The Pomp Podcast - #273: Igor Jablokov on Building an AI Ready Workforce to Improve Operational Resilience

Episode Date: April 18, 2020

Igor Jablokov is the CEO of Pryon, an AI company focused on augmented intelligence for the enterprise. He previously founded industry pioneer Yap, the world's first high-accuracy, fully-automated cloud platform for voice recognition. In this conversation, Anthony and Igor discuss voice recognition technology, machine learning, artificial intelligence, the best and worst current applications of these technologies, whether AI is evil or not, and how our society should think through educating more people to leverage these technologies in a productive manner. =============================== BlockFi allows you to keep your crypto, put it up as collateral, and receive a USD loan funded directly to your bank account. They do loans ranging from $2,000 to $10,000,000, and they're perfect for helping you reach your financial goals of all sizes. Visit BlockFi.com/Pomp to learn more about putting your crypto to work without having to sell it. ===============================  TaxBit is a refund-maximizing, cryptocurrency tax software you can depend on. Visit taxbit.com/invite/pomp and receive 10% off your tax plan today by signing up for a free trial. ===============================

What's up, everyone? This is Anthony Pompliano. Most of you know me as Pomp. You're listening to the Pomp Podcast, simply the best podcast out there. Let's kick this thing off. Igor Jablokov is the CEO of Prion, an AI company focused on augmented intelligence for the enterprise. He previously founded industry pioneer Yap, the world's first high-accuracy, fully automated cloud platform for voice recognition that was ultimately acquired by Amazon. In this conversation, we discussed voice recognition technology, machine learning, artificial intelligence, the best and worst current applications of these technologies, whether AI is evil or not, and how our society should think through educating more people to

leverage these technologies in a productive manner. I really enjoyed this conversation with Igor, and I think you will as well. Before we get into the episode, though, I want to quickly talk about our two sponsors. The first is BlockFi. BlockFi is an awesome product that I'm a big fan of. I'm an investor and a user. Today, they offer three products. The first is giving US dollar loans against your crypto as collateral. The second is an interest-bearing account for your crypto deposits. And the third is allowing you to buy or trade cryptocurrencies. Right now, if you want to earn interest on your assets, there's many different rates and they vary, but BlockFi is paying 6% APY on Bitcoin and 8.6% APY on GUSD and USDC deposits. Those are unheard

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form with a single click. The company was founded by tax attorneys and CPAs. Taxbit is the most trusted cryptocurrency tax solution. Get 10% off your tax plan today with a free trial by going to taxbit.com slash invite slash pomp. Taxbit.com slash invite slash pomp. Again, taxbit.com slash invite slash pomp. Go get your taxes done. All right, guys, let's get into this episode with Igor. Anthony Pompliano is a partner at Morgan Creek Digital. All opinions expressed by Pomp or his guests on this podcast are solely their opinions and do not reflect the opinions of Morgan Creek Digital or Morgan Creek Capital Management. You should not treat any opinion expressed by Pomp as a specific inducement to make a particular investment or follow a

particular strategy, but only as an expression of his opinion. This podcast is for informational purposes only. All right, guys. Bang, bang. I've got a special treat for you today. Igor is here and we have a lot to talk about, but I appreciate you taking the time to record this during quarantine. Yeah, thanks for having me. Let's just jump right in. Your background, you've spent a lot of time in technology, built a number of companies. Maybe just kind of take us from the beginning to how we get to today? Sure. So I used to lead the advanced multimodal research team at IBM. And when I was frustrated that they didn't want to commercialize AI as fast as I wanted to, ended up departing. We founded a company called Yap, which was one of the first, if not the first

AI cloud company. Microsoft ended up becoming a customer. Sprint was a customer. We had almost 50 million users on the platform. And then Google tried acquiring us, as did Amazon. And off we went to Amazon land in 2011. And it formed the nucleus of what everybody now knows as Alexa, the Amazon Echo, and these experiences. Yeah. So let's go back to the IBM time, because I think a lot of people look at IBM and they basically say, the company's been around forever. It's a blue chip. They've got hundreds of thousands of employees. How exactly does technology development happened inside of a large company like that and then obviously it led to some frustrations from your ends you guys went started your own company maybe talk to us just

about structurally how that happens in a big corporation sure and and look they're stewards of just some amazing technology right um the problem is that we uh ascertained is that um they couldn't take the inventions and commercialize them so that they could be um prepared for self consumption for interesting, which is what's driving a lot of this cloud adoption. Or even if you look at the number of enterprise software brands that got released into the wild at the workgroup level and they just tore it up, right? Things like Salesforce, Zoom that we're speaking on here, Tableau and the like were all released at the workgroup level and they made their way up. No CIO, CISOs, a CFO had to sign off on their adoption. And I think they hadn't figured that

out because a lot of the software that they create is unfinished, right? Because it's typically handed to their global services team, which then do a lot of the customizations for each individual customer. Whereas if you look at what Google and Amazon and other folks do, they write once and run everywhere and minimize the number of customizations that are required. And that's led to an upheaval in the tech provider space. Yeah. And then I guess as part of the exercise to spin out and go do this,

was the idea to end up building voice recognition software and kind of this AI cloud, knowing that these personal devices like the Alexas, the Google Homes, et cetera, would kind of be the end state? Or was it more just generalized? We think this technology is interesting,

let's develop it, and then we'll figure out the consumer applications later. No, I think we had a sense of the consumer applications. In fact, in 2006, I already had a prototype for what would become Alexa, right? So I carried around on a little flip phone where I could speak into it, and it was all cloud powered, meaning it would record the audio on the device and then stream it up to a remote server. and I could do things like Google searches, I could send messages, I can find things location-based on little mini maps and things of that sort.

So we already had a sense that I could have something that could answer anything, including why is the sky blue? So that was the sense. Now, a lot of folks were saying, we don't understand what the business model would be to support that.

