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.

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.

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.”

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.

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?”

Netflix could buy 10 top quality screenplays per year with the cash they’ll spend on that one job.  They must have big plans for AI.There are also a half dozen AI job openings at Disney. And the tech-based streamers (Apple, Amazon) already have made big investments in AI. Sony launched an AI business unit in April 2020—in order to “enhance human imagination and creativity, particularly in the realm of entertainment.”

When Spotify launched on the stock exchange in 2018, it was losing around $30 million per month. Now it’s much larger, and is losing money at the pace of more than $100 million per month.

But the real problem at Spotify isn’t just convincing people to pay more. It runs much deeper. Spotify finds itself in the awkward position of asking people to pay more for a lousy interface that degrades the entire user experience.

Boredom is built into the platform, because they lose money if you get too excited about music—you’re like the person at the all-you-can-eat buffet who goes back for a third helping. They make the most money from indifferent, lukewarm fans, and they created their interface with them in mind. In other words, Spotify’s highest aspiration is to be the Applebee’s of music.

They need to prepare for a possible royalty war against record labels and musicians—yes, that could actually happen—and they do that by creating a zombie world of brain dead listeners who don’t even know what artist they’re hearing. I know that sounds extreme, but spend some time on the platform and draw your own conclusions.

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