• We explored privacy and data protection in the AI age
• AI enables collection at scale and inference of sensitive data
• GDPR: Comprehensive rights (access, erasure, portability)
• US: Patchwork of sectoral laws
• Technical approaches (differential privacy, federated learning, synthetic data) help but have trade-offs
• Collection is the core problem. Same data enables benefits and surveillance
• Today: What does AI mean for jobs and the economy?

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

Historical fears of technological unemployment:

The pattern so far:

• Technology destroys some jobs
• But creates others
• Net effect has been positive over time
• Adjustment periods are painful

Previous automation:

• Targeted physical tasks
• Humans retained cognitive advantage
• Automation was task-specific

AI is different:

• Targets cognitive tasks
• General-purpose (not task-specific)
• Improving very rapidly
• “Computers can’t do intellectual work”
• “Computers can’t be creative”
• All of these are now questionable

Counter-argument:

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

A more useful framework:

• Don’t think about jobs being replaced
• Think about tasks being automated
• Most jobs contain many tasks
• AI might automate some but not all

Example: Lawyer

The effect:

• Fewer lawyers needed per case
• Role shifts toward higher-value tasks
• The lawyer doesn’t disappear, but what to do with the unemployed?

High exposure to AI:

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

Lower exposure (for now):

• Physical manipulation in unstructured environments
• Tasks requiring physical presence
• Tasks requiring real-time judgment in novel situations
• Tasks requiring deep personal relationships
• Tasks requiring embodied expertise

Exposure by occupation (various studies):

OpenAI’s research on ChatGPT use (2025):

Patterns:

• Most use is not work-related
• Learning and personal projects dominate
• Work adoption slower than headlines suggest, significant variation by sector and role

Studies finding positive effects:

• Customer service (Brynjolfsson et al., 2025): 14% productivity increase, biggest gains for novice workers
• Programmers (GitHub, 2022): 55% faster task completion with Copilot
• Consultants (BCG study, 2023): 40% better performance on applicable tasks
• Writers (Noy & Zhang, 2023): 40% faster, 18% better quality

Important caveats:

• Most studies are short-term
• Often sponsored by AI companies
• Lab settings vs real work differ
• Long-term effects unclear

The pattern emerging:

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

What the data shows (2024-2026):

• Entry-level job postings down significantly
• Particularly in tech, finance, consulting
• Companies saying AI reduces need for junior hires

Financial Times reporting (2026):

• Graduate hiring at lowest level in decade
• Tech companies leading the pullback
• Law and consulting firms reducing trainee intakes

The logic:

• Junior employees do tasks AI now handles
• Training juniors is expensive
• AI doesn’t need healthcare or vacation
• Companies facing pressure to cut costs

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
• Rising inequality

Why the disconnect?

• Adoption takes time
• Companies are cautious
• Many tasks don’t work well with AI
• Regulations and contracts slow change

Augmentation, not replacement:

• AI makes workers more productive
• One person can do what five did before
• But demand expands to absorb 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

Displacement dominates:

• AI replaces workers faster than new jobs emerge
• Transition period could be very long
• Many workers never recover
• Skills mismatch is severe

Polarisation:

• High-skill jobs get more valuable
• Low-skill service jobs persist (hard to automate)
• Middle gets hollowed out
• Inequality increases dramatically

Power dynamics:

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

Survey results (IGM Forum, 2024):

• Large majority: AI will significantly change labour markets
• Wide uncertainty about direction
• Most expect some job displacement, but not mass unemployment
• Strong agreement: inequality likely to increase

Acemoglu’s framework:

• AI can augment workers (make them more productive)
• Or replace them (automate their tasks)
• Current AI is biased toward replacement
• Policy could shift toward augmentation

Jones (2026) summary:

• Historical precedent supports optimism
• But speed and scope are unprecedented
• Transition will be rough even if outcome is good

Human-centric skills:

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

Metacognitive skills:

• Knowing when to use AI (and when not to)
• Evaluating AI outputs critically
• Asking the right questions
• Integrating AI into workflows
• Understanding AI limitations

Domain expertise:

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

Be AI-literate:

• Understand what AI can and can’t do
• Know how to use tools effectively
• Recognise AI hype vs reality
• This course is a start! 🤓

Develop complementary skills:

• If AI does X, learn to do Y (what AI enables)
• Focus on orchestration, not just execution
• Learning how to learn matters more than any specific 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

The tough reality:

• Entry-level hiring is down
• Competition is intense
• Traditional paths are narrowing
• “Just get a degree” isn’t enough

What might help:

• Demonstrated AI competence
• Evidence of applied projects
• Internship and work experience
• Skills beyond your major
• Network building (still human!)
• Willingness to take non-traditional paths

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?”

In groups (5 minutes):

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

Historical context

• Technology has always disrupted labour
• Net effect has been positive over time
• But this time may be different in speed and scope
• Cognitive tasks now exposed

Current evidence

• Most AI use is not work-related
• Productivity gains real but task-specific
• Graduate job market is challenging
• Aggregate effects still unclear

Economic scenarios

• Optimists: augmentation, new tasks
• Pessimists: displacement, polarisation
• Economists: uncertain, expect inequality
• Honest answer: nobody knows

For your career

• Focus on complementary skills
• Be AI-literate, not AI-resistant
• Develop adaptability over specifics
• Build human skills that resist automation

Key insights

• Think tasks not jobs
• Transition pain matters even if outcome is good
• Policy choices will shape outcomes
• Individual adaptation is necessary but not sufficient