Alfredo Andere - LatchBio: College Friends, Biotech Beginnings & Thoughtful Persistence (Part 2/4)

Intro - 00:00:00: This episode is brought to you by Excedr. Excedr provides life-signed startups with equipment leases on founder-friendly terms to accelerate R&D and commercialization. Lease the equipment you need with Excedr. Extend your runway, hit your milestones, raise your next round at a favorable valuation, and achieve a blockbuster exit while minimizing dilution. Know anyone who needs lab equipment? If so, join our referral program. Give your friends $1,000 and in return, earn $1,000 for each qualified referral. Start earning cash today by going to E-X-C-E-D-R dot com and click the yellow button in the bottom right to get your unique referral link. Additionally, as a podcast listener, you can redeem exclusive discounts with a growing list of biotech vendors and get $500 off your first equipment lease by using promo code TBSP on excedr.com/partners. Welcome to The Biotech Startups Podcast by Excedr. Join us as we speak with first-time founders, serial entrepreneurs, and experienced investors about the challenges and triumphs of running a biotech startup, from pre-seed to IPO, with your host, Jon Chee. In our last episode, we spoke with Alfredo Andere about his path to UC Berkeley, his early experiences in electrical engineering and computer science, and what eventually pulled him towards machine learning and biotech. If you missed it, be sure to check out part one. In part two, Alfredo shares how his college friendships evolved into co-founder dynamics and how the earliest seeds of LatchBio began to take shape. He opens up about the decision to drop out of Berkeley, the leap of faith it took to go all in, and why it wasn't one great idea, but a strong partnership that pushed them forward. We also dive into how the team explored different industries, what ultimately drew them back to biotech, and the pivotal moment they decided to commit the next decade of their lives to solving problems in life sciences. 

Jon - 00:02:09: And that's the thing I think, whether it's personal decisions you're making, business decisions you're making, there's always going to be a trade-off and there's always going to be a personal sacrifice. You open one door, you close another. 

Alfredo - 00:02:19: Yeah. 

Jon - 00:02:20: And it's kind of just being honest with yourself on like what doors you want to open, what doors you want to close and making sure that anyone who's like joining an organization or joining a movement, whatever it may be, that you are willing to make that sacrifice and everyone's on the same page. If you're not willing to make that sacrifice, it's probably not a good fit organizationally. I'll just give you an example outside of like business and school. Maybe if you're like, if you want to be in a band and a big part of being in a band is going on tour.

Alfredo - 00:02:44: Yeah. 

Jon - 00:02:45: If you don't want to go on tour, maybe being in a band isn't what you want to actually do. Right. You can apply that to anything, right? It's like, if you want to work in food, you've got to be on your feet a lot. If you don't want to be on your feet, maybe you don't want to be in the kitchen. I don't think there's a right or wrong thing. It's kind of a personal decision, a personal preference. And I respect that. Like I respect the screw it. I'm going in, I'm going in, I'm training. I'm putting on all the weights and I'm just going to get good. And then by the time you got out, you got good is what you did. 

Alfredo - 00:03:14: Yeah. 

Jon - 00:03:15: I love to hear that. And that's awesome that you were able to like basically squeeze in of like what a normal U.S. high school is like, would be like multiple semesters. You squeezed it in and got it done. So it seems like you won, not only got up to speed, but it almost seems like you started to get in a rhythm. And I know that you are also the president of the Machine Learning Club at Berkeley. I was also involved with kind of student run organizations. And my co-founder actually was too. He was the president of the Berkeley Investment Group. 

Alfredo - 00:03:45: Oh, nice. Yeah. 

Jon - 00:03:47: I did like law stuff and biotech too, but I'm curious, how did you get involved with the ML Club? Tell us a little bit about that organization and that experience. 

Alfredo - 00:03:55: Totally. No, and that's a great story. And by the way, I love that you did a kind of biotech student clubs because I think we need more of them in Berkeley and in every university. So I would love to 

Jon - 00:04:05: Yeah. 

