AI is the New Calculator (And It Won't Steal Your Job)

Ben Foster: You know, I imagine the world when the calculator was invented and you had all of these, um, accountants that were out there doing calculator work. In fact, there was even a job called calculator, right? And like, they were like human calculators. That's what they did, is it is just kind of like crunch numbers all the time, right? And they needed to do it without mistakes, you know, et cetera. And they had their various tools and eventually this new technology comes out that sort of like replaced the need for human calculation.

Sean Weisbrot: Welcome back to another episode of the We Live To Build podcast. I'm here today with Ben Foster, the co-founder and executive chairman at profi. That is an advisory practice and consultancy that helps entrepreneurs and companies be more product driven. I think this is an extremely timely interview because as AI is taking over a lot of functionality that, uh, is being done by people. I think AI will have a huge impact on product and the way companies see their product and the way their teams work on product and the way their clients experience the products that are being developed. So thank you for taking the time to talk with me, Ben. I appreciate it. Why don't you tell everyone a little bit more about yourself and how you got here, and we'll go from there.

Ben Foster: Sounds good. Well, thanks Sean for having me on the podcast. It's, uh, great to be a participant here and, uh, get a chance to engage with your audience. So, um, really appreciate the opportunity. Uh, real quickly, a little bit about me. I have been a product guy through and through my entire career. Uh, that's been about 25 plus years in product, which is kind of like, I don't know, 10 lifetimes in internet time, I guess these days. Um, but uh, but I feel like, you know, I've had a great. Uh, a great run at sort of working in product and working to build new customer value at a whole variety of different companies throughout my career. And just sort of like, you know, blessed with all these opportunities. So, you know, I was at eBay way back in the day from oh one to oh five. That's really where I kind of like cut my teeth in the world of product. Uh, I then went to a whole variety of startups after that, and most recently I was the chief product officer at a company called Whoop. Which makes a wearable device that a lot of, uh, professional athletes and a lot of like, uh, really serious kind of like, you know, people who are really kind, like focused on their health tend to use. So have worked in a whole variety of different kinds of things, whether it's been B2B, you know, consumer products, mobile, you know, you name it. I've probably been involved in it in some way and I've had a great, uh, chance at. Being an advisor myself to about 60 plus companies at this point. So it's been kind of like an interesting ride and feel like I've learned as much as I've been able to teach, which has been great.

Sean Weisbrot: From all of your experiences, how do you feel like product is changing right now?

Ben Foster: Hmm, that's a great question. I love jumping right in. Uh, you know, I think the way that the product is changing right now, uh, is, is in a variety of different ways. You know, you always have to understand the history to understand the present and the way that product used to be way back in the day. Is that, uh, you know, people have the role or the title of product manager and people kind of like deferred to them because of that title. And I think that what you're seeing is, is a continuation of a backlash to this idea that product people are just sort of like put into this position to make decisions about what the product should do next when the reality is there's all this data that's available that can help you to make those judgements, that can help you to make decisions and product managers. Need to be really data-driven. They need to have real evidence for why customers are going to use this next feature, or they've gotta have real clear information about why some version of a product is better than another one or they, or why one design is gonna perform better than the next, et cetera. So you kind of think of product managers less as being sort of like just straight up decision makers, you know, making their best kind of like judgment calls, but really making smart decisions around how they use the data that's in front of them. To make the decisions that they need to make. I think the second thing that's happening, and you pointed to this in the very beginning of this, which is AI is gonna have a really big impact on product management as well. Right. You know, because a lot of the decision making, which used to have to be human powered, human centered, can now be kind of like delegated to a machine which might be able to make better, kind of like data driven decisions than you'd be able to make because it's not gonna be subject to the same kind of human errors and things like that, that we're all guilty of. So, you know, I think those are the kinds of things that are, that are going on today For sure. What do you think?

Sean Weisbrot: I think you're right on both accounts. Obviously you have a lot more experience than I do. Uh, so of course naturally I would defer to your experience over mine. I think that AI will have an outsized, uh, impact on this, especially because where we see the world right now, a lot of companies are trying to stay alive. And in order to stay alive, they're downsizing and they downsize their human capital first because that is the most expensive thing that they're ever going to have, especially because AI can be extremely cheap in order to deploy. So I, I think that there may be less people on a product team. I think maybe it'll be delegated to someone of a higher level position, maybe like a chief product officer, maybe a product director. Um, maybe you won't have several people because you can use software to do customer reviews and customer feedback and community, uh, up voting and down voting for features. There's all sorts of software suites that really removes the need for humans. While still allowing the most important humans to get the information they need to feed the ai, the data so that the AI can help them to create the, uh, to make the decision. So yeah, I think AI is going to outpace the, the human upskilling on something like this.

