Offload Complexity, Don't Offload Learning

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A couple of months ago I read this excellent blog post by Steph Ango (better known as Kepano on Twitter), immediately this post struck a chord for me specifically while thinking about leveraging AI as a tool.

The post talks about a parasite and it's impact on us over time. Specifically this parasite is the act of delegating understanding, there are many parasites that take on the same format though (e.g. offloading your competitive advantage as a business). Broadly speaking, technology can sometimes be this same parasite masquerading as something beneficial - and that applies to how we leverage AI.

Fundamentally I look at AI as a tool for folks to leverage to help solve their problems, in the same way that most programmers look at frameworks or libraries. However, just as you and I would spend time evaluating the [[right tool for the job]], we should also carefully evaluate how we use AI.

Specifically - we should look to offload complexity to AI, but to not offload learning.

A similar mental model that folks have probably heard of before is maintaining a moat if you are a business. The moat represents your competitive advantage, it is what a business does (or maybe explicitly doesn't do), that makes it better than their competitors. It is the 11 herbs and spices, or the secret recipe.

If a business were to offload that competitive advantage to some other provider, or to another company - they'd cease to be a competitor within the market and others would soon take over.

In the same way - if you as an individual offload opportunities you have to learn, grow, or excel in your own - you will miss the follow on opportunities for greater achievements. This is why we should look to leveraging AIfn-1^{[1]Jump to footnote} as a tool to be used in some scenarios and not in others.

Since most of my audience for this blog are developers of some sort - I'll try to relate this to development concepts 🙂.

Imagine you're trying to learn game development, including the core pieces required to build a game. You probably wouldn't immediately jump to using a higher level game framework to make the game, you'd most likely want to start from the fundamentals and build up your knowledge along the way.

Similarly if you're looking to learn about web servers (think HTTP, TCP, routing, etc), you probably won't want to start with using a framework like Express or Gin and instead you'll again want to start from the basics that your programming language of choice supports.

However - if you're building a website to sell furniture for example - you'll probably won't want to dive into the basics of HTTP, TCP, and all the other concepts related to web hosting, and instead you'll reach for a framework that abstracts that complexity away from you.

In the same vein - you should look to focus on the desired outcome (your competitive advantage, or your core learning/growth opportunities), and leverage AI to solve the ancillary complexity.

While in some of these examples the decision criteria on when and where to use AI is maybe clear, it won't always be like that in practice. Sometimes it's hard to tell if a specific task or project will provide learning opportunities for you, or will be secretly hiding your competitive advantage, or if it is just filled with monotonous work that doesn't let you grow.

Footnotes:

👆Back to reference This can be said of technology more broadly as well!

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