Software Engineering

Everyone on the team writes code several times faster, but delivery cycles haven’t gotten any shorter. The surge of newly generated code simply overwhelmed the downstream review queue and integration pipeline. The bottleneck didn’t vanish; it moved from the text editor to code review and release verification. Coding Got Faster, Delivery Didn’t # AI has not made software delivery faster overall, and empirical industry data still fails to back that claim.

The conclusion first, so you don’t waste your read We used to split teams by craft: frontend, backend, product design. Now we split teams by stage: Prototyper — grabs the first idea and fails fast Builder — turns the prototype into a product you can take to market Maintainer — holds the fort once the software reaches scale Scaler — takes a proven product and scales it 10x, 100x Finisher — polishes the product and the code, sands off the burrs As for why we’re only splitting this way now — the answer involves AI. Read on.

Twelve articles, from the Think-Act-Observe loop all the way down into the 500,000-line engineering abyss. Now, standing at the end of the series and looking back over the whole evolutionary path, a bigger question surfaces: What will AI coding tools ultimately evolve into? A better Copilot? A more powerful IDE plugin? Or something new that we haven’t named yet? Three Evolutionary Paths # Looking back at the development of AI coding tools, you can see three clear stages of evolution:

The claude-code-from-scratch project implements a “mini Claude Code” in roughly 3,000 lines of TypeScript. It has the Think-Act-Observe loop, a tool system, a system prompt, permission control, memory, Skills, multi-agent, MCP — every core concept we tore down over the previous ten posts. And the official Claude Code source? Roughly 500K lines of TypeScript/TSX. That’s a 100x+ gap between 3,000 lines and 500K. What’s in it? The gap isn’t “number of features”. from-scratch already covers every core concept. The gap is the road each concept travels from “it runs” to “it’s usable” — that road is called productionization.

I have a strong intuition: some companies in the tech industry right now are going through a full-blown AI psychosis, and inside those companies, rational conversation is simply impossible. I’m not talking about strangers — I’m talking about personal friends I deeply respect. That leaves me worried about how this ends. This fear has a specific source. I lived through the great MTBF (mean time between failures) vs MTTR (mean time to recovery) debate when infrastructure moved from traditional ops to cloud and cloud automation. Now those exact same arguments are resurfacing in a new costume — only the scope has widened from infrastructure to the entire software industry, and possibly to the whole world.