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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