Skymind CEO Chris Nicholson Talks What’s Next in AI

Welcome to Make More Marbles. My name is Brad Hart and we're here to interview the game changers, the future makers, the co collaborators and creators who are here to collaborate with one another towards a better future for all of us. Enjoy the show. We've got a great guest coming up for you right now. Welcome to Make More Marbles. My name is Brad Hart and I'm here today with Mr. Chris Nicholson. What's going on, my friend?

Good to have you. Hi, Brad. What's going on?

Well, I'm just here in sunny San Diego, California. I know you're up in the bay.

Yeah.

It's sometimes sunny and sometimes Carl comes along, the fog, and says, hey, I'm going to be Carl the fog for a while.

It's like crashes the party. Yeah.

We even have a city named after Carl. It's called Carl's Bad. No, Carl, stop being the fog in the San Francisco Valley.

Very. A very bad boy. Yeah.

But, yeah, so let's just get kind of oriented for the folks listening at home, because I like to be silly and goofy, but also we have very serious business things to talk about.

AI is pretty serious, I have to say. Yeah.

Tell us a little bit about skymind and what's going on there.

Sure. Okay. So we're an artificial intelligence company. Cool, huh? It's the future. I come to you, I visit you from the future, Brad. It's foggy. So, AI. We're an AI company. We've got like 35 people. We've been around for three years. And what are we working on? We're like taking these algorithms that are coming out of really cool research groups. Right. Like Stanford or Google, or implementing them in a way that big companies can use them. Right. So we are. We're basically kind of bringing AI out of the lab and into the real world. That's what we do.

I love that. And I actually have a very dear friend of mine, I have a story about how I met Richard Prince and that whole thing. One of the other young hustlers there that night at that little charity gala that we had no business being at was a guy named Eric James is a friend of mine who runs Applied Intelligence and they run a lot of these systems and automation for traffic lights and things like that around different municipalities. So I'm a little bit into artificial intelligence. I kind of understand it. I understand machine learning. I've been following IBM, Watson and all that great stuff. What are some of the things that people like, just generally wouldn't know about that you're kind of geeking out on right now in the.

What would they not know? You know, I think the first thing people should know is that AI is just math and code, right? It's not magic. It's like stuff you learn in high school. If you did algebra in high school, right? Maybe a little bit of linear algebra from college. If you did that, you kind of get it right? So, like, AI just spits out decisions when it sees data. That's all it does. Right. It's really simple. So I think the thing people need to know about AI is it's really simple, right. A lot of coders can do it.

I like that. And then is there a analog to, like, the AWS type of thing? Like, so for capacity, a lot of big companies like GE are switching over to AWS or Amazon Cloud Services. Is there kind of an analog of that to, like, plug into a, you know, AI cloud infrastructure that now people can scale with? Is that something that exists already?

That's really interesting. That's an interesting idea. AI can run on pretty much any hardware, right? So you can do it on aws. You just need some extra help. So we're kind of like that, you know, we're a toolkit. We give people tools and then they can build AI with us. It's. You customize it a lot, right? There's a lot of little knobs to turn. It's like a fancy car. So the car can do a lot of cool stuff. Sometimes it breaks. You need somebody who knows how to fix it. That's one of the weird things about AI. But if you get it right, it can be more accurate than humans. Right? Like, if I walked my algorithm down the street, it would recognize my friends better than I do.

Yeah, that makes a lot of sense. And ultimately, the goal is to have a world in which we can perceive things and eliminate risks and spot opportunities more quickly and more efficiently than we ever could as humans, which will ultimately totally shift. Shift the fundamental nature of capitalism. Right? There's this old Winston Churchill saying. He says democracy is the worst form of government, except for all the others. I think that's true of a lot of systems like capitalism. Right. For the same reasons we don't use feudalism anymore as the best system at the time, but what will create it next? And in a world where AI and robotics are kind of ubiquitous and nobody can compete with that as an individual human being or even a team of human beings, what does that world look like? And those kind of questions that you think about.

Yeah, a lot. A lot. And you know, AI and capitalism have at least one thing in common, and that is that they are accelerants, right? Like what does capitalism do? It allocates capital, it allocates resources to things that it thinks work. Right? So millions of dollars going into something that you know might make you some money, that's an accelerant, right. It speeds things up, it makes them happen faster. AI is like that in the sense that it helps you predict things that you want to have happen and things that you don't want to have happen. Right. So people are using it for fraud detection there. They're avoiding things. But somebody like Amazon, if you go onto their website, they have a recommender system, right, that says, hey, Brad, you might also like this book, this mystery novel, because you read another that I saw, right? So what they're doing there is they're increasing the likelihood that you buy something from them, right? And that's an accelerant of your. They accelerate your purchasing. So AI is going to do that across the board. It's going to make companies better at what they do.

I really love that. And can you give us some examples just for, you know, real world, everyday things that we may not know are already happening, but are already things that we play with. Like Alexa is a really good example of that. Like, that's an artificial intelligence that's essentially, you know, listening to every single thing you ever say and recording it.

