#273: Igor Jablokov on Building an AI Ready Workforce to Improve Operational Resilience

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. ===============================

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Anthony Pompliano?

  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.

• 0:11Anthony Pompliano?

  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 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.

• 0:52Anthony Pompliano?

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• 1:36Anthony Pompliano?

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• 2:20Anthony Pompliano?

  Get 10% off your tax plan today with a free trial by going to taxbit.com/invite/pomp. Taxbit.com/invite/pomp. Again, taxbit.com/invite/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.

• 3:05Anthony Pompliano?

  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.

• 3:18Speaker 01

  Yeah, thanks for having me.

• 3:20Anthony Pompliano?

  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.

• 3:31Speaker 01

  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.

• 3:52Speaker 01

  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.

• 4:13Anthony Pompliano?

  Yeah. So let's go back to the IBM time, because I think a lot of people look at IBM and they basically say, you know, the company's been around forever. It's a blue chip. They've got hundreds of thousands of employees. Like, how exactly does technology development happen inside of a large company like that? And then obviously it led to some frustrations from your end. You guys went sort of your own company. Maybe talk to it just about structure, how that happens at a big corporation.

• 4:34Speaker 01

  Sure, and look, they're stewards of just some amazing technology, right? The problem is that we ascertained is that they couldn't take the inventions and commercialize them so that they could be 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, CISO, CFO had to sign off on their adoption.

• 5:17Speaker 01

  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.

• 5:47Anthony Pompliano?

  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, etc. 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?

• 6:12Speaker 01

  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 it 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, you know, 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.

• 6:56Speaker 01

  And back then, without app stores, right? 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.

• 7:10Anthony Pompliano?

  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, like, 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 and actually get the core of the technology?

• 7:39Speaker 01

  Well, all innovation, as you know, is nonlinear. 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, you know, 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.

• 8:23Speaker 01

  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 GotVoice, Umail, 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.

• 8:54Speaker 01

  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.

• 9:04Anthony Pompliano?

  Yeah, that's great when the people you're pitching end up doing business development for you, for sure.

• 9:09Speaker 01

  That's right.

• 9:11Anthony Pompliano?

  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?

• 9:27Speaker 01

  Yeah, so Pryon, 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, you know, 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, you know, some new fangled abilities

• 10:08Speaker 01

  that even to this day, we can't talk about because, you know, 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.

• 10:34Anthony Pompliano?

  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 then what's going on in the background before it presents me the answer?

• 10:54Speaker 01

  Yeah, 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 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 the third set of phonemes and says, is this the right word? And then you have this concept of bigrams, trigrams, quadragrams, 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?

• 11:38Speaker 01

  Because every time you have a word that it identifies, it also has something called an N best, 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.

• 11:57Anthony Pompliano?

  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.

• 12:17Speaker 01

  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 poorer accuracy.

• 13:02Speaker 01

  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 new fangled 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.

• 13:25Anthony Pompliano?

  Yeah, that's awesome. And then what is it like as you're going through kind of the acquisition process? You guys obviously have 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?

• 13:41Speaker 01

  Yeah, none of it starts from from a ground stop. You're building relationships with these folks at the whole time you're on your journey, right? So even to this day, right, with with the current company that we're that we're operating, you're always in communications with the Cisco's, the IBM's, 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 you're a member of that ecosystem, right? So you're not necessarily looking for investment, you're not necessarily looking for M and A, but you're always looking for

• 14:24Speaker 01

  partnerships, scaling partnerships and what have you. And it's just a normal part of business, as you know, based on the roles that you had.

• 14:33Anthony Pompliano?

  Yeah, for sure. And I don't think I've ever asked this before, but you guys sold the company for 90, 100 million bucks, somewhere in that range. What was it like the second you realized, hey, like, we did it, right? Like, what was that kind of feeling of, okay, this is real, this happened? Like, what do you guys say to each other? What was kind of the feeling internally?

• 14:54Speaker 01

  Yeah, I know the number was never published because 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 today. 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,

• 15:35Speaker 01

  No real founder that I know has an exit fantasy or exit strategy. Because why would they exit the thing that they enjoy doing on it on a day to day basis that they're, you know, highly passionate about. And so one of the common themes that I've heard with a lot of folks that do experiences actually is

• 15:57Speaker 01

  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, you know, right by customers and partners. They're trying to be relevant, you know, to this pandemic and help where they can. You get to work with these people day in, day out. Why would you want that to change? So that's, that's the thing that people don't, don't tend to talk about is that psychological change that happens.

