INFO 202 Chap.7 Automation and the Future of Work
Automation and the Future of Work
What the week covers
The seventh week of this course asks how artificial intelligence affects employment, how technology displaces labor, who wins and who loses from automation and generative AI, and how much weight to give the many forecasts on the topic.
The syllabus assigns three pieces: a 2015 NPR story by Bui on whether a machine will take over your job, a 2025 MIT Future Tech piece by Deming on how people use ChatGPT, and a 2024 Linde Center paper by Holtz on the distributional effects of generative AI.
Together they move from exposure, to actual use, to the spread of gains and losses.
Start from tasks, not occupations
A reliable starting point is to break any occupation into the tasks it contains. New tools rarely take over everything a worker does; they take over some activities, speed up others, and create fresh ones such as reviewing or correcting machine output.
Thinking in tasks explains why the same technology can shrink one role while expanding another, and why employers often redesign jobs instead of eliminating them. It also gives you concrete material for a short answer instead of a sweeping prediction.
Substitution and complementarity
Economists commonly distinguish two relationships between technology and labor.
When a tool performs a task instead of a person, it substitutes for that labor. When it makes a person more productive at a task, it complements them, and the worker's remaining skills can become more valuable. Most real cases involve both at once, in different proportions for different workers.
Name which relationship you mean and for whom.
Distribution, not just totals
Even when a technology raises output overall, the benefits and costs can fall very unevenly. Differences by occupation, experience, education, firm and region matter, and an average effect can hide groups that are clearly worse off. The Holtz title signals that the week wants this lens applied to generative AI specifically.
When you write, name at least two groups and the mechanism that separates them, for instance whether their work is mostly routine drafting or mostly judgment the tool supports.
A claim that workers as a whole gain or lose is almost always too broad for this topic.
Treat forecasts as conditional
Predictions about automation depend on assumptions about cost, adoption speed, how organizations absorb new systems, and policy choices.
History is a useful check: Kling, Rosenbaum and Sawyer recall that the early computer era produced speculation about mass unemployment, sharply shorter working weeks and a problem of excess leisure. Use that record to ask what a forecast assumes, not to conclude that concern is always misplaced.
The title of the Deming piece points to a related gap: what a tool can do in principle and what people actually use it for are separate questions, and forecasts built on the first often overstate the second.
What this chapter covers
- 01
AI, jobs and technological displacement of labor
- 02
Why tasks, not whole occupations, are where analysis starts
- 03
Substitution versus complementarity
- 04
New tasks created by new tools
- 05
Winners and losers: distributional effects of generative AI
- 06
How people actually use generative AI tools
- 07
Reading forecasts as conditional claims
- 08
Early computer era predictions of mass unemployment and leisure
Worked example · free
Analyze a customer support team adopting a generative AI assistant
- 3Break the support job into tasks and classify each as substituted, complemented or unchanged.
- 3Identify likely winners and losers within the team and explain the uneven distribution.
- 2Give one conditional reason the job-cut forecast may fail.
Key terms
- Task-Based Analysis
- An approach that studies the effect of technology on the individual activities inside a job rather than on the occupation as a whole, since tools usually change some tasks and not others.
- Substitution
- The relationship in which a technology performs a task in place of a human worker, reducing the labor needed for that task.
- Complementarity
- The relationship in which a technology makes a worker more productive at a task, so the worker's skills can become more valuable rather than redundant.
- Technological Displacement
- The movement of workers out of jobs or tasks because technology now performs that work, which may be followed by reemployment elsewhere or by lasting loss.
- Distributional Effects
- How the gains and losses from a change are spread across groups such as occupations, skill levels, firms or regions, as opposed to its total or average effect.
- Automation Forecast
- A prediction of how many jobs or tasks technology will change, which depends on assumptions about cost, adoption speed, organizational fit and policy.
Automation and the Future of Work FAQ
Will AI take my job or just change it?
For most occupations the better question is which of your tasks change. Tools usually take over some routine activities, speed up others and add new ones such as checking output. Whether the job shrinks depends on how much of it is routine, whether demand for the service grows when it gets cheaper, and how employers choose to reorganize work.
Why do predictions about automation and jobs so often miss?
Forecasts usually start from what a technology can do and underestimate what it takes to adopt it: cost, reliability, fit with existing workflows, regulation and worker responses. They also miss new tasks and industries that emerge. Early computer era writers expected mass unemployment and abundant leisure, which is why analysts now ask forecasters to state their assumptions.
Exam move
Choose three occupations you know well, such as a bank teller, a paralegal and a nurse. For each, list four tasks and label them as likely substituted, complemented or largely unchanged by generative AI. Then write two sentences per occupation on who within it gains and who loses. Finish with one paragraph naming an assumption that a job-loss forecast for one of them would need to be true.
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