And back then without app stores, without smartphones, without 3G networks, without any way to monetize this thing, people were unsure how we would create absorption for the consumer space of these technologies. Yeah. And really, I guess the technology ends up being a combination of multiple trends or technology developments. And so you've got machine learning, you've got voice recognition, you've got some level of artificial intelligence in there. Maybe help us understand, is that a sequential thing where one technology gets built

and it's added to another, or did you have to wait until all of those technologies reached a certain point and then you could kind of put them all together to actually get the core of the technology? Well, all innovation, as you know, is non-linear. So none of it is ready at the same time that some other function is ready. But people like us don't sit around twiddling our thumbs waiting for the perfect moment. So what you're doing is you're constantly iterating. And as best of breed pieces snap in, you go ahead and drop that module in there. Your engineers develop something else, you drop that module in there. Your scientists develop something else, you drop that module in there. So you may have, you know, start with a baseline acoustic model, but then

you have a better one that fits the use cases. You may have a baseline language model, you drop in a better one that supports your use cases, whether it was messaging, voicemail processing, call mining, a myriad of different use cases at the time. What was funny is as we were fundraising in the 2007-2008 timeframe for our VC round, a lot of people were scratching their heads saying, I'm not sure that I would use an AI assistant, but would you allow my other portfolio company here? I think some examples were Got Voice,

YouMail, RingCentral, and several others. Would you allow them access to your platform? And so by default, we ended up becoming this Twilio for speech recognition for AI services as an accidental discovery, if you will, because the VCs that we were pitching in some ways allowed their portfolio companies to diligence us by becoming customers.

Yeah, that's great when the people you're pitching end up doing business development for you, for sure. That's right. And so obviously you kind of described what you had on the technology front in 2006, 2007. By the time that you guys actually sold to Amazon, what did the technology do in Yap, right? Like how far did you guys get

before that acquisition occurred? Yeah, so Prion, which is the new company's name was actually the code name of the engine that eventually became the nucleus of Alexa. So we knew that we needed to have a totally independent stack because we didn't want to be at the beck and call, if you will, of other tech providers.

So we had a little bit of a secret mission behind the scenes, and we built a great research organization inside of a company, which is not something that you typically find in a startup, as we were plotting complete independence. And so it was a state-of-the-art speech engine that had some newfangled abilities that even to this day we can't talk about because a lot of people think that you invent things and you patent everything, but you typically don't. There are certain core capabilities you leave as trade secrets. For instance, Google always was annoyed that they published PageRank, where in hindsight,

they wish that they didn't publish PageRank because it was such a critical part of their search acumen. Yeah, that makes sense. And I guess, help us understand in kind of layman's terms, how exactly voice recognition works, right? We use it every day across all these different devices, but what's actually happening when I verbally verbalize a command or a question like how did the technology ingest that and what's going on in the background before it presents me the answer yeah the the easiest way for you to think

about that is it's turning audio files and converting them into text in order to do that it takes the audio stream and basically takes everything that you say and converts it into phonemes and then it clusters the phonemes together in in a search to see hey are these is this the right word for this combination of phonemes? And then it goes to the next set of phonemes. Is this the right word in the set of phonemes? And then it goes to a third set of phonemes and says, is this the right word? And then you have this concept of bigrams, trigrams, quadgrams, and what have you, where it says, hey, is this sequence of words together? Does it make sense? Or is it another combination of words? Because every time you have a word that

it identifies, it also has something called an NBEST, meaning it has a list of alternative words that it might be, right? So think of a homonym like two. Is it T-O, T-W-O, or T-O-O? Well, you need to know the context before and afterwards to figure out which one it might be. Yeah, that makes a lot of sense. And I guess then one of the limiting factors to the accuracy is how it ingests, like how clearly can you hear the audio, and then the conversion to text, and then what you're talking about here is almost like the contextual information that it's able to derive from the other parts of the sentence. Right. And that's why, you know, having a great engineering team paired with a great research team is so important, right? Because the research team works on the

models and the engines, all the natural language, everything that you think about. On the engineering team, we're blissfully lucky because we had some of the original folks that worked on the iPod engineering team worried about our audio capabilities, right? Our audio stack. And so they were able to find disconnects where sometimes some of the carriers that y'all use for your cellular networks may have had certain devices, recording devices misconfigured that were constraining the audio quality, which leads to poor accuracy. And so we had all sorts of routines to cleanse audio and then feed the best representation of your voice to the recognizers and the models in order to have the best accuracy. It took Google about 10 years with all these

newfangled methods that you heard of post-2012 to catch up to the level of accuracy that we had 10 years ago. That's how tight of a platform it was. Yeah, that's awesome. And then what is it like as you're going through kind of the acquisition process? You guys obviously had multiple suitors at the table to some degree, like maybe talk through a little bit. How do you get in touch with them? Is that like an ongoing conversation? Are they just coming inbound and what that process is like? Yeah, none of it starts from a ground stop. You're building relationships with these folks the whole time you're on your journey, right? So even to this day, right, with the current company that we're operating, you're always in communications with the Ciscos, the IBMs,

the Googles, the Amazons, the Apples, the Facebooks, so on and so forth, all of the big brands that you can think of, and even intermediate brands that are up and coming, like the Zooms, the ServiceNows, and everything else, because you're a member of that ecosystem, right? So you're not necessarily looking for investment, you're not necessarily looking for M&A, but you're always looking for partnerships, scaling partnerships, and what have you, and And it's just a normal part of business, as you know, based on the roles that you've had. Yeah, for sure. And I don't think I've ever asked this before, but you guys sold the company for $90, $100 million, somewhere in that range.

What was it like the second you realized, hey, we did it, right? What was that kind of feeling of, okay, this is real, this happened? What do you guys say to each other? What's kind of the feeling internally? yeah i know the number was never published because um we were on such a gag order with amazon because they never wanted anybody to know that they were coming for speech recognition and ai in a major way and obviously they're one of the dominant providers of ai services uh today um but frankly speaking nobody invents a company wanting to sell it which is counterintuitive because you think

that this is the way that value is unlocked you're literally building a company like the first days of Facebook or Google or Amazon or any one of those, and you're building it to last, you know, to be governed well, to be innovative, to hire a world-class team. And so in reality, no real founder that I know of has an exit fantasy or exit strategy, because why would they exit the thing that they enjoy doing on a day-to-day basis that they're, you know, highly passionate about. Um, and so, um, one of the common themes that I've heard with a lot of folks that do experience this actually is, um, um, they're actually quite sad on the other side of it, right? Because here, here they enjoyed working with this team. Look, look at the team

that we've built now, right? They're resilient folks. They're supporting each other. They're doing it right by customers and partners. They're trying to be relevant, uh, you know, to this pandemic and help where, where they can, you, you get to work with these people day in and day out. Why would you want that to change? So that's, that's the thing that people don't, um, don't tend to talk about is that psychological change that happens. Yeah. It's super interesting because it's very counterintuitive, right? A lot of people would say, Hey, the financial, um, aspect of this should be celebrated, but I think a lot of folks are less concerned with that. And it's more of, uh, the concerns around, well, do I still get to work on this? How's it going to change? I'm

handing it over to somebody else, uh, in some cases, et cetera. So it's pretty interesting there. Um, okay. So you guys go through the, uh, the acquisition to Amazon, obviously they ended up taking that technology, uh, and ultimately come out with, um, Alexa. What's kind of, uh, the view kind of post acquisition, uh, and over the last couple of years, as you've seen, you know, kind of your journey and then this final product of Alexa or kind of the current state of the product, good, bad, indifferent, kind of how do you, uh, evaluate what they've built? I think it's good. What the big tech companies can do, especially in the consumer space, is they de-risk things as an interaction method because they have other ways of supporting these assets because the majority of their businesses are built on ads and other forms of support. And so they can take these fantastic risks that expose, you know, 100 plus million users to a new way of being, right, and a new way of operating.