Alfredo - 00:04:06: Hopefully that's something I would like to contribute to in the future as students kind of start building more of those. But particularly for Machine learning at Berkeley, it was a very interesting experience. So as a starter, Machine Learning at Berkeley is a student-run, non-for-profit organization, student club at UC Berkeley that works on consulting, research, and educational machine learning projects. And it was a very valuable experience for me in two ways. So first of all, how did I get in? Because that was the first challenge, right? Because for context, usually there's like 500 applicants every semester to the Machine Learning at Berkeley club. And it's like 500, like some of the top Berkeley people because it's the EECS program, which is kind of very competitive. And then because of cultural and being able to develop these members, they only accept about 20 members, anywhere between 10 to 20. And so you can imagine it's very competitive. And I remember I applied for my first semester. As I was kind of starting to come out of this like time capsule, I was like, okay, I think I'm ready to expand a bit more, but still in this kind of more academic degree. And I started looking at student clubs. I didn't know much and I applied and I was really hopeful I would get in because I had spent quite a bit of time writing the essays. And then I had met one person at the club and I was like, oh my God, I should probably get in, right? And then not only did I get rejected in the first round. But my friend that had been a machine learning researcher at Facebook for two internships also got rejected in the first round. At the time, my resume had nothing. Like I barely had a resume. I think I had to make it for the application. I was very not pre-professional when I initially went into Berkeley. So this brings me to the first story. Remember the Viktor Frankl story that I told you about those kids sitting outside of his house? I was like, well, I really want to get into that place. How do I do that? Because I'm not going to get like an internship in the next semester. And so what I did over that next semester, I, A, started self-studying a lot of Machine learning. So I did all the typical kind of engineering and done all the like typical machine learning intro. I didn't really know much machine learning. So I started applying myself to that. I also joined the Decal. So you know what Decals are, but for other people, Decals are these student-run courses. And so Machine Learning at Berkeley, the club used to run this Decal on machine learning intro. And so I joined that. I attended every single class and I made sure to raise my hand every single lecture and ask some kind of interesting question that I was curious about and then go every time to the end to whoever was giving the lecture that day and introduce myself and ask them questions. So that was the second thing I did. The third thing I did is I coffee chatted over the semester, 10 different people from the club and genuinely ask them interesting questions about what being in the club was like and what the best ways of getting in was. And so between those things, by the time the next semester came on, I was still way on underqualified, but I think they kind of appreciated that I went to their workshops, that I went to their Decals, that I self-studied. And so they saw that high growth in me, thankfully, and then I ended up joining the club. And they ended up accepting me within like a class of about 16 students. And being in that club was one of the most transformational moments in my life too, because of the people, the people focus. So remember I told you like in my high school, it was very people focused, but it was people focused in a very kind of Mexican way. So people mostly care about kind of family, religion, going out on the weekends and then drinking a bit and then relationships. Both kind of romantic and friend relationship. And so when I came to Berkeley, I actually joined a fraternity because that's kind of like the main thing that kind of I knew from Mexico. That was the main social thing I knew from Mexico. Then when I went into that Moffitt kind of time capsule, I kind of dropped my fraternity mostly. I just was like focused on work, focused on work. And then kind of coming out on the other side and then joining Machine learning at Berkeley. And suddenly you find yourself in this group of 80, 90 people that like to have fun with each other, but they are genuinely just the most impressive people I had ever met. And there was like a lot of them. And you would talk to one and they were like, yeah, I just spend all my time doing reinforcement learning. I like Peter Beals Lab. And then I also on the side, I like have a fish tank, but then I also like to have fun with people, but I'm much more of like a hardworking, like I care about academics, but not that much. And so suddenly you meet a lot of these people and, in this community, it's fine to be curious. It's fine to care about smart, like theoretical concepts. It's okay to like really care and it's okay to try hard and it's okay to really explore that curiosity and to be really ambitious. And that kind of narrowing down of people in Berkeley to a group that where I was like, where have these people been all my life? It was really eye-opening coming from the culture of Mexico where everyone is very homogenous around the people, the things I told you about to now, oh, wow, these people really care about the same stuff I wanted to care about, but couldn't because there's no one else to care about them. I didn't care about them, but like no one else to care about them with. And so that was a huge turning point in my life. I ended up being extremely involved, led internal for a while, and then ended up being the pressing of the club and then leading the club by my senior year. And yeah, just still try to keep in touch with them. I still tried to give back to them. And yeah, it's a really awesome organization. 

