In The Timeless Way of Building, Christopher Alexander describes having strawberries for tea with a friend in Denmark. She slices them almost paper-thin. When he asks why she takes the extra time, she explains that this exposes more surface to taste. He writes: “I learned more about building in that one moment, than in ten years of building.”

For me, this is immediate, direct contact with what she’s doing. The way the fruit will be eaten guides the cut. It’s a small decision, but it helps me think about what we mean by taste when we’re making things with AI.

Alexander was trying to describe a quality that makes a place feel alive, with its parts belonging together and to the life lived there. He called it the “quality without a name” because words such as beautiful or comfortable each miss something. He also finds it in waves and a well-made fire. A fire can be too hot for comfort without losing that aliveness.

Think of a product that’s a joy to use, perhaps even a janky old motorcycle with worn paint and a few quirks. Something about the way it responds to you just… feels right. I think that can be a taste of the quality Alexander describes.

A product can look attractive and still be awkward to live with. Judging it involves paying attention to what happens when someone actually uses it, including things we might struggle to explain in a design brief.

The philosopher Eugene Gendlin describes how we can work with that difficulty. When searching for words, we can sense that a phrase doesn’t quite fit before we know what to say instead. He asks us to attend to a felt sense, the body’s sense of a whole situation, including more than we’ve already put into words. Trying another phrase can help us discover what we meant. In design, that might mean realising that the brief itself needs to change.

A language model can explain Alexander’s story and suggest cutting fruit that way. This doesn’t establish that it has tasted a strawberry, or that it can check its words against the bodily sense Gendlin describes.

A model could nevertheless learn useful distinctions from people who have those experiences. When we choose one design over another, we supply information even if we can’t fully explain the choice. I think a model could learn from those choices to propose things we value and help decide what to develop.

Perhaps some of what we call AI slop is work in which we feel this quality is missing. When we’re handed something that looks finished but leaves us struggling to make it useful, it can feel as though our time and circumstances weren’t worth much care.

Suppose a support team uses AI to draft courteous, accurate replies directing customers to another department. The departments close their tickets while the customer keeps having to explain the same unresolved problem to someone new.

If we’re reviewing only the replies, we might spend our time improving the tone. Following one customer’s attempt to get help could reveal that nobody has responsibility for resolving the whole problem. We might need to give one person responsibility for the case and authority to coordinate across departments.

The customer’s anger could help us notice this. Trying to make every exchange pleasant might mean soothing a justified complaint while leaving its cause untouched.

AI could help with that investigation as well as with writing replies. We could ask it to look across the case histories for repeated explanations or unresolved hand-offs, then check its findings with the people involved.

We could try a change on a recurring enquiry, then check whether people needed fewer repeat contacts and whether their problems were resolved. Staff and customers would need a way to tell us where the new arrangement still didn’t fit their needs.

Perhaps this fitness grows through contact: with the material, with the circumstances, and with the people who will live with what we make. We discover something we couldn’t fully specify beforehand, and allow it to change the work. Ideally, the people building the software encounter those situations directly: watch someone use it, hear what matters to them, and have the authority and supporting system to quickly respond to what they discover.