A (3:19)

For a long time I was jumping between cto, CPO and eventually I found this kind of role. And I think it's really big thanks to AI that the roles are now starting to. The lines are starting to blur for the roles and it's now one role. And early stage startups specifically love the CPTO because they have one person to go ask everything to. There's like no two people in the room now. And I think AI definitely has made it much more bearable and much more easier to manage the responsibilities that come with being both responsible for the product and the technology side of things. And most importantly, it cuts down the time required that's spent in transitioning ideas, transitioning information, and now directly goes into how things can be made possible.

B (4:09)

What I really like about this role is that you get rid of this false conflict between product and engineering, but unfortunately you do not get rid of, I wouldn't call it a conflict, but the debates and negotiations that happen between the product and engineering team and the CEO. And so I would love to dive into your first workflow, which is really about how to guide maybe an executive, whether they're your CEO or another leader, through an idea, and really solicit out kind of use cases, user experience, all that stuff in a single meeting using AI. So let's dive into that.

A (4:47)

I work in B2B enterprise applications mostly. So a very simple idea that came up, which I think I've built this over the years, like the last 17 years in my tech career, five different startups, I've built it at every startup and the timeline always shortened. First it took like six months, then it took a few months, then a few weeks. But now it's like in a matter of 30 minutes to two days, we're actually building it and also putting it into production and that's how fast it's become. So there's this one idea that there's a problem that I love a lot. How can we build a user journey map within the application? So a lot of applications that I work on is where customers want to be able to Tailor how the experience gets delivered to users. Normally, this, just imagine this. If we had no logic, we have to write down all the logic. A FIGMA designer has to go and design everything in figma. Then they have to think through all the possibilities. Next, engineering has to understand all the different permutations and combinations. Everything's written in a document. Impossible to think of. How much has to be done before we can even have something functional. So in this case, we are in a meeting multiple stakeholders and then the conversation comes up, can we build like a journey builder within our application? So first step, where I always start, I love starting with chatgpt because this kind of has become my brain dump of what I'm thinking through and then it helps me consolidate. So we'll start here, work through a very simple idea. So I want to be able to build a prototype here. Very simple explanations. Right. Natural language that we're just conversing. There's no complex like here's like a structured PRD or a structured statement. Just a pure brain dump of what everybody's talking.

B (6:27)

Yeah. One thing I'd like to call out here is as much as I like to say, of course, that AI can do a lot of the product job, one thing that experienced product managers are very good at, that you've shown you're good at, is taking probably an idea from your CEO that's like, I want a journey builder. Like, I want people to be able to like build whatever journey they want. And you've actually given some pretty precise language to that. So there is still a job out there for us, despite perhaps my, my past, my past forecasts of the death of pm. I do think the ability to clearly articulate, either for a human partner or AI, something a little bit more specific, which I think you've done here, help, you know, a single page canvas, user facing tool, map out personalized workflows. Like that's all very specific language that I think a great product manager can translate in very short order. So that's what you're putting into this prompt and then what are you trying to get out of it?

A (7:26)

So out of this I want actually a simple structured prompt that could go into another tool like lovable V0 or magic patterns, any of those. And the idea here is I'm starting with something and AI does a great job of understanding what else is needed because I'm saying I want it to be a lovable prompt or a v0 prompt. AI understands what else might be needed. So as we go in, it starts to summarize everything that's needed automatically picks like a pattern, like it's a left side vertical toolbar that could have some. And the great thing it does is AI helps normalize every good product manager's product sense or product feeling into very structured information. So that's what typically differentiates different product managers. They have each a different way of explaining how they want it. AI doesn't judge you. It's like, okay, I get it and I'm going to help you. And I think this is where the biggest shift has been. Even product managers who initially want to think a lot, my push always to them is just jump in and start typing. Don't think about what anybody's going to think, what AI is going to think, because it's very kind, very nice, just jump in, start putting in. So one prompt later, not even too complex. We have a very nice prompt that I could then use into lavable or V0. So from here that's all straight. It goes to directly, we go into lovable or V0, we put it in and start building the prototype. One thing I want to also mention here is it's not just limited to something I type here. I wanted to actually show in this use case, I was using the Limitless Pendant, something I was just experimenting with. This is actually live transcript that's coming from the conversation that we're having in the room. And I could go from this directly into ChatGPT as well. So helps normalize it. Or this could be.

