"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."
"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."
The software industry repeatedly chases silver-bullet productivity levers, most recently generative AI, promising that code-writing speed will dramatically increase delivery velocity. Development bottlenecks are primarily decision-making, design alignment, ecosystem integration, security and compliance, and operations rather than typing speed. Generative AI accelerates syntax, scaffolding, and boilerplate, but it also makes complexity cheap, risking more tangled systems. Platform strategies—paved roads, golden paths, and guardrails—constrain choices, preserve security and standards, and raise enterprise-wide productivity. Empirical results are mixed: controlled trials show slower outcomes in complex repositories, while isolated tasks with Copilot show faster completion and improved developer experience.
Read at InfoWorld
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