Author: IRPA AI Analyst & Senior Advisor, Chris Surdak

Recently, I caught up with two colleagues who worked primarily in the world of executive recruiting. One was focused in fulfilling high-tech roles in the United States, the other with filling executive roles across Latin America. Both I have known for a decade or more. Both agreed that their jobs were being dramatically transformed by Artificial Intelligence (AI). And both agreed that the changes that they were witnessing were not what they necessarily expected.

Since the release of transformer-based generative AI systems in 2022 there has been a great deal of worry that this technology would lead to substantial disruption and dislocation in the labor market. There is some evidence that AI is causing lay-offs while simultaneously creating astronomical demand for people who claim to have AI-related skills. The lower end of knowledge work stands to be replaced by automation while those who understand and can build these systems are extremely difficult to locate and retain. I wrote about this very topic over a decade ago in my column in The European Business Review. In this article, I discussed that one of the most-predictable effects of AI would be a deep polarization in the workforce. Low-skill, repetitive, administrative, commoditized, cost-sensitive work would rapidly be automated, while high-skill, intellectual, creative, revenue-generating work would command a large premium. Middle-of-the-road jobs would be partially-replaced by AI, but the really forceful changes to the labor market would be at the extremes.

Assessing the labor market a decade later, I find that those early predictions were fairly accurate. Low-value, cost-sensitive positions such as call center workers are rapidly being replaced by AI agents, while high-value, expertise-rich jobs such as data analysts or AI architects are seeing explosive demand. It is not uncommon for provably-talented AI architects to command high-six-figure base salaries, as companies scramble to keep up with the wave of AI adoption across the globe.

Last week I noted in my LinkedIn feed a position for a Vice President of Product Management with a mid-sized technology firm. This is notably a high-level position, requiring both technical and business acumen and decades of experience. The post showed that it was only 5 hours old, yet it had received over 2,800 applications in that time. As a recent article in the Society of HR Management suggests, an overall labor shortage should persist through 2025, despite the extremely high number of people who appear to be looking for employment.

This leads to a wide range of disquieting questions. If the labor market has been declared to be so strong for the last several years, why would a senior level position capture so many applications in such a short span of time? What percentage of those applications were truly qualified for such a senior position? If a high percentage of those applications were qualified is this role representative of the type of jobs that will be replaced by AI?

Battle of the Agents

The adoption of AI agents in recruiting will necessarily create a war of escalation between workers and employers. As recently reported in Forbes Magazine, AI agents can dramatically-increase the reach and effectiveness of job searches, while employers simultaneously deploy counter-agents to sift through an ever-growing flood of applicants. This whole process is further complicated by the persistent issue of AI hallucination and context loss, leading to situations where you can never fully trust the results of their efforts.

This also led to a discussion of the escalating war between agents in the recruiting process. As agentic AI grows in popularity its use is becoming more common amongst both job seekers and employers. As such, there is increasing evidence that candidates are using AI to flood the market with applications in recognition that getting employment is simply a numbers game, while employers are also using AI in an attempt to weed out the vast number of applications in an effort to find the “perfect” candidate.

It is easy to see a not-to-distant future where employers receive tens of thousands of applications for a position for which perhaps two or three candidates emerge as “perfect fits,” only to discover these “perfect fits” actually aren’t. They either don’t actually have the qualifications that they claim, or because they *do* actually have these qualifications they have dozens of companies chasing after them, and hence they can command substantially more compensation than their suitors are willing or able to pay. As I pointed out in my article from a decade ago, high-end talent hiring will look very much like professional sports, where only the top 3-4 teams are able to hire the best talent, further locking in their competitive advantage against smaller teams with smaller payrolls.

There’s no easy answer for how organizations must address this issue. In part, they must participate in this agentic numbers game, or otherwise be overwhelmed by applications. On the other hand, applying too much automation in an attempt to weed out less-qualified candidates may prove counterproductive, as it will lead to everyone chasing the same small handful of candidate which will amplify the imbalance in the market. As with most instances where automation is applied to human processes, the more we automate, the more human judgment, intuition and interaction will be the defining factor of success. As has been the case for most of human history, the more we rely upon technology, the more our success depends upon non-technical, humanistic factors.

About the Author


Chris Surdak is a Senior IRPA AI Advisor and was formerly White House Chief Transformation officer, Automation & AI Practice Lead at EY & Executive Partner for Digital Transformation at Gartner. He’s an engineer, futurist, transformation executive and best-selling author, with over 30 years’ experience in technology development and deployment, digital transformation, blockchain, data and analytics and AI & intelligent automation.


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Originally posted on 2025-01-31 in the IRPA AI Network — Enterprise AI

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