the smartphone, and a host of software technologies built on it,3 have birthed what is essentially a parallel, non-union, motion-picture industry consisting of YouTube, TikTok, Instagram, Twitch, Twitter, and their many other social-video rivals, all of which rely on the free or barely compensated labor product of people acting as de facto writers, directors, producers, actors, and crew. Even if they’d never see it this way, YouTubers and TikTokers are effectively competing with Hollywood over the idle hours of consumers everywhere; more to the point, they’re doing what any non-union workforce does in an insufficiently organized industry: driving down labor compensation.

Almost no one I know has work; most people’s agents and managers have more or less told them there won’t be jobs until 2025. An executive recently told a friend that the only things getting made this year are “ultra premium limiteds,” which sounds like a kind of tampon but actually just means “six-episode miniseries that an A-List star wants to do.”

YouTubers’ lack of collective bargaining power isn’t just bad for me and other guild members; it’s bad for the YouTubers themselves. Ask any professional or semi-professional streamer what they think of the platform and you’ll hear a litany of complaints about its opacity and inconsistency

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?”