In 1955, the U.S. Congress's Joint Economic Committee published a report on the economic consequences of automation. Analysts warned that new technologies posed a threat to workers who could not adapt to the changing labor market. Seventy years later, these conclusions are once again relevant.

The industrial era automated manual labor, displacing factory workers. Now, generative artificial intelligence is transforming the post-industrial economy. High-skilled professionals and those in intellectual fields—positions once thought secure from automation—are now at risk.

Drawing on a study from Tufts University and corporate trends, we analyzed the scale of this shift.

“Wired Belts”

Prestigious degrees and jobs in innovative clusters no longer guarantee career and financial stability.

Many specialists in STEM, applied mathematics, law, and the humanities are now at risk. Key risk factors include a close connection to digital technologies, standardized workflows, and information processing.

According to MIT researchers, AI could displace 11.7% of workers in the U.S. labor market in the near future. In monetary terms, this equates to $1.2 trillion in wages across finance, healthcare, and professional services.

This amount represents a significant portion of household income and municipal tax revenues. Automation could trigger a massive redistribution of global capital.

Leading intellectual hubs, which previously generated most of the added value, are rapidly becoming what we call “Wired Belts.” There is a risk that these innovative regions will turn into new depressed areas, facing structural unemployment, declining consumer demand, and long-term economic stagnation.

Vulnerability of Professions to AI

Analysts from the Digital Planet project at Tufts University distinguish between two key concepts: “exposure” and “vulnerability.”

Exposure refers to the technical ability of large language models (LLMs) to perform tasks within a given profession. It assesses how much the tasks of a profession overlap with the capabilities of current AI models, with a maximum score of 100. Source: Tufts University Digital Planet — AI Jobs Risk Index.

Vulnerability indicates the real economic risk of replacing a human with an algorithm. This metric considers the cost of implementation (ROI), infrastructure availability, regulatory barriers, and companies' willingness to restructure business processes.

Indices like the American AI Jobs Risk Index are built on three metrics:

  • Task-Based score — the ability of large language models to reduce task completion time by at least 50% without sacrificing quality;
  • Suitability for Machine Learning — the applicability of machine learning methods to business processes;
  • Advances in AI: the speed of AI development in related fields.

This data contradicts the notion that complex, creative, or intellectual work is immune to automation. For modern neural networks based on Transformer architecture, barriers such as human intuition, abstract logic, or creativity do not exist.

Labor market statistics show a consistent pattern: a 1% technological automation of tasks leads to a 0.75% reduction in jobs in that sector.

The greatest pressure is felt by professionals involved in generating and processing digital content. The most vulnerable include:

  • writers and copywriters (57.4%) — mass text generation leads to platform monopolization and declining freelancer incomes;
  • programmers (55.2%) — demand for junior developers is decreasing, and the outsourcing market is shrinking due to automated template code writing and refactoring;
  • web interface designers (54.6%) — specialists are being displaced by no-code tools that managers can use directly.

Applied mathematicians and sociologists are also at risk, as their tasks are gradually being taken over by statistical modeling and semantic analysis of big data.

The Productivity Trap: From Augmentation to Replacement

The tech community and PR campaigns from Silicon Valley giants promote a reassuring narrative: artificial intelligence is about augmentation, a tool for expanding capabilities. Algorithms are meant to complement humans, freeing them from cognitive routines for strategic and creative tasks.

However, an analysis of corporate practices reveals the opposite. In most cases, this narrative turns out to be classic AI-washing—an attempt to mask structural layoffs under the guise of innovation and productivity growth.

If generative AI halves the time required to complete a task, employees are unlikely to find themselves with more free time. In a market economy, the freed-up resources are either redistributed to new tasks or used as justification for layoffs.

Block Case: The Market Approves of Replacement

A clear example of the “cognitive automation” era is the restructuring of Block under Jack Dorsey.

In February 2026, the company announced it would cut nearly 4,000 employees. The workforce was reduced by almost half—from over 10,000 to fewer than 6,000. The official reason was a shift to a more compact and flat structure focused on AI.

The market reacted swiftly: Block's shares rose by 20% on the same day.

Investors reward companies for replacing human capital with algorithms—Block's stock continues to rise, with the fintech corporation's market capitalization exceeding $40 billion. Source: Google Finance.

SaaSpocalypse and the Resurrection of Taylorism

Macroeconomic research indicates that we are approaching a tipping point for 4.9 million high-skilled professionals in the U.S. In affected segments, the potential for replacement could rise from the current 10% to 40% within two years.