The business environment tends to be more conservative, right? So they kind of need things battle tested, and then they need layers built on top of those inventions, right? So security layers, governance layers, manageability layers, so on and so forth. And once those things are proven in the consumer space, there ends up being a sonic boom where these things eventually end up at work. And so, look, you have Facebook and Twitter first, and then you could have Slack and Yammer and these types of tools in the workplace. We have FaceTime before we really have the prominence of the WebExes and the Zooms and the like as well. So the consumer space in some ways is the premarital ooze, if you will, in terms of adoption of certain types of technology. And that's what Silicon Valley does very well.

And then other folks come in and say, okay, now how do we repackage this for business use? Yeah, makes a lot of sense. You have decided to go on the journey once again and started a new company. Obviously, we are investors in that company as well. Maybe talk a little bit about just kind of the problem set that you saw in the market and why you wanted to go bang your head against the wall again and build a company. Right. And it wasn't a decision. Right. I mean, again, I would I would consider there's there's an art and science to business that's entangled together. Right. And to startups in a way. And so it's think of yourself as a writer, as a musician, as a as a painter, as a sculptor. This literally hit me like a ton of bricks. And I actually ended up trying to give the idea away.

Right. So I called buddies and Amazon and I said, hey, I just thought of a new way that that's going to be a breakthrough for how AI is done. And they're like, oh, my gosh, get this funded and we'll resign and join you. And I dropped the phone like a hot potato. I'm like, oh, my gosh, I don't want another startup. It's crazy talk. And then I picked up the phone and called a buddy of mine that worked on Watson. And he's like, hold on, let me transfer you over to the CTO. And I started telling him what my idea was. And I said, hey, you guys should steal this and put this in Watson. I think it's going to be a breakthrough going forward.

And he's like, I'm in. And I'm like, what do you mean you're in? You know, imagine, you know, in Hollywood, how they have that dolly zoom where you get goosebumps because the backdrop, you know, falls back. That's the moment that I had. This guy's world famous, helped stand up the Watson division. and he's never gone to Google or Amazon or any of these places or any startup when they approached him. But here, this idea is big enough where he's finally able to take a risk with his

current credibility. And then sure enough, I called a friend that was working at Cisco reporting to the CEO and he said, yeah, this is big. And so in some ways, it's the reluctant a hero trope that you see in Greek mythology in some ways. And I think that's how you can tell that a company was authentically founded. It wasn't just, hey, let's go make some money. No, it was like, hey, here's this idea that I can be sure and I can pitch 100 investors. And even if 99 of them say no, my intuition, my spidey sense is screaming on all cylinders saying, hey, this is a big deal especially since it's battle tested and vetted by you know some amazing folks that work in these amazing brands including yourself right and the experiences you had so that was

that's what the initial process was like got it and then what was that original idea uh i guess we didn't announce it yet and we're gonna we were thinking of announcing it at at the wall street journal future of everything event in may but that got um um um re uh rescheduled until the fall timeframe, so we're going to hold it back for a while. It'll be surprising and not surprising all at the same time when we get it revealed, but it is in heady trials now. We even took investment from a consortium that includes AT&T, Goldman Sachs, UPS, Delta Airlines, and several other folks, New York Stock Exchange, Stanford Universities and Investors. So we have great backers that are, that certainly helped get us product market fit. And that's

something that I would suggest to anybody involved in startups. And I don't care where you are in the, in the food chain, you know, to have, you know, great folks, part of the journey from the get-go to help you, you know, figure out exactly how you're going to package price it, you know, exactly what they, what they need and what order in your roadmap is, is all important. Yeah. How do you go about finding some of those early, you know, kind of customer slash research partners, if you will, to some degree, given that, you know, there's tons of these big companies, but they're getting in dated every single day with the newest, shiniest toy, the latest request, et cetera.

How do you break through that noise and get them actually to work with you? Yeah, and that's why I think, you know, being involved, first of all, your backers, right? Our backers constantly have corporate development days, and I have to give them credit, you know, Greycroft Partners does that out of New York and LA, you know, Revolution does that at times, Digital Alpha has their own portfolio that we're tending to Bootstrap Labs and the rest. So remember, whenever you take investment, it comes with networks. that's actually more important than the money that you're getting from these folks. Of course,

you need their investment in order to make payroll. But more important than that, as we sit down and look at who's going to be on the cap table and on the journey with us as well, it's what networks do they bring to bear? Because you're using gravity assist whenever you sew these networks together. And it's irrelevant whether they're a fund or even an individual investor. You're weighing each one of them to say, all right, now you're part of this journey. How, you know, how are we going to help us help you in some cases? So it really isn't just, you know, you know, one thing, right? So you'll take one investment from Engage Ventures down in Atlanta, for instance, and

they have 10 different brands associated with them. You'll take an investment from Plug and Play Ventures in Silicon Valley, and they have a mess of brands that are associated with them and rinse repeat with all of them, right? They all have those connections. And that's normal. Whether you're running a florist shop or you're running a startup, you're consistently looking at your customers and partners and your backers for the networks that they provide. Yeah. And I guess as part of this, specifically in the artificial intelligence and voice recognition space, how are these large corporations doing on kind of the innovation front, right? Are they actually making progress? We see a lot of times kind of a lot of headlines and announcements and all this stuff. But do we see the progress outside of maybe the Google and Amazon's that you would expect? Or are they maybe ahead or behind schedule of where maybe they should be? Yeah. And that's always a touchy question, right? From our vantage point, living in the tech

industry and weighing their progress, if you will. But know this, right? We may be really good at tech, but they're really good at flying planes. They're really good at logistics. They're really good at operating banks. They're really good at all of these running hospitals, right? So in some ways we unfairly judge them to say, hey, what's your tech stack look like? And do you have the latest, you know, fangled, you know, AI chess playing thing, or can you win at Go and do all of these other things? Sure, they may not have that. But you know, they also know how to do all of these other things to clothe us and feed us and move us, right? That we don't know, right? We don't know how to build cars and planes and all of these other things. So I tend to be respectful

