AI and product management | Marily Nika (Meta, Google)

Marily is a computer scientist and an AI Product Leader currently working for Meta’s reality labs, and previously at Google for 8 years. In 2014 she completed a PhD in Machine Learning. She is also an Executive Fellow at Harvard Business School and she has taught numerous courses, actively teaching AI Product Management on Maven and at Harvard. Marily joins us in today's episode to shed light on the role of AI in product management. She shares her insights on how AI is empowering her work, and why she believes that every Product Manager will be an AI Product Manager in the future. We also discuss why PM’s should learn a bit of coding, where they can learn it, and best practices for working with data scientists. Marily shares some insight into building her AI Product Management course and also why she full-heartedly believes you should also create your own course. — Brought to you by Amplitude—Build better products: https://amplitude.com/ | Eppo—Run reliable, impactful experiments: https://www.geteppo.com/ | Pando—Always-on employee progression: https://www.pando.com/lenny Find the full transcript here: https://www.lennysnewsletter.com/p/ai-and-product-management-marily Where to find Marily Nika: • Instagram: http://www.instagram.com/marilynika • LinkedIn: https://www.linkedin.com/in/marilynika/ • YouTube: https://www.youtube.com/c/MarilyNikaPM • Website: https://bio.link/marilynika Where to find Lenny: • Newsletter: https://www.lennysnewsletter.com • Twitter: https://twitter.com/lennysan • LinkedIn: https://www.linkedin.com/in/lennyrachitsky/ Referenced: • The Download newsletter: https://www.technologyreview.com/topic/download-newsletter/ • TLDR newsletter: https://tldr.tech/ • ChatGPT: https://chat.openai.com/auth/login • MidJourney: https://discord.com/invite/midjourney • Whisper: https://whisper.ai/ • Machine Learning Specialization course: https://www.coursera.org/specializations/machine-learning-introduction • Career Foundry: https://careerfoundry.com • Coding Dojo: https://www.codingdojo.com/ • Building AI Products—For Current & Aspiring Product Managers course on Maven: https://maven.com/marily-nika/technical-product-management • arXiv: https://arxiv.org/ • Marginal Revolution blog: https://marginalrevolution.com/ • Automl: https://cloud.google.com/automl • Inspired: How to Create Tech Products Customers Love: https://www.amazon.com/INSPIRED-Create-Tech-Products-Customers/dp/1119387507 • You Look Like a Thing and I Love You: How Artificial Intelligence Works and Why It’s Making the World a Weirder Place: https://www.amazon.com/You-Look-Like-Thing-Love/dp/0316525227 • The Adventures of Women in Tech Workbook: A Life-Tested Guide to Building Your Career: https://www.amazon.com/Adventures-Women-Tech-Workbook/dp/1646871022 • Boz to the Future podcast: https://podcasts.apple.com/us/podcast/boz-to-the-future/id1574002430 • The White Lotus on HBO: https://www.hbo.com/the-white-lotus • Lensa: https://apps.apple.com/us/app/lensa-ai-photo-video-editor/id1436732536 In this episode, we cover: (00:00) Marily’s background (03:20) How Marily stays informed about the latest developments in AI (04:46) What is overhyped and underhyped in AI right now (05:59) How Marily uses ChatGPT for work (08:25) Why product managers will be AI product managers in the future (11:16) How to get started using AI (14:12) When not to use AI (15:47) How much data do you need for AI to work properly? (17:01) When should companies develop their own AI tools? (18:35) What an AI model is and how it is trained (21:25) How Google demonstrated the ability of AI to translate a conversation in real time (23:02) Why AI will not replace PMs (23:48) A case for learning to code (26:21) Where to learn to code (27:40) How to become a strong AI PM (29:25) Challenges that AI PMs face (31:16) Getting leadership on board with investing in AI (33:10) How PMs will work with data scientists and AI (35:29) Marily’s AI course (39:12) AutoML and how a renewable-energy company used it to improve its turbine maintenance procedure (40:31) How Marily built her course and the modifications she has made (42:53) Why you should create your own course (44:08) Lightning round Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  There is something called the shiny object trap. And I'm always telling people, hey, don't do AI for the sake of doing AI. Make sure there is a problem there. Make sure there is a pain point that needs to be solved in a smart way. Once you have identified what that problem is and what that's very, very high level solution is, then reach out and try to figure out how to actually implement it.

• 0:27Lenny Rachitsky?

  Welcome to Lenny's podcast where I interview world-class product leaders and growth experts to learn from their hard-won experiences building and scaling today's most successful companies. Today, my guest is Mary Lee Nika. Mary Lee teaches the most popular course on Maven on AI and product management. She's currently product lead at Meta, focusing on Metaverse, avatars and identity. Prior to Meta, she was at Google for over eight years, working on Google Glass, computer vision and machine learning around speech recognition. In our conversation, we touch on what PMs should be paying attention to when it comes to what's happening in AI. We talk about a bunch of resources that'll help you get started in the world of AI, how AI tools available today can already help you do your job better as a PM.

• 1:12Lenny Rachitsky?

  We also get relatively technical into what exactly is a model? How are models trained? All kinds of fun stuff like that. Enjoy this conversation with Mary Lee Nika after a short word from our wonderful sponsors.

• 1:26Lenny Rachitsky?

  This episode is brought to you by Amplitude. If you're setting up your analytics stack but not using Amplitude, what are you doing? Anyone can sell you analytics while Amplitude unlocks the power of your product and guides you every step of the way. Get the right data, ask the right questions, get the right answers and make growth happen. To get started with Amplitude for free, visit amplitude.com. Amplitude, power to your products.

• 1:53Lenny Rachitsky?