Jon - 00:09:38: That's freaking sweet. Even just hearing that that's a similar experience across student organizations at Berkeley. It's like, because the school is like what, like 30,000 people, like it's a lot of people, right? And when you go to your Math 1A, you're sitting in an auditorium that's massive. 

Alfredo - 00:09:56: Yeah. 

Jon - 00:09:56: Sometimes it's a lab, but it's just like, anyway, it's a smaller setting versus the lecture. And even in that, some people are there just to like check the box and like, I got to get it done. Some people may be interested in ML. And for my case, some people are interested in biotech. Most of the people wanted to go become a doctor. So even within that kind of cause them, it's a bit more random. And I think in student run organizations, exactly what you said, it's like this magnet for, it's like this gravitational pull for things that you're interested in. And to this day, I met my wife through the Berkeley Investment Group. 

Alfredo - 00:10:28: Oh, wow. No way. I just saw your ring. I was about to ask you if you were married. 

Jon - 00:10:32: Yes. Yes. So my co-founder actually introduced us via the Berkeley Investment Group. 

Alfredo - 00:10:38: No way. 

Jon - 00:10:39: Yeah. And so I still, with all the Berkeley Investment Group of that kind of cohort, we still hang out with each other all the time. 

Alfredo - 00:10:48: That's amazing. 

Jon - 00:10:49: So, and anyone who's out there listening or maybe at Berkeley or just like maybe student-run organizations are not exclusive to Berkeley. These exist elsewhere. I highly recommend you get involved. Like things like Nucleate too, like that's like a great avenue for biotech kind of student-run organization is a great way to kind of find these like-minded interests and you'll make connections and friendships that you'll carry through with you for life. So I love that.  

Alfredo - 00:11:17: Just to add something to that, the two founding engineers, both one of my co-founders are all from Machine Learning at Berkeley. 

Jon - 00:11:22: There we go. 

Alfredo - 00:11:23: I currently live with like seven people, four of which are from Machine learning. Are my friends, they're all from Machine learning. They're my friends, like you said, they're my friends from there. And now we've kind of built a much stronger relationship over the years, but no, I fully get behind that. And then especially in Berkeley, they're not a Berkeley thing, but Berkeley is special in that it's so huge. And so you really have to sub-select the people you want to be around rather than like, I don't know, like an Ivy League school where it really everyone knows each other. I think Berkeley has just the same amount of talent. It's just that the distribution is much wider. And so if you want to be massive, so no, yeah, totally.  

Jon - 00:11:58: I love to hear that because like, we talked about like you getting into Berkeley, inflection point, student-run organizations, it's particularly at Berkeley, another inflection point, massive.  

Alfredo - 00:12:09: For sure. 

Jon - 00:12:09: Like super massive. And even something too, like on the flip side of that, you can quickly learn by joining a student-run organization. If you might not like the thing, right? You can quickly ascertain too, right? Like you can join the club and be like, there is a world where you join the ML club. Like I actually don't like ML that much. Like, you know, right. Or you can be like, oh, this is awesome. And you can find your calling. So like it goes both ways. And it's always this thing where it is trial and error. Ultimately at the end of the day, it's all trial and error. And student-run organizations are great ways to figure out do you like something or not? And if you like it, you're surrounded by people who are sticking around because they like it. And then you, you keep going and you run with that. And I'm curious, I know, because like even, you know, it reminds me of your high school experience of like throwing the event. Running a student-run organization is also hard. Like itself, just running the organization is hard. So can you talk a little about actually running the organization? And, you know, it sounds like there's like-minded individuals who are into ML. Were you guys interfacing and doing like work for companies? Were you doing consulting? Can you talk a little bit about that as well? 

