Sub-shift
“EFFORT AS STATUS”
The finished result says less about the work. Some people will want their contribution to show.
A polished result used to offer a clue about the skill behind it. Not a perfect clue, but often enough to admire the person who had made it. As AI makes more accomplished outputs available to more people, that connection becomes less dependable.
For consumers who enjoy the recognition that comes with making something, the next question is what others can see of their contribution. The decisions behind a design. The practice behind a performance. The attention given to a gift. Over the next one to three years, we expect some of that process to become a more deliberate part of what people show and what others value.
“EFFORT AS STATUS” concerns the recognition people can gain from practice or effort they choose to retain when a similar-looking result could be generated. Someone else needs to understand what that work involved. The distinction is therefore different from enjoying a difficult activity entirely in private.
What deserves the credit?
Sociologist Richard Sennett's account of craft connects developing skill with self-respect and recognition. His work predates this wave of AI; the application here is ours. If an accomplished surface tells us less about a person's skill, communities may look more closely at how the work came about. [59]
That need not mean counting hours. Time can reflect care, but also an inefficient process. A meaningful revision may tell a better story than a long record of activity. Newman and Bloom's experiments on original artworks likewise point to creative history and connection to the maker, rather than appearance alone, as part of perceived value. [53]
The distinction matters for experiences as well as objects. Someone attending a workshop may want to leave with a finished piece and a way to explain the choices they made. A learning community may offer recognition for a technique genuinely mastered. A maker selling their work may want the buyer to understand a decision that would otherwise go unnoticed.
Give contribution a form people can recognize
For brands serving makers, learners and enthusiasts, this opens possibilities beyond a gallery of finished results. You could help participants show a meaningful stage in the process or describe why they changed direction. You could make room for peers to recognize progress that is difficult to capture in a flawless final image.
Be careful about what you reward. A leaderboard of hours can favour people with the most leisure. A machine-free badge can discount the work of someone who relies on assistive technology. Useful assistance may remove preparation while leaving substantial judgment, practice and care with the person.
The opportunity is to help customers receive credit for a contribution they actually made. Look for changes in how other people respond: what they admire, ask about, choose or recommend. If the value remains entirely in the pleasure of doing, that is a worthwhile experience too—but it is a different reason to buy.
THE SOURCE NOTESFollow the thinking 2 sources · what they show and where their limits lie
Sources and further reading
These sources inform the article. A product announcement describes an offer, not how widely people use it. Arguments and imagined futures are kept separate from observed results.
Sennett examines craft, skill and self-respect. The connection to AI-assisted consumer experiences is our interpretation, not his forecast.
Participants assessed hypothetical original and duplicate objects. Creative history and connection to the maker affected value, but this does not establish that today’s buyers pay more for human work because of AI.
About this research edition
This article draws on selected sources and our interpretation of their implications for consumers. The wider collection contains 50 articles and original illustrations. We have not read everything by everyone on the reading list, calculated the probability of each future or established worldwide adoption. The collection is not exhaustive.
The illustrations are AI-generated, with a visual approach informed by TrendWatching’s 2024 and 2025 reports. They depict ideas rather than observed events. If an article’s central idea changes, its image needs another look. Small edits need not mean a new illustration; automatic replacement is not active.
Each innovation has a source, a review date and an explanation of its relevance and limits. Some are early signs of the change; others offer a useful comparison. None proves the forecast. Monthly monitoring and automatic selection of new examples are not running.