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

[00:00] 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." [00:07] Make sure there's a problem there. [00:09] Make sure there is a pain point that needs to be solved in a smart way. [00:13] Once you have identified what that [00:17] problem is and what that very, very high level solution is, then reach out and try to figure out how [00:24] to actually implement it. 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. [00:36] Today, my guest is Marilee Nika. Marilee teaches the most popular course on Maven, on AI and product management, [00:44] She's currently product lead at Meta, [00:46] 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. We also get [01:16] power models train, all kinds of fun stuff like that. Enjoy this conversation with Marilee Nika after a short word from our wonderful sponsors. [01:25] 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.

[01:56] 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. [02:25] 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 cycles, and helping you easily get to the root cause of any issue you discover. Epo lets you go beyond basic click-through metrics, and instead you turn 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. [02:55] Check out EPPO at getepo.com, get-e-p-p-o dot com, and 10x your experiment velocity. [03:03] Merrilee, welcome to the podcast. Thank you. Hello. Thank you for having me. It's very much my pleasure. [03:12] 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... [03:25] to have you on and help us all understand what the hell is happening in AI and especially AI and product.

[03:31] So thanks again for being here. [03:33] Jens, thank you. I'm really excited. [03:35] I would love your help as a [03:38] former full-time PM/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. If you're trying to pay attention to what's happening, it's really hard to [03:53] 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, [04:01] I have a bunch of questions along these lines. The first is just like, what media do you pay attention to, to stay on top of what's happening and what's new and what's interesting in the world of AI and machine learning? [04:11] 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 MIT Technology Review. [04:24] or TLR. [04:25] and they're not necessarily AI-centric, but [04:28] what i'm advocating for and what i'm telling people is that in the future [04:32] Everything will be AI by default. [04:34] So even if you have something that's technology focused, you will see a lot of AI starting to get sprinkled in there. 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? [04:47] in the space of AI right now? What do you think is under-hyped and under-valued? [04:52] I would like to discuss strategy between which is both under Hype [04:56] and overhacked at the same time. [04:58] 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 going to die. Everything we're studying for is going to be in place. They're going to take our jobs and so on. And I'm just like, no, no, no, no.

[05:14] Charging BT and technology is enhancing our work, it's enhancing us, it does not spill from us. [05:21] So that's [05:22] what comes across right now. And then there are other things that are under hype. Like, obviously, ChoteauBit is amazing. I'm using it day to day. But there are other things AI can do in an amazing manner. 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. [05:46] So I encourage people to go to these newsletters and go to these online blogs, and so on, and just read what's happening. It's not all about chatty beating. There is more. There is more about AI, but you should read about it. [05:58] You mentioned they use ChatGPT in your work life. Talk about that. What are you actually using it for? [06:04] even when I'm at work and I am trying to come up with a nice mission statement. [06:09] When we're pianos, we come up with mission statements. It's just a crucial part. And it's where the core begins. You want to get people excited. You want to get people inspired. [06:19] There is nothing I can write that's going to be as good as what ties with you for it. [06:23] So what I do is I literally go to the strategy and I say, [06:26] rewrite this mission statement for me. [06:29] And even the first try to produce something which is fantastic. So that, number two, it helps me create user segments in a fantastic way. [06:39] it will think of user segments that your mind wouldn't even go there. Like, it just wouldn't go there. And it will provide the motivations, it will provide the pinpoints,

