E16: Artificial Intelligence goes Radical with Jordan Jacobs

Foreign.

I am here to help explain complex subjects in an easy to understand way. Warning the information provided in this podcast is for informational purposes only and should not be taken as investment advice. Any actions taken by the listener based on the content of this podcast are taken at their own risk. Consult your own financial advisor before making any investment investment decisions. This disclaimer was generated by ChatGPT AI, so don't trust everything it says. After all, it's not like AI can predict stock market trends better than a crystal ball. Or can it?

Yes, that was Chat GPT AI that did the disclaimer and even threw in a joke. Amazing. How does it work? Well, today we're talking with Jordan Jacobs, Managing partner and co founder of Radical Ventures, one of the leading AI focused venture capital firms in the world. Jordan gives us a AI 101 lesson how it's revolutionizing the world and will continue to do so what they're doing at Radical Ventures and he even throws in a story about his time with Elton John and meeting Roger Federer. This is a good one. Let's. Foreign. Welcome everyone to the Ginser Wealth Unlimited Podcast. Really, really pleased today to welcome Jordan Jacobs on the topic of artificial intelligence. We're sitting in mid January and the excitement over AI seems to be at a pretty high point. So we've got a topic on artificial intelligence, but we've got real intelligence here, which is Jordan. So he'll he'll give us the lay of the land. I keep saying I'm not going to read the bios of our guests, but lucky enough we keep getting such impressive people that I feel like I have to so a real quick intro or a moderately quick intro to Jordan. Jordan Jacobs is the Managing Partner and co founder of Radical Ventures. Before Radicle, Jordan Co founded Layer 6 AI and was co CEO prior to its acquisition for reported nine figures by TD Bank Group, where he joined as Chief AI Officer. Jordan is also a founder of the Vector Institute for Artificial Intelligence in Toronto, a concept he devised with Radical Ventures and Layer 6 co founder Tomi Putanen and Professor, Pardon me, Geoffrey Hinton, which hopefully we'll talk about later as well. Jordan is a Director of the Canadian Institute for Advanced Research, which administers on behalf of the Government of Canada the world's first national AI strategy, which Jordan helped author. He is also a member of the University of Waterloo President's International Advisory Board, a director of Tennis Canada, one of my favorite things, and has been selected as one of Toronto's 50 most influential people. Jordan previously co founded Mill and we'll hopefully hear the story of that because I think that leads to where he is today with Radical and that was a cultural content discovery platform and early adopter of machine learning for personalization and really exciting for me. He also founded Spybox Media through which he developed and partnered with Elton John's Rocket Pictures to executive produce the Emmy nominated music TV series Spectacle hosted by Elvis Costello. I'm going to stop there because we're going to take up the whole time with Jordan's bio here. But let me just say, Jordan, really, really excited to have this chat with you and very appreciative of your time in this frenzied AI point in time.

Thanks so much for having me, Brian.

No, no problem. So I, you know I, I gave, I gave the long bio here which I keep saying I wasn't going to do, but I did. But maybe if I could turn it back to you, not necessarily for the bio, but can you just give us your story as to, in relation to AI like how did you get here? If I recall correctly, it might have been quasi accidental, I don't know. But you know, it's worked out unbelievably well. Can you give us that sort of story for our listeners?

Sure, yeah. It's a little bit of a circuitous route. It's not the average path to AI. I don't think so. I did not go into university to study this in a PhD. I started my career as a lawyer doing financings for tech and media companies and start first at a big firm and then continued at my own firm. Ended up, as you said, starting a media company and TV series that I made with Elton John and Elvis Costello.

And by the way, I'm going to ask you for some good stories later on that, but that'll be later that

there are even better stories there than in AI but well, maybe we'll do

podcast part two another time but you know, you may be too busy so we'll see.

Sure, happy to do that. So in the making of that show we decided to partner with a charity. The charity ended up being the Product Red, Bono's charity

product. Red is Bono's charity brand that raises money for the fight against HIV AIDS in Africa.

The guy who was a co founder and the president of RED, Don McKinnon and I became very good friends through the course of making the TV series and are working with them and found out we had a mutual love of music and cultural content and Roger Federer and concluded that the way that the world was sharing content online was not very effective. So in, you know, your social media feed at the time, we're talking about like 2008, 9, you would get everything in a. In a. Just kind of an endless stream. So, you know, the greatest song ever squished between my next door neighbor's daughter's ballet recital video and, you know, someone's dancing cat video. And we felt like one of these things probably deserves its own place. The great song. So we talked about this for a long time and then decided we should really maybe pursue it a bit more. We ended up in a meeting at one of the giant tech companies. And we kind of told this senior executive, we knew the idea. And he said to us, okay, we'll partner with you on it and we'll build it with you. And we were kind of shocked and we looked at each other, kind of shocked, and he saw that. So he said, okay, go build it yourself. We'll buy it from you later. And he was mostly kidding. But we walked out of the meeting, which was in New York, and we were standing on the sidewalk and we basically said to each other, let's do this. He would go, quit red. I would dispose of the media and law firm assets and we would focus concentrate on this exclusively. The idea that I had for it was to use this technology I've been reading about called deep learning, which could do prediction really well and I thought should be able to do personalization. Although at that point I was naive enough to not know that no one had done that yet. And the tech wasn't even really proven outside of an academic setting. Very coincidentally, literally, as we walked out on the street, I got a text from one of my clients who I represented for a long time. They'd started the first company that went public and then the second company. And he said, I just, FYI, I just came out of a meeting with the CEO of this giant tech company. They're going to buy us. And I said, great, I will call you shortly. And just FYI, I came out of a meeting. Remember that idea? I floated past you and you said, you've got someone I should talk to as a partner or we're going to do the idea. So I want the intro. So he introduced. That was Dan DeBeau. It was the moment that they decided to sell their company to Salesforce. And he introduced me to Tomi, co founder, subsequently became co founder. Um, so we met Tomi. Don and I met Tomi first on the phone, but a couple weeks later in a meeting in person in Toronto, Don flew up from New York where he was living. And about 20 minutes into the lunch, Tony went up to. Got up to go to the bathroom and we turned. Don and I turned to each other and said let's just do this. A third, a third, a third.

Oh nice.

And so Tony came back and we proposed it to him and he said yes. And that was. That was it. So Tony was really the technical founder.

Yeah.

Dawn had done stuff in culture before but Tony had had built a company in undergrad at Waterloo that he sold to Microsoft, studied with Jeff Hinton, gone to the Valley, ran search at Ink to me, was bought by Yahoo, ran international search at Yahoo left and then built and sold became the ranking engine of B. So I had said to tell me look, I believe in this thing deep Learning that Hinton's lab is doing that I keep that I'm reading about like these academic papers. There's nothing in the news. And Tony said that they weren't even using that technology on the west coast and the tech companies like when he launched what he sold to Bing, it was the most sophisticated machine learning stock in use in the world. Google was then behind. So I felt like this is like the perfect guy for us to meet and it's turned out to be an incredible partnership for dec. More than 12 years, 13 years now. So we basically committed to building this thing and having machine learning at the very core of it. Our first hire was a PhD out of the machine learning group at University of Toronto. Jeff Hinton's group that he led. There were six of them graduating the class of 2012. Ilia who now runs OpenAI. A guy named Alex who much of computer vision was named after one of the things he created, guy who was running self driving cars at Apple, two senior guys at DeepMind and the guy we hired Max. Just an unbelievable group.

Unbelievable.

This was before imagenet.

Imagenet is a large scale image database used for object recognition and computer vision. It contains over 14 million images and more than 22,000 categories.

And so what trans. So we started to build the company and you know, my friends called me and said oh now I know you don't know what you're doing when you're starting a tech company because the first thing you did was hire a PhD in machine learning. And you have no machine and you have nothing for it to learn from. You have no product, you have no data, you have nothing. So we know you're screwed.

