Artificial intelligence is increasingly presented as a transformative force, one that will drive productivity, unlock innovation, and reshape the global economy.
But a closer look at its early impact, particularly in developed economies where data is more transparent and widely scrutinised, suggests a more complex reality.
Much of the AI narrative focuses on opportunity.
Far less attention is given to how unevenly these changes will be distributed. Rather than creating shared progress, AI risks deepening existing divides, accelerating the emergence of a system where access to opportunity becomes increasingly unequal.
AI is not entering a level playing field. It is entering a world already defined by economic inequality, fragile labour markets, and limited institutional capacity across large parts of the global economy.
In that context, its immediate impact is unlikely to be neutral. Less developed economies are likely to feel the effects more sharply, particularly among younger populations, where employment opportunities are already limited.
Recent statements from figures such as Elon Musk and Dario Amodei highlight the speed and scale of the changes underway.
Their warnings point to a reality that is already becoming clear: the pace of AI development is outpacing most governments’ ability or willingness to respond effectively.
This creates a growing gap between technological advancement and institutional preparedness.
The Illusion of Opportunity
The dominant narrative around AI emphasises efficiency and growth.
Firms adopt AI to reduce costs, increase output, and streamline operations. In developed economies, this may translate into higher productivity and new forms of employment. However, the scale of opportunities being created is unlikely to match the scale of displacement already underway.
There is a growing sense that policy focus remains on headline indicators such as GDP, while the underlying question of livelihoods and job security receives far less attention.
The benefits frequently highlighted by media, politicians, and corporate leaders are highly concentrated.
They accrue primarily to capital-rich firms, often a small number of dominant players, alongside highly skilled workers and economies with strong institutions, adaptable labour markets, and established welfare systems.
At the same time, prominent figures such as Elon Musk have repeatedly suggested that large-scale job displacement may eventually require forms of universal income support. The implication is clear: a future in which a growing share of the population is no longer integrated into traditional labour markets.
Similarly, Dario Amodei has warned that a significant portion of white-collar work, including entry-level roles in law, consulting, finance, and technology, could be automated within a relatively short timeframe.
These are not marginal sectors. They represent core pathways into professional employment.
In many parts of the world, AI does not arrive as an opportunity; it arrives as displacement.
Since the release and rapid adoption of large language models, a key question remains largely unanswered: how many jobs are being created compared to those being eliminated?
Early signs suggest that even sectors once considered secure, such as technology, are already experiencing disruption.
This alone should be treated as a signal of how far-reaching these changes may become.
The First to Be Affected: Youth
The most immediate impact of AI will be felt at the entry level of the labour market.
These are roles that require limited experience, involve repetitive or administrative tasks, and traditionally serve as the primary pathways into employment.
They are also the easiest to automate.
Customer support, data entry, basic content creation, and administrative assistance are not future risks. They are already being reshaped.
For young people, this creates a structural problem.
The traditional pathway that has defined labour markets for decades, education leading to an entry-level role, followed by experience and progression, is beginning to break down. In many developed economies, this shift is already visible.
Without entry points, there is no progression.
At the same time, several broader pressures are already affecting younger generations. Economic growth in many developed countries has slowed relative to previous decades, competition from emerging economies has intensified, and regulatory and tax environments have, in many cases, constrained business expansion and job creation.
This has reduced the availability of opportunities even before the full impact of AI is felt.
In parallel, the cost of education has continued to rise. In many countries, young people accumulate significant levels of debt in pursuit of qualifications, only to face increasingly uncertain employment prospects upon graduation.
This mismatch is beginning to reshape public discourse, while also fueling a growing sense of frustration among young people, increasingly visible across social media.
In countries such as the United Kingdom, there has been a noticeable shift in messaging, from universal higher education toward vocational pathways, apprenticeships, and skilled trades. Professions such as electricians, plumbers, and construction workers are increasingly presented as more stable alternatives.
This reflects a broader recognition that traditional white-collar pathways are no longer as secure as they once were.
In less developed economies, the impact is likely to be more severe.
Where opportunities are already limited and labour markets are less resilient, the removal of entry-level roles does not simply create competition; it removes access entirely.
