Company culture isn’t what you say you believe. It’s what you’re willing to stand behind when it gets difficult. This week on Founder Mode, @clairewaves joins @jasonshafton and me to share what 12 years inside Google and YouTube taught her about culture, leadership, and the gap between a company’s promises and its actions. She helped tell Google’s story, believed in its mission, and later helped organize a walkout when that story no longer matched what she was seeing. It’s a reminder that building a great company isn’t just about creating a mission people believe in. It’s about making sure your decisions still reflect it as the company grows. CHAPTERS 00:00 – Cold open 00:41 – Is culture load-bearing or just marketing? 04:07 – Selling meaning as a perk 05:42 – Ghostwriting Larry Page and the cure-death era 08:35 – Google+ and the corporate bonus 10:29 – When the story cracked: Creative Lab 13:18 – YouTube and the brand vulnerability briefing 15:54 – Was advertising the real mission all along? 18:10 – Marketing to executives instead of the public 21:31 – Maven, the walkout, and why workers went quiet 25:21 – Would she organize the walkout again? 28:08 – The subway ride home 32:06 – Where to find Claire and Don't Be Evil 33:13 – The Founder Mode Five

I'm addicted to AI. Not because it makes me smarter. Because it reminds me of having a bot that mines gold in a video game while you sleep. Back in the early days of online gaming, some players wrote bots. Little scripts that ran all night. Grinding resources. Collecting gold. Leveling up. And when you logged in the next morning? Monster pot of gold. While everyone else was still grinding manually. That's what’s possible with AI today, only it’s not a video game. Right now, while I'm sleeping, my agents are: - Researching to inbound leads - Analyzing call data from our healthcare clients - Flagging anomalies in our platform - Prepping briefs for my morning meetings I wake up every day to a monster pot of gold. Most executives I know think about AI as a cost-cutting tool. Cut headcount. Reduce overhead. Trim the fat. That's the wrong frame entirely. AI is a leverage machine. The question isn't "How do I do the same things with fewer people?" The question is "What can I now do that wasn't possible before?" I started writing code in Basic with my mom’s help at 8 years old because I could see the leverage. Write code once, run it a million times. AI is that same realization multiplied by a thousand. The people who understand this are already operating at a different level. The people who don't will wonder what happened to their industry. Which camp are you in? And what's the one AI tool in your stack that's actually changed how you work?

The biggest AI decision isn’t which model to use. It’s deciding where trust ends and judgment begins. This week on Founder Mode, @AlexFinn and @vpetersson return to compare two very different ways of building AI-first companies. One gives agents access to almost everything. The other believes every permission should be earned. Neither is trying to use less AI. They’re both trying to build companies that can trust it for the right things. The future isn’t AI or no AI. It’s knowing where to draw the line. CHAPTERS 00:00 – Cold open: "What's your concern?" 01:51 – Cage the agents vs. release the agents 02:42 – How they start the day with AI 05:14 – A new favorite agent every week 07:28 – Would you give AI your email? 11:44 – Building a company with a tiny team 16:53 – Are skills and context stores a short-term fix? 18:54 – Why Alex isn't rushing to ship Henry 21:04 – Local models vs. frontier models 23:31 – Ambient AI and hacking your hardware 27:49 – Babysitting agents: the new bottleneck 30:02 – Context switching and the perfect week 31:17 – Raising money vs. bootstrapping 35:07 – Lightning round 39:28 – Founder Mode top five

We deployed AI on a medical practice's phone lines. Day one: 38% of calls handled automatically. Day three: more than 50%. It was supposed to be a small test, but the staff asked us if we could just…never turn it off. Here's what nobody tells you about AI adoption in healthcare: The biggest risk isn't patient rejection. It's the staff’s resistance and fear of the shift happening with AI right now. We solved that problem with one decision: We named the AI. In this particular example, her name was Sarah. And something interesting happened almost immediately. Instead of staff asking "why is the robot on the phones?" They started asking: "Can Sarah handle this too?" Within a week, the whole staff was brainstorming new tasks for Sarah. They weren't threatened. They were relieved. Because Sarah (also known as @prettygoodai) handled the calls that used to eat up their whole day: - Appointment scheduling and rescheduling - Prescription refill requests - Insurance verification - Directions, FAQs, provider matching The stuff that doesn't require a human, but it was consuming humans’ time anyway. Now the practice has hold times under 5 seconds. 13% of their appointments are booked after hours- revenue they were leaving on the table every single night. And their staff is spending time on what actually requires them. The technology wasn't the hard part, the adoption was. And the answer to foster that adoption was pretty simple. Give the AI a name. Make it part of the team. Healthcare practices: if your phones are still the biggest source of operational friction in your building, we should talk.