  This episode is brought to you by EPO. EPO is a next generation A/B testing platform built by Airbnb alums for modern growth teams. Companies like Netlify, Contentful, and Cameo rely on EPO to power their experiments. Wherever you work, running experiments is increasingly essential, but there are no commercial tools that integrate with a modern growth team stack. This leads to wasted time building internal tools or trying to run your experiments through a clunky marketing tool. When I was at Airbnb, one of the things that I loved about our experimentation platform was being able to easily slice results by device, by country, and by user stage. EPO does all that and more, delivering results quickly, avoiding annoying prolonged analytics

• 2:36Lenny Rachitsky?

  cycles, and helping you easily get to the root cause of any issue you discovered. EPO lets you go beyond basic click-through metrics and instead use your North Star metrics like activation, retention, subscriptions, and payments. And EPO supports tests on the front end, the back end, email marketing, and even machine learning clients. Check out EPO at geteppo.com, get E P P O dot com, and 10x your experiment velocity.

• 3:06Lenny Rachitsky?

  Marily, welcome to the podcast.

• 3:08Speaker 00

  Thank you. Hello. Thank you for having me.

• 3:11Lenny Rachitsky?

  It's very much my pleasure. We've interacted a little bit on Twitter. We've never actually talked before, just right now. I've seen your course just kind of all over the place, your course on AI and PM. And so I just thought it'd be really fun to have you on and help us all understand what the hell is happening in AI and especially AI and product. So thanks again for being here.

• 3:33Speaker 00

  Yes, thank you. I'm really excited.

• 3:36Lenny Rachitsky?

  I would love your help as a former full-time PM slash everyone listening that is a current PM to help us understand what is going on with AI and product. Tech in general and tools in general move really fast. You know, if you're trying to pay attention to like what's happening, it's really hard to stay up to date on where things are going. And it feels especially hard in AI. It feels like there's just something coming out every day. And so. I have a bunch of questions along these lines. The first is just like, what media do you pay attention to to stand top of what's happening? And what's new and what's interesting in the world of AI and machine learning?

• 4:11Speaker 00

  As you know very well, subscribing to newsletters is something that's really, really impactful. And of course, I subscribe to your newsletter. But I am a big, big, big fan of the download by NIP Technology Review or T L E R. And they're not necessarily AI centric, but what I'm advocating for and what I'm telling people is that in the future, everything will be AI by default. So even if you have something that's technology focused, you will see a lot of AI starting to get sprinkled in there.

• 4:40Lenny Rachitsky?

  I want to follow up on what you just said there, but maybe we'll save it a little bit. Maybe going in a different direction first. What do you think is overhyped in the space of AI right now? What do you think is underhyped and undervalued?

• 4:52Speaker 00

  I would like to discuss ChatGPT, which is both underhyped and overhyped at the same time. I was reading this article this morning where there are writers complaining and they're very, very fearful. And they think, oh, writing online is gonna die. Everything we're studying for is gonna be replaced or gonna take our jobs and so on. And I'm just like, no, no, no, no. ChatGPT and technology is enhancing our work. It's enhancing us. It does not spill from us. So that's what comes across right now. And there are other things that are underhyped, like obviously ChatGPT is amazing. I'm using it day to day. But there are other things AI can do in an amazing manner.

• 5:33Speaker 00

  Like I was reading a research article the other day that said that AI can now detect lies. So lie detection, whether it is for security reasons or at work or anything like that, is now possible. So I encourage people to go to these newsletters and go to these online blogs, tenkrenz and so on, and just read what's happening. It's not all about ChatGPT. There's more, there's more about AI that you should read about.

• 5:58Lenny Rachitsky?

  You mentioned that you use ChatGPT in your work life. Talk about that. What are you actually using it for?

• 6:04Speaker 00

  Even when I'm at work and I am trying to come up with a nice mission statement, right? When we're PMs or come up with mission statements, it's just crucial part. And it's where the core begins. You want to get people excited. You want to get people inspired. There is nothing I can write that's gonna be as good as what ChatGPT will write. So what I do is I literally go to ChatGPT and I say, Rewrite this mission statement for me. And it just, even first try produces something which is fantastic. So that, number two, it helps me create user segments in a fantastic way. It will think of user segments that your mind wouldn't even go there. Like it's just wouldn't go there. And it will provide the motivations, it will provide the pain points.

• 6:49Speaker 00

  And you just come up with ideas as you breathe it. And then the last thing that it does is it provides ideas for you that are AI enhanced. So I just use it day to day, even personal day to day workflow. But I'm not making it do my job for me. I'm asking it after I have already had a mission in my head and what it is I want to do.

• 7:10Lenny Rachitsky?

  So is the way you're approaching it is you just put in, come up with a better mission statement then, and then you give it your version of the mission statement?

• 7:18Speaker 00

  Exactly.

• 7:19Lenny Rachitsky?

  Interesting. And you're saying that that comes up with a better mission statement than the one you had?

• 7:25Speaker 00

  It's better because the mission statement is going to be read by all disciplines. It's not just going to be read by PMs that already have a lot of context and understand. It's going to be read by leadership, by junior people, by stakeholders, by other departments, by competitors. And you need it to be on point and in the words that are meant to be understood by everyone. You're going to keep going on that. And they would get inspired by it as well.

• 7:49Lenny Rachitsky?

  And then you also said you use it for personas. How do you actually frame that prompt with chat GPT?

• 7:55Speaker 00

  Let's say you're working for a specific product area and you know you want to create some fitness band. So you would say something like, who would be interested in a fitness band that doesn't have a screen? And it will provide a bulleted list of people like, hey, young professionals that they're interested, but don't have enough time. People that do not want to charge their wearables every day. Then the list goes on. It's just fantastic.

