| This post first appeared on Mike Amundsen’s Signals from Our Futures Past newsletter and is being republished here with the author’s permission. |
We’re long past the novelty phase of AI-assisted coding. The new challenge is measurement. How do we know whether all this augmentation—Copilot, Cursor, Goose, Gemini—is actually making us better at what matters?
The team at DX offers one of the first credible attempts to answer that question. Their AI Measurement Framework focuses on three dimensions: utilization, impact, and cost. They pair these with the DX Core 4: 1) change failure rate, 2) PR throughput, 3) perceived delivery speed, and 4) developer experience. Together they help companies observe how AI shifts the dynamics of production systems.
For example, at Booking.com that meant a 16 percent throughput lift in a few months. At Block, it informed the design of their internal AI agent, goose. The broader context for this work was captured in Gergely Orosz’s Pragmatic Engineer deep dive, which connects DX’s CTO Laura Tacho’s research to how 18 major tech firms are learning to track AI’s effect on engineering performance.
Agents as Extensions
The message running through DX’s framework is both simple and radical: treat coding agents as extensions of teams, not as independent contributors. That idea changes everything. It reframes productivity as a property of hybrid teams (humans plus their AI extensions) and measures performance the way we already measure leadership: by how effectively humans guide their “teams” of agents.
It also calls for a rebalancing of our metrics. AI speed gains can’t come at the cost of maintainability or clarity. The most mature orgs are tracking time saved and time lost because every gain in automation creates new complexity somewhere else in the system. When that feedback loop closes, AI stops being a novelty and becomes an affordance that highlights a living part of the organization’s ecology.
Shared Understanding
The deeper signal here isn’t about dashboards or KPIs. It’s about how we adapt meaningfully to a world where the boundaries between developer, agent, and system blur.
The DX framework reminds us that metrics are only useful when they reflect shared understanding. Not fear, not surveillance. Used poorly, measurement becomes control. Used wisely, it becomes learning. In that sense, this isn’t just a framework for tracking AI adoption. It’s a field guide for co-evolution. For designing the new interfaces between people and their digital counterparts.
Because in the end, the question isn’t how fast AI can code. It’s whether it’s helping us build human, technical, and organizational systems that can learn, adapt, and stay coherent as they grow.
Key Takeaway
Every developer will increasingly operate as a lead for a team of AI agents.