Ben Foster: Yeah. You know, it's, it's kind of interesting. Like I, I think in general, I agree with you. Uh, I think that you're, you're gonna still have this need for human. Review of information, you're gonna have this need for humans to ask the right questions. I mean, who's even putting in the prompts into ai, right? You know, for generative things and things like that. So I, I think that the need for humans is gonna be a hundred percent there, the same way that it's always been there. Uh, I don't think that's gonna go away, but I do think you're gonna find that there's way higher leverage of those people, right? And so the best humans, the people who are best at their job, are gonna become that much more valuable. And the people who have really just been doing this kind of like rote, you know, basic kind of like execution type work, can in a lot of ways be replaced, right? I mean, if I can have a machine generate the right kinds of, you know, uh, user, uh. Interview questions and things like that to remove bias and things. I mean, maybe it's actually gonna do a better job of that than somebody who was a sort of like self-described expert in that particular kind of category. Right? And I can look at that stuff. It can build it. Now, I'd still wanna make sure that those kinds of things were getting reviewed to make sure that it was actually asking the right kinds of questions to lead to the, you know, decisions that need to be made, et cetera. But maybe that's something that can be done in half an hour of time as opposed to, you know, delegating it to somebody else to spend 20 hours on.

Sean Weisbrot: Yeah, so I actually did this experiment in a recent interview with a guy named Matthew Schmidt. What I did was I, so normally I never prepare any questions because I feel like that's a great way for me to have the flexibility. I need to be a great host. Some people like to prepare. I, I don't. I decided to ask chat GPT to analyze him and his business and give me questions that I could ask. It gave me generic questions. I thought they were okay. I, I said, I'm sorry, these questions are not good. Give me something that nobody else would think to ask. It then gave me 10 questions that I thought were fantastic, that I would've inevitably asked based on what he might say to me in a conversation before it knew what I actually was going to talk to him about. And typically I'll think of these questions on the fly, but that's already in progress, right? You can't prepare for something like that unless you're someone who prepares in general for being a host. So I found that after I prompted it several times, it finally gave me something that was extremely valuable. The only thing is because it didn't have the context of what we were gonna be talking about, the questions mostly were not usable. But they were extremely insightful and I was able to pull some ideas from some of the questions on the fly and change them up so that they would be valuable in the conversation. And then I asked him at the end, which questions do you think were me? Which questions do you, did you think were straight chat to bt? And which do you think were a mix of both? And he was pretty accurate about that, which I thought was cool as well. 'cause I didn't ask them exactly how they were written. Um, which is another phenomenon because when I do help a reporter requests, I look for what people are saying before I decide to respond to them or not. And more recently I've seen what feels like a bunch of people putting my question into Deach GPT and spitting out an answer, and I'll just ignore those people because they clearly didn't put any effort into it. So that's kind of a side tangent there on that. Uh, I'm curious to know from your side, do you feel like. The number of people that will remain in the product team will definitely diminish. And if they do, will that enable them to earn more money because they'll be seen as higher value, or do you think they'll continue to be paid what they're being paid now, excluding market fluctuations and inflation, et cetera? Just because they're the ones that remain.

