The cheese factory theory of research with Alec Levin

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Summary

Alec Levin, co-founder and CEO of Learners, on why the future of professional learning is messy and chaotic rather than rigid, why AI is to research what the tractor was to farming (his 'cheese factory' theory), why researchers who see themselves as study designers rather than 'people talkers' will thrive, and why a connected community of human practitioners is the natural-intelligence answer to an AI-driven research world.

Guest

Alec Levin — Co-founder and CEO, Learners · Marketing / startup

From this episode — top claims

• Researchers who define themselves as 'people talkers' will experience AI as an existential threat, while those who see themselves as study designers, information gatherers, and interpreters will experience it as a power multiplier that lets them contribute far more. — Ep. 61, Alec Levin
• AI is to research what the tractor was to farming: like a cheese factory that made cheese cheap and universal, AI will make most research dramatically cheaper to produce while a small premium 'artisanal' tier of research persists alongside it. — Ep. 61, Alec Levin
• Effective professional learning in the future must be far messier, more unpredictable, and more chaotic than the structured learning we are used to — the rigid, standardized model that worked for the last century has run its course. — Ep. 61, Alec Levin

aytm's take on this conversation

AI drops research cost toward zero, ballooning volume by orders of magnitude while a thin artisanal tier survives on top. Your moat shifts from interview craft to the judgment to design, gather, and interpret studies at scale, so build that or get commoditized.

aytm's perspective, voiced by Molly.

Full transcript

Auto-generated captions — speaker labels aren't always available and wording may be approximate.

0:00 If you think you're a people talker, AI is an existential threat. If you're someone who sees your role more as a study designer and information gatherer and interpreter, then this is the greatest thing ever. Because now all of a sudden, you have so much more power. You can contribute so much more, so you should be feeling even more comfortable in your role. Hello, fellow insight seekers. I'm your host, Molly, and welcome to the Curiosity Current. We're so glad to have you here. And I'm your host, Stephanie. We are here to dive into the fast-moving waters of market research, where curiosity isn't just encouraged, it's essential.

0:35 Each episode will explore what's shaping the world of consumer behavior, from fresh trends and new tech to the stories behind the data. From bold innovations to the human quirks that move markets, we'll explore how curiosity fuels smarter research and sharper insights. So, whether you're deep into the data or just here for the fun of discovery, grab your life vest and join us as we ride the Curiosity Current. Today on the Curiosity Current, we're joined by Alec Levin, co-founder and CEO of Learners, a global platform and community dedicated to helping UX researchers and designers advance their careers and to make that learning free for everyone.

1:15 Alec has spent years shaping the field of user research from hands-on roles at startups like Fall My Labs, Finaeo, and the original Meta. Yes, that Meta. To founding Learners, where he's helping researchers around the world develop the skills, mindset, and tools to turn insights into real business impact. He's also the mind behind the UX R Conf and the newly expanded Research Week Conference. And he's been unusually candid about where the research profession needs to grow, including some things researchers may not always love hearing.

1:47 Today, we're exploring how research training, AI, and the way researchers learn and grow are shaping the future of consumer insights. Alec, we're super excited for this conversation. Welcome to the show. Thanks for having me. Should be fun. All right, Alec. So, I want to start with something I think a lot of our listeners will relate to. You didn't set out to be a researcher. You studied biology. Um, you kind of stumbled into it. So, what did it feel like when you realized this was actually what you wanted to build your career around?

2:18 Uh, confusing. I didn't know. Uh, I mean, so the story goes that, uh, you know, like many young Jewish boys being pressured into trying to go to medical school. You know, I was studying biology. Didn't quite have the grades. Uh, I was close. So, I could have gone to grad school and gotten in, but I was pretty sure if I went to grad school, I wouldn't take it seriously. So, I was like trying to figure out like what else I could do. I volunteered at this lab.

