Hello, fellow humans! Today, we have some real data about how AI is impacting job markets and isolated three factors that can help you understand how you should adapt. We also have a self-assessment so that you can see where your role lands.

In Thursday’s newsletter, we’ll get into how you can strategically position yourself and outline a roadmap for how you can navigate the AI transition.

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Uncertainty is running high in the technology space. As everyone is making their predictions for 2026 and AI in general, we’re all asking the big questions about whether AI a bubble, if AI going to take over our jobs, or how to strategize in an AI-enabled world.

It’s easy to look into an unknown future and frame outcomes as binary—employed or not, bubble or not—when the real outcomes will probably be much more nuanced. Now that AI has been a factor in the economy for about a year, we can see some job data and how those nuances start to take shape. The International Monetary Fund has done some groundbreaking research on AI and labor markets in Europe, with Denmark being a revealing case due to the high rate of AI adoption there. The headline data seems stark: 60% of jobs in advanced economies are exposed to AI. Exposure to AI: Degree of overlap between AI applications and human abilities in occupations (Felten et al., 2021;2023).

But that exposure produces dramatically different outcomes for workers depending on a factor that the report calls complementarity. Complementarity is itself a data composite based on six aspects of the occupation. Six components signal that the role has high AI complementarity and is likely to see productivity boosts from AI:

However, “shielding” provides protection against AI exposure through factors like social norms, laws, organizational structures, human networks, information confidentiality, professional accountability, and other kinds of occupational moats.

For example:

(Source: GenAI and the Future of Work)

The IMF's analysis of 119 occupations tells us that the professional barrier isn’t exposure to AI technology, but whether AI complements the work or substitutes for it. Two workers with identical levels of AI exposure can face entirely opposite futures—one experiencing productivity gains and wage growth by leveraging the AI complementarity and shielding factors, the other facing displacement and income loss by overlooking those potential advantages.

The IMF's research analyzes real labor market data over 2023 and 2024 from advanced economies like the UK and emerging markets like Brazil and reveals clear patterns of winners and losers as AI adoption accelerates. Understanding where you sit in this landscape is the first step toward navigating it successfully.

The IMF framework sorts occupations into three distinct categories based on exposure and complementarity. Think of these as career climate zones, each with different opportunities and risks.

In advanced economies, 27% of workers fall into this category. These are roles where AI significantly impacts the work, but human oversight, judgment, and interaction remain essential. The technology enhances productivity rather than displacing the worker.

These roles are typically defined by high degrees of responsibility for others' well-being, require complex ethical reasoning, demand significant interpersonal interaction, or operate in contexts where unsupervised AI can face social, regulatory, complexity, or ethical constraints.

For example, business executives making strategic decisions, engineers designing complex systems, surgeons performing medical procedures, lawyers handling nuanced legal matters, and financial analysts developing investment strategies. These roles share a common thread—AI can process information and identify patterns, but humans must interpret context, make judgment calls, and take responsibility for outcomes. Humans still have a much greater capacity for understanding context and managing complexity, so for now, these roles will be able to offload some portions of their work to AI, but require human expertise to synthesize it.

The first indicator in the data is that HEHC employment is concentrated in the upper income deciles. The correlation between earnings and complementarity potential is stark, particularly in countries like India and the United States, where complementarity rises steadily at the top of the income distribution.

Workers in these positions face the best outcomes from AI adoption in terms of productivity gains, increased labor demand, and wage growth. The caveat is that you need AI-related skills to capture these benefits. Without the ability to effectively leverage AI tools, even workers in high-complementarity roles risk being displaced by colleagues who can.

The next category of work faces a bigger challenge. In advanced economies, 33% of employment—a larger share than HEHC—falls into the high-exposure, low-complementarity category. These are jobs where AI can autonomously complete tasks with minimal human input.

The IMF study looks at telecommunications workers but can also include data entry clerks, administrative assistants, payroll specialists, basic accounting roles, medical transcriptionists, and entry-level legal research positions. These jobs involve routine cognitive tasks, pattern recognition, and data processing—tasks well-suited to Large Language Model (LLM) strengths.

The outlook for these occupations is a challenging labor market with reduced labor demand, slower wage growth, and significant job displacement risk. The research documents that in geographic areas with higher AI adoption rates, vacancies in HELC occupations expanded slower than in areas with lower AI adoption by 0.4 percentage points. That’s a measurable signal of reduced demand.

The IMF and other job market research groups are pointing out that HELC roles typically serve as stepping stones for junior workers in their career progression. Young college graduates have historically started in these positions before moving into higher-complementarity roles. If these entry-level positions disappear, we face a career ladder problem that will become a leadership succession problem for how to gain the experience needed to grow, advance, and eventually lead?

The remaining 40% of employment in advanced economies (22.5-53.6% depending on the country) falls into low-exposure occupations. These roles face minimal potential for AI application, typically due to physical requirements, in-person presence needs, or non-routine manual tasks.

Examples include skilled trades (construction workers, electricians, plumbers), personal care workers, performers, food service workers, and hairdressers. Interestingly, surgeons—despite being highly skilled professionals—score as low exposure because while AI assists in diagnostics, the physical act of surgery requires human dexterity and real-time decision-making in unpredictable circumstances.

Lower exposure doesn't mean lower value. Skilled trades face persistent worker shortages, with 94% of construction companies reporting difficulty sourcing workers. These occupations remain largely shielded from both the risks and opportunities of AI disruption.

Understanding the three zones is step one. Step two is recognizing that people don't stay static—they transition between occupations throughout their careers. The IMF's longitudinal data from Brazil and the UK reveals clear patterns in who successfully navigates these transitions.

College-educated workers in both countries frequently move from HELC to HEHC occupations, particularly before age 40. This "upward" transition is especially common among young workers and correlates with wage increases of 10-15%. The data shows the steepest upward slope of HEHC employment shares occurs before age 40, with young college-educated workers making the most frequent HELC → HEHC transitions.

This pattern holds remarkably consistent across vastly different economies—the UK (an advanced economy) and Brazil (an emerging market)—suggesting the dynamics of AI impact on highly educated workers may be similar globally.

Non-college workers face a different reality. In Brazil, they show markedly higher chances of moving from better-paid HELC occupations to low-exposure ones, suggesting higher risk of income loss if AI reduces labor demand for HELC positions. The UK shows more employment stability but limited upward mobility for non-college workers.

The IMF data reveals clear demographic patterns that should inform both individual strategy and public policy:

Highest HELC exposure risk:

Positioned for HEHC benefits:

The paradox embedded in this data deserves emphasis: highly educated workers face greater occupational exposure to AI at both high and low complementarity. Education creates exposure, but whether that exposure becomes opportunity or risk depends entirely on the complementarity factor.

Answer these questions to identify your position:

Scoring:

60% of jobs in advanced economies are AI-exposed, with 27% positioned for enhancement and 33% at displacement risk. Understanding which zone you occupy is the foundation for strategic career decisions in an AI-transformed labor market. The question isn't whether your job is exposed to AI. The question of whether that exposure creates complementarity or substitution could be the deciding factor.