"The software industry is collectively hallucinating a familiar fantasy. We visited versions of it in the 2000s with offshoring and again in the 2010s with microservices. Each time, the dream was identical: a silver bullet for developer productivity, a lever managers can pull to make delivery faster, cheaper, and better. Today, that lever is generative AI, and the pitch is seductively simple: If shipping is bottlenecked by writing code, and large language models can write code instantly, then using an LLM means velocity should explode."
"But software development has rarely been constrained by typing speed. The bottleneck is almost always everything except typing: deciding what to build, aligning on an approach, integrating it into an ecosystem that already exists, getting it through security and compliance, and then operating what you shipped. AI can help with syntax, scaffolding, and the drudgery of boilerplate. It can also make a different problem much worse: It makes complexity cheap. So how do we tackle that problem? The answer is platforms."
Generative AI promises faster coding but seldom addresses the real bottlenecks of software delivery: deciding what to build, aligning on approach, integrating with existing ecosystems, passing security and compliance, and operating software. AI helps with syntax, scaffolding, and boilerplate but lowers the cost of complexity, encouraging riskier designs. Evidence of productivity gains is mixed: controlled trials show both slower completion in complex repositories and faster isolated task completion with Copilot. Constraining AI through platforms, paved roads, or golden paths provides developer guardrails that keep code within ecosystem and security guidelines and thereby improves enterprise productivity.
Read at InfoWorld
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