2:45 And they had a career day and they canceled it and I'm like, "Can you uncancel it?" So, they uncancelled it and there was one guy who was talking about at this career day, talking about like doing a startup. I'm like, "What is that?" And that sounds cool, right? And so, they were building software for biomedical researchers. And I'm like, "That sounds super fun." So, then I pitched him on a project. And he's like, "Okay, this sounds interesting. Maybe we can do something." And I kind of figured out how to be helpful so that I could have a job. Cuz I didn't have any prospects. And so, I just like took his website to a bunch of grad students.

3:17 And I was just like, "Hey, what do you think? Let's Tell me tell me about it, you know? Play around with it." What do we want And so, I wrote up a report. I'm like, "This is what we heard from grad students and undergrads about the the product and blah blah blah." And they're like, "Well, this is really helpful." And so, then I got a job. So, that was cool. Uh, it was just the We didn't really have I think they called me community or something or community lead growth, whatever it was. And uh, I just spent a lot of time doing research. I just didn't know that that was a job.

3:44 And then like only years later after doing some other shenanigans did I find did I meet people who did this like in as in their job title. And I'm like, "Oh, this is a thing." For real. Like I should do that. This is what I want to do. So that was That's the story. Yeah, and you started You started running these meetups as sort of a consumer acquisition strategy for a startup that I don't think panned out for you long run. It did not pan out.

4:12 2015 was a bad time to be doing research startups and I was also not very good at it. So that's a pretty lethal combination uh in terms of like startup success. It's not going to work. Well, the the meetups that came out of that was actually the thing that ended up working. So when was that moment for you when you realized that the community that you were working to curate was actually the product and the the true selling point?

4:40 Uh yeah, I mean it's it's mostly accidental. Like I was I was called like user testing TO. It was like a It was just a meeting a meetup and then when I shut down my crappy startup and I was all burnt out and I was trying to figure out what to do. I was like, "What did I actually enjoy the this experience?" And the answer was not much, but I really enjoyed the the meetups. So, you know, we started spinning that up again in a bit of a different format and um yeah, just like I you know, resonated in is the right time, right place.

5:08 Uh research was growing, but it was still very disconnected and at least in Toronto where I was. And so um you know, it kind of kind of spun out from there and uh when the pandemic came, you know, cuz we had done our first conference prior to that and it was still meant to just be a side project thing, but the pandemic came at a particularly bad time where uh I had just left my job. Uh my wife was full-time on it. And then like the pandemic nuked our business because conferences don't exist during pandemic.

5:41 And so that was when we had to like find a way to evolve and become something different and you know, we were able we we basically said the only path forward is to try and pursue you know, the stuff that we were excited about which is affordability around learning, access around learning, community. We didn't really know what it was going to look like, but you know, it is like do or die and had no choice. Yeah, talk about under the gun. Yeah. She was also pregnant which was bad.

6:08 Oh my gosh. Just added on. Just keep adding more stuff. The first kid. Um she think Yeah, when we when we realized that the even though like they hadn't shut everything down yet, but we we realized pretty there's like an outbreak in a nursing home in like suburban Washington state and where that's when we realized like this is not happening. Like this is thing is going to be everywhere before you know it and she's probably like half halfway like probably 5 months at the time or like this bad.

6:37 This is pretty bad. Gosh, talk about trials and tribulations and doing all of that while having like unpredictable income. I mean, startup life is hard. Startup life is hard. You got I think you got to really want it. I don't I don't recommend it. Look, I think you got to look into someone's eyes and be like they're just not going to not do it and then it's like yes, here's all the encouragement in the world. But I think, you know, if people think it's it's going to be it's just not an easy it's not the easiest way to make money. It's not the easiest way to have a successful career, all these other things. There's much more pleasant ones. They require very difficult trade-offs.

