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- [00:01]
A
Marketing has been in the lifeblood of the company for a long time, mostly from the perspective, how do we explain this very complicated idea with a lot of moving parts to potential customers in a way that we can really clearly showcase how useful the technology is?
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B
Welcome back to another episode of Builders. As always, this show is brought to you by Frontlines IO, Silicon Valley's leading B2B podcast production studio. If you're bringing technology to market and want to learn from your peers, we have a library of more than 1200 interviews with Venture backed founders and marketers. Where they talk, all things go to market. Of course, if you want to launch your own podcast, we offer podcasts as a service to more than 80 tech startups. The idea there is very simple. You show up and host and we do everything else. Now, with all that said, let's jump into today's episode. Today we're speaking with Alexander, co founder and CEO of the biological computing company. Alexander, welcome to the show.
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A
Awesome. Thanks for having me.
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B
Of course. So let's imagine that you're on a flight and there's a chatty person next to you. They ask you what you do for a living. How do you answer that question?
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A
Good question. So I build an AI company, but it's a very different AI company. We take brain cells, we grow them, an addition of electrodes, and from there we modify AI systems and make them much more efficient. And so currently what we're building is software tools, optimized AI algorithms currently for video generation, but it's certainly applicable to other architectures as well.
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B
I know this is the second company that you founded. What's the biggest difference in terms of approach and company building philosophy and product philosophy in this one compared to the first one?
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A
Yeah, definitely. So two very different ideas. The similarities are essentially that and my background, we can get into litters in academia. And the similarities between the first company I helped build and this one is that some ideas need to be out of academics and requirements, either venture backing or the market to determine what's built and how. And so both of those ideas or those companies had those similarities. However, the difference is in scale and actual product. And so the first one was clinical use. The second one is to help optimize AI systems, which, as you know, as we've seen today, are extremely inefficient and ripe for disruption.
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B
And if we look at tbc, who's the ICP that you're marketing, that you're selling to? And is that the same ICP that you identified three years earlier or four Years earlier when you founded the company.
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A
Yeah. So the answer to the latter question is no, definitely not who we thought we were going to be marking towards. But we have a couple. So first, any company that's building video generation models that have performed all of the standard techniques to try to optimize them and they're still having a hard time doing it in terms of either efficiency or in terms of the evals and outputs, things like how much video can you create, the resolution of the video, we have this dish of neurons that we pull concepts from, we perform targeted experiments in that no one else has. And so we can perform optimization techniques and that are directed towards the model at hand and no one else can. So first ICP is video gen model companies that need optimization. We also are building our own PVC optimized open source models for video generation. And so there anyone that wants to create a video either for advertising or anything else, you know, we'll soon be able to go in the open marketplace and use a TBC enhanced model through the marketplace which will have improved or better benchmarks, have better metrics in terms of quality, but also will cost less in terms of inference.
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B
What about on the marketing side? I feel like with a name like that you must have been thinking about marketing early on. Is that fair to say? Or when did you start to think about marketing?
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A
Yeah, I mean, so marketing has been in the lifeblood of the company for a long time, mostly from the perspective how do we explain this very complicated idea with a lot of moving parts to potential customers in a way that we can really clearly showcase how useful the technology is. It also informs what we're building as well. And so, you know, certainly that kind of combines marketing and brand, but certainly over the last, I'd say year or so, which by the way, we were stealth for the first three years of the company last year or so, we've really invested a lot of time and effort into rebranding this to our new name, actually the Biological Computing Company as an idea and a company that's more palatable in terms of understanding what we're doing, but then also marketing to potential customers. So certainly, you know, since the beginning.
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B
And what does that marketing strategy look like, that marketing program? Can you take us behind the scenes?