• 8:24Lenny Rachitsky?

  You were talking about how you think the future of AI is it's the default. And is what you mean there that it's basically baked into every product we use? And it helps the user do better things. It helps the product work better. Is that what you mean or is it something else?

• 8:39Speaker 00

  I believe that all product managers will be AI product managers in the future. And this is because we see all products needing to have a personalized experience. A recommender system that is actually good. I mean, you cannot watch Netflix, you cannot even watch a movie without needing that. After you you watch White Lotus or like Stranger Things, you will want something similar to watch. You're not gonna want like a romantic thing to be suggested or recommended to you, right? Also, automation is another thing. We need to keep improving our society. We need to keep making technological advancements. You're not gonna be able to do that if you don't have an AI centric view in every sector that you're working on.

• 9:20Lenny Rachitsky?

  When you say that every PM will be an AIPM, is you're thinking that you'll be using AI tools in your job as a PM? Or that you'll be building AI into everything you're building with? How do you think about that?

• 9:34Speaker 00

  I think it's that you need to get comfortable with having a partner that's a research scientist. And you don't need to understand that these people can produce a smart model. They'll be able to do some automations, some personalizations, some recommendations on. In a lot of people, it's really uncomfortable. A lot of people don't know how to approach the researchers. A lot of people don't like the uncertainty that research has. A lot of PMs are very, very used to, okay, I'm gonna do this, I'm gonna launch, I'm gonna do this, I'm gonna launch. Whereas when you're working with research, it's more like, we're gonna try this. And then in a year, if it doesn't work out, we're gonna shut everything down and people are completing.

• 10:18Speaker 00

  So I feel that if people get more used to uncertainty and research, things are gonna be good in the end for them.

• 10:28Lenny Rachitsky?

  I thought you were comparing ChatGPT as like a researcher you're working with, but you're actually saying people will have PhD researchers on their teams, helping them build models into their product to make their product better. Is that, is that what you're saying?

• 10:43Speaker 00

  Correct. This is exactly what I'm. And um, and from a product perspective, like in imagining like three bubbles in my head. So you want to find the intersection of something that's desirable by users, something that is going to be a viable business, and something that is going to be feasible from a research scientist and technical perspective. And then when you have that, it's just going to be a fantastic product for the next launch that you can run with. So yeah, whenever I say researcher, I mean, research scientists that can produce an AI machine learning model.

• 11:15Lenny Rachitsky?

  Wow, didn't think about how every cross-functional team might end up with a research scientist. Interesting, interesting. For PMs who are curious about learning how to do this stuff, what are a couple of things that PMs today, who have no experience with AI, what can they do to start learning how to build AI tooling into their products? Understand what the hell is happening in the space of AI?

• 11:40Speaker 00

  This is a good question, and I guess that the message I want to pass is you shouldn't be overwhelmed by these technologies if you don't have a technical background, because you can learn these things, and as a PM, you'll never need to actually train or code. Also, even if you want to train, there are no code approaches for training models. But to answer the question, if you're working on any product, you can always sprinkle in a smarter feature. So you can make it more secure, you can personalize it, you can enhance it with fraud detection, you can make it more ethical. If it's healthcare, you can make it faster, you can make it more accurate. If it's shopping, you can create better recommendations.

• 12:22Speaker 00

  Basically, anything where you can get data behind the behavior of your users can be improved with AI. So I guess it's all about changing the mindset for PMs, taking a step back and just thinking about, okay, I have all this data that's just lying and sitting around. What is it that I can do with it? I've been meeting PMs that said, oh, we don't have any, we're not collecting any data, we don't have any dashboard. So even that is a huge first step towards AI. And then just start thinking about it, what you could do, just hire and get a data science intern and just see what they they are going to do. There's there's just so much people can do.

• 13:00Lenny Rachitsky?

  So say you want to start investing in some sort of model, some sort of AI within your team. You're saying maybe hire data scientists who can help you start to build something that you can start integrating. Is that your advice on the first step of once you start, you want to start getting serious about building some sort of AI component?

• 13:19Speaker 00

  There is something called the shiny object trap. And I'm always telling people, hey, don't do AI for the sake of doing AI. Make sure there is a problem there, make sure there is a pain point that needs to be solved in a smart way. Once you have identified what that problem is and what that's very, very high level solution is, then reach out and try to figure out how to actually implement it. And there's a definition I like giving that I usually say that a general PM helps their team in their company build and ship the right product. But the AI PM helps their team and company solve the right problem. So if you want to get into AI PM, figure out what the problem is,

• 14:02Speaker 00

  that you will get a data scientist to create a model for solving. But there needs to be a problem, there needs to be audience, there needs to be a user and a team going for it.

• 14:11Lenny Rachitsky?

  What are signs that AI may not be a good approach to solving a problem? You said that, you know, and this happened on a lot of my teams, oh, we're gonna build a really cool model. It's gonna do something really smart in this case. And it often ended up being very low ROI investment and took like six months to a year before you even knew what the hell was happening. Do you have any thoughts on signs that maybe this isn't a place you should be putting a lot of time into AI? Versus like, this is definitely an opportunity. Yes, we should invest a lot of time into this.

• 14:42Speaker 00

  Don't do it for your MVP. It makes zero sense. Do not waste time of data scientists that can train models with using powerful machines that are going to take weeks to train. This is because if you have an MVP and you just wanna get buy-in for an idea or a feature that may use AI in the future, fake it, create a little Figma prototype and just show it to some users and just fake what the AI is going to be doing. So I have a lot of young, early-stage entrepreneurs who are talking to me and they say, "Oh, how should we train this model to do this and that? Because we want to prove that there is a market." No, do not use AI.