Fragile Economies, Greater Exposure
The impact is amplified in less developed and fragile economies.
These systems already face high youth unemployment, limited job creation, widespread informal labour markets, and education systems that are often misaligned with market needs. In many cases, long-term economic planning and strategic development have been weak or entirely absent.
In such environments, entry-level opportunities are scarce. Access to employment is frequently shaped by informal networks, personal connections, or the ability to pay, rather than merit.
In countries such as Iraq, this has contributed to a labour market that many view as deeply distorted, where opportunities are limited and pathways into employment are increasingly difficult to navigate.
AI reduces these opportunities further.
This creates a compounding effect: fewer jobs for young people, increased competition, downward pressure on wages, and rising instability.
In countries such as Iraq, this pressure is layered on top of existing structural weaknesses. The economy has become heavily import-dependent, with limited industrial capacity, underdeveloped infrastructure, and a weak private sector unable to absorb large numbers of graduates entering the labour market each year.
At the same time, corruption continues to distort access to employment, further narrowing already limited pathways.
The result is a generation facing constrained options.
Opportunities become so limited that some are pushed toward informal or high-risk alternatives, migration, criminal activity, or involvement in armed groups.
Unlike developed economies, there are few buffers to absorb this pressure.
Large-scale retraining systems are largely absent, social safety nets remain weak, and labour market policies lack the flexibility needed to adapt to rapid technological change.
In Iraq, proposals have recently emerged from some political figures to reintroduce mandatory military service for young people.
These measures are often presented as a way to instil discipline, provide structure, and address unemployment. However, the broader implications raise important questions.
In a system already affected by corruption, such policies risk creating new avenues for unequal treatment. Those with access and resources may find ways to avoid conscription, while others are left with limited choice.
At the same time, compulsory service can function as a temporary buffer, delaying entry into an already constrained labour market rather than addressing its underlying weaknesses.
It may also introduce additional layers of economic activity tied to procurement, training, and logistics, areas that, in many contexts, have historically been vulnerable to misuse and political influence, effectively creating new avenues for corruption.
Beyond the economic dimension, such policies can also shape patterns of loyalty and dependence, particularly in systems where the relationship between the state and society is already under strain.
The Collapse of Outsourcing-Based Models
Many developing economies have built significant parts of their labour markets around outsourcing.
This includes call centres, customer service, basic IT support, and administrative processing. In previous years, there were already growing calls in developed economies to re-shore some of these functions due to concerns over domestic employment, language and communication barriers, and data security risks.
AI introduces a more immediate disruption.
With the rapid advancement of language models, many of these roles can now be automated or significantly reduced. Tasks that once required large, low-cost workforces can increasingly be handled by systems operating at scale and with consistent output.
These sectors rely on two key factors: lower labour costs and high-volume, repetitive tasks.
They are precisely the areas where AI is most effective.
Automation in these sectors is already underway, with direct consequences for labour markets that depend on them.
The result is a contraction in employment opportunities in economies that previously relied on outsourcing as a key source of job creation. This has wider implications, including pressure on tax revenues, rising unemployment, and increasing dissatisfaction with existing political and economic models.
Entire segments of employment that once absorbed large numbers of young workers are now shrinking rapidly.
The Freelance Economy
In recent years, many young people in less developed economies have turned to freelance work as a means of survival, and in some cases, as a pathway to building independent income.
Online platforms have provided access to global markets, allowing individuals to offer services to clients across different countries. For many, this created an alternative to weak domestic labour markets.
Although individual payments are often low, these small contracts can accumulate into a stable income relative to the local cost of living, enabling a degree of financial independence.
The types of work involved are varied:
* Graphic design and image editing
* Writing, translation, and transcription
* Virtual assistance and data entry
* Video and audio production
* Social media and digital marketing
* Basic programming and technical tasks
These roles share a common feature: they are largely digital, task-based, and in many cases repetitive.
They are also among the most exposed to AI disruption.
As automation expands into these areas, the freelance economy faces increasing pressure. Tasks that once required human input can now be completed faster and at lower cost through AI systems.
For many young workers, this removes one of the few accessible sources of income.
The result is a narrowing of options, pushing individuals back toward state dependence where it exists, or forcing them to seek alternative opportunities, often under more difficult conditions.