[06:49] And you just come up with ideas as you breathe it. [06:53] And then the last thing that it does is it provides ideas for you that are AI-enhanced. [06:58] So I just use it day to day, you can approach my day to day workflow, but I'm not making it do my job for me. [07:04] I'm asking it after I have already had a mission in my head and what it is I want to do. [07:10] So the way you're approaching it is you just put in, come up with a better... [07:14] mission statement then, and then you give it your version of the mission statement. [07:18] Exactly. Interesting. [07:20] And you're saying that that comes up with a better mission statement than the one you had. [07:25] It's better because the admission statement is going to be read by all disciplines. [07:29] it's not just going to be read by pms that already have a lot of context and understand it's going to [07:34] be read by leadership, by junior people, by stakeholders, by other departments, by competitors, [07:39] And he needed to be on breathe, ending the words, [07:42] that are meant to be understood by everyone. [07:45] Even a kid couldn't understand it. And they would get inspired by it as well. [07:49] And then you also said you use it for personas. How do you actually frame that prompt with chat GPT? [07:55] Let's say you're working for a specific product area and you know you want to create some fitness band. [08:01] So you would say something like, [08:04] Who would be [08:05] interested in the fitness band. [08:08] that doesn't have his cream [08:10] And it will provide a bulleted list of people like, hey, young professionals that they're interested in, 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...

[08:22] Fantastic. [08:23] 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. [08:32] and it helps the user do better things, it helps the product work better. Is that what you mean, or is it something else? [08:38] I believe that all product managers will be AI product managers in the future. And this is because... [08:45] We see all products needing to have a personalized experience, [08:49] a recommender system that is actually good. I mean, you cannot watch Netflix, you cannot even watch a movie without needing that. After you watch [08:58] white lotus or like stranger things you will want something similar to watch you're not going to want like a romantic thing to be suggested or recommended to you right [09:06] Also, automation is another thing. We need to keep improving in society. We need to keep making technological advancements. You might not be able to do that if you don't have an AI-centric view in every sector that you're working on. [09:19] When you say that every PM will be an AI PM, is you're thinking that [09:24] you'll be using AI tools in your job as a PM, or that you'll be building [09:30] AI into everything you're building with. [09:32] How do you think about that? [09:34] I think it's that you need to get comfortable with [09:38] having a partner that's a research scientist. [09:42] And you'll need to understand that these people can produce a smart model. They'll be able to do some automation, some personalization, some recommendations, and so on.

[09:53] In a lot of people, [09:55] I feel 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. [10:03] A lot of PMs are very, very used to, "Okay, I'm going to do this, I'm going to launch, I'm going to do this, I'm going to launch." [10:08] Whereas when you're working with research, it's more like, [10:10] We're going to try this. [10:12] And then in a year, if it doesn't work out, we're going to shut everything down and pivot completely into it. [10:18] So I feel that if people [10:21] get more used to uncertainty and research, [10:24] things are going to be good in the end for them. I thought you were comparing ChagibT as like a researcher you're working with, but you're actually saying people will have... [10:35] PhD researchers on their teams, helping them build [10:39] models into their product to make their product better. Is that what you're saying? [10:43] Correct. This is exactly what I mean. [10:45] Interesting. [10:47] And from a product perspective, I can imagine three bubbles in my head. So you want to find the intersection of something that's desirable by users, [10:56] something that is going to be a viable business, [10:59] and something that is going to be feasible from your research. [11:02] scientist and technical perspective. And then when you have that, [11:06] It's just going to be a fantastic product for a fantastic launch that you can run with. So yeah, whenever I say researcher, I mean research scientists that can produce an AI machine learning model. [11:15] Wow, didn't think about how every cross-functional team might end up with a research scientist. Interesting, interesting. [11:22] For PMs who are curious about learning how to do this stuff, what are a couple things that PMs today

[11:29] who have no experience with AI, what can they do to start learning how to build [11:35] AI tooling into their products, understand what the hell is happening. [11:38] in the space of AI. [11:40] This is a good question. And I guess that the message I want to pass is, [11:43] You shouldn't be overwhelmed by these technologies if you don't have a technical background. [11:48] because you can learn these things. And as a PM, you will 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, [11:59] If you're working on [12:02] any product, you can always sprinkle in a smarter feature. [12:07] 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. [12:19] If it's shopping, you can create better recommendations. Basically, anything where you can get [12:24] data behind the behavior with users, [12:27] can be improved with AI. [12:30] So I guess, [12:31] It's all about... [12:32] changing the mindset of 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 they can do with it?" [12:41] I've been meeting PMs that said, "Oh, we don't have any, we're not collecting any data, we don't have any dashboards." So even that is a huge first step towards AI. [12:50] And then just start thinking about it, what you could do. Just hire and get a data science intern and just see what they are going to do. There's just so much people can do. [12:59] So say you want to start investing in some sort of model, some sort of AI.