Yeah.

And my answer was no, no, no. We're going to architect the product to produce the data for the machine. To learn from. So it learns in real time, basically, which I turned out to be right. But, you know, I think a lot of naivete helped, that if I realized that no one had ever made it work before, I don't know that I would have walked away from the businesses that I'd spent my life building up to do it and pouring money in instead of taking no salary for as long as we did. But it was great to have these two partners. Incredible guys. We worked really hard, and we started to build something that we realized was ahead of the big tech companies. At some point along the way, we decided to spin the AI out into a second company, Layer six, which told me I would run and Don would continue to run the original company, Milk. And we did that all very amicably and stayed, you know, very good friends and very close. And, you know, one thing led to another. We ended up carrying on Flare six and leading to Radical. But that's kind of the origin story.

And what was Layer six relative to Milk? What was Layer six doing? Building that AI engine.

So the. We built an AI engine in Milk that we realized was far ahead of the competition. Yeah, it was this. It was basically designed to do personalization. So the idea was, in Milk, we would partner with, like, the NBA and the New York Times and others, and you could ask a cultural question, like the NBA would say, what are the best Ulster game dunks? And fans could come in and they could post collaboratively into a Playlist video from YouTube and Vimeo and Instagram audio from places. And then we would order it based on your taste.

Right.

Happens in every feed now. That's what in your Facebook feed and Twitter, in YouTube and when you shop at Amazon. At the time, no one was doing it, so we were ahead of everyone in using deep learning to do this. If they were doing it, they were using much more rudimentary tools. So the benefit of what we built was, it turned out, was it would ingest all kinds of different data points. So audio, video, time series, data, personalization, history, but any other data that we could feed it, and it would make a prediction, a unified prediction. Instead of doing these different predictions based on each different data type and then kind of adding it together, it would take all the data and make one prediction, and we could do it in real time and at scale. And that those qualities we realized were far ahead of the competition, we decided to enter this contest called the Rexis Challenge, that is administered by the organization that gives the Turing Award.

The Rexis Challenge is an annual competition that Encourages research and development in the field of recommendation systems. It features various tasks and is open to academic and industry participants. The Turing Award is often referred to as the Nobel Prize of Computing and is awarded for outstanding contributions to the field of computer science.

And we knew that the big tech companies competed in it and we won it and we blew them away. And that coincided us with having decided to build out this as a separate business. We just felt it was more valuable to be able to use this in many other ways and sell as a product to enterprises. So we spun it out in 2016. The end of 2016, won the competition, had a bunch of acquisition offers like within two weeks of launching the company, wow, decided we're going to focus on customers. The first group of customers with banks who have tons of data but aren't very good at turning it into value at that time and, and saw a really nice ramp of customer base and revenue. And then through the course of that year kept getting acquisition offers in the fall of 2017. So about 12 months in, we're raising a pretty significant fundraising round. And a couple things happened. One, we had a guy named Kai Fu Lee who is very, very famous data scientist in China who actually studied in the U.S. you know, huge, like hundreds of millions of social media followers in China, has written books. He came and wanted to invest in our company and join the board as the, the first western board he would join. And we saw that he, he had built this AI fund in China. Second was goo. Some Google brain researchers released a paper called attention is all you need which described the invention of this new architecture in AI called Transformers. And I can back up a minute, give a little more background to the AI story and how it coincided with our timeline. But that was a very seminal moment. I can talk about again in relation to GPT3, which is chat GPT, which is based on all its modern AI. Based on it.

Talk about that later for sure.

But when we saw that, the third thing that happened was we had one tech company come to us with like five successive offers to buy us out. And I turned to Tomi and I said, after that we just kept saying, go away, we're raising a fund, we're raising money. Rather, we're, we're just going to keep going. And on the sixth offer, I turned to Tomi and said, look, we should talk about why we're saying no, because we're like seven years into this thing. We go another six, seven years and we fail, we'll have blown all our money. We're going to Be moving our families into, like, tents in a forest. We're going to have a very hard conversation with our wives who've supported us all the way along. Why didn't we take all that money, you know, six, seven years ago? And it will be a very bad answer if the answer is because.

Right.

So we better have a real reason for why we're saying no.

And your wife's a lawyer too, so she could really get this out of you. Okay.

Exactly. Yeah. Definitely would know how to interrogate a good answer. So we thought about it, and a couple of things led to us deciding we could sell the company and build the world's leading AI investor. First was we really, truly believe that AI is going to be the most impactful technology in the future of the world. Not just in the way that people use it, but also economically. And that's been validated since. I mean, PwC has put out a report that says AI will add 15 trillion net new to the economy by the end of this decade. Global.

Yeah.

There's other numbers that show that when we were, like, at this moment, 40, 45 years ago. Sorry. In 2018, the global AI software product market was $19 billion. In 2022, it was 450 billion. And in 2026, it's projected to be 900 billion plus. So there's this massive curve of growth. And we really believe that AI would replace all software in the world. It would just eat software. The reason for that is software, when it ships, is static. You code it, you ship it. It doesn't get any better until you ship the next version. In fact, you find holes and then you gotta ship patches. Right. Whereas AI is basically, you can think of it as learning software. It's either in real time or near real time, learning and improving itself all the time and the output of what it's doing. So our belief is every piece of software on earth gets replaced by AI in the next decade. So that we thought the market opportunity is absolutely as big as any market opportunity in the history of any. Like, anything you could possibly look at.

Yeah.

Two, we had exceptionally unique technical and business expertise in AI. The cohort of companies that started when we did, like, the deep minds of the world, if they were good, tended to sell to the big tech companies and never build product externally because we stayed independent for so long. We actually not only wrote the algorithms that were unique and leading, but build product around it that we can then deploy over and over without customization. That part like adding the software that you can sell as a Product and not customizing is as hard as the algorithms in the first place and then the business learnings of how you sell that in. Because as AI software, what we often found was the person we're selling to is the chief data scientist who's threatened by the fact that you're going to give something, a tool to the company that is better than what they can do themselves.

Right.

So how do you make those people your evangelists instead of your antagonists? Yeah, that whole process of writing the algorithms, knowing what's real and what's not, helping be able companies that can do those things, we felt like really had unique expertise. And then the third thing that made us do this was the relationships that we had, we felt were totally unique. So Geoffrey Hinton you mentioned, considered the godfather of AI.

Geoffrey Hinton is a pioneer in the field of artificial intelligence and machine learning, known for his work on deep learning and neural networks, and has won the Turing Award for his contributions.

Jeff is a, was a professor at University of Toronto. Tomi had studied with him in the 90s when actually Tomi's then girlfriend, now wife's father, who was a professor at U of T, said, don't do your thesis in neural nets. It's a dead end. Coincidentally, Tony did it in cryptocurrency. This is like 1997. But he still studied with Jeff. So Tony was ahead of his time on many fronts.

Yeah.

So Jeff ended up being truly like the leading figure in the world. In 2012, he and two of his students won the Stanford competition called ImageNet, that proved that neural nets, his vision of AI, worked better than anything else. And that led to all the big tech companies buying up him and his former students to bring into their companies. So his former students ended up being the heads of AI or the leadership at Google, Brain, DeepMind, OpenAI, Uber, Tesla, Microsoft, Baidu, Facebook, Apple, all led by these people from Toronto from this one little lab in Toronto.

Amazing.

So the leading edge of the, like, the leading tech companies, all from this one little lab. And we have this great relationship with them. And those people had a unique view of where the world was going that we shared. Most of them were considered crazy, including Jeff. No one believed them before they proved it with Imagenet in 2012. And so after 2012, what is that Imagenet?

Just describe that for a second.

So this is. There's a professor at Stanford named Fei Fei Li.