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A
Yeah, a little bit behind the scenes. So awareness and credibility, those are the two main buckets that we think about. So if you look at our team on our website, on the credibility side, you know, multidisciplinary group, the fact that we're actually able to communicate together as a group and try to solve this very complicated problem is really the magic, by the way, of the company. But on the credibility side, it's our blog as an example. So we build in the open. We want people to see exactly what we're doing. We want people to criticize it, to email us, tell us what they think. You know, we're not hiding from the rest of the world. We want people to see it. So that helps to increase the credibility. You know, certainly publishing in archive and paper in that academic journal is showing the world. Going to academic conferences, showing the world what we're doing, I think is a clear way to get credibility. In addition to the fact that, I mean, our team is just incredible on the awareness side, you know, things like this, right? You know, going out to the world, evangelizing, you know, what we're up to, it is quite unique, right? And I think people are interested in hearing our story and so just telling people about it as much as we can. So, you know, shout out to you guys, thanks for having us in the podcast, allowing people to see, to hear our story. You know, going to conferences and speaking on it, going to panels, you know, those are the two main buckets that we try to live in.
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B
On the awareness side, was there anything early on that you put cash into, invested in, that just didn't generate the results that you would have hoped? And same thing on the credibility side,
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A
you know, I think I'm not answering your question directly because we just really started this. You know, the audience may be interested in this because, like, you know, we were in stealth until two months ago, right? Like, people weren't aware of us. And so, like, we didn't have any awareness for credibility campaigns. We were just kind of building, you know, to get enough data to start showing the world, right? And so, like, for example, the stuff that's on our blog right now, again, you know, we were building that for a little while, but once we've launched out of stealth, you know, so far, I think it's been quite fruitful. Again, with this idea that what we're building is very unique, right? But it also has a lot of meat to it. Like, we have tons and tons of data which we've shared with the world. We have massive improvements in the efficiency and the quality video generation models. And so, again, so far it's been fruitful. So maybe ask me that question in six months and I'll tell you what hasn't worked.
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B
This show is brought to you by Frontlines Media, a podcast production studio that helps B2B founders launch, manage and grow their own podcast. Now, if you're a founder, you may be thinking, I don't have time to host a podcast. I've got a company to build. Well, that's exactly what we built our service to do. You show up and host, and we handle literally everything else. To set up a call to discuss launching your own podcast, visit Frontlines I.O. podcast. Now back to today's episode. How did you approach coming out of stealth and launching the business and really going public?
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A
I think the main goal was to have enough data that we can show the world that, you know, we're not just saying what we can do, we're not just saying what we're doing. Right. You know, we're actually showing people. And so I think the decision point was, you know, get enough data out there. So we did that. The second was a rebrand. Right. We learned very early on through pitching to investors, you know, talking to folks about it. The way we were positioning it was way too academic. And this is my co founder, John and I, both coming out of academia, both of us in our former lives, academic neurosurgeons, academic neuroscientists. And so we kind of had to, you know, beat that out of ourselves in terms of the way we were positioning the company, what we're doing. And that also took brand work. And so we rebranded the company in the. With this idea that, you know, what we actually are building is products and which is the case. And that needs to be the thing that we describe in the most kind of forward light.
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B
It sounds like to me that this is a category creation, play. Is that fair to say?
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A
Yeah, absolutely. So this concept of biological computing, this concept of using brain cells to perform some kind of computation, has been around for a little while. And I've been thinking about it for about 20 years, ever since I was a college student working on these neural cultures. But it wasn't until recently until we started a company that someone has started thinking about the application layer. What do you apply these brain cells in a dish to? And the computation that happens in them too. How do you actually create products from this? And so, again, as we've done, we create software tools from this. And that in my mind is the category is, you know, the applied biological compute. And that's what we're building.
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B
I had on this was years ago, but I had Godard abelon, founder of G2. And, you know, that's one of the things that they do, is they effectively create categories for software buyers. I know it's a little different for you. But I asked him, you know, what advice would you have to founders who are trying to create a category? And his advice was, go out, partner with your competition, go out and build the ecosystem. And once you build the ecosystem, that category will be created and there can't be a category of one. What's your thinking there? Are you about this as you need to really build the entire ecosystem up?
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A
Absolutely. We are actively partnering with academic labs to answer questions that aren't part of our timeline or part of our path. We're actively talking to, and we'll announce at some point soon, partnerships with other industry players that, for example, create the electrodes for our neurons. And so we a hundred percent believe that this is a very nascent feel and that we need anyone interested in this that continues to be interested in this needs to hold each other up and really bring the category together. And we're happy to be leading that.
