Generative AI’s Workforce Impact

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Stanford Study on Generative AI’s Workforce Impact

Now that we’ve completed our summer school series in the Friday editions of our newsletter, instead of returning to our regularly scheduled programming, giving you insights into tips and tricks to use generative AI tools effectively, we thought we’d dig in a bit into a Stanford study that was released a few weeks back about the impact of generative AI on entry-level workers.

The study in question, “Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence” by Stanford researchers Erik Brynjolfsson, Bharat Chandar, and Ruyu Chen, has been showing up in the headlines in the past few weeks:

These headlines don’t paint a rosy picture. So what exactly does the study tell us?

The six facts reference in the title of the study are as follows (these are subsection headings taken directly from the main section of the article):

• Fact 1: Employment for young workers has declined in AI-exposed occupations

• Fact 2: Though overall employment continues to grow, employment growth for young workers in particular has been stagnant

• Fact 3: Entry-level employment has declined in applications of AI that automate work, with muted changes for augmentation

• Fact 4: Employment declines for young, AI-exposed workers remain after conditioning on firm-time shocks

• Fact 5: Labor market adjustments are visible in employment more than compensation

• Fact 6: Findings are largely consistent under alternative sample constructions

Without going into any of the details of the study, the long and short of it is that entry-level jobs in areas like software engineering and customer service have declined significantly (to be precise, a 13 percent relative decline).

Here I want to focus particularly on two aspects of the study, namely (1) the difference in impact comparing AI applications that automate work with those that augment work, and (2) the lack of a decline in employment for non-entry-level workers due to AI.

Automation vs augmentation

The third fact listed above is that while entry-level jobs are declining in settings where AI can automate work, this is not the case in settings where AI is used to augment the work being done. In other words, employment has remained steady for those jobs that fundamentally rely on human experience and expertise.

For educators like us who are concerned about preparing our students to enter a world of work in which AI is playing an increasingly central role, this insight reinforces our strategy of encouraging students to be well-rounded, not just specializing in one narrow area, but developing a broader range of skills and competencies that are not so easily automated, such as critical thinking, entrepreneurship, cultural agility, design sensibility, communication skills, and organizational leadership.

But this impulse shouldn’t just apply to entry-level workers as we consider the possibility that a wider range of the workforce is increasingly exposed to the impact of AI. How can we equip the workforce to be better prepared to have AI augment their work rather than simply replace it?

Codified knowledge vs tacit knowledge

The second aspect of the study to highlight concerns the question as to why non-entry level workers have so far resisted being automated? Here the authors discuss this question:

“Why might AI adversely affect exposed entry-level workers more than other age groups? One possibility is that, by nature of the model training process, AI replaces codified knowledge, the ‘book-learning’ that forms the core of formal education. AI may be less capable of replacing tacit knowledge, the idiosyncratic tips and tricks that accumulate with experience. As young workers supply relatively more codified knowledge than tacit knowledge, they may face greater task replacement from AI in exposed occupations, leading to greater employment reallocation. In contrast older workers with accumulated tacit knowledge may face less task replacement.”

If Brynjolfsson, Chandar, and Chen are right, then we have another valuable takeaway for educators: We need to find opportunities for our students to develop the sort of tacit knowledge that cannot so easily be automated, for example, through experiential learning, internships, and project-based learning. Similar, we just be sensitive to current members of the workforce. How can we help build upon their codified knowledge to further develop tacit knowledge?

Food for thought

Surely more studies along the lines of the present one are forthcoming as AI is further assimilated into workflows and business infrastructure. In the meantime, the practical move is to audit curricula and job ladders for places where AI can complement—rather than replace—human judgment, and to redesign early-career roles to emphasize mentored practice, cross-functional rotations, and real client exposure. Treat entry-level jobs as launchpads for tacit-skill formation and you blunt short-term displacement while compounding long-term productivity and opportunity.

If you want to look at the study for yourself, take a look below: