On this week's episode of Inside Outside Innovation, Brian Ardinger, Inside Outside Innovation Founder, sits down with Paul Powers. Paul is the CEO and co-founder of Physna. They talk about innovation in the manufacturing space, 3-D data, trends Paul is seeing from the CES conference, and building a startup outside the Valley in Cincinnati, Ohio.

Inside Outside Innovation is the podcast that brings you the best and the brightest in the world of startups and innovation. I'm your host Brian Ardinger, founder of insideoutside.io, a provider of research, events, and consulting services that help innovators and entrepreneurs build better products, launch new ideas, and compete in a world of change and disruption. Each week we'll give you a front row seat to the latest thinking tools, tactics, and trends, and collaborative innovation. Let's get started.

To read the interview transcript, go to insideoutside.io

Brian Ardinger:  Welcome to another episode of Inside Outside Innovation. I'm your host Brian Ardinger, and as always, we have another amazing guest. Today we have Paul Powers. Paul is a Forbes 30 under 30, a graduate of Heidelberg University with a law degree.  He is an astronomy and astrophysics alumni at Harvard. He's a serial entrepreneur, and his most recent startup company is Physna, which he started in 2015. Welcome to the show, Paul. 

Paul Powers: Thank you. 

Brian Ardinger: You've got a pretty extraordinary background.  I wanted to have you on the show for a couple different reasons. One, because you're a young founder out there in the world building some interesting things.  Your company Physna is in the manufacturing space, and we haven't had a lot of folks on the show to talk about manufacturing innovation.  I thought it'd be a really good opportunity to start the conversation with, tell us a little bit about Physna and what does it do. 

Paul Powers: So Physna is short for physical DNA. And what we do is we take three-dimensional data and we normalize that down into something that software can actually read. And we help to bridge the gap between software applications that are tech space of two dimensional, and the real world essentially, which is obviously physical and three-dimensional. We do that through a series of proprietary algorithms and we applied machine learning to our technology so that we can actually not only break down and comprehend what we're looking at, but also make predictions about how humans might classify that, that might be used for, how you might make it, what are my costs, how's my performance, certain situations, et cetera. The most common use cases for the technology are to use it to help with engineering, to speed up the process so that you're not redesigning things from scratch and helps you make predictions about what you're trying to design and speed it up.

It helps in procurement by understanding what options you have. What suppliers might be able to provide the components that this thing has inside of it and who might be able to manufacture it, at what costs, et cetera. And then under the manufacturing side, understanding how to manufacture those, how it might turn out qualitatively, predicting quality, and a number of users out there who use it for a couple of other things marked miscellaneous use cases. We do have some work that we do together with the military, for example, to identify parts in the fields that aren't necessarily even a CAD model at that point. They can use AI or an image or even a 3-D scan to figure out what something is and more information about it. 

Brian Ardinger:  Tell us a little bit about how you got started in this space. My understanding is you started with a law degree and a law background. How did you get to designing software to attack the patent problems and everything else in the physical world? 

Paul Powers: It's not obviously a very direct line between those two things. What happened was I studied law because I wanted to be an entrepreneur and I thought that might be given me an edge or it might just be a different way of looking at starting a business. I focused on intellectual property. That was the closest thing to technology it felt like. It was cool. You got to see a lot of neat things, but I knew that being in that field bet, it was really easy to find a patent, like, you know violations of people's logos or music, texts written as a book or whatever.  It's easy to find digital copies of that, that are not legally obtained. But as soon as it comes in those 3-D models, it was very difficult. We can never really predict violations that might be about to occur, and that really is because it was hard to even search for a 3-D model with a 3-D model or with other input.

They're hard to identify. They're hard to understand. There's so many file formats out there, and you certainly can't really find it without like a perfect match of a 3 D model, it seemed. We started the company for that because there's so much cost to that problem. It's trillions of dollars annually and global loss for patent violations. And we thought if we can tackle that problem, that a benefit to society, not just because you can make more money off of your ideas, but also because it helps promote research and development by lowering the likelihood of the theft of your IP. We launched the company, we tried out everything in the world we could find that had geometric search or shape search or anything like that that we thought would be relevant.  And we tried out a lot of stuff and everything was extremely disappointing to us.

Everything in the market, we always keep an eye to trying out other tools, but they were very disappointing because they didn't really do what we thought. They weren't actually breaking the stuff down into 3-D. We found a way after a lot of time, but eventually we figured out our own way to break down through the models and to truly break them down so that you could find parts with a subsection. For example, let's say you have a screw and you want to use that to find a machine that it goes into. You can do that, or if you have half of a part, you can use that to find the rest of it. You can identify what's inside of the part even if you don't have data like this is the parent file these with the children's files, but you don't have to have that.  We can figure that out. 

Once we had that, we went out to conventions and started telling people about this technology, and very quickly we started hearing about all these other issues that existed that, frankly, I had no idea existed, right. That engineers, manufacturing, mechanical engineers, electrical engineers, anyone who engineers something physical, you know, not a software engineer, their productivity is only 20% of what it should be. If you compare how effective software engineers are compared to engineers of physical goods, that's over a five to one ratio actually. That's because these tools are missing. They redesign things from scratch, etc. We also found issues in quality control and inspection automation, and even in healthcare, and all these other areas. And we got overwhelmed and realized that, wow, the reason that we're finding so many issues and people are coming to us with them, we weren't thinking of problems they were just showing up on our doorstep, very quickly and we realized that there was this big gap between the 2-D world of, software, where most innovation seems to take place and the physical world that we live in where most of our economy is actually based. And most software doesn't deal with the physical 3-D world in a 3-D form.

If you look at those top five companies in the U S right now, they're all tech companies. There's a lot of money being made in software, but software is actually a very small portion of our economy. The majority of it's still in physical goods and will be for as long as we are human physical beings. To bridge that gap you would think would be very tantalizing to these companies. But the technology gap is pretty significant, and less than 1% of software actually deals with 3-D data. And because of that, the software that is actually used for most of our economy is way behind the software that's used for things like social media and stuff like that. And we decided that because we had figured out this really cool way to approach this problem over a long time with a lot of effort, we thought, why don't we go ahead and change our business model of that so that we really bridge that gap versus just trying to solve IP in and of itself.

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Brian Ardinger: You mentioned manufacturing and the majority of the world's economies built on manufacturing and physical goods, and that. What are the trends that you're seeing when it comes to manufacturing innovation? You see a lot of innovation, a lot of talk about different industries that are being disrupted by innovation. How's that compare in the manufacturing space? 

Paul Powers: Manufacturing is about to undergo what a lot of people would call the fourth industrial revolution. In Germany it's Industry 4.0.  Right. Which is basically the digitalization of it. You can use that definition if you want. I think there's a more substantial change that's going to happen here in the coming years. I think we're going to be a big part of that, I hope. And that is the democratization and consumerization of physical innovation. Right. What I mean by that is if you think about the ability to design like an app or software, in the 1980s very few people knew how to code, and it was very hard. It was a small percentage of our population that did anything with the actual coding, and not many people would actually write software that other people would be using.

If you look at. 2020, right now everybody, if they want to, can get online and go to Code Academy or another website and learn how to code for free and very quickly figure out how to actually code applications, that your friends can start using. That same capability does not exist yet in manufacturing. If you have an idea for, let's say a mechanical watch, for example. First of all, you have the 3-D model, which is going to be really hard compared to a lot of other per suits.  But let's say you create that 3-D model and it's perfect, and it's ready to go in every way, shape, and form. Well, it's still going to take you on average, a pretty long time I'd say over two years right now, before you've actually found all the distributors, suppliers, all the different pieces that have to go together to actually produce that watch.  What if you could do that automatically? Now, 3-D printing is part of that, but you can't 3-D print an entire watch today, right. Because there's so many mechanical parts and they're all different materials. It's very complicated. You'd have to use the supply chain. 

