Every major disruption eventually forces us to change how we measure things, and getting the unit of measurement wrong leads to badly wrong conclusions. Consider light bulbs. For a century, incandescent bulbs were sold by the power they consumed, rated at 40, 60, or 100 watts. When LED bulbs arrived, that metric stopped making sense, because LEDs produce the same light using a small fraction of the energy. Comparing bulbs by wattage no longer told you anything useful, so the industry switched to measuring what people actually cared about: light output, in lumens, and color, in degrees Kelvin. The new technology demanded a new metric. In the disruption of labor, the old and misleading metric is the job.
The trouble is that a job is not a single thing, it is actually a bundle of many different tasks. Each of which requires its own mix of training, experience, and skill, wrapped in contracts and tangled up with a person's career, identity, and community. A humanoid robot, so long as it is not sentient, does not have a job, a career, or an identity. It performs tasks. Measuring the disruption in "jobs lost" therefore misframes what is actually happening, because the machine is not stepping into a role the way a new hire would, it is absorbing tasks.
Thinking in tasks reveals things that thinking in jobs conceals. The most important is that jobs are automated piece by piece, not all at once. Almost every job contains a large share of tasks that can be automated, even though very few jobs can be automated in their entirety today. This is precisely why the early phase of the disruption looks like helpful augmentation rather than replacement. As the robot quietly takes over some of a worker's tasks while the human keeps the rest, it feels like empowerment. Counting jobs makes you miss that the ground is already shifting underneath. It also dismantles the comforting idea of jobs being uniquely human. A job only survives as long as enough of its tasks resist automation, and as the technology improves that machine-proof group keeps shrinking.
Tasks also give us a metric we can actually track and forecast, in the same way lumens did for light bulbs. The right cost-capability measure for this disruption is tasks per hour per dollar. At the start, the tasks a robot can do will be narrow, the hours it can work will be limited, and its upfront cost will be high, but each of those improves rapidly. You can chart that trajectory. You cannot chart jobs in the same way, which is part of why job-based forecasts tend to either cry wolf or offer false comfort.
Finally, the task lens leads to the right response. Because a robot can be re-skilled with a software update and shared across many different roles, the very idea of a fixed job dissolves, and the task becomes the only meaningful unit. Once you see the work breaking down into automatable tasks, it becomes clear that trying to preserve jobs is fighting the wrong battle. The sensible goal is to protect people, not jobs, and to manage the transition for them as the tasks that make up their work are progressively taken over.
Explore the evidence...
- Our blog The Painful Truth about AI & Robotics builds on the same idea, noting that even the jobs the disruption creates are themselves bundles of tasks that machines will go on to absorb. Read the blog here.
- Because AI can move between robotic bodies and any robot can become any kind of expert on demand, the familiar notions of training, specialization, and fixed roles begin to lose meaning, leaving the task as the unit that matters. We explore this in AI Telepresence: The Aliens Have Landed. Read the blog here.
Witness the transformation
In the span of just fifteen years, the working horse went from providing the vast majority of road travel to a tiny fraction of it. The automobile had arrived, and the fate of the horse was sealed. We are now on the cusp of a disruption every bit as swift and complete, except this time, we humans are the horses.
A convergence of sensors, computing, actuators, and batteries now gives humanoid robots the capability to perform both cognitive and physical work. AI is already taking on cognitive tasks once reserved for people, and humanoid robots are bringing the same capability to physical tasks. For the first time, the supply of available labor can expand as fast as machines can be built and trained. These systems are already approaching cost parity with human labor across much of the global economy, and their cost will keep falling while their capability keeps rising.
This is about far more than cheaper labor. Robots will create an entirely new and vastly larger labor system in which the marginal cost of labor approaches zero. The result will be a sweeping tide of falling costs, rising quality, and explosive productivity that forms the foundation of an era of superabundance. The nations, industries, and individuals who recognize this early, and who choose to protect people rather than jobs, will be best positioned to navigate the transformation and capture its extraordinary benefits.
Learn more about the disruption of labor and its implications for jobs, society, and the economy.
