The AI wage premium reaches 62% and education systems have not kept pace
A position requiring artificial intelligence skills is remunerated 62% more than a comparable position without this requirement — this is what the PwC 2026 Global AI Jobs Barometer measures, based on analysis of one billion job postings across 27 countries. These jobs are progressing, according to the same source, approximately eight times faster than the rest of the market, with growth of 69% for AI positions versus 9% for the overall market. For its part, the IMF published in 2026 a flagship note — SDN/2026/001, titled Bridging Skill Gaps for the Future: New Jobs Creation in the AI Age — which analyzes millions of job postings via Lightcast data in six countries (Brazil, Denmark, Germany, South Africa, United Kingdom, United States) and measures wage premiums of 3% to 15% for new IT/AI skills depending on the number of skills required. These two sources, which measure different realities, converge on the same diagnosis: the gap between countries capable of producing graduates equal to this demand and those unable to do so widens every year.
This finding deserves to be read with precision. The question is not whether AI creates or destroys jobs on net — that debate remains open. The immediate question is more concrete: are education systems producing, in sufficient quantity and quality, the profiles that employers are demanding today? And the answer, according to the IMF, varies massively from country to country. It is not the level of automation that reshapes the map of global wage inequality. It is the capacity of universities, technical secondary schools, and training programs to adapt.
Key Points
- The wage premium associated with AI skills reaches 62% on average globally according to the PwC 2026 Global AI Jobs Barometer (one billion postings across 27 countries); the IMF, in its SDN/2026/001 report covering six countries, measures wage premiums of 3% to 15% for new IT/AI skills — two measurements that reflect different realities.
- Job postings requiring AI skills are progressing approximately eight times faster than all postings overall, with growth of 69% versus 9% for the rest of the market according to PwC.
- The IMF introduces a Skill Readiness Index that measures the gap between demand for AI skills and the capacity of education systems to meet it: advanced economies dominate, but with significant internal disparities.
- The preparedness gap between rich countries and middle-income countries often exceeds that between middle-income and poor countries — meaning that dynamic emerging economies risk being left behind due to lack of educational reform.
- Several countries — Singapore, South Korea, certain Gulf states — have launched national AI upskilling programs that already constitute relevant case studies.
62%: What This Premium Really Reveals
A wage gap of 62% should not be read as a simple market figure. It says something about scarcity. When an employer accepts paying 62% more for a given profile, it is because they cannot find that profile in sufficient quantity. The wage premium is an inverse measure of available skill supply.
This is what the PwC 2026 Global AI Jobs Barometer constructed at scale, by analyzing one billion job postings across 27 countries, classified according to whether they require skills explicitly linked to AI — machine learning, natural language processing, generative models, large-scale data analysis. By controlling for sector, required qualification level, and company size, the analysis reveals a net wage gap: 62% on average, compared to 57% measured the previous year. The IMF report SDN/2026/001, which relies on Lightcast data in six countries over the 2021-2024 period, measures wage premiums of 3% to 15% for new IT/AI skills depending on their number — a more granular measurement that reflects the diversity of profiles concerned.
An earlier article in this journal had already documented a similar dynamic at the individual level: AI skills yield, in the American job market, significantly more than a traditional master’s degree. The work of PwC and the IMF broadens this finding across several dozen countries and adds a comparative dimension that was previously lacking.
The temporal dynamic is equally striking. In 2019, job postings mentioning AI skills represented a niche. According to the PwC 2026 AI Jobs Barometer, their share of all postings has progressed at a rate approximately eight times that of other categories — 69% growth for AI jobs versus 9% for the overall market. Demand is not progressing: it is accelerating. What the market is demanding today, education systems from five years ago had no reason to prepare for.
The Map of Inequality: Education, Not Automation
The common intuition suggests that AI-related inequalities widen between automated and non-automated countries. The IMF report says otherwise. It constructs a Skill Readiness Index — a composite indicator that crosses the demand for AI skills measured by job postings with the capacity of local educational supply to meet it, taking into account the higher education enrollment rate, the quality of STEM training, and the density of specialized programs in computer science and data.
The result is counterintuitive. Several middle-income economies — including certain Southeast Asian, Central European, and Gulf countries — display higher preparedness levels than more advanced economies despite being more automated. What determines the score is not income level or degree of industrialization: it is the quality of educational investment over the past fifteen years.
Conversely, middle-income economies that had dynamic labor markets find themselves today with university systems that produce masses of graduates in law, administration, or accounting — fields weakly exposed to AI demand — and very few data engineers or machine learning specialists. These countries risk not being overtaken by poor countries, but being distanced by neighbors who made different curricular choices.
This decoupling between automation and educational preparedness is perhaps the central lesson of the report. AI does not mechanically penalize developing countries. It penalizes countries whose universities have not reoriented their training.
What Countries That Are Making Progress Are Doing
Singapore is the most documented case. The government launched the AI Singapore program there starting in 2019, funded with 500 million Singapore dollars over five years, with an explicit training component: 12,000 professionals trained annually through the AI Apprenticeship program, mandatory partnerships between universities and companies for project oversight, and integration of AI modules into engineering, economics, and social sciences curricula. The stated objective is not to produce AI researchers — there are few of them — but advanced users capable of deploying tools across diverse economic sectors.
South Korea followed a different logic, centered on technical secondary schools. Starting in 2021, the Ministry of Education integrated mandatory programming and machine learning introduction courses into professional tracks, in partnership with Samsung and Kakao for pedagogical content. The result is still too recent to be measured in wages, but enrollment data in STEM tracks at university shows an 18% increase between 2021 and 2024 according to the South Korean Ministry of Education.
