Broadly speaking, Alphabet, Amazon, Apple, Meta, Microsoft, Nvidia, and Tesla are all “technology” companies. Looking more specifically, though, each company occupies fundamentally different categories of tech. Apple is a consumer computing hardware manufacturer. Its primary products are smartphones, laptops, desktop computers, and tablets. Other products that it makes, including the so-called “services,” are primarily accessories to or supportive of their consumer computing hardware: e.g., App Store, Apple Music, and iCloud. Apple’s specific product focus has remained unchanged since its founding as “Apple Computer Company.”

Meta has tried to pivot to the so-called “metaverse,” symbolically renaming the whole company from “Facebook” and continuing to pour $billions every year into the effort, yet with not much more return on investment than Apple’s own “spatial computing”, i.e., Vision Pro. And now Meta is trying to pivot to A.I., pouring a ton of money into that too, but with nothing much to show for it. We’re supposed to be impressed by Meta poaching individual Apple engineers with nine-figure pay packages, which in one sense is impressive, just not impressive in the sense of paying off for Meta. Perhaps it will pay off for Meta in the future. Or perhaps not. Meanwhile, Meta is still practically printing money at its old, core business: selling ads on social media

Jobs did not just make tech products willy-nilly, for no other reason than to maximize profit and stockholder returns. He was always focused specifically on consumer computing devices and platforms. That’s what he cared about, and where his experienced rested. When Jobs left Apple in the 1980s, what did he do? Again, he created a new personal computing platform, NeXT, a combination of hardware and operating system, just like the Apple II, Lisa, and Macintosh that came before. Jobs was innovating… on a theme, almost like a classical composer. Jobs was eventually able to return to Apple and become CEO precisely because Jobs made what Apple needed: a personal computer operating system, NeXTSTEP, which became Mac OS X.

It’s instructive to recall that the iPod, Apple’s second hit product under CEO Jobs after the iMac, was not only a consumer electronics device but also originally an accessory to the Mac.

I feel that McGee and other critics of Tim Cook fallaciously lump Apple in with other tech companies that are not Apple competitors. Tesla is not an Apple competitor. Neither are Nvidia or Meta, or for that matter, Amazon. You have to ask what makes Amazon a “tech” company. Amazon is primarily a retailer of physical goods. It sells those goods over the internet, which was novel in the 1990s but unremarkable today. I can order food online, but that doesn’t make the restaurant a tech company. If any product qualifies Amazon for the label, I’d say it would be Amazon Web Services. This is a business product, though, not a consumer product.

Why are we comparing Apple to Meta and Nvidia rather than to Samsung and Xiaomi on mobile, Lenovo and HP on desktop? Perhaps those markets have become saturated and don’t provide as much room for growth as other potential markets. So what? I get the impression that commentators complaining about Tim Cook’s lack of innovation simply want “growth,” unlimited growth, without any purpose behind that growth, technology without the intersection of the liberal arts, to use a metaphor from Steve Jobs, who always had a purpose, his innovation always oriented toward consumer computing hardware

You see glimpses of it in how Epic Games evolved from game engines to virtual worlds to digital marketplaces, or how Stripe started as a payments processing platform but expanded into publishing books on technological progress, funding atmospheric carbon removal, and running an AI research lab.

Think about what an operating system is: the fundamental architecture that determines what's possible within a system. It manages resources, enables or constrains actions, and creates the environment in which everything else runs.

The dominant view looks at narrative as fundamentally extractive: something to be mined for short-term gain rather than built upon. Companies create compelling stories to sell something, manipulate perception for quick wins, package experiences into consumable soundbites. Oil companies, for example, like to run campaigns about being "energy companies" committed to sustainability, while their main game is still extracting fossil fuels. Vision and mission statements claim to be the DNA of a business, when in reality they're just bumper stickers.

When a narrative truly functions as an operating system, it creates the parameters of understanding, determines what questions can be asked, and what solutions are possible. Xerox PARC's focus on the architecture of information wasn't a fancy summary of their work. It was a narrative that shaped their entire approach to imagining and building things that didn't exist yet. The "how" became downstream of that deeper understanding. So if your narrative isn't generating new realities, you don't have a narrative. You have a tagline.

Most companies think they have an execution problem when, really, they have a meaning problem.

They optimise processes, streamline workflows, and measure outcomes, all while avoiding the harder work of truly understanding what unique value they're creating in the world. Execution becomes a convenient distraction from the more challenging philosophical work of asking what their business means.

A narrative operating system fundamentally shifts this dynamic from what a business does to how it thinks. The business itself becomes almost a vehicle or a social technology for manifesting that narrative, rather than the narrative being a thin veneer over a profit-making mechanism. The conversation shifts, excitingly, from “What does this business do?" to "What can this business mean?" The narrative becomes a reality-construction mechanism: not prescriptive, but generative.

