AI is going to make engineers faster.
The fear is that it means we need fewer of them.
History says the opposite.
Geoffrey Hinton predicted in 2016 that radiologists would be obsolete within five years. By 2023, there were 17% more radiologists in the US than in 2014. AI made analysis cheaper, so hospitals ordered more scans, screened earlier, and expanded the total volume of imaging work. This is the Jevons Paradox: when a resource gets cheaper to use, total consumption goes up, not down.
The same logic is already showing up in AEC.
WSP posted a record backlog of $17.1 billion. They’re heavily investing in AI and saying the same thing the data says: AI augments engineers, it doesn’t replace them. If AI halves the time it takes to process a geotechnical investigation, or run a mechanical load calculation, or model a fire compliance scenario, the work doesn’t disappear. Clients who couldn’t afford a comprehensive analysis are starting to order it. Projects that weren’t viable become viable. The bottleneck shifts. It doesn’t disappear.
More efficiency doesn’t reduce demand for expertise. It expands the surface area where expertise gets applied.
But there’s a catch.
More demand for experienced engineers doesn’t solve the pipeline that produces them. Junior engineers traditionally learned by doing repetitive work. If you’ve always reviewed AI output rather than written the first draft yourself, you don’t develop the instinct for what good looks like. It’s the GPS problem: if you only ever follow directions, you never build a sense of direction. In structural or geotechnical or fire engineering, not having that instinct isn’t an inconvenience; it’s a liability.
The knowledge crisis in AEC is actually two problems: capturing what the current generation knows before they leave, and making sure the next generation develops the judgment to use that knowledge.
The firms need to ask how they will develop engineers fast enough.
