How Automation Used to Work
Every business is searching for a way to remove people from their operations. People are expensive, temperamental and difficult to control. They ask for vacations, slack off, sexually harass each other and generally cause chaos for companies.
Automation gives businesses what they want: lowered costs, higher profits, increased productivity and far fewer headaches. Employers have understood this dynamic since the first machines arrived during the Industrial Revolution, and they've been utilizing automation ever since.
In previous eras, automation meant that jobs were replaced — but it always ended up being for the best. More jobs were created than replaced in the long run, and automation has undoubtedly saved countless hours of human labor from being wasted on pointless, repetitive tasks.
Rather than spending our time raising crops and assembling widgets by hand, we can now engage in work that means something to us. I think we can all agree that this has been a positive development.
The examples of this type of automation are all over the place. During World War 2, people (mostly women) manually calculated shell trajectories, ideal bombing altitudes and all kinds of other data points needed for battle. That was a boring, difficult job and the inevitable errors that come from having people doing that kind of work manually put lives at risk.
Automating that kind of work is a good thing, even if it puts people out of a job. When we started to utilize computers for tasks that only require calculation (not creativity), we made our collective lives better because we could use our minds for things besides simple number crunching.
This style of automation — taking people out of the picture for tasks that require high speed and precision — has been the norm since the dawn of the Information Age. If the primary constraint on people is speed and/or precision, computers have largely taken their place.
The only limitation on automating like this is access to computing power, and that problem has been solved. Once you have the CPUs to do the task, it's just a matter of defining a clear set of steps (aka an algorithm) and putting it all into place.
That's why manufacturing has become a domain dominated by robots. An assembly line worker is there to perform a single task (or a small set of tasks) over and over again, and the economic incentive from the factory owner's perspective revolves around speed.
The more widgets a factory pumps out, the more money can be made. If a factory owner can increase the number of widgets while simultaneously slashing costs related to workers, then they'll always be incentivized to do so.
Knowing all this, it makes sense that people have been taken out of factories and factory-like environments. A very simple robot can do the same thing over and over again for a fixed cost and at high levels of both precision and speed. Robots don't demand raises, ask for breaks or go on strike.
But the nature of automation has changed dramatically in the last few years. While the focus has historically been on simple tasks that can be easily defined and optimized, automation in the form of machine learning is now tackling tasks that were once deemed impossible to automate.