Software Paradigm Revolution: From "Human-Written Code" to "System-Generated Code"

Code generation capability, developer efficiency, and business efficiency are three different problems. A 2023 controlled experiment with GitHub Copilot had participants implement an HTTP server in JavaScript; the AI-assisted group completed the task 55.8% faster. Three field randomized experiments published in 2025 combined data from 4,867 developers, estimating a 26.08% increase in tasks completed after using AI tools. But METR's 2025 experiment with 16 senior developers found that allowing AI tools increased task completion time by 19%, while participants still believed they had sped up. These studies collectively suggest: AI benefits depend on task structure.

The first change in requirements is that requirements shift from static handoff materials to business constraints that can be repeatedly executed and verified. LLMs can read interview transcripts, support tickets, existing pages, and code, summarize conflicts, ask clarifying questions, and quickly generate actionable prototypes. But models cannot know a company's actual business rules out of thin air. The complete documents it generates may simply fill in missing information as plausible assumptions. Therefore, each key requirement should distinguish its source: which come from customer's own words, which from existing policies, which are system inferences, and which remain to be confirmed.

Assume coding takes 30% of total delivery time, with the rest used for requirements, coordination, verification, and release; even if coding speed improves fivefold, overall speed is only about 1.32x the original. To achieve greater improvement, other components must change simultaneously, along with the waiting and handoff patterns between them. This is precisely the difference between a "paradigm revolution" and "better code completion." In traditional development, customers express needs, product managers organize documents, designers create interfaces, and engineers translate them into data models and program logic. Every handoff involves information loss. LLMs can read interview transcripts, support tickets, existing pages, and code, summarize conflicts, and ask clarifying questions.

For OPCs, this change directly shortens the distance from customer conversation to product experiment. Founders can complete interviews, specification revisions, and prototype validation on the same feedback chain, reducing cross-role explanation. But there are also blind spots unique to solo organizations: no colleagues to challenge incorrect assumptions. Therefore, real customer operation records, clear acceptance examples, and payment behavior are more valuable than model agreement with the solution. Organizational capability thus has the opportunity to exceed the total amount an individual can personally execute. But coordination costs do not automatically disappear. Context inconsistency, duplicate tasks, handoff omissions, and multiple roles jointly believing errors all require governance.