• 15:25Speaker 00

  You should use AI where you think you already have some data or data from an adjacent product that you feel you can leverage for your own product to create something that's meaningful, recommendation, automation, or whatever. But not for an MVP. Please, people, this is my advice.

• 15:46Lenny Rachitsky?

  How much data do you think you need for AI, ML, to have a chance to contribute? Do you have like a heuristic of if you have anything less than this, it's not gonna work at all?

• 15:59Speaker 00

  This is a good question and it honestly depends on what you're trying to do. If you're trying to classify if the photo is a cat or a dog, obviously, even if you have, I don't know, like fifteen, twenty labeled photos, this can work. But if you wanna create voice recognizers or complicated NLP applications, you're gonna need thousands and thousands of data. And this is what's making this not be easy, right? AI systems are not easy to build. There is a lifecycle of a machine learning project. And after scoping, you need to figure out, oh, God, how much data do I need? Where do I find this data as well, right? How much data? Sometimes I've seen people synthesizing their own fake data just so that they can have something to train with and test their models.

• 16:44Speaker 00

  But the exact amount is hard to be.

• 16:48Speaker 00

  It's really from a PM, like I'm sure data scientists have a different opinion.

• 16:53Lenny Rachitsky?

  Yeah, my guess is most startups are gonna have nowhere near enough data to build their own model and make it something really interesting. So do you have a thought on when it makes sense to try to build your own model? Try to train your own GPT type thing versus use something that's already out there like say GPT or Midjourney or all those guys.

• 17:17Speaker 00

  If you are a big tech company and you're offering a service that is gonna do speech recognition or then it's gonna have like their own chat GPT, you want to use more data and more diverse data to train and retain. Train because if you don't, then your quality is gonna be the same as every other companies. There are agencies that are selling data, actually have data that are ready, so you can get them and train your models. But the question is, if everyone takes that exact dataset, then the quality that every single company is producing is gonna be the exact same. So you do wanna diversify, you do wanna collect your own data.

• 18:00Speaker 00

  And I guess a good question from a PM perspective is, when is the quality of your product good enough to launch? And that is like a really interesting point because it's totally your responsibility as a PM to decide, okay, the recognition of whether this photo is a category is good enough for the users. It's like 70% accurate, 80% accurate, where is the bar, where do we launch? And that's why I'm like, the AI PM role is so cool because you have problems like that to solve that no one else has kind of tackled before. So it's all on you.

• 18:34Lenny Rachitsky?

  We've thrown out these words model and we talk about training models. Do you have a good succinct kind of explanation for what a model is for folks that haven't, you know, that aren't that technical? And then just the general idea of training a model, like what is a simple way to think of yours what a model is?

• 18:52Speaker 00

  So I have a three-year-old girl and I'm teaching her about life and everything. So I was recently teaching her about the animals. And, you know, you explain things to her once or twice, like what the mammoth is or a rhino and so on. But you will end up training your kid's brain by repeating the same information again. So you will say, hey, here's what the rhino looks like. Here's what the elephant looks like. Here's what the rhino looks like. Here's what the elephant looks like. And once you've done this enough times, then your kid will see an animal on the street and they'll be able to remember next and say, oh, yeah, that's like the rhino we were talking about. This is exactly what a model is. The model is like a kid's brain.

• 19:36Speaker 00

  It has the ability to take an input, which means it has the ability to take an image and say, oh, I recognize what this is. That looks like a rhino, but I'm seventy percent sure about this. So it will output the probability as well of this certain thing.

• 19:51Lenny Rachitsky?

  And you said image, but it could be text for, say, chat GPT in the future. I imagine video, there's also voice like whisper. That's an awesome explanation. Basically, it's try to recreate the human brain is a nice way of thinking about it. And then training a model, can you talk about what that means?

• 20:08Speaker 00

  The process of training the model, for example, is providing a lot of images that are label and say, hey, here's what a cat looks like. Here's what a dog looks like. And we're talking about thousands and thousands of data, data sets for this. And once you do this, there's a process where the model is just processing this information and it's learning, it's finding patterns through it. And the patterns are not in the form of, oh, if this is gray, then this means this. No, it just learns in the smart brain how to identify specific things that we don't even understand. And then it's able to output the probability of whether a photo is going to contain the cat or the dog.

• 20:49Lenny Rachitsky?

  Just conceptually, what is the output of the training? Is it code that is auto-generated with these decision trees and weights and things like that? Is it a database of weight? Like, just conceptually, what is the output of a training that becomes a model? What's the simplest way to think about that?

• 21:05Speaker 00

  So let's imagine speech. Speech is a great example. For example, I'm talking with a device which is like a home assistant and I say, hey, what is the weather like today? This is gonna take my voice and audio and it's gonna process it. And the output is gonna be a transcription. So it's literally gonna be text that corresponds to what I said to it.

• 21:24Lenny Rachitsky?

  Thinking about the stuff you've worked on at Google, at Meta, anywhere else you've worked on side projects even, what are some of the cooler applications of AI, machine learning that you've worked on, contributed to, or even seen that you can talk about? I imagine there's a lot of sensitive stuff too going on.

• 21:40Speaker 00

  One thing I want to talk about is the team I used to work for, for Google, which was the ARVR team. And they were working on an air glass. And actually, they, they had a video on last year's Google I O. They were able to have the Google Glass on someone that spoke one language. And then this other person was standing in front of him that spoke a different language. And the glass would take as an input the audio that came from that other person. And it would transcribe it, it would translate it and show it on the screen for that person in their language.

