Another ChatGPT conversation

It started with a quote presented to me.....

"A Stanford University study found that AI adoption has caused a 13% decline in employment for workers aged 22-25 in AI-exposed occupations, while older workers in identical roles remain largely unaffected"

I put it to ChatGPT

"What are the possibilities that some of the decline is due to employers using AI as an excuse to streamline their workforce. In other words, AI is an excuse and not necessarily a cause"

The response is below, but it also offered to dig into "the dataset appendix to see how much of this decline is explained by actual AI adoption intensity at firms versus sector-wide trends"

I said yes, and after a lengthy response this was presented at the tail end..

What this means for your “AI as excuse” hypothesis

• The dataset does control for non-AI shocks, so the measured effect isn’t just macro noise.

• But, the mechanism isn’t necessarily “robots replacing humans” — it’s very plausible firms are using AI adoption as a rationale to stop hiring younger workers and keep a leaner, older, more experienced staff.

• In other words: AI isn’t always the cause — sometimes it’s the justification.

ChatGPT

That’s exactly the right skeptical angle. Just because the data show a 13% employment drop among 22–25-year-olds in AI-exposed occupations doesn’t prove AI itself caused the loss. It could be correlation, camouflage, or opportunism. Let’s unpack the possible explanations:

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1. AI as genuine displacement (direct causal)

• Employers actually adopt AI tools (chatbots, code assistants, text generation) and find they can do the same work with fewer junior employees.

• Early-career staff are most at risk because they tend to handle repetitive or entry-level tasks — exactly the low-hanging fruit for generative AI.

• This is the interpretation the Stanford study leans toward.

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2. AI as cover for cost-cutting (excuse mechanism)

• Companies might already want to trim headcount (because of inflation, slow growth, shareholder pressure, or restructuring).

• AI then provides a convenient narrative: “We’re innovating, not just cutting costs.”

• Investors often reward “AI adoption” announcements — so there’s a signaling incentive to attribute layoffs to AI, even if the tech isn’t really replacing that much work yet.

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3. Generational seniority dynamics (not AI per se)

• Employers may be keeping older, more experienced workers while cutting junior roles.

• Reason: senior staff already know company systems, handle client relationships, and manage risk better.

• AI doesn’t necessarily replace juniors — it just weakens the argument for keeping them as a “pipeline,” since senior staff can use AI to offload some grunt work.

• The effect looks like “AI hit the young workers,” but it might really be an HR strategy about maintaining experience.

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4. Broader macroeconomic noise

• The Stanford paper does control for firm-level shocks, but the period studied (2022–2025) includes:

  • Post-COVID hiring corrections (many companies over-hired in 2020–21).

  • Tech-sector overexpansion and layoffs unrelated to AI.

  • Rising interest rates and capital costs.

• So part of that 13% may not be AI at all, just cyclical correction — and “AI” gets blamed (or credited) because it’s the hot narrative.

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5. Strategic under-hiring of juniors

• Instead of laying off existing staff, firms might just not hire new grads.

• That creates the same statistical outcome: employment among 22–25 year olds shrinks.

• In this case, AI isn’t pushing people out of jobs so much as giving employers an excuse to stop bringing in fresh talent.

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Bottom line

• AI is almost certainly a factor — entry-level work overlaps strongly with what genAI automates.

• But it’s not the only factor. Some of the 13% “AI effect” could be opportunistic cost-cutting, generational workforce strategy, or macro conditions disguised as “AI disruption.”

• Right now, disentangling those effects is difficult — even the Stanford authors caution they can’t prove pure causation, only a strong correlation.