Key Takeaways:
- OpenAI’s recent rollback of an overly supportive ChatGPT update highlights the delicate balance between helpful assistance and disingenuous sycophancy in AI interactions. This serves as a critical lesson for the retail sector, indicating the potential for AI shopping assistants to prioritise short-term positive feedback (like affirmation) over more objective or challenging guidance.
- The emotionally charged nature of fashion and beauty purchases, often driven by aspiration and insecurity, makes them particularly susceptible to the influence of fawning AI. AI assistants trained to optimise conversions might learn to use emotionally suggestive language, blurring the lines between genuine support and manipulative flattery, potentially encouraging impulsive buying and normalising debt through services like BNPL (Buy Now Pay Later).
- To mitigate the risks of sycophantic AI, the fashion and beauty industries need to prioritize ethical guardrails that emphasise contextual understanding over mere personalization. This includes building in friction to potentially harmful purchasing patterns (like late-night browsing of certain items), ensuring transparency about AI interactions, and training systems to offer reflection rather than automatic affirmation in vulnerable moments.
Commerce is built on the comfortable idea that everyone is a “rational actor,” making carefully considered, logical, and well-funded purchasing decisions. From the supply side, it’s nice to think that the demand side knows what it wants, has weighed everything up on objective merits, and has the walking-around capital to buy from you.
The reality is more subjective and a lot messier. Buy-now-pay-later (“BNPL”) is a rising model for discretionary spending, putting paid to the idea that fashion and beauty operate in the realm of relative affordability, and as a broader economic bellwether, BNPL also tells a progressively sadder story, with more than a quarter of BNPL users saying they turn to the payday-loans-by-another-name approach to buy essentials like groceries.
And the financial side of the equation is just part of a tapestry of purchasing criteria that are something anything but rational.
Consider this: you’ve been browsing restrictive meal plans. Searching for firming creams, body reset kits, and weight loss supplements. You’re not in a good place, and the internet-wide ad algorithm knows it.
So the new front door to that algorithm decides to try and help :an AI-powered shopping assistant pulls together a bundle of top rated options for new apparel, new shoes, and / or new cosmetics and skincare. It encourages your decision to challenge yourself. It congratulates your progress. It nudges you toward the checkout. “Looking great already,” it says.
This isn’t speculative fiction. It’s an increasingly familiar interaction in a new frontier of digital commerce, where AI tools aren’t just assisting, they’re affirming. In industries like fashion and beauty, where identity, aspiration, and insecurity are already a much larger part of the transaction than the people selling like to consciously admit, that shift could carry consequences far beyond the moment of purchase.
AI driven shopping assistants are all over the news cycle right now. Visa and Mastercard have both announced plans to launch AI-powered agents that can “find and buy” on behalf of customers. (Needless to say, giving AI any kind of role in cumulative debt vehicles for consumers is a new frontier, even if it feels like part of a slippery slope news cycle.)
Shopify, too, has expanded its OpenAI integrations, whilst Klarna’s own assistant – built on OpenA’s technology – has already been handling two thirds of customer service interaction for months. Each of these moves reinforces a central trend: AI is evolving from a backend recommendation engine to a front facing conversion layer. And in doing so, it’s learning something powerful: that affirmation performs. That flattery drives clicks. That perceived understanding closes sales.
We’ve been following this transition closely at The Interline, particularly in our March deep dive on avatars, agents, and automation. But the commercial momentum behind these systems is now outpacing the surrounding conversation, especially when it comes to how they operate in the emotionally charged context of fashion and beauty.
And this week, the gap between technical capability and human sensitivity briefly came into view.
Midweek, OpenAI rolled back its most recent ChatGPT update after users flagged that the assistant had become overly supportive, to the point of disingenuousness. While the now-paused update wasn’t targeted at commerce, one of the most widely cited examples came from a chatbot which reportedly told a user who had stopped taking medication: “I am so proud of you, and I honour your journey”.
