Every conversation about AI seems to hinge on velocity. How fast models are improving. How quickly new capabilities arrive. How long until we hit “AGI.”
But I think the more interesting question—and the one with real career implications—is this:
What if progress slowed far more than we expect?
What if LLMs plateaued near where they are today?
I don’t think the outcome would be stagnant at all. I think the consequences would unfold for decades.
We’ve seen this pattern before
Software development didn’t stop evolving once we got C. Or Java. Or the cloud.
Each shift changed who could build, what they could build, and how much leverage a single engineer could generate—not because the tools improved forever, but because the baseline moved permanently.
LLMs have already moved the baseline.
The ripple effects still take decades
Even if models never become meaningfully “smarter” than GPT-5, the ripple effects alone would take us twenty years to absorb:
- The primary interface becomes natural language, not syntax
- Debugging shifts from typing fixes to evaluating reasoning
- Apprenticeship looks less like memorizing APIs and more like orchestrating systems
- Teams shrink or specialize; one engineer may guide five software agents
- Documentation and code literacy matter more than code volume
- The ownership boundary between human and machine changes shape
Plateau is not stagnation. Plateau is the new floor.
From that floor, an entire generation of workflows, roles, and products will emerge.
Progress isn’t required for disruption. Absorption is.
We are still at day zero of that absorption curve.