I recently listened to Diana Hu’s interview on building AI-native companies. A lot of it resonated.
Her argument: AI shouldn’t be a tool the company uses. It should be the operating system the company runs on. Not faster execution. Not better features. A different substrate underneath.
I’ve been building something along these lines for the past year. Hearing the same structural argument from a completely different vantage point is what made me want to write this.
What I kept thinking about was everyone else.
Most of the people I know aren’t founding new companies right now. They’re working as individuals — practitioners, creators, operators in their own practices. Or they’re inside organizations that already exist, with histories, IP, and accumulated ways of working. So what does AI-native mean for them?
That’s the question this piece is trying to answer. Or really, the first half of it — what AI-native means at individual scale. The other half, what it means inside an existing organization, is what I’ll write about next.
For an individual working with AI, there’s a progression that tends to unfold as a sequence.
You come to know it — what it is, what it can do, where its grain runs. A working model of what the technology actually is.
You start to use it — drafting, editing, prototyping. The tool enters your daily practice. Your output changes shape as a result.
And eventually, if you stay with it long enough, you start to build with it. Designing structure on top. Your own workflows, your own systems, your own architecture for how AI lives inside your thinking. Not just using off-the-shelf tools — composing the substrate underneath.
Know. Use. Build. Three modes, mostly progressing in that order.
Most of the visible AI conversation lives between knowing and using. Productivity threads, hot takes, prompt tips, tool comparisons. That’s the loud part.
The quieter part — past the productivity threads and the philosophy threads — is the layer where individual creators, indie operators, and small builders have moved into building. They post less. Talk less. Demo less. Not because the work is less; it’s more. Building just doesn’t optimize for sharing.
That’s the layer I’ve been working at for the past year.
So at individual scale, this is already happening. There’s a layer of people who’ve moved into building, and they didn’t need to wait for permission or for a new company to do it. They just stayed with the work long enough.
For an individual practitioner today, the question isn’t really whether AI-native applies. It’s where you are in the progression, and whether you’re willing to stay long enough to reach the third mode. Building takes longer than people expect, because it isn’t technical work. It’s the slower work of figuring out how AI changes what you think creative practice is for — before you can design any structure that actually holds.
The other side of this — what happens inside organizations that already exist — is structurally different and harder. An established team can’t pause production to redesign. It can’t pull architecture out from under live work. So most don’t. AI ends up bolted onto what was already there, which isn’t the same thing. What it would actually take to close that gap, and what the structural answer looks like in practice, is what the next essay takes up.
About the author
Qinqin (Stella) Yang is a creative director and strategist working at the intersection of innovation, technology, and futures thinking. Over the past decade she has built brand experiences, product design and speculative projects for Tencent, IKEA SPACE10, Burberry, and the Palace Museum etc. More recently she has been building AI-native systems through her practice, Actual Fiction, to help creative teams move from ambiguity to clarity.
This newsletter is where she thinks out loud about creativity, innovation and what it means to build towards a future that doesn’t exist yet.
For more details: qinqinyang.com