Applying Jevons Paradox, as AI makes code (the "digital brick") cheaper and easier to produce, it doesn't reduce the need for builders. Instead, it unlocks previously uneconomical or overly complex projects, thereby increasing the overall demand for software engineers to build more ambitious things.

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.

The Jevons Paradox, an 1865 economic principle, states that efficiency gains increase total consumption, not decrease it. Applied to AI, as coding becomes cheaper and more efficient, the total demand for software will explode into new applications, ultimately creating a net positive number of jobs.

The idea that AI code generation reduces demand for engineers is backward. It leads to more complex systems, which in turn creates more challenges around system upgrades, downtime, and security incidents, ultimately requiring more engineering oversight and expertise.

While AI automates many tasks, it also makes software development cheaper and faster. This lowers the barrier to entry for new projects, increasing the overall demand for programmers to build and manage these newly feasible applications, especially for smaller companies.

AI tools make software development drastically cheaper. Rather than replacing engineers, this efficiency will likely trigger the Jevons paradox: the unlocked demand for new, more powerful software will skyrocket, increasing the overall need for people who can direct these new capabilities.

Jevons Paradox states that as a resource becomes more efficient, consumption increases. Applied to AI, making software development faster won't eliminate developer jobs. Instead, it will create a surge in demand by enabling new applications like internal tools and personal apps.

Counterintuitively, AI tools that make software engineering more efficient are increasing the demand for engineers. By lowering the cost of development (Jevons Paradox), AI is unlocking latent demand from non-tech industries that previously couldn't afford a large engineering workforce.

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.