DATASCI 101: Introduction to AI Applications

Lecture 21: Labour Markets and Economic Effects

Danilo Freire
danilo.freire@emory.edu

Department of Data and Decision Sciences
Emory University

Welcome back! 💼

Recap of last class

  • AI strains privacy: collection at scale plus inference of sensitive data
  • GDPR (EU): rights of access, erasure, portability
  • US: patchwork of sectoral laws (healthcare, finance) plus 23 state privacy laws
  • Differential privacy, federated learning, synthetic data help, but have trade-offs
  • Collection is the core problem: the same data enables benefits and surveillance
  • Today: what does AI mean for jobs and the economy?

Source: X.com

Lecture overview

What we will cover today

Part 1: The automation question

  • Historical perspective on technology and jobs
  • Why “this time might be different”
  • Task-based framework for thinking about AI

Part 2: Current evidence

  • What are people using AI for?
  • Productivity effects
  • Early job market signals

Part 3: Economic scenarios

  • Optimistic views: augmentation and new tasks
  • Pessimistic views: displacement and polarisation
  • What economists actually think

Part 4: What does this mean for you?

  • Skills that may remain valuable
  • Adapting to an AI-influenced economy
  • The graduate job market in 2026

Meme of the day 😄

Source: r/ProgrammerHumor

The automation question

This isn’t the first time

Historical fears of technological unemployment:

Era Technology Fear Outcome
1810s Power looms Luddites destroy machines Textile jobs changed but grew
1930s Mechanisation Keynes: “technological unemployment” Post-war prosperity
1960s Mainframes JFK warns of job losses Service sector expansion
1990s Personal computers End of clerical work New jobs created
2010s Robots Manufacturing decline Local job and wage losses

The pattern so far:

  • Technology destroys some jobs and creates others
  • The net effect has been positive over time
  • The adjustment periods are painful

Rage against the machine: the Luddites

Source: Current Affairs

Every wave of automation sparked fears of mass unemployment. So far, those fears haven’t materialised. But past performance doesn’t guarantee future results! 😅

Why this time might be different

Previous automation:

  • Targeted physical tasks, so humans kept a cognitive advantage
  • Task-specific: a loom weaves cloth, a lathe cuts metal, an ATM dispenses cash

AI is different:

  • Targets cognitive tasks too
  • General-purpose: the same model writes essays, analyses data, and generates images
  • Improving very rapidly
  • “Computers can’t do intellectual work” and “computers can’t be creative” both look shaky now

Counter-argument:

  • Similar claims were made before
  • We keep underestimating human adaptability
  • New tasks we can’t imagine will emerge

Source: Edelson Institute

AI will change work. Everyone agrees on that much. The real unknowns are how fast it happens and who pays the price while the rest of us adjust.

Tasks vs jobs

A more useful framework (Acemoglu & Restrepo, 2019; HBR explainer):

  • Think about tasks being automated, not jobs being replaced
  • Most jobs contain many tasks, and AI might automate some but not all

Example: Lawyer

Task AI capability
Document review High
Legal research High
Contract drafting Medium-high
Client counselling Medium
Court appearance Low
Negotiation Low
Judgment calls Low

The effect:

  • AI handles the grunt work, so firms need fewer lawyers per case
  • The role shifts toward higher-value tasks (counselling clients, arguing in court)
  • The lawyer doesn’t disappear, but firms hire fewer of them. What happens to the rest?

Source: The Economist

Which tasks are most exposed?

High exposure to AI:

  • Content generation (text, code, images)
  • Data analysis and pattern recognition
  • Translation and summarisation
  • Customer service (text-based)

Lower exposure (for now):

  • Physical work in unpredictable settings (construction, plumbing)
  • Jobs needing your physical presence (nursing, firefighting)
  • Real-time judgment in new situations (emergency medicine, crisis management)
  • Work built on deep personal relationships (therapy, social work)

Exposure by occupation: (AI exposure rises with wages, but there are exceptions)

Occupation Exposure
Telemarketers Very high
Accountants High
Paralegals High
Software developers Medium-high
Teachers Medium
Nurses Medium-low
Electricians Low
Plumbers Low

Sources: Frey & Osborne (2017); Eloundou et al. (2023); IMF (2024)

Current evidence

What are people using AI for?

