• We saw how privacy and data protection are challenged by AI models
• AI enables collection at scale and inference of sensitive data
• General Data Protection Regulation (GDPR - EU): Comprehensive rights (access, erasure, portability)
• US: Patchwork of sectoral laws (healthcare, finance) and state laws (CCPA in California)
• 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

• Anthropic shipped a debug file with 512K lines of source code inside a routine update (Decrypt)
• Spotted within minutes, posted on X
• 21 million views before the team woke up
• Code mirrored across GitHub; DMCA takedowns couldn’t keep up
• Sigrid Jin (most active Claude Code user in the world, 25B tokens/year per the WSJ) rewrote it all in Python before sunrise
• His repo hit 50K stars in 2 hours 🚀
• Anthropic had built “Undercover Mode” to stop Claude from leaking secrets. Then leaked their own code 🤦🏻‍♂️
• I guess our jobs are safe for now? 😅

Historical fears of technological unemployment:

The pattern so far:

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

Previous automation:

• Targeted physical tasks
• Humans retained cognitive advantage
• Automation was task-specific (a loom weaves cloth, a lathe cuts metal, an ATM dispenses cash)

AI is different:

• Targets cognitive tasks, not just physical ones
• General-purpose: the same model writes essays, analyses data, and generates images
• Improving very rapidly
• People used to say “computers can’t do intellectual work” or “computers can’t be creative”. Both claims look shaky now

Counter-argument:

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

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

• 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, because AI handles the grunt work
• 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?

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 environments (construction, plumbing)
• Jobs where you need to be there in person (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)

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

OpenAI’s research on ChatGPT use (2025):

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

What this tells us:

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

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 (Rest of World; IEEE Spectrum)
• Brynjolfsson et al. (2025): 13% employment decline for workers aged 22-25 in AI-exposed jobs (mainly in tech, finance, consulting)

Financial Times reporting (2026):

• Graduate hiring at lowest level in a decade
• 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

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

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 (2025) 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 (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)
• Recognising where AI falls short

Domain expertise (McKinsey, 2025):

• 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! 🤓

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

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:

• Show that 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

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

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

• Build skills that complement AI
• Be AI-literate, not AI-resistant
• Adaptability beats any specific bet on the future
• “Soft skills” may be the hardest to automate

Key insights

• Think tasks not jobs
• Transition pain matters even if outcome is good
• Policy choices will shape outcomes
• Learning AI tools helps, but policy choices will matter more