Fei Fei Li is a computer scientist and researcher in artificial intelligence. She is known for her work on how computers can understand images and the connection between the brain and technology,

who runs now their Stanford AI Institute. And she's actually joined us as a partner. She created a 15 million images data set. So images of dogs and cats and people on motor scooters and container ships, and you name it, 22,000 of the images are labeled the other, you know, 14.9 million not labeled. So that you feed the images to the machine. And the idea is the machine has to label the rest of the images and then they measure the accuracy of the labeling. And it turned. So Jeff showed that a neural net system, deep learning, which is this approach that he had of, you know, there's different versions of AI, but this version is, it's. You show examples to a neural net which is basically predicated on the human brain layers of neurons. You don't tell it what to do, you just tell it what you want it to do. So you say to it, I want you to label all the images and you say to it, here are some examples. But you don't tell it any other instructions about how to do it. Yeah, it has to figure out how to do it. It has to figure out in the example, the 22,000 examples, how do you extrapolate from that to the 14.9 million others and get them right? So they did that with a neural net and they won that competition by a pretty significant margin, which proved that neural nets could work for image identification. Neural nets have been adopted for everything else you could imagine from, you know, it's what underlies Google Maps and Waze when you're traveling in your car and does real time mapping for you. All the translation, all the autocomplete, you know, in Gmail or on your phone. Cancer diagnostics, disease cure discovery, like self driving cars, it's all based on this architecture of neural nets.

Wow.

So Hinton subsequently won the Turing Award along with Yoshua Bengio based, who's based in Montreal, and Jan Lecuna, who was one of Hinton's students, who is now at New York University and runs AI at Facebook or Meta rather. So that's kind of the background to it. But all of that kind of background and the fact that we had started the Vector Institute led us this conclusion that we could sell the company and build the world's leading AI investor. So that was a very, very long answer to.

Now we're at radical question. Okay, now that's an amazing answer. And you touched on a couple concepts that we probably have to dive in a little bit deeper on. So why don't we actually step Back for a moment, just for folks that are listening to this that may not be totally in the loop and in the know and understanding AI. Can we do a little bit of AI 101, like you know, starting right from the beginning and you know, can be quick, doesn't have to be super deep, but you know, in simple terms, what is artificial intelligence? How does it differ from traditional computer programming? I think you've already touched on it a little bit, but just in maybe a little bit of snippets here, like maybe. Yeah, what explain artificial intelligence sort of in a pretty basic way.

I guess the very basic conception is just a machine that thinks for itself. It's the simplest way to think about it. And the conception of what that meant has changed. But the idea going back to Alan Turing, the 40s and into a group at Dartmouth University in the 50s, was that you could have a machine that would think for on its own. The way that that ended up being manifest for basically until 2012, for most of the time was actually not really a machine. Hinton would say it's not a machine thinking for itself. It's just programming if then statements. So essentially decision trees that are pre programmed by people and you. And so if you think about like, you know, you're driving down the road, you're pre programming every single possible route you could take. So you turn left and again you program all the variables going forward. And then you turn right, you program all the variables again. So all the variables are there. But Hinton would say that's not thinking for itself. You're just programming. Right, you're hard coding all the variables, all the.

Not sort of the past. That's the way it would have been done pre AI. Is that what you're saying, that that's

the way we would have. No, that would have been called AI for most of, oh, history up till about 2012, until they went Internet. Okay, Hinton had this vision him, there are others. But Hinton was like a primary leader since his PhD in 1972 and then ended up at University of Toronto in the mid-80s. That, that's not AI at all. AI is the way the brain works. The brain works from you, you give it examples of things and it processes the information between seven layers of neurons in the brain. Sorry, six layers of neurons, sorry. And it makes a decision. And so, you know, if you think about the way little kids learn things, a little kid might go burn their hand on an oven and, and they, they learn, that's hot. I'm not gonna do that.

Again, Right.

Or you know, sees something and learns what that thing is and recalls it the next time they see it and can start to make inferences about things going forward. So it's basically example led learning. But you're not telling it what to do, you're just giving the examples that it learns from. And we are very good machines at figuring out what to do. So you know, comparable to self driving cars we are very good at when we're driving down the road as a driver you see something in the corner you recognize that is a mailbox, it's not going to end up in the middle of the street in front of me.

Yeah.

Versus a car that sees it and says is that a mailbox or is that a little girl about to run into the street? Do I need to break or not?

Right.

And you know, you might see something that you've never seen before in a corner, but you know that it's a static object, it's not going to move versus it's not a static object or it's so windy that that might get blown into the street. So your brain can do all these pro processes that by learning from examples and inferring the next thing. So Hinton's view was that's if you really want artificial intelligence, you're, that's what you're doing is you're, you're learning from the brain, you're trying to mimic the brain. And his goal actually was never doing to create artificial intelligence, it was to understand the brain better. And so that was using neural nets and this, this example driven approach. And so since 2012, before that he was considered really an outlier and most people in this field considered him nuts and wouldn't get let his papers be published at conferences. Now he's been proven right. He probably is responsible for more technological impact in the future of the world than maybe almost anybody certainly in the last hundred plus years there. I would say that there's others alongside him too, like Yoshua and others, but just these people have ushered in this era that is changing everything.

Unreal. So okay, so that's clear that that's great and you've again you touched on it a little when you talked about self driving this and that. But maybe a few more concrete examples I guess of how AI is going to change the way we work and live. How would you describe that?

The simplest way to think about it is AI can process things that we can't. So let's use radiology as an example. A radiologist who can train for Years, go to medical school, work in the field for years, apparently retain about 20,000 images in their head at a time. A machine, that's unlimited. So a machine can train on tens of or hundreds of millions of images and basically have the recall of all of them in real time. So if you think about diagnosing hard to diagnose diseases, I think that, you know, that's a very good example of that. Machines can do that better now than people can. Within three years after ImageNet, the error rate for humans of recognizing Images is about 3%. The error rate for machines had dropped below that. They completely shut the competition down a few years later because it didn't make sense anymore that machines had far exceeded the capacity of humans. So that's one example of just like a real life and death diagnosis of something very unusual. You can have the supplemental benefit of the machine.

So, so I guess I definitely want to hear you continue, but the first question that pops into my mind is, and I don't know what a radiologist earns and I don't know what that costs. You know, in Ontario, for example, when we're struggling under ridiculous healthcare costing, does this mean that we don't need, we may not need radiologists and 12 or 15 or a hundred or a thousand other jobs in the next number of years? Or am I, you know, getting too far ahead of myself?

I do think that the jobs will change as they have through history. You know, we don't have milkmen anymore who drive around on milk, you know, horse and buggy and drop off the milk at your house.

Right. I mean, well, we do. I got, well out by Sobeys and Grocery Gateway and so that wasn't a plug, by the way. I get no advertisers here.

Yeah, yeah, well, the past comes back. So, I mean, I think things will change, but I think they'll change gradually and regulation will limit some of that. I don't think the radiologists are going away anytime soon. I think what we do, the image volumes are so big that we just need supplemental help. And what really is valuable is there are many places in the world where there aren't radiologists. So if you're in the middle of nowhere in a rural area, they might have a machine to do an X ray or an ultrasound, but they don't have a local radiologist to interpret it. So it gets sent and you're in a queue and you're waiting for results. Imagine if that can be done by a machine and you can get results in five minutes. And so I think for lots of parts of the world where there are no radiologists, it's a huge benefit. You're suddenly putting the world's best radiologist everywhere, diagnosing, understanding disease and identifying drug targets. So machine learning is being used really extensively now to identify targets to cure disease. And we're invested in a company that does this for cancer. Another company we're invested in out of Vancouver, called Aspect biosystems, uses computer vision and other machine learning to combined with this process to print human tissue using stem cells to cure disease. So, for example, they. They're able to print pancreatic patches which they've implanted in rats, and reverse type 1 diabetes, like. And it stayed reversed.

Yeah.