Social and Political Consequences
The economic impact cannot be separated from its social effects.
Large numbers of young people entering adulthood without access to stable employment create pressure across multiple dimensions. This includes increased migration, expansion of informal economies, greater exposure to exploitation, and a growing sense of frustration alongside declining trust in institutions.
These dynamics are not limited to less developed economies; they are increasingly visible on a global scale.
The effects extend further.
Reduced earning power directly influences broader areas of life, including housing access, education, family formation, and long-term financial stability. Employment is a foundation for social and economic participation.
Periods of disruption have already shown how sensitive this system is. During the COVID-19 pandemic, even short-term labour market shocks led to widespread job losses, business closures, and a noticeable rise in mental health pressures. In many cases, re-entry into the workforce proved difficult, highlighting how fragile employment structures can be once disrupted.
At scale, sustained unemployment would have stronger and more lasting effects, reshaping expectations around work, reducing long-term participation in the labour market, and altering how societies function and develop.
When that foundation weakens, the consequences are interconnected.
This can manifest in rising social tension, increased criminal activity, and a broader deterioration in economic stability. Over time, these pressures begin to reshape societies.
In more fragile states, this easily translates into higher crime rates, increased recruitment into armed groups and militias, and rising social unrest.
These are not theoretical risks. They reflect patterns already observed in economies with limited capacity to absorb labour.
AI accelerates these dynamics.
A System Unprepared or Unwilling
Perhaps the most significant issue is the lack of preparedness, or, in many cases, the lack of willingness to prepare.
In many countries, there is no clear labour transition strategy, limited investment in large-scale retraining, and no coherent policy framework addressing AI’s impact on employment.
In a number of less developed economies, large segments of the workforce remain heavily dependent on government employment. These roles are often low-skilled and, in some cases, contribute limited economic value, reflecting broader inefficiencies within public sector structures.
At the same time, governments themselves are beginning to adopt AI to reduce costs and improve efficiency.
This raises a fundamental question: where does that workforce go?
The private sector, in many of these economies, lacks the capacity to absorb large numbers of displaced workers. It often operates within constrained environments, shaped not only by competition but also by market concentration, where a small number of dominant players control significant portions of economic activity.
As these same actors adopt AI to increase efficiency, the space for labour absorption narrows even further.
At the same time, global technological development is not slowing down.
This creates a widening disconnect between rapid technological change and slow, or non-existent, institutional response, particularly in regions where welfare systems are weak or absent.
This is a recipe for instability.
Inequality by Design
AI does not inherently create inequality.
However, the structure through which it is developed and deployed, concentrated among a limited number of dominant firms controlling infrastructure, capital, and technology, means that its benefits are unlikely to be evenly distributed.
In its current trajectory, AI risks reinforcing and accelerating existing divides:
* Between developed and developing economies
* Between high-skilled and low-skilled workers
* Between those with access to capital and those without
For young people in fragile economies, this represents an immediate structural shift.
It reshapes expectations around opportunity. Employment pathways, whether in the public or private sector, become increasingly distant or, in some cases, inaccessible.
This is a significant erosion of opportunity for younger generations.
Even in developed economies, concerns are already emerging. Job seekers report submitting large numbers of applications without securing interviews, highlighting how competitive and constrained entry-level pathways are becoming.
In less developed countries, the situation is likely more difficult.
Access to reliable data is often limited, and public visibility of these trends is reduced by more controlled media environments, including social media platforms. As a result, the scale of the issue may be underreported, even as its effects intensify.
Conclusion
Artificial intelligence will reshape the global economy, but it will not do so evenly.
For younger generations in more stable systems, it may open new opportunities, but for a limited number of people compared to those who risk losing their jobs and struggling to find alternatives.
For those in fragile economies, it risks removing the few pathways that already exist.
AI is a structural challenge that requires serious policy responses to protect livelihoods and maintain stability.
The scale of this shift should not be underestimated. Whether fully acknowledged or not, the direction is becoming increasingly clear: labour markets globally are entering a period of significant disruption.
Unless these structural issues are addressed, AI will deepen the inequalities that already define the global economy, and the earliest and most severe impact is likely to be felt in less developed economies.