Great.

So how are you feel about that? And then making distinctions on that data based on, oh, you called out my name.

What do you need?

You know, let me find that for you.

Yeah.

What are some things like that that people just don't really get that are all around them at all times?

Yeah, it's gonna make my. It's gonna make life more convenient kind thermostat. Right. So we all use email every day, Right. One of the things that is annoying about email is spam, but if you use Gmail, you don't see much spam anymore. We got really good at spam detection, which cleared away the noise. If anybody on email in the 90s remembers, it was like all noise. Now we filter it out. I say it's like a thermostat in that nobody thinks about the thermostat, especially because they just expect the temperature to be okay. So AI is going to do that for us. It's going to filter out the things that we don't want invisibly and infuse products all around us in ways that, where they might not wear it on their sleeve. You Mentioned Alexa. That's a smart object in the home. But you can make all kinds of other things smart. We can have smart locks, for example, that do image recognition. Can see you. Hey, that's Brad. I'll let him in. That's his mom. She's supposed to come today. I'll let him in. Her in, too. It can infuse smart objects, and those objects are going to be all kinds of things. They're going to be drones for deliveries. They're going to be cars to take us places. And those cars are not just smart on the outside navigating the world. They're going to be smart on the inside. It's going to be like living inside your smartphone, where all of a sudden they're recommending media to you. So AI will introduce us to new environments that try to serve our needs better.

I love that, Chris. And I think this is really cool stuff. And I dove right into it because I wanted to just show people, you know, who it is we're talking to as a function of what you do now. But also, I think it's important that, you know, people respect you for your successes, but they'll really. They'll love you for their failures. They'll connect with your failures. Because not everybody can relate to success, but everybody can relate to failure. So can you talk about some of the heroes, journeys, the things that have gone on in your life and some of the ways you failed in order to get to where you ultimately are today?

Oh, my God. Crap. Yeah. It's a hard question, man. Okay. I like it. Well, I think people in your audience, some people in your audience have probably built their own small businesses and startups before. So what I'm about to say now will not come as a surprise to them. But I'll say startups are a grind, right? Where the magic ingredient is just sheer persistence. You hammer at it and then, like, nothing gives way. And you keep on hammering at it for years, right? And it only builds very slowly. So I would say for the first year and a half, and we're a little over three years old now, first year, it felt like failure every day, right? I was knocking on doors, I was trying to raise money, and the doors were not opening for me. And what I realized after a while, because I had been pitching VCs, hundreds of them, and you could say every single one of those pitches was a failure. What I realized after a while was it was not a waste of time because I was rehearsing, right? Which is to say each one of those pitches which failed was helping me get better. And the first 10,000 pitches were the hardest. Right. But after a while, you start to polish. You start to polish your message. I guess you could say that. And by the time we applied to Y Combinator, which we came through last year, we were really good at pitching. Right. And we had built a company that was kind of more solid than a lot of startups trying to get into yc. So, you know, it sounds trite to say, what doesn't kill you makes you stronger, but we got stronger through that.

Hey, it's funny, these little quips and things that we use in these little quotes, they actually make a lot of sense, but they only make sense in when you've got the lesson, when you can emotionally resonate with the lesson if you just kind of, oh, I get that. It doesn't quite land until you have that experience in life. And I really like what you said. I like calling these little threads, you know, unravel the sweater. The naked truth appears where you said that every single one of these pitches was a failure. But it was also a rehearsal. And I think I have an actor from my best friend, Sean Nugent. We bartended together in New York City when I was 22 years old. And now he's an actor and he has that same mindset of every single time I go to an audition. It's a rehearsal for the part that I'm ultimately going to get. That's going to be the right part for me and just to never give up. And consistency really can move mountains, it can carve rivers, it could carve valleys and grand canyons over time, little trips and drops. It doesn't seem like much, but it adds up.

I love that. Yeah, a lot of people give up. So, like, one of the only differentiators is between the people who hold on the ones who don't.

So what do you look forward seeing in the next year? 3, 5 years for Skymind and also for artificial intelligence as an industry, what do you see coming faster and faster into your horizon?

Yeah. Okay. Well, for skymind, we're an open source company, which means that we help spread AI, Right? We help spread it to lots of countries, companies like tens of thousands of them. So I would expect to see this. This mission. We have to bring it out of the lab into the real world. That'll just continue. It's going to spread like an oil slick on the sea. How's that for an image? It's irritating.

Ooh, that's good. Write that down.

