John Stackhouse: [00:00:00] Hi, it’s John here, and welcome to Disruptors x CDL: the Innovation Era. We’re doing a special two part series on the space economy, and I’m joined by my co-host, Sonia Sennik, the CEO of Creative Destruction Lab, which has its own special space stream. That just tells you how big the space economy is these days.

Sonia, great to be with you again.

Sonia Sennik: Thanks so much, John. It’s awesome to be here. Today, we’re hosting part two of our discussion on the space economy. And this time we’re diving into examples of Canadian companies who are building the future of space. To put it into perspective, Morgan Stanley’s space team estimates that the roughly $350 billion global space industry could surge to over a trillion dollars by 2040.

John Stackhouse: That’s right. This is not just the stuff of Nassau and maybe Elon Musk and Jeff Bezos. There are thousands of companies, including many Canadians and two that we’ll hear from today, that are active way up there as well as down [00:01:00] here. If you listen to part one of our special series, you would have heard Commander Chris Hadfield, who of course has been to outer space and back, talk about just how big the opportunity and ambition is.

Sonia Sennik: And he contrasted that against just how little we know. And how much more there is for us to explore and learn about outer space. With the cost of accessing lower earth orbit dramatically lower than it’s ever been, the opportunities are truly endless.

John Stackhouse: Sonia, after a conversation with Chris, I was thinking about what the container ship did to globalization and how space transportation may do the same.

For history nerds, you’ll know that the container ship was born out of the Vietnam War. Where the U. S. military had to ship all sorts of stuff to Asia and then were sending back empty containers. So some bright entrepreneurs developed a business out of container ships, and thus was born in many ways what we now call globalization.

We may be seeing the same thing with what Musk and Bezos are doing [00:02:00] with space transportation today.

Sonia Sennik: And now our global supply chain is going to include reusable rockets.

John Stackhouse: So what do you call globalization when it includes another planet? Universalization, John. I’ll go with that. Joining us today will be Christine Tovey, the former CTO of Airbus Group North America and an aerospace veteran, who will tell us what her new company, Wyvern, is doing up there in space, as well as Mina Mitri, who’s the CEO and founder of Kepler Communications, a Toronto based satellite company.

Let’s dig in. We caught up with Christine at the CDL Space Session. Have a listen. Christine, thanks for being on the podcast.

Christine Tovee: It’s a pleasure to be here. Thanks, John.

John Stackhouse: So a bit of background. Why VERN came through CDL was founded with the broad goal of providing actionable intelligence of Earth from space to enable a sustainable future for humanity.

So no small ambition there. Why think small when you’re in space? Give us a sense of what inspired the vision.

Christine Tovee: Well, I think [00:03:00] Wyvern was unique in terms of its enthusiasm and cohesion of its founding team. So four young people, mostly out of the University of Alberta. Unique in the sense that two of the founders were women.

So we had a 50 percent female founder team and truly their joy to work with the enthusiasm, the collaboration, the insight that we have, but also one of the other reasons why I think Wyvern was amazing is we were combining a disruption of how the business of space was happening with real technical innovation.

And I would say going through CDL, that combination of both business and technology disruption is relatively unique and one of the reasons why I thought Wyvern had a great future in front of it.

John Stackhouse: And six years on, tell us a bit about your progress.

Christine Tovee: Six years on, we’re now at approximately 35 employees. We grew up in COVID, so we’re [00:04:00] practically a virtual company.

So there are engineers and employees from all the way from Halifax, all the way to Campbell River, Vancouver, as well as we have a few American employees down in Colorado. We have just finished another financing round, so we just raised six million dollars U. S., you know, in a very difficult financing environment.

We’ve got three satellites on orbit. We’ve got two more launching in 2025. Space is hard, so there’s a lot more lessons to learn about getting the imagery down. If you don’t know what hyperspectral imagery does, is we take images of the Earth in many different colors. So beyond what the eye can see. And you can combine these different colors to learn different information about what’s happening on the earth in terms of chemical composition, soil moisture, temperatures as well.

So you can learn a lot about what’s going on and therefore get into decision making and to optimize a number of things. So. We have a number of [00:05:00] clients in agriculture, in mining, defense, obviously is still very much interested in imaging, that’s naturally the number one client for this type of imaging.

