The AI Disruption Part 1: How AI is Reshaping Work Before Our Institutions are Ready

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Originally published on Medium on 27 May 2026

AI is not just another productivity tool. It is changing who gets paid for productivity.

In this first part of the series, I look past the hype and examine what the data actually says about how AI is reshaping work, who is feeling it first, and why trust is collapsing even as adoption soars.

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The AI dividend isn’t evenly shared

Most public debates about AI and jobs still start and end with “Will it replace us?”. That’s the wrong question.

The World Employment and Social Outlook by the International Labour Organization (ILO) shows that from 2004 to 2024, global workers’ output per hour rose by 58%, yet their share of income fell by 1.6% over the same period. That translates into a USD 2.4 trillion annual shortfall in labour income and a steadily shrinking share of the pie going to workers. The ILO explicitly warns that breakthroughs in generative AI could push workers’ share down further without deliberate policy intervention.

For businesses and policymakers, that divergence is the macro backdrop AI is arriving into, not a distant future scenario.

A separate IMF study goes further by modelling what happens when AI spreads through an advanced economy. It finds something that feels counterintuitive at first: wage inequality can actually fall slightly, while wealth inequality rises sharply. How so?

• AI heavily exposes high-income, white-collar roles; but these roles are also highly complementary to AI, so many of their incumbents see productivity gains rather than straight displacement.
• Those same workers hold a disproportionate share of AI-appreciating assets (equities, corporate bonds, pension funds), so they benefit again as capital owners.

In other words, we’re not just talking about “jobs lost vs jobs gained”. We’re talking about who captures the AI dividend. The early evidence says capital and already-advantaged workers are winning that race.

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The canaries in the coal mine: entry-level white-collar

If there is one labour market signal everyone should be paying attention to, it’s early-career employment.

Stanford’s Digital Economy Lab recently analysed high-frequency payroll data from the largest US payroll processor to see how AI is actually showing up in jobs. They found that:

• Early-career workers aged 22–25 in the most AI-exposed occupations have seen a 16% relative decline in employment since the widespread adoption of generative AI.
• More experienced workers in the same occupations saw employment remain stable or even grow.
• The declines are concentrated exactly in roles where AI is more likely to automate tasks rather than augment them.

A separate study from Anthropic, using a new AI-exposure measure based on what their models can actually do today, finds a similar pattern at the hiring margin: job-finding rates for 22–25-year-olds in AI-exposed roles have dropped by about 14% since the release of ChatGPT. Interestingly, aggregate unemployment hasn’t spiked; many of these young workers simply never enter the statistics as “unemployed” in the first place.

And these are not routine factory jobs. The most AI-exposed occupations skew more educated, better paid, and more likely to be female or Asian.

In other words, AI is quietly attacking the on-ramp into white-collar careers, long before we see anything in headline unemployment numbers.

If you’re a manager or policymaker, this is your future leadership bench — and they are the cohort seeing the quietest erosion of opportunity.

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The APAC paradox: adoption leaders, fear leaders

APAC is where this story becomes very real, very quickly.

BCG’s 2025 “AI at Work” survey of more than 4,500 employees shows that Asia-Pacific leads the world in workplace AI usage:

• 78% of APAC employees use AI at work at least weekly, vs 72% globally.
• 70% of APAC frontline workers use generative AI regularly, vs just 51% globally.

But high adoption hasn’t translated into confidence. The same survey finds that workers in markets like Singapore, South Korea and Thailand report some of the highest fears about AI-related job loss in the region.

And the governance lag is clear. Only about 13% of respondents say AI agents are properly integrated into their workflows; most usage is informal, unstructured, and self-driven.

Put simply: in APAC, AI isn’t a future scenario. It’s already on the frontline. But organisational design, skills, and governance have not caught up, and workers can feel it.

For leaders outside APAC, this isn’t a niche regional story; it’s an early glimpse of what ‘normal’ might look like once AI saturates day-to-day work.

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Singapore as a stress test

Singapore is arguably the most interesting lab in the world for AI and labour.

The IMF’s 2024 deep dive into Singapore’s labour market estimates that around 77% of workers are in occupations with high AI exposure, compared with roughly 60% in other advanced economies. That’s no surprise; Singapore has intentionally built a high-skill, services-heavy economy.

