It starts with a surprise version number: Linux Mint 27 instead of 24. But the next release brings far more changes than that.

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.

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Whatever happened to the future we were promised? Time for a Geek Out! To celebrate the end of the opening history bits, Richard pulls together all the most popular topics of "things we were supposed to get in the future." From flying and self-driving cars to fusion power, personal jetpacks, Star Trek replicators, space tourism, and more! While some things are coming true, in a form different from what was expected, other things continue to be very difficult - and there is no sign of when that future will come. The future is here, just not exactly the one we thought it would be!



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Teddy Schleifer is a New York Times reporter who covers billionaires and their influence on politics. Schleifer joins Big Technology Podcast to discuss what the Democrats’ midterm sweep means for the AI buildout. Tune in to hear why voters are turning against data centers and how much a Democratic Congress can do to slow their construction with Trump still in the White House. We also cover the prospect of tech CEOs facing subpoenas, what the election means for Elon Musk, and California’s proposed billionaire tax. Hit play for an emergency episode on what happens when Silicon Valley embraces a president and voters give the other party control of Congress.

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Dave Kerr created hacker laws, and now he finds himself reviewing agent-written code for them. Back on the show after six years, Dave names what he sees limiting teams today: keeping everyone’s mental model in sync with what’s actually been built. Adam declares it Kerr’s Law on the spot. We get into why Dave’s team treats code review as a deliberate slowdown, how Shellwright lets agents bring evidence to a pull request, and what living with bipolar disorder has taught Dave about noticing when the excitement of these tools stops being healthy.

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Featuring:

• Dave Kerr – Website, GitHub, X
• Adam Stacoviak – Website, GitHub, LinkedIn, Mastodon, X

Show Notes:

Kerr’s Law — A team’s ability to scale is limited by its ability to keep a shared mental model in sync with the system it builds.

• Dave Kerr’s website
• Hacker Laws
• Effective Shell
• Ark (Agents at Scale)
• Shellwright
• Tailscale
• Playwright
• asciinema
• k9s

Something missing or broken? PRs welcome!

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