Pod Insights Equity Podcast 'If you build it, they won't come': Bison Ventures founders on scaling frontier tech About this episode How can founders scale frontier tech? Today on Equity, Rebecca Bellan is finding out with Tom Biegala and Ben Hemani, the co-founders of early-stage venture firm, Bison. Bison invests in what they call 'frontier tech' companies building in the material science, robotics, biotech, climate, and sustainability verticals.
Listen to the full episode to hear Rebecca and Bison's founding team discuss:
Why AI isn't a 'silver bullet' for biotech companies, although it is central to modern drug discovery
Which robotics startups are catching investors' eyes
How - and how not - to find product-market-fit when building frontier technology
When to seek out venture funding over grant funding
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Transcript Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.
0:00 Anthony Ha? This episode of Equity is brought to you by Schneider Electric. Schneider Electric, your energy technology partner.
0:17 Rebecca Bellan? Hello and welcome back to Equity, TechCrunch's flagship podcast about the business of startups. I'm Rebecca Beland and this is our Wednesday episode where we home in on a trend in the tech world and dive deep. Today, we're talking to Tom Begala and Ben Himmann, the co-founders of early-stage VC firm Bison. Bison invests in a range of, quote, "frontier tech companies," and that can mean anything from fighting climate change, to treating disease, to meeting the world's insatiable demand for compute. So Ben and Tom, welcome to the show.
0:44 Speaker 02 Thanks for having us.
0:45 Rebecca Bellan? I'd love to hear a little bit more about how you got around to starting Bison, which I think wasn't even founded that long ago. You both started at Cascade. Tom, can you give me a little bit of a rundown?
0:55 Speaker 03 Absolutely. So Bison Ventures is focused, as you mentioned, on investing in early-stage frontier technology companies. And typically, these are companies that have some technical risk and are going after very scientifically ambitious products or services at the end of the day. And what we saw at our time as investors and as folks operating in these particular sectors is that there's not a lot of venture capital firms that focus on these types of businesses at the early stage, when there still is some technical risk left to tackle. And that created an amazing opportunity for us to come in and start a dedicated early-stage venture capital fund to invest in some of these businesses, because entrepreneurs really need that support at the early stage. They need that capital at the early stage. And frankly, this is an absolutely massive category.
1:40 Speaker 03 So looking at frontier tech, we think that it can address over 50% of global GDP. So areas like healthcare, or energy, or transportation logistics, these are absolutely massive sectors of global GDP. And frontier tech businesses go after all of these. And certainly, as we think about our focus areas, we go after all of these. So it's a massive opportunity and a huge unmet need. So for us, it was just a natural kind of next step, after having been at Cascade, to go off and do something entrepreneurial and support the entrepreneurs and back them in a way that can help create these generational businesses that, to your earlier point, do everything from solving climate change to curing diseases that were previously untreatable or uncurable.
2:24 Rebecca Bellan? Yeah, and you know, these seem like slightly, I suppose, riskier investments, or at least maybe not riskier, but maybe more capital intensive in a lot of ways. And there's like probably a longer runway to profits. I'm wondering, has it always been this way that VC wasn't interested in solving these hard problems at the early stage? Has there been a value shift, Ben? Can you speak about that at all?
2:45 Speaker 02 I mean, the history of Silicon Valley, of course, is investing in things like semiconductors and taking on ambitious novel technology challenges to build these great generational companies. The point in time when Tom and I were starting our venture careers, though, it felt like the conversation really shifted to SaaS, social media, and everything measured in terms of LTV to cap ratios. And so as we thought about where we wanted to take the future of venture and Bill Bison, we wanted to, in some ways, go back to the future and get back to investing in very non incremental technology solutions, solving big problems. The idea that these have to be more capital intensive or have to take longer, have a different risk profile, I think is a bit of a misconception. We are very intentional about going after businesses where we think that there is a pathway
3:30 Speaker 02 to solving a clear and obvious market need and, you know, a relevant timeframe for best in class venture returns. And that means that we're coming in after a lot of technology has been matured and developed in a research laboratory, a national lab or a university. And it's time to bring it to market and bring it to its customers. And those businesses often see tremendous pull from their users. And the scaling can be just as rapid, if not faster, than we see in software businesses.