• 16:26Anthony Pompliano?

  Yeah, it's super interesting because it's very counterintuitive, right? A lot of people would say, hey, the, the financial aspect of this should be celebrated. But I think a lot of folks are less concerned with that. And it's more of 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 in some cases, etc. So it's pretty interesting there. Okay, so you guys go through the, the acquisition to Amazon. Obviously, they ended up taking that technology and ultimately come out with Alexa. What's kind of the view kind of post acquisition? And over the last couple of years, as you've seen, you know, kind of your journey and then this final product of Alexa, kind of the current state of the product, good, bad, indifferent? Kind of how do you evaluate what they've built?

• 17:12Speaker 01

  I think it's good. I mean, they, you know, what, what the big tech companies can do, especially in the consumer space is the, they de-risk things as an interaction method because they have other ways of supporting these assets, right? Because the majority of their businesses are, are built on ads and, 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, of being, right, and a new, 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.

• 17:56Speaker 01

  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 know, you have Facebook and Twitter first, and then you could have Slack and things and Yammer and these types of tools in the workplace. You know, we have FaceTime before we really have the, you know, the prominence of, of the WebExes and the Zooms and, and the like as well. So the consumer space in some ways is, is the primordial ooze, if you will, in terms of adoption of certain types of technology. And that's what Silicon Valley does very well. And then, and then other folks come in and say, okay, now how do we repackage this for business use?

• 18:37Anthony Pompliano?

  Yeah, makes a lot of sense. You have decided to go on the journey once again and started a new company. You know, obviously we are investors in that company as well. Maybe talk a little bit about just kind of the problems that you saw in the market and, and why you wanted to go bang your head against the wall again and build a company.

• 18:58Speaker 01

  Right.

• 19:00Speaker 01

  And it wasn't a decision, right? I mean, again, I would, I would consider there's, there's an art to science and science to business that's entangled together, right? And, 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, you know, 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.

• 19:43Speaker 01

  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, the CTO. And I started telling him what my idea was. 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. You know, gone to Google or Amazon or any of these places or any startup when they approached him, but here, you know, this idea is big enough where he's finally able to take a risk with his.

• 20:27Speaker 01

  Career and credibility. And then sure enough, I called, you know, 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, it's the reluctant hero trope that you see in Greek mythology in some ways. And, and I think that's how you can tell that a company was authentically founded. Right? It wasn't just, hey, you know, let's go make some money. No, it was like, hey, here's this. Idea that I can be sure and I can pitch a hundred 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.

• 21:09Speaker 01

  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.

• 21:21Anthony Pompliano?

  Got it. And then what was that original idea?

• 21:24Speaker 01

  Ah, 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.

• 21:35Speaker 01

  A re, uh, rescheduled until the fall timeframe. So we're kinda, we're going to hold it back for a while.

• 21:43Speaker 01

  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 University's an investor. So we have great backers that are, that certainly helped.

• 22:06Speaker 01

  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 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 all important.

• 22:30Anthony Pompliano?

  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 inundated 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?

• 22:54Speaker 01

  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, 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, 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, right?

• 23:36Speaker 01

  Because you're using Gravity Assist whenever you sew these networks together. And it's irrelevant whether they're a fund or even individual investor, right? 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

• 24:21Speaker 01

  and your backers for the networks that they provide.

• 24:24Anthony Pompliano?

  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 Amazons that you would expect? Or are they maybe ahead or behind schedule of where maybe they should be?

• 24:55Speaker 01

  Yeah. And that's always a touchy question, right? From, 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, you know, 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.

• 25:38Speaker 01

  So I tend to be respectful where, 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, you know, for the community. And, and that becomes a respectful dialogue where we can play on each other's strengths then.

• 25:57Anthony Pompliano?

  Yeah. I guess it's really interesting too, 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, you know, hundreds of thousands or millions of people. Whereas many of those large companies, to your point, they're, they're experts at kind of the whole package, right? That the final product is successful and we're trying to accomplish.

• 26:19Speaker 01

  Yeah, 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 cyberattacks from all sorts of 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.

• 26:46Anthony Pompliano?

  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.

• 27:02Speaker 01

  Yeah, that's...

