Generative AI is coming for white collar jobs. Or is it? With generative AI we can even now imagine how machines can now make us more creative, caring, and connected. As burgeoning tools like advanced language models and image generators become standard in our creative, inventive, and communicative arsenal, we're forced to reevaluate our conception of human intellect and, by extension, our perception of work.
When considering the future of work it’s helpful to understand some key principles about human work.
The nature of human work isn't monolithic. A job is an intricate composition of numerous tasks. This view enables us to discern that some tasks are ripe for machine takeover, while others remain stubbornly human.
Automating routine tasks within a job is akin to refining raw ore: it removes the less valuable parts, leaving only the precious minerals. The mundane, repetitive tasks are filtered out, making room for the complex, high-value tasks to take center stage. This enrichment of the job role, in turn, increases its overall worth, especially if it stimulates the demand for the output.
Jobs, in this sense, are more fluid than fixed. They aren’t isolated islands in the vast ocean of work. Instead, they are dynamic, evolving entities, prone to fragmentation and fusion as technology and business models evolve. A job doesn't simply vanish into obsolescence; it unravels into its constituent tasks, which then become threads in new, unimagined work tapestries.
A fundamental truth about AI is its role as a expert predictor. As we dissect work into tasks, it's crucial to identify those that hinge on prediction or could significantly improve with more affordable forecasts or inferences. The challenge lies in redefining our perception of prediction. We need to move beyond seeing it merely as a forecast of the future and recognize it as a tool for completing an incomplete picture. The art here lies in envisioning how predictions could elevate the value of a task.
As AI reduces the cost of prediction, it inadvertently enhances the value of judgment and action. Why? Because cheaper predictions open up a broader realm of possibilities for decisions and actions. The more we know about the possible outcomes, the more nuanced and strategic our judgments can become, and the more precise our actions can be. Therefore, AI's prediction prowess amplifies the value of human judgment and action.
Peering into the workings of a human mind reveals a process far more complex than executing a single task step. An analyst crafting a financial model is simultaneously distilling wisdom from a sea of data. A doctor arriving at a diagnosis is concurrently gauging the potential repercussions on the patient's health and formulating a context-sensitive, personalized treatment plan. A programmer must weigh the importance of a slew of software tests based on the client's unique context.
The essence of human work is decision making. A decision is a context-laden, back-and-forth between prediction and judgment, often under broad uncertainty. Unconsciously, we fuse these two elements in our cognitive process—prediction, a process of filling gaps or foretelling the future, and judgment, a quality assessment of the decision or a confidence check. However, when machines pry prediction apart from judgment, the role of AI becomes more distinct, allowing us to discern the tasks that remain uniquely human.