[13:05] within your team. You're saying maybe hire data scientists who can help you start to build something that you can start integrating. [13:12] Is that your advice on the first step of once you start, you want to start getting serious about building some sort of AI? [13:18] component. [13:19] 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. [13:27] make sure there is a problem there [13:28] Make sure there is a pain point. [13:30] that needs to be solved in a smart way. [13:33] Once you have identified what that [13:36] problem is and what that very very high level solution is then reach out and try to figure out how [13:43] to actually implement it. [13:46] There's a definition I like hearing. I usually say that a generalist PM helps their team and 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. [14:02] that you will get the data science to create a moment for solving. But there needs to be a problem, there needs to be audience, there needs to be a user, anything going for it. [14:11] What are signs that [14:13] 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 going to build a really cool model. It's going to do something really smart in this case. And it, [14:24] often ended up being [14:26] 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 [14:35] 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.

[14:42] Don't do it for your MVP. [14:45] It makes zero sense. Do not [14:48] waste time of data scientists that can train models with using powerful machines that are [14:54] peak weeks. [14:56] to train. This is because [14:58] If you have an MVP and you just want to get buy-in for an idea or a feature that may use AI in the future, [15:05] Think it. Creative little stigma... [15:07] and just show it to some users and just debate what the AI is going to be doing. So I have [15:14] A lot of young early stage entrepreneurs talk 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. [15:24] Do not use AI. [15:25] You should use AI where you think you already have some data or [15:32] data from an adjacent product that you feel you can leverage for your own product, [15:37] to create something that's meaningful, the accommodation, automation, what we talked about, but not... [15:42] for an MVP, please people. This is my advice. [15:46] How much data do you think you need for... [15:49] AI, ML, to have a chance to contribute. You have like a heuristic of [15:55] If you have anything less than this, it's not going to work at all. [15:59] This is a good question and it honestly depends on what you're trying to do. [16:02] If you're trying to classify if the photo is a cat or a dog, obviously, even if you have 15, 20 labeled photos, that's going to work. [16:11] But if you want to create voice organizers or

[16:15] complicated nlp applications you're gonna need thousands and thousands of data and and this is what [16:20] making this not be easy, right? AI systems are not easy to develop. There is a life cycle of a machine learning project, [16:28] And after scoping, you need to figure out, oh, God, how much data do we need? [16:31] where do I find this data as well, right? How much data? [16:35] Sometimes I've seen people synthesizing their own fake data, just so that they can have something to train with and test their models. [16:44] But the exact amount is hard to be on the card. [16:49] Especially from a team. I'm sure data scientists have a different opinion. Yeah, my guess is most startups are going to 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... [17:05] model, try to train your own [17:08] GPT-type thing versus use something that's already out there, like, say, GPT or Majorney or all those guys. [17:17] If you are a big company and you're offering a service, [17:21] that [17:22] is going to do speech recognition or that it's going to have their own tragedy. [17:28] You [17:29] Wants [17:30] to [17:31] use more data and more diverse data to train and retain and train because if you don't then your quality is going to be the same as every other companies there are agencies that are selling [17:43] data, packages of data that are ready so you can get them and train your models.

