AI won’t kill your job — it will kill the path to your first one

Two inconsistent phenomena seemingly can be true at the same time: AI is seen as disrupting jobs, and, yet, on the surface, it appears as if less is happening than meets the eye. Where you stand on AI depends on whom you talk to. Schools now feverishly compete to prepare graduates with simplistic educational remedies driven by competitive branding agendas, providing symbolic curriculum overhauls as recruiting and job-placement signals, regardless of whether such courses share a coherent body of core knowledge. With recent New York Federal Reserve Bank research showing that computer science majors now have more trouble finding jobs than humanities majors, the risk of misleading students with false curriculum certainties is genuine.

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As a core course developer at one of the world’s top three business schools confessed to us:

“Our faculty are passionate, but there are two problems. One is that the AI models are developing so quickly and proliferating across so many uses that it’s hard for teachers to put together courses that aren’t quickly outdated. The second problem is that a growing number of students have experience with these models, in some cases a lot of experience, which an amount that far outpaces that of the faculty, so it’s hard to develop course material that adds to what they already know.”

The workforce warnings, in particular, are getting louder, with a mix of smart alerts and a cacophony of cliches. Verizon CEO Dan Schulman has bluntly predicted that AI will cause unemployment to rise by up to 30% in the next two to five years. The Boston Consulting Group (BCG) issued a clear-eyed report suggesting that 10%-15% of existing jobs could be eliminated as soon as 2031. Then, of course, Anthropic CEO Dario Amodei has made many headlines forecasting that AI could wipe out half of all entry-level white-collar jobs within five years and push unemployment into double digits.

Others see something very different: a productivity boom, not a wave of layoffs. Yet their data tells a similarly contradictory story. A recent Goldman Sachs analysis, for instance, estimates AI is already reducing U.S. employment by roughly 16,000 jobs per month. At the same time, demand is rising in adjacent areas—from data centers to AI development—creating new roles even as others disappear. In a sweeping global study, the National Bureau of Economic Research found that AI has had little to no impact on employment or productivity in almost 90% of firms over the past three years, based on responses from nearly 6,000 C-suite executives.

On the surface, the broader labor market also looks fine. Unemployment remains near historic lows, around 4%. But look closer, and the cracks begin to appear: unemployment among recent graduates has climbed to nearly 6%, rising twice as fast as the rest of the workforce since 2022.

Both sides are right, and both are missing the point. Technological advancements are only beginning. Agentic AI is the next frontier and the real productivity driver that enterprises desire.

The problem is not that the AI jobs debate is exaggerated. It is being framed incorrectly. We keep asking whether AI will trigger mass layoffs, as if disruption must show up all at once. But that is not how this transition unfolds. Across industries, as Agentic AI scales, the changes are already happening—just quietly.

Agentic AI Is Steadily Scaling

The nature of work inside firms is also changing. Early generative AI tools accelerated discrete tasks—drafting text, summarizing documents, writing code, or answering customer questions. Agentic AI will go even further.

Unlike chatbots that respond to prompts, agents can take on broader objectives. They break work into sub-tasks, invoke tools, move across systems, and revise their approach with limited human input. The shift is no longer just from human work to machine assistance—it is from task automation to workflow automation. Currently, the technology is primarily used for low-risk, highly repeatable tasks, but successful applications to more ambitious use cases are emerging.

An analysis from the Yale Chief Executive Leadership Institute tracked how this transition is already underway.

Major banks are deploying agentic systems across retail workflows and credit underwriting, including credit-risk memo production, delivering productivity gains of 20% to 60% and reducing turnaround times by roughly 30%.

Telecommunications operators are implementing agents for customer service and network remediation, with some deployments reporting a more than 60% reduction in manual network operations through automated provisioning.

Manufacturers are using multi-agent systems to reduce R&D cycle times by approximately 50% and increase order intake by 40% in early deployments.

Logistics giant C.H. Robinson is handling approximately 29% more Less-Than-Truckload (LTL) volume while employing 30% fewer employees than in early 2019 and roughly half of carrier bookings are no