Well, the big trend that I think we're going to feel the most, you know, there's definitely small trends about machines and stuff like that, but I think what every, the average person's going to feel, and what's going to matter the most, is that people will, over time, start to be able to create things more simply. Work with existing data more easily, so if somebody designed a screw you don't have to redesign it and be able to quickly turn that idea into reality by simplifying that process, which right now is very manual. We're finding all these suppliers and how things go together. But artificial intelligence, let machine learning figure out how to do that. Automate the process for you and simplify that so that anyone, with just a little bit of training can innovate new products and have them manufactured for much less than now. 

Because even though you might not have a $2 million, 3-D printer at home, that can make all the parts for a watch. It's pretty easy to imagine how, if there are large distribution centers, which we're already seeing with those kinds of machines that are really good at changing their settings for a new type of product on the fly. If you can order something from down the road that has the rest of the parts that you need, you could have that watch in a very short period of time, maybe within days or hours and at a fraction of the cost you'd see today.

Brian Ardinger: Right and the ability to prototype and build things and test things in the physical world. If you can even just speed that up a little bit, the ability, just like you see in software where you can test things and throw things up and see if there's a market available or whatever the case may be. That democratization is, I think, going to be quite impactful.

Paul Powers: A big part of what we're doing this year is we're going to be releasing a project we've been working on for a while. We haven't made a formal announcement of it yet. I wouldn't count this as one, but the project is called Thangs, T. H. A. N. G. S.  I know that that's kind of a funny name, but it seems to work and all of the studies we've done, and that really is our project to try to do everything I said. Over the course of the year we're releasing features to automate that process, to be almost as straightforward for innovating parts and also simplifying supply chain like that outlined there. 

Brian Ardinger: That's pretty crazy.  I think you've just got back from CES. Anything you saw there that was interesting. 

Paul Powers: I think there's a lot of stuff at CES that's worth talking about and looking at. I'm talking about a very, very large convention with a lot of different products, but I think is more important than any individual product that was on display was just how it reinforces that there are certain trends that are going to matter in this coming decade, which I think can be simplified into basically two categories. 

One being artificial intelligence or machine learning. The other one being 5G, and the reason that those two are running matters so much and why those two I think are logical. And it's definitely confirmed that suspicion seeing the products that are on display there. Machine learning can do so much for you. I mean, it really helps to customize experiences for users and adds value in a way that would otherwise require lots of algorithmic input. You know, think about it, like if you wanted to figure out how to create the ideal prescription for a patient based off of body rate and all this other stuff. There'd be a lot of other rooms you have to write. You probably get it wrong, but with machine learning, there are ways that you could figure that out automatically and the more cases, the process is more accurate. It gets so it gets more accurate over time. And you can customize an application like that without having to actually write it up from scratch. And we're going to see so many use cases for that that just don't exist today. We already have a lot of machine learning and AI in our lives, but we'll see a lot more of it because of 5G. 

5G is going to connect to everything better, right.  So 5G is just much faster form of internet. The simplest version I can give is think of their internet on steroids, right. Much faster than what we're used to. And when you get to speed like that, that's what I still allow things to happen that weren't really possible before. You can have data being transferred mobilely, wirelessly, that in the past it would just be impossible. And that starts to mean that it's cheaper and more realistic to have more types of devices connected, which means there's more types of information that you have, which means there's more that machine money can learn from and learn for. So I think what we're going to see is that between those two areas of technology our lives are going to see a lot more physical devices being connected to the internet and experiences with and software, and maybe even within that hardware, software bridge there, they're going to become a lot more customized. 

Brian Ardinger: Paul, I wanted to switch gears a little bit and talk more about your company. And one of the interesting things about it, not only is it in manufacturing, you started the company in Cincinnati, Ohio, so in the Midwest, and obviously I'm a Midwest person here, and we talk a lot on the show about what it's like to build a company outside the Valley. Let's talk a little bit about what the Cincinnati startup scene's like and what it was like to build a company in the Midwest.

Paul Powers: Well, I lived in Germany for a long time. You mentioned that I graduated from Heidelberg. I lived in Germany. or over in Europe in general, for about 10 years. And when I moved back, or when I was going to move back to start this company, if you told me that I was moving back to Cincinnati, I would have laughed cause I was like, yeah, there's no way I'm moving back there. That's where I was born, and I thought there's no way I’m moving there. And I was 100% sure I was moving to San Francisco. I was looking for apartments there and everything. I thought for sure I'm going be in the Bay area. Because if you're going to start a company, that's where I going to go to succeed. 

