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Future of Work

A reasoned guide to automation, gig labor, the four-day week, the universal-basic-income experiments, and the awkward question of meaning after the job.

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A reasoned guide to automation, gig labor, the four-day week, the universal-basic-income experiments, and the awkward question of meaning after the job. Key sections include: Will work exist?; Where work is, before AI gets to it; Three waves of automation, again; What the labor share has done; The remote experiment; Which jobs, which tasks; What platforms did to labor; What the trials say; Working less, on purpose; If work shrinks, what fills the room?.

Key sections

  • 01Will work exist?
  • 02Where work is, before AI gets to it
  • 03Three waves of automation, again
  • 04What the labor share has done
  • 05The remote experiment
  • 06Which jobs, which tasks
  • 07What platforms did to labor
  • 08What the trials say
  • 09Working less, on purpose
  • 10If work shrinks, what fills the room?
  • 11Recommended source
  • 12Three plausible 2050s
  • 13Indicators · 2026–2030

Topics covered

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Slide outline
  1. 01Will work exist?
  2. 02Where work is, before AI gets to it
  3. 03Three waves of automation, again
  4. 04What the labor share has done
  5. 05The remote experiment
  6. 06Which jobs, which tasks
  7. 07What platforms did to labor
  8. 08What the trials say
  9. 09Working less, on purpose
  10. 10If work shrinks, what fills the room?
  11. 11Recommended source
  12. 12Three plausible 2050s
  13. 13Indicators · 2026–2030
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Presentation Transcript

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Slide 01

Will work exist?

  • A long read · Volume 6 · Number 08
  • A reasoned guide to automation, gig labor, the four-day week, the universal-basic-income experiments, and the awkward question of meaning after the job.
  • By the editors · with notes from Acemoglu, Susskind & Autor
Slide 02

Where work is, before AI gets to it

  • § Vital signs · 2026
  • Global labor force
  • ~3.6 B
  • ILO 2024 estimate
  • Unemployment (global)
  • ~5 %
  • Stable around the long-run average
  • Remote-eligible roles (US)
  • ~37 %
  • Of jobs · Bloom et al. 2024
  • Gig / platform workers
  • ~12 %
  • Of OECD workforce; mostly part-time supplement
Slide 03

Three waves of automation, again

  • §1 · Automation
  • Every generation since the Luddites has watched a new technology threaten work. Power looms, internal combustion, electrification, the computer, the spreadsheet. Each wave displaced specific tasks; in aggregate, employment grew. The question for AI is whether the pattern holds.
  • Daron Acemoglu & Pascual Restrepo, in a series of papers (2018–24), distinguish automation (replacing labor) from augmentation (complementing labor). The mix matters: automation-heavy phases see wage stagnation and inequality; augmentation-heavy phases see broad-based gains. The 1985–2018 wave was, on their analysis, more automation than the rhetoric admitted.
  • The AI question is open. Generative AI looks more like augmentation in early studies (Brynjolfsson, Li, Raymond, 2023; Noy & Zhang, 2023; Peng et al. on Copilot, 2023). But the same tools, deployed end-to-end with agents, look much more like automation.
  • The honest answer for 2026 is: we don't know which wave AI will be. The institutional choices we make in the next ten years will shape it.
Slide 04

What the labor share has done

  • §2 · S-curve · figure
  • After Karabarbounis & Neiman 2014, Acemoglu & Restrepo 2018; BEA series. Schematic.
Slide 05

The remote experiment

  • §3 · The remote question
  • The 2020 lockdowns ran a global natural experiment on remote work. Five years on, the new equilibrium is hybrid. Stanford's WFH Research (Bloom, Barrero, Davis) finds:
  • ~28% of all paid working days remote in the US (2024)
  • Hybrid-3 (3 office days/wk) the modal pattern in white-collar
  • Productivity neutral to slightly positive on rigorous studies; preference asymmetric — workers value WFH at ~8% of wages
  • Office occupancy stuck at ~50% of pre-2020 levels in major US cities
  • Geography: secondary cities (Austin, Boise, Tampa) absorbed remote-first inflows
  • The downstream consequences — commercial real estate distress, downtown retail vacancy, school district financing — are still working through.
  • Photo · the office has not died; it has lost its weekday monopoly.
Slide 06

Which jobs, which tasks

  • §4 · AI & tasks
  • The right unit of analysis is the task, not the job. Eloundou et al. (OpenAI, 2023) coded 950+ occupations against GPT-4 capabilities; ~80% of US workers had at least 10% of their tasks exposed; ~19% had at least 50% exposed. Highest exposure: tax preparers, writers, mathematicians, web designers.
  • Occupation clusterTask exposureLikely shift
  • Tax prep · bookkeepingVery highCompression of mid-tier; higher-end advisory persists
  • Software developmentHighProductivity multiplier, not displacement (so far)
  • Writing · editorial · marketingHighVolume up, per-piece value down
  • Customer serviceHighTier-1 hollow-out; Tier-3 expert demand stable
  • Healthcare diagnosisAugment-leaningImaging, triage, transcription · clinician judgement remains
  • Skilled trades · plumbing · electricalLowWages rise; embodiment is the moat
  • Caregiving · early-childhoodLowDemographic demand grows
Slide 07