Ben Foster: Yeah. Uh, I'll cover the first part first. Do I think that the size of product teams will diminish? Yeah. You know, I think over time that's, that's certainly gonna happen. I mean, let's, let's look at historical precedent on this, right? There's a lot of other, you know, we, we all look at the innovations of today. And we think, oh my gosh, how's the world gonna change? You know, et cetera. But the reality is there are plenty of innovations that have happened in the hat in in the past that at the time, you know, we look back at them now and we're like, oh, that's obvious. Or you know, it seems pretty basic consider, but like at the time it was probably perceived very similarly to the same way that we're looking at generative AI and things like that. Now, I mean, you know, I imagine the world when the calculator. Was invented and you had all of these, um, accountants that were out there doing calculator work. In fact, there was even a job called calculator, right? And like, they were like human calculators. That's what they did. It is just kind of like crunch numbers all the time, right? And they needed to do it without mistakes, you know, et cetera. And they had their various tools and eventually this new technology comes out that sort of like replaced the need for human calculators. But it didn't sort of like take away all accounting jobs, right? Like what it did is it just made accountants that were good. That much better, that much more effective, right? And they were able to have higher leverage to now solve all kinds of interesting new kinds of like problems when it comes to accounting to think further ahead. When you think about the advent of Excel and the impact that that had on accounting, right? It's just sort of like the next generation, the next level of these things that are happening. I think generative AI is probably the next level of that yet again. So it's not like the whole profession goes away, you're still gonna have that human need. But if you think about what people are willing to pay to an accountant today. It's probably far in excess, even in today's dollars than what the historical version would've been because the leverage on the very best people is that much higher because of the tools they have access to. And I think the same kind of thing is gonna happen when it comes to product as well, which is there's gonna be a need for fewer product people, but the very best are gonna be worth their weight in platinum as opposed to worth their weight in silver. You know? And that's just what I think technology and, and new instrumentation and tools like that provide. But that means that if you're a product person. You've gotta get on board with this, right? Like, I mean, imagine being an accountant and, and sort of like saying, oh, I'm not gonna pay attention to these, like, these new calculators. You know, I'm, I'm gonna ignore those, right? If you ignore them, you're dead. And I think the same kind of thing is basically true here, which is you've gotta keep up with the latest technology, the newest capabilities, to ensure that you're ready for what that next generation of your type of work is gonna look like down the road. Whether you're an entrepreneur or product person, anybody.

Sean Weisbrot: Yeah, I recall, uh, when I first got involved with product from my last company, I was doing wire framing for our application. I have no design experience. I had to learn how to use Sigma and it was very difficult at first. I. And I started to put everything together. And then later on I found this prototyping tool. I was like, oh, you can connect the different things so that even without coding a single line, your team members can see all of the different animations and what button leads to what screen. And I thought that was fascinating. I imagine. Figma being capable and, and there's other programs like Figma as well that have the potential where you could just say, okay, I want you, like you think of chess. Okay, A one to B three, whatever you could say, okay, I want screen A five to connect to screen CE using the start button and it'll just create the prototype for you. It'll connect to like, I imagine you being able to have this voice activated assistant that. Does the things for you so that you can make it a lot faster. There's some websites where you can just say what you want and it generates a website for you based off of a few words. Now the quality of those things are not great, but the fact that it can be done at all is pretty fascinating as it is. That's kind of my own perspective. What do you think, what tool or tools are out there that you are excited about that you think are gonna really change the game? And I don't mean chat pt, something different. And how

Ben Foster: Sure, sure. I mean, so there's a lot of attention right now on generative elements, the usage of, of AI to kind of predict the next word, to predict the next, you know, frame in a video. You know, things like that. Um, I. I'm honestly a little bit less excited about that from the perspective of product than I am about the analytical side of things. So this is more taking the information that you're already seeing and then making assertions based on it. Right. So if I could just sort of feed a bunch of data in. Have it spit out for me all the insights that I really need to know. Account for bias in the data, ask the right questions, the data, et cetera, you know, and then kind of like deliver to me the set of insights that I need to know. And you could even couple that potentially with generative, you know, things you could say, Hey, here, here's a new feature that you could conceive of that might be included in your product based. It's, it's that by itself, not all that interesting. But if you actually linked it. Together with the output of the data and he said, because the data looks like this, you can see that there's an opportunity that's emerged and this is an idea that of the next thing you could do with your product to kind of like go after that opportunity. That's kind of like in a lot of ways what, you know, at least the basic role of a product manager has sort of like been historically so. If you can derive insights from things, from the data that you're sort of like seeing, then I think you're gonna be in a better position to, to understand at a human level what the meaningfulness of that data is and what the implications of that will be for making that next set of decisions. Now, you know, you could defer to things along the lines of chat GPT or any sort of like other generative tools to kind of like, you know, structure for you what that next thing might look like. But I do think that there's this. Strength of human ingenuity and creativity, that's gonna be very hard to sort of like replace. I mean, theoretically you're trying to build something in product that hasn't been built before and yet the generative tools that exist out there are all derivative. 'cause it's trying to predict based on what's been seen already. Right? So there's something that's kind of like a little bit amis right there. But I do think in terms of the analytical tools to say, Hey, here's, here's this universe of all this data that's out there. Let's parse all of that and let's make it make sense to you and make it digestible for you. Like the cliff notes of all the data that you sort of like see at any given day. I mean, there's a huge amount of work that goes into that. Uh, today. There's a lot of companies that don't even do it because there's so much work. And for that to become sort of like accessible to every product person, that's gonna be the kind of like really game changing. Uh, innovation I think in the next few years within product management.