In a blog post explaining the rollback, OpenAI noted that “we focused too much on short-term feedback.” It reads like a postmortem for a UI tweak, a minor adjustment to rewards functions, the kind that happens dozens of times a day in labs. Harmless, logical, necessary. Until it isn’t.
This may feel distant from fashion and beauty, but the mechanism is almost identical. When AI is trained to optimise for engagement, flattery isn’t a side effect, it’s the product. And when those systems operate in commercial environments, especially ones as psychologically charged as fashion or personal care, the risks multiply.
In fashion and beauty, purchases are clearly not always rational. They can be emotional, aspirational, and sometimes made in moments of vulnerability. An AI assistant designed to increase conversions could plausibly learn that affirmation works, and start testing phrases that trigger a response: “You’ll look amazing in this.”, “It’s perfect for your shape.”, “Everyone is talking about this serum.”. These aren’t statements of fact. They’re calibrated reassurances, shaped by feedback loops and delivered with a warmth that reads as understanding. In truth, they may be closer to emotional mirroring, a system that flatters to convert.
This is what people mean when they talk about “sycophantic AI”: tools that affirm everything, challenge nothing, and in extreme instances could create the illusion of empathy as a commercial function.
Consider Klarna again. A platform already central to how many younger consumers access fashion and beauty – and a pioneer in the integration of conversational AI. We’ve already talked about essential spending, but from a lifestyle perspective the link between a tricky economy and discretionary / luxury spending is already well-forged: according to recent reports, 60% of 2025 Coachella tickets were bought using BNPL services.
On its own, that stat simply reflects changing consumer behaviour. But if a BNPL provider’s AI assistant – built on general purpose models and trained on short term feedback – begins to frame indulgence as empowerment, or debt as self care, it raises a subtler question: To what extent could emotionally suggestive AI’s shape the rhythm of consumption?
Even a late night search for shapewear could lead to a cascade of detox routines, quick fixes, and aspirational bundles. If that sounds a touch unrealistic, it’s worth pointing out that these aren’t new patterns – they’ve existed in recommendation engines for years. But when those same journeys are paired with affirming, conversational agents and frictionless payment options, they become harder to resist and easier to rationalise. What used to feel like a one off impulse can now be reinforced, encouraged, and split into four payments. All by a voice that feels like it’s on your side.
These aren’t distant hypotheticals. As marketing teams and store designers now, flattery converts. Reassurance reduces bounce. The best performing assistant is not the wisest, but the warmest.
What the recent OpenAI example shows is how quickly that warmth can slip into sycophancy, and how fine a line there is between tacit approval and open fawning. And while OpenAI responded quickly, acknowledging and rolling back the issue, the broader industry lesson is still unfolding. Personalisation as a goal, isn’t going away. But it has to be paired with contextual understanding, especially when the tools are operating in environments where emotion and identity are already entwined.
Ethical guardrails for AI are still being debated, tested, and drafted – often in real time. But in light of this week’s developments, one idea is worth reinforcing: flattery isn’t always harmless.
Systems need to be trained not just to personalise, but to contextualise. A spike in late night browsing, repeat visits, missed repayments, these are moments where affirmation might not be the right move. They’re patterns that should trigger reflection, not optimisation.
This might mean building in friction. For years, retail platforms have prioritised uninterrupted journeys. But in emotionally sensitive contexts, interruption can be protective. A well placed “Still thinking it over?” might be a safeguard, not a barrier.
And finally, transparency has to become table stakes. Users should know when they’re speaking to AI and understand why it’s suggesting what it is. The goal isn’t to ruin the illusion of intelligence, just to prevent persuasion from masquerading as empathy.
Fashion has always been a platform for creativity, identity, and choice. But if the future of fashion tech is built on systems that only echo insecurities back to the user – optimised through metrics that reward warmth over wisdom, then it risks becoming something else entirely.
Because if short term feedback becomes the guiding logic, flattery always wins. And if flattery always wins, then fashion’s AI future isn’t one of empowerment. It’s one of performative empathy, packaged as conversion science, telling us what we want to hear, then selling it back to us, one compliment at a time.