where we show up and say, of course, you guys wouldn't have this because you're not a tech company, you're this other company, and you have all of these things that I don't have. And I need you to create a unit for the community. And, and that becomes a respectful dialogue where we can play on each other's strengths. Yeah, I guess it's really interesting to when you when you kind of break it down to the tech companies that are building technology, just because you have technology doesn't necessarily mean you have a product that can go to the end consumer and serve hundreds of thousands or millions of people. Whereas many of those large companies, to your point um they're experts at kind of the whole package right that the final product is successful

and we're trying to accomplish yeah and and they're iterating as well right and they frankly speaking based on the breadth of their operations they are looking for things that are battle tested right so they care about the security of these things because obviously we have you know never ending cyber attacks from all sorts of non-nonsensical folks out there that like kicking sandcastles down instead of building them. And there's many other attributes that they need before they can adopt innovations. Yeah. Let's talk a little bit about kind of the general state of some of these technologies. So let's start with voice recognition in general. Kind of give us an update as to where that is as an industry and then kind of how you see that

developing over the next 10 years or so. Yeah, it ended up in a place where it's more of a commodity now than in table stakes for each of the major cloud vendors. So that's where it is today. I mean, you can still be surprised by certain applications of the technology. So for instance, everybody assumed that text-to-speech was commoditized, but in the last week, Google announced a novel implementation where to backfill video conferencing, they actually took portions of your own voice, synthesize it, and they would insert them, phonemes into these video conferencing feeds so that you wouldn't have dropouts, for instance. So you have these sort of little applications that are moving the ball forward, but none of the grand

big bang style innovations anymore in that particular field, especially post-2012. So it's easier than ever to build new languages. Obviously, you need to have a clean signal and do as best as you can. Now, you match models and you pair it with machine translation. And at some point in time, you'll expect where you could be in interview in Russian and Chinese and French and talking to me. and in your earpiece you'll hear it in your native language and vice versa but you know there's still some areas where where language processing is still a little bit iffy and that's all the metaphors that we tend to use and and the jests that we tend to use and the fact that language is constantly evolving with slang and so that's a never-ending part of the work um but the big breakthroughs are are elsewhere nowadays yeah one of the things i've been very surprised that uh both positively at some point and also negatively uh is podcast transcripts right just having done so many of them

um everyone and their mom has come to us and said hey look at you know we've got this tool that can basically take the audio recording transcribe it and uh the best ones are still like the googles of the world right i mean people who are specifically focused on uh podcast audio to text they're just not very high quality right and what ends up happening to your point is either one, they're too literal, right? So every time I say the word write, like, and all these things get kind of ingested in, and so you get kind of a non-clean version, or they just miss words, right? And it's actually the conversion from audio to text. And so it's been pretty surprising. But then on the other side, it's not as surprising because Google probably has one of

the best ones from what we've seen. Well, because they were incented to create one of the best ones, because they actually developed it to operate on YouTube so that they can figure out what was in the video so that they can pair the best ads. So where there's a will, there's a way. There are basically two major places where media mining gets perfected. One is in the ad space and the second is in the intelligence community monitoring television broadcasts. In the case of voicemails, for instance, one of the things that we discovered is that when we used to leave more voicemails, and there were a lot of voicemails on our previous platforms, we would rewrite spoken English into written English. And part of that was to remove those pause words, like you mentioned, the ums

and the ahs and things of that sort. Yeah, super interesting. Machine learning, what's kind of the lay of the land or, you know, a state of where that technology is? And then how do you see that playing out over the next 10 years or so? Yeah, I think it's more the applications of it, right? So there's a lot of work being done in that space, certainly in the academic environment, but you have to, let me relate it to the past. So Carnegie Mellon, right, is a fantastic school for speech recognition, and they have this academic engine called Sphinx. But for the most part, you know, it's there for anybody to download, but you can't really put that into production, right? It doesn't have the accuracy, the scale, and the resilience and optimizations necessary

to ever be put in a heady, large-scale implementation, right? And so it's the same thing, right? You may be hearing about all of these advancements in deep learning and in natural language processing and what have you. But in order to take that work, that research and all the ideas that you're hearing and actually put it in such a way and package them

so that they could be consumed by these large-scale enterprises with tens of millions or hundreds of millions or billions of transactions per month, that's a totally different thing. And frankly, you have to start from scratch in many places. Yeah, and I guess as people go to build that, that seems to me to be one of the technologies

that's going to be core to every industry, right? And it's just the longer that we have to run data sets through these machine learning models, the models will get better, but also the insights that we're able to glean from this. And you'll actually see almost like an acceleration of the impact uh the longer we go into the industry right yeah there's basically three main places you would place um ai technology in in the workplace right in one hand it's analytics

right measuring what you do right the second is actually doing the thing that you do right so workflows right so powering all sorts of workflows and the third is all manners of human language technologies right because humans are an important part of of these workflows and measurements and And as a result, there has to be easy ways to onboard their intelligence and their abilities as well. So those are the three main areas that we see AI applied in business. Yeah. And now you're interchanging somewhat the terminology, artificial intelligence, machine

learning. And there's a lot of people, I think, that say, hey, look, machine learning is kind of the foundation for artificial intelligence. There's other people who use them interchangeably. How do you think about either the difference or the similarities between those two really vernacular used to describe something that is quite similar? Yeah, I guess there's a running joke, right? AI is what you tell the press, and ML is what you tell practitioners. So that's the reality of it.

Look, people like me never called it AI, right? It's just something that started taking off just like cloud as a word started taking off. Are you kidding me? I was stumbling. Oh, you know, and our marketing team last time was stumbling over ourselves. We didn't know how to call our hosted speech recognition or, um, client server or any of this stuff. And then, and then cloud started getting used and we're like, Oh, okay. It's, it's a cloud. Uh, it's a cloud service. That's what it is. Software as a service SAS. That's what it is. Um, and so in, in some cases it's different words describing the same thing for

different, uh, audiences. Yeah. Super interesting. And I guess then like, what are some of the applications for this that you're most excited about, right. In terms of, um, I'll hold constant the assumption that, uh, technologists will continue to do what they're best at. They'll, they'll build the technology. It will improve. We'll get to some of these, uh, end States that I think people have, uh, have been calling for for a while. Um, but where are the applications that you think have either the biggest impacts in the world or that you're most excited about? Oh, my God, there's endless, endless applications, right? It would be nice not to have disinformation, right? It would be nice to have smarter chatbots. It would be nice never to be on hold as you call into a contact center and potentially have an AI deal with stuff where we don't have to reintroduce ourselves every time we get a human on the other side. it would be nice to actually find the things that we're looking for right and actually get

