Discussions of artificial intelligence (AI) often dwell on the disappearance of entry-level jobs. This focus misses a more consequential structural shift: AI does not merely remove the bottom rung; it compresses the career ladder into a single floor. By making baseline competence widely and quickly accessible, it narrows the gap between novices and veterans, eroding the traditional link between experience and excellence.
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When a three-month hire can match a seasoned professional, skill-based hierarchies lose their logic. This “great compression” calls for a fundamental redesign of how institutions operate.
For many years, organisational hierarchies rested on the premise that experience begets expertise and superior performance. AI weakens that link, much as Henry Ford’s assembly line reduced the premium on master artisans a century ago.
A recent study at a Fortune 500 company illustrates the pattern. AI improved overall performance, but unevenly: the bottom 20 per cent – as defined by the study’s skill index – underwent a productivity increase of more than 35 per cent, the middle group experienced modest gains and the top 20 per cent remained essentially unchanged. More strikingly, learning curves compressed – workers with three months of tenure performed about as well as those with a full year on the job.
In effect, the dispersion of performance shrank: AI pulled the lower tail towards the mean while leaving the top essentially unchanged, creating a narrow band of solid, reliable output.
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Another recent study on Japanese taxi drivers shows a similar effect. AI navigation systems that anticipate demand reduced the time spent searching for the next fare. Low-skilled drivers improved by about 7 per cent, while veterans saw little change or even slight declines. The productivity gap between novices and veterans narrowed.