3:57 Rebecca Bellan? You know, this just occurred to me, but I'm wondering, a lot of these companies that you back, you know, I'm looking at Alonia is one, I think, that Tom has invested in, leveraging biology to solve industrial climate and sustainability challenges. I think you, Ben, have invested in Juvena, am I saying that right? Using AI to unlock the power of stem cells. These are all very academic kind of pursuits. When does a founder move from seeking out grant funding to seeking out VC funding?
4:24 Speaker 03 Yeah, I think VC funding is great. Once you have a proof of concept or maybe even a prototype. Obviously, things like pre-seed and seed investing, that's the earliest part of the VC ecosystem. But you don't want it to be so much of a science project that there is a lot of that very binary risk associated as to whether the science will, you know, work, period. I think you want to be able to demonstrate that the science will work and maybe have that proof of concept. And that's when something like the earliest stage investors tend to back a company when it's a very nascent team, you have that proof of concept, and they know that there's at least a decent chance of getting something to market. I'd say where we come in is a stage beyond those pre-seed investors. We typically focus on seed and Series A investments.
5:08 Speaker 03 And this means that we've got not only that proof of concept or maybe even a prototype, but there was initial signs of product market fit, which look totally different in frontier tech than they do in other verticals within ventures. So to Ben's earlier point, it's not about an LTV to CAC ratio. It's certainly not about ARR. It's more about what types of joint development agreements and with whom are you partnered more so than some of those revenue metrics that you see. In some cases, these businesses have pilot agreements in place where they're actually sharing prototypes with prospective customers. So it's those types of signals that we like to see. In addition to, of course, having that proof of concept or prototype on the technology
5:50 Speaker 03 side, and that allows us to back companies that are more going from that one to a billion as opposed to the pure zero to one, which I think is far better suited for either those earlier stages of VC or things like government funding or university funding.
6:04 Speaker 02 Yeah, true scientific breakthroughs can be very challenging and not impossible to put a timeline to, build a Gantt chart around, put a budget to. And so what we find is that once researchers and innovators have gotten to a point where they've retired that novel scientific risk, then they can start to put together a real plan that is fundable against set milestones and a fixed amount of runway from a Series A financing or a Series C financing, then it makes the right time for venture to commence.
6:30 Rebecca Bellan? And then your team at Bison, how big is your team so far? And how do you structure it in terms of domain expertise? Because I imagine you really want to do your due diligence here, right?
6:41 Speaker 03 Yeah, absolutely. So because we're investing in these technically ambitious areas, we are all engineers by training. So we all have at least undergraduate degrees in engineering. Some of us have been practicing engineers, such as myself. Others have just simply spent time in these verticals as investors. So we have a relatively small investment team. There's four of us. So Ben and myself and two others on the team, wonderful principals that we brought in over the years. As we think about expertise on the team, we are all generalists at the end of the day, but we do have our sub-verticals that we tend to spend a little bit more time in. Because my background is in material science and semiconductors, I tend to spend a little bit more time there. Ben, for example, has spent a lot of time in robotics, so he tends to focus his time in areas like that. We have a couple principals
7:25 Speaker 03 in our team, one of whom focuses very much on the biotech and life sciences aspects of our investing strategy, and another which focuses heavily on the climate and sustainability side. So we have kind of sub-verticals that we tend to spend a little bit more time in, but we think it's important to be generalists. And the key reason for that is because many of the businesses that we've invested in and plan to invest in the future cut across multiple different verticals. You know, you have to be an expert not only in life sciences and biotechnology, but the latest and greatest in AI innovation. And we have a business that's combining those two, as well as robotics and automation technology.
