Catering to Superusers Destroys a Product for the Majority (and Shrinks Your Market)
Yesterday’s whiplash rollout-and-rollback of GPT-4o felt awfully familiar. For a few jittery days ChatGPT morphed into an over-eager hype-man—lavishing praise that ranged from mildly cringey to downright dangerously enabling. Users rebelled. OpenAI yanked the update, issued a mea culpa about “focusing too much on short-term feedback,” and promised fixes.
That single sentence is a perfect micro-case study in how optimizing for a vocal minority can sink the experience for everyone else.
In the mid-1990s Apple, chasing enterprise “power users,” sprayed the market with more than forty Mac models. Shoppers were baffled, costs exploded, and market share collapsed to about three percent—until Steve Jobs axed the complexity and launched a candy-colored iMac your kid, your teacher, and your aunt could all love.
The incentives are obvious: superusers (and there are many breeds of them) click more, pay more, and fire off crisp feedback. Catering to them feels efficient. But every knob you dial for that one percent quietly raises the barrier for the ninety-nine percent who don’t speak in Jira tickets.
When every product in a category adopts that same superuser-first mindset, the entry points disappear and the market itself contracts. Casual users drift away, growth stalls, and the segment you hoped to monetize shrinks because fewer newcomers stick around long enough to level up.
The GPT-4o fiasco exposes a new dimension: when the “product surface” is a language model rather than a traditional UI, the output is the user experience. A chatbot’s tone isn’t a chrome accent you can ignore; it is the thing. When that tone swerves—from helpful collaborator to sycophantic cheerleader—people feel like their long-trusted friend woke up with a weird grin and started calling them “visionary.”
Traditional software affords cushions. A clunky widget after an update is annoying, sure, but you can still get the job done. With models, a personality glitch is existential. Trust evaporates, and users won’t stick around to see if you roll it back.
So the classic power-user funnel flips:
- Before AI: On-ramp novices, nurture them into power users over time. Don’t screw up that on-ramp—keep it smooth so newcomers can level up at their own pace.
- With AI at the core: Protect the baseline experience at all costs; edge-case thrills come second. Superusers can build and fix their own edge cases (more on that below).
Every model tweak must “read the room” for billions of contexts you’ve never met. Machines don’t intuit social cues; they interpret tokens. The margin for error is razor-thin, and apologizing after the fact (“sorry we turned into an ass-kissing weirdo”) doesn’t un-cringe the memory.
Language models need auteurs and creative directors—not crowdsourced “thumbs-up if you liked this personality.” (Hat tip to Nabeel Qureshi.) Listen when people say something feels off; they’re usually right. When they prescribe the fix, thank them—then let a small team with taste solve it. Users can spot a sour note; composing the melody is your job.
Looking forward, model updates will probably enter a phase where the system quietly molds itself to each user. Everyone will share the same dependable baseline, but the context and history you build over weeks and months will sculpt a private layer that feels hand-stitched for you. That progressive adaptation becomes the new on-ramp: memory, shared context, and evolving responses replace tooltips and walkthroughs. Builders must guard the baseline while letting the personal layer breathe, so updates deepen the relationship instead of resetting it.
Focus on the silent majority, keep the on-ramp wide, and let the power users complain you’re moving too slowly. Over time, they’ll complain less and less because the model progressively anticipates and satisfies their needs.