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In this episode

Most conversations about AI and software argue about what the machines can do. This one is about what stays with us. Who cares whether the work is good? Who is accountable when it goes wrong? And how people actually learn to do hard things.

For this bonus edition I sat down with my oldest, Beth Andres-Beck — a software engineer who once wrote self-driving-car software for the DARPA Grand Challenge, took a degree in theater, learned to run large groups of people through community organizing, and is now running for Congress in Massachusetts’ 6th district. We start with a cheese pun and end up somewhere serious: why engineers don’t write tests and how you actually get a team to start, what it means that an AI agent has no drive of its own, and why “the computer did it” is never the whole story.

Takeaways from Beth

1. The geek’s real superpower is asking why people do what they do. Beth’s through-line across every interest she’s ever had — chickens, road-grinding, theater, code — is that people always act for reasons. Maybe not reasons you agree with, maybe reasons no one thought through, but there’s always something. Get curious about the reason and you can solve problems you can’t even see otherwise, like why no one on a team is writing tests.

2. Meta-intelligence beats raw smarts. She told me about a kindergarten class that had all learned to count to thirty. One kid gets to the end and says “thirty-one.” Another kid asks: “How do you know that?” The first kid is smart. The second one goes further and faster, because she isn’t just solving the problem, she’s learning how the solving gets done. That skill has a name, it can be taught.

3. People usually don’t skip tests out of laziness. Beth didn’t write tests for the first seven years of her career, and it wasn’t for lack of caring. There was no off-the-shelf test framework, no Stack Overflow, no examples — she was learning frameworks from a book printed in Japanese, working off the English code snippets alone. If you want more of a behavior, first ask what’s actually stopping it.

4. Learn to write testable code before you worry about the tests. The thing that finally unlocked testing for her wasn’t discipline, it was design. Once she was writing code that was easy to reason about, the tests became easy to add later. A colleague came to her about a feature she’d built and said it was beautifully laid out and trivial to test, even though she’d written no tests for it yet.

5. To get a team testing, lead by pretending — then let the dopamine do the work. Her move as a first-time manager was to just start saying “we’re the mobile team, and we write tests,” until not writing one felt like not being on the team. Then, in code reviews, she stopped pointing out bugs and started pointing out the missing test. Every test someone added caught a real bug, and that loop is more persuasive than any lecture.

6. The best moment in testing is the surprise. A test that passes when you expected it to pass tells you nothing. The rush is predicting red, seeing red, and finding a bug you’re grateful never reached production. For engineers straight out of school especially, each test becomes a small discovery.

7. An AI agent has no endocrine system. I asked, half-joking, how we get the model excited about writing a test. Beth’s says it can’t be. A model has no enthusiasm or drive of its own. The thing that actually does the work is “us plus the genie.” A human brain never just sits there; the computer is a lump until we bring the caring. The wanting-it-to-be-good is still ours.

8. The urge to take humans out of the loop is often a flight from responsibility. When people say an agent can prescribe your medication, what they usually mean is that patients will prescribe their own medication as long as they route it through this program and the company that built the program takes no responsibility. Removing the human doesn’t remove the accountability. It just hides who holds it.

9. Calling a system “objective” is how bias hides. Beth’s bugaboo is the “objective” performance review. The moment you tell people a system is objective, they stop scrutinizing it, and you can smuggle in enormous bias. There’s no platonic ideal of a job someone is objectively good at; there’s only this job, and whether they’re good at the thing you actually need done.

10. It gets harder to see who is responsible when nobody is in the car.
We talked about a driverless car that made an illegal U-turn in front of a police car, got pulled over, and had no one to ticket. “The computer is driving” is the easy story. The real one: software a person wrote is driving, under incentives set by an organization, shaped by tax law, venture capital, and whether that engineer had a fight with their spouse that morning.

11. Systems do exactly what you tell them, not what you mean. Writing self-driving software in 2008, her team gave the car a rule: if you’re stuck, relax constraints until you free yourself. It hit a roadblock, thought for a while, and relaxed “don’t drive on sidewalks” — so it climbed onto the sidewalk and drove around. Exactly what she told it. Not at all what she wanted. Anyone working with agents today knows the feeling.

12. Good engineering can get you fired, and the real AI fear isn’t rogue machines. Beth has watched teams do everything right — refactor, test, integrate, collaborate — ship genuinely good software, and get cut anyway, because management didn’t actually want working software. And when I asked what keeps her up at night, it wasn’t machines doing whatever they want. It was machines doing exactly what a powerful few tell them: a “personal army” that no longer needs anyone else’s cooperation to do great harm.

