The American job market has split into two distinct tiers, with white-collar professionals experiencing what economists are calling a white-collar recession while lower-earning workers continue to find employment relatively easily. Jon Bach, a director at eBay with 30 years of tech industry experience, exemplifies this trend after being laid off in January and subsequently applying to 135 positions with only 2 callbacks and zero offers.
New data from LinkedIn reveals the sectors hit hardest by the hiring freeze. Tech-related positions have been devastated: IT hiring is down 27%, quality assurance down 32%, product management down 23%, program and project management down 25%, and even traditionally recession-proof engineering roles have declined 26%. Human resources hiring has slumped 28%, while marketing is down 23%. In stark contrast, healthcare hiring is up 10%, and community services are down only 3%.
The causes of this tech hiring freeze are multifaceted. First, tech companies over-hired during the pandemic, with post-pandemic recruitment spiking 89% for product managers, 79% for HR professionals, and 43% for engineers. When economic conditions shifted, companies implemented hiring freezes rather than mass layoffs to reduce headcount through attrition. Second, employees are choosing stability over job-hopping, with voluntary turnover at tech companies dropping from 27% in 2022 to under 20% in 2024.
Artificial Intelligence is emerging as a significant factor in the hiring slowdown. Tools like ChatGPT enable tech workers to complete tasks more efficiently, reducing the need for additional headcount. The productivity gains are particularly evident in coding, where AI-assisted programmers work 56% faster than those working alone. Google recently revealed that more than a quarter of its new code is now AI-generated. Jon Stross of Greenhouse notes that companies may not need to grow as quickly because they can “automate more things and be more efficient.”
The hiring process itself has become painfully slow, extending from an average of 52 days in mid-2021 to 66 days in early 2024. Job openings now receive an average of 222 applications—nearly triple the number from late 2021. Data scientist Santiago Rodriguez applied to 669 positions and created a dashboard to track his search. However, there are signs of recovery: tech job postings have rebounded from 144,000 at year-end 2023 to 223,000 currently, approaching pre-pandemic levels of 322,000.
Key Quotes
I don’t know what’s going on. I’ve been doing this for a minute, and I’ve proven my value. And then you apply to one place, two places, 10 places, 50 places, 135 places. And you go, ‘Am I the guy I think I am?’
Jon Bach, a former eBay director with 30 years of tech experience, expresses the psychological toll of the white-collar job market collapse. His struggle despite extensive qualifications illustrates how dramatically the tech hiring landscape has shifted.
It’s not like you’re going to lay off a whole team. But maybe we don’t have to grow quite as fast because we can automate more things and be more efficient. My guess is there are folks who are trying to make that happen.
Jon Stross, cofounder of Greenhouse (a major hiring software provider), explains how AI is enabling companies to reduce hiring needs without layoffs. This reveals AI’s subtle but significant role in the current hiring freeze.
We have not seen the hiring slowdown everywhere. But there are particular areas where it’s been very dramatic.
Kory Kantenga, an economist at LinkedIn, highlights the uneven nature of the job market downturn. His analysis underscores how tech and white-collar roles are disproportionately affected compared to healthcare and service positions.
In the recruiting world, we call it See More Disease. We really need to coach our managers that even in this market, people with highly qualified, in-demand skills aren’t going to be sitting around forever.
Jenny Diani, senior director of global technical recruiting at Autodesk, describes how the flood of applications paradoxically slows hiring decisions. Companies reviewing three times as many applications are taking longer to make decisions despite abundant qualified candidates.
Our Take
This article provides crucial evidence that AI’s impact on employment is no longer theoretical—it’s happening now in the tech sector itself. The 56% productivity gain from AI coding assistants and Google’s 25% AI-generated code represent a tipping point where AI tools meaningfully reduce labor demand. What’s particularly striking is the irony: the industry creating AI is the first to experience its labor market disruption.
The two-tiered economy emerging here—where AI augments some workers while reducing demand for others—may define the next decade of employment. Tech workers who once seemed invulnerable are discovering that expertise alone doesn’t guarantee job security when AI can replicate significant portions of their work. The recovery in job postings offers hope, but the fundamental equation has changed. Companies have learned they can accomplish more with fewer people when AI tools are properly deployed, and that lesson won’t be unlearned even as hiring eventually rebounds.
Why This Matters
This story reveals a critical inflection point where AI technology is fundamentally reshaping the tech labor market. The fact that AI-assisted programmers are 56% more productive and that Google generates over 25% of its code through AI demonstrates that artificial intelligence isn’t just a future threat to jobs—it’s actively reducing hiring demand right now. This represents a paradigm shift for an industry that historically treated technical talent as an irreplaceable resource.
The implications extend beyond tech workers. If AI can reduce hiring needs in one of the economy’s most dynamic sectors, similar patterns will likely emerge across other white-collar professions. The two-tiered job market—where lower-wage workers find opportunities while six-figure earners struggle—suggests AI’s impact may be felt most acutely by knowledge workers who previously considered themselves immune to automation.
For businesses, this signals both opportunity and risk: AI tools can boost productivity and reduce labor costs, but companies must balance efficiency gains against the need for innovation and growth. The recovery in job postings suggests the market may be stabilizing, but the fundamental relationship between AI capabilities and workforce requirements has permanently changed.
Recommended Reading
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