Theme of inquiry
How does AI adoption reshape employment and wage structures?
A question within its area, explored through 9 lines of inquiry below — each a family of specific questions the research asks.
99 specific questions
- Do workers succeed with AI tools when formal deployment stalls?
- Are organizational incentives blocking AI's return on investment?
- Why do only large firms currently adopt AI technologies?
- Why do organizations fail to integrate AI into existing work cultures?
- How much does platform design influence AI adoption rates?
- Why do external partnerships outperform internal AI team builds?
- Does AI adoption create returns to scale in internal firm capability?
26 specific questions
- How quickly do firms substitute labor for AI compared to their actual capability?
- Why does AI adoption favor automation over augmentation in female-dominated work?
- Which AI technologies actually create new jobs versus replace workers?
- Can we classify AI technologies by their actual effects on worker value?
- How do augmentation and automation differ as organizational responses to AI?
- Which AI technologies actually benefit workers versus displacing them?
- How much does AI actually automate versus augment in real workplace tasks?
40 specific questions
- Can employers tell when applicants use generative AI tools?
- Do candidates prefer being screened by AI or by humans?
- Do job candidates prefer or want to be screened by AI systems?
- What counts as AI deception in job applications versus legitimate use?
- Would human recruiters supervised by AI show similar self-preference patterns?
- Do institutional records like reviews substitute for written job applications?
- Do recruiters understand what their hiring algorithms actually prioritize?
68 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?
- Does individual AI productivity gain translate to enterprise-level business results?
- Do executives perceive larger AI productivity gains than independent measures show?
- Can worker time savings exist without visible productivity gains?
- What explains the gap between perceived and measured AI productivity gains?
80 specific questions
- Does AI assistance erode skill development over time among professionals?
- Does AI assistance help people learn skills or just delegate the task?
- Does AI assistance harm skill formation across different professional domains?
- Does AI assistance improve worker learning on the job?
- Does outsourcing tasks to AI reduce opportunities for skill development?
- Can we measure perceived skill change against actual independent task performance?
- Does constraining AI access during early task phases preserve skill formation?
86 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 building inference infrastructure differ economically from building AI applications?
- Does annualized capex or operating expense drive inference pricing?
- What efficiency gains could slow the growth in AI training costs?
- Does competitive pricing pressure explain most AI cost decline patterns?
72 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?
- What skilling outcomes does each human-GenAI collaboration mode produce?
68 specific questions
- Does deploying AI uniformly across task types increase or decrease workplace inequality?
- Does discretionary AI adoption widen gender and skill gaps in workplaces?
- Do less experienced workers gain more from AI assistance than experienced ones?
- How does concentration of AI capability across firms affect labor market outcomes?
- Does AI adoption rise or fall as worker education and wages increase?
- Does AI assistance reduce effort differently for novice versus expert workers?
- Does democratizing AI access actually improve or impair human skill development?
68 specific questions
- Are reduced hires or worker departures driving the AI-exposed occupation shortfall?
- How does concentrated AI exposure across workers affect firm-level employment demand?
- Does the gap in AI-exposed occupations reflect lower pay or fewer jobs?
- Do firms with high AI exposure shed jobs or reshape roles?
- Does task-level AI exposure predict which jobs will be rehired versus eliminated?
- Does widespread AI exposure hurt workers more than concentrated exposure?
- Why do some AI-affected occupations see earnings fall while others don't?