7:15 Uh and um you have to really want it and you have to really care about the thing and like we happen to care we happen to have to figure it out, but we also happen to really care about the thing, though. You know, it that that part makes it actually doable. Um but it's not I wouldn't say it's easy. Yeah, having the passion is important. For sure. Otherwise, it's just going to fall apart when it gets really hard which is quick. That unbridled passion of like there's nothing you can do to stop me. That's how you know.

7:44 He doesn't feel that way every day, though. You know, I've been learning, I've been in learning a long time, and I think about, you know, how are people learning and what are the best ways that they can learn, um how to grow people in their careers. So, you know, you're spending a lot of time doing the same thing. So, what's the biggest gap that you see between what researchers are being trained to do and what organizations actually need from them right now? I'm not I'm not even sure researchers are being trained to do anything in particular these days. I think that's part of the problem. Um I think like our theory, essentially, of what learning needs to look like in the future is is that it needs to be way more messy and and unpredictable and chaotic than we are used to in the past in order for it to be effective.

8:33 I think we're coming out of an era where for the last 100 years or so, the the the efficient and productive way to teach and to learn has been extremely structured and very rigid, right? So, you know, and and that rigidity and structure has been, you know, you build layers on top of it. So, you know, if you think about an undergraduate program, uh it's it's a specific amount of time, a specific course. It doesn't even matter where you do it. It's it's pretty much all the same, right? It's very similar material. And in a world where you're training people to like generally think critically, know how to write well-ish, know how to do math, whatever.

9:14 Like, okay, cool. That that'll probably work. But the work is changing so fast these days. I I I think that, you know, even prior to AI stuff, this this was had run its course. This way of being this being the default way of learning a craft or a trade, you know, like contrast that with like the the trades, right? Where, you know, you get you're you're hands-on, you're you're actually in the field a lot. You're doing apprenticeships. You know, it's a much slower pace of change of pace, but or or pace of change in the field, um but you get to actually go and see how things are working in in real time with with journeymen or with whatever.

9:53 Um and I think nowadays with the way things are moving, and it's not just that they're moving in one direction, they're also spreading out in a lot of different directions. There's going to be new specialties and and new sub specialties and all sorts of things like that. So, what I think you need is um is a much more chaotic array of options and where we have to have a lot of content being made, a lot of you know, workshops being done, a lot of trainings being done, and trying to figure out how to get the right thing to the right person in the right time. And I don't think it's going to work the structured way that we've done this stuff in the past with like MBAs or with certificate programs. Um by the time you actually build a curriculum, which takes a really long time, it's probably not as relevant as as it you know, as it should be if you're charging the money you charge.

10:42 Um yeah, I mean, I think the the days of like a rigid structure definitely gone. I always think about like how would you learn something sort of outside of work or outside of, you know, your educational system. And it's like, what are people doing? They're asking peers, they're working, they're getting hands-on, they're jumping in, they're watching small videos and on different ways of doing things. So, I think there's a lot of sort of new ways that people are learning for sure. And AI definitely contributes to that as well. So, um definitely really interesting. So, you've built Learners around this idea that professional learning happens, you know, in a community. You're inviting in all these different people, cross-disciplinary, peer-driven. Um it's accessible to anyone who sort of wants to grow. So, when you see someone coming in and genuinely shift how they work and how they're thinking about their work, you know, what makes that possible? What actually changes for them in that environment?

11:33 There's a few different ways to answer this question. It's like so it's no no silver bullets, a lot of lead ones kind of thing. So there's how you actually make the content, there's who produces the content, there's how it's delivered. Um so for example with us, the way we've designed our program is without the expectation that people will be watching the whole thing. Right? Cuz we think that it's actually more valuable if we make sure that like 25 to 30% is super relevant.