So, you know, Startups are growth machines, right? So what does growth mean? Growth means change, and change means a bit of pain. So startup is going to be a very painful, growing experience for the next few years. We're going to hire more people. There's 35 of us now. There were like 10 of about a year ago, right. So I will do my best not to become a corporation full of BS like most of the corporations out there. But we'll also probably realize there are reasons why they're full of bs. Because it's hard to be big, right? So that's, that's where skymind will be for AI. The fundamental thing to understand about AI is that it marches hand in hand with the power of our hardware, with our chips, right? So chips are getting more powerful, right? And as chips get more powerful, AI can get more powerful, right? So these chips that we have now are, okay, what we're running on the chips we have now are pretty much old algorithms that we had in the 80s, right? And in the 80s everybody laughed at them. They're called neural networks, right. Nobody thought they would be any good, and they were good. They just needed really powerful hardware.

Everybody thought Skynet or Terminator, right? They didn't really take it seriously, but now it's real.

Seriously, they scoffed at it and now it's real. So that hardware, like you've heard of Moore's Law, right? Every year and a half, two years, we have, we have a doubling in the number of transistors that can fit in the same space, right? They get smaller, you pack more of them onto a chip, one chip becomes more powerful. And what we've done now is construct giant clusters of chips so you can, even if Moore's Law slows down, which it has, you can connect all those chips together in new ways to build your own supercomputer on AWS or wherever you have it. So those supercomputers are the foundation of exciting new AI. The kind of AI that can win games of Google like AlphaGo or video games, right? Where humans, you know, the best humans are really good and they click really fast. AI is kicking their butts, right? So those forms of AI are actually going to move through more and more complex situations, meaning towards real life, right? Where that they can navigate and find rewards, right? Because that's what AI basically does it, you teach it which reward to go after and then it goes and does that, learns how to do that. So you need more compute to navigate, more complexity. So that's where AI is to going.

I like that. And you know the point at which I got really excited was when it beat Ken Jennings, who's basically a freaking supercomputer, at Jeopardy. So when, as soon as he could beat Ken, I was like, all right, all right, you win. And then go. And all these other things. Where do you see quantum computing fitting in? Just to use your analogy, like, okay, the faster things get. You know, first of all, if you could break down what quantum computing is to a layman and then understanding how that integrates with A.I.

Yeah, I'll do my best with quantum computing.

It's a little bit nutty. Yeah, I get them.

I'm not an expert there, but it seems to involve a couple things. It seems to involve spooky action at a distance, right? Like, so things are kind of little. Little quantum bits are relating to each other and maybe in ways that we don't wholly understand because quantum mechanics is weird. And secondly, those particles are able to store more information than traditional bits, right? So A traditional bit bit stands for binary digit. A binary digit has two states, 0 and 1, right? Now, qubit, which is the fundamental element of quantum computing, actually has more than one state, right? So if you have more than one state in your fundamental element, suddenly you can store more stuff in a smaller space, which is important because deep learning uses a lot of data. And if you've got spooky action at a distance, right? Like, if you're able to make those things change really quickly, then you can also compute. You can process information stored there really fast, right? That's my understanding. It could be wrong. People may laugh at me tomorrow because I'm explaining a highly technical subject that I'm not an expert in. But what I do, regardless of how quantum computing works, we both know, everybody knows it's going to be much faster than the kind of traditional mechanical computing we have now. And that goes back to what I just said. More powerful computing means more powerful AI. Now, the next 12 months, here's another thing I know. Without even understanding how quantum computing works, in the next 12 months, we're going to hit a moment called quantum supremacy, right? Where for certain algorithms, quantum computers will demonstrably perform better than traditional computers. And over the next three or four years, the number of algorithms that we can actually compute with quantum computers will expand, right? So, like, in the next 12 months, it will not be useful for machine learning because it's too specialized. It only does one little thing, right? Over, you know, maybe the next three, four, five years, it'll actually become useful for machine learning. And that's when I'm going to care about it.

Right, Yeah. I think that's really exciting because for a lot of reasons binary computing is relatively constrained. Like you talked about, it's a 0 and 1. And then you think about qubits as having like, I think there's eight different states that you can have in a qubit. So you go from 2 to the 1 and now 2 to the 8. And so now every single cycle can now process 256 times more information than it could before or store that as well. And now this, the whole cryptography system breaks down and prime numbers and everything we've been doing up until now with binary has got to be rethought and AI as well. So does it allow more? Like how could we relate? It is more of a flow issue, like there's not enough processing power. Is it more of a RAM issue? Is there not enough capacity to then take on those algorithms? And what do you think quantum supremacy would achieve in deconstraining either one or the other or both in order to then graduate, if you will, to the next level and that becomes the norm and the standard by which everybody operates?

Yeah, I believe it's both. And I think there's going to be a so called quantum leap in both memory and processing and both for the AI we have now both we need lots of both, so increasing both is going to help.

So all right, the next question would be AI as a creation mechanism.

Right.

Because currently, you know, going back to your quantum thing, it's like we actually have this vibrational quality about us as humans that an AI doesn't have, a robot doesn't have that we're only now beginning to understand and begin to put real science behind. Yeah. So at what point does a. We're, you know, I like to believe we're conscious co creators. Right. We're conscious co creators creating a creator at some point. And where does that start to come into play and what does that kind of look like in your opinion? You know, I don't buy into the whole Terminator scenario. Maybe, you know, maybe you do, I don't, I don't know. But I like to believe that things are happening for the best and highest good overall, you know, for life.