8:03 Rebecca Bellan? Oh, yeah. What's that business? Give us a for instance.
8:05 Speaker 03 Yeah, that's a business by the name of Viva9. It's actually a fascinating business that's doing high-throughput experimentation to create massive, massive data sets for AI drug discovery.
8:15 Rebecca Bellan? Oh, wow.
8:15 Speaker 03 And those data sets are basically missing today. So that's why AI drug discovery has, frankly, not fulfilled all the promise that's been put out there around that particular sector. It's because you don't have enough data. So in this case, Viva9 has very advanced biology, has the latest and greatest in AI technology and machine vision, and has robotic systems to basically automate all of the high-throughput data experimentation. So that's a super unique business that cuts across multiple different categories that a traditional life science investor that only does biotech therapeutics probably wouldn't get comfortable with. And at least at this stage, a traditional tech investor also wouldn't get comfortable with because of the biology that's deeply ingrained in this particular business. So
8:57 Speaker 03 that's a great example of a business that Bison is perfectly suited to partner with because it cuts across all those categories that we care about, and we have an understanding of those various technologies and can help the entrepreneur bring that to market.
9:08 Rebecca Bellan? Yeah, and I did want to talk to you a little bit about this biotech AI mix because quite a few of the companies that you invest in are biotech. And at TechCrunch, we're covering pretty much every day, every week, a different round of converged bio. We recently covered a $5.5 million seed round. We've got Archon, another stealth company that came out building antibody cages to power up drug development. You know, there's just this wave since the second wave of AI of, you know, life sciences, biotech companies adding AI to their names and then just getting a heap of funding. How much is hype and how much is realistic? And I think I know what you're going to say based on the previous database problem that you brought up, but go ahead.
9:47 Speaker 02 Yeah, I mean, historically, deep tech investors were sort of code for meaning that you were willing to work in hardware as well as software. You would touch a semiconductor or robotic solution, what have you. I think we think about the opportunities that as being actually having a third stool now where it's software, it's hardware, but it also could be wetware or biology. And that might be involved in therapeutic development or it might be taking those tools and applying them to industrial use cases, using microbial solutions to help in mining and waste problems, bringing novel technology in the agricultural space and pushing forward efficiencies and productivity in the field. With respect to AI-enabled drug discovery, I mean, as you have alluded to, I think that the idea that we're going to be able to have kind of an end-to-end black box
10:29 Speaker 02 where you type in a patient profile and disease and outcomes, the molecular design of a cure is purely science fiction. These are very data sparse regimes and there's a lot of different elements in there where we don't have even, you know, one millionth, one trillionth in some cases of the data sizes we would need to have that kind of holistic understanding. The opportunity that we see in bringing AI to drug discovery is in targeted, vertical-specific applications where you have a well-defined question you're trying to answer, you have novel data sets you can bring to bear. Maybe you also have physics-based methods to help kind of bootstrap into those data sets and double-check your answers.
11:10 Speaker 02 So when you start to close a loop on either experimental data or in silico kind of quantification with the AI, that's when you can get the learning going and really increase the power of the insights you're getting out of there. We think AI is central to modern drug discovery.
11:25 Rebecca Bellan? Yes, table stakes at this point.
11:26 Speaker 02 But not in the sort of black box way that people talk about.
11:29 Rebecca Bellan? Yeah, it's not like a magic wand. Like now we have AI and we can figure it all out like.
11:33 Speaker 03 Yeah, it's it's not a silver bullet at all. And I think that's the the common misconception around AI, that it's going to be this, you know, silver bullet in the near term that's going to solve all the world's ills. In in reality, it's another tool in the toolset. And it's a very important tool, don't get me wrong. But it is another tool in the toolset. And every company that's developing novel technology today, if they're not using AI, they're missing out because it is such a powerful tool.