- [09:40]
B
Now, when it comes to category creation, like, one thing that I've seen is there's always ends up being kind of this like battle of definitions and everyone defines the category in a different way. Are you seeing that here? Like in the way that you describe and you define the category, is that different from, I don't even want to say competitors, but just others in the ecosystem, or is there a shared kind of definition here?
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A
I think I agree with you. First of all, I have seen this like, mincing of words in terms of like, what is applied biocomputer. I think in the end the market's going to decide. I think that, you know, if you can get customers that will use the tools that you're building from brain cells in a dish. That's in the end what's going to define biological computing? Because in the end that's what's going to drive further investment, that's what's going to drive revenue, that's what's going to drive more results and very targeted results. And so, yeah, I mean, I think that's what's going to end up defining it.
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B
One of the other, I think, downsides that comes from when you're on the cutting edge of technology is it requires just a tremendous amount of education and evangelism. I know you touched on the awareness side and that's obviously why you're even talking to me today. How are you thinking about the long term kind of evangelism playbook to get this out into the world and educate the market?
- [10:47]
A
So a bunch of different ways, like I said, just interviews like this and talking about it. One, number two is, you know, academic publications, academic conferences. Three, kind of bigger AI conferences and going around and kind of showing folks what we're doing, demos. You know, we did a demo here in SF at TED AI, which was awesome because what we did was we brought brain cells to the Ferry Building and showed people how we encode information. Right now that we're in San Francisco bringing people to the lab, there's an open invitation, certainly, Brett, to you and any of your interested audience to come check it out. I mean, I think a part of what we're doing is awareness, is telling people about it, but another part is people intuitively understanding what we're doing. And sometimes that just means coming to the lab.
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B
And if I came into the lab, like, what am I looking at? What am I seeing? Exactly.
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A
So the first and probably most important thing is all of our team and we do this thing where whenever we have investors or we have media or anyone come by, everyone kind of stands up and gives a background. And it gets. Now that we're up to 25 folks, and soon it'll be 35, you know, it gets a little bit tedious. But the takeaway point from that is how eclectic everyone's background is, how impressive every single one of our engineers and biologists is. And the other probably most important thing is that they sit around thinking about the same problem from completely different perspectives. And you can see our whiteboards. It's either how to grow stem cells or how to compute very complicated algorithms from the neurons, or how to apply it to these very complicated architectures. And you have all of these different folks trained in different disciplines thinking about this, talking about it, and I think that's the first thing you'll notice on our tour. The next thing you'll notice is there's this clear entry point from what we call the dry lab, which is where we build our software tools to plug into AI systems. So then you enter a clean, slash, sterile biology lab, right? So, like, there's this clear separation between the two, but they're also very highly connected by a door, right? So you go from one place to another and you see, wow, like, these guys are actually using real cells. The next thing you'll see is, you know, you gown up and you actually look at the brain cells in a dish. And so I think, again, another aha moment here that a lot of people will come in and have is that we are actually using real brain cells, right? And while we were in stealth, we developed the tools to encode information into the Biology and decode what the response from the biology into algorithms. And so another thing that we have people see is how to actually encode our old logo versus our new logo. And I think another aha moment that happens, like, you know, oh, you can actually take a pattern from an image, send it into a dish of neurons, and the neurons will respond to it. And the conversation with how we actually build AI software from that. So that's what you'll see on the tour. But again, I can talk about it for days. There's nothing like actually seeing it.
- [13:25]
B
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- [13:27]
A
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- [13:35]
B
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- [13:42]
A
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B
i'm gonna take you up on it for sure. I wanna come and check it out.
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A
Definitely.
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B
In terms of stealth, yeah, I feel like there's one camp that's like, stealth is dumb. You should never stay in stealth. The other one that sticks in my mind is Mowat Aaron. I had him on the podcast. He founded cohesity notanics. That's two $10 billion plus companies. His third one, he remains in stealth. So basically saying, like, the guy knows what he's doing.
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A
He.
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B
He's remaining in stealth. So I think there's these, like two camps, like, for you. Do you think you got the timing right when you came out of stealth? Like, do you regret it at all that you stayed in stealth when you did? What's the thinking there?