[17:47] But the question is, if everyone takes that exact dataset, then the quality that every single company is producing is going to be the exact same. So you do want to diversify, you do want to collect [17:58] your own data. [18:00] And, [18:00] I guess a good question from a pre-em perspective is... [18:03] When is the quality of your product good enough to launch? [18:08] And that is like a really interesting point because [18:13] It's totally your responsibility as a PM to decide, OK, [18:16] the recognition of whether this folder is a cat or a dog is good enough for the users. It's like 70% accurate, 80% accurate. Where is the bar? Where do we launch? [18:25] 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. [18:34] We've thrown out these words "model," and we talk about training models. [18:39] Do you have a good... [18:40] succinct kind of explanation for what a model is for folks that haven't [18:45] that aren't that technical, and then just the general idea of training a model. What is a simple way to think of here is what a model is. [18:52] 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. [19:00] and you know you explain things to her once or twice like what the mammoth is or a rhino and so on [19:06] but you will end up training [19:09] your kids bring, [19:11] by repeating the same information again. So you will say, "Hey, here's what the rhino looks like." [19:16] Here's what the Nellethon looks like. Here's what the Rino looks like. Here's what the Nellethon looks like.

[19:20] And once you've done this enough times, [19:23] then your kid will see an animal on the street and they'll be able to recognize and say, "Oh, yeah, that's like the rhino we were talking about." [19:32] This is exactly what a model is. A model is like a kid's brain. It has the ability to take an input, which means [19:40] It has the ability to take an image and say, "Oh, I recognize what this is. That looks like a rhino, but I'm 70% sure about this." So it will output the probability as well of this identity. [19:50] And you said image, but it could be text for, say, chat GPT. In the future, imagine video. There's also voice, like whisper. [19:58] That's an awesome explanation. Basically, it's trying to recreate the human brain. [20:03] is a nice way of thinking about it. And then training a model, can you talk about what that means? [20:08] The process of training the model, for example, is providing a lot of images that are labeled and say, "Hey, here's what cat looks like. Here's what the dog looks like." [20:17] And we're talking about thousands and thousands of data sets for this. And once you do this, there's a process where the model is just [20:25] processing this information and it's learning, it's finding patterns, [20:29] through it. [20:31] and the patterns are not in the form of [20:33] "Oh, if this is gray, then this means this." No, it just learns in a smart way how to identify specific things. [20:41] We don't even understand. [20:43] And then it's able to output them [20:45] the probability of whether a photo is going to contain the Catherine Dawn. [20:49] Just conceptually, what is the output of the training? Is it code?

[20:53] that is auto-generated with these decision trees and weights and things like that? Is it a database of weight? Like, [20:59] Just conceptually, what is the output of a training that becomes a model? What's the simplest way to think about that? [21:05] So let's imagine speech. Speech is a great example. For example, I'm talking to a device which is like a home assistant and I say, hey, what is the weather like today? This is going to take my voice and audio, it's going to process it. [21:17] and the output is going to be a transcription. So it's literally going to be text that corresponds to what I sent to it. [21:24] Thinking about the stuff you've worked on at Google, at Meta, [21:27] anywhere else you've worked on side projects even, what are some of the cooler applications of [21:32] AI machine learning that you've [21:34] worked on, contributed to, or even seen that you can talk about. I imagine there's a lot of sensitive stuff too going on. [21:40] One thing I want to talk about is the team I used to work for for Google, which was the AR/VR team, and they were working on an air glass. [21:51] And actually they had a video on last year's Google I/O [21:56] They were able to have the Google Glass on someone that spoke one language, and then this other person was sat in front of them that spoke a different language. [22:06] and [22:07] The glass would take as an input the audio that came from that other person, [22:12] and it will transcribe it, it will translate it, and show it on the screen for that person [22:18] in their language. [22:20] So we're talking about the ability for this [22:23] devices,

