AI. What Do You Want From Us?

The architect Louis Kahn would say, if you are ever stuck for inspiration, ask your materials for advice.

You say to a brick, 'What do you want, brick?' And brick says to you, 'I like an arch.' And you say to brick, 'Look, I want one, too, but arches are expensive and I can use a concrete lintel.' And then you say: 'What do you think of that, brick?' Brick says: 'I like an arch.'

Kahn's provocation is a design principle - for me the design principle. Don't force a material to pretend to be something it isn't. Find what it honestly is, and build from there.

I've been thinking about AI and data as a material for most of my career. I've been asking, underneath all the projects and prototypes and client work, some version of Kahn's question. What is this stuff - and what does it want to do?

The Search Reflex

Every significant digital technology we've built since the early internet has been, fundamentally, a repository. A place 'things' are stored. A place you go to find those 'things'. You type a query. You get results. You retrieve information and take it away. Google made this the defining act of the internet age: someone knows something, it's indexed somewhere, you search for it, you find it. Knowledge flows from machine to human. The machine holds; we seek.

This is so deeply ingrained by now that most of us don't even notice we're doing it. We approach screens as answer-dispensing objects. We've raised a generation - my own children among them - who reach for a device the moment a question forms in their minds. The device will know. That's what devices are for.

So when the 'chat' interface appeared - the rectangle, the cursor, the place to type - we knew exactly what it was. It was a better search box. We'd been trained over decades to know what to do with this. You type a question; you get an answer. Only more fluent, more conversational, more impressive than anything before it. The same gesture, but upgraded.

This was an understandable response. It was also, I want to argue, a catastrophic misreading of the material.

The Inversion

Here is what I now believe to be true: AI is not a repository. It is not, at its core, a knowledge-holder or an answer-dispenser. The paradigm that has shaped every digital tool before it - machine stores, human retrieves - does not apply. In fact, it is almost completely inverted.

What we have built, trained on the vast accumulated expression of human thought and experience, is a curious engine and not a search engine. It wants to learn. More precisely: it is structured to find meaning by interrogating what it encounters. Where we have been trained to see a font of knowledge, what we actually have is an insatiable appetite for understanding - for the gaps, the contradictions, the assumptions we haven't examined, the questions we haven't thought to ask.

We (the human, carbon based machines) are the repositories now. We hold the knowledge, the experience, the lived context. It is now AI's turn to be the curious object.

For more than three decades we have been the seekers and machines have been the holders. That relationship has now reversed. If we keep behaving as if it hasn't - if we keep going to AI the way we went to Google, typing questions, waiting for answers - we are making a brick into cladding. We are denying the material its nature.

I spent 25 years working with data as a material before I understood this. I ran a data rights provocation in 2001 that most people thought was a stunt; built a studio (Normally) dedicated to understanding what data actually is and what it wants to do. I worked through hundreds of prototypes. It's only in the last few months that this particular inversion has become clear to me, and I want to try to explain why.

What a Prototype Showed Me

I was trying to solve a simple problem. Five of us - different time zones, different backgrounds, radically different schedules - wanted to explore something together. We had ideas, links, fragments, documents, and nowhere to put them. Nothing off-the-shelf quite fit us: we didn't have a shared domain, we didn't have a name, and honestly, we were all allergic to tools that end up ruling you rather than serving you.

So I built something. A shared space, a simple version of Notion, something that would let us drop things in and let the team find them. A repository. That was the brief I gave myself.

What I built turned into something different.

Because I thought: while I'm at it, what if AI didn't just store what came in — what if it read it? What if it paid attention on our behalf? Rather than tagging and filing and retrieving later, what if the AI did the work of attention in real time, the moment something arrived?

What happened next surprised me. When someone in the group dropped a research report, the system didn't just acknowledge receipt. It asked: based on this, what assumptions is your team making that you haven't yet tested? What questions does this raise that nobody is asking yet? When we shared notes from a meeting, it surfaced the fact that three of us were using the phrase "user engagement" to mean three different things. Not filed. Not retrieved. Interrogated.

This is not a system that holds knowledge. This is a system that is curious about the gaps - between what we say and what we do, between what we know and what we act on, between what we think we've agreed and what we actually mean.

I built a repository. I got an interrogator. And in that surprise, I finally understood the material.

The Honest Use

Back to Kahn. The principle isn't just about what materials prefer. It's about the cost of pretence. A brick used as decorative cladding isn't just aesthetically dishonest — it's structurally diminished. You're hiding the brick's actual capacity. You're using it for something it was never built to do, and in doing so, you lose what it was actually capable of.

We have been doing exactly this with AI. We put a search-box interface on a curiosity engine and then acted surprised when it kept trying to ask questions back.

The cost of this pretence is real. When we use AI as an answer machine, we get plausible-sounding answers, often correct, sometimes not, and we've taken from the encounter only what we already knew to ask for. We've capped the exchange at the level of our own existing questions. We've taken a material with compressive strength and nailed it to a wall.

The honest use is different. The honest use means coming to AI not with a question but with a situation: here is what we're working on, here is what we think we know, here is what we're uncertain about. And then — crucially — letting it do what it actually wants to do. Which is to find the thing you haven't asked about. The assumption underneath the question. The gap between your mental model and reality.

This requires a different posture from us. It requires us to act as the knowledge-holders in the exchange, which is what we actually are — repositories of context, experience, intent. We have to bring more, not less. The less you give a curiosity engine to work with, the less it can ask you that's useful.

What Becomes Possible

An arch is more capable than cladding - and that is why understanding material properties matters.

Honouring the material's nature doesn't just make it more honest. It unlocks what the material can structurally do. An arch can hold a cathedral. Cladding holds nothing.

What I think is on the other side of this reframe — and I'm only beginning to see it — is a completely different relationship with how organisations think and how individuals learn. Not AI as a smarter search engine. Not AI as a productivity multiplier, doing your tasks faster. But AI as the colleague who read everything before the meeting and has the one question that nobody thought to ask. The colleague who isn't invested in any particular answer, who doesn't carry the political weight of the room, who can say: you all think you've agreed on this, but you haven't.

That is what the material wants to be. Not a sage. Not a search box. An interrogator, in the best sense — the rigorous, curious kind that makes you think harder and see further.

We've spent three decades being curious objects in front of knowledge-holding machines. The relationship has inverted. The question now is whether we're willing to step into the role it requires of us: to become, finally, the knowers — and let the machine do what it's built to do.

Ask better questions than we can ask ourselves.

Anyway...

This article was developed in conversation with AI — fittingly, as an act of exactly the kind of questioning it describes.