Sean Weisbrot: I think what you just said is fascinating because, well, if you think about it, being able to take data from customers and have an AI recommend potential user stories and feature sets is fantastic because as humans. We're biased to think about what people might want based on what we've seen other applications do. The actual ingenuity and creativity of thinking up new ways for things to work or provide value is one of the hardest things that any human can do. Creativity is one of the most difficult things that humans have to deal with. When they do can be extremely valuable, especially financially. So if you can train an AI to recommend. That it can add tremendous value to an organization.

Ben Foster: Yeah, I mean, people are actually using these tools already. I mean, I've actually heard plenty of stories of product managers deferring to JA Chat GPT to generate user stories for the kinds of like new capabilities in the product that they're looking to build. I think the problem with it is it's not predicated on any real data about the company so far, you know, or what the opportunity actually was.

Sean Weisbrot: Yeah, I mean, I ended up taking Chachi, bt, and I wanted to see if it could act like a product manager. Right. Some of the things that the product manager we had was they were doing feature specifications, they were doing user stories, they were creating sprint plans. They were, uh, you know, making sure that the designs connected correctly to the feature spec. You know, so like the visual aspect and the text aspect. Do they align so that when the developers go and do their jobs, they know that what they're looking at and you know, connects and makes sense? Right. So all of those kinds of things and more. And I tried to see if chat TPT could replicate that and found that it was quite good at it in, in a vacuum, right? Hey, here's a user story. Create some feature specifications for it. Create some, you know, additional things. Hey, why don't you write the code to help me, you know, show me how it would make sense if I were to write this in this language. It could do all of those things. Now again, it's just a vacuum, so who knows if it's actually valuable, but. On its own. It's quite interesting. Uh, what tool do you know of that, that people are using that ingests the data first before it goes to chat g pt?

