Disruptors x CDL: Canada's Innovation Journey

John Stackhouse: [00:00:00] Hi, it’s John here. Welcome to a new season of Disruptors.

On this, our eighth season, we’re trying something new. Disruptive, you might say. Over the next eight episodes, I’ll be collaborating with my friend and fellow Disruptor, Sonia Sennik, the CEO of Creative Destruction Lab. Together, we’ll explore how advanced technology is disrupting a range of Canadian industries from entertainment to transportation and manufacturing.

All those tech revolutions out there are critical to Canada’s economic success, which has become a national debate. If we’re going to get the economy growing again, we will need to do much more with the extraordinary technologies that are defining the 2020s. Generative AI, 5G, quantum computing, and so much more.

And at the forefront of this challenge is Canada’s Creative Destruction Lab. Now, if you don’t know CDL, as it’s known, it’s one of Canada’s great success stories. It’s a global startup program for seed [00:01:00] stage, science based companies, and a bit of a startup of its own. It’s now in six countries and 11 universities, and has graduated nearly 5, 000 startups.

I was at something that CDL calls its Super Session in June, which had more than 500 startups pretty much from all over the world. It was breathtaking, and so was the CDL story. Initially, it set a goal of generating $50 million in equity value created by the graduates of the program. Within five years.

Today, companies participating in CDL have generated more than $36 billion in equity value. Now that scale. Sonia,

Sonia Sennik: it’s great to share the mic with you. Thank you so much, John. It’s a pleasure to be here. At Creative Destruction Lab, we connect early stage companies with experienced entrepreneurs, investors, and scientists through our structured, objectives based mentorship program.

Our mission at CDL is to enhance the commercialization of science for the betterment of humankind. In other words, we don’t want science to go to waste. [00:02:00] And that’s why partnering with RBC on this series is such a great fit. We both believe that innovation is key to Canada’s future. And this series is going to allow us to explore these big questions.

How can we harness tech to stay competitive? How can we foster a culture of innovation across our country?

John Stackhouse: That’s a great setup for the conversations that we’re going to have. But let me pause there, Sonia, for a minute and ask you to tell us a bit about you. What got you first interested in tech?

Sonia Sennik: I’m an engineer by trade and so I spent a decade managing large capital projects in the mining and metallurgy industry with a Canadian engineering consulting company called Hatch Limited. And over that decade, I saw the emerging need for new technologies. And there was a lot of buzz about big data sort of in the mid 2010s. I started to get really interested.

And when I found out about Creative Destruction Labs AI program that they started in 2015, I got even more excited thinking this could be. really impactful I thought for the mining and minerals industry, that this is something that refineries smelters really could benefit from. And as I dug more into it, I realized that [00:03:00] there was a real opportunity to get involved on the thin edge of the wedge science.

And yeah, I went from one of the oldest industries in the world with mining and metallurgy and jumped to Creative Destruction Lab where I was working right away in AI. We started our quantum stream the year that I started as well as our health streams and really, really deep tech areas. I got excited about the prospect of what is possible when you bring these new emerging technologies to our resource industry was really my first curiosity.

John Stackhouse: And that speaks so perfectly to the Canadian challenge because too often we think about tech as uh, some inaccessible. aspect of society and of the economy, that there are these algorithms being written in labs somewhere that aren’t really going to touch me, but it’s about mining. It’s about agriculture.

It’s about the guts of the Canadian economy and positioning it smartly for the 2030s. I think we started talking about the idea for this podcast, Sonia, a number of months ago, when you and I and a group of people were in Silicon Valley with some of the biggest AI brains in the world, uh, [00:04:00] scientists, technologists, investors, uh, as well as executives talking about some of the great challenges of AI.

And this was a collaboration between CDL and the Stanford Digital Lab. My brain hurt after those, uh, those few days, but it opened my eyes to the potential of AI, as well as the risks, but the potential that Canada is not seizing enough. What did you come away with?

Sonia Sennik: So transformative AI is AI that enables machines to do virtually all of the tasks that humans currently do.