• 22:20Speaker 00

  So we're talking about the ability for this devices to unlock the borders of communication. And that is not science fiction. This is what amazing and mind blowing. There's no science fiction anymore. These things are real, the technology is here. It's just a matter of connecting the pieces to the puzzle in order to see them coming to life. So I think that one was the most, one of the most impactful things I've ever seen.

• 22:46Lenny Rachitsky?

  I remember that demo and it was pretty incredible. Okay, so thinking a little more broadly, do you think ChatGPT or just say GPT four or GPT five, GPT six, do you think at some point this will replace product managers? Something I see on Twitter, a lot of people are like, oh my God, product management's dead. This thing made my product requirements document for me. Or you talked about how it makes your mission statement better. Do you think there's a place where PMs aren't necessary anymore?

• 23:13Speaker 00

  Oh, absolutely not. As I said, like it makes everything better. If anything, it's gonna free up time for me to do other things that are less tedious. For example, I am running so many projects and they all need their PRD. And the PRDs have all these areas that are common across all of them. If I had a system that can actually write the tedious stuff for me so that I can focus on more strategic side of things, that would be incredible. It will make us smarter, if anything. It will unlock new areas of product management that we hadn't realized that are there.

• 23:48Lenny Rachitsky?

  Are there areas, do you think, with your kind of vision of all PMs will be AI PMs? Are there areas that you think PMs should invest more skill wise? Or areas they should less focus on and invest? Because say some machine learning model is gonna do that for them.

• 24:04Speaker 00

  I'd like to see people being less overwhelmed, less intimidated, less afraid to start learning how to code, how to train a little model on their own. This is because even if, you know, ChatGPT or these no-code applications may be able to do this for us, it gives you a different approach, a different mindset, a different, if you want, confidence to know how things work. And here's a silly example. I was learning how to play the piano when I was young. And when my teacher came in, I was like, oh, I wanna learn how to play this cool song. There were some songs that I read night. And she said, no, you need to start with a classical music. And I just hated it at the time. And I said, why do I have to do this?

• 24:48Speaker 00

  Because she said, if you learn the fundamentals and how, you know, where things started and the beginning of music, it's gonna help you along the way to create music on your own if you want to. And she went right. Like, I just loved it. So it's the same with coding. I encourage people to just take an online course, understand more, get your hands dirty, pair up with someone else that's in the same boat as you, because this is going to give you the skill set to understand how that tool that's going to help you in your day to day was even created in the first place, instead of blindfoldedly just trust it to do your job.

• 25:23Lenny Rachitsky?

  This episode is brought to you by Pando, the always on employee performance platform. How much do you love the performance review process? Hmm, yeah. It's time consuming, subjective, biased, and there's rarely any transparency. With the rapid shift to distributed work, it's a struggle to create the structure and transparency that you want to help your employees have the highest impact and growth in their careers. Pando is disrupting the old paradigm of performance management, including a continuous employee centric approach, so employees stay engaged, see their progression in real time, and know exactly when and how they can level up. With Pando, managers can leverage competency based frameworks to effectively coach and develop their teams and align on consistent growth standards,

• 26:07Lenny Rachitsky?

  resulting in higher quality feedback and higher performing teams. Visit pando.com/lenny for more info and get a special discount when you sign up and reference this podcast. That's pando.com/lenny.

• 26:21Lenny Rachitsky?

  For someone that actually wants to do that and learn to code, which I love that advice, do you have any resources, places that you point people to for learning to code, getting started down that path?

• 26:31Speaker 00

  It depends on what type of learner you are. There are some people that like to learn offline. So just go to Coursera, there are so many courses. There is an amazing one, actually, Introduction to AI by Stanford, that I would encourage people to take a look at. But I know that a lot of people don't like, don't have the time, don't have the discipline to actually, you know, take time off or like after work, after they put their kids to sleep to just do it. So if you enjoy learning with others, if you enjoy being part of a team, if you enjoy going through a journey together, then I recommend these resources. So there is something called Career Foundry, which is a fantastic online coding school, General Assembly, and then Coding Dojo.

• 27:13Speaker 00

  I was, I actually gave a talk ages ago at Coding Dojo, Python. And all it takes is just a few weeks of your time and passion and just for you to roll up your sleeves and just realize that this is not intimidating and realize the benefits you can get by learning.

• 27:31Lenny Rachitsky?

  Awesome. Thanks for sharing those. We'll include links in the show notes. Going back to a PM, trying to become better in AI. If you think about a PM that's kind of early in their career and wants to become a very strong AI PM. I know you have a whole course about this, which we can talk about now or later, whatever is easier. What should that PM be doing? We talked a bit about learn to code, maybe start playing with tools. What else do you suggest PMs that want to become really strong AI PMs do now and invest in?

• 28:02Speaker 00

  So I do have a course that's coming out on February 6th on Maven, which is for current and aspiring product managers that want to build AI products. But I also have offline recordings. So I have the same course and then offline basis on my website. I'd be happy to talk to you, very chat to me about this. What I feel people should understand is what it takes to manage an AI product. Of course, people are very familiar with the stages of product development in general, but AI product development is different. As I mentioned before, sometimes you're actually managing the problem and not the product. And you're trying to figure out if there is a problem that makes sense to be answered by a smart solution.

• 28:45Speaker 00

  So it's kind of a very interesting and more complicated process than regular product management. So number one, figure out how it differs from general product management. Number two, if you're already at the company that is actually having AI researchers and AI research scientists, I encourage people to just reach out to them and shadow them and spend an hour of their week just talking to them and experiencing what they're doing. This is going to open your mind. This is going to give you so much context as to what it is and the endless potential that you can identify there.

• 29:22Lenny Rachitsky?

  Awesome. And is there anything else you want to share from your course that you think might be interesting to folks?