answers instead of a list of documents it would be nice if the right information was was sent to us you know if we're a morning person or a night person so there's everywhere you can think of you know there's there's opportunities for ai and as bezo said this is first pitch first inning time right we haven't even seen all the places where it can optimize things and for all the folks that are, that are worried about AI, uh, removing you from the workforce, you know, and I know we're in a, uh, a challenging time where a lot of folks are going to be losing their jobs and it's going to take a while until, um, you know, we can literally reboot, uh, the economy. Um, but there's more work than there are people, right. And there's more work than we have machines. And

these algorithms are still in their nascent stages as well. So we have, we have plenty of opportunities in front of us. I tend to trend towards an optimistic view of humanity and technology working together to solve these problems. Yeah. The part to me that in that entire debate, it's just like, this is not a new problem set, right? I mean, it used to be what, 70, 80% of people were farmers, right? It was like, oh my God, you know, what's going to happen when there's no need for humans to do all of the farming? And what it actually ended up doing is humans as a whole became more productive and they started to actually use their intelligence and kind of ingenuity to drive innovation in other areas that had a broader impact than just farming

right and i think that's somewhat similar here is a lot of the applications i've seen are going after more of the repetitive work right rather than going after replacing lawyers or you know know, things that humans are, I think, innately better at than machines, but maybe you're seeing something different. Yeah, look, and look, we had record low unemployment, you know, until this pandemic hit, right? So, and yet with all of this advanced technologies, why did so many of us, you know, have these jobs? Certainly the jobs you and I and many of our peers have weren't even a vision a generation or two generations ago as well. And there's a lot of arrogance for us to think that as even our jobs get displaced, that there aren't new ones

that come and take their place in a generation or two. We think that we would know everything and we sound as silly as the head of the patent office around the turn of the last century who thought that everything that could ever be invented has been invented. Yeah, that's a pretty obnoxious statement. I want to spend some time talking about security for a little bit. So the first is anytime that you start to have machines gathering lots of data, I think people are just worried about like, hey, where's the data get stored? How do you make sure it's secure? How does that get treated? Who does it get shared with, etc? How do you think about some of the best practices there and like at what level of maybe awareness should people have given these certain devices like, you know, the Alexa's in their homes, Google Homes, etc., listening versus not listening and all that? Yeah, and that's why we have to give the Apple team a lot of credit, right? If you look at how they're architecting, you know, how they're going to find things or in their architecture for how they may be, you know, showing if somebody that had that virus cross paths with you or not, right? They're really looking for ways of, you know, how do you have your cake and eat it too, right?

So how do you have the power of technology to help you in certain regards by finding things or making sure that you're not getting sick while at the same time preserving your privacy? And so I think people are being a lot more thoughtful towards architecting ways of essentially putting bumpers around what you're sharing with brands. Because I think even Facebook, right, you know, everybody's turning the corner to say, all right, we're not going to have this unfettered access anymore. And, you know, we need a lot more trust between us and our users as well. Otherwise, our users are going to go elsewhere. Yeah. What about the debate around is AI evil or not, right? Which I think is a pretty funny debate in the idea of like technology being evil or not. But there's definitely people who would argue artificial intelligence is the greatest risk to humanity. And there's others who would argue it's the most beneficial thing that could possibly happen. I guess, how do you just think through that trade-off and do you give any credence to the whole AI is evil or is a big

threat to humanity? Right. So that's like trying to project morality to a hammer, right? If you put a hammer in Ted Bundy's hand versus Jimmy Carter's, you're going to have two different outcomes, right? Jimmy, Ted Bundy's going to harm somebody and Jimmy Carter's going to build your habitat for humanity. So it really is. I would not be looking at a form of technology and automatically assuming it's positive or negative. I mean, it has no such spin, if you will, right? It's really what are you going to do with it, right? How are you going to apply it? In some cases, it's going to affect your privacy. In other cases, it's going to help you hunt down vaccines. So it really depends on how you use the tool. Yeah. And do you think there's things that

we can do to maybe not prevent, but at least disincentivize the negative use cases, right? Kind of to your point of if the technology falls in the wrong people's hands or it's applied in the wrong way, are there things that can be done to try to shepherd us away from that? Or is it just like other technologies where bad people are going to do bad things and good people are going to do good things yeah i think it organically it meets its end if you will right where the majority of people are going to be using it for positive means because the community in the market was would essentially um um not reward negative uses of this technology now it doesn't happen overnight but certainly there's there's no reward for it in the long term yeah and we're talking i

think the applications of artificial intelligence, machine learning, voice recognition, et cetera, in what I'll call kind of the current state and the short to medium term. I think one of the long-term implications that people are paying attention to or long-term applications is things like Neuralink, et cetera, where actually you start to merge the computational power with the human body. And so there's applications that have been discussed of literally taking it, putting in your brain, all the way to some people would argue, you know, something like an Apple watch is starting to get closer and closer, AirPods, et cetera. How do you just think about kind of the meeting of those two things with such powerful technology augmenting humans in some way?

Yeah, that's an interesting one. Look, we're worried about disinformation in our newsfeeds, right, in Twitter and Facebook and stuff like that. So once the machines are blended into our minds, are we actually the ones voting for the people that we're voting for? Or is there some undue influence that's being applied towards, you know, parts of our mind? It's going to be an interesting debate, you know, to see where we end and where they begin. And then how do we ensure that it's our influence that's creating these decisions and it's not some sort of external influence? That's a tough one. I mean, it's hard enough for us to eat information responsibly now when it's separate from us and and so that's uh those

are new issues that uh future generations are going to have to confront yeah and i guess it really begs the question of like where is the opportunity for something to go wrong right because there's what data are you collecting then how are you actually categorizing the data how are you analyzing the data and then like what's kind of the the output of that entire process and you There's a lot of people who, frankly, it scares the shit out of them if Google and Amazon and those companies are the ones doing that stuff and kind of feeding them the information or doing the analysis. And there's a lot of people who say, wait a minute, they're the smartest ones, you know, kind of on a holistic basis. And so we'd much rather have them do it rather than, you know, Joe Blow in his basement who is kind of untrusted, unchecked, and can kind of do whatever he wants without any transparency. Yeah, that's the paradox, right?