When Stripe first articulated their mission to "increase the GDP of the internet" and “think at planetary scale”, it became a lens to see beyond just economic output. It revealed broader, more exciting questions about what makes the internet more generative: not just financially, but intellectually and culturally. Through this frame emerged problems worth solving that stretched far beyond payments:  What actually prevents more people from contributing to the internet's growth? Why has our civilisation's progress slowed? What creates the conditions for ambitious building? These questions led them down unexpected paths that seem obvious in retrospect. Stripe Atlas enables more participants in the internet economy by removing the complexity of incorporating a company anywhere in the world. Stripe Climate makes climate action as easy as processing a payment by embedding carbon removal into the financial infrastructure itself. Their research arm investigates why human progress has slowed, from the declining productivity of science to the bureaucratisation of building. And finally, Stripe Press—my favourite example—publishes new and evergreen ideas about technological progress.

The very metrics meant to help the organisation coordinate end up drawing boundaries around what it can imagine [1]. The problem here again, is that we’re looking at narratives as proclamations rather than living practices.

I don’t mean painted slogans on walls and meeting rooms—I mean in how teams are structured, how decisions get made, what gets celebrated, what questions are encouraged, and even in what feels possible to imagine.

The question to ask isn't always "What story are we telling?" but also "What reality are we generating?”

Patagonia is a great example of this. Their narrative is, quite simply: “We’re in business to save our home planet”. It shows up in their unconventional decision to use regenerative agriculture for their cotton, yes, but also in their famous "Don't Buy This Jacket" Black Friday campaign, and in their policy to bail out employees arrested for peaceful socio-environmental protests. When they eventually restructured their entire ownership model to "make Earth our only shareholder," it felt less like a radical move and more like the natural next step in their narrative's evolution. The most powerful proof of their narrative operating system was that these decisions felt obvious to insiders long before it made sense to the outside world.

Most narrative operating systems face their toughest test when they encounter market realities and competing incentives. There are players in the system—investors, board members, shareholders—who become active narrative controllers but often have fundamentally different ideas about what the company should be. The pressure to deliver quarterly results, to show predictable growth, to fit into recognisable business models: all of these forces push against maintaining a truly generative narrative.

The magic of "what could be" gets sacrificed for the certainty of "what already works." Initiatives that don't show immediate commercial potential get killed. Questions about meaning and possibility get replaced by questions about efficiency and optimisation.

a narrative operating system's true worth shows up in stranger, more interesting places than a balance sheet.

adaptability and interpretive range. How many different domains can the narrative be applied to? Can it generate unexpected connections? Does it create new questions more than provide answers? What kind of novel use cases or applications outside original context can it generate, while maintaining a clear through-line? Does it have what I call a ‘narrative surplus’: ideas and initiatives that might not fit current market conditions but expand the organisation's possibility space?

rate of internal idea generation. How many ideas come out of the lab? And how many of them don’t have immediate (or direct) commercial viability? A truly generative narrative creates a constant bubbling up of possibilities, not all of which will make sense in the current market or at all.

evolutionary resilience, or how well the narrative can incorporate new developments and contexts while maintaining its core integrity. Generative narratives should be able to evolve without fracturing at the core.

cross-pollination potential. How effectively does the narrative enable different groups to coordinate and build upon each other's work? The open source software movement shows this beautifully: its narrative about collaborative creation enables distributed innovation and actively generates new forms of cooperation we couldn't have imagined before.

There are, of course, other failure modes of narrative operating systems. What happens when narratives become dogmatic and self-referential? When they turn into mechanisms of exclusion rather than generation? When they become so focused on their own internal logic that they lose touch with the realities they're trying to change? Those are meaty questions that deserve their own essay.

Who decided these features should go in the WWDC keynote, with a promise they’d arrive in the coming year, when, at the time, they were in such an unfinished state they could not be demoed to the media even in a controlled environment? Three months later, who decided Apple should double down and advertise these features in a TV commercial, and promote them as a selling point of the iPhone 16 lineup — not just any products, but the very crown jewels of the company and the envy of the entire industry — when those features still remained in such an unfinished or perhaps even downright non-functional state that they still could not be demoed to the press? Not just couldn’t be shipped as beta software. Not just couldn’t be used by members of the press in a hands-on experience, but could not even be shown to work by Apple employees on Apple-controlled devices in an Apple-controlled environment? But yet they advertised them in a commercial for the iPhone 16, when it turns out they won’t ship, in the best case scenario, until months after the iPhone 17 lineup is unveiled?

