When an AI bubble pops, roles focused on narrow, vendor-specific tools (e.g., orchestrating API calls) are most vulnerable. Practitioners with deep, fundamental skills—model architecture, optimization, and connecting work to business value—will be more insulated from layoffs and highly sought after as the industry refocuses on real value.
While disastrous for many investors, historical bubbles like the dot-com boom and railway mania left behind massively overbuilt infrastructure (fiber optics, rail networks). This infrastructure became cheap and abundant post-crash, enabling subsequent waves of innovation that benefited society for decades.
History shows that markets can remain irrational longer than investors can remain solvent. For instance, the Nasdaq was 40% higher at its post-crash low in 2002 than when media first called the dot-com market "nutty" in 1995. Selling too early, even with sound analysis, often means missing substantial gains.
A technology race involves many complementary products (e.g., power grids, chips, models). High asset prices signal the technology is viable, encouraging parallel investments across these layers. This coordinated spending ensures the entire ecosystem develops, preventing any single part from becoming a stranded asset.