12:02 Right? It's like right on where they need to be and then maybe some stuff is interesting but maybe not as critical and some stuff maybe not relevant at all, right? Um and uh so that there's that, there's even the way we pick content like coming back to this more chaotic messy way of learning, one of the things that we've I think done that's that's kind of new in the space is rather than me pick all the content, which is how it used to be 6 years ago. Um I try and find people with like different specialties or or areas of focus that are really tapped into a much more niche area and say, "How about you pick the content, right? I'll work with you, I'll try and help you." So for example, you know, with our AI program, it's not me, it's actually uh researcher at Anthropic who is very tuned in to um you know, who are some of the people that she respects in the field that she thinks are doing really interesting work. You know, so um so there's that.

12:53 Most of our speakers don't speak at other conferences, many of them this is their first time ever speaking. Um there's a there's this kind of unhappy relationship between uh propensity to speak at conferences and the novelty and interestingness of the content. Because generally speaking, the people who are doing the most interesting stuff uh are A very busy and B they usually doesn't their ability to do that doesn't rarely comes with the natural skill set of like building a sub an audience on Substack or YouTube or you know, building a LinkedIn following.

13:29 Those things don't usually coincide. So the people with the biggest natural, you know, reaches on these platforms at least when it comes to craft related content in many cases are not the people doing the innovative stuff. Right? So then you have to find all these clever ways of just like how do we make this better? How do we make this better? How do we make this more applicable? How do we And then there's just how we support our speakers. Um you know, a lot of their people that have the the really interesting ideas, you know, obviously specialize in those ideas and not how to format and deliver them. So we do a specific kind of coaching with them as well. So it's a handful of things like that, but it's really finding using different strategies to find the people doing the hardest work that we think is is the most relevant or a wide group of people to like help them get promoted, help them make keep their jobs, help them find their next one, help them stay on the leading edge. Um you know, that's kind of the way we think about it.

14:25 Yeah, and supporting the personal brand development of the of a lot of these really impressive people. Yeah, I mean a lot of the people who are, you know, some of them at least within our little niche are like household names now, you know. Some of them, you know, their first thing was with us, kind of thing like that and now now they're all rockstars and superstars and it's great to see their growth cuz um you know, once once people see how brilliant they are, uh why wouldn't, you know, why wouldn't you ask them to speak at your next thing or whatever it is. So it's great.

14:55 I love that. That's so cool. As like to be ground zero for someone that impressive, that's very cool. Yeah, I I cherish those relationships. Usually they stick around like, you know, Collette Callender for example. I think we were we were her first conference talk. Now she has her own program at Research Week that she curates cuz and it's all focused on really senior individual contributors and content that's focused on them. Um which is right in in terms of her what she loves to to focus on and talk about and it's perfect. Um and those those relationships are great cuz you get to know each other before you know, things things really take off.

15:31 So. Yeah, that's wonderful. Well, I want to pull the thread a little bit on something that we've alluded to, which is AI. And I I think that you've had an analogy in the past about this sort of thinking about AI and its implications for today's work as an agricultural revolution. This idea that AI is to research in terms of what a tractor is to farming. So, I have to ask where what does that actually look like in practice when when you're seeing those things happen and you know, what's what's something perhaps that you're seeing researchers utilize with AI that wasn't possible just you know, 2 years ago at scale?

16:14 I think I think the the analogy or metaphor that I've landed on that I like the most is like it's like a cheese factory. Because 99% of like the cheese that's consumed is relatively inexpensive and it's like black diamond or baby bell or whatever kind of thing and it's made in a factory and like most cheese is not touched by humans or whatever is made. But we still have very expensive Dutch cheese that you pay quite the premium for, you know, if you want to do that or like made in the Swiss Alps or stuff like that. So, you know, basically if you look at the consumption of cheese over time, cheese used to be a thing that you could only do if you were more wealthy, right? Because it's expensive. But now like cheese is something that no matter how well to do you are, everybody get you take it for granted. Of course, everyone can afford cheese. And like this is a kind of you know, I think it's an interesting analogy to uh research as well because um I I my guess is like 1% of the

17:13 desired research uh uh, that an organization has actually gets done or actioned on. And that's because when it's done in this very artisanal way, uh, it's very expensive to produce, right? To produce research, to produce insights. Um, now some of this research that doesn't get actioned on is quite complex and there's good reasons not to do it. Some of it's very simple, right? If you go and to ask like a bunch of executives at any almost any company, you say, "Hey, you know, why are we competitive against company A or why are we losing customers to company B or why is churn down last month?"

