AI Is Splitting Companies Into Two Groups
A quiet but consequential split is happening inside organizations everywhere. On one side: engineering teams deploying full agent fleets in production, writing code almost entirely with LLMs. On the other: teams still in evaluation mode, running sparse experiments with no agents in sight. The gap is widening fast. McKinsey's latest data puts AI adoption in at least one business function at 88% — but only about 23% of firms are actually scaling agentic systems. The two-speed enterprise isn't a warning anymore; it's already here.
For developers using tools like GitHub Copilot and OpenAI Codex, the implications are direct. Teams leaning in are pulling ahead on velocity, reducing review cycles, and shipping faster. What's perhaps most surprising is the labor market signal underneath all this: engineering job postings hit 67,665 open roles as of March 2026 — up 78% from recent lows. AI isn't shrinking the field; teams that know how to wield these tools are increasingly in demand, and organizations willing to invest are finding the payoff is real.
The challenge now isn't access to tools — it's organizational will. The hedge fund engineers and the retail bank engineers aren't operating in different technological universes; they're making different strategic bets. The companies choosing to move aren't waiting for a perfect playbook. They're building it as they go, and the distance between the two groups is only growing.