You're tired of hearing that you need to pick up AI, so today I'm not going to add to that pile. Instead, we'll look back at two moments in history that look a lot like this one: the arrival of Fortran in the 1950s, and the spread of autopilot in commercial aviation. Both show how new abstractions change the shape of our work without being purely good or purely bad.

You've heard plenty of engineering leaders tell you to use more AI and more agents, often in a way that seems out of touch with your day-to-day work. I'm not going to add to that today. Instead, I want to share some history. If it feels like our industry is in a completely unique moment, it helps to look at earlier times that had a similar shape. In this episode, I look at the early skepticism around Fortran and the effects of autopilot on pilot skill. Then I explain why a dualistic "good or bad" view of AI leaves out most of what is actually happening.

Fortran and the Skeptics: In the 1950s, Fortran became widely known as the first high-level language, generating machine code so programmers didn't have to write it by hand. Skeptics doubted it. John von Neumann reportedly questioned why a serious scientific machine should do "clerical work," and many engineers didn't trust the generated code.
The Critics Were Partly Right: Early generated code wasn't very good and needed a lot of massaging. The Fortran team didn't go back to the old way. They put their effort into improving the new abstraction. The criticisms of the new tool became the fuel for making it better.
Autopilot and Cognitive Saturation: Commercial aviation is one of the safest forms of travel, and automation is a big reason why. Taking the manual work of flying off the pilot frees up mental capacity for radio calls, navigation math, and diagnosing problems. That adds a safety margin to every flight.
Children of the Magenta Line: Automation also brought a new risk. When pilots rely on GPS and autopilot, their manual flying skills can atrophy, and that matters in the rare cases where the automation fails. Something similar can happen to engineers who lean heavily on tools like Claude Code. Some might choose to work in their codebase without AI now and then, or practice LeetCode-style problems, just to keep those skills sharp.
Beware Dualistic Thinking: New technology rarely pushes things in only one direction. It brings new easier things and new harder things, more quality and new risks to quality, skill atrophy and room to focus on completely new problems. Whenever you catch yourself framing something as a simple good-or-bad duality, ask whether your brain is just trying to make it easier to understand.
A Warning for Team Leaders: It's a mistake to push agents into your existing workflow as if they were a simple accelerant. These tools change who is attracted to the work. They may draw in new people who were never interested in engineering before, and they may push away engineers who loved the job as it was five years ago.
Where This Is Probably Headed: If this follows the pattern of past technological revolutions, today's rapid pace will eventually level off. The unreliability we see now is likely to drive future reliability, and wider adoption tends to follow once tools stabilize. In the meantime, the fatigue is real, and it's worth paying attention to what it's doing to your mindset.
Episode Homework: Pick one opinion you hold about AI in your work. Ask yourself whether you've framed it as all good or all bad, then list the new risks and new opportunities it creates together.

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How History Can Inform a Dynamic Understanding of Technological Revolutions Mirroring this Moment In Software Engineering

Developer Tea

How History Can Inform a Dynamic Understanding of Technological Revolutions Mirroring this Moment In Software Engineering