The purpose of our workshop, Organizing Collaboration with Stanford Digital Economy, the AI Center for the Governance of AI, and Creative Destruction Lab, was to address this question. If we knew with certainty that transformative AI would occur within 10 years, then what should economists and economic policy makers do today to prepare?

The first takeaway was on the issue of abundance versus distribution. So a lot of people believe that technology will create a world of more abundance and that technology can enable us to spend more time on [00:05:00] things we want to do and enjoy doing. However, we already have an issue of distribution in our world today.

So in a world of transformative AI, distribution is a problem that may only get worse. So how do we address the distribution issue? The second takeaway is that there were significantly differing opinions on the major breakthroughs needed to achieve artificial general intelligence, or AGI. So AGI is a form of technology that can understand, learn, and apply knowledge across a broad range of tasks.

This is a far more advanced technology compared to today’s AI systems that you may be familiar with, which are focused on more narrow tasks. So some believe we need major breakthroughs to achieve AGI. Others think we can scale our way to AGI. So they think, make our current models bigger and we’ll get there.

And there’s a lot of technologists that believe models need continuous retraining, and they should perform off policy learning. This means they need the space to experiment and grow without rigorous structures or rules or highly controlled policies around them. Of course, that brings up the issue of now we have this [00:06:00] really powerful tool.

How do we regulate it and manage it so that it can be safely integrated into our world?

John Stackhouse: Such a challenge for Canadians because we tend to gravitate to questions of safety. It’s almost how we’re wired as, uh, as a country. Maybe it’s all the winters that we, uh, grow up with. And I had my eyes open to the American ambition around AI.

And of course, Americans are mindful of safety, but they tend to index towards innovation. They’ll take a chance. Europeans will index towards safety. Canada’s usually somewhere in the middle. But with AI, things are moving so fast and America’s leading the way. I mean, China’s trying to keep up, but I think we know there’s one superpower with AI, and that’s the United States.

And Canada has a lot of opportunity there because of the interdependencies, but also the connectivity between our economies, between our education systems, our universities, cross border travel, cross border data. That’s a challenge for us as a country. [00:07:00] How do we keep safety in mind? Don’t do any harm, but also embrace risk a bit more.

And it’s not just the scientists, it’s companies, it’s small companies, it’s big companies, it’s governments, it’s hospitals. How do we open our minds a bit more to the opportunity and maybe take a bit more chance with this?

Sonia Sennik: So I think at this moment in time, John, you put your finger on it perfectly, the pace of technological improvement with AI is profound.

We’ve never seen this rate of change and improvement. So I think what Canadian companies and scientists are starting to understand is that never has the cost of not doing anything been higher. Because the longer you wait, the further behind you’re going to get in adopting these technologies and learning how to harness them.

So this isn’t a matter of offshoring, bringing in a few smart minds that are in that ecosystem, having a few conversations. This is learning. new skills, leveraging these tools and harnessing them for [00:08:00] the betterment of your company and your competitiveness. And it’s possible. So again, why I am so jazzed that we’re doing this podcast together is to build that bridge in conversation between what is really happening at the forefront and how can small, medium sized businesses and large enterprises start to really adopt and embrace the technology in a measured way.

John Stackhouse: I love that expression, the cost of not doing something. has never been higher. Every organization, business, nonprofit, government should be probably challenging themselves that way. Not what’s the cost of adopting AI. There’s the financial cost. I’ve got to hire a bunch of expensive techies and spend a lot on compute power.

We may get into some of those challenges, but the cost also of taking a bit more risk, but we should all be thinking about the cost of not doing something because right now, somewhere, someone is doing something. Yeah. Probably in. Our backyard, maybe our digital backyard, but whatever you’re doing, they’re innovating.[00:09:00]

One of the facts I came across recently that I found really alarming is that Canada is now firmly in last place in the G7 for AI computing. So, last place. We just saw the Olympics. No one likes to be in last place. No one likes to be in seventh place. We want to be on the podium. What are some of the things we need to think about as a country to get on the AI podium?

