Evidence that AI is destroying jobs for young people

Timing vs. AI Adoption

• Several commenters note that hiring drops for software engineers and customer service roles begin in mid‑2022 / early‑2023, before widespread LLM deployment in mid‑ to late‑2023.
• This timing mismatch fuels skepticism that AI itself is the primary initial cause; AI may instead be riding on pre‑existing trends and later used as a justification.

Alternative Explanations: Rates, Overhiring, Tax Code, Macro

• End of zero‑interest‑rate policy and rapid rate hikes are repeatedly cited as major drivers: cheap-money overhiring in 2020–22, then sharp reversals when capital got expensive.
• Pandemic overhiring and subsequent “corrections” are seen as a core story; many argue that junior workers always suffer most in downturns.
• Multiple comments focus on U.S. tax changes (especially Section 174/179 under the 2017 tax act) that suddenly made R&D and software salaries more expensive starting 2022, possibly triggering tech layoffs; later partial reversals may not yet have had time to show in the data.
• Broader macro factors mentioned: post‑COVID hangover, inflation, tariffs, geopolitical tensions, global youth unemployment, and general “uncertainty” discouraging new hiring.

Offshoring, Immigration, and Coordination Theories

• Some argue jobs aren’t disappearing but moving to cheaper geographies (BPO/call centers, offshore dev), with AI used as a scapegoat.
• Others blame immigration and visa policy (e.g., H‑1B) for depressing entry‑level opportunities.
• A minority push explicit collusion/cartel narratives: coordinated suppression of wages and junior hiring under the cover of AI “efficiency.”

Critiques of the Study and Data

• Commenters question whether the paper adequately controls for ZIRP, Section 174, and sector‑specific shocks.
• One detailed reading suggests the headline charts are misleading and that the key AI‑exposure signal for young workers only becomes clear in mid‑2024.
• Others build toy models showing that demographic bucketing (people aging out of “young” cohorts) alone can mimic the observed patterns.

Collapse of Junior Hiring and Training Pipeline

• Many report teams explicitly stopping junior hiring since COVID, citing lack of mentoring capacity and fear of training people who will quickly leave.
• AI and “do more with less” rhetoric now provide an easy justification to formalize this: new roles must be “AI‑literate” and senior, shutting out true entrants.
• Several see this as a long‑term problem: no juniors now means no seniors later, but firms treat training as someone else’s problem.

What AI Is Actually Doing

• Mixed views on real productivity gains: some firms adjusted staffing in 2022 anticipating AI; others see AI projects stalled while outsourcing and cost cuts advance.
• Clear displacement is reported in translation, copywriting, illustration, and some customer service; elsewhere, AI is viewed more as fancy autocomplete that may cut marginal headcount but not whole teams.
• A recurring distinction is drawn between “AI actually doing the work” vs. “AI hype driving executive decisions and capital away from hiring.”

Narratives, Media, and Ideology

• Some see “AI is killing jobs” as useful hype for AI vendors, investors, and media clickbait; others frame anti‑AI reactions as neo‑Luddite but rooted in real inequality concerns.
• Commenters also note partisan or institutional biases in outlets pushing the story, and warn against treating heavily confounded 2020–25 data as clean evidence of AI’s impact.