Right. Yeah.

Or otherwise we would have nuked ourselves to death 60 years ago as soon as we had the ability. You know, I feel like there's something more for humanity like that unleashes this new epoch. And what does that look like and what do you think the other pieces of that puzzle are?

Yeah, I do know that deep learning, even as we know it now, is getting really good at creating certain kinds of art, especially paintings. Right. There's some really impressive stuff. There's a guy named Mario Klingemann, I think his name is out of Germany, who's producing really cool stuff. Google came up with an algorithm called Deep Dream where these algorithms are, you know, they can study all the paintings in the world and all the photographs and come up with odd combinations of looking at things that we know. Right. And those odd combinations of looking at things we know kind of teach us, give us a new perspective on the world. Right. Which is what you would say is the purpose of art, to expand our perspective on the world, give it new meanings. So deep Learning is already doing that to a certain degree. Even if it doesn't have intent, it doesn't know what it means that it's producing. Right. It's just able to produce something strange that kind of wakes you up now in terms of creating a creator. Yeah. So we already have these little creators that are maybe good at one thing, like a painting, but they can't do music. So what we'll see over time, I think. Okay, here's something interesting. What does art do? Art produces an effect in you. Right. It produces feelings and thoughts and probably like a thought feeling combo. Right. And it's. That alters our state. Right. And that's why we appreciate it. We want that art buzz in a way, like, it's like getting high when you're kind of in relation to good art. So what we need to do, this is my dream, is gather more data, like, get really good at fmris. They can actually see the thought feeling state inside your brain. Right. And correlate that. Study what the sensory data was going into your eyes and ears at the time so that we can actually learn very precisely what states are caused by what art. And then we can actually create art with purpose, which is to say our computers could begin to create art that would correlate highly with the powerful effects that art puts us into, whether it's a Wagnerian opera or a painting by Rothko or whatever. So we. With more data, we do have the potential to create these creators you're talking about. And I think that would be really interesting. Basically, we would bypass. We wouldn't even have to think about the notes anymore. We could just aim straight for the buzz if you know. But as far as, like, creating independent conscious beings, I think strong AI is possible, Brad. Like, I don't want to. It's a religious schism. In Silicon Valley right now. Like some people say yes, some people say no. I'm not a strong believer either way. But I would say if we look at the march of hardware, if we say, okay, strong AI follows strong hardware and hardware is getting stronger really quick, then we'll probably get really strong AI someday. And that may be an autonomous AI and it may be smarter than us. And if it is smarter than us, what do you think it's going to do? Personally, I don't know. So I think the thinking in some of these labs, the really strong research groups like OpenAI or DeepMind, is hey, let's create strong AI before anybody else, before the military, for example, does, because we know they want to use it as a weapon. So let's create it before anybody else. And if we can get there first, we can figure out how to make it safe because we're going to create it in a controlled environment. Right. So if I had. So that's what Elon Musk is doing. That's why he created OpenAI. Basically he wants to give himself a window of reprieve, right, by getting there first to make strong AI safe. And I think that's one plausible path through the future. Right. Like I wouldn't, I'd have to be stupid to bet against Elon given everything he's done. Oh yeah, right, yeah. So those are my feelings on it.

So, yeah, I mean you're, you're touching a lot of different subjects. I think that, that scare the crap out of a lot of people because our natural state of the unknown is like, oh, fear the unknown. Because that's the way we're wired. It's kept us alive for quite a while. I think it's healthy to have a rational fear of things you don't understand, but not allowing that to turn over into an irrational fear that keeps you from pressing forward. One of the things I think that's really fascinating about what you said is that you have this capacity to create a creator. Right. And what that might do or may not do, I think is really just a function of its programming.

Right.

If it's programmed to go out and win at all costs, well then the ultimate result of that is winning at all costs. What are those costs?

Right.

Doesn't matter about people's feelings. It doesn't have a value set, but it could theoretically have a programmable value set based on our best ideas and reasons. And the best part about it is it won't have the emotionality that a lot of ego driven individuals would have in that same position of power.

Right.

It's like you notice that leaders tend to gravitate towards their highest and best values. And as an organizational leader yourself, I'm sure you realize that your company can only grow as you do.

Yeah.

And maybe that's the way we need to grow AI is to start with those values and beliefs that it has about what's right and good in the world, and do our best with that and then allow it kind of the space to create, but not also the space to destroy, because it would be against its ultimate program, which goes back to asimov in the iRobot books. You know, it's like, what are the. What are the different pieces that we need to have in place in order for this thing to create strong AI, but also have it be safe? Yeah.

Yeah.

That's a really brilliant reflection. I wanted to say thank you for that.