So if you think about AI being part of that process, it's not the whole process, but part of the process of effectively, you know, if this works in humans, eliminating type 1 diabetes for a percentage of the population, that can take that kind of therapeutic. So. And they're now applying that to other tissue types, liver, et cetera. So, like, the applications in healthcare, I think are enormous, all over the place. Schedule, scheduling better is obvious and all kinds of other applications, but really we're going to just see it in every piece of our lives because the technology is so powerful. It can do things that we've never been able to do before, and it can do things a lot more cheaply than before, which, on one hand there is a threat potentially to humans. But I think really what it will do is what we've always done with technology, which is scale up the bottom and allow people to do more interesting, more intellectual, more challenging jobs that humans are uniquely adapted to do. So I don't think we're going to see in the short term or even medium term, like massive job losses. I think over the long term, you'll just see different jobs that didn't exist before.

Okay, you used two phrases there that lead right into my next question. You said something along the lines of it unbelievably or astonishingly powerful. And then you said threat to humans, but you were talking about job losses. But Elon Musk has been pretty vocal about his concern that AI might not only be so wonderful, it might take over the world and, you know, kill all the humans. I don't know what. You're in the depths of this and you see, you know, you see a ton. And what does that sound like to you? Is that, is that realistic?

I don't think. Yeah, I don't think you or I have to worry about this in our lifetimes.

Right.

You know, like, look, at the end of the day, the, the AIs are created by humans. So, you know, part of it is like, what are you telling it to do?

Right?

So the idea of artificial general intelligence, which is what he's talking about and what OpenAI is pursuing and some others, is an AI that can do many, many different things. And ChatGPT is maybe the first example of people being able to play with something like that. It can do lots of different things. It can code for you, do math, write stories, answer questions. So the power and the speed of advancement is really amazing. I don't think we're gonna have, we're have to worry about machines taking over the world. I mean, you can always just pull the plug out of the wall, right?

I hope so. I hope so. I've seen Terminator too many times. Maybe I'm a little nervous.

You know, I can't guarantee this for your great grandkids, but I would say we're ways away from this. I mean, I do think there's way

more

present and proximate threats like climate change and other things rather than Terminators.

AI is going to help with that too, I suspect. You mentioned ChatGPT. This is probably where a lot of excitement is coming from. Can you just explain for folks who may not know what this is and we might even do a little bit demo a test of this thing in a minute. But what is ChatGPT and what's it doing and why is it so exciting, I guess right now?

So it's a basically a chat bot that was put out by OpenAI. OpenAI is a. Originally started as a research institute for AI that was the chief scientist is one of Hinton's students from that class of 2012.

I see a theme developing there.

The technology, the underpinning technology they're using are transformers. So that technology I mentioned that we were looking at, we were considering selling the company was Transformers and we.

What. What does that mean, Transformers?

So it's an architecture that it. It basically is taking the neural nets architecture and it is effectively allowing it to scale up and do much more. And the I. It's a little bit technical to get into like exactly how it works. But let's think of it as it's able to look at huge corpuses of data and understand the relationship, the weights, values of things to each other as opposed to in a linear way. So an example of that is when it reads a sentence, it doesn't read the sentence from the first word, second word, third word. For it to the end and then, and then decide what it means. It looks at the whole sentence and it recognizes the words in relation to each other and decides which words matter most in that sentence in relation to each other. And in the context of what it's learned before.

Wow.

Which is actually the way that humans think, believe it or not. But it's able. So it's able to go out and read like Wikipedia or the Internet and then learn so much about the world that when you ask a question in ChatGPT, it's able to answer things about everything. They. This system that they released is a few weeks old when we're recording this.

Yeah.

It allows so examples of questions. You could ask it to. To write code for you. You. And so the way that you're doing that is you're. You're asking it in what's called a prompt, but basically it's just you're writing something saying, do this. You could ask it to create a piece of text or to summarize something, or you could say to it, write me a Seinfeld episode where Kramer invents the transformer. And, and it, you know, and it'll write a pretty authentic sounding in the sound of what Seinfeld is like and with those characters and, and so you can really use it in a very powerful way.

And I guess that's what people are doing and they're testing it out and being, I guess, amazed by what the. And maybe the first time the average person can see an output of what AI really is all about.

Exactly. I mean, I've had a bunch of our LPs who are in tech call me and say, I've had three moments in my life where I really believed everything just changed. One was the Internet, two is the iPhone one, and three is ChatGPT right now.

Okay, so, so let's just see something here. If we, if we can do this and see if this works with. With our listeners as well. So, you know, for those who are listening. But we may put a video clip here. I've got Chat GPT up on my screen. It's chat.OpenAI.com you have to. It's actually. They want you to give feedback, so you gotta log in. But we're here. So Jordan, who's definitely the AI expert, has given us a description of AI. Let's just see what Chat GPT says. So I'm just typing now. Explain what? Let's just say explain AI to a kindergarten student in 50 words or less. Because we don't have Time to do this for too long and it's thinking and it writes right away. AI is like a robot brain that can think and learn like a person. It can do things like play games, answer questions and help people pretty darn good. I guess.

Maybe take off the limitation of the kindergarten students, see if it does a better job.

Okay. Oh yeah, well, for sure. Explain. Oops, I can't type here. Explain AI in 50 words or less. And so I'm, I'm, I'm continuing this chat for the listener. This is not a new thing. We're just continuing down. AI is technology that can mimic human intelligence and perform tasks such as decision making, problem solving and learning. It can be used in areas like self driving cars, voice assistants and medical diagnosis. So that's a little bit more sophisticated and better. But to Jordan's comment about the Seinfeld script and hopefully listeners know I love Seinfeld, but we won't do that. How about explain AI in 50 words or less in the way Donald Trump would explain it. Let's see what happens now. I don't know if I should do an impression, but this is typing as we go. AI is a big deal, folks. It's like a computer brain that can think and learn just like us. We're using it to make America great again with smart cars and technology that will make life easier for everybody. Believe me, I'm not, I'm not a, I'm not a Donald Trump impression or impressionist rather. But anyway, so you know, this, you can see and I guess the question is, you know, I guess I could have typed into Google what is AI? And I would get some description. But I, I think that these neck, you know, the next steps, down and down. Google's not going to be, you know, creating a, an AI explanation like Donald Trump or like some other whatever, whatever the heck you want to do. So what does this mean actually for if ChatGPT can answer all my questions and answer them frankly how I want them to be answered. What does this mean for Google in Microsoft, Bing? Although I think they may be doing something with OpenAI but Google is, you know, dominates any. Any thoughts? I'm going to shut this now, but shut my screen share any thoughts? Jordan, what this means for Google and who should be afraid here. Forget about the good stuff AI is going to do, who should be shaking in their boots?

Any company that has not already got an AI strategy, it should be on top of it yesterday. This is one of those things that if you wait you will get wipes. I think As a business.

Right.

A new company will come and become the incumbent and displace you or when your competitors will adopt it faster. So this is not one of those technologies that I think, I mean if you wait, you know, if you've played wait and see till now, you should stop and you should really get on it. So that's one thing. The big incumbents, I think there is a risk. So we're invested in a company called U.com, started by a guy named Richard Socer, who's one of the most well known NLP language researchers out of Stanford.

Natural Language Processing is a field of artificial intelligence that deals with the interaction between computers and human languages.

Ran built a company, sold it to Salesforce. Ran AI at Salesforce, built their AI products there. He started this company to basically be a new search engine to do things a little bit differently. And they were the first to incorporate chat in their search results. They did it December 23rd. So a few weeks ago. Yeah, they immediately saw their traffic go vertical and a lot of freaking out from the big companies.

Yeah.

To the point where with like over the Christmas holidays ended up in the seat on the phone with the CEO of some of the big companies.

Yeah.