“Can anyone tell me what MobileMe is supposed to do?” Having received a satisfactory answer, he continued, “So why the fuck doesn’t it do that?” For the next half-hour Jobs berated the group. “You’ve tarnished Apple’s reputation,” he told them. “You should hate each other for having let each other down.” The public humiliation particularly infuriated Jobs. Walt Mossberg, the influential Wall Street Journal gadget columnist, had panned MobileMe. “Mossberg, our friend, is no longer writing good things about us,” Jobs said. On the spot, Jobs named a new executive to run the group. Tim Cook should have already held a meeting like that to address and rectify this Siri and Apple Intelligence debacle. If such a meeting hasn’t yet occurred or doesn’t happen soon, then, I fear, that’s all she wrote. The ride is over. When mediocrity, excuses, and bullshit take root, they take over. A culture of excellence, accountability, and integrity cannot abide the acceptance of any of those things, and will quickly collapse upon itself with the acceptance of all three.

Google is facing many of the same challenges after its decades long dominance of the open web: all of the products shown yesterday rely on a different business model than advertising, and to properly execute and deliver on them will require a cultural shift to supporting customers instead of tolerating them. What hasn’t changed — because it is the company’s nature, and thus cannot — is the reliance on scale and an overwhelming infrastructure advantage. That, more than anything, is what defines Google, and it was encouraging to see that so explicitly put forward as an advantage.

The “hard tech” in AI are the LLMs available for rent from OpenAI, Anthropic, Cohere, and others, or available as open source with Llama, Bloom, Mistral and others. The hard-tech is a level playing field; startups do not have an advantage over incumbents.

There can be differentiation in prompt engineering, problem break-down, use of vector databases, and more. However, this isn’t something where startups have an edge, such as being willing to take more risks or be more creative. At best, it is neutral; certainly not an advantage.

This doesn’t mean it’s impossible for a startup to succeed; surely many will. It means that you need a strategy that creates differentiation and distribution, even more quickly and dramatically than is normally required

Whether you’re training existing models, developing models from scratch, or simply testing theories, high-quality data is crucial. Incumbents have the data because they have the customers. They can immediately leverage customers’ data to train models and tune algorithms, so long as they maintain secrecy and privacy.

Intercom’s AI strategy is built on the foundation of hundreds of millions of customer interactions. This gives them an advantage over a newcomer developing a chatbot from scratch. Similarly, Google has an advantage in AI video because they own the entire YouTube library. GitHub has an advantage with Copilot because they trained their AI on their vast code repository (including changes, with human-written explanations of the changes).

While there will always be individuals preferring the startup environment, the allure of working on AI at an incumbent is equally strong for many, especially pure computer and data scientsts who, more than anything else, want to work on interesting AI projects. They get to work in the code, with a large budget, with all the data, with above-market compensation, and a built-in large customer base that will enjoy the fruits of their labor, all without having to do sales, marketing, tech support, accounting, raising money, or anything else that isn’t the pure joy of writing interesting code. This is heaven for many.

A chatbot is in the chatbot market, and an SEO tool is in the SEO market. Adding AI to those tools is obviously a good idea; indeed companies who fail to add AI will likely become irrelevant in the long run. Thus we see that “AI” is a new tool for developing within existing markets, not itself a new market (except for actual hard-tech AI companies).

AI is in the solution-space, not the problem-space, as we say in product management. The customer problem you’re solving is still the same as ever. The problem a chatbot is solving is the same as ever: Talk to customers 24/7 in any language. AI enables completely new solutions that none of us were imagining a few years ago; that’s what’s so exciting and truly transformative. However, the customer problems remain the same, even though the solutions are different

Companies will pay more for chatbots where the AI is excellent, more support contacts are deferred from reaching a human, more languages are supported, and more kinds of questions can be answered, so existing chatbot customers might pay more, which grows the market. Furthermore, some companies who previously (rightly) saw chatbots as a terrible customer experience, will change their mind with sufficiently good AI, and will enter the chatbot market, which again grows that market.

the right way to analyze this is not to say “the AI market is big and growing” but rather: “Here is how AI will transform this existing market.” And then: “Here’s how we fit into that growth.”

We still believe that there will be a separation between the “application layer” companies and foundation model providers, with model companies specializing in scale and research and application layer companies specializing in product and UI. In reality, that separation hasn’t cleanly happened yet. In fact, the most successful user-facing applications out of the gate have been vertically integrated.

We predicted that the best generative AI companies could generate a sustainable competitive advantage through a data flywheel: more usage → more data → better model → more usage. While this is still somewhat true, especially in domains with very specialized and hard-to-get data, the “data moats” are on shaky ground: the data that application companies generate does not create an insurmountable moat, and the next generations of foundation models may very well obliterate any data moats that startups generate. Rather, workflows and user networks seem to be creating more durable sources of competitive advantage.

Some of the best consumer companies have 60-65% DAU/MAU; WhatsApp’s is 85%. By contrast, generative AI apps have a median of 14% (with the notable exception of Character and the “AI companionship” category). This means that users are not finding enough value in Generative AI products to use them every day yet.

generative AI’s biggest problem is not finding use cases or demand or distribution, it is proving value. As our colleague David Cahn writes, “the $200B question is: What are you going to use all this infrastructure to do? How is it going to change people’s lives?”