B (9:15)

I have to pause you. I have to pause you. Do you have the Limitless pendant right now? Do you have it close?

A (9:19)

Yeah.

B (9:20)

Wait, you got to show it. Show it to us.

A (9:22)

Give me one second.

B (9:23)

Yeah, yeah. While you're, While you're looking at it or looking for it, I have to shout out my old pal Dan, who's the CEO and founder of Limitless and will be thrilled to see the pendant in real life. This is my.

A (9:39)

Yep, this is my little pendant. I was actually one of the first few people to pre order this. I. I've worked with pendants my entire life because I used to work in senior living. So we used to build like the medical alert buttons. So when this form factor came on, I'm like, I'm trying this. And this has been such a game changer. Especially you're sitting in a coffee shop, you're talking with stakeholders, partners, colleagues, ideas flow. There should be no friction. Oh, let me take a paper and write it down. Or let me remember it. So this just completely changed the game there. So this is transcript, for example from a meeting. We start here and then directly go into AI. So straightforward and simple. So thanks for building this.

B (10:25)

Thanks. Thanks Dan and other friends over at Limitless. I have to say you did not tell me about the Limitless pendant in our prep and this has been a How I AI first. So just to recap for folks that are maybe not watching or don't know what this pendant is, you wore this basically AI recording device in your meeting with your CEO. I'm sure ascribe into all applicable disclosure laws about recording.

A (10:53)

Of course.

B (10:54)

Of course, yes. And you basically recorded this meeting and then that's streaming into the Limitless AI app. Then you're just taking the transcript of that meeting and you are pasting it into ChatGPT to generate this prompt. We totally buried the lead on the like new How I AI workflow here. So again it'll be interesting to see. I think we've seen a lot of, you know, desktop driven meeting recording apps. It'll be really interesting to see as these like wearables and devices come into the workplace how that might shift. And I just have to ask you, you know, why, why the pendant? Why a hardware device or wearable versus like turning on something like granola or another meeting recording kind of product?

A (11:41)

I think, you know, sometimes you want it to be non intrusive. I think that's, you know, I'm a big fan of also the Meta glasses. I'm waiting for better versions, lighter versions to come out. But this is such a non intrusive device, has amazing transcription and the great thing is it's not limited by language too. So sometimes we have voice recorders. You have to open your phone, you have to set it on the table. Oh, let me make sure I do it. So that's not always possible when you're talking, you just want to talk, you're thinking through and you're naturally converging. So it does a great. That's one great reason why I think I'm very bullish about hardware that's going to carry AI and it's going to completely change what's possible.

B (12:25)

Okay, quick request for folks watching or listening in the comments. If you are also a hardware aficionado or have a use case around that I would love to hear from you. I too love the Meta the Meta glasses. They are my daily drivers for headphones and things like that. And I would love to hear more about how people are using hardware on how I AI because this has been a first. Okay, so side detour through recording but you're basically taking these meeting transcripts in the meeting, you're saying, great, let's get it to a prototype. And then you're getting it into Lovable, for example. And this is an example of what you're getting out of it, which is this nice, beautiful workflow builder with a left hand kind of card nav, which we had seen in the prompt and some interactivity, I suppose.

A (13:12)

Yep, fully interactive. So here now I started with this prompt in ChatGPT as I thought through this and again, this is one of the great things, right when you're doing product and technology, you're constantly thinking of how this idea is going to evolve. So ChatGPT gave me a good starting point. I then went into Lovable and as I was pasting in, I'm adding a few more points there. So hey, I remembered I, you know, ran into a library called React Flow that does great job at making like a canvas based builder. So I put that in here and I did not expect Lovable to know what React Flow is. There's always this like, oh, maybe it doesn't know, but AI always surprises me here. Somehow it knew React Flow and exactly that's what it went ahead to implement. And you know, one of the things I always recommend is break down your concepts for AI and you'll have a lot more success. So I broke it down first, starting with the basic idea and slowly, as things progress, kept melting and building on it. And at the end of this, you know, I have a fully functional now drag and drop canvas builder that could build a full entire user journey. And this, as I mentioned, I've built this so many times in my career and it has literally been like 17, 18 page documents that fully summarize what's possible, pencil sketches, then, then UX designers go, put it in Figma, all of that stuff, which is still okay to do now. But I think nobody wants to spend time reading 17 pages and then arriving at a conclusion they would rather see. And this exactly does that. So very easy to do and most importantly talk about. So just imagine going to engineering team, you're not giving them an 18 page document, but you're rather stating, let me show a demo of what I'm thinking. Now we're moving from oh, let's spend a week aligning to here's the alignment in less than 30 minutes to an hour. And it's the same with the CEO too. Now that's a high stakes meeting. They want something in their mind and they're very, you know, most product led or more sales led. CEOs are very clear in terms of what they want. They might not explain it always, but now here they're visually seeing this and they might say, you know what, I want something else here that'll make it possible. So for example, I don't want too many variables or I, I want to be able to edit each part of whatever block that you have. And that's the great thing with these prototypes that we generate. Everything is editable. It's no longer just static images that like, okay, let me put it on a board and explain it. It's fully explanatory, it's fully interactive. And then we go from this to first alignment with the stakeholders, which is like the leaders of the business. Everybody's on the page. Let's now move on to the next step.