The nuance is in the split:

• 38.9% of workers are in high-exposure, high-complementarity roles (managers, engineers, professionals) where AI is more likely to augment than replace.
• 38.6% are in high-exposure, low-complementarity roles (clerical, admin, some ICT and sales roles) where AI can more easily automate core tasks.

The structural risk is not evenly distributed:

On the demand side, Singapore has already become a global outlier in AI job postings.

• The Stanford 2025 AI Index finds that 3.27% of all job ads in Singapore mention AI, nearly double the US figure.

• Indeed data from late 2025 shows that roughly one in six Singapore job postings referenced AI tools.

Yet there is an underlying paradox. In April 2026, Singapore’s Ministry of Manpower (MOM) released an AI adoption study that revealed the following:

• AI-skilled engineers and professionals see a 13–25% increase in their salary.
• AI mentions in Singapore job postings have surged, even with the cyclical decline in the number of postings by around 16% year-on-year.

So fewer roles overall; a larger share require AI skills just to get in the door; and those through that door enjoy a measurable wage bump.

There’s another important layer. Surveying 2,560 firms employing about half a million workers, MOM found that:

• 71.5% of firms had not yet adopted AI.
• Among adopters, only 3.8% had integrated AI into core processes; most were still in planning or pilot stages.
• 70.7% of AI-using firms reported higher worker productivity.
• Only 6.2% reported headcount reductions attributable to AI; 18.9% redesigned job functions, and 13.9% created new AI-related roles.

The ministry’s conclusion is cautious but clear: at this stage, AI is “complementing rather than displacing labour”, but workers who do not upskill, especially in SMEs, risk being left behind.

In other words, Singapore has bought time through strong institutions and proactive policy, but not immunity from structural pressure.

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The trust and legitimacy gap

All of this is happening against a backdrop of sharp, measurable collapse in trust.

In the developer community, AI adoption is up, but trust is down. Stack Overflow’s 2025 survey shows trust in AI output falling from 40% to 30% year-on-year, even as usage rises from 76% to 84%.

There is a similar pattern among the broader public. A study by KPMG across 47 countries, including multiple APAC markets, found:

• On average, 58% of respondents consider AI systems reliable, but only 46% say they are willing to trust them.
• Emerging economies (many in APAC) show higher trust than advanced economies: roughly 3 in 5 in emerging markets vs 2 in 5 in advanced economies.

In Singapore specifically:

• 50% of Singaporeans are willing to trust AI, with the rest unwilling or unsure.
• 75% accept or approve of AI, but 81% are concerned about negative outcomes.
• 75% are unsure online content can be trusted because it may be AI-generated; 89% want laws to combat AI-generated misinformation; and 67% believe AI regulation is required.

That combination, of high perceived benefit, high perceived risk, and strong demand for regulation, is the textbook definition of a trust gap.

And then there are the cultural signals. In May 2026, graduates at multiple US universities openly booed Eric Schmidt and other commencement speakers for optimistic AI remarks. That kind of visible pushback would have been almost unthinkable for an earlier generation of “tech optimism” narratives.

Ex-Google CEO Eric Schmidt booed after AI remarks at Arizona commencement
Pew research shows Americans are more worried than excited about AI as graduates voice fears over jobs

Taken together, these are not just vibes. They’re social signals that the adoption curve is running ahead of the trust curve, and that the cohort most exposed to AI’s labour market impacts is already voting with its voice.

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Where this series goes next

Part 1 is deliberately diagnostic. The aim is not to sensationalise, but to ground the discussion in what we can actually see in the data:

• Productivity gains are real, but the AI dividend is not being shared evenly.
• The first clear labour market impact is a quiet erosion of entry-level white-collar opportunities.
• APAC and Singapore show what happens when adoption outpaces governance, and how strong institutions can buy time, but not immunity.
• Trust is falling faster than many leaders realise.

In Part 2, the series can move from diagnosis to trajectory: What plausible futures do these signals point to for APAC and Singapore? What happens if we treat the current signal as a Luddite moment; not in the cartoon version of “anti-tech”, but in the historical sense of skilled workers asking for fair terms in a structural transition?

If you had to pick one lens for Part 2 — global macro, APAC, or specifically Singapore — which would be most useful for you and your organisation? I’ll be using these signals to shape Part 2 of this series, so I’d love to hear your view.