But we also get very unique requests, we’ve been asked about the health of coral reefs, the Great Barrier Reef, we also are looking at invasive species, we’ve just done a use case on forestry where we can actually identify different kinds of trees in a forest. What’s also exciting is this data used to be quite bespoke, and not a lot of people had access to it, nor did they have the expertise to work with it.

So it’s a real discovery journey, both for us in terms of that space journey, but also for clients wondering what more can we do that’s going to impact so many other areas.

Sonia Sennik: Christine, I think you beautifully illustrated in your response there, just how many different industries space companies can touch with the hyperspectral data that you get and the opportunity with AI to really [00:06:00] leverage that and harness those prediction tools to better support their businesses.

It’s all very new and exciting. So what is the biggest piece of education you’re giving to business leaders as you’re discussing with them the opportunities with your tools and technology?

Christine Tovee: Yeah, so first off Wyvern focuses on the data. We certainly do have an AI and ML deployment plan, but we’re also looking to partner with people who can do the application.

So on the layer of data is the applications. Now, what has been an education? Of such is what can people rely on the data for? So it’s quite a process to take down zeros and ones from a sensor that’s detecting light over 500 kilometers away from the earth and then turn that into something called an image.

And that’s what we do really well. Then it’s a matter of, well, what does this image tell you, and how do we deal with some of the discontinuities, the discrepancies in it, and what does it mean, and certainly [00:07:00] something that’s really relevant right now is what’s truth in an image, and what is processed. AI is making this even more of a challenge to explain it, because AI can do a whole bunch of stuff, and you don’t really understand what it’s doing to the image.

But how do we maintain what we’ve kind of called pixel truth? So making sure that the customer, whatever their application, whatever their analytics is, can trust that the data is of ground truth.

Sonia Sennik: Just to pivot to how you get those images and how you get that data, is you actually send CubeSats into lower Earth orbit. Now, not all satellites are created equally. Can you give us just a very brief primer on the difference between a CubeSat and satellites that we’d be more familiar with?

Christine Tovee: So it’s true.

Our first three satellites are CubeSats. If you’re in the know, we talk about CubeSats in terms of units. So this is a [00:08:00] 6u or 6 unit size. CubeSat. It’s small. It’s small. It’s the size of a microwave, essentially. It was literally packed in a suitcase and hand carried onto a British Airways flight to Vandenberg to be launched.

Yes, it had its own seat on the BA flight. I come from military satellite communications where we’re talking about a satellite that is in the tons that would fill this room and would be launched into an orbit that’s 36,000 kilometers away from the earth, which means it’s moving very slow compared to the earth.

In fact, it’s stationary over a point. With smaller sats, you put them into what we call low Earth orbit, which, like I said, is around 500 kilometers above the Earth, and they’re moving very fast. In one day, in a sun synchronous orbit, we’re going over the Earth 24 times. As it slowly moves along the equator, So it covers the full circumference of the earth in one day and it does that every day.

So it’s a very fast moving [00:09:00] satellite and we want more of them, but also we’re actually moving to what we’re calling a small satellite. So about a hundred kilograms next. And there’s some very big advantages to that as well. The orbits won’t change too much. But with a larger satellite, you actually get better pointing accuracy just because you have more mass, your conservation of momentum stabilizes the platform.

And certainly if you’re taking photos, if anybody’s sort of had a shaky hand when they’ve been trying to take a photo, you want stability for that platform. And so a small sat, which is about a hundred kilometers, provides extra stability. It also provides extra what we call space, weight and power. And so with power, you can also do a lot more on board processing.

So I talk about the technology challenges that we have is there’s, there’s two really that Wyvern’s trying to solve. It’s how do we take a good image? And we’re working on deployable optics for that. And then there’s, how do I manage the data? Because [00:10:00] one of the big challenges of hyperspectral, when I talk about multiple colored images, it means every time we’re taking 32 images of the same spot.

And that’s a lot of data, like we’re into the gigabytes of data. In 32 different spectroscopies. Yes, in 32 different colors. And the next generation is going to be even more colours. So it’s a data management problem as well. So being able to move into a constellation or an architecture where I do some of the processing and the A.

I., the M. L., the analysis of the images on board before I have to move all that data to the ground is also a big change.

Sonia Sennik: So what’s wonderful about this is it’s such a chain of innovation. It is. An innovation in the hardware, an innovation in how you take the photos, an innovation in how you power and process on board can lead to an innovation to the data that you get.

