Race Bannon sees AI (or really machine learning) changing many jobs, such as technical writing, in the near future.
“I believe within 5-10 years much of technical documentation will be written by AI. Certainly, the basic procedural stuff (Step 1, Step 2, and so on) will be written by AI, but even the contextual stuff surrounding the procedural documentation (use cases, examples, and implementation tips) will be written by AI eventually too.” —The Future of Technical Writing
In The Atlantic, Derek Thompson thinks that creativity will not save our jobs from AI.
We may be in a “golden age” of AI, as many have claimed. But we are also in a golden age of grifters and Potemkin inventions and aphoristic nincompoops posing as techno-oracles. The dawn of generative AI that I envision will not necessarily come to pass. So far, this technology hasn’t replaced any journalists, or created any best-selling books or video games, or designed some sparkling-water advertisement, much less invented a horrible new form of cancer. But you don’t need a wild imagination to see that the future cracked open by these technologies is full of awful and awesome possibilities. —The Atlantic 2022-12-01
It seems that machine learning is making many jobs that humans do at this time feel a bit obsolesced. In 2014 I wrote that preempting automation was necessary in all workplaces. I suggested that jobs and roles have to become more flexible and open to change and cannot stay tied to the concept of the ‘job‘ as the primary unit of work. Building ways to constantly change roles could be a small change to preempt automation. However, it requires a new way of looking at work, collecting good data, engaging workers in the process, and being transparent about it all. If AI (ML) is really here, we need to face it.
The human work that emerges from mass automation will be increasingly complex and creative. This is a challenge for training and education systems run by any central authority. Standardized curricula are only effective when developing instruction to deal with complicated phenomena, where all the components are understandable and can be analyzed and mapped. Best practices and good practices can align easily with a curriculum. But in complex environments, emergent practices need to be developed while simultaneously engaging the problem.
Social learning then is the best process for groups of people to cooperate and learn with and from each other. As discourse replaces the centralized training authority, social learning in knowledge networks augments and even replaces training and education. This is how we stay ahead of the machines. Let’s make sure the future is not all it’s cracked up to be.