While some argue AI will augment and increase demand for engineers, a strong counter-opinion emerged predicting a sharp decline. The consensus among some hosts, citing sources who make hiring decisions, is that the current 400,000 software engineering jobs in the Bay Area could drop to 200,000-300,000 within three years.
Beyond displacing current workers, AI will lead to hiring "abatement," where companies proactively eliminate roles from their hiring plans altogether. This is a subtle but profound workforce shift, as entire job categories may vanish from the market before employees can be retrained.
October saw the highest number of U.S. job cuts in two decades, with consulting firm Challenger, Gray & Christmas explicitly citing AI adoption as a key driver. This data confirms that AI's impact on employment is an ongoing event, moving beyond speculation into measurable, significant job displacement.
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.
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.
An informal poll of the podcast's audience shows nearly a quarter of companies have already reduced hiring for entry-level roles. This is a tangible, early indicator that AI-driven efficiency gains are displacing junior talent, not just automating tasks.
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.
Companies are preemptively slowing hiring for roles they anticipate AI will automate within two years. This "quiet hiring freeze" avoids the cost of hiring, training, and then laying off staff. It is a subtle but powerful leading indicator of labor market disruption, happening long before official unemployment figures reflect the shift.
Instead of immediate, widespread job cuts, the initial effect of AI on employment is a reduction in hiring for roles like entry-level software engineers. Companies realize AI tools boost existing staff productivity, thus slowing the need for new hires, which acts as a leading indicator of labor shifts.
In a sobering essay, the CEO of leading AI lab Anthropic has offered a concrete, near-term economic prediction. He forecasts massive job disruption for knowledge workers, moving beyond abstract existential risks to a specific warning about the immediate future of work.
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.