Honest writing on AI product strategy, team leadership, and what it actually means to build in the age of intelligent systems.
AI doesn't just change what we build. It changes what it means to be excellent at building. For years, PM "output" has been confused with PM "value."
In 2018, I wrote about how Cambridge Analytica forced the tech industry to reckon with privacy. In 2019, I argued that personalization and trust could coexist if you were disciplined about it.
Last month I watched a demo of an AI feature that looked incredible. The presenter typed a complex query, the model produced a beautiful, detailed response, and the room was impressed.
Every week, someone pitches me an AI feature that starts with "What if we used GPT to..." and I have to resist the urge to stop them right there.
If you've been around product long enough, you've lived through the integration tax. Every new tool your product needs to talk to requires a custom integration.
Last month, OpenAI announced that ChatGPT had reached 400 million weekly active users. Four hundred million. To put that in perspective, it took the product roughly two years.
Every time you ask a cloud-based AI a question, your words travel to a data center, get processed by a model running on expensive GPUs, and the response travels back.
In July, I left Tinder and joined Mozilla to help bootstrap AI for Firefox. A few people asked me why. Why leave a consumer product used by 75 million people?
Last quarter, my team ran an A/B test on a re-engagement campaign. Version A versus Version B. We let it run for two weeks, declared a winner, and rolled it out.
I remember exactly where I was when GPT-4 launched in March. Sitting at my kitchen table, coffee going cold, running prompt after prompt and watching it handle tasks I didn't think were possible.
Open any app on your phone. Scroll for thirty seconds. Everything you see, every recommendation, every suggested connection, every search result, was shaped by a graph.
There's a moment, early in any PM role, where the weight of the product hits you. At my last company, that moment was about metrics and revenue. At Tinder, it was about people.
Last month, I spent a Tuesday evening doing two things almost simultaneously. First, I typed a prompt into ChatGPT and watched it produce a coherent, well-structured essay.
Last year, my team spent three months building a feature I was genuinely excited about. The research was solid. The design was clean. Engineering executed well.
A few weeks ago I asked my team a simple question: "When was the last time any of us actually talked to a customer?" Not looked at a dashboard. Not read a support ticket.
At our last sprint retro, someone asked a question that landed harder than expected: "We shipped 14 features last quarter. Can anyone name one that meaningfully moved a metric?"
Last December I wrote about AI meeting education's crisis, the billions of students pushed online and the infrastructure that buckled under the weight.
Earlier this year, I was trying to align a cross-functional team on the direction for a new product. We were four weeks in, still in the ambiguity phase.
I became a mom in March. For the next six months, I disappeared into the fog of early parenthood: the sleepless nights, the constant Googling, the slow realization that every app matters.
This summer, I spent an evening reading GPT-3 demos on Twitter. Poetry, code, essays, business emails, all generated by a machine. Some of it was mediocre. Some of it was extraordinary.
In the first week of March, I was sitting in a conference room with my team, whiteboarding a roadmap for Q2. By the second week of March, the office was empty.
Last week I signed up for two project management tools on the same afternoon. The first dropped me into an empty dashboard with a "Getting Started" link buried in the corner.
Zoom's free plan has a 40-minute limit on group meetings. It sounds generous, and it is. Forty minutes is long enough to run a real meeting.
In the last two weeks, my team adopted three new tools. Zoom for video calls. Figma for design collaboration. Notion for documentation. In every case, someone on the team just started using it.
A few weeks ago, I was troubleshooting an issue with an online order. I opened the chat widget expecting the usual: "Please select from the following options."
Last week, a streaming service recommended a documentary about competitive jigsaw puzzling. I'd never searched for anything remotely related. But I'd just finished a series about obsessive hobbyists.
A few months ago, I was running late for dinner and asked my phone to "find a quiet Italian place near me that's not too expensive and has outdoor seating."
I was at a product meetup a few weeks ago and someone asked the question that never dies: "Should product managers learn to code?" The room split predictably.
In March, the Cambridge Analytica story broke. By May, GDPR went into effect across Europe. In the span of a few weeks, the conversation around personalization shifted permanently.
If you've been to a marketing conference this year, you already know: 2018 is the year of personalization. It's on every trend list, every keynote slide.
Product management is having a moment. Job openings have surged over the past year. LinkedIn named it one of the top ten most promising jobs of 2018.
Too often, we are solving the wrong problem. Not because we don't understand the problem itself, but because we don't understand "why" there is a problem.
User research is the bridge between a business and the people it serves, yet it's often overlooked. Whenever I ask, "Have you done any user research to understand this?"
Looking for a restaurant nearby? Ask Marsbot, a bot that learns about the types of places you like to go and texts you with suggestions for nearby eateries.
Not until recent years, Artificial Intelligence (AI) was far from ubiquitous in people's lives. We celebrated the moment when IBM's Deep Blue defeated the world chess champion.