When implementation becomes cheap, what's left? I stopped writing code, ventured into machine learning and 3D game development, and discovered that AI made me remarkably efficient at making mistakes. Fortunately, it also made me much faster at learning from them.
As a kid, your skills could only get you so far in a game. Sometimes the game itself was the problem. AI fixed a decades-old goalkeeper bug from a short video, and the real shift is what that means for anyone with an idea.
A startup ran out of runway and handled the layoff badly. The engineer found out from his broker. The argument here is that the company's real output was never the platform, it was the engineers it built, and no month of handover meetings can transfer that.
AI has a confidence problem. Human intelligence was shaped by an uncertain world, and uncertainty is exactly the ingredient AI is missing.
Hitting a wall with AI usually means hitting the wall of the chat box, not the wall of AI itself. Give a model memory, tool access and a browser, and the interfaces we built for humans become the integration. The bottleneck stops being the model and starts being your imagination.
An AI-assisted note system captures faster than you can understand, and the gap compounds into internalisation debt: a dense graph of links attached to a thin mental model. The test of a knowledge base is not how much it holds, but how much you could still explain with the file closed.
Writing code by hand used to be the job. Soon it will be a hobby, like woodworking or growing your own veg. What a company pays for now is judgment: knowing what to build, catching the answer that looks right and isn't, deciding when it's good enough to ship.
Most architecture methods try to predict which failures will hit. Residuality Theory does the opposite: it throws random stressors at a design and keeps whatever survives. The randomness is the point, because resilience that depends on predicting the future is not resilience at all.
Hand two developers the same tool and you get opposite verdicts. 'Useless' is rarely a fact about the thing. It's a fact about the person holding it. The rare skill was never operating the tool. It's imagining the door a tool opens before the use is obvious.
AI is now a reader of your documentation. When context lives outside the repository, gaps appear and assumptions creep in. Moving documentation closer to the code makes the system easier to reason about, for people and for machines.
Senior engineers carry the front of every decision, every code review, every on-call shift. The peloton runs best when roles rotate. When the star eventually leaves, someone else needs to know what wind feels like.
Using Claude Code as the orchestrator and Codex as the engine gives you the best of both tools: Claude's UI, Codex's depth, and minimal Claude token spend. The economics only work when the task is big enough to justify two layers.
The bottleneck in software engineering has shifted from typing to understanding. As AI handles implementation, engineers who thrive will be those who recognise patterns, curate guardrails, and connect technical decisions to business outcomes.
A head-to-head of pencil.dev vs Claude Design on the same markdown input. Claude Design produced the more polished deck and handled speaker notes; pencil.dev still wins for iterative UI work where direct manipulation matters.
AI is making the knowledge side of consulting cheaper by the day. Teams still avoid the legacy system. The gap is not expertise. It is the structural courage to act on what everyone already knows.