The more I read, the less I believe that. And the more I read, the more I'm realizing, Oh, well maybe I actually am going back. I wouldn't necessarily say that just Cincinnati specifically. There are, I mean, Columbus has an amazing ecosystem and we've got an office in Columbus now too. And there are other cities, many cities throughout the Midwest that have just great ecosystems that are starting to pop up. The reason that Silicon Valley made less sense, and keep in mind when you're flying over from Germany, it doesn't matter so much if you're flying to California or Ohio. Right.  It's really based on hard data. 

The reason why Silicon Valley became a thing, you know, it was largely because of Stanford University and also because it's called Silicon Valley, because back in the days before the cloud, you really need to be out there if you're going to have a large data center to have help setting it up and that's just where you would have to be essentially for practical reasons on the tech side. Nowadays we're so much further with technology and so much more as on the cloud that the arguments for why you have to move out there, they've really shifted to, that's where the money is, right. When people say you have to go to Silicon Valley, they don't talk about the server knowledge, there's so much, much more because of the access to capital, and that's still true, although that is starting to change a little bit. 

Trends have started to show that a lot of the VCs out in the Valley area have started to look for companies that are not in the Valley area because the expenses are so high to start a company out there. And also, it breaks the first rule of business, which is always be close to the customer. If you're out in Silicon Valley and you're like in manufacturing like we are, for example, our areas, engineering, manufacturing, and supply chain. So if you look at where the HQ of the companies doing that in the U S would be, that's in the Midwest, you know, it's the rust belt, that still has, and will have for a while, the center of gravity for manufacturing, engineering and supply chain. This is where you want to be, but costs are lower for one thing, but more importantly, you're closer to the customers. Out West, you're around a lot of tech companies. 

If you're going to build a product for software companies, you probably should move out there.  If you're building a product for non-software companies like we are, then you probably should not, just because it's better to be close to the customer physically. You can store stuff on the cloud now that you can get employees to work for you from abroad or remotely, that's fine. But when it comes to customers, especially if you're in B2B, you know, or even business the enterprise, you'd be surprised how often you still really have to meet with them to close certain types of deals. And there's something about that physical proximity. You know, even though we have Skype and you and I are doing this call remotely, there's still something to be said for being physically present. And so, it made sense for us because of that to be out here in the Midwest. 

Brian Ardinger: Well, Paul, what's next for you and the company?

Paul Powers: We're focusing a lot on THANGS and creating this public platform that'll serve as the combination of Google, Get Hub and Amazon, but more specifically for 3-D models, right. Where it's got the ability to find 3-D models. You have the ability to collaborate in 3-D models, but also make your 3-D models private if you want, and you have the ability to use it for automating the procurement side of things. If you have an idea, being able to let the AI figure out for you who really needs to be involved in supplying those parts to you, what it should cost and figure out all the formalities and the logistics behind it. And let you just order it with a click of a button. That's something that is for us, a very big priority this year.

We're adding a lot of new technology that we've been working on for a while. A lot of, 2-D to 3-D. things like being able to search for complex model with pulling a subsection of a part of a picture. Being able to help 2-D image recognition through 3-D AI by understanding that a picture of the bottom left corner of something might actually correspond to a larger item and understanding scale and stuff like that. From a tech perspective, that's what we're focused on. 

And then from a hiring perspective, we're bringing on some really, really world-class talent. We're bringing on some really all-star household names to really take this to the next level. And surprisingly enough, even though these people are from the West coast, moving to Ohio was not as big of a challenge as you might think. 

Brian Ardinger: That's awesome. Well, Paul, thank you very much for being on the Inside Outside Innovation, sharing some of the insights that you had in this space, and I look forward to everything that's going on in the future. 

Paul Powers: Thanks so much. Appreciate it. 

Brian Ardinger: That's it for another episode of Inside Outside Innovation. If you want to learn more about our team, our content, our services, check out InsideOutside.io or follow us on Twitter @theIOpodcast or @Ardinger. Until next time, go out and innovate.

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