Curriculum Automation, AI & the Future of Work
Automation, AI & the Future of Work
What automation and artificial intelligence actually mean for jobs, wages and the skills worth building next.
- What Automation Actually Means for Jobs The difference between automating a task and automating a whole job - and why that distinction shapes almost everything else in this module. 📄 Read-aloud
- Which Jobs Are Most at Risk - and Which Aren't The real factors that determine how exposed a job is to automation, beyond just the industry it's in. 📄 Read-aloud
- Reskilling: Preparing for a Changing Labor Market How workers and economies adapt when the skills a job requires shift faster than in the past. 📄 Read-aloud
- The Productivity Paradox Why new technology doesn't always show up right away in the economic statistics that are supposed to measure it. 📄 Read-aloud
- Automation, AI & the Future of Work: Checkpoint 1 Covers what automation actually changes, which jobs are most exposed, reskilling, and the productivity paradox. 5 questions
- Universal Basic Income: The Debate The case for and against giving everyone a regular, unconditional cash payment - and why automation has revived interest in the idea. 📄 Read-aloud
- How AI Is Changing Hiring and Pay How employers increasingly use AI in recruiting and compensation decisions - and what that means for job seekers and workers. 📄 Read-aloud
- The Gig Economy Meets Automation How algorithmic management shapes gig work day to day, and what that means for workers who don't have a traditional employer. 📄 Read-aloud
- Historical Parallels: Past Waves of Technological Disruption What earlier technological revolutions - from the printing press to the assembly line - can teach us about how automation actually plays out over time. 📄 Read-aloud
- Automation, AI & the Future of Work: Checkpoint 2 Covers universal basic income, AI in hiring and pay, algorithmic management in gig work, and lessons from past technological disruptions. 5 questions
- The Economics of AI Training Costs and Compute Why building a large AI model costs so much, and why that cost shapes which companies can even attempt it. 📄 Read-aloud
- Skill-Biased Technological Change and Wage Inequality Why new technology tends to raise pay for some workers while leaving others behind, and what that does to the overall wage gap. 📄 Read-aloud
- Capital vs. Labor: Who Captures the Gains From Automation When a machine replaces a worker, the value that worker used to earn doesn't vanish - it goes somewhere else. This lesson looks at where. 📄 Read-aloud
- Automation and the 'Lump of Labor' Fallacy Why the idea that there's only a fixed amount of work to go around is one of the most persistent misconceptions in economics. 📄 Read-aloud
- Automation in the Warehouse and Logistics Industry How robots and software transformed warehouse work, and why full automation is harder to reach than it looks. 📄 Read-aloud
- The Four-Day Workweek Debate Why rising productivity has revived the century-old argument for working fewer days without losing pay. 📄 Read-aloud
- AI and the Economics of Creative Work Why generative AI is reshaping creative industries differently than earlier automation reshaped factory work. 📄 Read-aloud
- Retraining Programs: Do They Actually Work? What the evidence says about government and employer job retraining programs meant to help displaced workers. 📄 Read-aloud
- Generative AI and White-Collar Work How AI systems that write, code and analyse are changing office jobs, which tasks are most exposed, and what early studies show. 📄 Read-aloud
- Robot Taxes: The Debate Whether governments should tax automation to slow job losses or fund support for workers, and why most economists are sceptical. 📄 Read-aloud
- AI and Productivity: What the Evidence Shows So Far What studies of AI in real workplaces have found about productivity, why economy-wide effects take time, and the range of forecasts. 📄 Read-aloud
- Algorithmic Management: When Software Is the Boss How apps and algorithms assign tasks, set pay and monitor workers in delivery, warehouses and offices, and the debates this raises. 📄 Read-aloud