And then how I do or don’t manage my farmland or how I do or don’t manage my mine site. And so that broad spectrum of [00:11:00] industries that you’re about to touch is really exciting. What do you see as being in the next 10 years, the biggest barrier for you bringing that to life and getting engaged with businesses all around the world?

Christine Tovee: Well, I don’t think it’s going to take 10 years. I think it’s a real five year planet as such. The barriers are, I’d love to see more launch companies and more launch opportunities because these are not massive constellations in terms of the Starlink thousands of satellites, but to cover the earth and to get the latency and the timing, you need 40, 50, 60 satellites to cover the earth in a meaningful way.

So access to launches is one thing. Reliability and being able to launch to exactly where I want it to be. I’m really interested in getting to a point in space that is really meaningful, especially when you’re trying to coordinate 40 satellites, I do not need them all bunched up into the same orbit.

Thank you very much. The challenge is also going to be managing technology from different generations and obsolescence and make sure it all works together. So there’s [00:12:00] a massive coordination, change management, change management, again, the operations are complex, the mathematical calculations are complex.

This is another area where I think AI and ML is going to help us just to manage that complexity and understand how performance varies across the satellites and the imaging capability. The space is also going to be an area where we’re going to be more security driven. We’re seeing the world change from everything from more space debris.

So we’ve got to monitor our satellites and make sure we keep them safe. Radiation, being able to de-orbit, but also there are cyber attacks. There’s a hypothetical where hack a satellite, hack a satellite, you know, take control of the TTC links. So those are the challenges.

John Stackhouse: As you laid out, you’ve got a great five year plan, but what’s your dream for Wyvern and what do you need to get there?

Christine Tovee: Well, the dream for Earth is to be able to see what’s happening on Earth at any moment. I remember [00:13:00] reading once about a story where a river up in Yukon essentially disappeared overnight and by the time people realized what had happened, they had no clue why this river was suddenly gone. I’m hoping Wyvern we never miss something like that again.

We’re living in such a connected and complex world that things happening in one area of the world actually have huge impacts somewhere else. And being able to see those two things at the same time, especially on a climate change level or something like that, understanding our connectivity across the globe, I think is one of the missions of Wyvern, and I think this data can really help.

The other way is There’s the moon next, there’s the solar system. Can we expand to a cislunar and a solar sort of environment where we’re doing hyperspectral imagery all over the place?

John Stackhouse: You don’t think small?

Christine Tovee: No.

Sonia Sennik: Never.

John Stackhouse: Christine, great to have you on the podcast. Thank you.

Sonia Sennik: It’s been a real pleasure.

Our [00:14:00] second guest is Mina Mitri, CEO and founder of Kepler Communications. Mina, welcome to the podcast. Thanks for having me. You have a background in aerospace engineering from the University of Toronto. How did you decide to build a startup?

Mina Mintry: Oh, well, that’s a long story. I’ll try to give you the bridge version.

During my undergrad and my master’s, I had the privilege of leading a not for profit called the University of Toronto Aerospace Team. We started out with about five people that were volunteering to build these heavy lift aircraft that were remote controlled and tried to carry as much weight up as they could.

three year period where I was responsible for the team. We took it to a group of about a hundred volunteers doing a range of things from building our own rocket engines from scratch, designing drones, and ultimately launching satellites. So we had the opportunity to put down a levy in the university of Toronto, where each student would pay a portion of their tuition to support our launch campaign going up into orbit that was unanimously [00:15:00] voted in, which was really cool.

But it was through that experience that I really learned what was happening inside of the space sector. Gone are the days where it’s nationally held by trillionaires, and I mean the government of the U. S. or the Soviets of the time, and now space has become democratized, it’s really accessible for all, where we could mix laughing gas, aluminum powder, and candle wax to make our own rocket engines from scratch.

Yep. Somebody permitted us to do that, but all the way through to developing and launching microbiology payloads on a student based levy is really a great opportunity to realize what was happening inside of the space sector. I got together with some of the smartest folks that I knew at the time, which were really top of their field in the world.

And we got together and decided to build Kepler.

John Stackhouse: Mina, I think you just gave away your IP. I didn’t realize laughing gas was the critical ingredient, but we’ll see who takes advantage of that. Tell us a bit about the Kepler story.