In the IT sector, this process has already been dubbed the SaaSpocalypse—a term describing the rapid devaluation of traditional development models. The emergence of autonomous software agents has wiped out about $285 billion in market capitalization for traditional software companies.

For decades, their business models relied on reselling routine intellectual labor from large teams of developers. When code is generated by machines at zero marginal cost, such models lose their competitiveness.

Taylorism for White-Collar Workers

Large corporations are reviving the principles of Taylorism, but applied to office workers.

IT giants are moving from recommendations to mandatory use of neural networks. Amazon Web Services has implemented digital dashboards to track how frequently employees use AI. Google and Microsoft have included this metric in their employee performance evaluation systems. Refusing to use AI tools is equated with professional inefficiency.

Those who designed this technological shift have suffered the most from the implementation of algorithms. The production of complex content and software is growing exponentially, but their market value is approaching zero—systematically undermining middle-class incomes.

The Geography of Risk and the Paradox of “Ghost GDP”

Adaptation to technology is currently reshaping economic geography. The highest risk areas are leading tech centers with a historically high concentration of well-paid cognitive professions.

Analysts have developed the Iceberg Index—a digital twin of the U.S. labor market modeling the employment of 151 million workers. Each worker is treated as an independent agent. The index shows how neural networks are transforming task structures long before changes are reflected in official unemployment statistics.

Spatial modeling yields an unexpected result. San Jose, the heart of Silicon Valley, tops the list of at-risk areas—9.9% of jobs here are under threat of displacement.

Small university towns, whose economies are built around serving knowledge workers, are particularly vulnerable. For such local economies, the loss of even 7–8% of jobs threatens to shrink consumer demand and depress the real estate market.

At the opposite end of the spectrum are regions historically dominated by manual labor. Here, the risk of AI displacement is statistically minimal. Therefore, regions that have historically lacked high-paying jobs are least affected by their disappearance.

This leads to a phenomenon analysts call “ghost GDP” (Ghost GDP). Gross domestic product continues to grow due to corporate productivity, but this growth is increasingly disconnected from household incomes: money accumulates in corporate profits rather than circulating within local communities.

Militarization of AI

The corporate sector is using AI implementation as a pretext for layoffs. In the military, however, the logic is different: AI is viewed as a tool for enhancing combat capability rather than reducing costs. The integration of artificial intelligence into intelligence and defense has been declared a strategic priority.

In December, the U.S. Department of Defense launched the GenAI.mil platform to use Gemini for Government from Google in national security. This initiative is part of a plan from the Trump administration, announced in July: federal agencies must accelerate the adoption of advanced AI systems.

The U.S. Army has initiated a retraining program—specialization 49B has been introduced. AI officers will manage high-tech systems, speed up decision-making cycles, and work with autonomous platforms.

Unlike the private sector, the military does not lay off personnel but invests in their retraining.

Strategies for the Transition Period

Traditional unions, unemployment benefit systems, and other social institutions were created for the realities of the industrial era. Will they be able to cope with the potential large-scale displacement of specific functions and levels of employment?

Researchers at Tufts University believe the situation requires fundamentally new mechanisms:

  • wage insurance — the government compensates the income difference for specialists displaced by algorithms to lower-skilled positions;
  • corporate transparency — public companies must regularly disclose data on how AI affects workforce size; investors and regulators should see the relationship between productivity growth and job cuts;
  • “augmentation first” model — the implementation of neural network technologies at the corporate level is tied to mandatory funding for employee retraining programs; for instance, in Germany and France, government subsidies for retraining workers whose tasks are automated are already in place;
  • “stacked qualifications” — traditional four-year education is giving way to short micro-modules updated every few months; the focus shifts to meta-skills: systems thinking, ethical arbitration, and empathy.

What’s Next

The widespread adoption of generative AI has long surpassed corporate efficiency. This is a structural shift of global proportions, marking the end of humanity's centuries-long monopoly on complex intellectual labor. The emergence of a new digital “rust belt” is becoming a painful socio-economic issue.

The window for soft, preventive adaptation has nearly closed. Digital transformation is spreading dozens of times faster than changes in legislation and education systems.

Future stability will not depend on attempts to slow down technology adoption. However, productivity growth and technological progress will lose their humanistic meaning if they come at the cost of destroying the global middle class and turning innovative hubs into zones of chronic economic decline.