TL;DR: The use of AI in development has shifted from a coding assistant to a team of agents doing the heavy lifting. This requires developers to skill up in management and forces a fundamental shift in how software roles collaborate.
The Managerial Migration
For those not watching closely (which is most people, and perhaps only a few reading this), the world of software delivery is in the midst of a tectonic shift. The use of AI is evolving from a simple coding assistant to a team of agents, or experts, performing the bulk of the work. This moves the developer role from a traditional software engineer to an agent manager.
The change in role definition and skills is another aspect of the paradigm shift the Age of AI is bringing. If this sounds new to you, it is because you may have missed the transition that the proliferation of the “WWW” subdomain brought to IT in the 90s. We are all going to come out better, but it is going to be a long haul as we re-learn lessons from last time and write new ones for this era.
The Expertise Gap in Management
One common misconception is that this automation means developers can be replaced by Product, Project, or Program managers. This is mostly the “Wall Street Rumor Mill,” which is only just now being revised from “replacing people with AI” to “shifting investment from employees to AI vendors”. At least that is more honest.
The “average” manager often lacks the technical depth to write a precise specification or review the complex output of an agentic workflow. Managing a digital workforce requires the same technical understanding and focus as writing source code. If you cannot perform a rigorous technical review of what AI agents produce, you should not put it into production (unless you suffer from a terminal case of technical hubris).
The Developer Drift
While many managers lack the depth to take over, developers are not guaranteed success in this new role without learning to view technical problems from different angles. Many developers tend to drift from the business context without a reminder, which is why lifecycle ceremonies exist to gather feedback from users and owners. For some, this is a forest-versus-trees effect, while for others, it is the temptation of a “cool” approach over a practical solution.
The speed of AI can take a minor gap in understanding and expand it into a costly chasm. When an agent can produce a week’s worth of logic in seconds, the cost of moving in the wrong direction scales exponentially. The team must find a way to collaborate where agents are a factor beyond just a tool choice.
Grit over Gift
This proficiency is not a magic gift: it is a byproduct of learning, practice, and pushing boundaries. There is a persistent myth that “prompt engineering” is an inherent talent or a shortcut for the lazy. It is actually the opposite. Real proficiency comes from hundreds of hours spent in invisible iteration. You have to break the agents to understand how to fix the workflow. These skills are then applied to context engineering, where the developer becomes the manager and the back-and-forth transitions to a human-in-the-loop system.
Deep experience can sometimes trigger intellectual rigor mortis, where you stop looking for a better way because you already know the “right” way. To succeed now, you need the grit to unlearn habits that are no longer efficient. High ROI in the age of AI belongs to the person who pushes boundaries through practice, not the one waiting for the “perfect” model to arrive.
The Practical Pivot
As we navigate this “.ai moment,” leadership, managers, and developers need a new way to interact. It is no longer about a ticket hand-off: it is about real-time orchestration.
- Developers: Start treating your AI tools as interns, not calculators. An intern needs guidance, a clear spec, and a rigorous peer review. If they produce garbage, it is a reflection of your management. Mentor your agents by providing better context and documentation.
- Managers: Help leadership understand that the “silver bullet” still requires expert aim. AI is a force multiplier, but it requires a human who knows where to point the barrel. Use these tools to bridge the communication gap, not to eliminate the experts.
- Everyone: Support each other in cross-training. Incorporate big-picture product thinking with low-level solutioning. Document the new workflows immediately, as your team now includes transient sessions that lack long-term memory.
Incorporating this new layer requires new connections, shifts in responsibility, and overlaps that act as double-checks from different perspectives rather than simple redundancies.
© Scott S. Nelson