References

Where to find Beth Andres-Beck:

• Campaign: https://bethfordemocracy.com

• Bluesky: @beth4ma.bsky.social

• Instagram: @beth4MA

• Ballotpedia: https://ballotpedia.org/Beth_Andres-Beck

Timestamps

• 00:00 Intro: a very special edition

• 02:13 When did you first know you were a geek?

• 03:02 Computer geekdom: Widget Workshop

• 03:45 Realizing not everyone’s a geek, and learning to read people

• 06:22 A multifaceted geek: the theater degree

• 07:26 The chicken phase

• 08:54 Following curiosity: people do things for reasons

• 10:26 Why people don’t write tests

• 13:33 Learning to write testable code first

• 14:19 Getting a team to test: “we write tests”

• 16:31 Getting the genie excited: the endocrine system

• 17:43 Taking humans out of the loop, and responsibility

• 19:23 The trouble with “objective” systems

• 20:59 Nobody’s in the car: accountability

• 22:43 The self-driving car that drove on the sidewalk

• 23:54 The software someone wrote, and its incentives

• 25:09 The Good Place: decisions inside complex systems

• 26:45 Work to rule, and the forest vs. the desert

• 29:01 Shipping great software and getting fired

• 29:36 Why agents give managers a sense of control

• 30:47 What awakened the geek in politics

• 32:13 Occupy and the logistics of organizing

• 33:51 The Guild of Guilds

• 36:51 “How do you know that?” — meta-intelligence

• 38:55 Failure is just finding what stops you

• 39:13 What keeps you up at night

Keith Adams has spent his career at the systems layer, with nine years at VMware, the founding of Facebook's HHVM team, and a run as chief architect at Slack. Now he backs frontier-tech founders at his firm Pebblebed. He and Kent sit by the fire to ask what happens when the twenty-year-old playbook for building software suddenly goes blank. They range across the economics of the shift, from Jevons paradox to software as "proof of work" to a future where the real moat is gigawatts of compute, before turning to what it costs us: the flow state that drew us to programming, now traded for something closer to air traffic control.

This season of Still Burning is sponsored by WorkOS and Augment Code.

The audio version can be found here.

Jessica Kerr joins Kent by the fire to argue that AI didn't take the programmer's job, it split it in two. The part we loved, crafting code by hand, has been commoditized like IKEA furniture. What's left is harder and more human: understanding what to build, proving it works, and stewarding the living "symmathesy" of people, code, and agents all learning from each other. They get into accelerated learning, why play is a signal you're learning, the loop that "becomes a noose," and choosing excitement over fear while the ground keeps shifting. You can find more of Jessica’s thoughts on her podcast at: Machines of Code and Grace

This season of Still Burning is sponsored by WorkOS and Augment Code.

Listen to the audio version here.

Randy Shoup set out to be an international lawyer. He studied in West Berlin when there was still a wall around it, spent a year at Stanford Law, and had what should have been the perfect summer internship on Sand Hill Road. Instead he spent it watching inventors light up whiteboards with brilliant ideas — then being told his job was just to write them down. That summer broke something open. He went back to Oracle that fall and never looked back. Kent and Randy dig into what it means to need to make things, why the people who wrote the original distributed systems playbook aren’t panicking about AI wiping it clean, and how Jevons paradox explains what happens when cognition gets cheap.

This season of Still Burning is sponsored by WorkOS and Augment Code.

Listen to the audio version here.

Michael Grinich has an unusual vantage point, his company powers enterprise infrastructure for hundreds of companies, so he sees how the AI transition is actually playing out across the industry, not just in the headlines.What he’s noticing: the whole ecosystem is accelerating, not just the AI companies. And most people are misreading what kind of moment this is. Kent and Michael talk about building when the marginal cost of software approaches zero, what the Red Queen theory tells us about AI competition, how engineering leadership is quietly changing, and what drew Michael to the itchy-brained compulsion to make things in the first place.

This season of Still Burning is sponsored by WorkOS and Augment Code.

Angie Jones spent years as the "geek whisperer" — translating technical possibility into human progress. Then she led one of the most ambitious AI adoption programs in the industry, and the rug got pulled out. Now at the Agentic AI Foundation, she's working to make sure the standards powering the next era of software are built in the open, by everyone. Kent and Angie dig into the impossible bargain AI has handed engineers, what it really means to hire junior developers right now, and what scares her most about where we're all headed.

This season of Still Burning is sponsored by WorkOS and Augment Code.

Listen to the audio version here.

Brought to you in partnership with WorkOS and Augment Code.