For two decades, capital was allocated around three bottlenecks: hiring engineers was hard, writing code was slow, and shipping products took months. Competitive advantage was a function of who could attract talent and ship fastest. AI is dissolving these constraints in real time. When production constraints disappear, advantages shift. When you can build anything, the question becomes what to build. When there are too many ideas, features, directions, and noise, the bottleneck is no longer execution — it is judgment.
This is constraint theory applied to the AI transition: removing the binding constraint doesn’t eliminate scarcity, it moves scarcity to the next bottleneck in the chain. The same pattern appears in Technology transitions create more of the 'dying' thing, not less — every technology transition creates more of the “dying” thing, not less, because the freed capacity finds new applications. Applied here: dissolving execution constraints doesn’t make software engineering easier — it makes the judgment layer (what to build, when to stop, what to say no to) the entire game.
The practical test: if you removed all execution constraints from your current project and could build anything instantly, would you know exactly what to build next? If the answer is uncertain, execution speed is not your bottleneck — judgment is. This connects to The intelligence-to-judgement ratio determines which professions AI automates first: the judgment layer is the last to automate precisely because it’s the binding constraint that remains after everything else is compressed.