OpenAI’s research on ChatGPT use (2025):

Category Jul 2024 Jun 2025
Practical Guidance ~29% ~29%
Writing ~36% ~24%
Seeking Information ~14% ~24%
Technical Help ~12% ~5%
Multimedia ~2% ~7%

Among work-related messages only (Jun 2025): Writing ~40%, Practical Guidance ~24%, Technical Help ~10%.

What this tells us:

  • Only 27% of use is work-related, 73% is personal
  • Writing’s share fell as people found other uses
  • Technical help may have moved to coding tools (Copilot, Codex)

Source: OpenAI (2025)

Productivity effects: early evidence

Studies finding positive effects:

Important caveats:

  • Most studies are short-term, so long-term effects stay unclear
  • Often sponsored by AI companies
  • Lab settings differ from real work

The pattern emerging:

  • AI compresses the skill distribution: novices improve more than experts
  • Experts sometimes do worse with AI
  • Benefits concentrated in specific tasks

The graduate job drought

What the data shows (2024-2026):

Financial Times reporting (2026):

  • UK graduate vacancies at a 10-year low
  • Tech companies leading the pullback
  • Law and consulting firms reducing trainee intakes

The logic (from the employer’s side):

  • Junior employees used to do the tasks AI now handles
  • AI doesn’t need healthcare, holidays, or mentoring

Keep in mind that this is UK data.

Source: Financial Times (2026)

This matters for you personally: if companies replace junior roles with AI, where do you get your first job?

But the aggregate numbers are confusing

What we’re not seeing (yet):

  • Mass unemployment
  • Dramatic productivity surge
  • Clear sectoral collapse

What we are seeing:

  • Slower hiring rather than mass firing
  • Some sectors growing, others shrinking
  • Geographic concentration of effects
  • Young workers hit first; experienced workers show no gap

Why the disconnect?

  • Adoption takes time (most firms are still experimenting)
  • Many tasks don’t work well with current AI
  • Regulations, contracts, and organisational inertia slow everything down

Source: The Economist

What careful estimates show

Humlum & Vestergaard (2026):

  • 25,000 Danish workers, 7,000 workplaces
  • Surveys linked to government payroll records, not self-reports
  • Difference-in-differences: adopters vs non-adopters, before vs after ChatGPT
  • No effect on earnings or hours after two years, ruling out anything above 2%
  • Adopters do save time, around 3% of hours

Yale Budget Lab tracker:

  • Share of US workers in low, middle and high AI-exposure occupations
  • Monthly CPS data, updated to August 2026
  • The three lines are flat since 2023

Economic scenarios

The optimistic view

Augmentation, not replacement:

  • AI makes workers more productive: one person does what five did before
  • Demand expands to absorb the extra capacity
  • Net effect: more output, same or more jobs

New tasks emerge:

  • AI creates new kinds of work: prompt engineering, AI training, oversight
  • Jobs we can’t imagine yet, like social media manager in 1990

Historical pattern continues:

  • Technology has always created more jobs than it destroyed
  • Higher productivity → higher incomes → more demand
  • “Lump of labour fallacy”: there isn’t a fixed amount of work

The pessimistic view

Displacement dominates:

  • AI replaces workers faster than new jobs emerge
  • The transition could be very long, and many workers never recover
  • Skills mismatch is severe

Polarisation:

  • High-skill jobs pay more
  • Low-skill service jobs persist (hard to automate)
  • Middle gets hollowed out, so inequality rises sharply

Power dynamics:

  • Productivity gains go to capital, not labour
  • “Race to the bottom” for wages

Source: Fortune

Pessimist’s argument: “The horse population never recovered from the automobile. What if humans are the horses?”

