- Tension: Marketers celebrated algorithmic personalization as a gift, ignoring that they were never the intended audience.
- Noise: The industry hype around AI-driven feeds obscured how little control brands actually gained over distribution.
- Direct Message: An algorithm that learns to think for users will eventually learn to think without marketers entirely.
To learn more about our editorial approach, explore The Direct Message methodology.
Most marketers got the Twitter AI story backward from the start. When the platform announced it was using deep learning to personalize content in users’ feeds, the prevailing narrative across marketing departments and agency Slack channels was celebratory.
Industry roundups in 2017 called the move “absolutely a positive” for marketers, framing algorithmic curation as a new lever brands could pull. The assumption was clean and comfortable: if Twitter’s feed got smarter, marketers would benefit because smarter targeting meant better reach, better engagement, better ROI.
I remember sitting in a strategy meeting at the time, watching a deck that described algorithmic feeds as “the great equalizer” for branded content. Everyone in the room nodded. Nobody asked the question that mattered: equalizer for whom? The algorithm was built to serve users, to keep them scrolling, to learn what kept individual humans engaged and deliver more of exactly that.
Marketers were never the customer. They were, at best, a tolerated passenger. And the gap between what the industry believed it was getting and what actually arrived has only widened in the years since.
The Uncomfortable Distance Between Access and Influence
Here’s what happened in the years following Twitter’s deep learning pivot, and what continues to unfold on the platform now called X. The algorithm got better at predicting what individual users wanted to see. It learned behavioral patterns, engagement rhythms, emotional triggers. It did what machine learning systems do: it optimized for attention. And for a brief window, marketers who understood content resonance did well. Organic reach rewarded posts that sparked genuine interaction. The algorithm seemed generous.
Then the floor shifted. As the system grew more sophisticated, it began prioritizing content that served its own engagement objectives with increasing precision. The feed became less of a public square and more of a curated tunnel. Brands that had been riding organic reach found their content deprioritized unless it triggered the specific behavioral responses the algorithm valued. What I’ve found analyzing consumer behavior data is that the transition was gradual enough that many marketing teams blamed their own creative rather than recognizing a structural change. They kept optimizing their posts when they should have been questioning the platform’s incentives.
As Kyle Wiggers noted when Twitter’s recommendation code was released publicly, “Twitter’s algorithm is fairly complex — but not necessarily surprising in any way from a technical standpoint.” That observation matters. The technology itself was standard. The surprise was never in the engineering. It was in how the marketing industry projected its own desires onto a system that had no obligation to fulfill them. Marketers saw personalization and assumed partnership. The algorithm saw users and optimized for retention. Those are fundamentally different goals, and the tension between them has only intensified as AI systems on every major platform have grown more autonomous in their decision-making.
During my time working with tech companies, I watched this same dynamic play out internally. Product teams building recommendation engines rarely consulted with ad sales until late in the development cycle. The algorithm’s primary directive was user engagement. Monetization was a layer added on top, sometimes clumsily, always secondarily. Marketers who treated algorithmic feeds as friendly territory were misreading the room.
The Industry Echo Chamber That Kept Everyone Comfortable
The marketing industry has a reliable pattern when a new technology emerges: declare it revolutionary, build a service model around it, sell that model to clients, and defend the narrative long past its expiration date. AI-driven feeds were no exception. Conference stages filled with speakers explaining how to “hack the algorithm.” Blog posts proliferated with tips for beating the feed. Entire consultancies were built on the premise that brands could reliably game a system designed to learn and adapt faster than any human strategy team could iterate.
This echo chamber drowned out the more uncomfortable conversation. A study published in Nature found that X’s AI-driven feed algorithm nudges users toward more conservative political attitudes, with effects persisting even after switching back to a chronological feed. The implications of that research extend far beyond politics. If an algorithm can shift political attitudes through content curation, it can shift consumption attitudes, brand perception, and purchase intent in directions that have nothing to do with a marketer’s campaign strategy. The algorithm is shaping the user. The marketer’s content is raw material the system uses or discards based on its own logic.
I keep a journal of marketing campaigns that failed spectacularly. I call it my anti-playbook. Flipping through it recently, I noticed a pattern among the social media failures from 2018 onward. The common thread was overconfidence in platform alignment. Brands assumed the algorithm was working with them because engagement metrics looked good in isolation. They confused visibility with influence. They mistook the algorithm’s temporary use of their content for a durable relationship. When the system’s priorities shifted, those same brands saw their reach collapse overnight with no explanation and no recourse. The failures were never loud. They were quiet, gradual erosions that most teams rationalized away.
The conventional wisdom that AI-powered feeds are inherently good for marketers persists because it serves too many stakeholders to die easily. Platforms sell ads on the promise of algorithmic targeting. Agencies sell services on the promise of algorithmic optimization. Neither has much incentive to say what is becoming increasingly obvious: the algorithm’s interests and the marketer’s interests overlap less with each passing year.
What Becomes Clear When the Assumptions Fall Away
An algorithm designed to learn what users want will eventually learn that users rarely want what marketers are offering. The front-row seat was always in someone else’s theater.
This is the insight the industry still resists. Algorithmic personalization does not extend the marketer’s reach. It extends the platform’s control. Every improvement in AI-driven content curation reduces the predictability of organic brand distribution. The smarter the algorithm gets, the less it needs branded content to keep users engaged, and the more leverage the platform holds over advertisers who want access to the audience the algorithm has so carefully cultivated.
Building Strategy on Solid Ground Instead of Borrowed Territory
So where does this leave marketers who depend on platforms like X for audience connection? It leaves them in a position that demands honesty. The era of treating algorithmic feeds as friendly infrastructure is ending. What replaces it requires a different posture entirely.
First, the separation between owned and rented audiences has never been more consequential. Every dollar of attention earned on an algorithmic platform is held at the algorithm’s discretion. Email lists, direct communities, proprietary content ecosystems: these remain the only channels where the relationship between brand and audience is unmediated. The unsexy work of building these assets is now the most strategically important work a marketing team can do.
Second, understanding what the algorithm actually rewards requires moving past surface metrics. Engagement rate means little if the algorithm is surfacing your content to users it has already decided are likely to interact with anything. The question is whether your content is being distributed to new audiences or recycled within an existing behavioral cluster. Most analytics dashboards do not make this distinction easy to see, which is by design.
Third, and most fundamentally, marketers need to reckon with the fact that AI-driven platforms are building relationships with users that are more responsive, more personalized, and more persistent than most brand relationships. When I taught a guest lecture series on the psychology of digital consumption at Berkeley, the graduate students grasped this faster than the industry veterans in the room. The students understood intuitively that an algorithm optimizing for engagement is competing with brands for the same psychological real estate: attention, trust, habit formation. The algorithm has structural advantages in all three.
The marketers who will navigate this landscape well are the ones who stop treating the algorithm as a distribution channel and start treating it as a competitor for user attention. The front-row seat was flattering. It was also a distraction. The real work happens offstage, where the relationship between a brand and its audience does not depend on a system that was never designed to care about either.