11:56 Speaker 02 You know, biology is inherently an incredibly complex, multifactorial problem set. And humans are just not great at understanding that complexity. Right? When you think about the classic monogenetic disease where you have a single mutation and you have a single symptom that comes from that, and you can imagine making a single pill that targets that, that whole system is very easy for people to understand. When you get to the realities of biology, it quickly escapes human intuition. And and that is an opportunity for AI to provide, you know, a tremendous amount of light and insight into the next generation of drug hunters. It just needs to be targeted.
12:29 Rebecca Bellan? Yeah, and I guess we've talked about this in terms of robotics as well. You need that level of data and it took ChatGPT or OpenAI and all the other companies building LLMs. It took them an internet scale level of data to get those breakthroughs. Do we need that for life sciences? Do we need that for robotics, specifically for robotics? I mean, to get generalized robotics, like the dataset just doesn't exist.
12:54 Speaker 02 To your point, it wasn't just that we needed internet scale data to get the large language models to work and that there were these kind of, you know, returns to scale on data. It was that they just didn't work. I mean, the functionality was essentially not existent until we got to those massive scales. There was sort of a so-called lift-off moment. And so if you think about a more data poor regime where we don't have the tokens to train transformer architectures, which are what large language models are based on for either biology or robotics, the idea that maybe we'll get partway there, I think, fails to acknowledge the challenges that transformer architectures have. And so we really need to, again, either have novel ways of creating data, we need to invent new pathways for architectures for the models,
13:36 Speaker 02 or we need to start asking more narrow questions, general purpose, end-to-end control systems for robotics, black box, patient shows up, drug gets spit out, drug discovery. These are fantasies that are either never going to happen or they're decades out.
13:51 Rebecca Bellan? Yeah. Tom, thoughts on that?
13:53 Speaker 03 Yeah, I mean, I very much agree. And as you think about how to invest in this space, finding those companies that can bootstrap that data problem is incredibly important. So thinking about one of our investments in a business by the name of Cobot, they're very much, they are bootstrapping, getting systems out to their customers, building that data set and eventually developing more and more applications and making it more broadly applicable to a variety of new and different and high-value applications. And, you know, it's no different than what Tesla is doing with their self-driving technology. They've got cameras on each one of their vehicles. They're aggregating massive, massive amounts of data that will eventually make their self-driving algorithms significantly
14:36 Speaker 03 more powerful, significantly safer, et cetera. So it just takes a lot of data because there's a lot of edge cases and you've got to train your AI on those edge cases. You've got to see those edge cases or you have to have the synthetic data to support them. And the same story is very much true in robotics.
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15:36 Speaker 02 A founder in frontier tech has to have the vision to see the opportunity for how a novel technology can change the world and solve a big commercial pain point. But that's necessary, but not sufficient for success. They also need to deeply internalize the limitations and obstacles that exist with their technology and build a product and company that is cognizant of those and navigates around them in a way they're still delivering value, they're still delivering reliable solutions that customers can trust and scale. And if you do both of those things, then I think that's a key recipe. And so when we talk about all of the limitations and the data that exist or the time scales it's going to take for general purpose humanoid robotics to become a reality, I think what
16:19 Speaker 02 we're trying to say is that that's a great future vision, but we're not there today. And it is not the case that we can't still do interesting things that solve customer pain points today. So let's solve those problems on the way to that path.
16:32 Speaker 03 Just one historical analogy for that is Intel did not start off by manufacturing and creating the most powerful microprocessors on earth. They started with watches and calculators and far less sophisticated types of computing devices. And you have to start at a certain level where your capabilities match the customer needs and the customer pain point and build from there. So I think just looking at historical examples of what Ben is trying to say in the humanoid space, that's one that I kind of fall back to.
17:02 Rebecca Bellan? So what does this look like on a pitch deck, right? When you're, when someone's pitching you and they've got this big vision, but you want to see that they're still kind of down to earth?