Mina Mintry: The Kepler story builds off of this idea that we were seeing space access become so democratized.[00:16:00]

You had sit and run competitions that were going out and building, launching, operating spacecraft, made us realize here is the opportunity where people have democratized access through launch or through regulations or through a myriad of different ways in which spaces become more accessible. And while there’s been a heavy amount of investment into launch, There’s been very little investment into communications, and so we set out with this vision to bring internet access outside of this world, the same internet that we’re so enjoying today, but bringing it into space so that any object in space becomes completely free.

indifferentiable from your networked printer and that might be a good or bad experience depending on how you’ve used network printers in the past and hopefully it’s been positive but generally it means that you’d be on your phone able to access any asset that’s in orbit in the same way that we so do here on earth and that was the grand vision.

And so we set out to do that in 2016. We raised a little bit of capital, [00:17:00] launched our first satellites that were proof of concept validation, allowed us to acquire spectrum rights. And then we moved into developing our product into 2018 and raised two successive financings. Thereafter, and today we’re a team of about 170 people split between the US, Canada, and Europe launching a constellation that’s entirely built in house here in Toronto, and that provides connectivity to the range of applications from human space flight to earth observation missions, to national scientific and defense oriented applications.

John Stackhouse: You and I were together recently, Mina, and I made an offhand reference that I thought Canada may need an Elon Musk to propel us with a bit more ambition into the space economy, and you glared at me. I needed no words to know what was on your mind, and that was that we have plenty of Elon Musk equivalents, you being, I think, one of them.

Tell us a bit about your ambition. How big can this get?

Mina Mintry: John, those are your words, not mine. So I think I’d say we have an [00:18:00] incredible opportunity here in Canada because we have the talent capacity to near do anything. We have highly motivated, highly skilled people that come out of university, just ripe for opportunity.

And if they’re provided that opportunity in Canada, we’ll see incredible growth inside of the space sector. If we don’t provide them that opportunity in Canada, they’ll actually just move. They’ll go to the US, they’ll go to Europe, they’ll go where they can match their ambition with their talents and be rewarded for it.

And so I think in Canada, we have an incredible moment where we can provide a massive space economy. We have a really unique talent base that could be put to work inside of this ecosystem. And we just need the right mission and ambition to support them.

Sonia Sennik: The European Space Agency awarded a group led by Kepler Communications a $36 million euro contract to develop a low Earth orbit optical relay network.

This is the first time ESA has awarded a contract to an [00:19:00] effort led by a Canadian company. Can you tell us a bit more about this project? And congratulations.

Mina Mintry: Thank you so much. I think this project follows a broader theme that we’re seeing, which is that governments are increasingly moving away from government owned, developed physical hardware, and moving towards procurement of services, where traditionally these government customers would go out and they procure the physical hardware, they’d specify a requirements list that was like an inch thick of paper, and you’d have to read every bit of that paperwork to deliver on a hardware good.

Now we’re seeing a shift in governments where they say, okay, you as a contractor, we’ll take on the liability. You’ll deliver the end service to me. And so we were successful in that opportunity because we are commercially led, commercially driven, developing our own technology, our own network. And the European Space Agency saw an opportunity to take advantage of our commercially developed infrastructure and fulfill their need just by buying [00:20:00] services instead of buying physical goods.

And so this is happening not just in Europe, but in the US and everywhere around the world. And giving governments access to capability on a timeline and a cost that otherwise would just not be possible. And so that is what ultimately led them to the decision to kind of say, okay, we’ll take advantage of the Kepler network.

We’ll use some of the services on there, but we have a few things we’d like you to fine tune. And that Delta design effort is very small for us. It allows us to move fast. And at the same time, provide something to the European Space Agency that they otherwise wouldn’t have been able to gain access to.

John Stackhouse: Mina, you’ve been talking about low orbit satellites and in our previous episode with Chris Hadfield, we talked a bit about that, but also about going back to the moon and all the opportunities out there on the moon and beyond. That’s very exciting. It opens our aperture. to literally a universe of opportunities.

A lot of what’s going on in space is actually [00:21:00] almost within arm’s reach in low orbit. Give us a sense of what’s going on out there up in the sky that we can literally see.

Mina Mintry: So we’re actually seeing activity in space and three main regimes and beyond. So the three are low earth orbit, medium earth orbit, geostationary orbit, and beyond would include lunar activity or other exploration type missions that are predominantly government led.

