I sat down to judge how well Claude Fable 5 writes code. The code turned out to be the smallest question it raised.
Last night I gave it an ambitious app idea and a long evening in Claude Code. About an hour describing what I wanted, two hours watching it work, and a final hour sorting out what it had actually built. I expected a rich, complex application. What I got was an elegant, stable MVP: it had scoped that idea down on its own, a reasonable first move and a judgment call I met after the fact.
Ethan Mollick published a piece this week describing the same shift. His old metaphor for working with AI was a wizard casting a spell. With this model he feels more like a patron: he sets the direction, pays for the work, and judges what comes back. I had read him before I got access and went in to check it, heavy on specifics where he stayed open-ended, and landed in the same place. My little experiment last night didn’t prove anything broader.
A week ago I wrote about the first wave of AI gains, where teams drain a backlog faster than it refills. Fable sharpens that picture rather than changing it. It did in a multi-hour session what would take a person days, and I have no doubt it is a large productivity gain. The shape of the work is the part that is new.
We spent twenty years learning to make work small, and those years wishing our tools could take on more. Agents like Devin enlarged the unit, but what they hand back is usually still a ticket or a feature. Fable handed back a working application, closer to a waterfall-sized deliverable than a user story.
The reasons we kept units small still hold: each increment was easy to validate, and short cycles left room for the pivots that show up over weeks as product owners and users react. A model writing the whole thing in a day does not remove those pivots. It turns them into refactors after the fact, and a deliverable you did not watch get built takes more verifying. Those costs are real, but they are an offset, not a reversal: the hours saved dwarf them.
None of this settles what people do now. Mollick looks at all this and sees a case for more engineers. Others look at the same thing and see a case for fewer. The honest position is that we haven’t settled what role, or roles, humans play in this work on an ongoing basis. These aren’t confident predictions. Working through them shows what the uncertainty looks like, which beats waiting for it to settle.
Fable surprised me less by how much it did than by how it did it. The shape of this work isn’t done surprising us.