Like during the Christmas holidays. So you can tell they're, they're concerned about the competitors here. Yeah, I mean that's an example of how seriously this could disrupt some of the biggest businesses in the world, including Google's, which is I think the greatest single business model in history. Their search product generates 150 billion plus a year in revenue. They've built internally what is equivalent to OpenAI called Lambda. They had an employee last summer claim it was so good that the employee said it was sentient and that person is no longer at the company. But they haven't released it because Google's concern is if we release it and it gives incorrect results, which these things tend to sometimes do that that really impugns the veracity of the search results and people's ability to rely on Google, not to mention their business model is the links that result when you do search. So they have to rethink the links. Cause it's not easy to put links into a chat result as easily.

Yeah.

So there are some big, big issues in the horizon here. Google I think is going to imminently respond with their own product like within weeks. But. And maybe by the time people are listening to this, it'll have happened. So I think the world is going to change pretty dramatically. The average person may not notice it day to day. But businesses should be aware of it right now.

Well, and my understanding is that Microsoft has made an investment and may be making a larger partnership with OpenAI. I don't know anything confidential, just sort of what I'm hearing. But I can imagine, you know, we use Microsoft Office Suite. I can imagine I'm in Word and I just say, hey, Bing, you know, prepare me a proposal for a new client based on the normal questions that we would ask. I don't know. And actually, actually I did. I should have said this up front. So I always prepare a nice agenda. I hope it's a nice agenda for these podcasts. I actually didn't prepare the agenda for the first portion of this podcast. ChatGPT prepared the agenda for the first portion of this podcast. So I said to ChatGPT give me 10 questions to ask an AI expert on what is AI? And so on and so forth. And I got 10 questions and I will say that three of them I deleted, didn't need them. Three of them I tweaked and, and four of them I guess I kept exactly the same. So it saved me. I used my brain, I used the real intelligence, but I used it after I had the AI give me a full sort of, you know, head start basically. So imagine that inside Microsoft that, you know, most of the world's businesses use, or many of the world's businesses, I should say. Unbelievable. Okay, we better keep.

I was gonna say the other. The immediate challenge is gonna be for teachers. So already many schools have started to ban the use of ChatGPT for essay writing because it can write us beautifully and better than many students can.

Yep.

And teachers can't tell the difference other than the fact that suddenly students are writing much better essays than they did before. So there's been a fairly widespread ban in elementary and middle and high school and then even in universities in using this stuff. It's going to be a cat and mouse game. I think that very hard. Yeah, probably you're going to have to have universities at least who can afford it, buy software that is AI, that identifies when things have been written by AI and so unreal, you know, so that, that is, that is happening in real time like literally within the last couple of weeks because of ChatGPT, the, the other answer to the question about where this goes. So we're investors in a company called Cohere, based in Toronto, founded by one of the inventors of Transformers. He was a 19 year old kid at Google Brain when he was a co inventor. They're a competitor to OpenAI, their approach is to build this technology and, and enable every business in the world to get access to it. None of whom can afford to build it themselves or hire people.

Right.

OpenAI is tied up with Microsoft. Microsoft is apparently making a 10 billion investment. Essentially it is becoming a subsidiary of Microsoft. It will get infused in all the products in Microsoft. They've already said that. It will be in everything, which goes to the point we made about. I made about AI will eat all software. So it will be infused in Word and in Excel and everywhere else. And you'll see that other companies do the same. Google will do it and it's all of its products. It's already done a lot of them, but lots of other companies too. And then for all of those organizations in the world that can't build their own AI divisions or spend 10 billion investing in AI, they'll hope is there'll be buyers from our company Cohere, which is growing really well and is really one of the primary competitors to OpenAI.

Amazing. So, speaking of your portfolio of companies, maybe we'll just shift over to Radical and what you're doing there. You've given us a little bit of the story about how you got there, but for our listeners, what is Radical, you know, started a few years ago. I know what, what types of things or companies I should say, are you invested in successes? What do you see in the future? Anyways, just a little bit of overview. Radical for our listeners. And by the way, I know you're, I think, I know you're raising a new fund now and I don't know if that's all institutional or, you know, private investors as well, but, you know, maybe give us a little bit of the story about Radical and what you're doing there.

Sure. We started Radical originally to consolidate the angel investing Tomi and I had been doing since we met really, in like a, you know, a decade, more than a decade ago in AI companies. So we were just writing personal, like little personal checks into AI companies which had gone well, I'm not sure if you asked my wife while I was doing a startup and not earning a salary if it was smart to continue to write checks, buy a new car for a decade. But anyway, she supported it and we did and it's gone well.

Yeah.

In, in 2017, we decided while we were running layer six and we'd spun it out and we'd also started the Vector Institute.

The Vector Institute is an independent research organization based in Toronto, Canada, focused on advancing the field of AI by training the Next generation of AI leaders and fostering cutting edge research

which has now grown to like 3,000 researchers. We had a lot on our plate and we just decided it was too challenging to write checks individually every time. And we always had people come to us and say hey can you get me in your deal? So we decided to just create a little dedicated pool of capital that we could do that like at our decision without having to go wrangle like checks from everybody. As, as you know, wealthy people are not the best at filling out their forms.

And so we hand we handle that for them if they like. To be clear, we're not investors in and we have no affiliation with Radical but in general we handle all that paperwork for clients.

That's good. It would've made that life easier back then. So anyway we created this little fund, a $7 million fund, 7 million US fund to write precede and seed checks like $250,000 checks. And then when we decided to do to actually sell the company and like go full into Radical. That was five years ago this week.

Oh, congrats.

And we told, we decided to keep it Canadian. The offers we had were from outside of Canada but we wanted to keep Canadian after how to create the paying Canadian strategy and Vector went to a couple of banks. TD ended up buying us and we told them we're going to do this fund and they were very good. They said stay until you get the company integrated and then we'll let you know and we'll let you go. So they called me six months later and said you've already generated a tremendous amount of value. We want to be in go raise your fund. We want to be an lp. Nice and but in the meantime stick around and if it doesn't work out stay. Which was fantastic of them. They were, you know, they made us very senior like one layer between us and the CEO and it was very generous for them to let me to do that while I remained on staff and you know, showing up to a few meetings a week, went out and raised the fund left in May of 2019. And you know when we started we had commitments from cpp, IB Canada Pension Plan, psp, Temasec, the Singapore Sovereign Wealth Fund. All of them were considered like a plus global institutional investors which was nice. PSP and CPP had not done venture before so we were the first venture investment. They've since done all kinds of great funds in the U.S. i think they've other done other Canadian funds. Maybe CPP has done one now. Tomasic had not invested in Canada Venture before, you know, institutional investors like td, Big family offices like Whittington and the Weston family, Also in the U.S. eric Schmidt, who'd run Google, which is the Newhouse family that owns Conde Nast and Reddit, big shareholder and Warner Discovery. So a bunch of great investors and then and then like down to kind of smaller family offices, people that invested in our company and done well with us and wanted to come back in and obviously our own money too. So we started May of 2019. We ended up closing that fund at 325 million. The focus is exclusively AI. We tend to invest early. So it's Series A is the sweet spot. We will go as early as doing a couple of incubations. Cohere is an example of a company that we got involved with even before they incorporated, when the guys were still at Google Brain.

Right.

And work with them on the business model and what they're doing and then put the money they were. We were the only check when they. The day they incorporated. And so our team has now grown. We've done 27 investments in that first institutional fund, finished investing out of that fund in new companies. We will have still follow ons in the existing companies. The performance has been fantastic. We now have a new institutional fund, which is our third fund, although it's actually our fourth fund because we have an opportunity fund as well. Because our companies have done so well, we need to follow on investments and we need more capital. So in total, in our new fund, we're targeting a $550 million fund. We've closed most of it. All the key LPs have come back, plus a bunch of additional people. Jeff Hinton's part of the management group, investment group in the fund. Fei Fei Li, as I say, runs AI at Stanford. Some other top luminaries in the world as well invested. So we're on the road to closing out that fund and we started investing out of it in the second half of 2022. The investment geography in our first institutional fund was just North America, about half Canada or a little more than half Canada. The balance in the U.S. mostly Bay Area.

