When AI takes the effort out of luxury

Luxury sells effort. The case a house makes to its clients rests on the hours and hands behind a handbag and on the idea that time spent is proof of worth. Inside those same houses, employees are now judged on speed. The dashboard or presentation that earns praise may well have been produced by AI. That contradiction sits within a wider shift that people across industries are struggling to name. For most of working life, effort and reward moved together: the work went in, and in time, something arrived that felt earned. Now, much of that effort is either compressed or completely delegated. Results still come in and targets are still hit, but the sense of having earned them often fails to follow. For an industry that prices its products on time and craft, that gap reaches beyond morale to brand coherence because the story a house tells clients has to hold up against the way it treats its own people. Ian Beacraft, CEO of the future-of-work consultancy Signal and Cipher, said at SXSW in March that disruption at work would produce an identity crisis on a mass scale, as whole industries reinvent themselves faster than people can reposition. The break between effort and reward is one early form that crisis takes. When work stops feeling earned Shortcuts are now built into ordinary work. AI can write drafts and generate dozens of campaign concepts in minutes. In a survey Beacraft cited at the same session, professionals were asked what they did with the time AI freed up. They answered almost unanimously: more work. The output arrives. What goes missing is the feeling that it was earned. And since professional identity is built on that feeling, its absence is unsettling. Michael Easter’s book Scarcity Brain offers one explanation. Its central argument is that rewards designed to be unpredictable and instantly repeatable — the slot-machine model now built into social feeds and shopping apps — keep people chasing without leaving them satisfied. In other words, when work becomes easier, we gain efficiency but may lose some of the process that makes achievement feel rewarding. Easter’s counterexample is a woman who hunts for shed antlers. His term for her pursuit is an “abundance loop,” because the searching itself is the reward. Remove the effort from professional work, and results start to behave like the first kind of reward: plenty of payoff, little satisfaction. Craft vs. corporate speed Tension runs highest where the product itself is sold on process. Houses tell clients about the years artisans spend learning a technique and the hours each piece spends at the bench. That story supports the price. The people who tell the story and run the business around it work in a different system: one that rewards turnaround and output and treats the slow route as a cost. An employee can spend the morning explaining that a bag is worth its price because of the time behind it, then spend the afternoon being assessed on how quickly an AI-assisted report was delivered. Staff notice the gap. If effort makes the product precious outside the building and speed is what gets rewarded inside it, employees will draw their own conclusions about which of the two the company actually believes in. New markers of competence Old markers of competence, such as time served and visible struggle, no longer map cleanly onto how work gets done. Many companies have yet to replace them, so employees improvise alone and assume everyone else has worked it out. A colleague who seems to be coasting may be questioning whether their output is still theirs. A manager who seems certain may be recalculating, in real time, what makes someone qualified. Beacraft’s view is that competing with AI on speed and scale is futile. As execution gets cheap, he said, human value moves to judgment and to designing the systems through which work gets done. That puts the new skill set in places that are hard to automate: knowing when to move fast, when to slow down on purpose, and when to make a call with incomplete information. Workforce data points the same way. Degreed, which sells learning software, found that seven of the 10 skills professionals most wanted to build for 2026 were human or business-centric, led by leadership and communication. The ranking is based on learning pathways created on the platform in 2025 and includes technical skills such as data analytics and Python. Degreed’s reading is that judgment and influence gain value as automation scales because a competitor does not acquire them simply by buying the same AI tools. The World Economic Forum’s Future of Jobs Report 2025 reached a similar conclusion from employers. It projected that creative thinking and resilience would keep rising in importance through 2030, alongside the technology skills in fastest demand, with leadership and social influence also among the top 10 skills on the rise. Judgment of that kind depends on curiosity, and curiosity has its own sameness problem. Brand strategist Jasmine Bina said in an essay that the same AI platforms and industry reports now feed everyone the same average information. Her remedy is to commit to an idea fully rather than learn about it by routine. That depth is hard to fake and harder to shortcut, which makes it a signal of whom to trust. The value of experience Mentoring is where this gets complicated. Mentors built their authority on the old formula, through years of mistakes made and lessons absorbed. Passing that knowledge on assumed the person receiving it would travel a similar road. Junior staff now work with tools that compress that road, producing quickly what took their mentors years to learn. The mentor’s experience keeps its value, but what it offers changes. Technique can now be acquired fast. Judgment still cannot, and it is what junior staff most need: a sense of what is worth keeping and what a result means once it exists. The questions every brand must answer Every professional now faces a blunt question: Would they hire themselves today? For those whose value is mostly operational, following a process, or producing the same output faster, the pressure arrives first, because that is the work AI typically absorbs. By Beacraft’s estimate, about 95% of work today sits at this operating level. The adjustment is to build judgment and the habit of questioning a result before delivering it. Leaders carry a different responsibility. It falls to them to treat those skills as real work, and to say so openly to their teams instead of leaving everyone to guess. Two questions make a useful start: Are staff evaluated on the time they spent or the judgment they applied? And what would a manager need to see in someone’s work to trust it without seeing the effort behind it. Mentors need mentoring too. Houses built their value on time and craft as proof of worth. The people who make and sell their products are now being asked the question the industry puts to its clients: What still deserves to be called precious, and why? Getting that answer right inside the company, as well as in the boutique, may decide which houses adapt and which only talk about adapting.

When AI takes the effort out of luxury

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When AI takes the effort out of luxury

How Bottega Veneta weaved China’s forever it bag