Oh, well, no, thank you. I think you're right. Like, AI, we program their values. Right. They learn in their direction. We point them in. And it's called an objective function. We literally give them their objective. And that's. That's the direction in which they try to minimize error. Right. That error could be, I'm classifying photos correctly. Or that error could be, wow, I avoided killing humans today. Right. Like, you can point it in all kinds of directions, give them all kinds of values. And you're right, it does go back to Asimov and it goes back to his three Laws of robotics. Or four. I can't remember three or four. Like, don't harm humans and other stuff like that. The funny thing is, I hate to be like an Elon Musk fanboy, but I've been thinking about this a lot recently.

No VR talk in the hot tub. No, what is it? Not VR. It was simulation talk in the hot tub.

Well, okay, so Isaac Asimov wrote Elon Musk's biography before Elon Musk was born, basically. Right. Like, he wrote the guy in the foundation series. He wrote about the guy who made humanity and multi planetary species. He wrote the three laws of robotics, which is what's happening over at OpenAI right now. They're trying to make robots, autonomous, conscious robots. Safe. Right. So. So, yeah, Asimov is weirdly influential in this age we live in.

I've got a really good story for you. You ready? So my other friend that I mentioned and alluded to at the beginning of this, at this podcast is a guy named Eric James, who's a dear friend of mine I've known for many years. He actually met Elon randomly at an art gallery. And Elon Musk, being who he is, he's kind of an interesting cat, walked right up to him, said, hi, I'm Elon Musk.

Okay.

You know, and it was one of those things. And this is before Tesla really got going and all that. And it was in Colorado at the time, and they were talking to each other. And what ended up transpiring out of that conversation, there's a bunch of little pieces to it, was that he got the idea to use tax credits to then make the. The Tesla affordable for most people, competitive with a lot of other things. And it's just this funny kind of happenstance. So you're talking about Elon. I'm talking about an AI guy that actually helped him kind of, you know, catapult. And that's, that's just like these little nuggets are what I like to pull out of these conversations. Like, you don't know how you're affecting the whole world through just showing up and being in your best and highest service to each other and humanity.

Right.

So this, this idea of this make more marbles thing, it's like a fun, goofy, you know, don't be the hungry hippos and grab for all the marbles thing, but collaboration is the way we win. You know, when we talk about Darwin, Darwin's been misquoted so many times before, guy about saying that survival of the fittest is the only law in evolution. But in fact, in this book, if you actually read it, he talks about over 90 times how cooperation is the number one quality and biggest competitive advantage of species, and cooperation of groups is the thing that actually makes it work. So I have all these people running around in my life, and I'm sure you do too, that are hyper competitive to a fault and they're actually missing the fact like that. Third, fourth, fifth order Consequences, which is another great book I'm reading Cool Principles by Ray Dalio, best hedge fund manager that's ever lived, where he talks about, like, you got to think in third, fourth, fifth order consequences of your actions. And that's not easy to do because most people think about themselves.

Yeah. It's true what you're saying, and I'm ashamed to admit it because. Okay, so you talked about, like, founders and organizations kind of, and kind of having an influence on those organizations. You know, when you create a company, you do have an outsized impact. Impact on how other people behave within the company because you're. You're kind of modeling something. Right. And so somebody like me who's used to being kind of joking about everything. You know, sometimes I forget, right, that I'm under the magnifying glass and that I really shouldn't be making comments about everything because. Because it's actually not what I want to model, right? I'm just having fun. And so that's like.

This is the other thing. It's like sarcasm. It's only really, it kills intimacy. That's one thing to understand, sarcasm. I'm from New York and I moved to California and sarcastic here and it's only entertaining to me and other sarcastic people. It's like our little love language that we came up with, you know? But I can see what you mean because you'll say things offhandedly that people take seriously and you're like, oh, no, I just ruined the whole thing. It's over now and I have to go crawl into a hole and die somewhere.

Yeah, you watch with it. You walk with this guilt and you're like, man, stop acting like a teenager.

Yeah, it's all growth, brother. It's all good. We're still humans at the end of the day, even though we're doing these amazing things. Kris, you're doing some amazing stuff in the world and I just want to give you every bit of credit. You totally deserve it. But then at the end of the day it's like, alright, well, what about Chris? What about the human that is before me? What he, what's he all about? What drives him to do what he does? So you artfully dodged it earlier. I'm going to ask it again. What has been some of the things that have shaped you into the man you are today? Like, how did you grow up and what were you influenced by early on and the painful struggles that you've been through that have created this human that is standing in front of me?

Okay, I didn't mean to dodge it, but.

No, no, I didn't say it was even intentional, but your subconscious programming took over and took you in a different direction.

Maybe it's just that I don't want to talk your ear off all day about that, but. Okay, I'll tell you a little bit about myself. I was born in Montana and I grew up there. Montana is a very rural state. And one thing that a guy growing up in Montana kind of obscurely intuits is that there's a big, big world out there, right? There's a big world out there that has to be explored and you just don't know what it is.

You mean there's a world beyond big sky country.

