The AI Workforce Shift and Who Benefits, Who Pays, and What Happens Next
- 2 days ago
- 7 min read
Written by Rosie Hewat, Founder & CEO of Rosie’s People
Rosie Hewat is a Board and Executive Advisor and the Founder & CEO of Rosie’s People, a leadership and organisational advisory platform. She works with founders, boards, and senior leaders navigating complexity, scale, and high-stakes decision-making across global and regulated environments.
Across industries, a significant shift is taking place, one that many people can feel, but far fewer are openly discussing. Workers are increasingly being asked to train the very systems that may eventually replace them. This is no longer theoretical, it is already embedded into workflows across customer service, marketing, operations, and technology. Employees are documenting processes, refining outputs, labeling data, and improving systems designed to replicate parts of their roles.
Most understand what is happening. Many can see the direction of travel. And yet, they continue, not because they are unaware, but because they feel they have little choice. If they do not participate, someone else will. If they resist, they risk being excluded from a system that is already moving forward without them.
This is where the conversation becomes more complex.
The acceleration of workforce change
Recent developments across the technology sector provide a clear signal of the direction of travel.
In 2026 alone, tens of thousands of roles have been cut globally across major organizations. Companies including Meta and Microsoft have announced significant restructuring efforts while simultaneously increasing investment in AI.
Reporting from CNBC, Business Insider, The Guardian, and the Wall Street Journal highlights a consistent pattern, layoffs are increasingly aligned with strategic shifts toward automation, efficiency, and AI-led operating models.
In April 2026, estimates suggested that approximately 40,000 tech jobs were lost in a single month, contributing to over 90,000 job cuts globally in the first four months of the year.
Not all of these reductions are driven solely by AI. Factors such as over-hiring during previous growth cycles, interest rate pressures, and broader market adjustments continue to play a role. However, what is changing is the increasing use of AI as both a driver and a justification for leaner workforce structures.
The pace of change is what matters.
The workforce is being asked to adapt to structural transformation that is moving faster than most systems, including training, policy, and organizational design, are equipped to support.
The illusion of new opportunity
Alongside this shift, a parallel narrative has emerged.
Across social media and digital platforms, individuals are being encouraged to “build with AI,” “monetize automation,” and “replace traditional income streams.” The message is clear, while traditional employment may be changing, new opportunities are everywhere. But we need to be honest about who this message is reaching.
Many of the individuals being targeted by these promises are not operating from a position of security. Some are unemployed. Others have been made redundant. Many are underemployed or struggling to maintain financial stability despite being employed. In today’s economy, even roles that would once have been considered stable are often no longer sufficient on their own, pushing individuals toward additional income streams simply to keep up.
This context matters. Because when insecurity rises, certainty becomes highly marketable.
A course for £7, a “money-making-automation” platform for £27, a lifetime tool for £97 that will do it all while you sleep, a system that promises to automate outreach, generate leads, or create passive income. These offers are often positioned as accessible entry points into a new economy. What is less visible is the structure behind them.
Increasingly, individuals are participating in systems that encourage them to sell similar solutions to others, often with minimal differentiation. Many are not operating as established experts, but as participants in a cycle where knowledge is acquired just enough to be repackaged and resold.
The result begins to resemble a large-scale digital extraction loop. Not because individuals are acting in bad faith, but because they are responding rationally to pressure within a system that offers limited alternatives. The language may be modern. The mechanism is familiar.
We have seen versions of this before in earlier waves of speculative selling and low-barrier entry markets. What is different now is the scale and speed. The internet has created a single global marketplace, and AI has standardized the tools, messaging, and offers being sold.
When everyone is selling similar solutions to the same audience, saturation is inevitable. And when saturation arrives, value declines.
A pattern already visible in the music industry
The music industry can be seen as an early signal, a testing ground where digitization, automation, and value redistribution have already collided.
Artists have raised ongoing concerns about their work being used to train AI systems without clear consent or compensation. At the same time, platforms have seen an increase in AI-generated content, alongside cases of artificial streaming activity that distort how revenue is distributed.
Legal disputes involving companies such as Suno and Udio highlight the growing tension between technological capability and creative ownership. At the same time, streaming platforms have been forced to introduce mechanisms to distinguish authentic artists from automated or synthetic accounts after the system itself became saturated.