[22:24] to unlock the borders of communication. [22:27] and that [22:28] is not science fiction. This is what's amazing and mind-blowing. There's no science fiction anymore. These things are real. The technology is here. [22:37] It's just a matter of connecting the pieces to the puzzle in order to see them come into life. [22:42] So I think that one was the most one of the most impactful things I've ever seen. [22:46] I remember that demo, and it was pretty incredible. Okay, so thinking a little more broadly, [22:51] Do you think ChatGPT or just say GPT-4 [22:55] or GPT-5, GPT-6, do you think [22:59] 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... [23:06] product requirements document for me, or you talked about how it makes your mission statement better. You think there's a place where PMs aren't necessary anymore? [23:13] Oh, absolutely not. As I said, it makes everything better. If anything, it's going to free up time for me to do other things that are less tedious. For example, [23:24] I am running so many projects and they all need their PRD, and the PRDs have all these areas that are common across the world. [23:31] if I had a system that can actually write the tedious stuff for me, so that I can focus on the 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 [23:45] We kind of realized that, but are there? [23:47] Are there areas that you think with your kind of vision of all PMs will be AI PMs, [23:53] Are there areas that you think PM should invest more skill-wise or...

[23:58] areas they should less focus on and invest because, say, some machine learning model is going to do that for them. [24:04] 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 [24:17] even if, you know, TriGPD or these no code applications may be able to do this for us, [24:23] It gives you different [24:25] approach a different mindset a different if you want confidence [24:30] to know how things work and here's a silly example i was learning how to play the piano when i was young [24:35] and when my teacher came in i was like oh i want to learn how to play this cool song there were some songs that i really like [24:41] And she said, "No, you need to start with classical music." And I just hated it at the time. [24:46] And they said, why do I have to do this? [24:48] Because she said if you learn the fundamentals and how, you know, where things started and the beginning of music, it's going to help you along the way to create music on your own if you want to. [24:58] And she wouldn't write like I just loved it. So it's the same with coding. I encourage people to just take an online course. [25:04] understand more, get your hands dirty, [25:07] Pair up with someone else that's in the same boat as you, because this is going to give you the skill set [25:12] to understand how [25:15] that tool that's going to help you in your day-to-day was even created in the first place instead of blindfoldedly just trusted to do your job this episode is brought to you by pando the always-on employee performance platform how much do you love the performance review process yeah it's time-consuming subjective bias 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

[25:45] 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, resulting in higher quality feedback and higher performing teams. Visit [26:15] discount when you sign up and reference this podcast. That's pando.com slash Lenny. [26:21] For someone that actually wants to do that and learn to code, which I love, that advice, [26:24] Do you have any resources, places that you point people to for learning to code, getting started down that path? [26:31] It depends on what type of learner you are. There are some people that like to learn offline, [26:36] So just go to Coursera, there are so many courses. There is an amazing one, actually, Introduction to AI by Stanford. Let's take a look at that. [26:46] 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. [26:56] So if you enjoyed learning, [26:57] 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 college school, General Assembly, [27:12] and then coding dojo i i was actually giving talks ages ago i called the dojo about python and

[27:19] 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. 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... [27:43] 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 [27:53] Learn to code maybe. [27:54] start playing with tools. What else do you suggest PMs that want to become really strong AI PMs do now and invest in? [28:02] 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. I have the same course and an offline basis on my website. I'd be happy to talk to you if you're rich as me about this. [28:18] What I feel people should understand is, [28:22] what it takes to manage an AI product. [28:25] Of course, Google Library is familiar with the stages of product development in general, [28:29] But AI product development is different. [28:32] 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, [28:44] solution. [28:45] 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.

