Your brain is constantly trying to simplify the world. It takes a snapshot of what it sees, builds a picture that makes sense, and then works from that picture. Most of the time this works well enough. But when we plan projects, set goals, or try to predict where something is headed, these default modes of thinking can lead us in the wrong direction. In today's episode, I cover three of these defaults: linear thinking, static thinking, and infinite or non-discrete thinking. I also talk about how they show up in your career and in the way engineering leaders set vision for their teams.

• Static Thinking and the Snapshot Problem: We tend to fix our beliefs about people very quickly, which is why first impressions carry so much weight. If someone acts a certain way in one meeting, we assume that's how they act in every meeting. This is closely related to the fundamental attribution error, where we treat a person's behavior in one moment as a permanent part of who they are instead of a response to their environment.
• Linear Thinking and Exponential Change: When something is changing, we assume it will keep changing at about the same rate. AI agents are a good counterexample. Research suggests their effectiveness roughly doubles on a timescale of a few months, and even if the exact numbers are off, the curve is exponential. That kind of growth is hard to picture intuitively, and it probably won't last forever. At some point it will likely level off into something closer to logarithmic.
• Where Our Intuition Comes From: Nonlinear patterns are all around us in nature. Think about how quickly heat rises as you move your hand toward a fire, or how loudness changes as you get closer to a speaker. But our social lives mostly involve slow, roughly linear change, and over the course of human history our minds have adapted to that kind of linear model.
• Your Career Isn't a Straight Line: Many of us expect our salary and growth to rise steadily, in steps, for our whole careers. That expectation often breaks down, especially later on, and it makes exponential income growth hard to even imagine.
• Infinite Thinking and Missing Endpoints: We aren't naturally good at thinking in discrete stages. We rarely design our careers in phases or plan for how they end until those moments are right in front of us. Cultural habits like contributing to a retirement account do some of that planning for us almost by accident, but most areas of our lives get no such help.
• Two Traps for Engineering Leaders: When setting direction for a team or department, leaders often fall into one of two traps. The first is a "forever vision," an unbounded picture of perfection that the org is supposed to chase. The second is purely linear planning, where you only do the next thing in front of you and never step back to look further ahead.
• The Productive Middle: The most useful vision work usually happens on a timescale of months to years. That's harder than it sounds, because it calls for a concrete plan, a lot of saying no, and putting the pieces together in a discrete way that doesn't come naturally to us.
• Episode Homework: Becoming aware of these defaults is the first step. Add linear, static, and infinite thinking to your planning checklist. Next time you set a goal or a vision, ask yourself which of these defaults might be shaping your picture of reality.

If you enjoyed this episode and would like me to discuss a question that you have on the show, drop it over at: developertea.com.

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Keeping up has always been part of the job for software engineers, and with AI it's harder than ever. In this episode, I talk about two signals that you, your coworkers, or the people you manage might have a regressive mindset, which shows up as quietly avoiding the learning you need to stay current.

Keeping up has always been part of being a software engineer, and right now it's harder than ever. A lot of us are tired, skeptical, or just overwhelmed by the constant stream of AI news, and it's easy to slide into habits that slowly cut us off from new information. In today's episode, I talk about two signals to watch for in yourself, your coworkers, or your reports that point to a regressive mindset. That's a pattern of avoiding progress and learning, often without realizing you're doing it.

• What a Regressive Mindset Actually Is: A progressive mindset doesn't require jumping on every new tool or throwing out your whole toolset. Constant upheaval isn't progress. What matters is that your understanding of the world keeps growing. The regressive mindset shows up as avoiding learning and leaning on defense mechanisms that shield you from more nuanced information.
• Signal #1: Avoiding Information by Category: Watch for the moment you click away from an article or video just because it mentions AI or agents. You don't have to read everything, but if you're skipping things purely because of the subject, it's worth figuring out why. Fatigue might mean you need a break. Skepticism or discomfort is a different story.
• The Ostrich Effect: Avoiding uncomfortable information is a lot like sticking your head in the sand. It feels protective, but over time it leaves you behind and out of touch with what it takes to stay competitive as the industry changes.
• What Is Your Alternative Path?: If your plan is to keep avoiding this information, ask yourself honestly what that looks like a year or five years from now. I'd argue that avoiding it forever just isn't possible.
• Signal #2: Tying a Concept to a Person: Many organizations have one or two people carrying the flag for AI adoption. If "the AI enthusiast" has become the whole of what that person means to you, you may be dismissing everything they say, even on topics that have nothing to do with agents.
• The Flip Side and Fundamental Attribution Error: The same trap works in reverse. If you don't trust someone, you may throw out good information simply because they're the one bringing it. This is closely related to fundamental attribution error, where we explain someone's choices by who they are and judge the information by the messenger.
• Episode Homework: This week, notice when you skip, dismiss, or tune out information. Ask yourself whether it's because of the topic or the person sharing it, and what your long-term alternative really is.

If you enjoyed this episode and would like me to discuss a question that you have on the show, drop it over at: developertea.com.

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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.

If you enjoyed this episode and would like me to discuss a question that you have on the show, drop it over at: developertea.com.

If you want to be a part of a supportive community of engineers (non-engineers welcome!) working to improve their lives and careers, join us on the Developer Tea Discord community today!

We are developing a brand new newsletter called The Tea Break! You can be the first in line to receive it by entering your email directly over at developertea.com.

If you're enjoying the show and want to support the content head over to iTunes and leave a review!