• 27:04Speaker 01

  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, synthesized 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,

• 27:47Speaker 01

  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 know, 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 then at some point in time, you'll expect where you could be an interviewer in Russian, in Chinese, in 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 language processing is still a little bit iffy, and that's all the metaphors that we tend to use and the jests that we tend to use

• 28:32Speaker 01

  and the fact that language is constantly evolving with slang. And so that's a never-ending part of the work. But the big breakthroughs are elsewhere nowadays.

• 28:42Anthony Pompliano?

  Yeah, one of the things I've been very surprised that both positively at some point and also negatively is podcast transcripts, right? Just having done so many of them. 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 the best ones are still like the Googles of the world, right? I mean, even people who are specifically focused on podcast audio to text, they're just not very high quality, right? And what is happening to your point is either one, they're too literal, right? So every time I say the word right, like, and um, 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?

• 29:27Anthony Pompliano?

  And it's actually the conversion from audio to text. And so it's been pretty surprising, but then it does, on the other side, it's not as surprising because Google probably has one of the best ones from what we've seen.

• 29:39Speaker 01

  Well, because they were incented to create one of the best ones, right? 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, right? So where there's a will, there's a way, right? There are basically two major places where media mining, you know, gets perfected. One is in the ad space and the second is in the intelligence community monitoring, you know, television broadcasts. In the case of voicemails, for instance, one of the things that we discovered is that, you know, when we used to leave more voicemails, and there were a lot of voicemails on our previous platforms, we would reroute 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.

• 30:22Anthony Pompliano?

  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?

• 30:33Speaker 01

  Yeah, I think it's more the applications of it, right? So in there is a lot of work being done in that space, certainly in the academic environment. But you have to, uh, let me relate it to, uh, to the past. So, uh, 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, into production, right? It doesn't have the accuracy, the scale and the resilience that's in, in optimizations necessary to ever be put in, in a heady, large scale, um, uh, implementation, right? And so it's the same thing, right?

• 31:13Speaker 01

  You may be hearing about all of these advancements in, uh, in deep learning and, 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, you know, 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, uh, in, in many places.

• 31:42Anthony Pompliano?

  Yeah. And I guess as people go to build that, that, that seems to me to be one of the technologies that's going to be core to every industry. Right? And, and it's just, uh, the longer that we have to run, um, data sets through these machine learning models. The models will get better, but also, uh, 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?

• 32:05Speaker 01

  Yeah, there's basically three main places you would place, um, AI technology in, in the workplace, right? And 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 then 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, in business.

• 32:38Anthony Pompliano?

  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 the foundation for artificial intelligence. There's other people who use them interchangeably. Like, how do you think about the either the difference or the similarities between those two, really vernacular use to describe something that that is quite similar?

• 33:04Speaker 01

  Yeah, I guess there's a running joke, right? AI is 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 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. It's a cloud service. That's what it is. Software as a service, SaaS, that's what it is. And so in some cases, it's different words describing the same thing for different audiences.

• 33:50Anthony Pompliano?

  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, I'll hold constant the assumption that technologists will continue to do what they're best at. They'll build the technology, it will improve, we'll get to some of these end states that I think people have have been calling for for a while. But where are the applications that you think have either the biggest impacts in the world or that you're most excited about?

• 34:17Speaker 01

  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. Everywhere you can think of, you know, there's, there's opportunities for AI. And as Bezos said, this is first pitch, first inning time.

• 35:00Speaker 01

  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, a, a challenging time where a lot of folks are going to be, uh, losing their jobs. And it's going to take a while until, um, you know, we can literally reboot, uh, the economy. But there's more work than there are people, right? And there's no 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, I tend to trend towards an optimistic view of, of humanity and, and technology working together to solve these, these problems.

• 35:40Anthony Pompliano?

  Yeah. The part to me that in that entire debate, uh, it's just like, this is not a new, uh, problem set. Right? I mean, it used to be what 70, 80% of, uh, people were farmers, right? I was like, oh my God, you know, what, what's going to happen when there's no, uh, 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, and kind of ingenuity, uh, to drive innovation in other areas that, uh, had a much broader impact than just farming. Right? And I think that's somewhat similar here is, uh, a lot of the applications I've seen are going after more of the repetitive work.

• 36:17Anthony Pompliano?

  Right? Rather than going after, um, replacing lawyers or, um, you know, things that humans are, I think you, uh, innately, uh, better at than machines. Uh, but, but maybe you're seeing something different.