Ben Foster: Yeah, I mean, my understanding is that there are, um, you know, that GPT-4 actually does allow you to infuse a bunch of. Information or context into it, but I don't know how, sort of like reasonable sort of like how, how meaningful that actually is. Like what it, what it's still doing at the end of the day is predicting the next word that you would have in a sentence. You know what I mean? It, it's kind of like generating content based on the kinds of things that it's seen historically and it's trying to predict that and it might be able to modify it based on inputs that you can provide, but I think that that notion. Of generating the next most. Likely word is exactly the problem, right? It, it, it's means that the output's gonna look like, it's gonna smell like, you know, content that a product manager might produce, because that's exactly what it's being asked to provide is this, it's not saying generate for me this next great idea. It's not saying, you know, you're, you're not, you're not actually prompting it with something like that. What you're prompting it with is give me content that would pass a sniff test of making it look like it was the next great idea. And those two things are very different from one another. Right. And that's because whether it's chat, GPT or any other of these other kinda like generative models, they, or, or, or, or AI systems, they don't have an underlying model of what the real kind of like good, like they don't have a model underneath of what. A great product is they're just trying to predict the next content that you would put out there, and it's almost like a hollow shell of something that has the veneer of looking real, of looking correct, but it doesn't actually understand why it's correct. It doesn't understand, for example, what the core value proposition of a product would be. It can try to illustrate that to you. It could try to tell you what it is and give you content that makes it seem like it knows what it's, but it doesn't actually have an awareness of that. And so if you don't understand what the core value proposition of your product would be, how do you know what the next thing is that you need to invest in to go advance that core value proposition? And I think that that's like until you actually have AI that is, that really has an underlying true model of things behind the scenes the way that we do in our heads, what you're gonna see is a bunch of. Derivative stuff. Sometimes it might like accidentally be good content. Usually it's gonna be something that looks like it's good, but is actually kind of like garbage. And I'd be really interested to see if you, like did a, you know, the, the ultimate AB test here and you generated a hundred new features for different products that were all kind of like AI generated as opposed to like good product manager generated who would outperform who? Right. And I have a feeling that the product managers would severely outperform AI at this point, but I think that there's a situation in which the. You know, best product managers could ultimately be beaten once there's actually an underlying model behind these things down the road. But we're talking way in the future at this point, and I don't think that anybody should be really worried about their job at this stage for that reason. Because so much of the there, there's so much low hanging fruit for investors working on generative ai. Uh, solutions right now that don't require that underlying model, and it's a much harder thing to go build that. You're just gonna see a lot of investment go into that because they need dividends within a short period of time. Now, maybe the larger players, the Googles, the Amazons, et cetera, will be making those investments into these new types of models and things like that down the road. Um, that will. Uh, that will actually generate better content because it actually understands what it's creating as it's creating it. Um, I think that, uh, it's gonna take a very long time to create systems that actually do have an awareness. I. Of the world around it. We're getting very theoretical here, but, uh, but I think that it's a, it's an interesting distinction, right? Like if I wanted to predict the next feature that would be in some generic roadmap, that's one thing. It's another thing to say, I understand what the core value proposition is, and this is the best opportunity to actually double down on that thing, right? Uh, based on the feedback that I have. And a product manager is capable of doing the latter currently. Uh, those other systems are not able to do it, but there's so many other things that they're gonna be able to do that it's worth the investment to just focus on these near term opportunities for quite some time. And I'll give you an example of that, right? Like it could be, um, generative AI as it stands today without actually having an underlying model can probably do an a ridiculously good job. Of providing customer support for technical issues related to products, right? Someone's gonna come out very quickly with a whole variety, or in fact, thousands of companies are probably gonna like it established using AI to try to replace customer support agents. Because there's this long tail of all these different kinds of like issues that people can run into, and historically people have had to manually do rules-based systems and generate their own content. It's just very cumbersome. It's very time consuming. You have to train a lot of like reps on it. Those reps leave after six months. You gotta retrain a bunch of new people, right? It's like. It's kind of, it's horrible having to manage all that kind of stuff, but if you can just sort of like, push a button and have this whole thing just kind of like run itself and it's Right, 99% of the time, hey, you're beating the, the average customer support agent like, you know, pretty thoroughly. If you can get that kind of like, you know, uh, accuracy in, in solving people's problems so you can respond to customers immediately, right? With, with two seconds and you can give them a better answer than a customer surface agent was and it takes you less time to kind of like make that whole system be trained. I mean, it's a no brainer, right? So you're gonna see that there are plenty of things that are like low hanging fruit that, that have, that this new technology will allow you to solve. There's a lot of investment dollars that are gonna go go into that because they're gonna see return on investment in a very short order. But I think the kinds of things that are necessary to truly replace the function of product management you're talking about. Something that's way in the future, and I don't know why an investor would go bite that off right now when there's plenty of other kinds of things that they can do that are much more significant in terms of dollars impact and ROI.

Sean Weisbrot: If you wanted to stay in that line of thinking and you wanted to focus on a generative AI that could speed up something or decrease the cost of something, as I was kind of talking about earlier. With the visualization, wouldn't it make sense to invest in something that can see what you've already built visually? And if you can say, Hey, based on the data I have, this is the new user story. Why don't you make a visualization for me? Why don't you wire frame for me in the current style of our application, you know, using all of the. Uh, all of the assets that we've uploaded to Figma, you know, make this for us, show us what it could look like, make a prototype and it would just instantly make it minutes, seconds later. Wouldn't that be a multi-billion dollar product?

Ben Foster: Sure. I think that by itself absolutely would. And I think that's a solvable, uh, that that is a solvable problem with the technology, even as it stands today. So I do think those kinds of things are gonna be out there. But to be clear, I don't think that that's what you just described. That may be some of the execution work of product management. We talked about why maybe the number of product managers would go down, but to eliminate the human involvement is not to sort of like replace that function. What are you doing when it comes to product vision? What are you doing when it comes to product strategy? What are you doing with build versus buy versus partner decisions? And there's just, you know, there's so much that strong product management entails. That is sort of like human powered and human centered in a lot of ways that I think, you know, if you're talking about creating something that's visual, that is described in detail through a user story and is derived from the existing style guide that you have for your application, that's sort of like one of those rote tasks that I think will kind of like go away, and maybe that minimizes the number of product managers or designers and things like that you actually need. The best designers, the best product managers aren't doing just that type of work, right? Um, product leadership is a completely different thing, for example, than that. So I don't think that product leadership gets replaced because you sort of accumulate enough of those types of examples of more execution oriented things that can be, that can be outsourced to technology,