B (15:53)

So, yeah, I, I really love this. I want to call out a couple things for folks that are listening or maybe not watching. One is, this is really, it looks great and Lovable has been a wonderful sponsor of the show. So I have to have to shout them out. We do love them and I do tend to pick Lovable, but I want something to look nice, you know, like, exactly. It tends to be out of the box. You know, it uses color intelligently. It has, it tends to have, you know, kind of beautiful, pretty, modern user experience. So I do tend to pick this particular tool for something like this. Especially where visual design is a differentiator, I think, you know, sometimes people ask me what, what prototyping tool should I pick? And you're probably like me, it's like, well, which, whichever one I feel like is going to do the job of the day. And you know, when I need something that, where UX is maybe a little bit of the differentiator or it's highly interactive like data visualizations or a workflow builder. I've also found that Lovable does quite, quite a nice job. So that's one thing I wanted to call out. The second thing is, you know, I, I did a little, little tour in healthcare once and these rules, builders, flow builders are like, like everybody's built one or they built 10. Yeah. And what's really hard for product managers in the past is you're exactly right. You have to sit there and write bullet points for every piece of logic, every, every customizable field. You have to go in here and say, okay, if I send an sms, I have to check that the SMS has been like opted in. I can't send too many SMS's in a row. Like you have to do all this logic. It's very hard to document, it's very hard to test. And what I like about prototypes as sort of the source of truth for something that is this complex is you can actually just click through and go, oh, you're not supposed to put two SMS components in a row that goes back into the prototype code. Or, you know, you shouldn't do a delay on a delay. And so I think these are particularly effective for highly complex products because the alignment on the requirements and the testing becomes much more natural. It's very unnatural to read rule sets. It's much more natural to interact with something, something like this. So I think it's a really effective tool. And then the third thing, I want to call out that in case people missed, because they're going to be very mad at you. They're like, oh, no, you've given my CEO the idea, one, I can get a prototype in 30 minutes, two, that I can ship it in two days. But I actually think the thing that you called out was really important, which is those are high stakes meetings and they're high stakes products. When your CEO is asking for something and the worst thing is you say, okay, I'm going to come back here two or three weeks and show it to you. And you show it to them and it's totally wrong. They're like, no, that is not all what I wanted, right? Not at all what I was thinking. The team is angry, the CEO is frustrated, no one feels good. And so this is like a cheap path to failure too, which is like, if you get this and CEO is like, she's like, no, not cost you what, 15 minutes. It's not a big, not a big deal. Move on with your life. So I think those three things are worth, worth noting in this particular workflow. But let's go to what you were talking about next, which is great. We have something like this CEO is going to be like, when can I have it? And sales is going to be like, when can I sell it? And a product manager is going to ask their favorite question, which is, why do people actually want this?

A (19:24)

Exactly.

B (19:25)

How do you do kind of testing? I'm curious what the next part of your flow is.

