Industrial Revolution for Software Development

We are going through a paradigm shift in how we build software. GenAI has changed software engineering faster than any other profession, and we are at the forefront of receiving the benefits from improvements in state-of-the-art models.

Until late 2025, CEOs were quoting headline figures about how much code is now being produced by AI (CNBC — Nadella). My own employer at the time expressed similar optimism and suggested mid-level software engineering work could be automated by the end of 2025 (Business Insider — Zuckerberg).

As a ground troop my reality was different for a while: I often found models were not very helpful for many coding tasks and their utility was largely limited to inline suggestions. That changed once agentic coding tools (Claude Code, Codex, etc.) and more powerful models (for example, Claude Opus 4.6, gpt-5.x) became available.

Suddenly, the amount of code being generated by AI is ~100%. This varies by domain and language but in 2026 the percentage of code written by AI is not even a question anymore. It all depends on the wielder and the use case/needs.

I believe we are now in an Industrial Revolution for coding agents. We have tools that can produce more code than a human can in the same amount of time.

This is puzzling and unsettling because the marginal cost of replicating software has always been effectively zero (LWN.net — The marginal cost of software). Once written, software can be copied indefinitely. That property has allowed the software industry to sustain gross margins higher than traditional manufacturers (B. Cantrill — The Economics of Software).

Those margins helped drive higher-than-market salaries for software engineers, because of their specialized skills and the leverage that comes from near-zero distribution costs.

With machines writing code, I’m not sure what the world will look like. I don’t want to be unduly pessimistic: the explosion of software could increase demand for the distinct skill of thinking algorithmically and understanding systems deeply.

There are extremes to the AI narrative. To be realistic, current models are impressive. Even if they stopped improving and only got cheaper, we could still use today’s capabilities to make meaningful improvements.

At the same time, as someone who understands code and systems, I see the need to steer models toward valuable outcomes.

The functional requirements require understanding customer needs, product strategy, business needs and then expressing them elegantly in a product which is commonly expressed as “taste” by the tech twitter.

The non-functional requirements are also challenging, scalability, reliability and security are hard and require continuous effort and deliberate planning and thinking.

The role of software engineers and other employees is now evolving to focus on the job while delegating tasks to the AI and using AI effectively to drive outcomes (Business Insider — Jensen Huang on task vs purpose).