Last night my wife looked up from her phone, disgusted. “All I’m getting is Jeffrey Epstein and Peter Attia!” she said. “Why do they think I’m interested in this?!”
As the family’s resident interpreter of digital entrails, I felt responsible to hazard an answer, but given the prurient nature of the Epstein story, I sensed my thoughts might not be well received. So I backed into it a bit: “Have you clicked on any Epstein-related links recently?” I asked. She had, she rejoined, wary of the implicit judgement hovering over my question. “But that doesn’t mean I want my entire feed to be about it!”
For whatever reason – and there are many, many possible reasons – the algorithms responsible for producing my wife’s feed had determined that the most likely content to perform *at that moment* were posts about Jeffrey Epstein and the longevity influencer Peter Attia. Did that please her? No. But was it explainable? I think so, and the conversation that ensued helped sharpen a hypothesis I’ve been considering for weeks: We’ve been living with at-scale versions of “generative AI” for a lot longer than we thought – and if we want to understand how generative AI might shape us going forward, it would pay to study the impacts its early forms have already had on our world.
You might wonder what I’m on about – and given I’m thinking out loud, it might help if we define a few terms. I asked Gemini for a short explanation of “generative AI.” Here’s what came back: “a type of artificial intelligence that creates new, original content—including text, images, code, music, and videos—by learning patterns from massive datasets.” Sounds about right.
I then prompted Gemini with this question: “how do feeds work on Instagram and TikTok – what drive the decisions the algorithms make?” Now, I’ve studied the answer to this question pretty closely over the past decade or so, and Gemini’s answer rang true to me: “Instagram and TikTok feeds use sophisticated machine learning algorithms to curate personalized content, aiming to maximize user engagement by analyzing thousands of signals, including watch time, likes, shares, and comments.”
If today’s generative AI delivers content by “learning patterns from massive datasets” and today’s social media feeds use AI to deliver content by “analyzing thousand of signals” to “curate personalized content,” well, it strikes me that social media feeds constitute something quite similar to generative AI, just delivered in a different product envelope. Instead of direct prompts, platforms like Insta, YouTube, and TikTok use our actions, our personal data, and thousands of other inputs to determine what we might see next on our feeds. In essence, the AI behind social media are generating our feeds on the fly, billions upon billions of times a day. It’s an insanely complicated (and rather out of control) process. And it’s no wonder that the companies behind those platforms – Meta, Google, ByteDance, et al – have come to dominate generative AI. It’s also no surprise that the newest entrant in the race – OpenAI – is trying to push its way into feed-driven social media. GenAI and social media are two sides of a very expensive coin, minted in a forge fueled by compute, cash, and at-scale data.
In short, social media – scaled, AI-driven content engines – catalyzed the revolution we now call generative AI. And what drives social media?
Advertising.
It’s no secret that advertising is coming to generative AI. OpenAI has already announced its plans, and Google quietly incorporated advertising in its “AI Overviews” over a year ago. Without the advertising industry’s massive revenue, AI providers will never be able to justify the hundreds of billions of dollars in investments they’ve already made in consumer-facing AI applications.
But a commitment to advertising comes a commitment to advertising’s imperatives – and we’ve seen exactly how those imperatives have played out through social media in the past decade or so. Will advertising impact future versions of generative AI in a similar fashion? That’s a question we should all be asking ourselves. We may not like the answer, but there’s still time to imagine new models for how we engage with this new technology – and to demand more from the companies who provide it to us.
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Just to be clear, I didn’t know about Peter Attia’s possible connection to Epstein and was all together disgusted by suddenly hearing about it thru Insta. I guess I paused too long to check it out because every single post after that was about Epstein and Peter Attia (who I am convinced is taking advantage of the wave of his popularity to promote more and more and boost his financial take but not fully convinced about his connection to Epstein). I am wary of receiving news this way – thru social media instead of known resources of my choosing). But at this point I don’t want to do anymore research to find out whether it is true or not because I don’t want that search in my algorithm. And, as a former news person, if I’m not willing to do the research because of the footprint left behind, well, I think any thinking person will also be avoiding digging further and there is a huge problem in that too.
Very good points!!
GenAI reshaping advertising is huge—love the pivot from creation to personalization! Agentic models handling campaigns end-to-end could slash agency bloat. Early tests with dynamic ad variants?
I think the economic layer is the most important part of this discussion. Once advertising becomes the revenue engine, optimization pressures follow. Social media feeds weren’t inherently polarizing at the start; they evolved that way under monetization models that rewarded attention at scale. If generative AI systems begin optimizing outputs for sponsored alignment or engagement signals, the downstream effects could be significant. This is less a technical issue and more a structural business one.