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The argument that AI will eliminate software jobs by making coding easy overlooks a key reality: most existing software is buggy and frustrating. The demand for better, more reliable products is practically infinite, suggesting AI will augment developer productivity to meet this demand rather than replace developers wholesale.

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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.

Don't view AI through a cost-cutting lens. If AI makes a single software developer 10x more productive—generating $5M in value instead of $500k—the rational business decision is to hire more developers to scale that value creation, not fewer.

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.

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.

Automating coding tasks won't eliminate engineers. Similar to the shift from assembly to higher-level languages, AI tools increase output potential, leading to an explosion in demand for software and the builders who can leverage these powerful new platforms.

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.

Despite creating code that could replace junior and senior developers, the author argues AI is a tool for enhancement. The key skills for future developers are not just coding, but the ability to effectively direct AI through prompting and validate its output via debugging. This mirrors how computers augmented, rather than eliminated, mathematicians.

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.