At first, I imagined knowledge work as work that is done by someone who is knowledgeable in a certain field. For example, if I wanted to do scientific work, I first have to grasp the basics of science, at least.
Grasping the basics of science is like the "knowledge base" that will allow me to do scientific work.
This was the train of thought that I followed until I encountered Peter Drucker's definition of knowledge workers as those who work primarily with information rather than physical goods.
Granted, there may not be much difference between information and physical goods, since the latter can be represented as information too.
What's specific with Peter Ducker's definition is it made me realize that knowledge work is inherently different from traditional work because its output is rather intangible and quality can't be measured by traditional metrics.
With physical labor, the core mechanics remain relatively stable, in that the fundamental processes and skills remain consistent over long periods.
A carpenter's basic tools and techniques haven't drastically changed in decades, for instance.
Knowledge work however, is on a constant redefinition through technological convergence.
Adaptation May Take Time
In Peter Diamandis's The Future Is Faster Than You Think, there's an interesting portrayal of how multiple technologies are speeding up and converging at this point, creating a positive feedback loop in knowledge work and also altering how value is created and captured in the modern economy.
The logic is that when core technologies like internet improve, they enable faster development of new technologies, which in turn accelerates knowledge creation and sharing in ways previously unimaginable.
I don't think this acceleration will slow down anytime soon. If anything, it's likely to intensify.
But it could be argued that despite the speed of developments, adoption may take time, since we humans find it hard to change, especially relatively abrupt changes.
It could also be argued that just applying existing knowledge wouldn't cut it anymore in terms of maintaining professional relevance and competitive advantage.
When AI can analyze data faster than any human, the value of a knowledge worker shifts from being a repository of information to being an interpreter of insights and/or a navigator of complexity.
Put in another way, our minds primarily storing information like a fridge that stores food is increasingly obsolete.
We need to move toward developing mental models that help us process and synthesize new information as more or less a default state of operating.
An Automated Market
Twenty years ago, a successful trader primarily relied on their knowledge of market fundamentals, technical analysis skills, and maybe also a network of industry contacts.
Their competitive edge came from being able to quickly process market information and make decisions faster than others.
Nowadays, this same trader operates in an environment where high-frequency trading algorithms execute millions of trades per second.
Did you know AI systems analyze satellite imagery of retail parking lots to predict retail sales before quarterly reports?
The modern trader's value isn't anymore in executing trades faster (machines will always win that race) or in analyzing traditional market data (AI can process vastly more information).
Identifying opportunities that machines will miss due to their lack of contextual understanding or developing strategies that combine human insight with technological capabilities are probably the main ways modern traders maintain their edge in an automated market.
In Closing
I think human judgement will always be needed to make ingenious decisions in complex, uncertain environments.
This is the basic criterion that will continue to give humans an edge and separates effective knowledge workers from mere "information processors".
On top of that, understanding how to leverage and interpret the outputs of multiple technological systems may be the meta-skill that enables adaptation in this new environment.
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