The “AI” Label is Bullsh*t

Jan 1, 2017 · 5 min read

The term “AI” has become overused, overextended, and marketed to oblivion like “HD” or “3D.” A new product with “AI” in the headline of its press release is thought to be more advanced. The time has come for us to speak clearly about “artificial intelligence” (“AI”) and arrive at a new, clean starting point from which to discuss things productively.

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Let’s begin with the words themselves, because if they are vague then we are already obscuring things. Let’s accept “artificial” at face value: it implies something synthetic, inorganic, not from nature, as in “artificial sweetener” or “artificial turf”. So be it.

The painfully overcharged word here when paired with the word artificial is “intelligence”.

In thinking about artificial intelligence, I won’t refer to Alan Turing or his famous “test,” for he himself pointed out (correctly) that this was meaningless. Nor will I quote Marvin Minsky, who passed away recently concerned we were repeating a so-called “AI Winter”: in that lofty expectations would lead to disappointment and an under-investment in the science for years. His worries are well founded, and that’s a different discussion. Another separate discussion is AI’s existential threat to humanity, which Bostrom, Musk, Kurzweil and others have pondered.

Instead let’s look at what nearly all of the software carrying the label “AI” is doing and how it relates to working with information.

I’ve been among the many who have long admired the thinking and writing of Peter Drucker. His clarity of thought is reminiscent of prior generation Austrian writers, including Zweig and Rilke.

So what would Drucker say about artificial intelligence?

Drucker would say that we are mostly talking about machines performing knowledge work. He would view the “intelligence” label as chimerical.

The term “knowledge worker” was coined by Drucker, who said in 1957 that “the most valuable asset of a 21st-century institution, whether business or non-business, will be its knowledge workers and their productivity.”

The utility of this term was to distinguish laborers (farmers, machinists, construction workers, etc.) in the workforce from a new emerging type of worker (accountants, architects, lawyers, etc.) who worked primarily with information.

Knowledge Work and Software

Here’s where it gets interesting. This new frontier of work “by thinking” certainly did not exclude machines — more accurately: computers — more specifically: software. That’s because the new knowledge-working genre that Drucker perceived in the 1950s was just beginning to interact with computers. Now of course, software has increasingly augmented and replaced human work as it relates to information, and today it is a pervasive phenomenon.

In fact, a software spreadsheet (one of the most useful and common pieces of software ever created) is capable of knowledge work. It’s doing a fair amount of the work that was done previously by calculators, and prior to that — a mano by number crunching humans. The spreadsheet is performing tasks that were once performed by a human knowledge worker. That is what it does.

We don’t refer to the work of an accounting package, a travel booking server, a payroll processor, CAD (computer aided design), and countless other software systems as “AI”.

Software has for a long time performed knowledge work and this work has evolved in complexity for decades. It has done so in narrowly defined tasks, always with a specific goal in mind. This is still true today.

The evolution of software as a knowledge worker

What about the “AI” that recognizes patterns in stock market data, translates writing from one language to another, transcribes audio or recognizes image patterns? This is also software applied to knowledge work.

We’re referring to a set of instructions applied to a computer system (CPU, memory, etc.) to move data around, calculate and output values. Today we have have a lot more “system” today than ever before, and we have a lot more data as well.

The fact that software is now doing its thing increasingly everywhere is a big thing. It is able to perform “knowledge work” in your car, in your hands, and in the world. It is able to do this kind of work while being connected to informational resources. Knowledge work, as Drucker pointed out, is intrinsically about information.

The primary difference between today’s knowledge worker and yesterday’s is the amount of processing and information at hand. What is deceivingly branded “AI” today is based on old algorithms (eg. neural networks, invented decades ago) applied to larger computing and datasets.


Herein lies the significance of the term “intelligence”. A laborer is undoubtedly intelligent, a farmer deals with extraordinary amounts of information about crops, soil, weather, etc. But farmers are not knowledge workers, because their craft is not predominantly working with information, that’s secondary to the actual task at hand. The accountant, also intelligent, is on the other hand primarily working with information.

This is not about “intelligence”, but rather what they are working on, the nature of the work.

Just as knowledge work can be the job of a person, artificial knowledge work can be the job of a software application. This is what the vast majority of software with the “AI” branding is actually doing. But just because it’s called Artificial Intelligence doesn’t mean the software has any intelligence. But that too may be changing.

A field known as “Artificial General Intelligence”, or “AGI” is examining the possibility of software that can “think” in the pure sense of the word. What is referred to as “AGI” should simply and properly be called “AI” because once machines can acquire knowledge, learn adaptively and make rational choices, they become not just knowledge workers, but truly intelligent.

In conclusion, software continues to evolve in its capacity to perform knowledge work: narrowly defined information-driven tasks with specific objectives. The label “intelligence” has to do with something much more fundamental and elusive.

The human accountant has the ability to learn to be an architect (a different type of knowledge worker) but today’s artificial knowledge worker cannot adapt this way. Software code can “learn” but thus far only within a specific type of task. DeepMind’s “AlphaGo” defeated a Go professional player but it cannot play checkers, tic-tac-toe, or any other game. The “smartest” software applied in consumer and business settings today lacks the capacity to adapt itself outside of its intended purpose. It is utilitarian.

The scientific pursuit of artificial intelligence aims to change this. Will we see real advancements on this front? Are you ready?


Written by


Philosopher, Entrepreneur, Investor

Intuition Machine

Deep Learning Patterns, Methodology and Strategy

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