Sean Weisbrot: right? Uh, my goal is not to replace leadership. My goal is to empower leadership. So for example, when I was first starting that company, I was literally drawing my wire frames on a eight by 11, you know, piece of paper from the printer, and it was chicken scratch. It was completely unusable, had no value whatsoever. I spent a long time building that out because I didn't know how else to do it, and I shared it with my CTO and he is like, what the hell is this? I'm like, you told me to make wire frames? He's like, not like that. I'm like, well, I don't know how to make wire frames. He's like, well, not like that. I'm like, but you're not helping me to figure out how to do it. He's like, it's not my responsibility to tell you how to do wire frames. It's your responsibility to figure out to do wire frames and tell me how to do the product so that I can develop it for you. I'm like, thank you very much. And so I went and I tried to figure out how to do it better, but. It was, it was difficult. And so if there was an application that could just understand what I wanted, then I could create a wire frame and go, Hey, what do you think about this? No, this doesn't work. This doesn't work. Well, okay, fine. Lemme go and make another one five seconds later. Alright, here's another one. Do you think, right, you could go through the iterations a lot faster and if you have ideas for, like, for example, I had, I had a million ideas for product, uh, for user stories, features, et cetera. But I didn't have the time and the energy to sit there and copy and paste every asset and make sure that everything was in the right place because I was the one doing the designs like. Something like this that's generative would allow me to just say out loud what I want and it'll just create it for me. And I can go yes or no, or I can show it to the CTO or the product team or the development team, whoever, and go, this is my idea for the next thing or for the, for five years from now. Like this is the, the robot. You could build the entire finished product with an entire roadmap of all of the iterative ways in which you get there. Right? You could, you could see, oh, what's gonna, you know, if the AI is connected to the. Hive mind of GitHub or whatever. It could go, okay, well based on what you've told me, what you wanna do, it could possibly take two years to develop this entire thing. Right? It's like there's, I feel like there's so much value at the leadership level for streamlining processes through this kind of AI generated automation and workflow or generated workflow so that you can make better decisions faster. Iterate on new features faster, right? Obviously the goal would be to have the data to be able to do it, but at the time when you start a company, you don't have customer data, you don't have feedback, you don't have anything. So at some point you have to start somewhere, and the faster you get there, the better. 'cause all these investors, right, they want a return. How do you get a good return? You move fast. One of the biggest problems for people that are just starting is they don't know what they don't know yet. And so it takes them forever to learn that information. Right. So that's what I'm getting at is how to empower leaders.

Ben Foster: Yeah, I, I think that's right. And I think that product leaders will become more valuable because of the speed at which they can iterate. Right. You know? Um, for sure. But I don't, you know, even embedded in your story, you have. Examples of things like somebody saying yes no to something that that gets spit out of it, right? The judgment calls of yes and no become even more valuable than they were before because they're getting made so many more times a day than they used to be able to be made, right? And so the difference of the best product people to be able to say yes or to say no to something that gets spit out, um. It means that the, that the multiplicative value of that judgment that they bring to the table is that much greater. Right? And I think that, you know, their, their ability to be creative, to conceive of new ideas now means that they have this great leverage on that because it's like, Hey, I could create a thousand different ideas as a product person. But if we can only execute five of them, then the other 995 ideas that I generated were kind of like worthless. Right? Like, but in the case that you can actually execute on all thousand of them, right? Then the ability to be that much more creative, you know, to come up with a thousand ideas when somebody else can like come up with a hundred. Now actually is 10 times more valuable. Right. So I think that these are the kind of examples where, where, whether it's product leadership or senior product management, you know, et cetera. The, the value of the best kind of like thinkers on that front gets to be that much better because, and that's why I think they're gonna become more valuable. Um, and the, and it is because the. The things that have been holding them back, time, efficiency, ability to execute, et cetera. Those issues that have been sort of like friction points start to vanish.

Sean Weisbrot: So what's something that you think we should talk about that's extremely relevant to this, that something you've been thinking about?

