"Artificial intelligence will make large-scale immigration to support Western labor markets unnecessary."

AI, migration and the future of work

In January 2026, Alex Karp, the polarizing co-founder and CEO of data analytics giant Palantir, used the stage of the World Economic Forum in Davos for one of the most daring economic policy predictions of the decade.

His thesis struck a chord with a globalized world in flux: the rapid development and application of artificial intelligence, Karp argued, would render large-scale immigration to support Western labor markets virtually obsolete. At a time when societal aging and skills shortages dominate the agenda of industrialized nations, this statement initially appears to be a radical rejection of conventional economic doctrine. However, anyone who dismisses Karp's argument as mere political propaganda overlooks the fundamental technological transformation already clearly evident in economic data.

The debate Karp has initiated demands a closer examination beyond the usual partisan battles. It compels us to consider two of the most powerful forces of our time—rapid technological development and global migration—not separately, but in their interplay. Recent data from the Wharton Budget Model and the US Federal Reserve support the assumption that we are at the beginning of a productivity surge comparable to the advent of the internet or even electrification. If AI systems can significantly increase overall economic output and automate up to 40 percent of working hours by 2035, the question must be asked: Will we continue to need quantitative immigration on the scale we have seen so far, or will the demand shift toward a highly selective quality of workers?

At the same time, a closer look at the health, construction, and skilled trades sectors reveals the limitations of purely computational solutions. The vision of AI-supported self-sufficiency clashes with the physical and human reality of structural labor market gaps that software alone cannot close.

This article analyzes the validity of Karp's thesis in light of the latest economic data. We examine the tension between theoretical automation potential and practical labor market realities, investigate the role of targeted immigration models based on the Canadian example, and critically ask: Is AI the hoped-for savior of the welfare state or an accelerator of new social inequalities?

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The claim that automation does not necessarily mean job losses

Alex Karp's thesis deserves more careful consideration than the usual heated debate allows. The Palantir CEO's argument isn't unrealistic when considering recent economic evidence, but rather involves subtle nuances that many media outlets overlook. The core question isn't whether AI will displace humans, but how quickly this process will occur and which jobs will actually disappear and which will merely transform.

The Wharton Budget Model from September 2025 projects that artificial intelligence will increase overall productivity by 1.5 percent by 2035, by 3 percent by 2055, and by 3.7 percent by 2075. These cumulative effects are substantial. Labor savings through the use of AI are calculated at an average of 25 percent, with this figure potentially rising to 40 percent in the coming decades. At the same time, the model shows that 40 percent of current gross domestic product (GDP) could be significantly impacted by AI. Those in the top third of the income bracket are most affected, while those in the absolute top positions are less impacted, and low-income earners are least affected. The Federal Reserve Bank of St. Louis documents that AI-related sectors already contributed 0.97 percentage points to real economic growth in the first three quarters of 2025, surpassing the figures achieved during the dot-com boom. This is not a footnote, but a fundamental economic driver.

An often overlooked aspect of studies on AI adoption is the distinction between the automation of individual tasks and the loss of entire jobs. The MIT Sloan School of Management documented in 2025 that employment in highly affected occupations continued to grow despite automation. This is because when AI takes over individual tasks within a job, employees can focus on higher-value activities where AI is less efficient, such as critical thinking or idea generation. High-earning employees in roles heavily impacted by AI even saw an increase in their employment share of about three percent over five years. This contradicts the widespread fear of massive job losses due to AI. The OECD found in 2023 that despite high AI impact, there was no discernible slowdown in the demand for labor. Highly skilled workers even experienced employment gains compared to lower-skilled workers over a ten-year period. The productivity-enhancing effect outweighed the displacement effect in this early phase of adoption.

The historical perspective also supports Karp's position. Goldman Sachs points out that today, 60 percent of all American workers are employed in jobs that didn't exist in 1940. This means that more than 85 percent of job growth since 1940 has resulted from technology-driven job creation. Every wave of technological disruption, from electrification to computerization to the internet, initially led to fears of job losses but, in the long run, to net gains in employment and prosperity. AI could follow this pattern, but with one critical difference: the speed and scope of the disruption could be unprecedented.

New professions are indeed emerging. Positions such as data scientist, machine learning engineer, AI ethicist, and prompt engineer didn't exist a decade ago. The World Economic Forum 2025 forecasts significant growth in practical jobs like farmers, delivery drivers, and construction workers, as well as in the care sector with skilled workers, social workers, and personal caregivers, measured in absolute numbers. "Green" skills, including climate data analysts and sustainability specialists, are experiencing demand that exceeds supply. Hybrid roles that combine technical expertise with human judgment are proliferating. If the labor market is flexible enough to create new categories, unemployment caused by AI may well be limited.

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Targeted selection instead of mass immigration: A working model

A central element of Karp's thesis is not the general rejection of immigration, but its restructuring through selection. Countries like Canada, Australia, and New Zealand have implemented points-based selection systems that have proven effective. Canada has used such a system since 1967 and has regulated 85 percent of its economic immigration through a points system. These systems are transparent, resource-efficient, and attract highly skilled migrants far more effectively than family reunification or asylum seeker immigration. A comparison has shown that while the United States and Canada were attractive to similarly qualified applicants, Canada successfully managed immigration through its points system, whereas the US barely regulated this flow.