[28:55] Number two, if you're already at the company that is actually having AI researchers and AI research scientists, I encourage people. [29:04] to just [29:05] reach out to them and shadow them [29:08] 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. [29:22] Awesome. And is there anything else you want to share from your course that you think might be interesting to folks? [29:30] So we talked about why it's awesome to be an AIPAN, but [29:34] I do want to go out that there are a few challenges that people need to be aware of. [29:38] Number one, and I kind of mentioned it before, is the uncertainty. [29:41] You may have been working on all these incredible research and ideas in hypothesis, [29:48] But then when you actually train the model, the results you may be getting may not be [29:53] optimal may not be answering the questions or the hypothesis that you actually had in mind. So that's number one. [29:59] You need to... [30:00] be able to encourage the teams throughout this process, because you're like the captain of the ship. [30:05] you need to be the one that's kind of cheerleading the team, making sure everyone's going. [30:10] Number two. [30:12] you are going to have to be like, "Port a lot." You are going to have to change the emotion. And managing this from a leadership perspective can be tricky, [30:20] and it can be challenging. [30:22] Number three, we talked about data, but

[30:26] 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 for what is your dream you need to be able to [30:40] and willing to do everything. [30:42] And the last thing is from a career trajectory. [30:45] Usually, product managers get ahead the more they launch. But if you're in a research org, you're not going to launch as often. [30:53] So you need to make sure to clarify with the hiring managers early on, hey, what does this [30:59] progress how am i going to get a sense in the research work which is different than what i've been doing so far so it's challenging [31:07] But [31:08] I always encourage people to flex different muscles, and this is like the zero to one muscle that I think is just crucial when it comes to product management. 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 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? [31:38] out if it's worth the effort, but we think there's something here. [31:42] And then sometimes there's like a lot of energy initially and then, [31:45] you get some win, like your search ranking is smarter and it's great. But then maintaining that [31:50] Having all these really expensive people working on just tweaking this model and continuing to make it smarter and a little more efficient.

[31:56] often it's hard to continue to get buy-in. [31:59] for that sort of team. Do you have any advice on initial kind of buy-in? Let's try something here and then [32:04] down the road, just like [32:06] keeping a team going, trying to make this thing smarter and smarter. [32:09] People should know that there is an excellent source of inspiration and something that kind of do risk things. [32:16] which is adjacent for us. [32:18] Maybe the company has already launched a product that has been successful, those AI firsts. [32:24] And [32:25] Whenever I try to convince leadership about something that I want to do that's [32:29] kind of a big bet, 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. [32:39] And then I propose a little contingency claim. Like, hey, if that doesn't work out, [32:44] Here's the... [32:46] rollback plan, here's kind of the maximum impact [32:49] 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. And it's interesting because the more... [32:57] you work on this specific company, the more trust you get. [33:00] And if the culture is such, then [33:03] failing is going to be welcome. So I love companies that welcome pain because you can just go ahead and do this shortly. Do tell me if I'm wrong, but I feel like most investments in ML are not successes and [33:14] often not great uses of time. [33:16] 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. [33:22] I wonder if it becomes like, oh, OK, look, we'll put in three weeks. We'll get something really useful.

[33:27] 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? [33:36] We shouldn't underestimate academia and research blogs. And there's a website called Archive where you can see new papers come up. [33:44] because this is where... I mean, CHGBT and LIAC used to be there for a long time. Like, there was a lot of information on this kind of thing. [33:53] 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, the more PMs are going to add there, [34:13] then the more you're going to see this bridge kind of creating good [34:18] products that are created. [34:20] Sometimes you have amazing ideas by a research scientist, but you need a PM to take it and actually figure out ways [34:25] to also monetizing, right? [34:28] That's the other thing. If you're a PM, you need to come up with ways to actually be able to monetize. And ChatGPB is now free for everyone. But I don't know if you saw there was a... [34:37] there was a sign up forum that was kind of coming around saying, hey, [34:40] 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 BM's bridge that gap is crucial for companies to be able to take the research and actually come up as meaningful use cases for users. I think they actually started charging the other day. I think it's like $40, $42 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.