What? Crazy. Yeah, yeah, yeah. So. So, like, Montana is a beautiful place. My family still lives there. I love it. And yet I had to. I had to leave. Not, like, emotionally, I had to leave economically. There's not a lot of jobs, right? So Montana exports its children to the cities on coast, both coasts. And I ended up coming out to California many years ago for the first time in the 90s, and then left and then came back about four years ago. Anyway, so, you know, that migration, like being a country boy, you know, not with a Nashville accent, but just like being from a very remote place, does shape you in certain ways and like I said, stumble. Like, what are the great divisions in the world, right? Some people say it's like, oh, US versus China or like France versus Britain or whatever. Actually, no, it's rural versus urban, right? That is the great divide, right? It's one of the great divides in this country now. It's a great divide in our experience. So that was a divide I had to traverse to kind of get to know the world, right, in its current state. Like, what is it doing now? Because California is way ahead of many other states in the west in the problems it confronts and their laws, it rights, that kind of thing. I had to catch up with kind of what was happening now in America, coming out of Montana. That was one of the things. I'll try to get more personal, though. I was a journalist for 10 years. I was a foreign correspondent for the New York Times and Bloomberg News, and I was an editor for them. Spent about 10 years doing that in Paris, France. I went to France and to Paris with the dream of becoming a writer in kind of along the Hemingway model. And I didn't realize that Paris had changed a lot in, like, the 50 years since he. Since the Sun Also Rises was written. It's not a place for artists anymore. It's a very bourgeois city. It's very expensive to live in. It's not very welcoming like it was after the war destroyed it, right? So I had to kind of be disillusioned about my own expectations for Paris, France, and I had to watch journalism crumble around me for a number of years. And we all know journalism is a tough business these days, right? Like, the ad revenues have gone over to Google and Facebook, and things are just hard, right? Even if they're getting the Trump bump of viewership for the last 12 months or so, I wanted to write. I became a journalist. And after a while, I just realized, man, you're in the wrong business in the Wrong country, Right. And I had to get out of there. And so I came here about four years ago thinking I'm gonna go be a junior developer in Silicon Valley and start over. And that didn't happen either. You know, like, that's life, right? The many things you think will happen that don't. So I taught myself to code in the evenings while I was a journalist. I was gonna go be a junior Python developer. And I got here and all people wanted to talk to me about was pr, right? So I went into PR instead and I joined a startup. And then I found somebody who really knew how to code, like professionally. And they became my co founder in skymind. And that's just like a long series of things happening that I didn't plan on. Right. That I had to kind of pivot into because it was the most interesting thing at the time.

And it kind of just illustrates the fact that it didn't all come together all at once. In fact, you started an entirely different industry. But now the skills that you developed as a journalist, because you really gave it your all, even though that the economy didn't quite line up with your expectations, you now have skills which I really appreciate. Like, you actually have very distinct stops to your words. You choose them very carefully and you do it as quickly as you possibly can. And then you get to the point you stop. And that's what a journalist does instead of a weirdo that just talks forever, myself included, you know, but, you know, just that piece, now that you're the head of a company or CEO of a company, you are that PR person whether you like it or not. You are that leader whether you like it or not. Did you ever anticipate that you would be put in the powerful position that you are, that you now are literally creating the future that we're going to occupy?

Yeah. You know, four years ago I didn't. It grew on me the kind of perception that this was happening. And I don't, you know, until somebody builds an organization from scratch, they don't really know what that's like to live. Right. To be in the skin of the person who founded it. And it turns out it's a lot of responsibility, right? It's not, it's not all like, you know, vacations with Richard Branson and stuff like that. Right. It's actually, it's a big moral project to give people a place where they can do their best work, to create a supportive work environment since it's like 8, 10 hours of their lives per day and make sure Everybody's communicating well. So there's all kinds of things like knobs to twiddle to get it. Right, to create this culture, this organization or this community. Right. That has to work together to get something done.

I love that. And what are some of the leadership lessons you've learned along the way with running a large organization? You see a 35 people working there now.

Yeah, I think communicate more.

Right.

And this is coming from a former reporter, right? I should know that lesson already. I still don't do it enough. Right. So communicate more. More clearly. Like, and. And when I. And when I say communicate more, I don't mean just like, broadcast my message of people, but, like, get into contact with them and listen to them. Right. To figure out what's going on, what they need, what their problems are. Right. And where I can unblock them. Because especially, look, modern companies, people don't all gather in an office anymore. Most of the time we have a distributed team with, like, engineers in eight countries. So, like, conversations don't happen by chance around a water cooler. Right. We have to be deliberate and conscious about creating those conversations to make sure everybody's listening to each other properly.

And what are some of the ways that you do that from a practical standpoint? You know, I like to go practical tactical every once in a while because it helps to kind of bring it out of the cloud and into the. To the groundedness. Like, for example, we use zoom, and we have about 25 or so people that work on various projects around the world. What are some of the tools that you use?