17:12 Speaker 02 I think a lot of it shows up in their ability to meet customers where they are today. If a company is talking about being able to deliver a solution that is commercially relevant in seven to 10 years, then they haven't really found a pathway to acknowledging those limitations that exist today and still solving problems on that path. We want to back businesses at Bison that are turning that corner on technical innovation and are ready to get into customers' hands quickly. That doesn't mean that we're not willing to take on ambitious technical pathways and solve new problems. Very much our companies are all doing that, but they're starting with something that's tractable and relevant today as they move forward.
17:55 Speaker 02 And I mean, again, to use the sort of Tesla analogy, it's the Model S or even the Lotus supercar as a starting point, as a way to get to the mass market solution in the future.
18:05 Rebecca Bellan? Yeah. So what's a company in your portfolio, specifically in robotics, actually, that's that's doing this right, that's solving customer pain points today? And then using that to create, you know, the necessary means to scale into something, a bigger mission in the future?
18:21 Speaker 02 I think cobot is sort of a great example here. And unfortunately, they just came out of stealth with their first product in the past couple of weeks. So we can talk about it a little bit more. They are delivering a general purpose robot for commercial and industrial customers, working with amazing partners like Maersk and Moderna and the Mayo Clinic. I mean, there's a lot in their product stack that I think embraces this vision of how to build a frontier technology company. But maybe one tangible example is their product has wheels. Legs and bipedal motion is a really complicated controls algorithm problem. It consumes a lot of power, it adds a lot of weight, adds a lot of costs. And the vast majority of commercial and industrial environments are designed to accommodate wheels, not just ADA compliant,
19:04 Speaker 02 but also for moving carts around and just the practical operations of a warehouse or a hospital. And so I think adding all of that additional complexity, again, both in software architecture and in hardware and cost, is just unnecessary. You don't need to start with a bipedal device. You can be pragmatic, even if it looks less cool in your YouTube video when you're announcing the new product.
19:28 Rebecca Bellan? Yeah, I guess the argument that people make for bipedal, you know, robots is that they can be slotted into factory floors where your humans already are. And so, yeah, but I can see that you want to solve smaller problems first before kind of branching out into that harder problem, but always with the mission and with your eye on the prize, right? So I wanted to switch gears a little bit. We've all spoken about what to focus on when building, right? This tech development pathway. And I would love to, I think our listeners would love to hear a little bit more about how startups building frontier tech can really find that product market fit.
20:00 Speaker 03 Yeah, we spent a lot of time thinking about that and a lot of time both looking at that in due diligence as well as helping our entrepreneurs think about that and do that correctly or most efficiently. So I think a lot of historical frontier tech investing has followed a very linear path where first you do the technology and product development and then you do the commercial development. So you develop a product and then you start selling it. In our view, that's the wrong way of doing things. That sets you up in a position of an if you build it, they will come mentality. And what that results in is sometimes you develop the wrong product. And that means that either the market is too small or you don't meet a customer's needs whatsoever. And that's a bad outcome for everyone involved. That's a bad outcome for the entrepreneurs, for the employees, for the investors, et cetera. So we very much believe in our approach where you're
20:45 Speaker 03 doing both technical and commercial development in parallel, which means that you have to have an initial hypothesis around what your product is going to do and the problem that's going to solve. But you better be talking to customers on day two as opposed to day one, validating your hypothesis that the product that you want to create is actually going to solve those problems. And then you start building that product or developing that technology and continuing to develop commercial engagements in parallel. So as I mentioned earlier, you know, what we like to see is at least that prototype and some form of commercial engagement, some customers that we can talk to that tell us, yes, this is actually solving a massive problem for us and we would pay a lot of money for this
21:27 Speaker 03 product once it gets to market. That's the ideal scenario that we want to hear. So at the earliest stages, if frontier tech entrepreneurs are not having those commercial discussions and validating their hypothesis on the product, that's a major red flag for us. And by the way, that doesn't mean that that a frontier tech entrepreneur has to be both technically brilliant and commercially savvy or highly successful. The frontier tech entrepreneur could bring in folks around him or her that can handle some of those commercial discussions. They could leverage advisors. There's a whole variety of ways of having those commercial discussions. That doesn't mean that you need to find this, you know, rare unicorn that's both a PhD and a brilliant business development executive, although those do exist.