The United Arab Emirates bet on yet another strategy: importing skills by creating attractive conditions for foreign talent, while massively funding the Mohamed bin Zayed University of Artificial Intelligence in Abu Dhabi, created in 2019 and already ranked among the most cited programs in global academic publications in AI according to Google Scholar.
These three trajectories have little in common institutionally. What they share is the deliberate decision not to let the university system adapt at its own pace.
Europe and the Paradox of Advanced Training
Europe is in a paradoxical position. It trains excellent AI researchers — European academic publications in machine learning and natural language processing are among the most cited in the world, supported by institutions such as INRIA in France, the Max Planck Institute in Germany, or ETH Zurich. But this cutting-edge excellence coexists with a massive deficit at the intermediate level: developers, data engineers, specialists in integrating AI into industrial processes.
This intermediate level is precisely the one the labor market demands most massively. The job postings analyzed are not primarily seeking PhDs in deep learning. They seek profiles capable of deploying existing tools, assessing their limitations, integrating them into business systems, and interpreting their results. This is a profile of higher education level 3 to 5 (bac+3 to bac+5) with specialization, which neither large research universities nor general training programs naturally produce.
The European Commission has become aware of the problem. The AI Act, which entered into force in 2024, includes a training component that requires companies deploying high-risk AI systems to train their teams. But this obligation falls on companies, not education systems. University curriculum reform remains a national competency, which slows any coordination at the continental level.
Several member states are nonetheless making progress. Germany launched a reskilling program for working engineers in 2023 — targeting 50,000 people over three years, cofinanced by the Länder and chambers of commerce. France expanded its professional licenses in data science in vocational universities (IUTs), with gradual rollout from 2022. These initiatives remain modest relative to the scale of the need, but they show that reorientation is underway.
The Silent Fracture: Countries Watching the Train Pass By
The IMF report is harsh with a category of countries it does not explicitly name but which its index clearly maps: middle-income economies whose growth in the 2010s rested on a skilled workforce in service sectors — finance, law, consulting, administration — and which did not anticipate the shift in demand.
These countries are not poor. They have universities, graduates, active labor markets. But their training produces profiles whose wage premium is negative or zero in a context of AI acceleration, because these profiles correspond precisely to the most substitutable tasks. A junior lawyer searching for precedents in a database, a financial analyst consolidating Excel spreadsheets, a researcher compiling sectoral reports: these functions do not disappear overnight, but their market value compresses while that of AI-augmented functions explodes.
The share of labor in GDP is declining less than previously thought — this is a robust finding documented here. But this aggregate stability masks violent redistribution within the labor market. Wage gains concentrate on AI-competent profiles; stagnation strikes others. What the IMF adds is that this redistribution also operates between countries, according to the same logic.
The risk is not theoretical. Economies such as Brazil, South Africa, Morocco, or Thailand have university systems capable of producing graduates in quantity, but whose curricula have not yet shifted toward skills the market values. The lag between a curriculum reform decision and the graduation of the first trained cohort is four to six years. Every year without reform represents an additional cohort entering a labor market with the skills of a decade past.
Training in AI Does Not Mean Training AI Engineers
The final contribution of the IMF report may be the most useful for policymakers: market demand is not homogeneous, and the educational response should not be either.
The report distinguishes several levels of AI skills in the analyzed job postings. The first, the rarest and best paid, covers model design and training — the domain of researchers and specialized engineers, whose training requires high-level master’s and doctorate degrees. The second level concerns deployment and integration — engineers capable of implementing AI systems in existing environments, managing data pipelines, and ensuring maintenance. The third touches what the report calls augmentation: professionals from varied sectors — healthcare, law, finance, logistics, HR — capable of using AI tools in their daily practice, evaluating their outputs, and adapting their use.
It is this third level that concentrates a significant share of unmet demand. It does not require advanced mathematics training. It requires functional understanding of tools, capacity to assess their biases and limitations, and mastery of specific sectoral uses. This competency is accessible through two to three year programs if curricula are designed for it. Most universities have not yet designed them.
This is precisely the segment on which the most promising programs intervene. The OECD’s Skills for Jobs initiative, launched in 2023, works with approximately thirty countries to map sectoral needs and adapt certification frameworks. Several Nordic countries — Finland, Denmark, Sweden — have integrated applied AI modules into continuous training in healthcare and public administration sectors, targeting this third level directly.
The logic is clear: training an entire country in AI engineering is neither possible nor necessary. Training a significant proportion of the workforce in augmented use is both feasible and decisive for national competitiveness.
What the IMF measures in its report SDN/2026/001, and what the PwC 2026 AI Jobs Barometer confirms, is the magnitude of the gap to bridge. What the experiences of Singapore, South Korea, or Nordic countries show is that it is bridgeable. The variable is not a country’s development level. It is the speed at which its educational institutions accept reform — and the political priority its leaders choose to give it.
The real question for coming years is not how many countries will have sovereign language models. It is how many countries will have doctors, lawyers, engineers, and teachers capable of making something of them.
Sources
- IMF — IMF Staff Discussion Notes SDN/2026/001: Bridging Skill Gaps for the Future: New Jobs Creation in the AI Age
- PwC — PwC 2026 Global AI Jobs Barometer
- Journal d’un Progressiste — Les compétences IA rapportent cinq fois plus qu’un master
- Journal d’un Progressiste — La part du travail dans le PIB baisse moins qu’on ne le croyait
- AI Singapore — Official program and training data (Singapore National AI Strategy)
- South Korean Ministry of Education — STEM enrollment data 2021-2024 (South Korea AI Talent Development Plan)
- OECD — Skills for Jobs Initiative, 2023 report