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.

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?”

Over the past six months, I spoke with more than two dozen annotators from around the world, and while many of them were training cutting-edge chatbots, just as many were doing the mundane manual labor required to keep AI running. There are people classifying the emotional content of TikTok videos, new variants of email spam, and the precise sexual provocativeness of online ads. Others are looking at credit-card transactions and figuring out what sort of purchase they relate to or checking e-commerce recommendations and deciding whether that shirt is really something you might like after buying that other shirt. Humans are correcting customer-service chatbots, listening to Alexa requests, and categorizing the emotions of people on video calls. They are labeling food so that smart refrigerators don’t get confused by new packaging, checking automated security cameras before sounding alarms, and identifying corn for baffled autonomous tractors.

In other words, when Kylie Jenner posts a petition demanding that Meta “Make Instagram Instagram again”, the honest answer is that changing Instagram is the most Instagram-like behavior possible.

The first trend is the shift towards ever more immersive mediums. Facebook, for example, started with text but exploded with the addition of photos. Instagram started with photos and expanded into video. Gaming was the first to make this progression, and is well into the 3D era. The next step is full immersion — virtual reality — and while the format has yet to penetrate the mainstream this progression in mediums is perhaps the most obvious reason to be bullish about the possibility.

The second trend is the increase in artificial intelligence. I’m using the term colloquially to refer to the overall trend of computers getting smarter and more useful, even if those smarts are a function of simple algorithms, machine learning, or, perhaps someday, something approaching general intelligence.

The third trend is the change in interaction models from user-directed to computer-controlled. The first version of Facebook relied on users clicking on links to visit different profiles; the News Feed changed the interaction model to scrolling. Stories reduced that to tapping, and Reels/TikTok is about swiping. YouTube has gone further than anyone here: Autoplay simply plays the next video without any interaction required at all.