22:10 Speaker 03 Occasionally. So when we look at the founding teams, you know, we want to see both that technical and commercial sense. Sometimes it's in the same person, sometimes it's not. But at the very least, if it's a highly technical founder, he or she needs to understand that commercial is incredibly important and fundamental to the growth and building of a business. Because the last thing you want to do is end up in a position where you develop the wrong product. And that really kind of encompasses itself with an internal.
22:35 Rebecca Bellan? Spent millions on it already, like.
22:38 Speaker 03 I mean, you could even spend hundreds of millions of dollars on it. Like, sometimes entrepreneurs just get enamored with the technology that they've developed and the IP that they've patented at the USPTO. And they think that is the hammer and they're looking for a nail.
22:50 Rebecca Bellan? Yeah.
22:50 Speaker 03 So we've got an internal mantra that we call, if you build it, they won't come. Instead of you build it, they will come. And that's the mantra that we have that basically hammers home the point that if these entrepreneurs aren't talking to customers and validating their hypothesis around the pain point, around the value proposition, around the product specs, that's a major, major red flag for us.
23:09 Rebecca Bellan? Hmm. And then, you know, when you talk about the founding team, wanting people with that technical and that commercial knowledge, how important for these seed stage, early stage companies, how important, Ben, would you say is pedigree? Like, do you want to see that they've come from some of the best companies? Is it cool that they're right out of university? Like, what are you looking for?
23:28 Speaker 02 In terms of where we've historically seen, you know, profiles we've seen be successful, it's very diverse. I would say the sort of canonical Harvard or Stanford CS major who dropped out and started a company when they're 20 years old, obviously works very well for some software companies. I don't think it translates well to frontier tech. I think that's, that should have been red flag number one with Darren Oss, for example. But, you know, where we have seen, you know, entrepreneurs be successful, some of them are coming right out of the lab, where they were actively working on the research and they want to be the ones to bring it to market. Some of them are repeat founders that have been at the helm before or in senior executive roles of other companies. And occasionally, we even see people come out of large, you know, existing corporate businesses
24:11 Speaker 02 and, and want to take an entrepreneurial plunge and be successful in that. And what this results in is, is a pretty diverse pool of founders and CEOs. We have a large number of women and minority founders. We have a variety of ages of our CEOs. I think that the sort of getting too focused on the pattern recognition here tends to lead to a very non-diverse pool of, of leaders. And we've seen all of the above be successful historically, and we know we will again here in Bison.
24:40 Rebecca Bellan? Yeah, well said. And then is there anything that you would say to founders who are maybe listening to this and would like to pitch you, like, you know, we've talked a little bit about what you're looking for, but if there's anything else that they can put on their pitch deck or that you would advise they do even before pitching you, what would you say?
24:58 Speaker 03 Yeah, I mean, warm intros from folks that we know are always a good way to get our intention. But ultimately, at the end of the day, what we're looking for is the best technology going after the biggest market opportunities with a very viable pathway of getting to that big vision. And that's really kind of the key for us. We're pretty practical at the end of the day because we're all engineers on the team. So it's difficult for us to sometimes suspend belief in reality. But that just means that as you're pitching us, you know, tell us about the big vision, tell us about the huge market opportunity, tell us about the amazing technology that you're going to develop. But you got to baby step into it. You've got to have a realistic pathway and you've got to have multiple iteration cycles. That's something that a lot of entrepreneurs tend to forget. They think that they're going to get a product
25:43 Speaker 03 right on the first try, which never, ever, ever, ever happens. I think manufacturing is a whole different slew of problems. As Elon Musk very eloquently said that he was in manufacturing hell at Tesla. I've seen manufacturing and manufacturing truly is hell. And I think a lot of entrepreneurs, whether they come from the software world or whether they come from, you know, PhDs in academia, underestimate the challenge of manufacturing.
