Theme of inquiry
How does AI adoption reshape labor markets and organizational structures?
A question within its area, explored through 7 lines of inquiry below — each a family of specific questions the research asks.
127 specific questions
- Are reduced hires or worker departures driving the AI-exposed occupation shortfall?
- Does task automation or task reallocation drive wage effects?
- How does concentrated AI exposure across workers affect firm-level employment demand?
- Which AI technologies actually create new jobs versus replace workers?
- Do scope gains from AI create job instability despite higher output?
- When does task reorganization from AI actually translate into wage changes?
- How do skills demanded in AI-exposed occupations differ from other sectors?
89 specific questions
- Can we distinguish real AI productivity gains from statistical measurement artifacts?
- Does AI productivity translate into measurable enterprise profit gains?
- Why do most organizations lack reliable data on AI's actual impact on productivity?
- Can self-reported productivity surveys measure AI's real workplace impact?
- Can worker time savings exist without visible productivity gains?
- How much can self-reported AI use tell us about actual productivity changes?
- Do executives perceive larger AI productivity gains than independent measures show?
65 specific questions
- How much do typical users actually save from declining AI inference costs?
- Can inference prices fall faster than AI usage grows?
- Can inference demand itself change based on model price and speed?
- Does annualized capex or operating expense drive inference pricing?
- How do energy efficiency measurements compare to price-based algorithmic efficiency estimates?
- Does competitive pricing pressure explain most AI cost decline patterns?
- Does building inference infrastructure differ economically from building AI applications?
68 specific questions
- Does generative AI adoption shift work away from coordination tasks?
- Does AI adoption push knowledge work away from communication toward solo tool use?
- Does generative AI substitute for labor or complement worker productivity?
- Does generative AI push knowledge workers toward different types of tasks?
- How do work and nonwork adoption patterns differ for generative AI?
- Can individuals using AI match the output of teams without AI?
- Does AI shift knowledge work away from communication toward solo documentation tasks?
20 specific questions
- How much of frontier AI cost growth comes from chips versus skilled staff?
- How much of frontier AI training costs come from staff versus hardware?
- What drives consolidation of frontier training onto specific hardware vendors?
- Why did frontier training consolidate around specific chip designs after V100?
- Which spending categories drive the rising cost of frontier AI training?
- Did non-frontier AI training spread across more chip designs than frontier training?
- How long do leading AI chip designs stay in use for frontier training?
69 specific questions
- Do workers succeed with AI tools when formal deployment stalls?
- Why do organizations fail to integrate AI into existing work cultures?
- Why do only large firms currently adopt AI technologies?
- How do management practices shape whether AI adoption pays off?
- Are organizational incentives blocking AI's return on investment?
- Does AI adoption create returns to scale in internal firm capability?
- Do larger firms and smaller firms respond differently to AI adoption pressures?
37 specific questions
- Do candidates prefer being screened by AI or by humans?
- Can employers tell when applicants use generative AI tools?
- Do job candidates prefer or want to be screened by AI systems?
- Would human recruiters supervised by AI show similar self-preference patterns?
- What counts as AI deception in job applications versus legitimate use?
- Do institutional records like reviews substitute for written job applications?
- Do recruiters understand what their hiring algorithms actually prioritize?