Yep.

The new fund will continue to be focused in North America, but we've already done a deal in London and one in Israel. The other news is that we will soon open an office in London. People there. So, you know, a lot of stuff

going on interestingly and sorry to interrupt you. You said Toronto first of all is the home. And you, you explained earlier in the podcast how unbelievable the talent is here. And you just Said London's your next office.

I, you know we did open Palo Alto.

I was going to ask you about that too. Okay. Yeah. So you're, you're in, you're in the sort of the Silicon Valley area as well.

Yeah, we brought on a partner there about a year and a half ago and sort of build up that office, you know, work out of there regularly and have team members in California. So. Yeah, absolutely. So Toronto, Palo Alto. Second one of our leaders is in New York and then we'll add London.

Amazing. That's a huge, huge success. Congratulations. Maybe you talked a little bit about some of your portfolio companies. One that is exciting for me. So I drive a Tesla. I, A few years ago I paid for the full self driving. It cost, it was an additional 10,000 at the time. Now it's 19,000 if you want to turn on full self driving. So as it gets smarter and smarter. Speaking of AI, Elon Musk keeps making it more and more expensive. But it is that. Speaking of one of the, you said one of your investors early on the sort of one of those moments that everything has changed with Tesla. Just a quick story. So you buy the self driving for $10,000 at the time and you don't get it right away. You have, first of all it wasn't ready and you had to wait. And then you got to go on the, sorry, you have to apply to be on the beta program and then they start monitoring your driving for weeks. And so every second you're driving the car they're giving you a score. And if you follow too closely, turn too quickly, hit the brakes too fast, your score goes down. Which actually by the way is unbelievable. You think about from a safe driving and insurance perspective. Anyone who wanted that self driving was driving like, I don't want to say it but you know, like a little old person or whatever, like Kramer, when he was about when he was worried he was going to get hit and be in a coma in Seinfeld. But anyways, and then I got it and then, and, and it, and the way it works is it just downloads over WI Fi, which is pretty amazing for those who don't know. And the full self driving came on and it was at night because you know that's where my car's parked at home and it's attached to WI Fi. And I got in the car and I turned it on in my neighborhood and I realized like the world has changed from this point forward. Is it perfect? No, by any stretch. But it the feeling that I had and seeing What I saw, it was like I stepped into the next dimension, you know. And I called my brother in law who lives a few kilometers away and I said I'm coming to your house right now, you have to see this. And we took a drive around and anyways it was unbelievable and it actually gets smarter and smarter every few weeks as these updates go. You have a self driving startup in your portfolio, wabi I think it's called, if I'm pronouncing it right, run by Raquel Erdogan, formerly head of self driving at Uber. Maybe just share from your perspective what self driving looks like. What does that mean for the world? I don't know if there's a difference between what she's doing versus how Tesla is doing this, but I'm a real car enthusiast and a technology enthusiast so put these two things together. It's, I like those almost as much as tennis and music which we're going to talk about before, before you have to jump. But what's, what's what, what's happening at, at Wabi, one of your portfolio companies?

So yeah, Raquel is a professor at University of Toronto. She like one of the world leaders in computer vision but also in applying modern AI deep learning to self driving. So unlike a lot of the self driving companies who are kind of hard coding things, they were using a deep learning system which what they've done at Wabi is they've invented a simulator for the training of the car. So the car, the, the is digital and is sitting in a like created simulated digital world that constantly throws new things at the car or the car to experience and learn from and improve. And so the reason for that is the, for a machine to learn how to drive in a real environment requires so many tens of millions of miles to see everything that could possibly happen. You know there's so many edge cases that are unusual. A bag blowing across the road and obscuring your vision or just things that are very, you know, lightning strikes and a pole falls in front of you. Those things are important to know how to deal with if they happen because they're life or death in drive in a driving scenario. So they can massively accelerate the learning at a, at a virtually no cost by doing it through this AI simulator. They still need to go out on the road and I think they will very soon announce a partnership with a trucking company. They're focused on trucking.

Right?

Trucking. Trucking is an obvious use case. There are, there's a very significant lack of truckers in North America and a lot of the cohort of truckers will retire and age out and there's not a replacement.

Yeah.

So you're going to need machines to be able to do that. The idea is highway driving is much simpler than city driving where people on the road, pedestrians and scooters and bicycles and stop and starts and all those things that complicate the act of driving. So you'll see a pretty significant deployment in trucking I think over the next decade globally. And I think that it doesn't mean that truckers are going to lose their jobs. I think this is, it's the exact same as plane pilots and subway conductors and train conductors, boat conduct, you know, boat pilots. Many of these things that we are used to traveling in are run by AI. A plane is not flown by the pilot for most of the flight.

Right.

And I don't know if when people get in the plane, if they think about that or not. But for a long, long time there's an autopilot system that is flying most of the time. Yeah, just like that. I think the governments will regulate that. You need to have a driver in the cab of the truck and it is there alongside the AI system. But I think ultimately what we'll see is that most of trucking on highways for sure is, is self driving and eventually in cities too it'll, it'll just be. The predominant method of transportation will be self driving. If you go to San Francisco now, you can hail the equivalent of an Uber through Cruise, which is GM's self driving unit. They have a hundred cars in the road with no drivers. Like a hundred self driving cars that are just driving around. So you can, you can hail a cruise, get in and it'll take you to your destination and there's no people in it.

Well, you know, as I say. So I've got this on my car. I have not yet, I've not yet experienced a. In the city driving where I haven't had to take over, you know, if any in sort of as you say, these, these, these weird things that happen and especially in Toronto. Lots of construction, lots of potholes, lots of this and that. So to hear that, that actually happening with full self driving, that's, that's amazing. Okay, so you know, you run a, essentially an institutional focused venture capital firm. The average person, you know, unfortunately is likely not going to be able to become a limited partner at Radical. What's the best way for you know, the typical investor out there to get exposure to like direct exposure? I guess because if you say this is going to be involved in everything and revolutionize everything. Ultimately every company will have some AI component to it. But today, if someone wants to sort of ride this wave of excitement and technological innovation, how do investors get access to this?

You can buy shares in the public companies that are really AI companies. So the big tech companies are AI driven companies. Google, Microsoft, Amazon, like those are AI companies that like, they're all, it's all over the place. I mean Amazon uses it all their logistics in all their website, like it's just everywhere. So if you want exposure to companies that are on the leading edge of deploying it, those are examples that an average retail investor can make the investment in through the public markets. I think in the private markets, obviously there's credit investor limitations, but it might be in future ways for people to pool capital and get into some of these things. Ultimately, over the next five, 10 years, everything's going to be an AI company. You know, every bank, every insurance company, every medical company, every farming entity, everything. So I think, you know, if people are interested, they should keep an eye on, they should read, they should do some homework, see which companies are adopting this for real versus just talking about it. And you'll see, I think the companies that are on the early curve of adoption pulling ahead far ahead of others, they don't have to be developing this stuff themselves. If they're just licensing the technology from startups that we invest in, that is a very good way to get adoption without having to spend all the R and D expense.

Right? You got it. All right, well look, that, that's an amazing overview of your story. What AI is, what radical's doing, how AI is going to revolutionize the world before we end. If you can indulge me and us because as you know, you know me well. Music and tennis, my two favorite things. If we could combine them somehow, that would be amazing. And you've had a ton of involvement in both of these areas. Can you, you're okay with it? Can you tell us maybe your favorite story or an interesting story from your work with Elvis Costello and Elton John on Spectacle?

Yeah, sure. I can give you a couple stories. One is just an example of Elton's work ethic. Yeah, so when we, we had the concept for the show, so just to describe it to people, the idea is having been had a background where when I was a lawyer, I was doing representing tech entrepreneurs, but also musical artists and athletes. I just loved music and entertainment and tech and sports. So I was able to do all three and I was lucky yeah, One of the benefits of that was you'd be backstage with artists and you'd hear kind of stories behind the scenes, how they wrote this song, you know, talking, gossiping about other artists, like all the fun stuff.