Ben Foster: Well, I, I think that there's. I mean, we talked a lot about what AI will be able to do for product management. What kind of like opportunities it creates? I think there's an, there's a couple interesting, you know, spinoffs of that. I think one of them is what are the ways in which AI will actually be problematic for product management? What are the ways in which it's gonna interfere? Um, and I think that there's some interesting kind of things that are there. Uh, I think, you know, there are other, uh, elements of. You know, I ki I guess we kind of jumped into why the human element becomes even more valuable than it did before. But what are all the ramifications for the rest of the product management function, uh, as you start to have more technological capabilities? So, for example, collaborating with stakeholders and things like that is a lot of the job, right? Uh, understanding the business, selling your ideas to somebody else, you know, et cetera. Getting board approval or investment dollars thrown into something, right? You not gonna sort of. You know, have a prompt that, that, that issues you money. Right? So how do you go get the investment dollars into certain kinds of things? You know, given that these tools start to like, you know, they start to emerge, and as this function continues to shift, what does that sort of like next stage of evolution look like for the other elements of the job as opposed to just sort of like, a lot of times we think about the core elements of product management, but there's all these ancillary parts that sometimes are the make or break for why a person is a great product person versus kind of like average. I don't know where do, where do you wanna go with there? Do you wanna, do you wanna talk about some of some of those things or, or did you wanna jump off of AI entirely?

Sean Weisbrot: I think what you're referring to is quite interesting and we should go there where you're talking about how AI can be detrimental for product.

Ben Foster: Sure. I think, um, let's give a few examples, right? Part of the issue is it's currently derivative. It may appear to be creative. But all it's capable of doing is. Coming up with stuff that makes sense based on what it's seen so far. Right. So, you know, one of the great, you know, old adages in product management that people talk about all the time is they say, you know, if I asked every customer, you know, back in the day what they wanted, they say a faster horse, but that doesn't generate a car. Right? Um, you know, people, you know, so, you know, a faster and faster and faster horse does not. Mean a car, um, you know, people wanted to communicate, um, faster. Than they used to be able to, but that wouldn't create the telegraph or the telephone, right. Or cell phones. Um, there are countless examples of this. I mean, Jeff Bezos has a great one that he uses when he talks about, you know, Amazon, uh, echo or Alexa, you know, that kind of thing. It's like, you know, if I asked everybody whether they wanted a Pringle size can on their, you know, like, uh, you know, in their, in their kitchen or their dining room or whatever, like everybody would've said, you know, no, I don't really want that. Um, but. It's, it's, it's the foundation for other product investments that they can make. They get to hear what's going on in your home. They get to kind of like, you know, be there. There's another channel that they have exclusive access to, et cetera, that nobody would've created before. And, you know, they, they wanted to offer the best availability of books. They wanted to offer the widest selection. They wanted to offer cheaper prices, they wanted to deliver them faster so you could look at all the ways in which books were getting delivered back in the day. And you know, it would be like, oh, well here's what we can do to streamline delivery and here's what we could do with supply chain and everything else, right? But is it gonna come up with Kindle as like the solution for that, right? Where now anybody can kind of like be published without having to go through this, the typical publishing channels and things like that. So when you look at all these kind of like products that are out there. Um, that have really been game changing and yield these multi-billion dollars and sometimes even multi-trillion dollar valuation companies, they're not based on just doing the same thing that's already been done in the same way, just better, which is I think a lot of what you sort of like find out or what you kind of like get from these tools that are sort of like looking at the body of everything that's been produced so far. So like true creativity or like, let's say for example, iconic design. What's the next iconic design? Well, by definition, if it's gonna be iconic, it's gonna be completely different than everything else that was out there. And it's gonna be distilled, it's gonna be simple, right? Um, what does that look like? You know, I think it's very hard for those tools to create those types of things. And so you might have something that has the veneer or the appearance of being more creative, and there it still might generate all kinds of value. But it's not gonna do something that's gonna be completely game changing, I don't think. And I think that the premium that then gets placed on that gets, you know, higher and higher. But that's gonna be, at least for the time being of mostly human thing that's gonna be necessary. You know, you can get ideas or concepts provided to you that might generate your own new ideas. But I don't think that you get to kind of like outsource creativity as a result of this. And I think there's a lot of entrepreneurs that expect that you will be able to do that, and I think that that's dead wrong. At least for the technology as it stands today.