I mean, it's sort of like a comic book character, right? Be here long enough and you become a villain, right? So we all remember the nascent stages of the Googles and the Amazons and the Facebooks and everything else where they were considered heroic for helping communities reach each other, right? Users far flung throughout the globe to be able to communicate with one another, to find any sort of information. And now we have this new situation where they're viewed as the villains because they're so

dominant. successful and they employ so many folks and they have undue influence on political processes and the like. So you'll have new upstarts, but you'll also have the incumbents being part of that. Yeah, for sure. And I guess the big question most people have is just like, how did these technologies impact my life? And it's amazing that the Alexas, the Google Homes, have all, even Siri, have kind of infiltrated in a very consumer-friendly way because people realize, this is super helpful it does provide me solutions but then i always joke and say and then they see like a boston dynamics uh robot running through the forest and they're like holy shit you know

the robot's going to kill us all and so it's really funny how i think people's uh perspective is driven by uh the use cases that they have and also what they see um information online uh drives how their interest level is with technology and in some ways um it's it's scattershot and um you know for every positive use of technology you'll read a negative one and and positive one and negative one and and uh you're going back and forth like a yo-yo on is this good for us or bad in some ways you know we can't predict all the good that's going to happen and it's going to be surprising and we can't predict all the bad that's going to happen and it will surprise us as well um certainly nobody you know coming outside of new year's day expected um you know

everything to turn out the way that it did in the first half of of this year and and hopefully we have pleasant surprises in front of us just not unpleasant surprises too yeah one of the other things that um really fascinates me around this whole space is uh the idea of creating um automated uh content but doing it in reverse right so we talked previously about voice recognition being you hear my voice and it gets written to text there's analysis done etc uh but going in reverse being able to either take a book, let's say, and turn it into an audio book without a human having to sit there and read it, but it sounds like a human, right? It doesn't sound like the robotic voices that we've heard in the past, or even, you know, take a nightly news segment, right?

Be able to kind of ingest all of this information and then present that information back just like a newscaster would. Do we see kind of the early signs of that stuff actually being present? And And if so, like, how long does it take for us to get to more of that, like, utopian world that I think everyone is kind of intellectually curious about, but doesn't know if it's actually possible? Yeah, there's really neat use cases for that, right? You had a whole collection of natural language generation startups that would take financial news, for instance. So not all publicly traded companies have analysts covering them. Actually, the vast majority of them don't. And so it would actually take the bits and pieces of financial information that were delivered to regulators and automatically generate news pieces for those folks in order to approximate the type of coverage that the analysts were doing.

So, again, that's an example of not enough humans, and there was no ROI for humans to do that work. As you said before, it was repetitive. They did that for sports coverage, right? So again, the press doesn't cover every single game, but it would be nice to have a natural language version of the outcome of these different matches and what have you. And that's what that technology was able to do. And like you said, for every New York Times bestseller where you can get Samuel L. Jackson to read it, there's plenty of other books and content that it would be nice to have in an audio format. Why? For accessibility purposes, for people to listen to while they're driving. right if if this interview was just in written format it would be nice to listen to it if if

it's something that you cared about and you were you know driving on the interstate uh for three hours you know to your client's site so that's that's where the technology really fills in the blanks if you will again for highly automated things where where only um automation can help backstop something and make it affordable so that you have access to a different way of consuming content. Yeah. Last thing I want to talk about here on this technology before we move on is the education, right? So obviously one of the ways to accelerate the development of all of this is to get more people educated on the technologies and get them building and innovating. What do you see in kind of the talent pipelines and is it better for people who are interested in this to go get

trained kind of classically in the traditional education system? Or is this more of a get on YouTube, watch some videos, and if you're really dedicated, you can teach yourself the skill set that's necessary. I think it's honestly, it's all of the above. If you're truly passionate about the work, you're going to, you're going to be tireless and you're going to consume everything you can think of vis-a-vis formal, informal education. I've heard, you know, some of the things that go into, you know, good entrepreneurial teams is whether they're tinkers or not. So they're tinkering and they're honestly interested in the work before it becomes famous, for instance, or it becomes lucrative.

You know, the majority of our team have been working in the AI space, one, before it was called AI, and two, for decades before there was any resources, you know, being applied to it. Those are the folks that you want involved on this journey because you know that they just love the work. And anybody that loves the work, by the way,

it doesn't even matter if it's startups. You know, if you're the best plumber in the world, the best electrician in the world, the best cook in the world, you can tell when we look at your work, right? And I think we have to be a lot more respectful of the different roles that people have in society and say, hey, everybody's work has value. It's not just the AI people. It's not just the startup people. It's not just the bankers. If everything fires on all cylinder and everybody does the best work possible, that's how you can have AI.

By the way, you can't have AI without water, right? Something has to cool all of these technologies that do that. You can't have AI without electricity. There's all of these other things that go into it. By the way, you can't have AI without feeding the people that are creating these things, right? So if you're a farmer, you are actually part of AI. If you're in logistics, you are part of it. We're all part of developing these technologies. By the way, just like we're part of you doing your work as well. And so I'm kind of tired of these politicians trying to separate us from each other when we actually have to work on all of these things together. By the way, that's how we did the moonshot. That's how we won the Cold War. And

that's how we'll solve the pandemic. And so enough of that nonsense that we see in the political sphere. Yeah, I completely agree. In terms of crypto, anything there that you've paid attention to or any thoughts on Bitcoin or other cryptocurrencies? Yeah, it's interesting, Because I don't know how to solve the beginning or end of it. In the case of decentralization, it makes a lot of sense for objectively to look at it and then say nothing can have influence on the value of this thing other than itself. Right. And then on the on the flip side, though, as you look at this pandemic, you see, because we do have that centralization that they're able to apply certain stimulus in different places where, you know, imagine trying to do that with Bitcoin, where we can say, hey, you know what, now we're going to reallocate Bitcoin to all of the caregivers out there. Literally, all the doctors and nurses and this and that, or all the people that are out of the work, now we're going to allocate portions of the blockchain over to them to make sure that they can feed themselves, right? And pay their rent and utilities and stuff like that. So I don't know how to solve the pro and con, because both architectures have their place, right, depending on what the environment looks like, right? In some case, obviously, now we're worried about rampant inflation, because we don't agree with all of the ways that they're spending this money, right? Because I feel like it's a shotgun approach, right? Instead of being targeted where in Iceland, for instance, everybody was cooped up for

three weeks. We had door-to-door testing, right? They were able to find half the folks that tested positively were asymptomatic. Those folks were asked to stay home perhaps another couple of weeks, and they can have a measured way of rebooting their economy, if you will. That would be a nice way to use centralization, and it would also be nice if they would say, all right, we're going to backstop the economy for three weeks. we're going to freeze the stock market so we don't have opportunist hedge fund managers that

