There's a quiet trend emerging in 2026 that exposes a fundamental flaw in how we think about AI and headcount: companies that went all-in on AI to eliminate junior roles are now hiring junior engineers back — to babysit the AI.Let that sink in.The same executives who told their boards "we're replacing entry-level headcount with AI" are now running job reqs for engineers whose primary job is to clean up after LLMs, contain runaway token costs, and fix the technical debt that "vibe-coding" le

Everyone I talk to right now is focused on the same things when it comes to AI. Which model to use? How to structure prompts? Do I fine-tune or go with RAG? How many data scientists to hire? These are real questions worth asking. But in my experience, they're not the question that's actually blocking most organizations.The thing that's blocking most organizations isn't the AI. It's that the AI has nowhere to go.I've been in software long enough to have watched the same pattern play out with ever

In my last post, I introduced the idea of Logic Entombment. Business rules that are so tightly coupled with additional work overhead and infrastructure that no external system can reach them cleanly. Today, I want to get into why this is specifically bad for AI agents. Not just inconvenient. Actively hostile.I wrote about monoliths back in 2015, and the core observation I made then was that a monolith isn't a bad choice early on. It's actually the right choice for many organizations getting sta

My last post talked about the problems that Logic Entombment poses for AI; now, let's talk about how we can start to fix them. One thing I've noticed in every conversation about modernizing legacy systems is that the prescription arrives before the diagnosis. Someone reads about Domain-Driven Design or microservices, gets excited, and immediately wants to rearchitect everything. I've been guilty of this myself.The problem is that "rearchitect everything" is a multi-year program that requires sus