Highly skilled migrants pay higher taxes and require less social welfare. An influx of skilled workers could reduce income inequality by decreasing the relative scarcity of highly skilled domestic workers and thus reducing their wage premium, while lower-skilled domestic workers become relatively scarcer and could command higher wages. Skilled workers from abroad could boost innovation and productivity, promoting long-term economic growth and wage increases across the entire economy. In aging societies, where the burden of caring for the elderly is rising dramatically, migrants have an overall positive financial impact if they successfully integrate into the labor market. This is an argument for high-quality, not merely quantitative, migration.

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Structural gaps remain despite automation

Karp's thesis would fail, however, if genuine, irreplaceable labor market gaps persist. Indeed, such gaps exist. The US projects a shortage of 200,000 to 450,000 nurses by 2025, of nearly 40,000 to 124,000 physicians by 2034, and over three million other healthcare vacancies. Over 40 percent of nurses are considering leaving the profession due to burnout. These gaps are structural, not temporary. An aging population is driving demand, while workers are leaving the industry. Healthcare requires empathy, physical interaction, and ethical judgment—tasks that AI cannot fully replicate. Studies show that less than one percent of healthcare jobs are fully automatable.

In the construction sector, the figures are similarly dramatic. North America projected over eight million unfilled jobs by 2025, with gaps in healthcare, construction, and technology. In the construction sector, 74 percent of employers report difficulties finding domestic workers. Tens of thousands of truck driver positions remain vacant. Hospitality professionals, including chefs, are increasingly being recruited from abroad. The World Economic Forum forecasts growth in construction jobs in absolute terms. And in education, qualified teachers are leaving the profession due to burnout and underpayment faster than new graduates are entering the field.

These areas demonstrate that Karp's intuition is partially correct: not all jobs are equally automatable. Physically demanding, emotionally intense, and ethically complex jobs are resistant to automation. But there is a significant difference between filling a gap through technology and through strategic immigration. Canada, Australia, and other countries have proven that targeted skilled immigration, not open borders, can fill labor market gaps.

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The productivity puzzle and the time factor

Karp's thesis is complicated by a fundamental problem with the timescale. The International Monetary Fund (IMF) forecasts that AI could contribute between 0.3 and 0.8 percentage points to global economic growth in the medium term. This is substantial, but spread over decades. The Wharton model shows that the peak productivity contribution is around 0.2 percentage points in 2032, after which the effect declines. Following market saturation in the 2030s, the sustained boost drops to barely 0.04 percentage points. In other words, productivity gains are concentrated in the 2020s and early 2030s, after which the curve flattens dramatically.

This is crucial for the migration debate. If AI does indeed deliver moderate productivity gains for a decade before its impact diminishes, the labor market will still require immigration over the next five to ten years to fill demographic gaps while automation is still unfolding. Furthermore, the Wharton model and MIT research show that 95 percent of AI pilot projects in companies fail. Implementation is not a sure thing. The rollout of AI systems takes time, requires reprogramming processes, and retraining employees. Hardly any company can immediately deploy AI on a massive and widespread scale.

Regional and industry-specific differences

Another problem with Karp's generalization is its geographical and sectoral fragmentation. The gains in AI productivity are not uniform. Technical, professional, and managerial roles see the greatest demand for new skills. In the healthcare sector, telemedicine and digital skills are booming. In marketing, expertise in social media is growing. These benefits are concentrated in urban areas with many highly skilled workers and access to education. Rural and older industrial regions with lower-skilled workers benefit less. A labor market access policy that completely excludes migration could have a destabilizing effect on regions.

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Palantir's political stance and critical double standards

Karp himself embodies the ambiguity of his position. He is a highly educated intellectual whose academic career has focused on issues of power and society. His political shift from progressive to a critique of left-wing politics is well-known. And while Palantir is launching a fellowship for neurodivergent talent, offering salaries of $110,000 to $175,000 and explicitly not labeled a “diversity” initiative, the company works closely with the military and intelligence agencies and has assisted immigration authorities in deportation operations. This is not without its contradictions. One cannot simultaneously argue with AI optimism for the job market and provide the technology for mass deportations. Karp’s statements can be understood as part of a broader political agenda that uses technology to justify isolation.

The data situation: What are the arguments for and against?

What conclusions can be drawn from the totality of the data? Karp's thesis is not fundamentally wrong, but it requires important nuances. First, productivity gains through AI are real and significant, but they unfold over decades, not years. Second, the automation of individual tasks does not necessarily lead to job losses, but it can increase job requirements and lower wages for certain groups. Third, new jobs are created, but may follow historical patterns where 85 percent of growth is technology-driven. Fourth, structural gaps in health, construction, education, and care will not be closed by AI in the foreseeable future. Fifth, targeted, not mass, migration can be a viable model, as Canada and Australia demonstrate.

Karp's critical blind spot lies in the summary. If highly skilled workers benefit while low-skilled workers are displaced, and new jobs are created in other regions or sectors, but demographic gaps are acute locally, then a simple "AI makes migration unnecessary" is too simplistic. The correct answer is: AI may make mass unskilled migration unnecessary, but it does not preclude skilled, selective migration and cannot fill structural labor market gaps in certain sectors. This is less radical than Karp's statement, but it aligns better with the actual facts.

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