are shorting everything and causing disaster for our industries. And then we get everybody 80%. We get the economy back to 80%. And then the remaining folks that are still ill at home, we apply all the resources there, and then we bring them back online in the succeeding three weeks. That's the right way to do it. Now, these politicians have no problem coming to us door to door and shilling for votes, but nobody's knocked on my door to do a test and nobody's coming to our doors and doing an antibodies check and nobody's going to be coming to our door to give us a vaccine. So I don't know what's going on there because frankly speaking, I think the economy would be in a better place if we had very deterministic and scheduled items for how we're

remediating these problems. And so I think this is why people are curious about all of these new assets and a new way of essentially having a financial system because of the mismanagement that we see in other places. Now, if you want to apply centralized stimulus, fine, but then don't start firing inspector generals that are supposed to be watching how we manage these funds? Yeah. The data I just looked at, so it's the weekend of April 12th or so, is that there's still less than 3 million tests that have been administered in the United States, which means less than 1% of the population has been tested. And so really that brings two problems, right? One is you can't do anything that you just described because we don't know

who's sick and who's not, whether they're symptomatic or not. And so it's really hard to kind of go back or at least try to go back to the economy turned on because you basically have to over rotate or be overly cautious but two is then you're basically looking at biased data on the testing that you have to right because you know let's call it eight or 2.2 2.3 million tests that have been done uh a high percentage of those are people who thought that they're sick right so they go in and you get kind of skewed data there and i think the thing that this has really opened in my eyes too, is you'd like to think that experts in general, they're economic or health experts or government officials, they understand simple things like you got to have good data to

make good decisions and just very, very basic stuff. And you see some of the comments that are being made, both public and private market. You're just like, what the hell is going on? Why are we making these big sweeping decisions when we had tested 200,000 people and 80% of them more positive. So I don't think that's really the indicative of the entire country, but the sad part is we just don't know, right? Until you get better testing, you just don't know what the data is. I don't think it's the experts. I think experts are experts in all fields. Like I said before, if I have a leak in my basement, I'm not the expert. A plumber is an expert. If I have a short circuit, an electrician is an expert. We all need to be respectful of

experts in these different fields. I think what happens is the political process essentially mutes the voices of these experts and facts. We can't land something on the moon without facts. Facts matter, right? And objectively, if you look at the financial system, maybe we need a hybrid approach, right? You have this backstop out of a mutable blockchain where you can't screw with the store of value. It is. It's there. It's immutable. You own those assets. And then you have a separate portion of the economy that is under more centralized control so they can go in and, and, and, and smooth out the rough edges, if you will. Right. It's, it's using all of these assets are at our disposal. Well, I think makes a lot of sense. Right. Yeah, for sure. Um, before

I finish up, I always ask a couple of rapid fire questions and then you can ask me a question to end it. But, uh, what is your favorite book ever? Um, Asimov's foundation, Asimov's foundation. It was just a fantastic tome. I remember reading it in a, um, monastery by candlelight, which was, ironic given about how fantastical it was in terms of saying, hey, if you knew math, you could predict the future. And so now if you look at the world that we have now with all of these predictive models, if you will, just like the ones that you were talking about, it's come to pass, right? So, you know, again, this is why we have to be attuned to art, not just science fiction, right? Art in general, in some ways, is just a different language

for the things that we do. And by the way, that's why I'm not even anti-faith either, right? Faith may be a different language for saying the same thing. Just like you could ask me a question in French or Arabic or German or anything else, it would still be the same question that you just asked. It's just, you know, said in a different way for the folks that know how to speak that language. So art's a major deal. And look, they've been so inspirational, right? So many people have said in speech recognition, things like Star Trek inspired them as well, because again, they put it in art and it depicted what a future world might look like. And then guess what? For many of the folks in the audience, they view it as just, you know, something that's

fantastical, right? And yet for some folks sitting there, little kids, boys and girls and what have you that are watching it, they're like, hey, that's not make-believe. I know how to build that. I think I take this piece and then I put it with that piece and I do it with that piece or I need to learn this, or I need to learn that, or I need to talk to this mentor. And then sure enough, 10, 20, 30 years later, they're giving birth to that thing that they imagined long ago. That's what's wonderful about innovation. Yeah, I love it. I love the way you described that. Aliens, believer, non-believer, think they're real? I think there was a film that Carl Sagan was involved in which is it would be an awfully uh waste uh waste of space if you will so plausible

but too big for them to encounter and then you also have to think of the um of the lifetimes right in the lifespan of whether we can be uh in parallel with them one of the mind-blowing things you have to realize is all of the um historical characters that you know of in the past so think of your Caesars, your Hannibals, your, you know, Peter, Peter, the greats, George Washington's and everything else. These are only 1% of all humans that ever lived were all the past humans. 99% of humans haven't existed yet. They're still in our future. So there's a lot more to do in their future than, than there's water under our bridge yet. And so they may be the ones that discover it, but, you know, to think that the first 1% have discovered them, I'm not sure if

the timelines along or not but again i'm not the expert in that yeah it's uh a lot of people come on and they basically say uh it would be selfish of us to think that we're the only species you know in the galaxy and it's so big and math and probability and all this stuff uh but very few people talk about uh it doesn't mean that it all has to be simultaneous either right there definitely can be other um species on other planets just they happened a million years ago right or or intelligent right or or our definition of intelligent life right had if you visited here a million years ago you wouldn't have your definition of intelligent life and who knows you know million years in the future we may not have that anymore when they are right so we have we

have no idea yeah it's um sometimes i read in the news uh things like you know these two galaxies are uh um are colliding right now and in four billion years our galaxy is gonna fly this other one. And you're just like, that scares the shit out of me, frankly, right? Yeah, I worry more about the galaxies colliding in November 2020 right now than anything else. And I hope on the other side of that, we have leaders that care about all citizens, which would be great. And by the way, all citizens, I mean all citizens worldwide, right? Because this pandemic is a perfect uh reminder that our our systems of governance are interconnected right and so we got to look at those this thing in a systemic way for sure uh what one question do you have for me

to uh to wrap up here yeah so um so you you've obviously seen you know some of the best of of these future fintech um uh platforms that are up and coming what really what really excites you about the possibilities of those platforms in solving some of the issues that we're seeing with this pandemic. So how, you know, have they been applied correctly? How would they be responding to this crisis differently? Yeah, I think that there's two core trends that I see. One doesn't get talked about that much, except for in crypto. And then the other gets talked about a lot. And So the first one is just this idea of like, you and I have a certain set of banking services that is commonplace, like it's table stakes, right?