Sonia Sennik: No one likes being in fourth place either. I think that’s the most painful spot. We’re also. Bye for now. hosting the G7 meeting next year and been thinking about this a lot. We need to adopt AI. It’s as simple as that. We need to figure out ways in which we can embrace that in our workforce, in our companies, our small, medium sized businesses, in our enterprises, and do it in a way that feels like we demystify what it’s capable of doing.

With the introduction of ChatGPT, And these large language models, anyone can code. You know, we’ve taken the task of coding software. You don’t need to know [00:10:00] C or Rust or Python. You can just speak in your native language to a large language model to start coding and creating and building. There are these incredible opportunities for innovation and new types of innovation that could be embedded in everything from clinics, schools, hospitals.

Large enterprise, small, medium sized businesses, but really simplifying and creating a structured pathway to get there so it doesn’t feel overwhelming and it doesn’t feel impossible. So I think we need to adopt AI, but we first need to understand it before I think we’ll have the openness to adopt it.

And again, I hope that these conversations can really help and translate what is really going on with these technologies and what’s possible today.

John Stackhouse: So we need to adopt it, but. Most people already are adopting it and this is a challenge for organizations. I try to ask people whether it’s on the elevator or the street or waiting in line for a coffee.

Are you using cat GPT? And more often than not, the answer is yes. And I’m using it for my job in some way. And then I’ll ask, well, is your company, does it have [00:11:00] like a chat GPT policy for you or an AI policy? Yeah, well, I don’t know. Or it’s kind of like, I got to go to the tech department. And it reminds me a bit of going back more than a few years with the cell phone revolution and almost overnight, everyone had a cell phone.

And then lots of employers were sitting there still with their landlines and there were phones on every desk. And I used to ask employers like, why do you have phones on every desk? Because all your employees have a phone in their pocket and no one’s answering their desk phone anymore. And so we’ve gone through that change and we’re seeing it a bit with AI as well.

It’s employee led, it’s individual led, it’s consumer led. So your consumer is usually ahead of you. Whoever your consumer is, your user is usually ahead of you. Your student, if you’re in education, is usually ahead of you. And one of the things I’ve always admired about CDL is your ability to work with a range of organizations, big, small, in every sector, and excite them [00:12:00] about, uh, about these challenges.

But of course, the opportunities that go with them. What, Sonia, are you learning in, in, this revolution, if I can call it a revolution with AI of what the smart companies are thinking about or how is it, how are they thinking about things differently?

Sonia Sennik: So what we’re learning is that companies that have developed an AI strategy or have started working through these AI strategies, maybe they’re further down the line, they have a few structured implementations in their workforce versus companies who are at the starting blocks.

They’re just starting to wrap their heads around it. Those pathways, though they’re further along in the journey, There’s similar issues, right? So when I say things like AI for procurement, AI for customer service, AI for managing your HR, or AI for supporting your finance team, these are all core functions that whether you’re a clinic engaging with your clients, you’re a retail store engaging with customers, you’re an airline engaging with travelers, there’s more similarities than I think people would think. There [00:13:00] has been a Cambrian explosion of off the shelf AIs available. Up until prior to ChatGPT, it was a lot of internally developed tech. So you have to have a team of people in your organization dedicated to building in house AI models. Now I think even in the time that we’re having this podcast, there’s probably new AI models available off the shelf for people to apply to their personal finances, to managing their home, managing their energy usage at their office building.

So understanding how these prediction tools can be applied. used in your companies, used in your life in a meaningful way. What we’re learning is that that journey to adoption starts with a strategy. The buy in from the CEO and C suite level is so important that boards are starting to get very, very interested in AI governance.

I think last year we saw a big increase in the interest in privacy from boards. So cybersecurity and privacy, how can I understand what data is being used? In these large language models, is my company being exposed? Now that’s moving to, okay, how can we harness [00:14:00] this? How should we be governing our AI models that are in operation and leading the operations of our enterprise, both locally and potentially multinational or globally, depending on the size of the organization.