26:09 Rebecca Bellan? Well, with deep tech and manufacturing robot, you know, these things that are a hardware and a software play, and these are seed stage companies, maybe a lot of them come from academia, they don't have a lot of money, they're bootstrapping, how do they iterate fast enough?
26:20 Speaker 03 Yeah, I mean, there's multiple different ways of iterating rapidly. I think one is to use all of the latest and greatest computational tools in order to do as much as you can on the modeling and simulation side of things. I think the best companies and technologists in the hardware world will do a lot of work on the modeling and simulation side of things. But they're also very nimble on the on the iteration side of things. I think when you see companies such as the SpaceX's and Teslas in the world, they're very good at rapidly iterating products. And getting to the right answer at the end of the day. And that's an incredibly important characteristic that entrepreneurs need to have. If you can do everything that you possibly can to decrease that iteration cycle and do as much in
27:02 Speaker 03 parallel as possible, that gives you so many cycles of learning that you can then incorporate into what the final product looks like and you get there faster. And the best deep tech teams that are developing hardware know how to do this incredibly well. So again, kind of as we work with entrepreneurs, this is another one of my mantras. You've got to decrease the iteration cycle time. You've got to parallelize as much as possible. And that means that you might have some wasted cycles and engineers kind of come from this world of let's do an experiment, let's analyze the data and then let's do the next experiment. In reality, you should be doing 20 experiments in parallel, all of them as rapidly as possible. Analyze all the data at the end of the day and then move on to the next set of experiments.
27:43 Speaker 03 So that's a big, big, big part of getting a product to market as quickly as possible. And I think a lot of engineers don't truly grasp.
27:50 Rebecca Bellan? Yeah, good point, Ben.
27:52 Speaker 02 A kind of classic mistake that we see are companies or founders systematically overvaluing the importance of their contribution in the value chain and undervaluing those of the rest of the ecosystem. I like to point to the Apple example. Every Apple product says on it, "Designed in California." They rely on very efficient scale Asian manufacturing partners to produce their products. And there's not an ego about needing to outsource that piece of the work. They do what they do best, which is the design and R and D, and they use manufacturing partners. And I think that that ethos needs to be translated to startups too. And as Tom said, with the advent of kind of modern and social design tools for a wide range of the fields that we're operating in, the maturity of outside manufacturing,
28:37 Speaker 02 not just for a physical widget, but also biomanufacturing. Startups can focus on what they do best and then build an ecosystem of partners to help them be successful.
28:46 Rebecca Bellan? Yeah, absolutely. Good point. Know when to outsource and find, yes, you can do that from the earlier stages. Yeah.
28:52 Speaker 03 And know what your core area of expertise is and, you know, understand that.
28:56 Rebecca Bellan? Awesome. Well, I think that we are just about out of time, but if our listeners want to connect with you online, where can they find you?
29:03 Speaker 03 Anyone who wants to reach out can reach me at [email protected] and Ben is [email protected] . So very simple email addresses. Feel free to reach out if you resonate with what we're talking about here, our developing companies and products in the frontier tech space that you think will resonate with how we think about the world.
29:21 Rebecca Bellan? Great. Thanks, guys. And thanks so much for joining us on the show. To our listeners, you can find me at Rebecca Bellan on X, at RebeccaBallon underscore at rights. And then I just, I come on Blue Sky. I'm on Blue Sky, y'all. So find me there. They're long names. RebeccaBallon.Bluesky.social, I think. I don't know. And of course, there's equity@equitypod everywhere. So see you there and talk to you all Friday. Thanks for joining.
29:46 Speaker 04 Equity is produced by Teresa Loconsolo with editing by Kell. Bryce Durbin is our illustrator. And we'd like to give a big thanks to our audience development team and Henry Pickavet, who manages TechCrunch Audio Products. Thanks so much for listening and we'll talk to you next time.
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