AI Economics & Labor, in order.

Open questions 28 open

• AI Usage Cadences
  • Time-of-day rests on IP-inferred location; how much noise do VPNs, travel, and datacenter-routed API traffic inject into the "sleep advice pre-dawn" style claims?
  • The weekend personal-use spike is largest in high-income countries — is that a genuine work/life boundary difference, or a composition effect (who uses Claude for what, where)?
• Context Advantage, Not Taste
  • **Does the asymmetry regenerate faster than it transfers?** The whole human role, under this frame, rests on the answer. Nobody in the corpus has posed it.
  • Ng prefers the frame because it "gives us a clearer path to helping AI systems get better." That is a reason to *adopt* the frame, not evidence that it's true. What would distinguish a context asymmetry from a capability gap empirically? ([[returns-to-expertise]] is the closest thing to an instrument.)
  • If the human's contribution is context injection, is the human *replaceable by better context plumbing* — memory, retrieval, continuous production telemetry — rather than by a better model? That would put the expiry of human-in-the-loop on the infrastructure roadmap, not the scaling curve.
  • Ng writes from 0-to-1 consumer products. Does the frame survive contact with domains where the missing thing is a concept rather than a fact?
• Conversation Artifacts
  • Tokens are a proxy for both compute cost and output value, but verbose models inflate tokens per unit of intent (the same critique [[conversation-to-delegation-shift]] raises); how much of "compute tracks value" is genuine value vs. models simply emitting more?
  • The reading-level "+1 year" gap may be register (terse prompts, polished replies) rather than substance; can it be separated from genuine elevation of content?
  • Artifact classification is first-party and single-model-graded; do the 30+ categories and the work/personal/coursework split survive independent replication?
• Conversation-to-Delegation Shift
  • The token-share metric rewards *verbose* agentic output. How much of the 99.8% / 63.3% / 16.5% spread is a genuine work shift vs. agentic tools simply emitting more tokens per unit of human intent?
  • OpenAI-internal is a frontier preview *by assumption*. Does the external organizational curve actually trace the OpenAI path (the paper's implicit claim), or does it plateau where adoption frictions don't vanish?
  • "Asking is half of ChatGPT, doing is most of Codex" — but the two tools self-select different work. How much of the asking→doing contrast is the shift itself vs. routing pre-existing "doing" tasks to the tool built for them?
• Experimental Learning Impact of Generative AI
  • Time-on-task is held fixed by the lab; the authors flag that real-world learning depends on how students reallocate saved time. Does the augmentation dividend survive once students can spend the hour AI frees on something else entirely?
  • Gains skew to the able (upper GPA/SAT quartiles). Is the widening-gaps signal a durable property of unrestricted AI, or an artifact of a high-ceiling elite sample where the bottom quartile has little room to move?
  • The augmentation/automation choice is *endogenous to incentives* (grade inflation and signaling-motivated students push toward automation). Can incentive or interface design shift the mix toward augmentation at scale — and would that reverse the deskilling half?
  • Does the same use-mode split govern **workplace** skill accumulation (the open question [[automation-optimism-link]] and [[ai-brain-fry]] leave for workers), or is a proctored one-week academic task too unlike on-the-job learning to transfer?
• Exposure Taxonomy: Observed, Theoretical, Reported, Anticipated
  • Binned midpoint coding biases the exposure slopes toward zero; how much of the "uniform rising tide" is substance vs. coding artifact (the report checks robustness with a ≥60%-of-tasks indicator, but the levels remain self-reported)?
  • Reported exposure exceeds observed partly because the survey reaches heavy users; what does the reported/observed gap look like in a representative sample? **Sharpened:** [[market-priced-ai-exposure|realized-consumption measurement (Borri-Liu-Tsyvinski)]] adds a market-implied instrument built on 380T tokens of *actual* paid requests — but it is skewed toward developers/sophisticated users (OpenRouter is ~2% of global tokens), a *different* non-representativeness than the survey's heavy-user skew. The lesson: no current AI-exposure instrument is representative; each collection mechanism biases in its own direction, so the reported/observed/market-implied gaps are partly artifacts of *who each method reaches*. A representative census remains the open target.
• Firm AI-Spend Intensity and Headcount Growth
  • **What operational mechanism converts intensive AI spend into hiring?** The paper establishes the correlation (adopters, especially intensive ones, grow) but explicitly cannot say *why* — product acceleration, sales productivity, engineering leverage, support automation, faster analysis, or new business lines are all candidates, and the firms that cracked it have no incentive to share.
• Market-Priced AI Exposure (the AI Premium)
  • **How much does the developer skew move the answer?** OpenRouter's slice is unrepresentative; would a representative realized-consumption panel (if one existed) price the same firms and skills, or is the frontier/intensive-margin concentration partly a sampling artifact of who uses OpenRouter?
  • **Why is the market-implied skill map orthogonal to every task-based measure (<2% variance)?** Is market-implied exposure capturing genuinely different information (forward-looking rents, complement/substitute *value* rather than technical automability), or is it noisier — and which should labor-impact forecasts trust?
• Organizational Complements to AI
  • The "digital production diffuses faster than electrification" claim is asserted from one favorable internal case. Do external organizations actually redesign workflows quickly, or does the low cost of *tool* adoption mask slow, expensive *process* redesign (the real complement)?
  • Which complement is the true binding constraint — access/permissions, skills, or review capacity? The paper lists all; it doesn't decompose their relative weight.
  • If complements, not capability, gate value, does model progress have *diminishing* near-term returns until orgs catch up — and how long is that lag for agentic AI specifically?
• Returns to Expertise in Agentic Coding
  • Outcomes are transcript-inferred (verified success leans on git activity + explicit affirmation). How much of the management edge — and the whole success gradient — is *real* outcome vs. who-narrates-success-in-the-transcript?
  • The study excludes headless / SDK / IDE usage (a "substantial share"). Does the returns-to-expertise pattern hold in non-interactive and pipeline use, where there is no human steering mid-session at all?
• The Automation–Optimism Link
  • Selection vs. treatment: tenure controls attenuate but don't eliminate the enthusiast-selects-into-delegation story. Does a within-person design (sentiment before/after adopting automated workflows) hold the effect?
  • The sample is heavily computer/math + management and 88% men; how much of the automation–optimism link survives in a representative population?