09-02-2026, 10:41 PM
When AI writes almost all code, what happens to software engineering?
Author: Gergely Orosz — The Pragmatic Engineer
Published: January 6, 2026
Gergely Orosz argues that software engineering may have reached an important turning point: the newest generation of coding models can now generate a very large proportion of production code with surprisingly little human intervention. He points particularly to Claude Opus 4.5, GPT-5.2 and Gemini 3, released in late 2025, and cites experienced engineers who report that AI agents can complete substantial tasks, create pull requests, write tests, refactor code and sometimes produce entire implementations from relatively high-level instructions. Orosz says that in his own work, AI can already generate roughly 90% of the code he would otherwise write himself, especially for greenfield applications using mainstream technologies.
This does not necessarily mean software engineers become unnecessary. Instead, the value of different engineering skills changes. Routine implementation, prototyping, knowing the precise syntax of several programming languages, and narrow frontend/backend specialization may become less valuable because an AI agent can perform much of this work. What becomes more important is understanding architecture, defining the correct problem, evaluating trade-offs, testing and validating systems, debugging difficult failures, understanding product requirements and deciding what should actually be built. In other words, the valuable engineer increasingly resembles a technical lead or product-minded engineer supervising AI, rather than someone whose primary skill is manually producing code.
There are significant downsides. If generating code becomes extremely cheap, organizations may generate far more software and therefore far more potential bugs, security problems and technical debt. Weak testing, poor observability and bad architectural decisions could become painful much faster because AI amplifies the speed at which code is created. At the same time, the boundaries between product management and software engineering may blur: product managers can increasingly prototype software themselves, while engineers equipped with AI can take more responsibility for deciding product direction. Orosz therefore sees AI less as the end of software engineering than as a shift from writing code to making good engineering decisions at much greater scale.
Key takeaways
Central idea:When producing code becomes cheap, the scarce resource is no longer code itself—it is knowing what to build, how to design it, and whether the AI-generated system can actually be trusted.
ARTICLE
Author: Gergely Orosz — The Pragmatic Engineer
Published: January 6, 2026
Gergely Orosz argues that software engineering may have reached an important turning point: the newest generation of coding models can now generate a very large proportion of production code with surprisingly little human intervention. He points particularly to Claude Opus 4.5, GPT-5.2 and Gemini 3, released in late 2025, and cites experienced engineers who report that AI agents can complete substantial tasks, create pull requests, write tests, refactor code and sometimes produce entire implementations from relatively high-level instructions. Orosz says that in his own work, AI can already generate roughly 90% of the code he would otherwise write himself, especially for greenfield applications using mainstream technologies.
This does not necessarily mean software engineers become unnecessary. Instead, the value of different engineering skills changes. Routine implementation, prototyping, knowing the precise syntax of several programming languages, and narrow frontend/backend specialization may become less valuable because an AI agent can perform much of this work. What becomes more important is understanding architecture, defining the correct problem, evaluating trade-offs, testing and validating systems, debugging difficult failures, understanding product requirements and deciding what should actually be built. In other words, the valuable engineer increasingly resembles a technical lead or product-minded engineer supervising AI, rather than someone whose primary skill is manually producing code.
There are significant downsides. If generating code becomes extremely cheap, organizations may generate far more software and therefore far more potential bugs, security problems and technical debt. Weak testing, poor observability and bad architectural decisions could become painful much faster because AI amplifies the speed at which code is created. At the same time, the boundaries between product management and software engineering may blur: product managers can increasingly prototype software themselves, while engineers equipped with AI can take more responsibility for deciding product direction. Orosz therefore sees AI less as the end of software engineering than as a shift from writing code to making good engineering decisions at much greater scale.
Key takeaways
- AI-generated code may become the norm: for some developers and greenfield projects, 90%+ AI-generated code is already plausible.
- Coding syntax becomes less important; engineering judgment becomes more important.
- Architecture, testing, product thinking and technical leadership gain value, while narrow language or framework specialization may lose some value.
- AI will not eliminate engineering problems—it may multiply them, making strong engineering practices even more necessary.
Central idea:When producing code becomes cheap, the scarce resource is no longer code itself—it is knowing what to build, how to design it, and whether the AI-generated system can actually be trusted.
ARTICLE
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