What platforms did to labor

  • §5 · The gig economy
  • Uber, DoorDash, Deliveroo, Upwork, Mechanical Turk turned millions of jobs into discrete tasks priced at the margin. The 2010s brought efficiency and flexibility; also algorithmic management, opacity, and worker classification fights. Prop 22 (CA, 2020), the EU Platform Work Directive (2024), the UK Supreme Court's Uber v Aslam (2021) all push back on bright-line independent-contractor status.
  • What we now know
  • Platform pay is highly variable; mean often above legal wage, median below
  • Most gig workers also have a primary job
  • Algorithmic management produces fatigue and arbitrary discipline
  • Worker organizing is harder, not impossible (App Drivers & Couriers Union)
Slide 08

Slide 8

  • §6 · Voices
  • "The labour question is still open."
  • Daron AcemogluMIT · Nobel 2024 · institutions, automation, Power and Progress (2023)
  • David AutorMIT · the canonical "polarization" thesis; recent work on AI & middle skills
  • Daniel SusskindOxford · A World Without Work (2020)
  • Erik BrynjolfssonStanford Digital Economy Lab · the Turing Trap; productivity J-curve
  • Carl Benedikt FreyOxford Martin · 2013 "47% at risk" paper; corrections since
  • Mary Gray & Siddharth SuriGhost Work (2019) · the human labor inside automation
  • Anna SalomonsUtrecht · long-run wage compression evidence
  • Aaron BenanavAutomation and the Future of Work (2020) · skeptic case
Slide 09

What the trials say

  • §7 · UBI experiments
  • Universal Basic Income, once a fringe idea, has been tested at modest scale in Finland (2017–18, ~2,000 participants), Stockton CA (2019–21, $500/mo), GiveDirectly Kenya (2017–, full-saturation), Kela's earlier negative-income-tax pilots, OpenResearch (Sam Altman's, results 2024). Results are interesting and limited.
  • StudyDesignHeadline finding
  • Finland 2017–18€560/mo, unemployedNo employment effect; well-being up
  • Stockton SEED 2019–21$500/mo, 125 pplFull-time employment ↑; volatility ↓
  • GiveDirectly Kenya 2017–$22/mo, ~10yr saturation villageAsset accumulation, business formation
  • OpenResearch 2024$1k/mo, 3yr, 1k US adultsModest reduction in work hours; spending on basics
  • forecast By 2035 expect 5–10 OECD countries to run formal pilots; full UBI implementation remains politically distant.
Slide 10

Working less, on purpose

  • §8 · Four-day week
  • 4 Day Week Global's UK pilot (2022, 61 firms, ~2,900 employees) reported flat-or-improved revenue, lower burnout, lower turnover. 92% of firms continued. Iceland's 2015–19 trial (1.3% of workforce) had similar results. Spain, Portugal, Belgium have national-scale pilots underway.
  • The mechanism: a 100-80-100 commitment — 100% pay, 80% time, 100% output — forces meeting reform, deep-work protection, and elimination of low-value work. Critics note selection bias (high-knowledge-work firms self-select).
Slide 11

If work shrinks, what fills the room?

  • §9 · The meaning question
  • Susskind's World Without Work ends not with optimism or pessimism but with a problem: leisure is hard. Aristotle thought leisure was where philosophy lived. Modern industrial workers, mostly, have not used leisure that way.
  • The honest sociology says: identity, status, social ties, and time-structure are the four things work currently provides. Replacing it requires explicit institutions — community, creative practice, learning, civic life. Bregman, Standing, Frase argue this is the actually-hard problem, distinct from the income problem.
  • If we automate the wages, we still have to build the meaning somewhere else.
Slide 12

Recommended source

  • §10 · Watch
  • Lex Fridman × Daron Acemoglu
  • Three-hour conversation on Power and Progress, automation, AI, and institutions. The clearest articulation of the augmentation-vs-automation distinction available.
  • youtube.com/@lexfridman →
  • PBS Idea Channel · Future of Work series
  • Shorter, accessible takes; pair with Andrew McAfee's MIT lectures on YouTube.
  • youtube.com/@PBSIdeaChannel →
Slide 13

Three plausible 2050s

  • §11 · 2050 scenarios
  • A · Augmentation
  • 5–10× productivity
  • AI as bicycle for the mind. Wages rise broadly; new jobs emerge; 4-day week common; UBI partial & means-tested. forecast
  • B · Polarization
  • K-shaped
  • High-skill capital owners gain; middle hollows; expanded transfers buffer pain; political stress. scenario
  • C · Displacement
  • Post-labor
  • Most economic value generated by capital + AI; labor becomes optional for most; meaning crisis dominates politics. scenario
Slide 14

Indicators · 2026–2030

  • §12 · What to watch
  • Labor-share trend in OECD (BLS, ONS, Eurostat) — falling further?
  • Productivity J-curve · does measured TFP finally rise?
  • Worker classification rulings (US PRO Act, EU Platform Directive)
  • Frontier-AI agent-capability benchmarks (METR, GAIA) saturating?
  • UBI / NIT-style policies becoming live ballot questions
  • Office occupancy data (Kastle, Placer.ai) — recovery or new normal?
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