Mr. Musk had largely come to peace with a price of $100 a year for Blue. But during one meeting to discuss pricing, his top assistant, Jehn Balajadia, felt compelled to speak up. “There’s a lot of people who can’t even buy gas right now,” she said, according to two people in attendance. It was hard to see how any of those people would pony up $100 on the spot for a social media status symbol. Mr. Musk paused to think. “You know, like, what do people pay for Starbucks?” he asked. “Like $8?” Before anyone could raise objections, he whipped out his phone to set his word in stone. “Twitter’s current lords & peasants system for who has or doesn’t have a blue checkmark is bullshit,” he tweeted on Nov. 1. “Power to the people! Blue for $8/month.”

Being broadly effective, but somewhat inefficient, is better than being narrowly efficient, but less effective.

Targeting can increase the scale of effects, but this study suggests that the cheaper approach of not targeting so specifically, might actually deliver a greater financial outcome

As Wiberg’s findings point out, the problem with targeting towards conversion optimisation is you are effectively advertising to many people who were already going to buy you.

If I only sell to IT decision-makers, for example, I need some targeting, as I just can’t afford to talk to random consumers. I must pay for some targeting in my media buy, in order to reach a relatively niche audience.  Targeting is no longer a nice to do, but a must have. The interesting question then becomes not should I target, but how can I target effectively?

What they found was any form of second or third-party data led segmenting and targeting of advertising does not outperform a random sample when it comes to accuracy of reaching the actual target.

Contextual ads massively outperform even first party data

We can improve the quality of our targeting much better by just buying ads that appear in the right context, than we can by using my massive first party database to drive the buy, and it’s way cheaper to do that. Putting ads in contextually relevant places beats any form of targeting to individual characteristics. Even using your own data.

The secret to effective, immediate action-based advertising, is perhaps not so much about finding the right people with the right personas and serving them a tailored customised message. It’s to be in the right places. The places where they are already engaging with your category, and then use advertising to make buying easier from that place

Even hard, sales-driving advertising isn’t the tough guy we want it to be. Advertising mostly works when it makes things easier, much more often than when it tries to persuade or invoke a reluctant action.

Thinking about advertising as an ease-making mechanism is much more likely to set us on the right path

If your ad is in the right place, you automatically get the right people, and you also get them at the right time; when they are actually more interested in what you have to sell. You also spend much less to be there than crunching all that data

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?”

I wrote five scripts during my season and a half on TZ, and I was deeply involved in every aspect of every one of them.   I did not just write my script, turn it in, and go away.   I sat in on the casting sessions.   I worked with the directors.   I was present at the table reads.   “The Last Defender of Camelot” was the first of my scripts to go into production, and I was on set every day.   I watched the stuntmen rehearse the climactic sword fight (in the lobby of the ST ELSEWHERE set, as it turned out), and I was present when they shot that scene and someone zigged when he should have zagged and a stuntman’s nose was cut off… a visceral lesson as to the kind of thing that can go wrong.   With Phil and Jim and Harvey Frand (our line producer, another great guy who taught me a lot), I watched dailies every day.    After the episode was in the can, I sat in on some post-production, and watched the editors work their magic.   I learned from them too.

Streamers and shortened seasons have blown the ladder to splinters.   The way it works now, a show gets put in development, the showrunner assembles a “mini-room,” made up of a couple of senior writers and a couple newcomers, they meet for a month or two, beat out the season, break down the episodes, go off and write scripts, reassemble, get notes, give notes, rewrite, rinse and repeat… and finally turn into the scripts.   And show is greenlit (or not, some shows never get past the room) and sent into production.  The showrunner and his second, maybe his second and his third, take it from there.   The writer producers.   The ones who already know all the things that I learned on TWILIGHT ZONE. The junior writers?  They’re not there.   Once they delivered their scripts and did a revision of two, they were paid, sent home, their salary ended.   They are off looking for another gig.

In many cases they won’t be asked to set even when the episodes they wrote are being filmed.   (They may be ALLOWED on set, if the showrunner and execs are cool with that, but only as a visitor, with no authority, no role.   And no pay, of course.   They may even be told they are not allowed to speak to the actors).

One of the things the AMPTP put forward in their last offer to the WGA is that some writers might be brought onto sets as unpaid interns, to “shadow” and “observe.”   Even that will not be an absolute right.   Maybe they will be let in, maybe not.   These are the people who wrote the stories being filmed, who created the characters, who wrote the words the actors are saying.   I was WAY more than that in 1985, and so was every other staff writer in television at the time.

Mini-rooms are abominations, and the refusal of the AMPTP to pay writers to stay with their shows through production — as part of the JOB, for which they need to be paid, not as a tourist —  is not only wrong, it is incredibly short sighted.   If the Story Editors of 2023 are not allowed to get any production experience, where do the studios think the Showrunners of 2033 are going to come from?

numerous sources say they cannot discern what kind of material Salke and head of television Vernon Sanders want to make. A showrunner with ample experience at the studio says, “There’s no vision for what an Amazon Prime show is. You can’t say, ‘They stand for this kind of storytelling.’ It’s completely random what they make and how they make it.” Another showrunner with multiple series at Amazon finds it baffling that the streamer hasn’t had more success: Amazon has “more money than God,” this person says. “If they wanted to produce unbelievable television, they certainly have the resources to do it.”

Blockbuster responded by pricing Blockbuster Online 50 cents cheaper, accelerating Netflix’s stock slide. Netflix, though, knew that Blockbuster was carrying $1 billion in debt from its spin-off from Viacom, and decided to wait it out; Blockbuster cut the price again, taking an increasing share of new subscribers, and still Netflix waited.

Swelling trade tensions and the rise of “direct-to-consumer” platforms were bound to heighten the scrutiny on the import of mass media cultural products. But it’s also notable that the Marvel movies that did gain admittance in China were led by six heroes (The Avengers), five of whom were employed by the American military (with the sole outlier being an extraterrestrial) and all of whom were white. The current, rejected leads are more diverse in vocation, American allegiance, and ethnicity (among other attributes).

In 2017, Disney began a marketing integration with aerospace and defense giant Northrop Grumman encouraging those who use Google to research American defense contractor Stark Industries to join something like the real thing.

Avatar’s unprecedented achievements require us to examine not just its technological innovations, but also its narrative. The film’s “protagonist humans” are classic Western archetypes such as the taciturn soldier and the driven scientist. The villains are archetypes as well, but they are also particularly close to foreign caricatures of evil Americans: the tough-as-nails, violence-prone colonel and pillage-the-earth corporate executive. Furthermore, Avatar’s overarching message is one of collectivism, spiritualism, and alignment with nature. At the end of the movie, each of the Western heroes literally shed their individual identities (and white bodies) to become part of the cooperative aboriginal mind and save the day.