How Automation and Artificial Intelligence Strips us of our Humanity

Carlos E. Perez
Mar 12, 2018 · 5 min read
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In my most recent post, I offered the idea that the should be to support this idea of (生き甲斐): A reason for being.


Melody Wilding wrote a Medium article that may serve as a good introduction to the topic: “”. Wilding makes an astute observation that Ikigai “involves a commitment of time and belief, perhaps to a particular cause, skill, trade, or group of people.” If you examine Ikigai closely, you will realize that it revolves around the central idea of the importance of work. The goal of Automation and AI, in contrast, is precisely the elimination of work.

This idea also translates to worker productivity and innovation. Daniel Pink who wrote the bestseller “” says it simply enough:

The secret to high performance and satisfaction-at work, at school, and at home — is the deeply human need to direct our own lives, to learn and create new things, and to do better by ourselves and our world.

However AI is framed from a more Utopian idea focused on the elimination of inconvenient work: the kind of work that is supposedly unnecessary in our pursuit of more lofty goals. AI is like American fast food drive-thru on steroids.

Tim Wu has written one of the best essays on the idea of the importance of inconvenience in defining our humanity. He writes about the “”. I highly recommend inconveniently spending the time to read his highly insightful essay.

His first interesting thought (which is a bit of a digression) is that “convenience and monopoly seem to be natural bedfellows”. Our need for convenience leads to more convenience that is driven by network effects and stickiness in the form of human habit. I’ve written about this in “” where I argued for the importance of developing decentralized AI. The present monopoly of AI is detrimental to our own privacy and to the functioning of democratic societies.

Tim Berners Lee (Inventor of the WWW) has written about the . He writes:

I want the web to reflect our hopes and fulfill our dreams, rather than magnify our fears and deepen our divisions.

The centralized web grows because it is primarily driven by our need for convenience. It is too easy for us to give away our privacy in exchange for automation that will automate our daily news feed. Curating content takes a ton of effort so why not use automation and AI to handle this chore?

Tim Wu writes that the ‘first convenience revolution’ promised to make life and work easier for you. This, at first glance, doesn’t appear to be a bad thing. However, by the 1960’s it was recognized that this convenience was a form of conformity. Tim Wu writes:

Playing the guitar was not convenient. Neither was growing one’s own vegetables or fixing one’s own motorcycle. But such things were seen to have value nevertheless — or rather, as a result. People were looking for individuality again.

There is now a ‘second convenience revolution’ where its unrelenting drive for convenience leads to an alarming conclusion: What if we could make it convenient to express our own individuality? The second convenience revolution promises to make it easier for you to be you. Said differently, automation and AI that makes it easier for you to express your own identity becomes the tool that replaces your own identity. Tim Wu writes:

It is about minimizing the mental resources, the mental exertion, required to choose among the options that express ourselves. Convenience is one-click, one-stop shopping, the seamless experience of “plug and play.” The ideal is personal preference with no effort.

This is because what we have lost in this ‘fast food’ culture of ours is the reality that our humanity is defined by the inconvenient obstacles that we must overcome every day. Work is only truly meaningful to us when there is an investment in considerable time and effort. The more effort we spend on a pursuit, the more we can find meaning in it. You can climb to the top of a mountain or you can take a cable car up the mountain. The climb is hands down a more meaningful activity. Tim Wu says it best “convenience is all destination and no journey”. The journey gives us the experience that defines us, take that journey away and there is little that is left.

The most meaningful journey is going to be the ones that are steep and difficult. Automation and AI introduces every kind of convenience at scale. As a consequence, ikigai or that “reason for being” gets extinguished at scale.

Therefore, to preserve humanity within a reality of exponential progress, we must understand how to balance our activities into focusing on the inconvenient. It is in the inconvenient where one will find true meaning. To ignore these inconveniences is to ignore our own identity. The tasks that AI makes convenient are the also the same tasks that make us human.

It is our responsibility to recognize the value of these tasks and to ensure that they endure and continue to be available for future generations. A notable example of this preservation is the activity of of the people of the Pacific. It is a way to navigate the ocean with as few navigation tools as possible (without sextant, compass, clock, radio reports, or satellites). This is an art that was lost for several generations but is being revived to be available for future generations. In that little corner of the universe that is hopefully reserved for humans, we will spend our lives enjoying human activities like sailing. Not because sailing takes us from point A to point B, but because the activity and journey of sailing is meaningful.

Why has the open source movement become so valuable? Why do thousands of programmers devote endless hours on a mentally taxing endeavor like programming or writing only to give away their work for free? It is the expression of effort and sacrifice that makes us human. To quote from this :

Discomfort is the price of admission to a meaningful life.

Intuition Machine

Deep Learning Patterns, Methodology and Strategy

Carlos E. Perez

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Author of Artificial Intuition and the Deep Learning Playbook —

Intuition Machine

Deep Learning Patterns, Methodology and Strategy