What is the 'climbing wall' Raman describes?

It is his term for a career path where progress isn't always upward and workers chart their own routes based on their skills, interests and opportunities, rather than following a fixed ladder.

What did the Pew research find about AI exposure and employment?

In more AI-exposed occupations, men's employment grew 6.3% for each one-point increase in an occupation's exposure score, while women's barely changed and fell for women with a high school diploma or less.

Why does Raman think women could benefit from AI?

He argues women over-index on uniquely human skills like empathy and judgement that will become more valuable, and predicts more women will become CEOs and board representation could climb from roughly a third to half or more.

Large Language ModelsAI Business & IndustryFortune AI1h agoNov 3, 2026, 01:00 JST

Microsoft's Raman: AI Kills Career Ladder, Replaces It With 'Climbing Wall'

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3 Key Points

What happened
Microsoft chief economic opportunity officer Aneesh Raman said AI is replacing the predictable career ladder with a 'climbing wall' where progress isn't always upward, and he called for 'radical transparency' about how AI is changing jobs.
Why it matters
Raman acknowledged the shift could initially intimidate women and other historically marginalized groups who research suggests benefit from clear promotion criteria, and Pew's Nicole Smith found men's employment grew 6.3% per one-point rise in AI exposure while women's barely changed.

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Context & Analysis

Raman's 'climbing wall' framing has been part of his recent predictions about AI reshaping the workplace, but the equity question is where the argument gets tested. Research cited by Pew's Nicole Smith suggests that in occupations more exposed to AI, men's employment grew 6.3% for each one-point increase in exposure score, while women's barely changed and fell for those with a high school diploma or less.

Smith also pointed to a mindset gap, noting men are much more optimistic about AI than women and may be more inclined to use it to pursue new opportunities. Raman's counter is that women's strengths in empathy and judgement become more valuable as AI transforms work, and he predicts more women will become CEOs and that board representation could climb from roughly a third to half or more.

The two views are not mutually exclusive: Raman's proposed 'radical transparency' about how AI is changing jobs is meant to preserve clarity even as the path to advancement becomes less predictable. Whether employers can make advancement more flexible without making it less equitable is the open question.

FAQ

What is the 'climbing wall' Raman describes?: It is his term for a career path where progress isn't always upward and workers chart their own routes based on their skills, interests and opportunities, rather than following a fixed ladder.
What did the Pew research find about AI exposure and employment?: In more AI-exposed occupations, men's employment grew 6.3% for each one-point increase in an occupation's exposure score, while women's barely changed and fell for women with a high school diploma or less.
Why does Raman think women could benefit from AI?: He argues women over-index on uniquely human skills like empathy and judgement that will become more valuable, and predicts more women will become CEOs and board representation could climb from roughly a third to half or more.

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