augmentation
In plain wordsUsing AI to help people do their work better or faster while they stay in charge, instead of having the AI do the job on its own.
Work and economics
The use of AI or other technology to complement rather than replace human labor: the system takes over parts of a task, supplies information or drafts, or extends what a person can do, while the person remains responsible for the work and its outcome. Economically, augmentation raises the productivity and often the value of human skills, whereas automationautomation, n.: Using machines or software to do work that people would otherwise do, from factory robots to programs that sort email or write reports. substitutes for them; in practice the two coexist, since a tool that augments each worker can reduce how many workers are needed. Analyses of real assistant usage, such as the Anthropic Economic Index (from 2025), classify interactions as augmentative (iterating, learning, checking work) or automative (delegating a task outright) and track how the balance shifts as agents improve. Field studies often find the largest gains for less experienced workers.
The rollout was framed as augmentation: agents keep the case, and the model drafts replies for them to edit.
Origin
The contrast between augmenting and replacing people goes back to Douglas Engelbart's 'Augmenting Human Intellect' (1962) and the intelligence augmentationintelligence augmentation, n.: Using computers to make people smarter and more capable rather than building machines that think on their own: AI as a tool for human minds. tradition. In management and labor economics, automation versus augmentation became a standard frame in the late 2010s, formalized in Sebastian Raisch and Sebastian Krakowski's 'automation–augmentation paradox' (2021).
Usage note
Distinct from data augmentationdata augmentation, n.: Creating extra training examples by changing existing ones in ways that keep their meaning — a flipped, cropped or recolored photo of a cat still shows a cat — so the model learns more robustly. in machine learning, which creates modified training examples, and from biomedical human enhancement.
Related
- automationautomation, n.: Using machines or software to do work that people would otherwise do, from factory robots to programs that sort email or write reports.
- intelligence augmentationintelligence augmentation, n.: Using computers to make people smarter and more capable rather than building machines that think on their own: AI as a tool for human minds.
- centaurcentaur, n.: A human and an AI working as a team, each doing what it does best, like a chess player who consults a computer before every move. The name comes from the mythical half-human, half-horse creature.
- jagged frontierjagged frontier, n.: The uneven edge of what AI can do: it may excel at some hard tasks yet fail at seemingly easier ones, so how hard a task looks is a poor guide to whether AI can handle it.
- Turing trapTuring trap, n.: The risk that by building AI mainly to imitate and replace people, rather than to extend what people can do, society ends up with less widely shared prosperity and weaker bargaining power for workers.
- human-centered AIhuman-centered AI, n.: An approach to AI that puts people's needs, abilities and control at the center: systems designed to help humans do things better rather than to replace or overrule them.
- co-intelligenceco-intelligence, n.: Ethan Mollick's term for working and thinking alongside AI as a partner — treating it like a capable but odd colleague whose strengths and blind spots you learn through experience.
- automationautomation, n.: Using machines or software to do work that people would otherwise do, from factory robots to programs that sort email or write reports.
- data augmentationdata augmentation, n.: Creating extra training examples by changing existing ones in ways that keep their meaning — a flipped, cropped or recolored photo of a cat still shows a cat — so the model learns more robustly.
Sources
- Raisch, S. & Krakowski, S. (2021). Artificial Intelligence and Management: The Automation–Augmentation Paradox. Academy of Management Review 46(1), 192–210.
- Brynjolfsson, E., Li, D. & Raymond, L. R. (2025). Generative AI at Work. Quarterly Journal of Economics 140(2).
Mentioned in
automationautomation, n.: Using machines or software to do work that people would otherwise do, from factory robots to programs that sort email or write reports., centaurcentaur, n.: A human and an AI working as a team, each doing what it does best, like a chess player who consults a computer before every move. The name comes from the mythical half-human, half-horse creature., deskillingdeskilling, n.: The loss of people's skills when machines take over the work that used to keep those skills sharp, or when jobs are redesigned so that less skill is needed., Garry KasparovGarry Kasparov, prop. n.: A Russian chess world champion whose 1997 loss to IBM's Deep Blue computer became the symbol of machines overtaking people, and who later championed humans and computers working together., human-centered AIhuman-centered AI, n.: An approach to AI that puts people's needs, abilities and control at the center: systems designed to help humans do things better rather than to replace or overrule them., intelligence augmentationintelligence augmentation, n.: Using computers to make people smarter and more capable rather than building machines that think on their own: AI as a tool for human minds., jagged frontierjagged frontier, n.: The uneven edge of what AI can do: it may excel at some hard tasks yet fail at seemingly easier ones, so how hard a task looks is a poor guide to whether AI can handle it., task exposuretask exposure, n.: A measure of how much of a job's work AI could, in principle, do or speed up. High exposure means many of the job's tasks are within AI's reach, not that the job will vanish., technological unemploymenttechnological unemployment, n.: Joblessness caused by machines or software taking over work that people used to be paid for, faster than new kinds of work appear for them., workslopworkslop, n.: AI-generated work that looks polished but does not actually get the job done, quietly passing the real effort to whoever receives it, who must then puzzle it out or start over., cognitive debtcognitive debt, n.: The idea that letting AI do your thinking or writing saves effort now but weakens your own thinking, memory and learning over time., co-intelligenceco-intelligence, n.: Ethan Mollick's term for working and thinking alongside AI as a partner — treating it like a capable but odd colleague whose strengths and blind spots you learn through experience., Erik BrynjolfssonErik Brynjolfsson, prop. n.: An American economist who studies how digital technology and AI change productivity and jobs, and who warns against AI built to replace people rather than to augment them., Ethan MollickEthan Mollick, prop. n.: An American management professor at Wharton whose experiments and popular writing on how people use AI at work introduced ideas such as the 'jagged frontier'., J. C. R. LickliderJ. C. R. Licklider, prop. n.: An American psychologist who envisioned people and computers working in close partnership, and whose US government funding built early AI research and the precursor of the internet., Turing trapTuring trap, n.: The risk that by building AI mainly to imitate and replace people, rather than to extend what people can do, society ends up with less widely shared prosperity and weaker bargaining power for workers.