As the joke goes, there are 10 types of people in this world. Those who understand binary and those who don’t. Joking aside, it is common to divide the world into two groups of people, those who know how to code and those who don’t. I like to think of the world a little differently, and though the difference is quite subtle, it has significant implications for the future.
There are two kinds of people, those who don’t know how code works, and everyone else.
I’ve been around long enough to know that even experienced coders are constantly learning and unlearning, and there is always a new tool or framework they will boldly declare that they don’t know how to use. The plethora of programming languages and frameworks out there, and the fact new ones keep cropping up are a fixture of the world of working with technology systems and code. “My learning is done”, declared no programmer ever. So in order to more accurately capture the reality of the dispersion of knowledge about programming concepts, I think the more relaxed representation above is more apt. Hopefully, this work has made you into one of the ‘everyone else’ category, or affirmed your status in that group. Like I explained in the introduction to the driver labs, if technology is like a car, the users are passengers, not drivers. Only by learning how code works can you become anything beyond a hapless passenger. This is a trend that is only going to continue as abstraction continues to increase through the creation and deployment of AI driven tools.
Google took a complex network of link relationships between websites and page content and offered the users a single text entry box and a bold logo with primary colors - to respond with the most likely website that had information relevant to your search term, all while solving critical technical challenges around distributed computing and management of large datasets. Decades later, ChatGPT would do the same thing, also offering a simple text entry box as a window into a complicated world, after training a large language model using estimated 30,000 GPUs on over 300 billion words to respond with the most likely next words based on the accumulation of human text communication on the internet and elsewhere. Is it important to understand all the pieces that go into making these one page abstractions as powerful as they are today? Absolutely. There are many reasons why peeking behind the curtain of modern technology, whether you are a data analyst or a data consumer, is imperative for the survival and health of our technology ecosystems. Here are just a few. Allow me to wear my futurist hat for the next few sections.
If you have read the chapter on automation, you will be able to tell that a lot of the tasks that a business handles today have already been heavily automated for efficiency, speed and other reasons such as security and traceability.
Automatic transmissions made driving easier. That was the whole point. No clutch, no gear changes—just press the gas and go. But in simplifying the experience, something was lost: engagement, awareness, control. A recent study found that adolescent drivers with ADHD actually performed better with manual transmissions. Why? Because the act of shifting gears forced them to stay actively engaged with driving. Automation, it turns out, isn’t always the answer. Sometimes, requiring more effort makes us better at what we’re doing.
The same logic applies to technology. When companies compete on ease of use, they aren’t just making things more accessible—they’re also removing layers of depth. A tool that demands nothing from its users can only take them so far. This is why professional designers reach for Photoshop over Canva, why experienced programmers use Vim instead of a basic text editor, why Excel power users bristle at the limitations of Google Sheets. When people are forced to engage more deeply with a tool, they get better at using it. They become more skilled, more aware, more demanding. Their horizons for possibilities using the tool are expanded, and that demand is what drives meaningful innovation.
But what happens when everything is automated? The entire debate between manual and automatic transmissions may soon be irrelevant, because driverless cars will put us all in the passenger seat. No more shifting gears. No more driving at all. Just passive transportation, where decisions are made for us and engagement is reduced to a tap on a screen. This is the danger of prioritizing ease of use above all else—not just in cars, but in every corner of technology. If we let simplicity win every time, we might wake up one day to find that we don’t actually control anything anymore.
If companies compete on functionality, users get smarter. They demand more from their tools, and in turn, those tools become more powerful. But if competition is only about ease of use, we risk building a world where complexity is hidden behind sleek interfaces, where users never push past the surface, where the depth that once drove innovation disappears. And once the self-driving future arrives, that wheel might be out of our hands for good.
When I teach collaboration, I like to share this original quote about globalization. Even if your team members are not global, your customers are. Even if your customers are not global, your competition is!
Related to the previous idea is the need for the development of a philosophy of tools, a manifesto and a social contract that consumers can get behind to set the tone for the future.