So we’re really seeing an interest and a curiosity to be on the front foot as opposed to on the back foot of how do we protect.

John Stackhouse: Sonia, maybe we can talk about a few of the other episodes. We’re going to touch on in this series because we want to explore not just how our listeners can think about AI and apply AI in their lives and their organizations, but also what the opportunities are in the real economy. So we’ll be talking about your former sector, about mining and manufacturing, but also services.

So maybe I can start first with education. You’re situated on a campus. Tell us a bit about how education, which sometimes feels. Still a bit centuries old, is transforming itself with AI.

Sonia Sennik: Each of our CDL sites is situated on a university campus, from Canada, the [00:15:00] US, France, Germany, Estonia, and Australia. In post secondary, it’s very much student led.

They know it’s available to them. They want to harness it to make their education experience more interesting, to make it more efficient. So students are adopting AI and leveraging things like ChatGPT to support them in creating content and through their educational experience. You’re seeing professors understanding that.

And actually starting to give them tasks that intentionally include chat GPT. So giving them strategic projects that say, Hey, use chat GPT to compare these two economic issues. So you’ll see some professors that are adopting it and engaging with the students, understanding that they’re using it. And of course you have some processes and some educational areas that aren’t yet adopting it.

I think seeing the students in the creative instruction lab program take the course, seeing their excitement in getting engaged with ventures, being in the room and seeing that type of entrepreneurial [00:16:00] energy. I think there’s a real appetite for change and innovation in post secondary. And we are one of the world’s only experiential entrepreneurial courses.

Meaning if you’re a student in the CDL course, you’re matched with a company and you get to effectively work for and with that company for the nine months that they’re in the program. Yeah. And they get to actually understand what it takes to build these technologies, what it takes to build a business and make tough decisions, thousands of decisions a week.

So being able to bring them behind the curtain of innovation, there’s, I think there’s two pieces. One is them adopting innovation. And the other is students really getting exposure to how are these innovative tools brought to life? How does something like this actually get built?

John Stackhouse: I love those examples.

Can’t wait to get into that episode on education, but you mentioned economics. We at RBC are setting out to help our economics team become one of the world’s leading AI empowered economic shops in the world for the 2030s and keen to see what kind of students, what kind of future economists are coming out of our excellent universities [00:17:00] to help us on that journey and in so many other fields as well.

So stay tuned for the education episode. We’re also going to talk about life sciences and drug discovery. We all saw and benefited during the pandemic from a speed of drug discovery that was unprecedented. And one of the reasons it was unprecedented was because it was AI powered. AI got us out of the pandemic, and if we seize on that spirit, and there’s lots of scientists and labs across the country, Intrepid Labs run by Christine Allen, who’s a friend of both of ours, doing amazing things.

We can, in Canada, lead the world with new drugs and medical treatments for, for the 2030s. What should we look forward to in that episode, Sonia?

Sonia Sennik: This life sciences space is so exciting. You mentioned Dr. Christine Allen. She is an expert in drug formulation. And so one item of drug development is what should the actual contents of the drug be.

[00:18:00] Another element is how should we formulate it so it can best reach the goals and achieve the goals that we’re trying to with these drugs. Leveraging AI with Intrepid Labs at U of T, they’re able to simulate that without. the long laborious process that previously was required. So what you can do is you can simulate both the formulation and the contents of drugs in, it’s theoretically an endless number of combinations and be able to assess through simulation what can potentially be the most effective and not just what’s the most effective drug period.

What would be the most effective drug in formulation for John versus what would be the most effective drug in formulation for Sonia? Those could be different answers. So that space, as you mentioned, AI being leveraged for solving are biggest health related problems. It’s a very expansive area. And also at Creative Instruction Lab, our largest portfolio of streams are health related.

So we have our biomedical engineering stream, our health and wellness stream, our cancer stream, our general health stream, as well as [00:19:00] our advanced therapy stream, all focused on different approaches to leveraging innovation for improving healthcare outcomes.