• 36:30Speaker 01

  Yeah. Look, and, um, 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, uh, you know, have these, uh, uh, jobs? Certainly the jobs you and I, uh, and many of our peers have weren't even a vision a generation or two generations ago as well. And, and there, 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, um, the head of the patent office, you know, around the turn of the last century, who thought that everything that could ever be, um, invented has been invented.

• 37:15Anthony Pompliano?

  Yeah, that's a 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?

• 37:51Speaker 01

  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 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 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 your privacy? And so I think people are being a lot more thoughtful towards architecting ways of of essentially putting

• 38:32Speaker 01

  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, and 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 user is going to go elsewhere.

• 38:50Anthony Pompliano?

  Yeah. What about the 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, you know, 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, you know, AI is evil or is a big threat to humanity?

• 39:21Speaker 01

  Right. So that's that's like, you know, trying to

• 39:27Speaker 01

  Project morality to a hammer, right? If you put a hammer in Ted Bundy's hand versus Jimmy Carter's, you're gonna have two different outcomes, right? Jimmy, Ted Bundy's gonna harm somebody and Jimmy Carter's gonna build you a 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.

• 40:09Anthony Pompliano?

  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 is 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, you know, bad people are gonna do bad things and good people are gonna do good things?

• 40:32Speaker 01

  Yeah, I think 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

• 40:47Speaker 01

  not reward negative uses of this technology. Now, it doesn't happen overnight, but certainly there's no reward for it in the long term.

• 40:55Anthony Pompliano?

  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?

• 41:40Speaker 01

  Yeah, that's an interesting one. Look, we're worried about disinformation in our news feeds, 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.

• 42:25Speaker 01

  And so that's, those are new issues that future generations are going to have to confront.

• 42:31Anthony Pompliano?

  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 output of that entire process? And, you know, there's a lot of people who frankly, it scares the shit out of them. If Google and Amazon and, you know, 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.

• 43:15Speaker 01

  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, you know, communities reach each other, right? Users, you know, far flung throughout the globe to be able to communicate with one another, to find any sort of information. And now, you know, 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.

• 44:00Anthony Pompliano?

  Yeah, for sure. And I guess the the big question most people have is just like, how did these technologies impact my life? And, you know, it's amazing that the Alexa's, the Google Homes have all even Siri have kind of infiltrated in a very consumer friendly way. Because people realize, hey, look, this is super helpful. It does provide me solutions. But then I always joke and say, and then they see like a Boston Dynamics robot running through the forest. And they're like, holy shit, the robots are going to kill us all. And so it's really funny how I think people's perspective is driven by the use cases that they have, and also what they see information online drives how their interest level is with technology.

• 44:41Speaker 01

  And in some ways, it's scattershot. And you know, for every positive use of technology, you'll read a negative one and positive one and negative one. And 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. Certainly, nobody, you know, coming outside of New Year's Day, expected, 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.

• 45:21Anthony Pompliano?

  Yeah. Well, one of the other things that really fascinates me around this whole space is the idea of creating automated 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. 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, then present that information back, just like a newscaster would. Do we see kind of the early signs of that stuff actually

• 46:04Anthony Pompliano?

  being present? 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?

• 46:16Speaker 01

  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?

• 46:57Speaker 01

  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 this interview was just in written format, it would be nice to listen to it if it's something that you cared about and you were driving on the interstate for three hours to your client's site. So that's where the technology really fills in the blanks, if you will.

• 47:42Speaker 01

  Again, for highly automated things where only automation can help backstop something and make it affordable so that you have access to a different way of consuming content.

• 47:54Anthony Pompliano?

  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 the talent pipelines? And is it better for people who are interested in this to go get trained 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?

• 48:23Speaker 01

  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, 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, 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.

• 49:06Speaker 01

  And anybody that loves work, by the way, it doesn't even matter if it's startups. You know, if, if you're the best plumber in the world, the best electrician in the world, the best cook in the world, you can tell, you know, when, when we look at your work, right? And I think it, 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 the, 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.

• 49:47Speaker 01

  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, if you're in logistics, you are part of, 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, of, 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.

• 50:25Anthony Pompliano?

  Yeah, I completely agree. In terms of crypto, anything there that, that, that you've paid attention to or any thoughts on Bitcoin or other cryptocurrencies?

