Sub-shift
“ANTI-OPTIMIZATION”
A growing health service should not require a growing job from its customers.
A person can follow every prompt and still feel that the service is asking too much. Log the meal. Explain the night. Review the score. Decide whether the change matters. Each interaction may be small; together they can occupy a considerable part of a life.
“ANTI-OPTIMIZATION” is the possibility that some consumers will start choosing health support for how much of this work it removes. They may still want good care, useful information and appropriate monitoring. They do not necessarily want continuous self-improvement as the organizing principle of the day.
AI makes this a particularly interesting product choice. The same capability that can generate another tailored prompt could help consolidate information, prepare for a useful conversation or leave someone alone until an agreed reason to contact them arises.
Help that fits around being a person
The WHO’s 2020 framework connects healthy ageing with what people value being able to do, shaped by their environment as well as their body. [72] Applied to consumer services, that perspective makes room for a customer who lives well with illness, disability or numbers that will never be perfect.
You might help someone spend less time organizing appointments or make essential information easier to understand. You might separate optional wellness engagement from the requirements agreed with a care team. The person gains time and attention for whatever else matters to them, without being told to abandon necessary care.
This need is hardly confined to people who can afford a wellness retreat. A person managing unpredictable shifts, limited money or family responsibilities may have especially little capacity for another daily administrative task. A quieter interface is useful only if the underlying service remains accessible.
Allow the relationship to become less intensive
For businesses accustomed to measuring engagement, this creates a practical tension. A customer may use the product less because it is working. Your research needs to distinguish that outcome from someone giving up, becoming confused or losing access to help.
An agreed purpose provides a useful starting point. During a particular period, what does the person need from the service? Which interactions support it? What changes when that period ends? Build a way to revise the arrangement rather than allowing every feature to remain switched on indefinitely.
Some people will enjoy detailed tracking and find lasting value in it. This is a proposed countermarket, not a prediction that they will abandon their preferences. The expectation worth testing is whether customers can choose a less demanding relationship without receiving a worse standard of care.
If you can reduce the work a service creates, explain that benefit as plainly as any other feature. A parent who no longer spends an evening assembling information for tomorrow’s appointment has gained something tangible. You do not need to describe it as a better version of themselves.
THE SOURCE NOTESFollow the thinking 1 source · 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.
Normative framework; not evidence that a particular AI product improves health.
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.