all the rest.

Andy Jassy:

People aren’t paying as close attention as they will and making sure that those generative AI applications are operating where the rest of their data and infrastructure. Remember, a lot of generative AI inference is just going to be another building block like compute, storage and database. And so people are going to actually want to run those applications close to where the other applications are running, where their data is. There’s just so many more applications and data running in AWS than anywhere else.

Both Apple and Amazon’s arguments are very plausible! To summarize each:

Apple: Large language models are useful, but will be a commodity, and easily accessible on your iPhone; what is the most useful to people, however, is AI that has your private data as context, and only we can provide that. We will provide AI with your data as context at scale and at low cost — both in terms of CapEx and OpEx — by primarily running inference on device. People are also concerned about sharing their personal data with AI companies, so when we need more capabilities we will use our own compute infrastructure, which will run on our own chips, not Nvidia chips.

Amazon: Large language models are useful, but will be a commodity, and widely available on any cloud. What is the most useful to companies, however, is AI that has your enterprise data as context, and more enterprises are on AWS than anywhere else. We will provide AI with a company’s data as context at scale and at low cost — both in terms of CapEx and OpEx — by primarily running inference on our own AI chips, not Nvidia chips.

What is notable about both arguments — and again, this doesn’t mean they are wrong! — is how conveniently they align with how the companies operated in the previous era. Apple powered apps with Apple Silicon on the edge with an emphasis on privacy, and Amazon powered apps in the cloud with its own custom architecture focused first and foremost on low costs.

The AI Paradigm

The risk both companies are taking is the implicit assumption that AI is not a paradigm shift like mobile was. In Apple’s case, they assume that users want an iPhone first, and will ultimately be satisfied with good-enough local AI; in AWS’s case, they assume that AI is just another primitive like compute or storage that enterprises will tack onto their AWS bill. I wrote after last fall’s re:Invent:

The emphasis on “choice” in the presentation, first in terms of regular AWS, and then later in terms of AI, is another way to say that the options are, in the end, mere commodities. Sure, the cutting edge for both inference and especially training will be Nvidia, and AWS will offer Nvidia instances (to the extent they fit in AWS’ network), but AWS’s bet is that a necessary component of generative AI being productized is that models fade in importance. Note this bit from Garman leading up to his Bedrock discussion:

We talked about wanting this set of building blocks that builders could use to invent anything that they could imagine. We also talked about how many of the cases we walked through today, that we’ve redefined how people thought about these as applications change. Now people’s expectations are actually changing for applications again with generative AI, and increasingly my view is generative AI inference is going to be a core building block for every single application. In fact, I think generative AI actually has the potential to transform every single industry, every single company out there, every single workflow out there, every single user experience out there…

This expansive view of generative AI’s importance — notice how Garman put it on the same level as the compute, storage, and database primitives — emphasizes the importance of it becoming a commodity, with commodity-like concerns about price, performance, and flexibility. In other words, exactly what AWS excels at. To put it another way, AWS’s bet is that AI will be important enough that it won’t, in the end, be special at all, which is very much Amazon’s sweet spot.

Go back to that illustration from The End of the Beginning: Apple and Amazon are betting that AI is just another primitive in continuous computing that happens everywhere.

The most optimistic AI scenarios, however, point to something new:

A better word for “Anywhere” is probably autonomous, but I wanted to stick with the “Where” theme; what I’m talking about, however, is agents: AI doing work without any human involvement at all. The potential productivity gains for companies are obvious: there is a massive price umbrella for inference costs if the end result is that you don’t need to employ a human to do the same work. In this world what matters most is performance, not cost, which means that Amazon’s obsession with costs is missing the point; it’s also a world where the company’s lack of a competitive leading edge model makes it harder for them to compete, particularly when there is another company in the ecosystem — Google — that not only has its own custom chip strategy (TPUs), but also is integrating those chips with its competitive leading edge large language model (Gemini).

Tim Cook, meanwhile, has talked for years now about his excitement about AR glasses, which fit with the idea of augmentation; Mark Gurman reported in Bloomberg earlier this year:

Still, all of this is a stepping stone toward Cook’s grand vision, which hasn’t changed in a decade. He wants true augmented reality glasses — lightweight spectacles that a customer could wear all day. The AR element will overlay data and images onto real-world views. Cook has made this idea a top priority for the company and is hell-bent on creating an industry-leading product before Meta can. “Tim cares about nothing else,” says someone with knowledge of the matter. “It’s the only thing he’s really spending his time on from a product development standpoint.”

Still, it will take many years for true AR glasses to be ready. A variety of technologies need to be perfected, including extraordinarily high-resolution displays, a high-performance chip and a tiny battery that could offer hours of power each day. Apple also needs to figure out applications that make such a device as compelling as the iPhone. And all this has to be available in large quantities at a price that won’t turn off consumers.

What seems likely to me is that for this product to succeed, Apple will need to figure out generative AI as well; I posited last year that generative AI will undergird future user interfaces in The Gen AI Bridge to the Future. From a section recounting my experience with Meta’s Orion AR glasses:

This, I think, is the future: the exact UI you need — and nothing more — exactly when you need it, and at no time else. This specific example was, of course, programmed deterministically, but you can imagine a future where the glasses are smart enough to generate UI on the fly based on the context of not just your request, but also your broader surroundings and state.

This is where you start to see the bridge: what I am describing is an application of generative AI, specifically to on-demand UI interfaces. It’s also an application that you can imagine being useful on devices that already exist. A watch application, for example, would be much more usable if, instead of trying to navigate by touch like a small iPhone, it could simply show you the exact choices you need to make at a specific moment in time. Again, we get hints of that today through deterministic programming, but the ultimate application will be on-demand via generative AI.