Alfredo - 00:13:13: Totally. No, yeah. I mean, we definitely did, a little bit of both. So we did consulting. So we did about five to 10 projects every semester that we charge anywhere from $5,000 to $10,000 for, which it's all for the student org, which is kind of an interesting model, but I think it's good for the social dynamic. But anyway, so we did about 5 to 10 projects. And then at the same time, we did a career fair, which usually nets, regardless of the money, a lot of really good companies. So for the consulting, we've had everything from like Google and Cruise, and these really interesting research projects for the consulting arm, for the career fair, usually kind of like Citadel is there trying to recruit for that machine learning side, but also a lot of startups like Nuro used to always be there, the kind of self-driving bot. And then all these very interesting startups were paying to recruit students from, we organized the Machine learning career fair. And then there was also this education component. So we would organize the Decal initially, which turned into a Coursera course, I believe, or Udemy. And then, yeah, so it was like a really kind of this big thing. I usually, we even taught one that it was self-driving Decal at some point that actually our founding engineer founded the course and then developed. And you, over the course of the semester, you will learn how to build a self-driving car in simulation, not the hardware, just the Machine learning component, which was really cool. And so it was really those three branches and running it, I mean, running it is pretty involved. As you probably know, like running these student orgs, like you have the president, which then there's an executive team, which really, is the people doing a lot of the work. And so there's the internal person coordinating all the social events. There's the external person coordinating the Alfredoships. There's the projects lead, coordinating all these consulting projects. And then the education lead. It sounds funny because at the end of the day, it is a student club. And so the way I think about it is it taught me a lot about management from knowing more, a little bit more than my co-founders. I still knew nothing. I was still extremely naive about management and probably still am. But it taught me a lot about the basic principles of management, having to run first that internal committee. So each committee has like six people. And so it ends up being like, okay, you're managing like about 40 people. Now it is a student club. So I like to think that managing about 40 people for a student club teaches you up to managing like a eight person startup, because like, it's like every person also has their classes and like another club they're part of. And so it's really like a fractional, you're managing 40 kind of like part-time students versus managing eight people who are thinking all day and every day about this thing, which in many ways makes it easier, by the way, to manage those kinds of people. But in some other ways, it makes it harder on the logistical side. But yeah, so it did teach me a bit about kind of that management and how to like handle some of the people problems, how to organize some of the kind of things that seem obvious now to you and to me. But at the time when my co-founders and I had to make certain decisions, I was like, oh yeah, this feels like it should be this way because of this learning I had, when organizing an executive team. So yeah, it was actually quite an interesting learning. 

Jon - 00:16:20: I've always thought about student-run orgs, like being involved. Like it's one thing to join, it's another thing to run. And I almost feel like it was like a quick trial run in startup life. 

Alfredo - 00:16:31: Yeah. 

Jon - 00:16:32: And you're exactly right. Like with a student-run org, there's like competing interests. Like I gotta do my classes. Like I have to make grades. I got, some people have like prioritized like social life too. So I think there's all these things, but like as a leader of a student-run org, you have to balance that and make it all work, which I think was like for me, that my first kind of like hand at everyone is different. And even within a company, everyone's different, different learning styles, different working, like the habits. And you kind of like to make the machine kind of work seamlessly, you have to accommodate and like be strategic about how you know your audience, like, right. And I learned that really quick at Berkeley because like, again, it's super diverse and it's like very different. Everyone has competing interests. So I love hearing that. And also too, there's like, running a student-run org is not free either. Like these things, like you have like membership dues, how do you like throw these events and stuff like that? So it's kind of like, you're just like getting a quick kind of like business kind of like crash course. So I love to hear that. And so you're very involved in the ML club. And one thing I will add to, that was awesome about how you kind of like thought orthogonally about how to get in. You took it upon yourself to just really like, again, just like level up and be persistent, thoughtfully, like thoughtfully persistent, and again, for anyone out there, it's just like, you will be surprised what you can achieve with thoughtful persistence. Like, and show up authentically, right? And you take it. You're like, yo, like, I really care about this. Like I really do. And I like, I, you know, and I mean it. And so, and you demonstrated it, like demonstrating the commitment to it goes a long way. And so ML at Berkeley, doing EECS, you're minoring in math now, I guess during this period, was this when you got exposed to, kind of Biology and like life science or was that a later point? And you can, if this is not when you got like kind of the biotech spark, totally cool. But I'm just like curious, was it at Berkeley where you found that? 