A (19:29)

Right? So next part from here is three things. I try to put together some slides. Thanks. Gamma here or gamma the same prompt that we were just using. I start here with the same ChatGPT prompt. Whatever came out, I go and iterate on this. So the next question. And again, all of this can be done with a custom GPT tool that you can build for yourself. But I think the new models have gotten good where it understands what your Persona is, where you're asking from. So we don't even have to go through that effort anymore. I just start like I want to be able to do some market research. Can you now make this a little bit better? I can put it into perplexity or do some research on it. So that's the next step that I start here. Try to build a very simple just statement of what we're trying to do. What is a little bit more about the actual use cases, what the customer types are. So on one side I can use the same prompt, go into perplexity. This particular use case that I just did. So here one of the great things to do is deep research which cuts down the TAM analysis time by like, like three like, brings it down to three minutes from whatever it used to take. I think, I think the main advantage with a lot of this is it reduces the immediate emotional or cognitive burden on product managers or leaders in general to focus on what makes strategic value and forget about every other thing that's involved. So if we have to do an analysis, every product manager has their way of what all they want to analyze put into a competitive matrix or any of this. We get past all of that. That's what deep research for example in this case does for us. We give it some information. Again, as I come in here, I put in some more information. I want to build a market analysis on the following feature I'm working on. Paste the rest of the prompt. It goes and builds everything that's needed as a starting point. So review this. So now your market analysis to a certain extent is done. This is again to build conviction within first the PM or the technology leader that I know what I'm working on is of meaningful value and I can monetize it. Very important in an early stage startup, even in late stage. But I think even more important early stage because if we're not seeing ROI immediately, that's more money we're burning building products that don't generate immediate roi. So once that's done, export it as a PDF or goes into kind of gamma. From here build very basic slides, makes it look pretty. So when we're Talking to other PMs, other stakeholders, it's like work is already done that immediately attracts them, makes them bring to the understanding that we have thought this through, which is true, which is all happening in our minds, but now AI is helping implement it. And then very basic slides to be able to talk through talks about what are competitive advantages, strategic benefits, and then what the product is supposed to do.

B (22:20)

This episode is brought to you by Lovable. If you've ever had an idea for an app but didn't know where to start, Lovable is for you. Lovable lets you build working apps and websites by simply chatting with AI. Then you can customize it, add automations and deploy it to a live domain. It's perfect for marketers spinning up tools, product managers prototyping new ideas, or founders launching their next business. Unlike no code tools, Lovable isn't about static pages. It builds full apps with real functionality. And it's fast. What used to take weeks, months or even years you can now do over the weekend. So if you've been sitting on an idea, now's the time to bring it to life. Get started for free at Lovable Dot Dev. That's Lovable Dot Dev. One of the things that I find challenging, working with AI exciting and challenging is AI has tends to have an abundance mindset in that it tends to think all my ideas are great and everything is opportunity. And so I'm curious if you've gotten a critical market analysis out of AI or if it's always sort of seeking the data that tells you something is a good idea versus figuring out reasons why it might not work or it's not the right investment. I'm curious what your experience has been there.

A (23:38)

Yeah, I mean one of the things always, as you said, AI is bullish about everything that needs to happen. So I think some follow ups are critical here. So in perplexity, for example, when I'm doing it, I always have a follow up at the end saying can you play devil's advocate and tell me if it's really worth it or do you see really value in any of this? In other words, if you're going to ask AI once more to just make sure it is right, it is going to be accurate then to say compared to all of these other things that are generated. I think this is the reasons why it might be unique. And a pro con analysis is again so easy, right? So you could just build it into your prompt or ask it and I'll tell you there are more features that AI has helped me say no to because it's all in front of me and the data is readily available, which I think is again, what is the most critical. Right? You want to save your time for the ones that are really valuable, really meaningful. But now you're no longer carrying the burden that oh, my God. I spent three weeks working on this just to say no. It's done in an hour. And now you can feel more, you know, you can more freely say no to things and only focus on the things that are important. So I think it goes back to the earlier point you mentioned too. Good productions is still critical. Product managers are still critical. It doesn't replace PMs or what they do. It just makes them to focus on what the outcomes could be rather than the entire process. So that's, that's, that's where I have seen it be useful and helpful.

B (25:05)

Okay, so in our stakeholder Olympics, you have hurt, you know, cross the hurdle of the CEO. We've got the CEO excited. You made a deck. So now all the product managers and executives are you deck with numbers. That's the thing that makes product managers and executives happy. What, what's your next stakeholder?

