Sep 22, 2026 · 1 hr 9 min · 13 segments
Today's guest is Naveen Rao, co-founder and CEO of Unconventional AI. Naveen has built two startups before Unconventional AI, both at critical inflection points in the AI market, often early to…
Naveen RaoGuest
Brian SchechterHostGaby LorenziHost
And back then, this is 2014, mind you, like beginning of 2014, started thinking about at the end of 2013.
No one knew, most investors didn't know what machine learning was or why it was important, much less why we need to build hardware around it, right? It was kind of obvious to us at the time, but convincing investors was a large part of my job and it was very hard to raise money.
We had to take a bit more of an incremental bite, but it was still a pretty big leap by most people's standards.
Like, oh, you're going to completely change how architecture looks for processing neural networks.
That and highly distributed matrix multiply was really the main two innovations, which are now standard in GPUs and other things.
And if you kind of back up a little bit further, why that even mattered at that time was linear algebra and tensor math was kind of the thing that was built up in the high performance computing days.
And it became the natural basis to build neural networks because it's already parallel.
It's a better basis than serial compute, but it's really not the most natural fit.
And I think that's what we're going toward now is something even even stronger affinity to neural networks.
It was already pretty hard to get people over the line to invest in us, but we did and we started to build it.
But through that, you know, we saw this evolution of the whole industry happen much faster than any of us ever anticipated in 2014.
And back then, this is 2014, mind you, like beginning of 2014, started thinking about at the end of 2013.
No one knew, most investors didn't know what machine learning was or why it was important, much less why we need to build hardware around it, right? It was kind of obvious to us at the time, but convincing investors was a large part of my job and it was very hard to raise money.
We had to take a bit more of an incremental bite, but it was still a pretty big leap by most people's standards.
Like, oh, you're going to completely change how architecture looks for processing neural networks.
That and highly distributed matrix multiply was really the main two innovations, which are now standard in GPUs and other things.
And if you kind of back up a little bit further, why that even mattered at that time was linear algebra and tensor math was kind of the thing that was built up in the high performance computing days.
And it became the natural basis to build neural networks because it's already parallel.
It's a better basis than serial compute, but it's really not the most natural fit.
And I think that's what we're going toward now is something even even stronger affinity to neural networks.
It was already pretty hard to get people over the line to invest in us, but we did and we started to build it.
But through that, you know, we saw this evolution of the whole industry happen much faster than any of us ever anticipated in 2014.
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