The pattern is instructive. Human input creates value. That value is used to train systems. Those systems scale rapidly. And the original contributors find themselves competing within a system reshaped by their own work.
The overall pool may not increase proportionally. It may simply be redistributed.
Why Mitchly exists
This is also part of the reason I am building Mitchly. The music industry has made the imbalance between value creation and value distribution highly visible. Artists create the work, platforms scale the work. Technology reshapes the output. But the economics do not always follow the people whose creativity made the system valuable.
Mitchly is not just a platform, it is an attempt to explore a different model of alignment.
If value can be more transparently attributed and shared in creative industries such as music, it raises a broader question, "Can similar principles be applied to other forms of labor, where human input is currently being absorbed into systems without ongoing participation in the value created?"
What is often overlooked is that this process is not just about automation, it is also about transfer.
Workers are not only being replaced, in many cases, they are contributing the knowledge, patterns, and intellectual input that make that replacement possible, often without any form of long-term participation in the value created.
This is not simply a technological shift. It is a question of how intellectual equity is being redistributed.
The workforce is now following a similar trajectory
What we are beginning to see in the broader workforce mirrors this dynamic.
Roles are not simply disappearing, they are being restructured. Tasks that were once performed by individuals are being absorbed into systems, redistributed across platforms, or removed altogether. In many cases, these systems perform tasks faster, more consistently, and at lower cost.
From an operational perspective, this is rational. From a system perspective, it raises a more difficult question. What happens when large numbers of people are displaced faster than new forms of work are created? At its core, this is a timing problem.
The speed at which roles are being reshaped is accelerating, while the systems designed to retrain and redeploy people are adapting far more slowly.
The economic implications and the gaps
This is not just a labor market issue. It is an economic one.
Employment underpins income, consumption, and participation in the broader economy. When access to stable income becomes less predictable, the effects extend beyond individuals to markets, institutions, and public systems.
Recent data suggests that unemployment duration is increasing in multiple markets, including the UK, where more individuals are experiencing longer periods out of work compared to previous years. Youth unemployment has also risen significantly, reinforcing concerns about entry into the workforce.
At the same time, global workforce engagement remains low, with studies indicating that a significant proportion of employees feel disconnected from their work, their organizations, or their long-term prospects. This creates a structural tension.
Organizations are optimizing for efficiency and cost reduction, while the broader system relies on a workforce that is able to earn, spend, and sustain economic activity. If these dynamics fall out of alignment, the consequences will not remain confined to the workplace.
Governments are beginning to respond through increased investment in training, apprenticeships, and reskilling initiatives. Organizations are experimenting with new operating models, including remote, fractional, and project-based work. These are necessary steps, but they do not yet form a complete strategy.
There remains a gap between the speed of technological change, the pace of workforce adaptation, and the structure of economic systems designed for a more stable era.
This is not a failure of intent. It is a challenge of alignment.
Conclusion
AI is not just changing how work is done. It is changing how value is created and who is able to participate in that process. The question is not whether this shift will continue. It will. The question is whether we are prepared for what happens next.
Because we are not simply building more efficient systems. We are reshaping the foundations of how people earn, contribute, and sustain themselves within the economy. And that requires more than innovation. It requires clarity, alignment, ethics, and intention.
Read more from Rosie Hewat
Rosie Hewat, Founder & CEO of Rosie’s People
Rosie Hewat is a Board and Executive Advisor and Founder & CEO of Rosie’s People, a leadership and organisational advisory platform. A former Group Chief People Officer and Non-Executive Director, she has supported leadership teams and boards operating in high-growth and regulated environments. Rosie is also a trustee and an Executive Contributor to Brainz Magazine, where she writes on leadership, governance, power, and organisational risk.
References:
This article draws on a combination of industry research, policy developments, real-world workforce observations, and current global reporting.
ONS (UK Labour Market Data, 2026)
Learning and Work Institute (Youth Unemployment, 2026)
Gallup – State of the Global Workplace
CNBC – AI and labour market impact reporting (2026)
Business Insider – Meta layoffs and workforce restructuring (2026)
The Guardian – Tech layoffs and AI transition coverage (2026)
Wall Street Journal – Layoff tracking and corporate restructuring (2026)
Business Today – Global tech layoffs data (2026)
Billboard – AI music and industry impact timeline
The Verge – Spotify verification and platform integrity