A (25:23)

You go after more details. Right. So there's always going to be a junior PM or the engineering leader who's going to ask, okay, tell me more. I want to know what exactly you mean by this. This is where chat PRD is like extremely great for me because I use Chat PRD almost like a gate to the point that you mentioned. Are you sure you want to do this? Are you sure you have everything? That's where I find the tool to be extremely helpful. Because while ChatGPT is there, it's generic. Unless you give it like a huge Persona overload of what it needs to do, it's very generic. But chat PRD on the other side is geared towards a requirements document or a product sense that needs to be validated. And this is where I use the same prompt. But now rather than chat PRD going and saying, let me generate everything for you just because you asked me, it goes through a series of validations. Have you thought through this? Have you thought about what could be the disadvantages if you're building something? Do you have any specific inspirations? This is where I find Chat PRD specifically helpful. I have a template that I've built for myself. I call it the CPTO stack, where there is a particular type of PRD that my team likes. So I sat with them, discussed with them, came to alignment, and then I went forward. Now I validate all the questions here. This is kind of core or the last step before it's handed over to the engineering counterparts. Now they're going to spend actual mental time thinking through the details. So all of it is done. It goes through a series of discussions and we have Now a fully functional prd. Easy to read, very small bullet points, no large paragraphs, no large certificates yet. And then it's now ready. And this is one that caught me by surprise. There's future ideas and enhancements. Right. So this is something I've been dumping into the chats as I go through ChatGPT, perplexity, all of it. But Chat PRD did a good job of pulling this separately. What are future ideas and enhancements? What could be possible? Almost like a teaser to let your engineering team know, hey, I'm going to come back with a V2 that's going to be all this too. But it's a very subtle hint that gets their, you know, creative juices flowing and I think makes, makes the job easier.

B (27:38)

It's a, it's a very strange experience as a, as a founder or as a product person to have somebody explain your own product back to you. But I will say for folks that have not experienced this product that I made, it really is just a replica of going through a product review with me. My poor, my poor product teams, which is, I've done so many, like what we call be called prdr. We have a little song, there's like P R P rdr. It's very fun. But we have this product requirements or a product requirements document review meeting. And I just found myself consistently asking like, who else is doing this? Well, are there other products that we, we like here? What's next? Like, is this V0? If this is V0, what's V1? It's V2. And I do think this idea of using AI no matter what your function is as a gate to make sure that you've checked all the boxes of the next step without having to go to your boss or having to go to, you know, your other stakeholders is, is a really effective way to sort of bring AI into the loop in a way that ultimately saves everybody time, right?

A (28:41)

Yep, definitely. And I think, you know, the one thing I would love which you're building already is integrations. So from here it goes now to V0, which makes the job a lot more easier to keep iterating and refining. But I think as time progresses, more tools become integrated. It's going to be a big game changer for everything PMs and CTOs are doing day to day.

B (29:03)

Yeah, I'm sorry to the engineers out there, but we will be building a Jira integration to turn all of this into tickets. SATs, sad to say. Okay, so you've gotten again through a couple key stakeholders. Your CEO Your PM cohort, your AI CPTO here in chat prd, you know, let's get to customers. What do we do when we want to actually get this in the hands of customers and validate it? What's your next AI driven next step?

A (29:30)

Yeah, so customer success teams love this as well. So we built what is almost called like a living product library or demo library of pages especially, which are like close to the design language of our actual platform and have these all as individual microsites that we deployed. So now either sales teams or customer success teams because it's intuitive, there's no places where it's going to fail suddenly or I can't click because the figma interaction was not done. No challenges of that nature. They have now the flexibility to be able to completely take this and show it to customers, get real time feedback so they're no longer worried about how to navigate. There is a fully interactive prototype. So in this situation, as soon as I built this, it was so funny. We had a stakeholder meeting in one meeting room. Thirty minutes later we're with the customer actually demoing this because they wanted to see it. And what would have taken again like two, three months of discussions going back and forth, especially if they're a high stakes customer, is now done almost immediately. And it does two things right. Customers see it, they align and say, you know what, this would be really helpful for me. They're no longer reviewing slides, they're no longer having to just listen to long talks about what a product could do. They're seeing what it's going to be and if they don't like something, they're right there telling us, you know what, I think something could be different. And I think the outcome of all of this is like extremely fast alignment, which in my view is kind of what is super critical now. As long as you're aligned with your stakeholders, with your customers, building anything else is easier, is more tangible because there is no longer an uncertainty of, okay, let me put it into build, will this work or not? So that's what we do next. Take it to customers, show them anybody on the team can do it. Even I could trust my CEO to do it because, you know, it's a demo library, it's restricted to that. There's no page he has to go to where it's like suddenly going to give a 404. So all of it is very well bounded and this living demo library keeps growing and it most importantly doesn't put any burden on the engineering team, which is what I love. About being a cpto. I can almost do my entire product function without worrying the engineering team. Oh please build this demo two days time, one day time. I just want this one button to work. It's all now gone well.