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A
No, I think it's very specific to the company and depends on what you're building. Right. You know, the decision point for us, you know, to stay in relatively stealth mode while, you know, accruing enough data to, when we show the company to the world, that people will actually believe what we're doing? I think that was the critical point. You know, if we had done this two years ago, you know, we had some interesting experiments, but, you know, people, you know, would think it's just research. Right. At that point, you know, we really needed people to see that we're building real tools, they're productized and they're ready for production now. Right. And so I think that was the critical inflection point and we need enough data to show what we're actually doing we're doing. Right? So I think that was the critical inflection point while we decided to come out of stealth. And I thought it was the right decision for us.
- [15:01]
B
How'd you go about recruiting a team that wouldn't just get stuck in the R and D side and they were ready to go out and actually commercialize this technology? Because I feel like a lot of great technology just never makes it out.
- [15:11]
A
Yeah, so that's, that's in the lifeblood and culture of the company from the beginning, from the top down. And like I said, you know, John and I did come from academia and, you know, he comes from an MBA background, which certainly is helpful. I come from a PhD background, but we're both neurosurgeons. But again, we approach things differently. But we were able to come to drive a culture in the company not only of being product focused and product oriented, but also deeply exploratory. We're building something new that no one's ever built. And so you do have to have a way of innovating, but that innovation has to happen quickly. And so we have team meetings twice a week. Every single person describes the approach they're taking. And we have a method that if an adapter someone's building for a transformer model, for example, doesn't work in two weeks, we get rid of it and we try the next thing. Right. And so I think you have to walk that fine line. I think the other part of it is we look for a combination of folks coming out of industry and academia. So, you know, we have, as I mentioned, our team, our people that have come from Meta, Apple, Amazon, but also folks that have come from Hopkins, Stanford, ucsf. And so it's a nice healthy balance between that exploratory mindset that lives in the academic world and that, you know, need to push products forward. That comes from a lot of these big companies and startups. And it's really striking, that healthy balance that is allows us to do what we do.
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B
And final question for you. Let's talk about the big picture vision here. What does this look like in 3 years, 5 years, 10 years, however far you want to go out on the timeline.
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A
Yeah. So three years, I'd say we're making current AI systems a lot more. So many, many fold hundreds, thousands fold more efficient, with massively better improvements in evals. And we're also applying this not just towards video generation, but towards language models, world models, et cetera. Three to five years from now, continuing down the path of augmenting and improving AI systems through what we observe in the biology is we have novel architectures that come from this. And so we are in active talks with some of the hyperscalers and some of the larger labs about, you know, what is the thing that happens beyond transformers? How do we define that architecture that is more brain like or more similar to the brain? You know, how do we know what to build? And so that's what that looks like. The big picture vision, the ten year vision. And this is what everyone wants my computing to be is what we call real time compute. And what that means is actually using the dish of neurons themselves as part of the circuit of compute or part of inference. And so what we're building now through these software modules is a foundation to get to that point. And what that actually means is efficiencies that are many, many fold better than what we have now. Applications that personalized AI tools, for example, that learn to solve continual learning problem and things like that. And so that's what it looks like in 10 years and beyond. Real time biological compute.
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B
Amazing. I love it. Alex, love what you're building. Super, super fascinating company. I'll take you up on the lab offer. I want to come check it out before we wrap. Where should we send people who are listening who just want to follow along with you?
- [18:09]
A
PPC Co is our website, but PPC Co Blog is our blog. So, you know, every person I talk to, I try to encourage to go to the blog because again, that's the meat, you know, and at this point it's maybe a little outdated and you can probably add another 5x or 10x to some of the efficiency gains we're seeing just at this point. But we'll have another blog post coming up in the coming weeks, so we're constantly updating it.
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B
Amazing. Love it. Thanks so much, Alex.
- [18:34]
A
All right, thanks, Brad.
- [18:37]
B
Well, that's all for today's episode of Builders, brought to you by the front. If you want more amazing content like this, visit Frontlines IO where you'll find a library of more than 1500 interviews with founders, marketers and other GTM leaders, where we unpack the tactical lessons from their journey. And of course, as always, if you do want to launch your own podcast, we'd love to have a conversation with you. Visit Frontlines IO podcast as a service. Mention that you listen, mention you love the show, and we'll give you a 10% discount. Thanks for listening. We'll catch you on the next episode.
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A
It.