• 50:35Speaker 01

  Yeah, it's it's it's interesting, right? Because I don't I don't know how to solve the beginning or end of it, right? In in the case of decentralization, it's it's it makes a lot of sense, right? For for objectively to look at and then say, no, 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.

• 51:18Speaker 01

  Now we're going to allocate portions of the blockchain over to them to make sure that they can feed themselves, right? And and 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? For depending on on 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.

• 52:01Speaker 01

  Those folks were asked to stay home, perhaps another couple of weeks, and 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 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 shelling for votes,

• 52:45Speaker 01

  but nobody's knocked on my door to do a test and nobody's coming to our doors and 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 mediating these problems. And so I think this is why people are curious about all of these new assets and a new way of of essentially having a financial system because of the mismanagement that we see in in other places. Now, if you want to apply, you know, centralized stimulus,

• 53:30Speaker 01

  fine, but then don't start, you know, you know, firing inspector generals that are supposed to be watching how we manage these funds.

• 53:37Anthony Pompliano?

  Yeah, it's the data I just looked at. So it's the weekend of April 12 or so is that there's still less than three million tests that have been administered in the United States, which means, you know, 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, a high percentage of

• 54:20Anthony Pompliano?

  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 my eyes to is you'd like to think that experts in general, their economic or health experts or government officials, they understand simple things like you got to have good data to make good decisions and, you know, just very, very basic stuff. And you see some of the comments that are being made, you know, both public and private marketers, like what the hell is going on, right? Why are we making these big sweeping decisions when we had tested, you know, 200,000 people and 80% of them were positive. So I don't think that's really the, you know, indicative of the entire country, but the sad part, we just don't know, right? Until you get better testing, you just don't know what the data is.

• 55:05Speaker 01

  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, electrician's an expert. We all need to be respectful of experts in these, in this different fields. I think what happens is the political process essentially mutes the voices of these experts and facts. We, 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.

• 55:49Speaker 01

  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, but I think makes a lot of sense. All right?

• 56:01Anthony Pompliano?

  Yeah, for sure. 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 what is your favorite book ever?

• 56:12Speaker 01

  Asimov's Foundation. Asimov's Foundation, it was just a fantastic tome. I remember reading it in a 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, 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, in French or Arabic or German or anything else.

• 56:58Speaker 01

  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 arts, arts are a major deal. And, and look, they, 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,

• 57:34Speaker 01

  That's not make believe. I know how to build that. I think I I take this piece and then I'm 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, ten, twenty, thirty years later, they're giving birth to that thing that they imagined long ago. That's that's what's wonderful about about innovation.

• 57:56Anthony Pompliano?

  Yeah, I love it. I love what you described that. Aliens, believer, non believer, think they're real?

• 58:03Speaker 01

  I think there was a film that Carl Sagan was involved in, which is it would be an awfully waste, waste of space, if you will. So plausible, but too big for them to encounter. And then you also have to think of the of the lifetimes, right, in the lifespan of whether we can be in parallel with them. One of the mind blowing things you have to realize is all of the historical characters that you know of in the past. So think of your Caesars, your Hannibal's, your, you know, Peter, the Peter, the greats, George Washington's and everything else.

• 58:39Speaker 01

  These are only one percent of all humans that ever lived were all the past humans. Ninety nine percent 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 one percent have discovered them. I'm not sure if the timelines align or not, but again, I'm not the expert in that.

• 59:04Anthony Pompliano?

  Yeah, it's a lot of people come on and they basically say it would be selfish of us to think that we're the only species that, you know, in the galaxy and it's so big and math and probability and all this stuff. But very few people talk about it. Doesn't mean that it all has to be simultaneous either, right? There definitely can be other species on other planets, just they happened a million years ago, right?

• 59:26Speaker 01

  Or or intelligent, right? Or or our definition of intelligent life, right? Had 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.

• 59:43Anthony Pompliano?

  Yeah, it's sometimes I read in the news, things like, you know, these two galaxies are are colliding right now. And in four billion years, our galaxy is going to find this other one. And you're just like, that scares the shit out of me, frankly, right?

• 59:59Speaker 01

  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, you know, we have, you know, 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 reminder that our systems of governance are interconnected. Right? And so we got to look at this thing in a systemic way.

• 1:00:29Anthony Pompliano?

  For sure. What one question do you have for me to wrap up here?