B (31:50)

And so I think this is worth repeating because I think it's both very inspirational and also is going to give a whole bunch of teams big heart attack. And so the thing that you've done here is you've taken not just this prototype, but basically your library of good ideas, fairly high fidelity prototypes. You've put them in a kind of company wide live demo library. So anybody has access to these links and then you're like, customer success, go ahead. Like Sales, go ahead. CEOs go ahead. And I know that you work for a relatively small company and so that cultural change is a little easier for you, but I see so many product teams being like, oh my God, don't, don't tell customers we're working on this or don't show them the figma. It's not going to be like that. And I do think it's worth reconsidering how tightly we hold product ideas in the product organization. And I do think you're showing a different model, which is customers are understanding that things are in prototype phase and they're not live yet. Your team is intelligent and capable enough to manage customer expectations around things that may or may not come soon or later. And it's overall better for the product, it's overall better for the teams, and it's overall better for customers to just see what we're working on versus kind of keep it secret. And so I think the AI side of this is interesting because it becomes cheap to sort of show customer stuff, which I have also worked in the past where sales needs a demo of this or a prototype of that and you're scrambling two or three engineers to just try to make something happen that's not great. So now you've changed the cost of that, but then you've also changed the culture around just being more generous with showing your roadmap, showing your prototype, showing your things, things to customers. And so I think it's something that folks generally should consider is going to become a shift in how product and sales teams work together. And I think you're totally leading, leading the way here in terms of collaboration and openness.

A (33:53)

Yep, yep. Yeah, that's totally the product and sales collaboration. You know, I had my, all my previous sales leaders always said like, if only you could show me what's possible, I can make this happen. So for the longest time, what was friction? It could easily now be converted to a partnership. And I think that's where AI is playing a big role. How can you reduce friction across different teams? How can you make the impossible possible? And, you know.

B (34:20)

Yeah. And one thing I want to say, which is probably the fear in the back of product teams minds or engineering teams minds, is they say, if I give this to sales and a customer likes it, I'm going to be asked to build it. I'm like, I'm going to be stressed because all of a sudden we're going to have demand. And I, as a product leader, I will tell you this. This is before AI. I have always said, wouldn't that be a great problem to have? Like, wouldn't it be a great problem to have that a customer wants to buy something so much for us that there's like demand on our time to build something very specific. I'm like, the most likely outcome of all of this is a customer looks at it and goes, eh, like, maybe, you know, like they're like, eh, kind of. And I just, I think people are so afraid of preserving product energy and engineering energy that they forget that those are like what I call rich people problems. Like having too many customers ask you for too much product is like the best problem a product manager can have.

A (35:15)

Yeah.

B (35:16)

And so I love this idea of just like getting in there and creating that problem for yourself, which is ultimately a good one.

A (35:23)

Right. I mean, like, it's almost like you would rather have this prototype and alignment rather than randomly finding it on a quote that has already been signed and executed.

B (35:34)

Okay, you're. You're talking about the other thing, which is the. Well, I had to close it for Q1.

A (35:38)

Right.

B (35:39)

Feature. Feature commit. Tied to. To revenue recognition, which, you know, as, as a good old B2B girl. I have been, I have been there. I've been there as well. We've all been there. Okay, great. So you're not only you as a product manager getting this front of customers, but you are letting your team get this in front of customers. So I think you've managed stakeholders. It all sounds great. It sounds lovely to me. But we know there's still conflicts, there's still debates. So tell me a little bit more about how you're breaking deadlocks with AI.