Yeah.

That they, you wouldn't hear when they would show up on the late night talk show and do play a song and promote a new record for three minutes on the couch.

Right.

The idea was, could you have an artist interview them where they would be at ease and they would talk about the things they love and their motivations and how they wrote these songs. So that was the idea for the show. The artist that was hosting it was Elvis Costello. Elton was an executive producer with me and a few others. And we would get great guests like Bottom on the edge from U2 and the police and Lou Reed, Herbie Hancock, Nora Jones. I'm getting all kinds of great Bruce Springsteen who would basically sit and talk and perform. They would demonstrate, like Sting explained how he wrote Roxanne and how it transformed as he was writing it. And then they would also perform like full out performances too. So when we were pitching the show, you know, the same with trying to raise a fund or raise money for your company. Most people think you're nuts if you're trying something big. And so, and we were going around and we were pitching and we were, we were in discussions with the channel that ended up running it in the us, Sundance channel. We were having, we're going to have a big meeting in New York and Elton's husband, David Furnish said, why don't we do it at our hotel and we'll meet on, I think it was like a 9am on a Monday morning. And so a couple of us from Toronto who are partners in this and David down. And what David, David didn't tell the broadcaster is what I found out when I walked in the room. And he didn't even promise us, he said, you know, he said to us, like, just show up a bit like half an hour early. I get them like 8:30 in the morning and Elton's in the room.

Right.

That was a Monday morning. Elton had started the day. I unders, this is what I recall. And it's a while ago. Elton started the day on Sunday in London, flew to Eastern Europe to do a performance, then flew overnight to New York, landed at like 4 o' clock in the morning, went straight to the hotel, you know, maybe got a bit of sleep, showered, whatever, was dressed perfectly to the nines. When this crew walked in from the like eight or nine people from the broadcaster. And they walk in and Elton John's in the room and he was like, amazing. He knew the story perfectly. He, like, he, you know, he dazzled them, as superstars can do.

Yeah.

You know, like there are these people in the world who just have extraordinary level of charisma, but also are brilliant and, you know, can carry conversation, can make you feel very comfortable. And so just his willingness to put in that effort to do that was an example, I think, of why people who achieve something extraordinary and reach really the apex of their field and something incredibly competitive, how you. How you stay there, is that you're willing to do as much work or more even, than you did on the way up.

Right.

Not take things for granted, not expect it just to come to you. There is a very good example, I think, of that of someone who, you know, has been at the top of their game for 40 plus years, almost 50 years at this point, which is unbelievable. Like, if you think about the history of music, how many artists achieve incredible success and then disappear after two or three or four years, or 10 years, or 20 years, stay there at that level for that long is a dedicate, like an example of his dedication and work ethic in addition to his musical genius.

Yeah.

So that was a really nice example for me. We had lots of funny things happen along the way in making the show. One of the most amazing things was we were booked to have you two perform, like, be on the show bought on the Edge in our second season as our first episode, and we were shooting the show in New York and we bookended this. We had to shoot them together one after another through the course of a week or week and a half, two weeks, in order to make the budgeting work. And we were in season two at the Apollo Theater in New York, and about two weeks before we started, so we were going to shoot YouTube, like the bottom Edge episode is the first episode of the season and Springsteen is the last episode. And about two weeks before, I got a call from YouTube's management saying, we can't do it. We're on this 360 tour. It's the biggest tour in the history of the world.

Yeah, I was there. Remember that?

We're squeezing you in between shows. Bono's voice needs a rest. We can't do it.

Yeah.

And I was like, oh, that's bad. You know, I'm. I'm gonna just so you know, So I said to the manager, you know, we're partnered with his charity.

Right.

Last year, Elton did a song for his charity for Bono's charity with the Killers.

Yeah.

So, you know, I mean, you're gonna have to let Elton know that even though he did that, now these guys are backing out. And I'm, you know, that's. So he's probably gonna hear from Elton. Mono's probably gonna hear from Elton.

Right.

But she said to me, okay, don't do anything. I'll call you back.

I think you know what you're doing, eh? I love it.

Call me back in about 10 minutes. And said, okay, we can do Toronto in nine days. I think it was like, this was a Thursday, or maybe it was a 10 days, 11 days. Anyway, it was very short. And she didn't know I was in Toronto. She knew the show was being shot in New York. And so I said, yes. I said, okay, yes. And she's, she's like, what? I expected me to say, no, that's impossible, right? But I said yes. And then we got off the phone, I called the head of CTV who was our partner in Canada, and said to her, great news. You know that Bono Edge episode we're going to shoot in New York? She said, yeah. I said, we're going to shoot in Toronto instead. She said, oh, amazing. Where? I said, in your studio, the Masonic Temple based. And she's like, oh, when? I said a week Monday. And she stopped and she said, you know, we shoot in there on Mondays. I said, yeah, but you'll just, you won't shoot there that day. You'll take the stuff out. You know, we'll move in on Sunday, we'll shoot on Monday, it'll be gone. You can shoot again on Tuesday. And then she paused for a while and she said, how many tickets can I have? And I said, well, how many does it hold? She's like 120. And I said, well, how many do you want? She's like, you know, I'm making up the numbers now, but she's like 90. And I said 30. And then she's like 80 and I'm like 40. And, you know, we had a haggle over it at the end of the day, she got as many as she needed, but we were able to do this move the episode. And because we were shooting in Toronto and I had been running and we were. We had been shooting the show in New York before, and I'd been doing that in parallel to running the law firm and doing other stuff. I really had not seen my friends much in a couple of years. Yeah, and they, you know, they, they. I think there was. They believed it Was maybe true that I'd been making the show, right.

Maybe you really know Elton John, right? Maybe.

So I mean it had been on the air at that point, they knew, but, but, but so we ended up doing the taping in Toronto and we historically would invite in the fan club of that band to populate the seats. Like just give them the seats for free.

Yeah.

And I said to like our day to day producers and director, I'm like look, I, I can fill this with my friends and I mean let's go get their, the fan club but I can also fill it up. So we basically I invited like a lot of my friends and so when we were taping the show I was standing with the director and he's like who's that? Like that's my brother. Who's that? That's you know, my friend, my friend from grade school. Who's that? And so when they produce the output of the show at the end, you know, they label them like it's you know, season two, episode one is episode 201 and you know it's spectacle with Bono Mia. So in this case they gave me a dub of it, like a copy of it on CD and it was labeled episode 201, Jordan's Bar Mitzvah show with Bono and the X.

Amazing.

So I mean it was for me it was fun to be able to do that in Toronto. I our apartment, when we conceived the show we lived on an apartment on that same block. So I used to walk past that place. YouTube is one of my favorite groups of all time. So I used to walk past there imagining we would do this show there. When we were doing the show, we were doing it all in New York in Saturday Live Studio then the Apollo. So if there was no idea that we were going to ever do it there and just a cork of fate ended up having it there.

So.

And that was, it was a lot of fun. So you know, I was able to take at the time our two year old over there while like, you know, the opening chords are being played of where the Streets of no Name. Like you know, five of us in, in the crew in, in the rehearsal and stuff. So that it was a lot of

fun, amazing, amazing experience. And you know you talk about being at the top of your game of the people that you're working with. Top of the game and working so hard and having a big vision and that's how you make things happen. Frankly, that's my view of you. I mean you've created all this, all these different things One of the greatest AI companies sold to td, I think at the time the largest venture firm focused on AI with your first capital raising. Sorry. In North America. At the very least, you're a big thinker and you're. You're at the top of the game, it seems like. So it's absolutely amazing. Good, Good lessons to share with our listeners too. Any, any interesting or great things on the tennis side? I know you're a big Federer fan. Your director of Tennis Canada. Excuse me, wait. Director of. I'm still doing the bar mitzvah thing with my voice cracking there. Director of Tennis Canada. Any. You know, and I know it would be. You've been very generous with your time. So quickly. Any interesting tennis stories? My favorite things to do.