• 29:30Speaker 00

  So we talked about why it's awesome to be an AIPM, but I do acknowledge that there are a few challenges that people need to be aware of. Number one, and I kind of mentioned it before, is the uncertainty. You may have been working on all these incredible research and ideas and hypothesis, but then when you actually train the model, the results you may be getting may not be optimal, may not be answering the questions or the hypothesis that you actually had in mind. So that's number one. You need to be able to encourage the team throughout this process because you're like the captain of the ship. You need to be the one that's truly leading the team, making sure everyone keeps going. Number two, you are gonna have to be a coordinator.

• 30:14Speaker 00

  You are gonna have to change the direction and managing this from a leadership perspective can be tricky and it can be challenging. Number three, we talked about data, but getting good data is hard. Like you may need to be creative, figure out ways for data collection that you never thought you'd do. You may get on the street and ask for people to actually contribute data so while you're doing that, you need to be able to and willing to do everything. And the last thing is from a career trajectory. Usually product managers get ahead the more they launch, but if you're in a research org, you're not gonna launch as often.

• 30:53Speaker 00

  So you need to make sure to clarify with the hiring managers early on, hey, what does progress mean? How am I gonna get assessed in a research work which is different than what I've been doing so far? So it's challenging, but I always encourage people to flex different muscles. And this is like the zero to one muscle that I think is is just crucial when it comes to product management.

• 31:15Lenny Rachitsky?

  This actually is a great segue to a question I definitely wanted to ask, which is around getting buy-in for investment at a company for ML. So there's sometimes like all this energy for like a zero to one, let's just try something. Sometimes not, but that maybe that's maybe there's a two-part question here. Do you have any advice on just getting buy-in for we want to try something with ML? It's going to take us six months to figure out if it's worth the effort. But we think there's something here. And then sometimes there's like a lot of energy initially and then you get some win like your search ranking smarter and it's great. But then maintaining that, having like all these really expensive people working on just tweaking this model and continue to make it smarter and a little more efficient. Often it's hard to continue to get buy-in for that sort of team.

• 32:00Lenny Rachitsky?

  Do you have any advice on initial kind of buy-in? Let's try something here and then down the road, just like keeping a team going, trying to make this thing smarter and smarter.

• 32:09Speaker 00

  People should know that there is an excellent source of inspiration and something that can kind of do raise things, which is adjacent products. Maybe the company has already launched a product that has been successful, those AI firsts. And whenever I try to convince leadership about something that I want to do, that's kind of a big bad. I always use examples and I'm like, hey, this seemed crazy at the time. Here's how it works. What I'm proposing is very similar to this crazy thing. And then I propose a little contingency plan, like, hey, if that doesn't work out, here's the rollback plan. Here's kind of the maximum impact it will have done in a negative way, which is not going to be too much. And you kind of take it all on zero.

• 32:55Speaker 00

  And it's interesting because the more you work on a specific company, the more trust you get. And if the culture is such, then failing is going to be welcome. So a lot of companies are welcome paying because you can just go ahead and do this.

• 33:09Lenny Rachitsky?

  Do tell me if I'm wrong, but I feel like most investments in ML are not successes and often not great uses of time. I'm curious if that changes with more tooling and more kind of public models that people can plug into without having to build their own. I wonder if it becomes like, oh, okay, look, we'll put in three weeks, we'll get something really useful.

• 33:27Speaker 00

  Exactly. And also the other thing, and I wanted to add on the question you asked before about, hey, how do you keep updated about new niche tech? We shouldn't underestimate academia and the research blogs. And there's a website called Archive where you can see new papers come up. Because this is where, I mean, ChatGPT and like, it used to be there for a long time, but there was a lot of information on this sort of thing. But it's now recent where we see that research scientists and research orgs are kind of not as silent as they used to be. So the more companies invest on staffing this layer between productionizing and research, academic research,

• 34:11Speaker 00

  the more PMs you're gonna add there, then the more you're gonna see this bridge kind of creating good products. Okay, so sometimes you have amazing ideas by research scientists, but you need a PM to take it and actually figure out ways to also monetize it, right? That's the other thing. If you're a PM, you need to come up with ways to actually be able to monetize. And ChatGPT is now free for everyone, but I don't know if you, if you saw, there was a, there was a signup forum that was kind of coming around saying, hey, would you pay for this? What would be the minimum you would pay? What would be the maximum you would pay? What would you like to see if you paid? So having BMs bridge that gap is crucial for companies to be able to take the research and actually come up with meaningful use cases for users.

• 34:56Lenny Rachitsky?

  I think they actually started charging the other day. I think it's like forty dollars, forty-two dollars a month to start using it. I think. People have been talking about it on Twitter. I don't know if that's live yet. And then you talked about research papers. When I think that, I always think of Tyler Cohen. He has this awesome blog, Marginal Revolution, and he's really good at sharing insights from research papers that he's reading. So that's another place for folks to check out. He's just like, this really smart dude. He's really excited about AI and ChatGPT in general, and so he shares a lot of really interesting insights about it all. Seguing a little bit to your course, I have a couple of questions about it. One is just like, can you just talk about like the broad framework of your course? Like, how long is it? What do you learn? What are the workshops broadly? And then I have a couple of follow-up questions.

• 35:39Speaker 00

  My course is three weeks long. It's meant for people that are either aspiring or current PMs that want to understand how to sprinkle in AI solutions or they want to become full-time AI firms. Week one is more of an introduction, what the product development lifecycle is for regular products and how it differs for AIP specifically. And then we talk about idea creation. How on earth do you come up with ideas? And I love what Steve Jobs says, where he, he used to say, "Well, users don't know what they want until you show it to them." And that's exactly the mindset I want to invite to people and say, "Hey, people don't know how on earth to use AI." People would never have imagined, "Chad, you can do what it is." And then we take that and we dive deep.