Alfredo - 00:18:28: It was not the way I got into Biology. Machine learning was the way that I got into science through actually neuroscience. So not Biology. Biology was much later through my co-founder, actually, him telling me about his work and stuff like that. But initially, I got really interested in neuroscience from the angle of, hey, neuroscience is mostly, at least from a specific branch of neuroscience, it's mostly signals. Whereas EfMRI, more invasive stuff, it's all signals. And machine learning models process signals. So is there a way to process these signals to do different cool stuff with machine learning? And that might get us a little bit into my experience at UCSF doing a project like this, like what I did. But also, just generally, I was really interested on just several Machine learning projects, exploring how to use brain sensors to predict and look at different things. I was reading papers and exploring the cutting edge research. So Neuralink first released their major paper for their big sewing machine at the time. Berkeley's NeuroDust, which then later turned into Iota Labs, which I believe later sold to a biopharma company. And what's really, really interesting is actually one of my co-founders, one of the things we bonded over a lot was talking about the future of neuroscience and eventually shifted that focus towards Biology after a conversation with my co-founder, Kenney, learning about the exciting developments there, but also learning about the viability of building there. And what I mean by that is I actually tend to see a lot of founders in Deep Tech. It's like a pipeline from getting really very ambitious, but excited about science, getting really excited about neuroscience, and about just EEG and brain waves, realizing there's no money to be made in neuroscience, but that science is really cool. And then being like, well, what other Deep Tech that is close enough, that is cool, can I do? And then finding either hardware, because they were doing EEG headsets, or finding even AI, or finding, for us, it was biotech. And a lot of biotech founders, even the young therapeutic biotech company, you ask them, and then their background is neuroscience. And so it's really interesting seeing this trend. And hopefully, it'll stop one day. I really do hope it'll stop one day, because neuroscience will be good enough to build businesses there. But today, the reality is it's still very early. And it's still mostly a research field. And so I started applying that to Biology. As Kenney started telling me about it, I was like, this is equally exciting, but seems like there's a lot of potential for stuff to build here. And that was like our early inklings into biotech. 

Jon - 00:21:13: Very cool. Very cool. And I think, too, you go down these paths, you open the door, and you're like, okay, maybe we need to close this door. Maybe we need to go in a different direction. Again, it's this trial and error kind of exercise. And so I know during your time at Berkeley, you also had some stints at Facebook. You mentioned UCSF and Google as well. Can you talk about those internships and what those experiences... What was the first one? What was the first internship? And what did you learn there? 

Alfredo - 00:21:41: So my first internship was at Facebook, doing a data engineering role for the ads integrity team. So I kind of automated the creation and delivery of a report. But I won't get too much into the boring details. But the point is, it kind of contributed. I contributed, real code to a real problem that was then solved at the company, hopefully, for the other people in the team. And that was a really cool experience because, A, I got to see what working at FANG looks like, right? From we were in person, I remember I was just in awe at the campus at the time, like 32 different restaurants. Like, I think it was like 40 different buildings, like thousands of people kind of running around. There was like, I remember there was an internal Uber app that would take you between the different buildings. And then you, yeah, it was crazy. It was, it was, Facebook is insane. But also the tools internally that we had, I think for me, that is the most, at least productive learning I had while working at Facebook. I got to work with this tool called DaiQuery. And DaiQuery is, you can think about it like BigQuery. And if you don't know what BigQuery is at Google, it's very similar to what Snowflake, at least initially, is. It's a way to do analytics on large databases and give data science reports. So first, you kind of pipe this data into these neat tables that then you can explore. And so I got to see really incredible internal tools being used in all these productive ways, while also kind of checking off the box of FANG. And I'll get more into that on the Google side, because there it was a little more telling. But then I go to UCSF. I'm doing an internship kind of project with them on EG brain waves. So using self-supervised learning to decode sleep data, on EG brain waves, particularly sleep data and categorize the phase of sleep that a person is in. And it was really, really awesome. This kind of taught me like, hey, you can do these self-led projects because it was a very self-led project. You can code serious things because we ended up beating the benchmarks for the paper that we were going after replicating. We didn't do much. We mostly just added more weights to the model. But still, being able to replicate this paper and then get better results was incredible by using self-supervised learning. So we were warming up the weights. And we were doing all these cool techniques at the time. And so it was kind of my first time doing real Machine learning. And so that was really cool. I got to work with this neural data and actually explore it myself. And that actually led in the future to what initially was the early inklings of Latch. And so me and my co-founder, when that project finished and we ended up kind of like shipping the code to GitHub and just open sourcing it. Me and my now co-founder at the time, just best friend, Kyle. Kyle was like, oh, you're the best. I was working at an fMRI in Berkeley, way west. And he was working with this data. And we wanted to continue doing work with neural data. I remember COVID was just starting. I was at home in Guadalajara. We would Zoom just to catch up. And we would talk about cool projects we could do with EEG and machine learning models. And our first project that we started working with was using EEG to predict people's emotions and focus with an EEG headset, and then give that software to marketers to use on their advertisements. And we realized a lot of things shipping that project. And Kenney, actually, Kenney, our third co-founder, our CTO now, he joined us like probably a few weeks into us starting this project. And we learned a lot of things. Well, first of all, we realized EEG was really hard to use. EEG tech is at least four years ago. I don't know where it is at today, but I imagine it hasn't advanced that much, given, a keep up lightly with it. And EEG was really hard to use. It was really expensive. And then when you put it on, it didn't really quite work unless you had like a very perfect environment for it. So that was the first learning. But we kind of pivoted to doing facial, emotional recognition and focus just with your camera. But even then, we had then our biggest learning for that project, which was probably the biggest kind of startup founders sin of all of them, which for many people listening, they're probably already kind of caught it. But I don't think I knew a single marketer at the time. And definitely, we did not talk to a single marketer before building out the whole product and then releasing it and being like, hey, guys, you can use this. Here's this MVP. Go use it. And that might tell you about how many users we actually got. Yeah. Zero for anyone not clear on these terrible decisions to do when you're initially building projects. We weren't building a startup. We were building projects that we wanted people to use. And even then, you should still talk to users because you want to build something people actually need. And so that was around the point we looked at each other. We were like, we really like working with each other. We want to keep doing that together. But this ain't it. 

