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(And who recognized the quote on the last page?)
While working on the episodes, I started to develop a shift map; a thinking tool that helps to understand where this pressure is strongest and where it may be weaker for the time being.
AI & Work: Mapping the Shift
The public discussion about AI often circles around one dramatic question: Which jobs will disappear? But in practice, jobs do not vanish overnight. What changes first are the tasks within a profession. Some tasks become cheaper, faster, and easier to scale, while other parts remain stubbornly human. Over time, the whole profession shifts.
The map has two axes (yes, I studied economics, why). The first is predictability. This category stands for: How repeatable are the patterns in this line of work task? How much data is available? Is there right and wrong and fast feedback when something goes wrong so that errors are corrected fast?
The second axis concerns the work environment. Does the work mainly happen on screens? Or does it unfold in physical presence, embodied, with nervous systems involved and questions of power, responsibility, risk, or trust?
When you combine these two axes, four fields appear, which of course overlap in real life: A, B, C, and D.
A is the zone where work tasks are both predictable and screen-based. That is where AI moves fastest. Patterns repeat. Errors are visible. Output can be tested. This is (standard) coding, reporting, etc… and this explains why so many AI people are right now so afraid to lose their jobs soon.
B is the zone where work follows standards but happens in messy reality, such as classrooms, or construction sites. Here AI helps with documentation and pattern recognition, but someone still needs to stand there and take responsibility and fix surprises.
C is the zone where work happens on screens but outcomes are new, for example in research, journalism, or political communication. Here AI does not simply replace. It floods. Production and competition increase. The real bottlenecks becomes our attention, verification, and attachment.
D includes work situations that are both unpredictable and physical, such as negotiations, live performances, or high-stakes decisions. AI may assist in the background, but this work depend on who is in the room, who carries which kind of power, and who takes risks when stakes are high and uncertainty rules.
In short, AI wins where prediction is queen and screens are king. Plus: the map is not static.
I am currently working on a longer written version of this framework. I am in conversation with a few people about publishing it as an essay, because I believe it can be a useful lens not only for institutions, but also for individuals trying to understand where they stand.
More soon.
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