What economists actually think

Economist polls (Clark Center, 2023 and 2026):

  • Most are uncertain whether AI will raise inequality (10 agree, 19 uncertain, 3 disagree)
  • Few expect a big rise in unemployment (5 agree, 25 uncertain, 11 disagree)
  • Most expect less certain returns to education (27 of 41 agree)

MIT economists:

Jones (2026), now at Anthropic:

  • The economy is only as fast as its weakest links: tasks AI cannot yet do
  • Big gains may take decades, which gives workers time to adjust

Source: Noah Smith

Nobody knows. Anyone who claims certainty is selling something. The range of possible outcomes is very wide.

Discussion: your predictions

Quick exercise:

Think about a job you might want after graduation.

  1. List 3-4 main tasks in that job
  2. For each task: Could AI do this?
    • Now?
    • In 5 years?
    • In 10 years?
  3. What tasks would remain human?
  4. How might the job change rather than disappear?

Share with us!

  • What job did you pick?
  • Which tasks seemed most automatable?
  • Which seemed most human?
  • Did this make you more or less worried?

What does this mean for you?

Skills that may remain valuable

Human-centric skills (Deming, 2017):

  • Building relationships and trust
  • Emotional intelligence
  • Cultural and contextual judgment

Knowing how to work with AI (WEF Future of Jobs Report, 2025):

  • Knowing when to use it and when not to
  • Evaluating AI outputs with a critical eye
  • Asking the right questions (garbage in, garbage out)

Domain expertise (McKinsey, 2025):

  • Judgment in novel situations
  • Ethical reasoning and values
  • Accountability and responsibility

Source: Harvard Business School

The irony: “soft skills”, the ones people used to dismiss as fluffy, may turn out to be the hardest to automate.

Adapting to an AI-influenced economy

Be AI-literate:

  • Understand what AI can and can’t do
  • Use the tools well and tell AI hype from reality
  • This course is a start! 🤓

Build skills that complement AI:

  • If AI does X, learn to do Y (what AI makes possible but can’t do alone)
  • Focus on orchestration: directing, checking, and combining AI outputs
  • Learning how to learn matters more than any single skill
  • Don’t over-specialise too early

Maintain perspective:

  • Your career will span 40+ years. AI in 2066 will be very different from AI today
  • Adaptability beats any specific prediction

Become someone who can keep learning!

The graduate market in 2026

The tough reality:

  • Entry-level hiring is down in AI-exposed jobs and competition is intense
  • Traditional paths are narrowing
  • “Just get a degree” isn’t enough

What might help:

  • Show you can actually use AI tools, not just talk about them
  • Build applied projects people can see (a portfolio beats a transcript)
  • Get internship or work experience while you can
  • Pick up skills outside your major
  • Build a network (still a human activity!)
  • Be open to non-traditional paths

The job market shifted while you were in university. That is unsettling, and I won’t pretend otherwise. Learning to work with AI won’t fix everything, but ignoring it is worse.

Source: Fortune

What would you advise?

Scenario:

Your younger sibling is about to start university. They ask:

“What should I study? I keep hearing AI will take all the jobs. Should I even bother with a degree? What career should I aim for?”

  • What would you tell them?
  • What field would you recommend?
  • What skills should they prioritise?
  • How would you address their anxiety?

Some thoughts to consider:

  • The value of education beyond job training
  • The danger of chasing “hot” fields
  • The importance of genuine interest
  • The limits of prediction

Summary

Main takeaways

What we know

  • Technology has disrupted labour before, with a positive net effect over time
  • What is new is the exposure of cognitive tasks
  • Productivity gains are real but task-specific
  • Careful estimates find no aggregate effect on earnings or hours yet
  • Entry-level hiring is where the strain shows

What we don’t know

  • Whether this ends in augmentation or displacement, how long the transition takes, and who pays for it

For your career

  • Think tasks, not jobs
  • Complement AI rather than compete with it
  • Adaptability beats any specific bet on the future
  • Policy choices will matter more than your tool choices

Nobody predicted “social media manager” in 1990 or “prompt engineer” in 2020. The jobs of 2036 probably don’t have names yet!

… and that’s all for today!