• 36:24Speaker 00

  And we talk about how on earth do you productionize something like this? What are the different partners you're working with? What is a research scientist and how on earth do you collaborate? And how do you partner with them? How do you convince them of what you have in mind for their precious research? To be converted into a product? How on earth do you convince them to trust you? And, and how do you influence them? And then at the end, we're talking about how you actually will be able to pave your path with PM, all the way from interviewing for this role, from what good resumes look like, and doing some local interviews, because the more you practice, the better.

• 37:03Lenny Rachitsky?

  How many workshops are there through the course?

• 37:05Speaker 00

  Nine workshops.

• 37:06Lenny Rachitsky?

  Nine workshops, okay. Of the nine workshops, which of them are you finding is the most exciting, game-changing for someone, most interesting?

• 37:15Speaker 00

  So throughout the duration of all these workshops, people have homework in the actually take-home exercise, where they need to create and develop their own AI product end-to-end. And they can pair up with each other. By the way, there was this, this two C and S part app, and actually where I would raise funding, which is mind-blowing to me, which is really, really good.

• 37:33Lenny Rachitsky?

  That's awesome.

• 37:34Speaker 00

  Um, but to continue, the most exciting part is when everyone at the very end are actually presenting their work. And they're actually asking questions and getting feedback. And they're just really excited and proud for what they created.

• 37:49Lenny Rachitsky?

  That's a good reminder of a lot of the learning that you do is just doing it, not just kind of reading about it and following Twitter. Can you share any examples of stuff people built after the course?

• 37:57Speaker 00

  Someone was able to actually, and I can't do not, create a loan model that was able to take us an input, X-rays that they found online, and was able to tell us what was wrong, if something was wrong with that patient. And it's just crazy to think that you can do that within three weeks. Obviously, it was just by photos we were able to crawl online for X-rays. But the concept is there, that you can build something like that, you can create it. And to take it a bit further, they wanted to create a low over commander system and say, hey, we think this is what's wrong with you. Here are the steps to solve.

• 38:36Speaker 00

  Obviously, we're not trying to play doctors or to to pretend that we're medical in any way. But being able to see that actually functioning is just, it's very.

• 38:48Lenny Rachitsky?

  That's amazing. Did they already know how to code, this team that built this thing?

• 38:51Speaker 00

  They did not, but part of the course is to teach people the basics that you are gonna need from a PM lens. And there are some no code tools, as I mentioned, that are gonna allow you to drag and drop and train these models and input photos in it and be able to.

• 39:07Lenny Rachitsky?

  Can you mention those tools again? Because that is really interesting. And it was just like a peek at your course. But if someone wanted to start building something like this, what what are some of these tools they could check out?

• 39:16Speaker 00

  One of the tools I would like to recommend to people is actually AutoML. This is offered by Royal Cloud. And essentially it allows you to train high quality custom machine learning models with minimal effort. You don't need to be able to understand code or anything like that. You need to have a lot of photos and images that you have already corrected, but it's not gonna do the correction for you. And a great application I had to see, there's actually a YouTube video about this is. There was this company that actually had a lot of winter bans. And what they did anyways, in order to maintain this, they would actually have people manually have huge ladders and go take a look and see if everything was okay.

• 39:57Speaker 00

  So eventually they just got drones and they had these drones fly on all of these machines and take photos on everything. And then they downloaded all these photos and they uploaded on AutoML and they were able to see which ones need in maintenance and which did not. And I think they reduced the time from like three weeks of work to like a few hours of knowing which need maintenance and just be able to send people there. So it's this type of thing that you can do on your own by applying this sort of tools.

• 40:25Lenny Rachitsky?

  So and that tool is called AutoML?

• 40:28Speaker 00

  Yes, AutoML.

• 40:28Lenny Rachitsky?

  Amazing. We'll link to that in the show notes. Coming back to your course and maybe just a couple more questions. Can you just talk about what it takes to build a course like the course you built? Like, how much time did it take you? How much work did it take? Anything there you want to share?

• 40:43Speaker 00

  I treated creating my course like a product. This is like what I did is I came up with some hypothesis as to who the audience was and as to what they were looking to get out of it. And I started reaching out to people and I started saying, hey, first of all, would you like to learn from me? Second of all, what would you like to learn? What are the specific questions that you would need answered? Because these are people that are working full time, that have families, right? In order to take a break from all that, you need to provide something that is meaningful. And there were quite a few iterations. In the beginning, I was focusing the course more for software engineers that wanted to become AI product managers.

• 41:25Speaker 00

  But then I realized, no, there are a lot of PMs that want to become AI product managers. So I did a little online shift there. So what it takes is make sure you find the right audience. Make sure to figure out what that audience wants. Make sure to have the right duration. One week I find it too short, two weeks, it will still be rushed. Three weeks is excellent because you give the opportunity to everyone to present and to get to know each other on like an offline Discord community, which is another important part. And then the last thing, you need to have a personal relationship with everyone. So I've messaged everyone, I've seen everyone's application. I met with some people as well, just to make sure to answer any questions and concerns.

• 42:09Speaker 00

  Because I wanted to make sure that people were comfortable just trusting a stranger like me and paying them for, to provide knowledge for their course. So it was, it took quite a few iterations, but I was able to get there and I'm very, very happy about it. And I recorded it offline as well for people.

• 42:27Lenny Rachitsky?

  Has anything had to change in this course? Maybe that's just as a last question, things are moving so fast. Is there anything you've had to rethink, redo since you first built it?