[35:07] When I think that, I always think of Tyler Cohen. He has this awesome blog, Marginal Revolution, and he's [35:14] 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, [35:21] This is a really smart dude. He's really excited about AI and GPT in general, and so he shares a lot of really interesting insights about it all. [35:27] - Okay. [35:28] Segwaying a little bit to your course, I have a couple 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 follow up questions. [35:39] My course is three weeks long. [35:41] It's meant for people that they're either aspiring or current PMs. [35:44] that want to understand how to sprinkle in AI solutions, or they want to become full-time AI. [35:52] Week one is more about introduction, what the product development lifecycle is for regular products and how it differs for AIP and specifically. [36:00] And then we talk about idea creation. How on earth do you come up with ideas? And I love what Steve Jobs said, where 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 embed to people and say, hey, people don't know how on earth to use AI. People would never have imagined a chance if you can do what it is. And then we take that and we dive deep and we talk about how on earth [36:26] 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

[36:40] to be converted into a product, how on earth do you convince them to [36:44] Trust you and and [36:46] How do you influence them? And then at the end, we're talking about how you actually will be able to pave your path to AFPN, all the way from interviewing for this role, [36:55] from what good resumes look like and doing some interviews, because the more you're practiced, the better it's going to be. [37:02] How many workshops are there? [37:04] Through the course? Networks. [37:06] Nine workshops, okay. Of the nine workshops, which of them [37:10] are you finding is the most exciting, game-changing for someone most interesting? [37:15] So throughout the duration of all these workshops, people have homework, and they actually take home an exercise where they need to create and develop their own AI product end to end. And they can pair up with each other. [37:27] By the way, there was this two students per DAP and actually where I would raise funding, which is mind-blowing to me. This is really great. That's awesome. 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've created. [37:48] 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? [37:56] after the course. [37:57] someone was able to actually, and I came to you not, create a little model that was able to take as an input [38:06] a tree that is defined online, [38:08] and was able to tell us what was wrong.

[38:10] if something was wrong with that patient. And it's just crazy to think that you can do that within three weeks. [38:18] 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, [38:29] Further, they wanted to create a little recommender system and say, "Hey, we think this is what's wrong with you. Here are the steps you should follow." [38:36] Obviously, we're not trying to play doctors or to pretend that we're medical in any way. [38:43] But being able to see that actually functioning is just, it's very powerful. That's amazing. Do they already know how to code, this team that built this thing? [38:51] They did not, but part of the course is to teach people the basics that you are going to need for the PM rents. And there are some no-click tools, as I mentioned, that are going to allow you to drag and drop and train these models and input photos in it and be able to do it. [39:07] Can you mention those tools again? Because that is really interesting. And it's just like a peek at your course. But if someone wanted to start building something like this, [39:14] What are some of these tools they could check out? [39:16] One of the tools I would like to recommend to people is actually AutoML. This is offered by World Wide 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 [39:34] you have already corrected, but it's not going to do the collection for you. And a great application I had to see, there's actually a YouTube video about this, is

[39:44] There was this company that actually had a lot of wind turbines. And what they did is, in order to maintain these, they would actually have people manually have huge ladders and go take a look and see if everything was okay. So eventually they just got drones and they had these drones fly on all of these machines and take photos and everything. [40:04] And then they downloaded all these photos and they uploaded on AutoML, and they were able to see which ones need maintenance and which did not. And I think they reduced time from like three weeks of work to like... [40:16] a few hours of knowing which lead maintenance and just be able to send people there. So it's this type of thing that you can do on your own, [40:23] by applying this sort of tools. And that tool is called AutoML? Yes, AutoML. Amazing. We'll link to that in the show notes. Coming back to your course, and maybe just a couple more questions, [40:34] 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? [40:39] How much work did it take? Anything there you want to share? [40:42] They treated creating my course like a product. [40:46] 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. [40:54] And I started reaching out to people. [40:57] 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? [41:06] 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 to them that is meaningful.

