AI is moving from a technology conversation to a question about how jobs are designed.
Two recent numbers offer an interesting, if incomplete, picture.
In its regional economic outlook for Asia-Pacific published last week, IMF analysed 40 million online job postings across 19 Asian economies. It found that employer demand in occupations more exposed to AI was nearly 10% lower than in less-exposed occupations, compared with November 2022.
In India, active tech openings reached 123,000 in October 2026, the highest level in 18 months, according to Xpheno’s Active Tech Jobs Outlook – India, October 2026. Interesting insight: Mid-senior roles accounted for 68,000 openings (55% of total), up 19% year-on-year.
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I know these figures measure different things.
Yet, read together, they point to something worth pondering over:
The effects of AI on work are unlikely to show up as one simple story about jobs rising or falling.
AI is rather creating an uneven shift in the labour market.
Some tasks and roles are under pressure. And those are the ones typically requiring base levels of coding and testing, research and synthesis, problem solving and critical thinking - areas that AI is commoditising.
Demand is likely growing for people who can apply AI, work across business and technology, and make sound decisions in context - typical mid to senior role characteristics.
The multi-billion dollar questions that this trend leaves unanswered -
For junior roles: how do we develop mid-senior level skills without first exposing people to the fundamentals where they learn and practice the basics before they develop judgement, context, and taste to operate at a higher level? Essentially, how do we make them skip a few steps of the traditional on-the-job learning without depriving them of a strong foundation?
For mid-senior roles: how do we reliably segregate and measure the AI- accelerated parts of their role and channel those productivity gains into higher value activities best done by humans?
For the enterprise overall: how do we proactively orchestrate and capture the value from this shift? How should the gains be distributed among the clients/customers, shareholders, employees, and other stakeholders?
These are all hard questions. Organisations and leaders that work this out will shape more than their use and economics of AI. They will shape how people learn, contribute and grow at work.