Low earth orbit is where the majority of the activity is. That’s anywhere between, you know, 200 kilometers above the earth to about 2,000 kilometers above the earth. And there were Experiencing the range of Earth observation missions. So these are missions that want to observe the Earth or some property.

That could be the weather. That could be measuring the location of aircraft or shipping vessels. It could be taking just regular pictures. It could be radar data. We’re also seeing human spaceflight, and that’s predominantly being done in low earth orbit, so the International Space [00:22:00] Station is set to retire by 2031, if I’m not mistaken, and we’ll see the advent of private space stations that are meant to be replacing the International Space Station effort, and there’s a lot of interesting parties that are vying to do this, and partnering well outside the space sector to establish them.

I think Axiom Space is one of them that recently announced a partnership with Prada to develop their, their astronauts or the spacesuit. And in medium earth orbit, you’re seeing a lot of activity as well, where you’re looking at replacements for GPS, alternative pointing, navigation, and tracking mechanisms.

Geostationary is the historical most used orbit. And the reason why geostationary orbit is really interesting is because whenever you fly something in geostationary orbit, you stay fixed with respect to any point on the earth that you’re observing. So if I fly a geostationary satellite above Toronto, I will always see Toronto.

Cause it’s rotating at the same rate as the earth is rotation [00:23:00] and geostationary orbit is already experiencing some new and interesting technology applications where we’re seeing satellite servicing take place. There was actually commercial missions that went up to extend the life of geostationary satellites.

And then the exploration side of things where we have lunar mandates, we’re going to see, uh, Hopefully our first Canadian astronaut fly around the moon, Jeremy Hansen. We’re expecting the lunar gateway to take place, which is the next generation of the international space station, but hosted, you know, closer to the moon.

And there’s a whole range of both commercial and government led activities there, but in general, this is the result of democratized access to space.

Sonia Sennik: And Mina, as the number of satellites and missions increase, we hear a lot about space debris and managing all of the stuff that is now in lower Earth orbit or in orbit around planet Earth.

What do you think are the most pressing concerns for orbital management and sustainability?

Mina Mintry: Yeah, for us, the most pressing concern is really [00:24:00] uncontrolled space debris. Controlled space objects, or even large space objects. are really manageable. We’ll get notification of what’s called a conjunction.

That’s where there’s a probability that we will be within a defined orbital sphere of another object. And for large objects or controlled objects, there’s awesome operator to operator dialogue. That we can always see, and it cuts across geopolitical borders, where we have those conversations with near any operator who all have the same incentive, and it’s the safety of their space assets.

Where we struggle is for uncontrolled objects, so think like defunct bodies, and ones that are really small. There are some interesting recent news articles that talk to, you know, the micrometeorites, space debris, and the damage that can cause to solar arrays on board spacecraft, and that’s the type of stuff that we worry a little bit about.

Which is how do we build redundancy in our systems to make sure we can manage for those micrometeorites, which might be a loose bolt or a nut [00:25:00] or something that’s come off of a pre-existing satellite that is flying at 7. 4 kilometers a second and going through the body of your satellite.

Sonia Sennik: And it’s whipping around planet Earth effectively infinitum. So how much can we expect that we’ll have more conversations about management of this issue over the next few years?

Mina Mintry: Yeah, we’ll always have more conversations about it, but there’s two sort of real mitigating factors. One, space is really large. If you think of the surface area of the earth, we do okay to get billions of people.

In and around and can traffic manage individuals and in space. That’s just a much larger surface area covers the whole of Earth. And so space being so large really helps to mitigate for that problem. And then the second thing to keep in mind is building redundancy in your system so that they can tolerate one of your solar cells and of itself getting destroyed by micrometeorites and having redundancy to be able to manage it.

Regardless, we always want to have conversations about this because it’s an issue where you see increasing amounts of space debris, and we want to make sure we have [00:26:00] sustainable use of space for decades or hundreds of years to come.

Sonia Sennik: Mina, what advice would you give to other entrepreneurs aiming to grow their space ventures in this increasingly competitive landscape, but exciting landscape?