• 42:35Speaker 00

  I actually added bonus sections and one bonus section was JGBD and how it was trained. This is because I started this new cohort in December and on day one, the question I got is, what is this? How did it start? What is going on? How did they train it? So I added the dedicated section for it and I point people to it.

• 42:52Lenny Rachitsky?

  Amazing. Anything else that you'd like to share before we get to our very exciting lightning round?

• 42:58Speaker 00

  It was someone that recommended, I actually did a course and in the beginning I was, I was not. In the beginning I laughed and I said, wait, people would wanna learn from me? Really? And of course they did and I'm teaching so many people. So what I wanna tell people is, don't underestimate this. Try creating your own courses as well. People may want to learn what you take for granted. For them it may be game changing, it can be life changing. So building courses is an amazing thing and, you know, we're living in the whole collaboration era. And so the course is content, so go try this.

• 43:33Lenny Rachitsky?

  I find that teaching and at least crystallizing thoughts is one of the best ways to learn it yourself. I imagine you learned a lot about AI, much more than even came into it with just putting it together into a course.

• 43:44Speaker 00

  Absolutely, and I, I got some unconfident questions that I had no idea how to tackle. Like people on day one were like, how do I assess the trade offs between these two different models? And I had to figure out how to answer these things and how to incorporate them in my course. So learning from the students, learning from the course, learning from explaining is just so viable, so skills that you can get.

• 44:07Lenny Rachitsky?

  Well, with that, we've reached our very, very exciting lightning round. I've got five questions for you. I'm gonna go through them pretty quick. Whatever comes to mind, share. We'll see how it all goes. Sound good?

• 44:18Speaker 00

  Sounds good.

• 44:19Lenny Rachitsky?

  Two or three books that you recommend most to other people?

• 44:22Speaker 00

  Inspired. It taught me. It's all about how to create tech products people love.

• 44:28Lenny Rachitsky?

  By Marty Kagan, right?

• 44:29Speaker 00

  Yes, that's the one.

• 44:31Lenny Rachitsky?

  Cool. Anything else? Or that's the one. That's the one that comes to mind?

• 44:35Speaker 00

  You look like a thing and I love you and I have it right here. It's a great thing. Super, super cool. It's about how AI works and why it's making the world a weirder place. It's actually a very fun book. And there's one more, which is a book, a workbook I recently launched with Alana Carr. And it's about, um, it's a workbook for women in tech trying to navigate working in tech. It's called Adventures of Women in Tech Workbook. So that's another thing that I want to shamelessly plug in.

• 45:02Lenny Rachitsky?

  Uh, that's a great choice to plug. Where can folks find that? Is that on Amazon?

• 45:06Speaker 00

  Yeah, Amazon.

• 45:07Lenny Rachitsky?

  Amazing. What's a favorite other podcast that you like to listen to?

• 45:11Speaker 00

  I like Boz's podcast. I mean, if you're aware of it, Boz is the CTO of Facebook. He has a great podcast.

• 45:19Lenny Rachitsky?

  I have not heard it. I do know of Boz. I'll check that out. I didn't know he had a podcast. He had some great writing over the years. Maybe that's why he doesn't write anymore. He has this podcast. What is a favorite recent movie or TV show that you've loved?

• 45:30Speaker 00

  Oh my God, The White Lotus. People were talking about this thing. I, I ended up, you know, just trying it out. And me and my husband, we just binge watched the whole thing. It's just so different, so mind blowing. Get you excited about going to Hawaii again. It's just, it's really good.

• 45:45Lenny Rachitsky?

  Have you seen the second season?

• 45:46Speaker 00

  I've seen it and it's so much better than the first, which is rarer.

• 45:50Lenny Rachitsky?

  I agree. Awesome. Love that show. What is a favorite interview question you like to ask? And bonus point if it's AI related?

• 45:57Speaker 00

  I love to ask people, how would you explain a database to a three-year-old? And I know it's, it's kind of an AI, not very, very honest AI. And, but I love asking it because people are kind of thinking, they're boxing, like, what did you just ask me? But it's so important to be able to explain things in a simple way and have the storytelling to convince a kid and really explain technical terms to non-technical people.

• 46:23Lenny Rachitsky?

  Favorite AI-based tool that you think people should check out?

• 46:27Speaker 00

  I mean, talk about Tragically P now, my head is on Tragically P, this is what comes to mind. Um, well, the Lensa was pretty cool too, right? We all uploaded our photos and we were able to see what we would look like as fantastic heroes. I have to say, I tried being the nail version because it was so much cooler than the nail version. So that's what I recommend to people, try the nail version.

• 46:48Lenny Rachitsky?

  That's fun. And there's actually a, they actually have pets now? That's what got me to download and pay for it. You can take pictures of your pets and they look so fun. That's like a killer feature right there. Good job, Lensa. And the app is Lensa, right?

• 47:01Speaker 00

  Yeah, Lensa.

• 47:02Lenny Rachitsky?

  Amazing. Mary Lee, thank you so much for spending time with me, sharing your wisdom. Two final questions, where can folks find you online if they want to learn more and reach out? And how can listeners be useful to you?

• 47:13Speaker 00

  Thank you so much. Uh, people can find me on Instagram. I also have a product channel on YouTube that you can check out. I just started it. I'm getting used to the whole process. I'm also kicking off a newsletter. Just any social, reach out and you'll see all my links.

• 47:28Lenny Rachitsky?

  How do they find the YouTube channel? How do they find the newsletter?

• 47:31Speaker 00

  Type in Mary Lee Nika.

• 47:32Lenny Rachitsky?

  Mary Lee, thank you again for being here.

• 47:34Speaker 00

  Thank you so much, Lenny. It was a pleasure.

• 47:38Lenny Rachitsky?

  Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at lennyspodcast.com. See you in the next episode.