翻訳待ち：AI's Influencer Problem

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。

Sarah Gibbons Apr 21, 2026 Last Tuesday, Reese Witherspoon showed up on my Instagram feed telling me “it’s time we learn about AI before it gets too far ahead of us.” She shared how she was with her own book club and only 3 of 10 women use AI (and of those three, only one felt like they actually knew what they were doing). She framed AI literacy as future-proofing. The backlash was immediate. Roxane Gay commented: “Absolutely not.” Writers and illustrators pointed out that the AI Reese was endorsing is trained on the unauthorized work of authors (the people Reese’s famous Book Club was built to serve). One writer called generative AI as an act of feminism “vile.” Reese's reel on my feed last Tuesday, with 167K likes and 13.4K comments. Last Thursday, Ashley Striblet (of Early Insights Club) posted a screenshot of a different influencer moment: Carly Weinstein’s “dinner party with @claude.ai.” A quintessential influencer brand dinner: place cards, conversation starters, and vibey lighting. Paraphrasing Ashley: it would be nice to see AI labs make meaningful changes to how they develop AI, rather than manufacture consent through influencers. I’d seen Carly’s post earlier and had the same feeling. From Carly's story. Also at Claude's brand dinner: Deepa ('helping you get happy, healthy, hot, and rich'), Brittany Leighball ('a real-life rom-com'), Rawan ('career, travel, personal finance'). Back in February, Microsoft Copilot took a group of New York fashion influencers to the Super Bowl on an influencer brand trip (they had nosebleed seats). The Copilot use cases that came out of the trip, documented on TikTok, were “hey copilot how do you cure a hangover“ and “how to get a stain out”. To recap, a multi-trillion-dollar company sent a powerful, emerging technology to one of the largest cultural events in the country…to ask about hangover cures. Greta's TikToks from the Copilot-sponsored Super Bowl weekend. Short-form social content skews heavily to Gen Z, yet Gen Z is the demographic the most skeptical of AI. Gallup’s February survey of 14–29-year-olds shows excitement down 14 points year over year (to 22%), anger up 9 (to 31%), and only 18% feeling hopeful. Among employed Gen Z, 48% say the risks of AI at work outweigh the benefits—more than three times the 15% who say the reverse. The lower the use and trust, the higher the hostility. An aside, I actually don’t disagree with Reese’s main point. A Federal Reserve Bank of NY study found that 50% of men used generative AI in the past year, compared with about 33% of women. The gap carries into the workplace: LeanIn.Org’s 2026 survey found 33% of men use AI daily or constantly at work, versus 27% of women. And a meta-analysis of 18 studies found a roughly 25% gender gap in adoption overall. AI’s value can help its users succeed at work, which maps to pay, promotions, and raises. It’s easy to see all this as outside noise to our product/ux/strategy bubble. However, what the average person does with AI (or refuses to) is what steers the money. That money decides what this technology becomes, and who it ends up serving. My goal is just to noodle out loud and sketch what alternative could look like. This Matters, and It Could Look Different The influencer playbook we know today was built to sell goods. It emerged from beauty and fashion in the mid-2010s, designed around products you can hold up to a camera, style into an apartment, and wish for from a distance. AI is shaped differently. Its value lives entirely in what a human does with it. It cannot be held up to the camera. So far, the major AI labs have grabbed onto the old playbooks anyway. The underlying challenge, for design and marketing alike, is that AI is a product with a virtually endless set of uses, for nearly anyone, across an enormous range of fluency. It’s hard to market a technology clearly when you haven’t decided who you’re designing it for. Claude is what I use most, so the rest of this leans toward Anthropic, though most of it could be said of any majorAI lab. Anthropic has positioned itself, in my eyes, as the mainstream “thinking person’s AI.” Here’s what marketing could look like if it took the technology’s own promise—that AI can help humans do more intrinsically human things—as its core thread. Idea 1: Fund domain experts, not influencers. There is a population of people in this country whose authority is unimpeachable inside their own corner of the world, even if it’s entirely unknown outside it. For example, a labor lawyer well-known in her region, a history teacher in his twenty-fifth year teaching, or a social worker with a strong reputation. These people have the kind of credibility a fashion influencer cannot manufacture (and probably would not want). The shape of the best viral content right now is the long arc: someone on a journey, with a narrative audiences can follow over time. Influencer marketing borrows the surface of that shape, someone seemingly normal, doing something, but doesn’t have the structure and depth. The version of this that could work for AI is a patronage model (a tale as old as time), with AI labs as the patrons. Find the experts, resource them, pay them, teach them, and then partner them with someone who can help them tell their story. Let them share the actual arc of using the technology in their work. It’s rather simple—fund and make possible the technology doing something real and meaningful. Idea 2: Fund third spaces. “Claude is why the library is open on Saturdays again” is the sentence I want to see. Matter, a small marketing think tank whose work I’ve been following, published a piece last August called “Brands Should Fund Third Spaces”. Their argument: the loneliness epidemic is the unmet need of Gen Z and millennials, and brands should be enriching the spaces where humans gather to be with other humans, instead of staging hollow “experiences.” The AI labs have large budgets, no retail footprint, and a product that can, at its best, take other things off people’s plates so they can spend more time with each other doing the intrinsically human stuff. The opportunity is to fund the infrastructure of that life. The public library in my beach town gets by on town taxes, but also receives state funding and private donations. Most small libraries only have the first, and the director is usually running the place solo. (Made with Nana.) Imagine a neighborhood library, a “makers” space, a youth program, or a local arts council. Most of these are one or two people wearing ten hats, and programming gets thin because of the admin overhead: grant-writing, scheduling, and donor comms. A lab that funded AI tooling for a set of these orgs—and helped set up the workflows required—would be directly trading administrative load for human hours in the room. Idea 3: Build civic infrastructure. Embed the technology inside the institutions that carry civic trust but are historically underserved. Build the boring tools nobody films themselves using: SNAP enrollment, FAFSA prep, small-claims paperwork, Medicaid appeals, immigration forms, medical bill disputes, and eviction defense templates. Released for free, branded lightly or not at all. The humans this would serve are the ones who never appear in any AI marketing deck, because they are not the demographic the brand teams have been told to chase. The case for doing it anyway is (beyond ethics and the general hope to do good) a marketing case. Givsly’s research found 88% of US consumers buy from brands that align with their values, and 64% say they’d pay more for those brands. This figure jumps to 79% among Gen Z, the same cohort that is most skeptical of AI. The people spending money on AI tools want a partner who is ethically sound. “We are living in a world of monsters and we need more monster slayers” as Jasmine Bina of Concept Bureau says. Zooming Out If you zoom out, our digital social behaviors are probably going to change too. Sinead Bovell, hosting Gary Vaynerchuk on her podcast, discussed how we’re at the end of the social media era. She makes the point that AI breaks the signaling value of posting. If you can’t tell who’s human, the psychology of feedback collapses. Voice-first AI breaks the scrolling habit. AI is general-purpose, so it’ll produce new behaviors instead of faster versions of today’s behaviors. She believes glasses and AR will do to the phone what the phone did to TV. This all makes it even sillier that AI labs are spending their marketing budgets on influencer dinners and short-form video, aimed at the audience least likely to trust the product, at the exact moment something more interesting could be available and defined by them. At the end of the day, these alternative approaches would require time, effort, and new skills from the major AI labs’ marketing teams. There’s a reason they are using an unoriginal playbook—it’s defined and known.