Yeah. As you know, I love tennis, so I've been very privileged to be on the Tennis Canada board recently. An amazing thing was Tana. It was Canada winning the Davis cup, which is like the World cup of Tennis.

Yeah.

An amazing thing to witness kind of a little bit from the inside. I wasn't actually there, but have heard from many others who were. That it was just an incredible experience. I've been lucky. I mean, I've been able to as a fan be. I've met Federer. Excuse me. I've been able to go, after we sold the company, a bucket list was to go to Wimbledon. Yeah, we got to do that into the French Open final. I mean, these are all things before Tennis Canada. But Tennis Canada has really been an amazing experience because the organization is unique in that it is responsible for the grassroots development of players, like from little kids up to pro. It's also responsible for coaching, certification, things like that. But then it also has the tournaments and it's a co owner of the tours, right. The men's women's tour. So it's very plugged in at a global level and then all the way down to grassroots development level for to. To try and generate the next generation of superstar players. And so it's mostly self funding through the tournaments and revenues it derives in the revenues that gets from the tours as a co owner. So it's a really fascinating organization in this because it's very different from most other national sports organizations because of the ability to fund and it's got all these different moving parts. So it's really complicated. A lot of interesting business things at a global level. One of the things that's happening now is this new Netflix series from the producers of Drive to Survive, which is

coming out on the day we're recording this January 13th, I guess this evening. I can't wait.

Yeah, it is. I've heard that. It's really good. I'm looking forward to. There's a prohibition in our house that we're going to watch it together, so no one jumps the line and watches.

Oh yeah, you got a lot of tennis players in the house and tennis fans. So.

So, yeah. You know, I think meeting Federer was an amazing experience. It happened in 2012. I have two brothers, both of whom are getting married that year. You can tell what kind of family are. Because the stag event for my youngest brother was taking him to New York and going to an exhibition match of Federer versus Roddick.

Right. That was telling me about that.

We flew down there and a cousin of mine worked at Madison Square Garden and I, he. And the, even the, the afternoon before that match, I got a phone call that said, be at this gate at Madison Square Garden at 5:00'. Clock. And I'm like, why? He goes, don't ask questions, just be there.

Yeah.

I called my brothers and I was with my partner Don, who I'd mentioned earlier, and Don was going to come with us. Also a huge tennis fan. As I said, we were bonded over being Federer fans.

Yeah.

And so the, the stag was me and my two brothers and Don. But at this tennis match. But Don was going to go home or something and I said, don't go home, come with us. So we, we get there and they meet. It's the player's entrance to Madison Square Garden and they take in there like Mr. Jacobs, Mr. Jacobs, Mr. Jacobs,. Mr. McKinnon. Let's go. And they get up in the elevator and you go up and you exit on ice level, like floor level for Madison Square Garden is above ground. It's about five or six stories up, I think. And they, we walk out from the players elevator entrance and you, you kind of come out the corner into the playing area and the court is set up and they look around and you can see there's security people, there's TV people setting up cameras and lighting and stuff. And then there's Federer and Roddick on court, hitting with each other.

Yeah.

And on the near side when we come in is, is Roddick and the other far side is Federer. And they walk us along the side of the court and they've set up four chairs on the edge of the doubles alley on Federer's side by the baseline, like. And they, so they sat, they said, these are your seats. You know, watch the practice. And we sit down, we watch the practice, which Was. I mean, we're literally like 5, 8, 10ft away from him as he's hitting. Like, if he runs too far, he's going to hit. Run into us.

Yeah.

On his forehand. So.

Oh, my God.

Watching just his footwork in his hands was, as a. But a tennis fan generally just unbelievable. I'd been live tennis lots.

Yeah.

But being that close and just seeing how fast his hands were and how fast his feet were was amazing. And then it, when it ends, we were like, that was the greatest thing ever. He walks out of the tunnel and then he spins around and he comes back and he comes right straight up to us and he sticks out his hand, he goes, hi, I'm Roger and

did you say pooh?

Yeah, yeah, we know. We know who you are. And we had a really nice conversation with him where unlike almost every. I've had the privilege of meeting lots of celebrities. Their handlers are always like time bound. They pull them away. He wasn't. There was no one pulling him away. He was just having a totally normal conversation. He says, why guys here? Because he knew we're from Toronto. He said, these guys are getting married. And he's like, oh. And then he went to the whole story about, oh, you know, I met my wife. Her name is Mirka. I met her, you know, like, we know your wife is married. Yeah, met her. I met her in Australia or, you know, tells the whole story at the Olympics and, and kind of marriage is the greatest thing ever and, you know, changed my whole trajectory. And we just had a really lovely conversation with someone who was as genuine and authentic and pleasant and no rush to disappear as any celebrity, I would say as much or more as any celebrity I've ever met in my life. It made me love the guy even more amazing. And so that was a really fun kind of tennis experience.

Love it.

I've been lucky. There have been a lot of them.

Yeah, sounds like it. Well, you've made them happen. Okay, last question. And then we get to let you go and watch the tennis flicks tonight.

Yeah, exactly.

Yeah. Tonight, Tonight. Our theme at Gensler wealth is unlimited. Our view is we can look at any investment opportunity. We can help families with whatever they need on the financial side. And we love that, that, that word, unlimited. We ask every guest. And now you've done a lot of different things, so I don't know what your answer is going to be, but if, you know, like anyone on earth really, you have unlimited options. You can do anything you want. You know, if you weren't doing what you're doing right now, what would you be doing? Maybe you'd be doing the same thing, but what would you be doing? Unlimited? You can do anything in the world.

I would be doing this. I mean, I, you know, I wasn't doing this and I made this so I could do this.

I mean, right.

For me, it com. It kind of satisfies so many of the things I love. I love technology and thinking about the future and how you can make the world better. I'm curious by nature. So I love AI because it touches so many different areas. We get to invest in healthcare and we're not industry specific. I get to invest in the smartest people in the world and learn from them. Our team is incredible. So learning from our team and I'm incentivized that if we do it properly, I also get to make money from it, which, like, I would always do it without. I mean, I don't want to say this, but, you know, I'll tell your partners, yeah, don't, don't, don't mention that to our LPs, but. So it really is like a pretty unicorn type gig. It's a lot of hard work. I mean, building a fund from zero is a ton of work. I imagine it's much easier to go into a fund that's got a reputation and you know, it's easier to raise money and hire people and get into deals. But we, I. So our goal has been to build a reputation really fast and I think we have built an elite reputation now in the AI world and among AI founders and starting to be in the wider tech world. But I really like it. I mean, it's. I would, I can't imagine another job giving me like satiating my curiosity, getting to work with people this smart in so many different disciplines, learning about so many things and getting paid to do it.

So amazing. Sounds awesome. So look, thank you so much, Jordan for your time, for sharing with us the world of AI, how, how we look, how it looks today and how it's going to look in the future. You gave us a little bit of a vision into the future and your story. Wish you you've had huge success. I wish you continued success and appreciate time we, we spend together and enjoy the Tanner show tonight and look forward to talking to you again. Thanks so much.

Yeah, thank you so much for having me and, and hope everyone has a happy, healthy promise for year ahead.

Thanks.

This has been Ryan's conversation with Jordan Jacobs of Radical Ventures. This podcast is not to be taken as investment advice. And is not an endorsement of Radical Ventures. This has been a Ginsler WILT Audio production. The unlimited podcast is hosted and produced by Brian Ginsler and is edited and mixed by myself, David McMillan. Don't forget to rate the podcast and follow us on social media intleworld. Thanks for listening and see you next time.

SA.