Ninety-seven percent of developers now use AI coding tools. Impressive number. But it describes augmentation—AI helping humans write code faster—not the actual threshold ahead: when engineering work reorganizes around orchestrating autonomous agents rather than writing code directly.

The gap between these states is wider than it looks.

Consider what "orchestration as primary work" would actually require. Engineers would need observability systems showing not just what code agents produce, but how they arrived at solutions and what alternatives they considered. Without that visibility, orchestration means approving black-box outputs and hoping they're correct. That's not a sustainable engineering practice.

They'd need frameworks for codifying successful patterns so agents improve systematically rather than through scattered experimentation. Current AI coding assistants learn from broad training data, not from an organization's specific context and constraints. The infrastructure to capture and apply institutional knowledge at agent-execution time doesn't exist at enterprise scale.

Most critically: reliability guarantees that make orchestration the bottleneck rather than agent quality itself. When Anthropic introduced computer use capabilities in late 2024, they explicitly noted the feature was experimental. That's the current state. Impressive demonstrations, uncertain production readiness. You can't restructure teams around capabilities that work most of the time.

The threshold gets crossed when this infrastructure exists and proves itself reliable enough that companies find it more valuable to hire for orchestration skills than traditional coding ability. That's an organizational decision, not a technical one. But the organizational decision can't happen until the technical foundation is solid.

Right now, the industry is caught in definitional confusion. Some companies call their AI coding assistants "agents" and consider the transformation complete. Others reserve that term for autonomous systems that plan and execute multi-step workflows—capabilities that remain largely experimental. This ambiguity masks the real question: what needs to be built before orchestration becomes a viable primary mode of work?

Audit trails that make agent decisions traceable. Access controls that define what agents can touch. Error handling that fails gracefully rather than catastrophically. Monitoring systems that catch drift before it compounds. These aren't AI breakthroughs—they're reliability engineering. But without them, orchestration remains a demo, not a practice.

Companies investing in AI tools while maintaining traditional team structures aren't behind. They're accurately assessing where the industry actually is. The infrastructure enabling true orchestration needs to be built before the organizational transformation can happen. Pretending otherwise leads to premature restructuring around capabilities that don't reliably exist yet.

What triggers the crossing? Probably the first major company that builds this infrastructure, restructures around orchestration, and demonstrates measurable gains. Or the emergence of standard frameworks that make orchestration teachable and repeatable across organizations.

The trajectory is clear. The timeline depends on infrastructure work happening now, mostly invisible. The strategic insight isn't recognizing that engineering work will transform—everyone sees that coming. It's recognizing that the transformation waits on unglamorous foundation-building. The companies doing that work now, rather than assuming someone else already did it, will cross first.