We begin this week’s piece in a similar breadth as last’s. The influence of data on our lives is enormous, and therefore the ability to analyze it effectively an increasingly necessary skill. As its happened, this skill has been central to several of my past pursuits including engineering school, financial market education, and my SEO agency. Furthermore, as Becoming Polymathic develops, so will its reliance on effective data analysis. These facts all contributed to me pursuing my Microsoft PowerBI Analyst Certification in 2025.
An Interesting Starting Point
Shortly after entering the new year, I began the first module. To my surprise, it had little to do with analyzing data, Excel, or even examining data analysis career paths. It began with an overview of generative AI. “Interesting,” I thought. To an even greater extent than data, AI has been proselytized as the solution to every obstacle in modern society. The core argument is centered around automation of the first two stages of a venture – ideation and execution, the third and final stage being iteration.
To be more precise, AI is intended to quickly present a breadth of ideas during the ideation stage, gather preliminary data, then synthesize it into the necessary formats at the end of the execution stage. It removes us from repetitive, linear tasks such that we may focus on meaningful, non-linear ones for which our brains are optimized. As with many theories, it sounds reasonable upon first glance. However, unlike many theories, it has real merit.
At this point, it’s imperative to clarify the purpose of this piece. In this context, we’re going to be referring to AI as it relates to the performance of the aforementioned “boring” tasks, also known as automation. It’s understood AI can refer to a gamete ranging from chatbots (i.e. ChatGPT) to supercomputers (i.e. IBM Watson) to humanoid robots (i.e. Aria). We will not be jumping down those rabbit holes. We will be addressing the question posed by the title.
Automation – A Double-Edged Sword
It’s hypocritical to state automation is overly negative. Furthermore, it’s naïve to state it’s new. Automation has been central to human evolution. It first came in the form of simple machines – levers, pulleys, wheels, etc. The next iteration was complex machines – combinations of two or more simple machines. The following iteration was fuel-powered machines – steam engines, internal combustion engines, electric motors, etc. Each evolution moved humans further from the machine’s output and reconcentrated their effort to the initial ideation, monitored execution, and process improvement (iteration) microstages.
Modern digital processes follow a similar pattern of prompting, result monitoring, and prompt improvement. The most immediate example is ChatGPT. Extrapolation further leads us to prolific examples such as social media advertising, engineering analysis software, and data analytics tools such as PowerBI. As stated at the beginning, it’s influence on our lives is immeasurable.
The Other Edge
To begin elaborating on this “other edge”, we return to the mechanism by which we learn any skill. The brain learns best by immersion. The further removed we are, the less we understand about the skill, and the less potential there is to form new neural connections. Though its benefits are vast, automation is a form of removal. This finally returns us to the original question – how much should we automate?
First, it needs to be understood automation does not correlate to less human involvement – jobs. Despite estimates stating up to 30-40% of jobs could be overtaken by automation, as history has proven, there will be new avenues for the displaced.
Most research into this subject focuses on economic impact: rightfully so. But what about the non-economic, qualitative impact of automation? Specifically, if the ultimate goal of automation is to give us more time, then we must determine how to spend it. Work is a biological necessity. So, despite how appealing it seems, nobody will be happy leisurely meandering through the day. For some, the most satisfying work is primarily manual. For others, its primarily cerebral. Both are underpinned by immersion. Deducing further, any task ancillary to this immersion is unsettling, which in many cases are the responsibilities of our 8-12 hours per day jobs. Further deduction brings us to a familiar conclusion; our desire to automate is driven by what does, or does not interest us.
How Much Should We Automate?
If we are interested, we have zero desire to remove ourselves from that interest. Therefore, the question of automation is not one for society, or me, to answer. Society will eventually reach equilibrium, another statement forged in human history. It will be a long and iterative process, affording ample time to act on one’s interests. The question, therefore, is the same one we’ve been asking:
Are you disciplined enough to discover and act on your genuine interests?
Be More.
Become Polymathic.