We can't imagine not having a credit card or a debit card or being able to go into a bank branch and all these things. And that's not the norm globally, right? So there's a whole group of people who just, they kind of leapfrogged. They went from zero banks to mobile banking. So you see this all across like the African continent. but there's still a lot of people who have no access to banking services at all right there's simply just a cash-based society they get paid in cash they hold cash etc and so getting those

people onboarded into um you know some sort of banking services whether it looks more like the traditional world or it's more crypto based um just that is uh really good i think for kind of human uh production when you can give people access to things like credit and all that uh investment opportunities etc so that's one bucket and then the second bucket is actually the education side. So I think it's pretty clear we've all identified like, hey, we don't teach financial education in schools very well. Like people don't understand how money works and all that. In some weird way, I've told people that things like Bitcoin, cryptocurrency, and even blockchain technology to some degree, people come in through the speculative nature, right? So

there's a lot of volatility, there's price movements, there's a lot of people who say, oh, I think I can go get rich. Right now in the stock market, same thing, right? Lots of volatility, people, uh, either going to make a lot of money or lose a lot of money. And so when they start to actually be drawn in through these volatile price movements, uh, they begin to get educated, right. And, and, and that almost drives, um, the education is driven by the speculation, uh, in some weird way. And so, um, I think that that's something, uh, we know that the education is a problem, but we don't necessarily think through like, well, how do we solve it on like a mass scale um and you know look it's a hard problem right because frankly it's not just let

me teach you about uh how to open a bank account or let me teach you you know about a balance sheet uh there's an ever-moving target you know so i joke and i say if you were a teacher teaching financial education um at a university or online or something the the conversation you were having in january and february of this year is very different than the conversation you'd be having right now. And it's because all of the market conditions change. And so what could have been good advice or good, you know, kind of lesson, uh, in January and February actually may be horrible now, or it could be good. Right. But, but the environment really matters and something that's so dynamic, like an economy, et cetera. Um, and so it's just something I just pay attention to.

Cause I think if you can, you know, educate folks, then, uh, it's very similar to, you know, AI machine learning can give people the tools you're surprised by what they kind of end up doing. and so I think it's pretty important. Yeah it's best to give them the tools when there's not an emergency right because like you said there are experiences that are worked by just dealing with these emergencies that may just be black swan events instead of saying hey it's a normal part of your operating system if you will to know how to manage these affairs and by the way this is how you put things away from a rainy day by the way you'll be doing that for a decade or two decades so that when that black swan exists they'll they'll you know feast through your

your backstop, if you will, and maybe you'll be able to make your rent for three, six, 12 months because you've put away the small tokens, if you will, over time that turned into this nest egg, if you will, because we can show them, hey, every person's life is going to experience X number of these black swan events and you're going to be more resilient when they happen. No, you're right. It should be, in the same way that we talked about STEM or STEAM, to add the arts in there, that needs to be a fundamental part of certainly education in a capitalist society, if not any society. Because resilience starts with you, and then it works with the people around you, and then it works with the system.

But you have to have strong links of the chain across the board. And then we only help folks where, you know, disaster truly struck and there was nothing that they could have done to prevent it. And that's, you know, we have a lot more folks that were affected than probably should have been because of that lack of education that you said. So the system failed them then, right? Yeah, I said the other day, and I understand this is unpopular, but I do believe it, which is the people who are comfortable right now and are likely the ones that will thrive coming out of this economic situation are the ones who did the hard, unpopular, disciplined things for the last two, three, four, five years. and so you know if you were getting highly over leveraged and you were doing a bunch of speculating and kind of hey i'm so you know i'm a genius because everything keeps going up and you didn't do the discipline things then you're actually probably one of the people who's likely to be in a bad or worst position now right and so it's this weird thing where well that doesn't help to identify today right because that means that you would have had to do the things previously but it

is an explanation as to why some people are actually comfortable right now and other people are you know kind of sweating bullets and very worried right and rightfully so well i usually tell people look even as children we were read the story of the three little piggies right so build it out of straw build it out of wood or build it out of brick that was always your choice right and whether that's your career whether it's your education whether it's your nest egg whatever you were doing you know you know should have um you know and maybe that was too abstract for people to get, but it was always build a plan so you can operate with no revenue. In this company, that's what we did. Find different sorts of customers so that if you lose one sort of

customers, you still have that. Build a diversity of partners so that if you lose one, you get this other. Raise more investment than you think you need by even 2X because you never know what will happen. So think of the Gen Xers out there, right? Gen Xers are a group of folks that people don't talk about a lot, but we experienced the tail end of the Cold War. Then we experienced the boom and bust of the first dot-com craze. And then we had 9-11. And then we had 2008. And now we have the pandemic right so it's hit after hit after hit after hit uh in some ways and and most of of the folks that i know in in that field are actually very serious folks and maybe it was you know you know because of all the experiences uh that they had that even the boomers uh didn't have it in

some ways so you know i've yet to meet you know um you know gen x or maybe that's why we listened to music that was so down and and dark in some ways too but um yeah i think we'll make it through i think if if we stay innovative and and um and apply it through i always i think just like bill gates right the the you asked about favorite books but typically a lot of the things that that um we think about in some ways is you'll always have a lot more folks that build sand castles than knock them down and as long as that's the case then you know we'll make it through this and it'll be better, bigger and better. Unfortunately in humanity, we have to experience floods and tornadoes and hurricanes and earthquakes before we learn to how to backstop them and

prepare for the next one. And, and we needed a modern example of, of a pandemic in order to realize, you know, we need it to be more battle hardened for this particular eventuality. And so, you know, we'll get to the other side of it and we have to help as many folks as we can through that transition. Yeah, I definitely agree. Where can people find you online if they want to reach out and learn more about Prion? Sure. Our Twitter handle is at Prion, so P-R-Y-O-N as in Nancy. And I'm at I-J-A-B-L-O-K-O-V, also on Twitter. Awesome, man. Listen, I really appreciate you taking the time to do this. You are one of, if not the smartest person I know when it comes to a lot of this technology. So I am obviously cheering for you. And I think people will

learn a lot from this. I appreciate you doing it. Yeah. Stay safe, everybody. Hey, everyone. Pop here. If you like this episode of Off The Chain and want to help us take crypto to the top of the Apple, Spotify, and other podcast charts, please do us a favor and rate, review, and subscribe. To review, simply go to the Off The Chain homepage, scroll down until you see the five blank stars. Taking 15 seconds to fill those stars in and leave a quick review goes a long way in helping us take the entire crypto ecosystem to the top of the charts. I appreciate you listening and see you next time on Off The Chain.