This is a widely debated question that economists and researchers approach differently, so I'll give you the range of perspectives rather than a single verdict.
Arguments for AI Displacing More Jobs
Several studies point to significant disruption:
•Roles centered on routine, predictable tasks—data entry, basic bookkeeping, certain customer service functions—are highly automatable.
•Some research suggests generative AI could expose a large share of jobs to at least partial automation, particularly in white-collar work like writing, coding, and analysis that was previously thought "safe" from automation compared to physical labor.
•Historically, automation has often eliminated specific tasks faster than economies create new full jobs to absorb displaced workers, especially in the short term.
Arguments for AI Creating More Opportunities
Other perspectives emphasize job creation and transformation:
•New job categories emerge that didn't exist before—AI trainers, prompt engineers, AI ethics officers, human-AI collaboration specialists.
•AI often augments rather than replaces workers, handling repetitive sub-tasks while humans focus on judgment, creativity, and interpersonal work—potentially making existing jobs more productive rather than eliminating them.
•Historical precedent: previous technological revolutions (industrialization, computers, internet) caused short-term disruption but ultimately created more jobs than they destroyed, often in sectors that didn't exist before the technology arrived.
The More Likely Reality: Net Effect Is Uncertain and Uneven
Most rigorous analyses suggest:
1.Net job numbers may stay roughly stable or grow, but the composition of jobs will shift dramatically.
2.Displacement will be concentrated in certain sectors (customer service, transportation, administrative support, some coding/legal-support roles) while growth concentrates in others (AI oversight, healthcare, skilled trades, creative-strategic work).
3.Timing matters: short-term displacement often outpaces the emergence of new roles, creating painful transition periods even if long-term totals balance out.
4.Skill polarization is a real risk—workers who can adapt/reskill benefit, while those in easily automated roles without transition support face genuine hardship.
Bottom Line
There's no scientific consensus giving a definitive number either way—forecasts vary enormously depending on assumptions about how fast AI capabilities advance, how quickly businesses adopt it, and how effectively economies retrain workers. If you want, I can search for the most recent studies/reports (e.g., from WEF, McKinsey, or academic labor economists) with specific projected numbers—would that be helpful?