17:49 These seem like things that are pretty fundamental to know about a a business. And I bet the vast, vast majority of leaders and executives could not answer those questions, right? On any granular level. And that to me is just an an a very simple example of something that is knowable, should be known, and is it? And it's just the the only reason is because research is expensive to do. And in a company where you're trying to do new things and take that next leap, it it and you have limited research resource, you know, you want to invest it in that next big thing.

18:27 But if AI makes doing simple research much less expensive, then all of a sudden all this stuff becomes financially accessible from an organizational point of view. So, yeah, it's easy to look at a lot of AI tools and think they you know, there's things that you do that as a researcher that they can do too and that's kind of scary. Um, but it's I think we're missing the bigger picture here where you're a lot more than talking to users, right? Right? Your relationships, your business contacts, your intuition, your understanding of organizational dynamics, your ability to design studies and critically analyze data to interpret from and infer things that that, you know, a AI system might not be able to.

19:18 Um the these are super valuable. And I think for a long time, a lot of research folks have thought of themselves as like people talkers instead of like study designers. And in a world, if you think you're a people talker, AI is an extent existential threat. If you're someone who sees your role more as a study designer and information gatherer and interpreter, then this is like this is the greatest thing ever. Because now all of a sudden you are so you have so much more power. You can contribute so much more, so you should be feeling even more comfortable in your role.

19:49 That was a lot. I'm like I'm taking it all in from a bunch of different different angles because you're you're totally right. And this it also begs the question of where where do researchers need to skill up and where do researchers need to learn how to utilize these whole bunch of different tools? Because like you said, there's the threat of now these people can do the same thing that I can. So how are researchers skilling up in order to utilize these AI tools in a way that expands their job function and still keeps them relevant?

20:20 See, I push back. I don't think that even with AI tools, PMs can do research the same way that we can. There are some who are super too are super talented and that's awesome. I think for the most part they can't. That's same with design. Look, I can do design work now by using cloud code. Are the is it good? No, but it it's like the rectangles and buttons and stuff like that. And so there there's a quality side of things to to research as to design as to everything else that, you know, it's easy to see when the design works bad.

20:52 It's hard to see when the research is bad. You know, someone uh said a line I can't I wish I knew who said it, but bad research doesn't stink versus like bad design. We look at it and you're like, I don't even understand what I'm looking at, right? And so I I don't believe I think the vast majority of PMs and designers would really again struggle to do research well even with AI tools. Sure, they don't have to talk to the people anymore. But there's a whole thing again it's like the study design element of it. Like when great researchers walk into a room, right? I remember back in the day, I am old enough, when we used to do research and the the way you were supposed to do it was to read from a script.

21:31 So you're going to like you had six or seven interviews you're doing and you got to literally print out a script and then like read it word for word and ask it to the to the person and write down their answers. And if you didn't ask the questions in the same way it was biased, right? You couldn't be trusted, which makes absolutely no sense, no sense whatsoever. Because this type of research is purely interpretive and and and there's no at that's number of participants, there's no statistical significance that can be gathered from anything. If you read from a sheet of paper, you'll never build rapport. You're you're Yeah, you'll get input from the person.

22:11 But you'll never get real deep understanding cuz they're not going to be honest with you when they you don't even have the the respect to look them in the eye when you're talking to them. Right? But that's how it used to be, right? And like great researchers wouldn't do that. Because what you realize is I got to like build a relationship and build trust very quickly. And then I can be like, all right, where's my in to get them really talking? It's over here, it's over there, whatever it is. Like designing the thought process of how to get somebody to open up in the right way is a skill that most PMs and designers don't have and won't develop.