Mina Mintry: I think the main thing I would, I’d focus on is the persistence you need. In this role, because the sector, the domain, the talent wars, everything in between, it’s just that there’ll be day after day challenges. I’m sure this applies to any entrepreneurship, not just in the space field. If I were to give one piece of advice to any entrepreneur aspiring to be in the space sector or any other sectors, just persistence is the most important attribute.

Sonia Sennik: And yet he persisted. Mina, thank you so much for your time.

Mina Mintry: Thanks for having me.

John Stackhouse: A big thanks to Christine and Mina for sharing their perspectives. And if you didn’t get a chance to listen to episode one in this special series, you can find it wherever you get your podcasts and hear our extraordinary conversation with [00:27:00] Commander Chris Hadfield.

Sonia, it’s clear from both of these episodes that we’re at the dawn of a new space era and one that’s filled with potential as well as responsibility.

Sonia Sennik: Absolutely, John. Space is no longer just a destination. It is becoming a new platform for human innovation, imagination, and opportunity. We hope our episodes this week gave you insights into the opportunities ahead in the space economy.

Until next time, keep looking up and stay inspired.

John Stackhouse: And if you’re as passionate as we are about understanding the intersection of advanced technologies and real world applications, be sure to subscribe and leave a review. This has been Disruptors, an RBC podcast, in collaboration with Creative Destruction Lab.

I’m John Stackhouse.

Sonia Sennik: And I’m Sonia Sennik.

John Stackhouse: Thanks for listening, and talk to you soon!

John Stackhouse: [00:00:00] Hi, it’s John here. Welcome to Disruptors and CDL: The Innovation Era, where we explore the transformative ideas and leaders shaping our world. And as always, I’m joined by my co host Sonia Sennik, CEO of Creative Destruction Lab. Sonia, it’s great to be with you today in person.

Sonia Sennik: Yeah, we’re kicking off CDL Space Session #1 for the 2024/25 program year.

And I’m beaming with excitement to talk to you about what’s happening in this industry.

John Stackhouse: I’m not sure that there are any astronauts around. In fact, actually, I see one across the hallway, so check that. But the lab and the broader Rotman School at the University of Toronto is buzzing with space entrepreneurs and innovators.

Sonia Sennik: There are a few astronauts and astronaut hopefuls in our orbit today, John. And we’re looking beyond our atmosphere to the rapidly evolving space economy. Rockets are being launched on a weekly basis and there’s never been a more exciting time to take a look at the major steps we are taking towards making [00:01:00] access to space more affordable and what that means for us here on planet Earth.

John Stackhouse: In fact, there’s so much to talk about when it comes to space that we’re going to devote two episodes to the topic, both recorded here at CDL Space Stream. And our real focus is going to be the economy of space. What are the opportunities out there? And how will space transform every business and every sector down here?

Sonia Sennik: John, living in a world where there are reusable rockets has profoundly transformed the opportunities for innovation. Think about it this way. In the 1970s, the cost to get one kilogram of water to space was about 20,000. Today, it’s closer to 2,000. And if SpaceX’s Starship hits its target of 20 per kilogram, everything changes.

Whether it’s launching new machines, robotics, or satellites to space, or testing healthcare technologies in space.

John Stackhouse: You’ve got a great motto that every company is a space company, and in episode two, we’re going to get to know some of [00:02:00] the most interesting Canadian space companies that are testing all sorts of frontiers.

But first, we’re going to set the stage with where we’re at in space exploration and what opportunities are coming pretty fast at us, especially in the economy. And I can’t think of a better person to do that than Commander Chris Hadfield, who’s our first guest today on the special edition of Disruptors and CDL.

Sonia Sennik: Commander Chris Hadfield, of course, is the first Canadian commander of the International Space Station, and the first Canadian to do a spacewalk. He founded the CDL Space Stream with us in 2018 to start engaging with startups from all over the world who have new ideas that will drive the future of the space economy.

He’s also not so bad at guitar and singing. Commander Hadfield, welcome to the podcast.

John Stackhouse: Good to be talking with you. Chris, it’s great to see you. You took millions of Canadians, I suspect, to space virtually, and you’re still trying to take us to space in very different ways. Maybe you can frame the conversation a bit [00:03:00] for us in terms of what we should be thinking when we think about space today.

Chris Hadfield: I think one of the best ways to get a sense of framing is to look at historical comparators. And so when you invent a new, let’s say, transportation capability, then how does that change the fundamental human condition and how do we all start to incorporate that? We dreamed about flight for hundreds of thousands of years, and then suddenly in 1903 the Wright Brothers created a vehicle where people could start to leave the earth.