Jon - 00:26:50: Yeah. 

Alfredo - 00:26:51: Yeah. And so there we started exploring. And that actually coincided with my internship at Google. I was a software engineer on Google Brain, which is no longer around. It kind of merged with DeepMind. And I was contributing to TensorFlow, the machine learning framework that is very popular to this day, even though I think PyTorch is better. But I couldn't say that at the time. But indifference to Facebook, actually. Because I actually really enjoyed my time at Facebook. And I enjoyed some of the learnings at Google. But honestly, it was a remote internship during COVID. I think they almost resigned it, but they ended up not resigning it because it was COVID. Remember, all these internships were getting resigned. Actually, my Facebook friends all got resigned, the people that had gone back the next year. But honestly, my biggest learning from that time is it made it extremely clear that that was not the path that I wanted to pursue. And that is really nice when you're talking about such a high merit place like Google, when you're going to have to explain to people like, hey, you could have gone back to Google and instead you're dropping out. To like, work on a project. But it made it very clear being there at the time that this was not the place I wanted to spend any significant time on. And maybe it was the remote part, but I had not felt this extreme after working at Facebook. I actually think I would have even considered going back. And so I actually really appreciate that it gave me that realization because it was very freeing. It allowed me to realize that the joy and the pleasure and the just passion I was getting from working on these projects with Kyle and Kenney was unmatched by even the most glamorous FANG out there. And so I was like, hey, I do not want that. And it was a very freeing moment. And it allowed me to go all in on working on these projects with Kyle and Kenney, even though the whole thing was kind of a blur. Because by week three or four, I had already decided I do not want this. And so every hour that I had to spend on my internship was an hour. And I was getting taken away from being able to work with Kyle and Kenney on this extremely awesome projects we were kind of trying to ideate and that we didn't know what they were. So it was more frustrating because it was like, we need to figure it out. And meanwhile, I have to code these things for TensorFlow. And so the fact that I did a whole semester after that of school is actually like people are like, oh, but you only had one semester. I dropped out with one semester left to go. And people are like, oh, you dropped out one semester. The fact that I did that semester before that is actually crazy because all I, could think about was just building things with Kyle and Kenney. And we were just obsessed with just getting MVPs out and figuring out just different stuff to build. And so, yeah, it was an awesome experience of internships, both on kind of what it allowed me to learn that I wanted to do and what it taught me that I didn't want to do and not feeling that FOMO now that I'm very far from that world. 