• 1:00:34Speaker 01

  Yeah, so you've obviously seen, you know, some of the best of these future fintech platforms that are up and coming. What really excites you about the possibilities of those platforms in solving some of the issues that we're seeing with this pandemic? So have they been applied correctly? How would they be responding to this crisis differently?

• 1:01:00Anthony Pompliano?

  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?

• 1:01:42Anthony Pompliano?

  They're simply just a cash-based society. They get paid in cash, they hold cash, et cetera. And so getting those people on boarded into, you know, some sort of banking services, whether it looks more like the traditional world or it's more crypto-based, just that is really good, I think, for kind of human production, when you can give people access to things like credit and all of that, investment opportunities, et cetera. 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. Uh, in some weird way, I I've told people that uh, things like Bitcoin, cryptocurrency, and even blockchain technology to some degree.

• 1:02:24Anthony Pompliano?

  Uh, 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. Uh, right now in the stock market, same thing, right? Lots of volatility. People uh, either gonna 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 uh, 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 it's a hard problem, right?

• 1:03:04Anthony Pompliano?

  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 I joke and I say, if you were a teacher teaching financial education um, at a university or 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, etc.

• 1:03:42Anthony Pompliano?

  Um, and so it's just something I just pay attention to, because I think if you can, you know, educate folks, then uh, it's very similar to, you know, AI machine learning, if you can give people the tools. You're surprised by what they kind of end up doing. Um, and and so I think it's pretty important.

• 1:03:55Speaker 01

  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 backstop, if you will, and maybe you'll be able to, you know, make your rent for three, six, 12 months. Because you've you've done this small, you know, put away the small tokens, if you will, over time that turned into this this nest I give you all.

• 1:04:34Speaker 01

  Because, you know, 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 you're right. It should be in the in the same way that we talked about. You know, you know, STEM or STEAM, right, to add the arts in there. That needs to be a fundamental part of of of certainly education in a in a capitalist society, if not any society, right? Because resilience starts with you, and then it works with the people around you, and then it works with 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's nothing that they could have done to prevent it.

• 1:05:18Speaker 01

  And that's some, you know, we have a lot, a lot more folks that were affected than probably should have been because of of that lack of education that you you said. So the system failed them then, right?

• 1:05:30Anthony Pompliano?

  Yeah, I I said the other day, and I understand this is unpopular, but but I 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, you know, 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 I'm so, you know, I'm a genius because everything keeps going up and you didn't do the disciplined things, then you're actually probably one of the people who's likely to be in a better or a worse 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.

• 1:06:14Anthony Pompliano?

  And other people are, you know, kind of sweating bullets and very worried, right? And rightfully so.

• 1:06:19Speaker 01

  Well, I usually tell people, look, even as children, we read the story of the three little pigies, 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, you know, and maybe that was too abstract for people to get, but it was always, you know, build a plan so you can, you can operate with no revenue, you know, in this company, that's what we did, you know, find a difference, different sorts of customers, right? So that if you lose one sort of customers, you still have that build a different, a diversity of partners. So that if you lose one, you get this other raise more, you know, investment than

• 1:07:04Speaker 01

  you think you need, right? By even two X, because you never know what will happen. So think of, you know, think of the Gen Xers out there, right? Gen Xers are, you know,

• 1:07:16Speaker 01

  you know, 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, of the first dot com craze. And then we had nine 11. And then we had 2008. And now we have the pandemic, right? So it's hit after hit after hit after hit 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 that they had that even the boomers didn't have it in some ways. So, you know, I've yet to meet, you know,

• 1:07:56Speaker 01

  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 we stay innovative and, and, and play it through, I always I think just like Bill Gates, right? The you asked about favorite books, but typically a lot of the things that that we think about in some ways is you'll always have a lot more folks that build sandcastles 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

• 1:08:41Speaker 01

  a modern example of of a pandemic in order to realize, you know, we needed 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 all as many folks as we can through that transition.

• 1:08:57Anthony Pompliano?

  Yeah, I definitely agree. Where can people find you online if they want to reach out and learn more about Pryon?

• 1:09:03Speaker 01

  Sure. Our Twitter handle is at Pryon, 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.

• 1:09:16Anthony Pompliano?

  Awesome. And 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 learn a lot from this. I appreciate you doing it.

• 1:09:31Speaker 01

  Yeah, stay safe, everybody.

• 1:09:34Anthony Pompliano?

  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.