AI lowers the economic bar for building software, increasing the total market for development. Companies will need more high-leverage engineers to compete, creating a schism between those who adopt AI tools and those who fall behind and become obsolete.

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Contrary to fears of job replacement, AI coding systems expand what software can achieve, fueling a surge in project complexity and ambition. This trend increases the overall volume of code and the need for high-level human oversight, resulting in continued growth for developer roles rather than a reduction.

Increased developer productivity from AI won't lead to fewer jobs. Instead, it mirrors the Jevons paradox seen with electricity: as building software becomes cheaper and faster, the demand for it will dramatically increase. This boosts investment in new projects and ultimately grows the entire software engineering industry.

With AI agents automating raw code generation, an engineer's role is evolving beyond pure implementation. To stay valuable, engineers must now cultivate a deep understanding of business context and product taste to know *what* to build and *why*, not just *how*.

The narrative of AI replacing jobs is misleading. The real threat is competitive displacement. Professionals will be put out of business not by AI itself, but by more agile competitors who master AI tools to become faster, smarter, and more efficient.

Instead of fearing job loss, focus on skills in industries with elastic demand. When AI makes workers 10x more productive in these fields (e.g., software), the market will demand 100x more output, increasing the need for skilled humans who can leverage AI.

AI coding assistants won't make fundamental skills obsolete. Instead, they act as a force multiplier that separates engineers. Great engineers use AI to become exceptional by augmenting their deep understanding, while mediocre engineers who rely on it blindly will fall further behind.

The idea that AI will enable billion-dollar companies with tiny teams is a myth. Increased productivity from AI raises the competitive bar and opens up more opportunities, compelling ambitious companies to hire more people to build more product and win.

Experience alone no longer determines engineering productivity. An engineer's value is now a function of their experience plus their fluency with AI tools. Experienced coders who haven't adapted are now less valuable than AI-native recent graduates, who are in high demand.

Industries with fixed demand (accounting) will see job losses as AI handles the necessary workload. Sectors with expandable demand (software engineering) may absorb AI's productivity gains by creating vastly more output, thus preserving jobs for a longer period.

Mike Cannon-Brookes argues that AI makes developers more efficient, but since the demand for new technology is effectively unlimited, companies will simply build more. This will lead to a net increase in hiring for engineering talent, not a reduction.