The shift in corporate language often signals a deeper transformation in how we value human labor, moving from a culture of “colleagues” to a cold assessment of “human capital.” In this environment, the rise of artificial intelligence has prompted executives to categorize the workforce into three distinct groups: those who create, those who persuade, and those who track. Simon Glairy, a veteran in insurance and risk management, joins us to discuss the implications of this new taxonomy. We explore the chilling rise of the “Measurer” category, the psychological weight of being labeled high-risk for displacement, and the specific strategies professionals can use to anchor themselves in an increasingly automated landscape. Our conversation delves into the survival of the “unglamorous middle” of the workforce and how trust remains the ultimate human currency that no algorithm can yet mint.
The corporate world was recently shaken by the term “lower-value human capital” used to describe certain employees. From your perspective in risk management, what does this linguistic shift reveal about how leadership views the modern workforce?
This terminology is a stark departure from the language of “personnel” or “colleagues” that we grew up with, and it suggests a view of people as mere entries on a balance sheet rather than living parts of an organization. When a CEO looks at his staff and sees “lower-value human capital,” he has essentially spent so much time staring at spreadsheets that the numbers have lost their human faces. It is the language of efficiency over empathy, a mindset where a company can celebrate record revenues and 30% growth while simultaneously laying off 20% of its workforce because they are seen as redundant assets. This isn’t just about corporate gibberish or “synergy”; it is a fundamental neurological shift in how executives perceive the value of human effort. We see leaders posting on LinkedIn to walk back these comments, but the initial slip reveals a cold taxonomy that prioritizes task routinisability over the complex, messy reality of being a human at work.
Many organizations are now adopting a framework that divides staff into Builders, Sellers, or Measurers. Could you elaborate on which specific roles fall into these categories and why one group is facing a much higher risk than the others?
The “Measurer” category is currently standing in the crosshairs of the AI revolution, encompassing vital but vulnerable roles in finance, compliance, legal, operations, and internal audit. According to recent industry estimates, these roles often carry a risk score of 70 or higher because their tasks are highly structured and involve processing information that AI can now handle with a level of precision and tirelessness no human can match. On the other hand, Builders are the creators who design products and systems, while Sellers are those who drive revenue through persuasion and relationship building. While Builders and Sellers are often seen as “safe” because they require high-level creativity or human-to-human connection, Measurers are being viewed as replaceable by AI-native systems. This creates a terrifying reality for the “unglamorous middle” of the workforce—those reliably competent people who have spent decades reconciling accounts or managing middle-management workflows.
With AI displacement scores for some roles exceeding 70, how should a professional in the “Measurer” category assess their own vulnerability without falling into a state of panic?
The most honest way to evaluate your position is to look at whether a well-prompted AI could produce 80% of your daily output at an acceptable quality level. You have to ask yourself if your value lies primarily in processing and routing information, or if you are actually providing the judgment and accountability that keeps a system from breaking. If your organization would simply redistribute your calendar rather than feel a structural loss if you left tomorrow, that is a red flag. However, even within the Measurer category, those who hold deep contextual knowledge of a specific client or domain that isn’t written down anywhere are much harder to replace. The goal is to move from being an interchangeable job title to being the person colleagues ask for by name because of your unique handle on genuinely novel situations.
There is a growing concern for workers in their 50s who have mortgages and decades of experience in roles that are now being automated. What is the reality for these individuals who are being told they must “reposition” themselves?
Asking someone who is fifty-three years old and has spent twenty years being excellent at reconciling quarterly accounts to suddenly reinvent themselves is a massive, unglamorous burden. These individuals are being told to become “AI-native” on their own time and at their own expense, essentially competing with machines that do not need to sleep or pay a mortgage. It feels less like the creative renaissance promised by economists and more like a struggle for survival in a world that no longer values traditional competence. There is a profound gap between the optimistic corporate talk of “repositioning” and the reality of a person whose role has been reduced to a score on a risk assessment table. For these workers, the challenge isn’t just learning a new tool; it’s proving that their twenty years of experience provides a level of human context and trust that a language model simply cannot simulate.
If AI is becoming fluent and tireless in writing and data processing, what are the specific human traits that currently serve as a “moat” against automation?
The strongest defense against displacement is the ability to handle genuinely novel situations that have no established playbook or data set for an AI to learn from. Trust is the primary product in many high-stakes industries, and humans still fundamentally want to deal with other humans when things go wrong or when high-level accountability is required. Creativity that produces things people didn’t even know they wanted, along with the physical presence required for skilled trades or care work, remains very difficult for AI to replicate. Furthermore, having a deep, unwritten understanding of an organization’s internal politics and long-term relationships creates a layer of indispensability. While an AI can write a fluent report, it cannot show up personally to build a relationship or offer the “gut feeling” that often guides the most critical business decisions.
How does the current trend of AI-driven layoffs during periods of high growth change the traditional psychological contract between an employer and an employee?
The traditional contract, which suggested that loyalty and “reliable competence” would lead to job security, has been effectively shredded by the new corporate religion of measurability. When companies like Cloudflare grow at 30% yet still cut a fifth of their staff, it sends a clear message that no amount of success protects you if your role is deemed a “Measurer” task. This creates a culture of perpetual anxiety where employees feel they are constantly auditioning for their own jobs against an ever-improving algorithm. The irony is that by treating people as “lower-value human capital,” companies may be destroying the very trust and institutional knowledge they will need to survive when the next novel crisis hits. We are moving toward a future where the human connection isn’t just a soft skill, but the core product itself, as everything else becomes a commodity.
What is your forecast for the professional landscape over the next five years as these AI risk scores continue to evolve?
I expect we will see a dramatic thinning of the “middle” of the workforce, where anyone whose job description can be distilled into a series of repeatable data-processing steps will face a risk score that stays well above 70. This will lead to a bifurcated economy where you are either a Builder/Seller with high-level agency or a service worker providing the physical presence and empathy that machines lack. The “Measurer” roles won’t disappear entirely, but they will be radically transformed into “AI Orchestrator” roles, where one human manages the output of several systems rather than doing the work themselves. For the individual, the forecast is one of constant adaptation, where the most valuable skill won’t be what you know today, but how quickly you can pivot to handle the “novel situations” that the machines haven’t yet mastered. The human element will become a premium luxury, and those who can navigate the space between cold data and warm human relationships will be the ones who thrive.