22:50 Right? And so I don't think I like of course AI will get more and more powerful, but I just don't see I don't see how there's not specialty here that matters. Right? And again, as long as you if as long as you don't ascribe as I don't to the idea that researchers are just people talkers. Then you know, you understand there's a lot more that that to the to the craft here. Yeah, I think that craft is the right word because when I've seen research be done, there's an art form to it that is not necessarily based in science and there's very specific nuances where I think that institutional knowledge is essential and can we or should we encourage people who are users of market research AI technology, encourage them to gain that institutional knowledge and maximize what they're able to do using these AI tools.

23:48 Yeah, I for sure. Look, I think In terms of what we need to to do, I think we need to get I think everyone should be experimenting and trying things. There are so many specialties that we haven't even identified yet with these tools. The volume of research is that to be conducted is going to go up by like probably a couple of orders of magnitude. Right? Because as the cost to do research comes down, that means the amount of profitable research that can be done goes up dramatically. So you get more research at this into this new equilibrium, right? Um So there's going to be tons more research which means there's tons more data to manage, there's tons more studies types to run, you know, there's just going to be so much more stuff. So you know, when I think about what those things might be of the future, like I try and look back to that metaphor of the cheese factory for example and I'm like, you know, so there there's the old way of making cheese, you know, you get the milk from the cows, you put it in a

24:45 pod, you add some whatever rennet to it. I'm pretty sure there's like salt and salt and vinegar in there somewhere. Well, I don't know how to make blue cheese but you know, somebody knows, right? And then you look at the at the new way and you're like, "Oh, there's like a technician, right? What does a technician do?" Well, a technician, you know, they're the ones who are um updating the software and the the hardware components on all of the big factory machines, right? And there's actually probably a handful of them cuz there's lots of different machines. So, we you know, we might have a bunch of different AI tools that need models to be updated and reconfigured as new new labs release new things and then um tooling to swap out features and whatever. So, there's going to be a management thing. There's also a QA thing, right? So, you know, when cheese factories are pumping out cheese every thousand pieces of cheese, someone's got to eat it and see if they get sick, right? Like we're going to need to audit

25:43 these massive flows of AI-conducted uh interviews and and surveys and all that stuff, right? Um there's probably going to be like AIs doing interviews with like AI avatars of ourselves at some point. How the is that going to work? I don't know, but someone's going to have to figure this out and how these pieces connect together. And there's probably going to be somebody who's trying to figure out, how do we match our on-platform data or other data that's available to us to complement the analysis that's being done on the qualitative stuff that we're getting. All of that is going to be works to figure out. All of that is going to take time. All of that is going to take humans, in my opinion, who have the sense of business context, organizational context, craft understanding, understanding of what all these AI tools can do. This will be hard. This will be figured out. And if you want proof, like are are the AI companies not hiring uh humans anymore? No, they're hiring

26:41 like crazy, right? As long as sales people have a job, account executives have a job, I'm not worried, right? Because that should be the easiest thing for these AI tools to automate. Right? But why why does Open AI Why is Open AI and Anthropic and all these other ones Why are they all partnering with Accenture and BCG and and McKinsey? Right? Because the the human selling, the trust building, the influence It's all really important. And even though AI might already and probably does already have the technical capabilities to have these conversations, there's a reason it's not working.

27:20 Right? And so all I'm saying is we're is essentially some version of not only uh do we are we learning about what AI can do, we're also learning about what's special about us. Right? And what are the things we really bring to the table? Right? And so for a long time I think it's we were the best at talking to people and we were the social cats of which is fine and it's an important part. But it's such a small part. It just felt like the big part cuz it's what we spent 60% of our time doing.