Yeah, we. What do we do? We. We communicate asynchronously. Right? Like when you're eight, when you're in, like, eight different time zones, it means, like, people don't always have time at the same time to talk. So it means we write things down, we save them in a project manager called Basecamp, right? Where we kind of draw up documents, leave them for each other, leave comments on those documents. We do organize weekly calls, so we have to be disciplined about it. Like, we introduce structure to it. And then whenever you feel like something's going slow or there's a miscommunication, live conversation is often better than an email. As you know, emails are often ambiguous. And when things get emotional, you just got to get on the phone. Right? Even if that means, like, waking up at 5am to talk to somebody who's very far away, get on the phone, give them your tone of voice, listen to their tone of voice, widen the bandwidth on what you can understand about them. And start getting that loop going quickly so things don't fester. Yeah.

Does anybody has it. Has everybody in your company ever been in the same place at the same time and do you make that a priority?

They have not been in the same place at the same time. There are several of our most senior people. I've never met them face to face. We've only seen each other like you and me right now, and they're very far away and we're a startup, so it's going to, it's going to be a while before I kind of have skymind Con and, and we. I bring them all together in, in Tokyo or something. It'll happen someday.

That sounds rad. And I hope I get an invite. What? Okay, so what's the kind of roadmap for you guys? Are you looking for an exit? Are you looking to just be the next Google of AI or like what's the. Are you kind of open to both at this point? What would be the, the best case scenario in your mind right now? Or what does the team really want? Yeah, yeah, yeah.

Well, I'll tell you, Google is doing a very good job at being the Google of AI, so I'm not sure if I can unseat them in that regard. But we do have something big, right? We're riding something big with lots of growth and lots of users. We think we can take it a long ways, right? Maybe we can take it to the stars. It's not clear. So what do I hope happens? I hope we can take this community and this kind of team and culture and let other people share it to build something even bigger and better. Right? And that's going to require that we get better at selling things and better at raising money, better at recruiting people and all the things that you have to accumulate to build a really big organization. And those are all uncertainties, right? Like I wake up at 3am in cold sweats sometimes, right? Just wondering, oh, am I gonna be able to close that next big deal? Right? Because that's, that's the next thing to get us to the next stage. So, so there's lots of uncertainties, but we can live with them. And for the moment, you know, our aim is just like, let's say IPO remains guideline, IPO 10 years down the road.

Got it. Are you guys profitable right now? Are you still working towards profitability?

We are working towards profitability. We did hit, so we've, we've already crossed. We've had our first million dollar year. Congratulations. So it's A big step for any startup. And we think that the coming years are going to be a lot bigger than that.

And what would you say, since we're going to make more Marvels podcast, and we believe in collaboration to win more together, what are some of the connections, resources, opportunities, people, or systems that would alleviate the biggest constraint towards moving you forward faster?

What are the systems like? What's holding us back? Is that what you mean?

Yeah, I love that analogy. That crops, right? Just connections, resources, opportunities, people, systems. Normally that is the thing that can unblock or accelerate progress in the direction. And what are those things for you right now that you'd really just love for somebody to come along and say, hey, I heard you guys need this. Bam.

Yeah, yeah, yeah, That's. That's really interesting. So we've got this product, right? And no product like children. They keep growing for a long time, right? And I mean, kids may be perfect at different stages in their lives. Products are never perfect, right. Like, you keep on making them better and better. So, like, I can see us making this product better. And the choke point, all we need to do is, like, execute on the plan, right? We need to hit our milestone in December. We need to hit it in January. And as we make it better, we'll have something better to sell, right? And as we sell it more, we'll build out a whole organization of repeatable sales. Right. Right now it's just, you know, me and a couple salespeople, so. And obviously, you need very large teams to deal with very large organizations when you're serving enterprise, which we do, so that's one choke point. It doesn't. It doesn't feel like I'm blocked, exactly. It's just that I know where I want to go and I'm not there yet. Right? And you're right. Like, some crucial people, they will be necessary. And I haven't found them yet for sure. And that's like, recruiting is like dating, right? There's just like, you have to see a lot of people before actually, it's like getting married. You have to see a lot of people before you find the right one. And that takes a long time. And it's actually painful, right? It's emotionally painful because you can't say yes to everybody and they can't say yes to you. Right? So you got to lose some of them, right. Even though you thought, oh, I thought you were right for me, right. And they have to get rejected. Right. Which is awful. I have to reject people. And. And I've slowly learned over the Years that, like, it's not their fault. Right. And it's not my fault if one side says no. It's just that it's not the right fit. You know, just like with dating. Right. It's got to be a fit and bilateral fit like that. So, yeah, so talent can be a choke point, obviously. If I could close two more $10 million deals in December, I do that too. Right. So, yeah, there's lots of choke points.

So right now, I think you're saying your biggest hassle might be just having the right talent, the right. Especially the sales and marketing and people that can get the message out and also close big deals, investable or otherwise. Sales or investing deals would be the next big thing for you.

