As any child who’s heard Aesop knows, the point of a fable is its moral. We see the consequences of falsely crying wolf. We learn why slow and steady wins the hare race.
So what is the moral of Claude Fable 5, Anthropic’s newest model, which this week we called the best coding model in the world?
For engineers, the case is easy to make. For many knowledge workers, though, Fable might feel incremental. You may have one-shotted an impressive demo or two, but you’re probably not using it for your day-to-day work. Why would you? It costs twice as much and the results aren’t that much better.
But there is a certain class of developers who are feeling Fable’s full force. These are people like Cora general manager Kieran Klaassen, who are suddenly churning through his backlog of bug fixes and feature requests in hours instead of days. “This is my favorite model ever,” he told me.
What’s the difference between Kieran and everyone else? The difference between Kieran and most people using Fable isn’t simply that he’s a developer but that he’s at Level 7 or 8 on our scale of AI use: He delegates whole projects, lets agents work asynchronously, reviews the results, and feeds what he learns into the next run. In other words he writes—dare I say it, loops—not prompts.
For now, this might make Fable seem like a tool for developers. But in AI, developer workflows have a habit of spreading to the rest of knowledge work. Claude Code started as a developer tool, and now the same methodology is being used for everything from slide decks to spreadsheets inside of Cowork and Codex.
If you’re not feeling Fable’s force, that’s probably because you haven’t yet started to treat your work like gardening.
Gardening and loops
Two years ago I wrote:
“This era of creativity is going to look more like gardening. A gardener doesn’t grow plants directly. Instead, she sets up the conditions for the garden to grow.”
When you garden, you’re creating a loop. You water and weed, prune and stake, establishing and maintaining the optimal environment; the plant itself does the growing..
Likewise, developers are now creating the optimal conditions for a product to grow and improve. They’re collecting inputs like user feedback, turning it into actionable plans, which then become the basis for AI to do its work. They also create a system for reviewing that work, merging the changes, and integrating the lessons. Each stage’s output becomes the next one’s input; the developer’s role becomes determining the best order and process to help the product flourish. The process becomes the product.