Machines rely on energy, compute, and cryptography; human work is physical, cognitive, and emotional.

Expecting people to scale like software produces burnout that is far harder to reverse than to prevent.

Work is not only about outputs we produce, but about what we become through the process of doing it.

When the tech industry talks about the future of work, the narrative is aggressively focused on automation, efficiency, and replacing human effort, creativity, and ingenuity with software. We are told that artificial intelligence will automate most of the work of humanity, and that will usher in either a utopia or dystopia.

We are told that algorithms “learn,” software “reads,” and machines “work.” Newer campaigns go further, claiming software can “create,” “explore,” “reason,” and act as a “creative partner.” There is an excitement about collapsing the boundary between human and machine when there is no need, but there is a strong marketing drive to make the two indistinguishable.

Whether or not it is intended to be, this language is deceptive. Machines can complete an expanding number of tasks, but the process is not the same. When we borrow human terminology to describe the processes of software, we risk losing a clear definition of what meaningful human work is.

It helps to define the work of humans and computers in order to compare them. What follows is not a definitive computer-science account of computation, but a simplified framing that highlights the core inputs and constraints so the differences with human work become clearer.

Computer hardware and software can complete all sorts of tasks, many of which humans do regularly, and many more that are beyond even the most exceptional person. Computers can process vast datasets in seconds, generate reports from complex models, or simulate climate systems no individual could calculate by........

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