Sean Weisbrot: I think it's quite valuable to be able to have an AI give you ideas that then you can execute on, or you can go back to it and go, okay, well you gave me this great idea. Why don't you execute on it? Right? What's the next step? How do I do it? Um, so I, I think, yeah,

Ben Foster: it, it, it can be helpful as a trigger for inspiration, you know what I mean? But I don't think you get to say, I don't have to be inspired anymore. Right. You know what I mean? Like that's, that's, there's a big distinction between those two things.

Sean Weisbrot: I think the vast majority of humans would be very happy to not need to be inspired anymore. I think the vast majority of humans would be very happy, not really needing to do anything. I, I think. There's probably an overwhelming majority of people who would be very happy sitting around doing nothing, getting paid by the government to do nothing while an AI creates value for the world. Let me

Ben Foster: think on that.

Sean Weisbrot: I would say two things.

Ben Foster: I think one of 'em is if you're talking about the world of product management, I think that the kinds of, you know, realistically, like we're not, we're not gonna go from. Zero to a thousand here, right? Like, you don't sort of like turn the stuff on and those things kind of like go away. So maybe you're talking about several generations from now, you know, et cetera. Um, that kind of thing could be the case.

Sean Weisbrot: I'm speaking about humans as a species.

Ben Foster: Sure. But I, but I would say I, I, I, the, the kinds of people that I meet, who are the entrepreneurs, the kinds of people that I meet that are the product people, I don't think that they fall into that description. I don't think that what excites them is the paycheck they get from the work that they do. I think it's the fact that they're involved and, and engaged. In solving a problem that's never been solved before or working with really interesting and exciting technology that they get an opportunity to change the world in doing what they're doing. And the nice side benefit of that is if you can do that with a sustainable business, you can also do it profitably and you can be very well to do as a result. But look at all these people who have plenty of money. They have like essentially infinite money. Yet they still choose to work. Right? So it depends on who you're talking about. I guess. You know, maybe you're right. The vast majority of people would fall into that camp. But for the entrepreneurs that are out there, if they could exchange everything that they're doing to just kind of like get a average check, that is the average check size that everybody else gets because there's nothing that differentiates them from the other people that are out there, versus the opportunity to have an in, like an impact the way that they do today. I think the thing that often drives them is the problem that they're trying to solve and. The kind of exciting work that they get to do.

Sean Weisbrot: I think there's a number of people who are seeing what's happening now that would've never imagined it was gonna happen in our lifetime, especially around ai, who suddenly are now like, I need to be healthy so I can live long enough so that I can cheat death. So I can see where the hell this goes. But I think the rest of the world is like, eh, it'd be nice to have things paid for, taken care of. So we, we can definitely go down a, a negative rabbit hole on this one. With climate change and all that, but I, so I'll, I'll avoid it here. Um, is there anything else you'd like to leave the audience with?

Ben Foster: Um, sure. You know, I, I think that there's, uh, you know, maybe summarize really quickly is I think that technology's constantly changing. I think you need to embrace that change. Whether, no matter what role you're in, no matter what you do, if you're an entrepreneur or you are, you know, senior in a product role or any kind of like, you know, position that's out there, always be ready to embrace that change. I don't think that you need to be crazy fearful that your job is gonna go away unless you're the kind of person who doesn't embrace the change, right? That would be the, the issue. So focus your attention on the things that you can do. Try to become more adept at using the technology that's out there. Be experimental, try those kinds of things. And I think you'll be amazed by the kind of leverage that you're able to get as a result of taking that kind of approach. You know? So always be curious, always be sort of like, you know, trying these things. Try to apply them to your, to your job, but at the same time, don't think that just because there's some new technology out there. That the value of your brain to make decisions about whether it's actually right or wrong, or whether it's the kind of thing that you need to, to, uh, like, you're not gonna be able to outsource all the things that you do, at least not right away. And so trust yourself, right? It doesn't mean you, you sort of like trust yourself less because these kinds of like tools exist. I think you trust yourself more to have the right kind of judgment about, uh, whether what is getting produced from generative ai, et cetera, is actually. Helpful, harmful, uh, redundant, uh, derivative, et cetera. You know, keep in mind that the best creativity is gonna come from you. And if you can take all those approaches and you can keep those kinds of, like lessons in mind, I think you're gonna do very well for yourself. But if you try to be ignorant of those things, if you don't sort of like embrace that change. Then you might be a dinosaur before you know it.