http：//x.com/i/article/2064451362184671232

# Where AI coding ends up

I asked Fable：

> Let's work on a thought experiment. As AI models continue to improve at coding， as they become faster， smarter and more capable， but also more expensive and gated， where does this take us？

Where AI coding ends up

As AI coding agents improve， the progression runs from humans writing code， to AI assisting， to humans managing agents， to managing fleets of them. Followed to its end， code itself stops being the artifact anyone cares about. Human review becomes sampling， then spot-checking， then trust in tests and outcomes. Eventually asking "what does the code say？" becomes like asking what a compiled binary says - technically answerable， practically irrelevant. Software becomes specified by intent and verified by behavior， and the layer in between goes opaque， the way assembly did.

The human role compresses upward but doesn't vanish. Someone must still want things， decide what's worth building， take responsibility for failures， and arbitrate between competing intents. The end-state job looks less like engineering management and more like a blend of product owner， lawyer， and auditor - defining correctness and bearing liability. The uncomfortable corollary： the world likely needs far fewer such people than it employs engineers today.

The "expensive and gated" assumption is the wildcard. If frontier capability stays costly and access-controlled， we don't get democratized software abundance but bifurcation： well-resourced organizations compound their advantage with the best agents while everyone else runs cheaper， older models. Code becomes a capital good， with the constraint shifting from talent to compute budget. Whether costs stay high is the open question - compute historically gets cheap， but the frontier keeps moving， so the gate may simply relocate upward.

Implications for entrepreneurship