AI e mercato del lavoro: ci vuole tanta prudenza
Non bisogna affidarsi a emozioni o impressioni. Il rischio è di prendere decisioni sbagliate. E di correre dietro a mosche cocchiere.
Un recente studio suggerisce che le turbolenze del mercato del lavoro a cui abbiamo assistito siano cominciate prima della diffusione di ChatGPT e della GenAI. Non è ancora referato: potrebbe contenere errori o avere limiti di validità, ma lo cito perché il volume di affermazioni apodittiche sull’effetto dell’intelligenza artificiale sul mercato del lavoro raggiunge ogni giorno livelli sempre più insopportabili. I dati che emergono, da confermare, tendono a smorzare o a confutare le affermazioni più allarmistiche o apocalittiche (come quelle sull’impatto sui giovani di cui discutevo qualche giorno fa).
Eppure non c’è modo di contrastare la visione che, evidentemente, cattura la fantasia di chi vuole cavalcare questa onda tecnologica.
Il fatto che più mi sorprende è che troppi che si dicono “tecnici” o “studiosi” in realtà adottino posizioni fideistiche: “L’AI ha un impatto sul lavoro perché è così, non può essere altrimenti. I numeri sono vecchi e quindi non catturano i fenomeni.” E così via. È una corsa per convincersi e convincere che la visione di un’AI dall’impatto travolgente sia vera anche in assenza di evidenze solide.
Una volta dissi che troppe persone divinizzano l’AI e si riducono ad atteggiamenti fideistici (o interessati). Ovviamente, ricevetti risposte offese e piccate.
Ma io cerco di guardare i fatti, la struttura e la natura dei problemi, i numeri che abbiamo. Da questi traggo le mie conclusioni. Quando avrò evidenze o indicazioni diverse, cambierò idea. Ma la fede la vivo in chiesa, non sul lavoro.
Riporto qui le conclusioni dell’articolo che citavo, scritto da colleghi provenienti da università e centri di ricerca prestigiosi: University of Pittsburgh, Stanford University, AI Economy Institute – Microsoft, Revelio Labs, Chapman University, Columbia University. Le sottolineature in grassetto sono mie.
Taken together, our results indicate that worsening labor-market outcomes in 2022–2024 for LLM-exposed workers and graduates were already underway prior to the mass-market emergence of LLM applications. Unemployment risk in highly exposed occupations rose beginning in early 2022—well before ChatGPT—and in most occupations and states we observe no discrete break coincident with its introduction. Early-career workers were affected disproportionately: graduates from the 2021–2023 cohorts entered highly exposed jobs at lower rates and experienced longer observed delays to their first job than earlier cohorts, with gaps opening, again, before late 2022. At the same time, LLM-relevant education remained valuable within this environment. Graduates whose programs exhibited greater pre-2020 curricular emphasis on LLM-exposed tasks earned higher first-job pay and reached their first jobs more quickly in the post-ChatGPT period, particularly when entering highly exposed occupations. Together, these patterns suggest that LLM diffusion occurred in a labor market already shaped by macroeconomic and sectoral forces, while LLM-complementary skills retained—and possibly increased—their labor-market value.
These findings have implications for research and policy. First, they caution against treating ChatGPT’s launch as a clean natural experiment for AI’s labor-market impact: designs that attribute post-2022 labor-market weakness primarily to LLMs risk confounding AI diffusion with concurrent macroeconomic shifts (possible examples include monetary policy, sectoral demand, and/or post-pandemic adjustment). Second, they suggest that higher education and workforce programs should not abandon skills often labeled “AI-exposed,” such as writing, coding, and information synthesis. Instead, curricula that teach these skills in LLM-complementary ways (e.g., by emphasizing verification, evaluation, and effective human–LLM collaboration) may improve graduates’ resilience in an uncertain labor market.
Our analyses rely on administrative unemployment insurance records, online career histories, task-based exposure measures, and syllabus-derived proxies for curricular content. Each source involves measurement error and selection. In particular, our educational exposure measure is derived from syllabi that predate 2020 and therefore captures baseline differences across programs rather than curricular responses to ChatGPT. Our time-to-first-job analyses exclude individuals whose first observed job begins more than three years after degree completion, which may understate delays among graduates facing the greatest barriers to entry. We do not identify the causal effect of LLMs on labor-market outcomes. Nonetheless, by triangulating across independent data sources, we document timing patterns that are difficult to reconcile with a sudden post-ChatGPT collapse in exposed occupations, shifting attention toward broader labor-market dynamics and toward expanding access to AI-complementary education. Future work combining direct measures of LLM adoption with linked worker–firm data will be critical for distinguishing displacement from productivity gains and for identifying which workers, regions, and institutions benefit—or fall behind—in the next phase of diffusion.
© 2026 Alfonso Fuggetta & Sonia Montegiove. Salvo diversa indicazione, tutti i contenuti di questa pubblicazione sono protetti da copyright e rilasciati con licenza CC BY-NC-ND 4.0: https://creativecommons.org/licenses/by-nc-nd/4.0/deed.it