What’s the Endgame of Automation?

what do you do when there’s no need to do?

Mark Cleverley
8 min readJul 12, 2020


Machine learning has been driving a new wave of automation in the past decade. The speed of this wave is up for debate, but its direction and strength are beyond doubt — for it is driven by good old economic interest.
It always helps to remember:

Corporations swarm towards profit surely as maggots to a corpse.

And unimaginable profit lies just beyond the mechanical horizon. The lauded principles of the Free Market move companies to pay workers the absolute minimum, so the idea of a worker that needs no wages is a utopian dream for every CEO with their souls oriented towards the bottom line.

Many economists are skeptical of the coming robotic revolution. They point out how humans have always been automating our work (the water wheel, for example), and we simply make new jobs to fill out the slack.
This is good, I will concede, so long as the displaced workers can find meaningful work afterwards. I don’t imagine this to be the case in the future.

We Live in the Beginning

With the rapid development of computer vision through convolutional neural nets, we’re rapidly approaching the age self-driving cars. To put that in perspective, there’s ~3.5 million truckers in the US, nearly a million ride-service drivers and perhaps half a million bus drivers. That’s about 3% of the 165 million workforce who may have to look elsewhere when companies decide it’s much cheaper to buy and maintain a driverless vehicle.

How many jobs are actually at risk in general? Figures vary: 20 million US factory jobs by 2030, 25% of US jobs, 15–30% of workers displaced by 2030. There’s myriad factors influencing the speed of the wave.

Notably, the sources I linked were written pre-Covid. Visions of the “six-feet office” have pushed many firms to downsize and turn roles remote. Many companies are investing heavily in automation infrastructure instead of rehiring.

This, too, makes economic sense: The grand economic machine has been grievously disrupted, and nobody knows when things will return to “normal”. The drawbacks of automating your workforce — losing…



Mark Cleverley

data scientist, machine learning engineer. passionate about ecology, biotech and AI. https://www.linkedin.com/in/mark-s-cleverley/