In fact, programming has gone through several transformations of this kind already. Programmers went from writing assembler (“Move bit a to memory position b”) to C with its explicit memory allocation, and onto higher-level languages that allow the programmer to spend more effort on solving the business problem and less on the quirks and limitations of the programming language. Now, programmers are eagerly adopting the various GenAI copilots. With each such transition, the time needed to create a particular piece of functionality has plummeted—but the profession of programmer shows no signs of becoming obsolete.
The most fundamental version of this argument is that a job is sustainable if its added value exceeds the salary it commands. As knowledge workers embrace GenAI tools, their value added per hour will significantly increase, reinforcing their importance in the workforce and ensuring the economy’s continued support for such jobs.
This doesn’t mean that the content of these jobs won’t change—it almost certainly will. Not only will individual knowl- edge jobs evolve, but it’s also very likely that the very way in which firms are structured will change—just as the factories during the Industrial Revolution were not just larger artisan workshops.
One thing is clear: The widespread adoption of GenAI will not lead to fewer knowledge jobs, but rather, it will pave the way for their growth and evolution.