John Stackhouse: We can’t touch on any of these topics without getting to electricity because everything we’ve discussed requires electrons.

And we’re actually heading into a period where we may have an electrons shortage. Uh, we’ve all heard about the insatiable appetite that, uh, AI and all these algorithms have for electricity. I think the, uh, the common reference now is that a chat GPT query requires 10 times. The amount of electricity that a Google search does, and that’s only going to grow.

It’s insatiable. Now, Canada has an advantage. We’re really good at producing electricity. We have some of the world’s best and biggest hydro dams. We are really good at nuclear. We’re really good at renewables. And more of the world’s going to come to us wanting to put data centers here and wanting to harness that electricity.

We can also be a little more strategic in terms of using that electricity for [00:20:00] our own advantage and maybe gaining a step or two on our competitors in the AI race. But we’re going to need more energy. We’re going to need more electricity to do all these wonderful things. If we think about one question there, Sonia, what should it be?

Sonia Sennik: Maybe the question is, have we fully grasped that electricity provides us with compute and compute provides us with intelligence? So the natural outcome would be that the more electricity you have, the more intelligence you’re able to leverage, whether that’s in your enterprise or in your country. There is that central part, compute power, setting up data centers.

Of course, that infrastructure is being talked about widely, but John, I love that we’re doing an episode on electricity and the future of energy because it is at the core of absolutely everything. How widespread is that understanding would be my first question. And then I think we could dig into that to talk about how we can diversify the delivery methods of electricity in the future and how Canada, as you mentioned, is so well positioned to have a really broad range of opportunities to do that.

John Stackhouse: Well, I gotta wait for that [00:21:00] episode to remind you, Sonia, of that line and credit you with it. Electricity equals intelligence. That’s got to be Canada’s motto. So, lots more on that. And then lastly, we’re going to get to the live entertainment business. Something actually Canadians are very good at. And I can’t wait to talk about some of the Canadian expertise.

If you see a screen in a stadium, almost anywhere in the world. Odds are Canadians were behind both the hardware and the software. So we’ll talk a bit about that because AI is there as well and advancing the live entertainment experience. It can be baseball games, football games and concerts. And of course, here in Canada, the concert we’re all waiting for this fall is Taylor Swift.

Sonia, you’re a bit of a Swiftie. In fact, you’ve got, if I can break news here, a Swifty tribute concert in Toronto in November. Tell us a bit about your Swiftiness.

Sonia Sennik: Happy to. So it is called Tdot Swift 4 cats. It is Toronto’s only. Cat Fundraiser, where we are going to be playing Taylor Swift songs all night.

Our band is incredible. We typically do one fundraiser a year for Sick Kids in the spring. It’s a bunch of people from the tech ecosystem in Toronto. This time we’re harnessing all of our musical talents for all the stray Swifties. So, you know, 34 million Canadians tried to get tickets to the ERAs tour.

Only about 300, Got tickets? So where do those stray Swifties go, John? TDot Swift 4 cats. November 20th at the El Mocambo. Tickets are available and all proceeds go to cat shelters in the GTA.

John Stackhouse: We often hear the expression tech for good. I think that’s a beautiful illustration of tech for good. This is so exciting to share Disruptors with you and to think about all that we’ll get to discuss and explore and learn from during the coming season.

It’s going to be a lot of fun. Thank you for being part of it.

Sonia Sennik: John, thanks for inviting me to be on this journey.

John Stackhouse: Thanks to all our listeners for coming on this journey with us, for being on the journey for eight years and still going. If you’ve been with us from the start, you can subscribe to Disruptors and subscribe to the [00:23:00] Innovation Era on our websites, our special series with CDL.

Like and share the episodes you get to listen to and stay tuned for our next episode on AI and how it can make Canada more competitive. And make whatever organization or community you’re in more prosperous, more competitive, and more relevant for the coming years. I’m John Stackhouse. And I’m Sonia Sennik.

And this is Disruptors.

Sonia Sennik: And CDL

John Stackhouse: The Innovation Era. An RBC podcast. Talk to you soon.

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