Where previous generations of tech companies sold software to hotels and taxi companies, Airbnb and Uber used software to create new businesses and to redefine markets. Uber changed what we mean when we say ‘taxi’ and Airbnb changed hotels.

But for all sorts of reasons, the actual effect of that on the taxi and hotel industries was very different. The regulation is different. The supply of people with a car and few hours to spare is very different from the supply of people with a spare room to rent out (indeed, there is adverse selection in that difference). The delta between waving your hand on a street corner and pressing a button on your phone is different to the delta between booking a hotel room and booking a stranger’s apartment.

Sometimes disruption is much more about new demand than challenging the existing market, or only affects a peripheral business, as happened with Skype.

it’s always easier to shout ‘disruption!’ or ‘AI!’ than to ask what kind.

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?”