A (36:15)

Yeah, so one other situation where we ran into a particular challenge was an idea. Again, that is where it required almost like a mobile app to be built. And the whole idea there was, we have this concept, could it work and now we have never done it for mobile. There is no mobile developer on the team. And now the team is stuck. Engineering is like, nope, I don't want to even hear about this. Don't even tell me about this. And all we want to do is, okay, can we validate if it's possible, rather than like going hiring. That's the first step to do before we even start something. So what we did in this situation was a particular use case. Again, to kind of also show there is no limit to what could be possible is where we went into building a mobile app. This was an idea where we specifically wanted to be able to capture just a selfie of a person. And we are working with avatar, so we want to be able to test capi. Capture them in a happy way, in a sad way, in a normal way. And one of the big challenges we saw was enterprise customers or enterprise laptops always don't have the best webcam. So now how do we bring the solution to the customer with whatever they have, rather than shipping expensive kits? So we're like, okay, mobile phones are always great. The cameras are great. Now how do we build something very quick? So this is where, again, Roark comes into play. I think they're an underdog in this game board. A lot of people are using it, but when I found it, I was like, okay, this is like another frontier, completely unlocked. Because what was previously not possible was mobile apps with generative AI. So same prompt, right? Similar concept started with ChatGPT, a discussion in a coffee room where they're like, okay, what if we bring the mobile experience to the customers and have them just capture an avatar? I'm like, sure. And then I go off 10 minutes later. Just one prompt here. We're able to now have a fully functional iOS app or like an Expo app that does only two things, right? This is not a production app again, but it does the main thing of building something that you could test on your phones now and shows what could be possible. And this is where a little bit of sensitiveness is critical too, which requires understanding what engineering needs. We're not saying engineering here. I built it. I think that's not the right explanation to this or what we're doing here. It's more about it's possible, I think. Let's look at what is required to take this to production. So now this deadlock, where there is a lack of knowledge of what could be possible, is suddenly lifted to say, here's what could be possible. Could we explore more?

B (39:00)

Right, okay, so I'm laughing because. And I'm making this face. Usually when I'm excited, I make this face, but I'm making this face, which is. I. I love this. This is like Clairvaux Playbook. No, no lanes. Like, do whatever you want. Sure. Why don't we ship the CEO's idea? Like, yes, let's show it to, like, let's give it to sales and everything you're recommending. I can see the flip side of, like, a product manager being like, why in the world would you give the CEO a prototype? Why in the world would you tell sales we can do that? And then an engineer being like, great, this guy just like, whipped up a bubble app and told me that he can code something that I can't code. And what I want to call out here is, you know your intentions. And, you know, some. Someone like me that's really AI trying to adopt AI. Like, our intentions are good, which is there are sort of these, like, artificial rules and blocks that we in organizations have put into place. Like, you're supposed to argue with the CEO about the roadmap. You're supposed to never over promise to sales. You're supposed to, like, trust that engineering will always tell you what's possible and what's not possible. And the reality is, like, we're all human. We're limited in cognitive capacity. We have good days, we have bad days. We have experience. We lack experience in places. And there's just so much more access now to overcome those, like, misunderstandings, get more creative, be more inspired. And so you know what? I would encourage folks to maybe take away from this, or what I'm taking away is one, there's a different way you can show up to your team, and it may frazzle them a little bit, but I think on the Net, it's pretty inspiring and very generative. And you can see how this is going to deliver real value for customers first. And then I would say, on the other side of the table, let's be gentle friends. Which is like, yes, yes. I think people that are trying these new tools and trying these new technologies, you know, like, you maybe like me, are genuinely builders. Like, genuinely get excited about creating solutions, genuinely want to solve problems. And so it's not about threatening people's expertise or, you know, trying to take their role or questioning them. It's really about, like, I think this could be pretty cool to build for our customers. I think this would be great for our users. And I found something that maybe can inspire us to do that. And so I think there's like a very important cultural takeaway from this episode that I don't want people to miss. In addition to I gotta call out, I have not used Roark. So now you can AI prototype mobile apps. Did not know. Now I know to kind of figure out things that are possible with consumer devices. So I appreciate you bringing sort of both sides, the tool sides and the workflow sides, as well as the culture side to this conversation.

A (41:55)

Yeah, definitely. I think that the one word I would use is to echo what you're saying is alignment. The goal here is not to say something is easy. It's more so to are we all aligned and can we move fast? Which I think is the next superpower, as all these tools come out and what could be possible, Right? And to all the big unicorns today that are coming out are small teams that are moving extremely fast. And I think we're all seeing living proof of that with everything that's going on. So, yes, nothing to displace good old production, resilient code and processes. But this is all before that.