This may sound fanciful, but then again, I wrote in early 2022 that generative AI would be the key to making the metaverse viable:

In the very long run this points to a metaverse vision that is much less deterministic than your typical video game, yet much richer than what is generated on social media. Imagine environments that are not drawn by artists but rather created by AI: this not only increases the possibilities, but crucially, decreases the costs.

That may have also sounded fanciful at the time, but it’s already reality: just yesterday Google DeepMind announced Genie 3; from their blog post:

Today we are announcing Genie 3, a general purpose world model that can generate an unprecedented diversity of interactive environments. Given a text prompt, Genie 3 can generate dynamic worlds that you can navigate in real time at 24 frames per second, retaining consistency for a few minutes at a resolution of 720p.

[…] Achieving a high degree of controllability and real-time interactivity in Genie 3 required significant technical breakthroughs. During the auto-regressive generation of each frame, the model has to take into account the previously generated trajectory that grows with time. For example, if the user is revisiting a location after a minute, the model has to refer back to the relevant information from a minute ago. To achieve real-time interactivity, this computation must happen multiple times per second in response to new user inputs as they arrive…

Genie 3’s consistency is an emergent capability. Other methods such as NeRFs and Gaussian Splatting also allow consistent navigable 3D environments, but depend on the provision of an explicit 3D representation. By contrast, worlds generated by Genie 3 are far more dynamic and rich because they’re created frame by frame based on the world description and actions by the user.

We are still far from the metaverse, to be clear, or on-demand interfaces in general, but it’s stunning how much closer we are than a mere three years ago; to that end, betting on current paradigms may make logical sense — particularly if you dominate the current paradigm — but things really are changing with stunning speed. Apple and Amazon’s risk may be much larger than either appreciate.

Google Appreciation

Genie 3 is, as I noted, from Google, and thinking about these paradigm shifts — first the shift to mobile, and now the ongoing one to AI — has made me much more appreciative and respectful of Google. I recounted above how the company did what was necessary — including overhauling Android to mimic iOS — to capture its share of the mobile paradigm; as we approach the three year anniversary of ChatGPT, it’s hard to not be impressed at how the company has gone all-in on relevancy with AI.

This wasn’t a guarantee: two months after ChatGPT, in early 2023, I wrote AI and the Big Five, and expressed my concerns about the company’s potential disruption:

That, though, ought only increase the concern for Google’s management that generative AI may, in the specific context of search, represent a disruptive innovation instead of a sustaining one. Disruptive innovation is, at least in the beginning, not as good as what already exists; that’s why it is easily dismissed by managers who can avoid thinking about the business model challenges by (correctly!) telling themselves that their current product is better. The problem, of course, is that the disruptive product gets better, even as the incumbent’s product becomes ever more bloated and hard to use — and that certainly sounds a lot like Google Search’s current trajectory.

I’m not calling the top for Google; I did that previously and was hilariously wrong. Being wrong, though, is more often than not a matter of timing: yes, Google has its cloud and YouTube’s dominance only seems to be increasing, but the outline of Search’s peak seems clear even if it throws off cash and profits for years.

Meanwhile, I wasn’t worried about Apple and Amazon at all: I saw AI as being a complement for Apple, and predicted that the company would invest heavily in local inference; when it came to Amazon I was concerned that they might suffer from not have an integrated approach a la Google, but predicted that AI would slot in cleanly to their existing cloud business. In other words, exactly what Apple and Amazon’s executives are banking on.

I wonder, however, if there is a version of this analysis that, were it written in 2007, might have looked like this:

Nokia will be fine; once they make a modern OS, their existing manufacturing and distribution advantages will carry the day. Microsoft, meanwhile, will mimic the iPhone UI just like they once did the Mac, and then leverage their app advantage to dominate the lower end of the market. It’s Google, which depends on people clicking on links on a big desktop screen, that is in danger.

I don’t, with the benefit of having actually known myself in 2007, think that would have been my take (and, of course, much of the early years of Stratechery were spent arguing with those who held exactly those types of views). I was, however, a Google skeptic, and I’m humble about that. And, meanwhile, I have that 2023 Article, where, in retrospect, I was quite rooted in the existing paradigm — which favors Apple and Amazon — and skeptical of Google’s ability and willingness to adapt.

Today I feel differently. To go back to the smartphone paradigm, the best way to have analyzed what would happen to the market would have been to assume that the winners of the previous paradigm would be fundamentally handicapped in the new one, not despite their previous success, but because of it. Nokia and Microsoft pursued the wrong strategies because they thought they had advantages that ultimately didn’t matter in the face of a new paradigm.

If I take that same analytical approach to AI, and assume that the winners of the previous paradigm will be fundamentally handicapped in the new one, not despite their previous success, but because of it, then I ought to have been alarmed about Apple and Amazon’s prospects from the get-go. I’m not, for the record, ready to declare either of them doomed; I am, however, much more alert to the prospect of them making wrong choices for years, the consequences of which won’t be clear until it’s too late.

And, by the same token, I’m much more appreciative of Google’s amorphous nature and seeming lack of strategy. That makes them hard to analyze — again, I’ve been honest for years about the challenges I find in understanding Mountain View — but the company successfully navigated one paradigm shift, and is doing much better than I originally expected with this one. Larry Page and Sergey Brin famously weren’t particularly interested in business or in running a company; they just wanted to do cool things with computers in a college-like environment like they had at Stanford. That the company, nearly thirty years later, is still doing cool things with computers in a college-like environment may be maddening to analysts like me who want clarity and efficiency; it also may be the key to not just surviving but winning across multiple paradigms.

---