[41:15] and [41:16] 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. But then I realized, no, there are a lot of PMs that want to become AI product managers. [41:30] I did a little mind shift there. [41:33] So what it takes is make sure you find the right audience. [41:37] Make sure to figure out what the call means warrants. [41:40] 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 [41:48] You give the opportunity to everyone to present and to keep to know each other, unlike an offline Discord community, which is another important part. [41:56] And then the last thing, [41:58] 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 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. [42:18] Thank you. [42:19] So it took quite a few iterations, but I was able to get there. And now I'm very, very happy about it. And I recorded it offline as well for people. [42:27] 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 like rethink redo since you first built it? [42:35] I actually added bonus sections, and one bonus section was "JJBD" and how it was trained. This is because I started this new cohort in December, and on day one, the question I got is,

[42:46] What is this? How did it start? What is going on? How did they train? So I added the dedicated section for it and I put people to it. Amazing. Anything else that you'd like to share before we get to our very exciting lightning round? [42:58] It was someone that recommended I actually did the course and in the beginning I was [43:03] It was not... [43:04] In the beginning, I laughed and I said, wait, people would want to learn from me, really? And of course they did. And I'm teaching so many people. So what I want to tell people is don't underestimate this. [43:15] try creating your own courses as well people really want to learn what you take for granted for them it can 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 [43:30] The course is content, so go try this. [43:33] 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 you even came into it with just putting it together into a course. [43:44] Absolutely. And I got some uncomfortable 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 [43:59] Learning from the students, learning from the course, learning from explaining is just so viable, so skilled. Well, with that, we've reached our very, very exciting lightning round. [44:11] I've got five questions for you. I'm going to go through them pretty quick. Whatever comes to mind, share. We'll see how it all goes. Sound good?

[44:18] Sounds good. [44:19] two or three books that you recommend most to other people. [44:22] inspired. It taught me it's all about how to create tech products people love. Marty Kagan, right? [44:29] Yes, that's the one. Cool. Anything else? Or that's the one that comes to mind? [44:35] 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 and there's one more which is a book a workbook i recently launched with alana car and it's about um it's a workbook [44:53] for women in tech, [44:55] trying to navigate working in tech. It's called Adventures of Women in Tech Workbook. So that's another thing that they want to shamelessly plug in. That's a great choice to plug. Where can folks find that? Is that on Amazon? Yeah. [45:06] Yeah, Amazon. Amazing. What's a favorite other podcast that you like to listen to? [45:11] I like boz's [45:13] podcast. I don't know if you're aware of it. Boz is the CEO of Facebook. He has a great podcast. [45:18] 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. [45:26] What is a favorite recent movie or TV show that you've loved? [45:30] Oh my God, the White Lotus people were talking about this thing. I ended up just trying it out, and me and my husband, we just binge-watched the whole thing. It's just so different, so mind-blowing. It gets you excited about going to Hawaii again. It's really good. Have you seen the second season? I've seen it, and it's so much better than the first, which is rare. I agree. Awesome. Love that show. What is a favorite interview question you like to ask, and bonus points if it's AI-related?

[45:57] I love to ask people, how would you explain a database to a three-year-old? And I know it's [46:05] It's kind of an AI, not very much AI. But I love asking because people are kind of thinking, "Wait, what did you just ask me?" [46:12] 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, [46:19] explain technical terms to non-technical people. [46:23] favorite AI based tool that you think people should check out. [46:27] I mean, talk about tragedy. Now I have these on tragedy. But here this is what comes to mind. [46:32] Well, the lens app was pretty cool too, right? We were all uploading our photos and we were able to see what we would look like as fantastic heroes. I have to say, I tried being the male version because it was so much cooler than the female version. [46:45] That's what I recommend to people. Try the mail. That's fun. And there's actually a, they actually have pets now. That's what got me to download it 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, Lenza. And the app is Lenza, right? [47:01] Yeah, one song. Amazing. Marilee, 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? [47:12] Thank you so much. 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. How do they find the YouTube channel? How do they find the newsletter? Typing Marilyn Nica.

[47:32] Marilee, thank you again for being here. Thank you so much, Lenny. It was a pleasure.