B (42:34)

Well, and I'll also say, I mean, annoying. PMs have been doing this from the beginning of time, which is I have been guilty of being told no and saying like, I set up a, an Excel script that like kind of did the thing that I want or I set up like a zap. That is basically the flow I want. Can we. And at least now you get code. I mean, you know, you get code and something to click. So, you know, we will forever, perpetually, as product managers find very annoying ways to ask engineering to build too complicated things in scope creep. It will just happen till the end of time and now we just have more tools. Okay, so let's, let's stay on this topic. Let's get to lightning questions. We've been here a while, you know, zooming, zooming way out. And then the big takeaway from, for me from this conversation is roles are changing and the way companies work is changing. So I'm just curious, sort of what are your meta, you know, predictions on not how is this going to change for an individual product manager, but really, how are companies going to start to operate differently when they have access to these tools?

A (43:36)

I mean, I think companies are going to figure out a process that helps them validate things faster and move faster very much early on. I think whatever used to be the bottleneck of let's take time, let's wait for something to come back, let's wait for more analysis to be done is going to shrink, which I Think in turn is really good because you don't want confusion after you ship a product, which is what causes the most issues. Right. A good PM is now stuck managing disconnects rather than what should have gone out to be successful after spending all the time. So I think companies are going to evolve to where they're spending more time with these tools upfront and then being very sure of what they want as they move into building alignment is going to be faster. I think these prototypes from a perspective of aligning all the different departments I think about also customer support, right? Enterprise applications, you want to be able to support your customers. You have a customer support team. Rather than them reading booklets of information and preparing manual guides, they can now have a prototype. You could explain it to them with that. They're then training like other hundreds of team members, like, oh, this is how you would support if something happens. This is super valuable for large organizations. They don't have to wait until a product is done to be able to train. Now they can train on the fly. And you're no longer using obsolete technology. Right. You're always staying cutting edge, which could be a big differentiator for a lot of large companies too. So I think that's where I see companies evolving alignment fast and then being able to ship with. Ship with, I would say precision after that.

B (45:14)

Okay, I love it. It's like I'm smiling ear to ear. Let's move faster, let's build better things, let's enable, you know, our peers in the company a little earlier, have them more prepared and give our customers a better experience. Okay. And then you've showed us nothing but perfect outputs. So I've seen a perfect lovable prototype. I've seen perfect complexity analysis, gamma presentation, a chef's kiss chat purity analysis. I've seen all these beautiful things. Please tell me AI sometimes steers you wrong. And so what do you do when AI is not giving you what you want?

A (45:51)

I think one take a break. Sometimes it's like you just need to take a break and stop prompting until the end of credits. So yeah, I could be wrong. I mean, for the one demo that I showed, I have at least three demos that did not work in terms of building. When we're building something to perfection. That's where initially I was doing all of this inside V0 or lovable. And then I realized, okay, I'm spending way too much time iterating inside the tool, which should not be. So I think naturally found ChatGPT to be the first starting point. So when AI doesn't work, you'll take a step back and use something else more broader to be patient. Because AI can make mistakes. It's still a developing field, but the benefits far outweigh the failures.

B (46:36)

Yeah, and we were joking a little bit before the show saying that how people answer that question is often a reflection of their parenting strategy. And so I like the idea. Honestly, I need to hear this. Sometimes when you're like, kid is not doing what you want, like, just walk away is usually the answer. You're probably not going to win the argument with the, the five year old. So sometimes you just got to go, we're gonna, we're gonna take a little break. And so I'm gonna take that into my, into my AI strategies as well. Well, this has been such a great episode. I've just really enjoyed the conversation top to bottom. Where can we find you and how can we be helpful?

A (47:10)

Yeah, I'm on LinkedIn, Twitter as well, or X, both platforms. LinkedIn is the best place to reach. I'll leave my email here and happy to also talk if anybody needs insights on how to advocate for more AI in your product or technology workflows. Happy to always support with that. But I think we as a community need to step together to do this and not overselling your tool. But the chat purity community or Lenny's community is where I've had very meaningful discussions. You know, I've had a lot of PMs talk about what could be done different and I think, yeah, that's where you can find me.

B (47:49)

Yeah. Okay, so find you on LinkedIn and let's all chat about how we manage this transition together. Well, thank you so much. I really appreciate this PMs. This is one that I hope you've listened to the very end. Thank you so much. I'll see you later.

A (48:02)

Thank you.

B (48:04)

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