Jon - 00:29:44: I had a similar experience when, like I mentioned, for a moment in time, I was like thinking about becoming a lawyer. I was like-  

Alfredo - 00:29:51: No way. 

Jon - 00:29:52: Yeah. Yeah. So I was like, it was the combination of, I basically minored in philosophy. I took a crap ton of philosophy classes at Berkeley. And so that's kind of an interesting combination of like biochem and philosophy. And you're like, what the heck? And so I was like, kind of like logically thinking like, oh, this would work well in like intellectual property. Kind of how do I apply that? And this is no shade at anyone who's in, I have a lot of my closest friends are in that work, are practicing like lawyers and law firm life, but it just wasn't for me. I worked at a law firm for a little bit and I was just like, I have great respect, but this isn't for me. And that's okay. And that's okay. And I think you kind of have to live through those experiences to kind of like figure out, because now- Then you get more conviction on the other thing. It's like a control group, right? You kind of have to have something to compare to. And I get that exact feeling because I can remember like, and it's funny that you and your co-founders made that cardinal startup sin because back in your high school, you were saying when the Waze founder came by, he's like, start with the problem. Start with the problem, not the solution. Not the solution. Start with the problem. Talk to the marketers, man. Go see if there's a problem even exists. And it was interesting too, because while at Berkeley, I tried my best. I was like, okay, this is a finite amount of time where you can be on a campus where there's just so many smart, hardworking, sharp people working on things that they're passionate about. And it's a fleeting moment, right? Unless you become a professor and you're there forever. But I was like, I need to capitalize on this moment to go speak to the experts where they are willing to actually talk to me. I'm still a student. And my co-founder being part of the Berkeley Investment Group, you went through Haas. I was like, what are the Haas classes or groups? My lab was at the bottom by Shattuck. Haas was at the top of the hill. So it was like a polar opposite world. It's like fancy Haas land. And I'm in the lab dungeon over here at the bottom by PMB. So he was like, okay, go take these classes. And I learned about the whole market research thing. Learn the market. Learn the market. And, but for me, and I think people may, may be tired of me telling this story, but it is, it was true. I did the market research and even though it was the right thing, it was hard as hell because people like one, there's like stated preference. And like, I forgot what the saying is. It's like, what do people want versus what they tell you they want? You have to discern that. Yeah. Right. You're just like, okay, just because you're telling me you want this doesn't actually mean that's like a real problem for you. Like where rubber hits the road. It's like, are you willing to pay money for it? It might be just nice to you and say, yeah, that sounds like a problem that's worth solving, but like there's that aspect that's hard too. So like, even if you know, you go down that path, it's still a hard thing to do. So it's not easy. I guess what I'm getting at is I don't blame you and your co-founders for making that Cardinals and cause either way that shit is hard to figure it out. And so, you know, it sounds like you had a wonderful experience at Facebook and I'm going to imagine just like the pace in which they move and like ship code and just like do everything. It's like probably just like unparalleled. 

Alfredo - 00:33:11: Thanks for listening to this episode of The Biotech Startups Podcast with Alfredo Andere. In part three, we'll hear about his experience joining an international incubator in the midst of the pandemic and what the transition from Berkeley to Taiwan was like for him and his co-founders. He shares how they used that time to run hundreds of user interviews, identify critical pain points in biotech data infrastructure, and walk into investor meetings with real traction and paying customers. If you enjoyed this episode, subscribe, leave a review, and share it with a friend. See you next time. The Biotech Startups Podcast is produced by Excedr. Don't want to miss an episode? Search for The Biotech Startups Podcast wherever you get your podcasts and click subscribe. Excedr provides research labs with equipment leases on founder-friendly terms to support paths to exceptional outcomes. To learn more, visit our website www dot E-X-C-E-D-R dot com. On behalf of the team here at Excedr, thanks for listening. The Biotech Startups Podcast provides general insights into the life science sector through the experiences of its guests. The use of information on this podcast or materials linked from the podcast is at the user's own risk. The views expressed by the participants are their own and are not the views of Excedr or Alfredos. No reference to any product, service or company in the podcast is an endorsement by Excedr or its guests.