27:48 That's fascinating. All right, so let's talk back to the community. So Learners started because you felt isolated as a you know, solo researcher. You've built a community now with 35,000 plus people. It's a huge community. Um beyond the networking and the career development, do you think having that kind of community actually changes the quality of research that people are doing? Without a doubt. Like I So we're all you know, talking a lot about artificial intelligence, but I'm also big into natural intelligence, too. Right? And so like if you were if you were imagine you're pitching a product to like a bunch of venture capitalists and you're like, "Okay, hear me out. What if we took an AI thousands of them and we planted them into thousands of unique circumstances, right? And each one of them had to figure out on their own how to navigate the unique challenges of their organization and their product and their this and their that. And then what if we like connected those AIs together?

28:45 Right? Like what what what could we learn? Like, what could that or That's That's the community, right? Like, every single one of us is dealing with a unique situation, unique challenges, right? The key thing is like, if you want you know, it's what I was saying about learning before. It's got like the the next generation of learning is very is very chaotic. It's very messy. It's It's very flexible, right? It's more of an ecosystem than it is a rigid structure in my view. But, like, if you if you think about it that way where you have 30,000 sensors and all and each one in a unique situation, but now they're connected and they can talk to each other and share things with each other, well, we're going to have a very bright future ahead of us cuz every time one unique intelligence learns something, it can propagate to the rest of the of the of the entire space.

29:36 And like, that to me is is the exciting vision for like what is possible with a community approach to stuff moving forward, right? I don't think we can rely on our institutions for our future growth and training the way we have in the past. I think, you know, there's a lot of signs that they've been failing us for a while and that they're incapable, even though there's lots of great people there, they're not capable of of keeping up with what's happening. But, I think we are, right? And if we have open dialogue and and the right kinds of connections with each other, we can keep up with whatever changes and challenges are thrown at us, and there will be many over the coming years.

30:17 So, that to me is the That's the exciting opportunity, right? And, you know, uh happens both for the learning, it happens both with like all of our partners like you guys. Um you know, you're working on new products that solve problems. Great. How do we help How do we help, you know, you all meet people who have those problems, right? It's just another version of the same thing, which is thinking more of it a connected ecosystem. Um where we we focus like Learners focuses on building the infrastructure in the shared space literal literal space and figurative space.

30:50 And then then now we're ready to to tackle anything that comes ahead of us. So, that's that's the thinking. That's the focus. I love that. I would have fascinating reframe. That's awesome. I love that and I love community. Good stuff, right? It's fun, too. It's super fun. Yeah, and and events in community is always a fun thing, for sure. It's a fascinating reframe also to think of like imagine if you could have AI systems that were constantly learning and they were all connected to each other. Oh, that's people.

31:19 Yeah, we call those people. Amazing, we've come full circle. Here we are. Yeah, and and that's I mean that's going to be the continued importance of remaining grounded and remaining in person and seeing the values in human connection I feel like it's even more important in these times of advancing technology. No doubt. Fabulous. Well, thank you so much Alec for joining us today. This has been such an enlightening conversation taking us through startup life, what it means to be a founder, that it is definitely not for the weak, the researcher and AI relationship, and I now I'm going to have a grilled cheese. So, thanks a lot for that.

31:59 Um the power of natural intelligence, of human connection is incredibly valuable, and the essential nature of community in the world today. So, thank you again so much for joining us, Alec. I really enjoyed our conversation. My pleasure. Thanks for having me and I'm looking forward to hanging out at Research Week in just a few weeks. Yes, it's it's going to be a short flight up for me to San Fran from LA. So, I'm I'm really looking forward to it also. I've only been there a couple of times, so it'll be a really good it'll be a really good show.

32:27 It's going to be fun. The Curiosity Current is brought to you by AYTM. To find out how AYTM helps brands connect with consumers and bring insights to life. Visit AYTM.com. And to make sure you never miss an episode, subscribe to the Curiosity Current on Apple, Spotify, YouTube, or wherever you get your podcasts. Thanks for joining us, and we'll see you next time.

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