TBC’s Alex Ksendzovsky: The Neurosurgeon Using Brain Cells To Build Better AI

**Alex Konrad** (0:00)
What if I told you that the tech behind the best, cheapest, and fastest image and video models might not come from fancy AI alone, but from the same kinds of cells living inside one of the oldest technologies of all, your brain? May sound like science fiction, but at The Biological Computing Co, a startup that came out of stealth earlier this year, it's becoming reality.

**Alex Ksendzovsky** (0:20)
We're now building brain-inspired AI. We're building biologically-derived algorithms, right? And so this is a bit of a stepping stone towards real-time biological computing, but it builds the foundation to get there.

**Alex Konrad** (0:32)
This episode is brought to you by Rippling AI, the only AI built to give you full visibility into your startup and the ability to take action across every department. On this week's episode, we discuss how co-founder and CEO, Alex Ksendzovsky, went from neurosurgeon to tech entrepreneur, how human insight and living brain cells can help power a new generation of AI models, and why he thinks the data center of the future just might be alive.
I'm Alex Konrad, founder and editor of Upstarts Media. And this is The Upstarts Podcast, our weekly show where we talk to emerging startup founders who are punching above their weight and taking on the status quo. Alex, thanks so much for joining the show.

**Alex Ksendzovsky** (1:08)
Thanks a lot for having me.

**Alex Konrad** (1:09)
We've got an Alex Alex pod here in San Francisco today. What is The Biological Computing Co. besides a very sort of intriguing name that I think you guys settled upon after another one?

**Alex Ksendzovsky** (1:20)
Yeah, yeah.
So, I think the simplest way to think about it is we are building software from wetware. So, what that actually means is we take real biological neurons, so real brain cells, grow them in a dish of electrodes, and we build software tools and emulate how the brain processes information. And so, we're creating algorithms that are a lot more efficient, have much better quality.

**Alex Konrad** (1:42)
And so, you're working with these neurons that are living neurons that can die, and from them, you are essentially able to create other models that can be used for images and videos, more akin to how we would think of sort of traditional AI software.

**Alex Ksendzovsky** (1:55)
Exactly, yeah. So, the product we built itself is algorithms that are more efficient. And we're starting with video generation, so the output is much more higher quality.
But that can extend to language and other modalities as well. The premise of the company is that the brain processes information in a very special way. It's a lot more efficient in the way it processes it. It's a lot more dynamic and complex. And what we do is we encode information from the outside world into the biology. We observe how the neurons, the brain cells process the information. We mathematically model that and build adapters, learning rules that can be incorporated into current AI systems to make them better, faster, cheaper, stronger.

**Alex Konrad** (2:34)
So, I know you've only been out of stealth for a couple months. You guys announced a seed round of $25 million. Is this in the market today? Do you have customers? Or like, just how early are we with TBC?

**Alex Ksendzovsky** (2:45)
We have a product that's a TBC-enhanced open-source video generation model. We have a partnership which we'll announce in the coming months. It's serving this model on a NeoCloud. And so, we're going to ultimately, customers will be able to use it on the marketplace.
And so, yeah. And so, in the coming months, our product will be deployed and people will be able to use it.

**Alex Konrad** (3:05)
So, when I first heard about the idea of neurons helping to train AI, I'm sure my mind, like a lot of people, went to, oh, like, are these neurons in a data center? Are we going to see racks of, like, you know, living GPUs or something like that that are creating a much better output? But that's not quite what's going on here. Can you correct, for everyone else, the mental image that they might have?

**Alex Ksendzovsky** (3:28)
So, not yet.
We're getting there. We're building the foundation to perform what you're suggesting is real-time biological computing. But to get there, it's going to require probably five, ten years. And a lot needs to be figured out. What we realized is, in the interim, as we're building this foundation, we can really incorporate our findings into current day AI systems, right? And so, we can make significant strides on transform models, for example. We can build new algorithms that mimic the brain more closely. We're not building brain-inspired AI. We're building biologically derived algorithms, right? And so, this is a bit of a stepping stone towards real-time biological computing.