Yeah, yeah. Get the message out. But. But, you know, we're. For us, sales is a lot about listening, right. Like, I think a lot of people forget that, but, like, it's really about active listening, knowing what to ask and knowing where. How to follow your nose. Right. To get to the things that are really hurting the client. Right. And then knowing what in your product answers that need, if that in fact is the case. Right. So it's almost like it's very close to being like a psychoanalyst or something. Right. You got to dig with your question and then let them talk for a while.

I like it. And then when you hammer on something that is. You're totally in pioneering territory, right? There's so many problems to solve, and you have these new tools that can solve them, and you're really just gobbling up that opportunity as fast as possible to solve those problems. But then you got to decide, okay, what are the problems that we're really good at solving and what are the problems that we can scale? You know, and luckily, you work in, you know, qubits and bits and bytes and binary, and it can scale pretty far. But just getting that nailed, right, Nailing it so you can scale it. What are some of the challenges or things that you've.

You've.

You've come across, like, where you're like, oh, we can solve this problem, and you went down that rabbit hole. Just wasn't the thing for you guys. And how have you pivoted since?

That's interesting. Okay. AI. AI has a supply chain problem. And what I mean by that is that many different components are necessary for it to work. And if you're missing any single one of them, the whole thing collapses. Right? So when we first started this company, we knew some things about deep learning. Deep learning learns really well off of Raw data, right? You don't have to pre process it a lot to make it work. And we thought, holy cow, we can apply these algorithms to anything. Just give me your data, whatever it is, as big as it is, I will produce results for you. And it turns out that that's not quite the case. What you need AI starts with the gathering of data, with data collection. And that data has to be aligned with the outcomes that you want to predict, right? So a lot of companies over the last 10 years, they got all into big data. They got their data lake, they filled it up with their data soup. And that data does not always fit. It doesn't always contain the signal that will allow them to make the predictions they want that are important for their business. Right? So the first step for us now, right, to avoid any kind of embarrassing months long project, first step for us in the first week, we just say, okay, tell us what you want to do. Show us the data. After a week, I will just tell you. Go, no go. Can deep learning do something with this or not? Right? So that's one thing we've learned to avoid.

That's a really good process to have, by the way. It's just really understanding what the problem is you're trying to solve and then getting enough of it just to be like, is this going to really.

No.

You know, and you're. I'm sure these people really appreciate it because it's like you'd rather have a short no than a long maybe at.

The end of the day.

Long maybes cost lots of money and time and resources that could otherwise be put into more productive pursuits. So, Chris, I just want to say I'm so grateful I could talk to you for 10 hours, by the way, about AI. It's been a really beautiful conversation. What are some of the things that you wish you could leave the world as your legacy or some words of wisdom that you really wish that could reach the earballs of the people listening and we'll kind of end on that note.

The earballs, I love that. Listen, that's a frigging heavy question, man. I don't know if I could handle such a.

Not prepared. Hey, man, your CEO. I got, I got to challenge you. I got to stretch you a little bit today on this podcast.

Oh, man. Could I leave the world?

Or how will you know you've won? At the end of your life you'll know you hit all the pieces and you die with no regrets. Like, how do you know that you, Chris Nicholson, have done the thing that Chris Nicholson was here to do.

Yeah, well, my kids are still talking to me. It is their job to forgive me for my sins. Just like it's everyone's, right? What would I like to leave the world? I think God. So, you know, I guess it goes back to that persistence thing I was talking about. I don't want to get back into, like, trite advice territory. You got to stick with it, Right? What else is there? As a journalist, over the years, I realized how to follow my nose. Right? It's not something people talk about a lot, but when you're a journalist and you have to write a story that people are going to want to read, you start thinking a lot about what interests them, what leads them to go from one sentence to another. Right. What subjects draw them in. Right. And I slowly learned, you know, and it's a weird metaphor, but, like, you know, your nose kind of tingles. There's something in your brain that tingles when you're like, this is hot. Right. And it's not hot because other people think it's hot. Right? It's hot because there's something really interesting here that I care about, and it's complex. I can feel it call for something in me that makes life richer. Right? And that led me to the best stories in a way, that led me to the best stories just following my nose. And nobody teaches you that in high school. They don't even talk about it. It's hard to explain. You definitely can't quantify it. Right? But it was that I took that lesson when I came to Silicon Valley, and the reason why I ended up in AI was because of that, was because I could just feel it. I could feel that gravity, you know, the tingle of, like, wow, this feels like the future. So if more people did that, I think the world would be a more wonderful place because they would all be producing kind of crazy, wonderful things that we would share with one another. How about that?

I like that. That's beautiful. And it also. It really characterizes your journalistic journey as well, is now you're literally creating systems that go and do that for you. And that's that beautiful, harmonious piece that I was looking for. So I appreciate you very much for digging it out. Chris. It has been an absolute honor, the worldly pleasure to have this conversation with you. And on behalf of everybody at Make More Marbles, thank you so much for being here.

It's been fantastic. Yeah.

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