And it was a whole foreign idea and almost an inconceivable idea. And when an airplane flew over in 1910, it was a huge local event. It was, you know, the magnificent men and their flying machines and all that other stuff. Now, people hardly think about it. And they were kind of irritated if your plane’s 10 minutes late or if you didn’t know what you know.

type of peanuts you got on board. And so we are in the early stages of a major redefinition of a new mode of transportation. How is it that [00:04:00] we are going to incorporate access to space for our robots, for our virtual presence, but also for our human presence over the next generation? When I was born, no one had flown in space.

All of this has happened in my lifetime. So it’s, it’s incredibly new. And the technology. That gets people safely to space and back is accelerating and accelerating right now, like never before, so that now a private citizen for the price of a luxury car can fly in space and people buy luxury cars all the time, how that’s going to play out, how people are going to incorporate that with the regulatory environment is going to be, and then what the human applications of that are vacations on the moon, vacations on a space station, vacations on Mars.

It all sounds fanciful. So did air vacations one long lifetime ago. So since I was lucky enough to go when we were the first people to fly in space, I wanted people to get a sense of it. What’s it like to live on a space station? What, what’s your day to day? What are [00:05:00] the risks that go along with it? But also what are the beautiful parts of it?

What are the advantages of it? What are the things you see that you could never see any other way? And I really look at all technology that way. There are downsides to it. How does this allow you to see and do things that were otherwise impossible? And Spaceflight’s just a wonderful, visible, visceral version of that technology.

Sonia Sennik: We’ve been working together for many years when you founded the CDL Space Stream with us here in 2018. And I remember the first year that we were getting applications. It was really tough to find companies that were focused on space entrepreneurship. Fast forward six years later, we have more applications for the program than we know what to do with.

I’d be really interested on your reflections, Chris, on just the evolution of the types of technologies you’ve been seeing over the last six years through the stream.

Chris Hadfield: Yeah, it’s still a new thing. People have sort of been raised with the fact that space is a rare and esoteric and a very difficult to access thing, and it’s not normal [00:06:00] in daily lives.

But if you give people just a moment to reflect, going, oh, well, wait a minute. When, I came to this thing today, I was using, My phone in order to navigate for me, and I checked the weather before I left home this morning, and I used the internet, and gosh, it turns out the internet is coming directly from space to my house, and so it’s becoming much more integrated.

And that’s not happening through hoping and through magic, it’s happening because people recognize, hey, if we could, for a reasonable price, put things into orbit around the world, what can we do with that? Or, if there are these things that are already in orbit, what can we do with that information and apply it in a way that becomes a viable business?

And that realization, it’s like a wave that’s been slowly building out in the ocean. And then as it gets closer to shore, the wave gets bigger and more and more people can ride it. And that’s where we are right now in the space stream [00:07:00] is people are recognizing what reusable spaceships have done to the cost of access to space that now suddenly there’s constellations of thousands of things up there.

And we have some amazing applications coming through in space medicine, remote medicine, things like earthquake prediction, but until you make that technological beachhead and then make that part of people’s common understanding and expectation of what’s happening, then no one’s going to spend any time thinking about it.

But we’re at that moment in history, and I think Creative Destruction Lab has been really pressing in recognizing, hey, let’s be on the start of that, and let’s start encouraging these space businesses that have been started here.

Sonia Sennik: I’ve been saying to John, every company is a space company. And one of the examples you gave there of the intersection of healthcare and space, we had a company last year in our Cancer Stream called Encapsulate. They created a tumor on a chip so that you could test treatments and therapeutics, [00:08:00] but they actually partnered with the International Space Station to test their tumor on a chip in microgravity.

And they were able to get tremendous results because the impact of microgravity on the actual tumor and the behavior of the cells was very different. Can you speak a little bit to the opportunity of testing healthcare treatments and therapeutics in space?

Chris Hadfield: Sure. There’s a couple of things. One is just what you just referred to, the straight environment itself.

If you’re trying to grow tissue or human cells or some, some part of the body, gravity is a big omnipresent downer. Yeah. Gravity is a downer as the line of the day. Gravity is an invisible heel grinding us into the ground all the time. And so if you’re trying to build something subtle or delicate, it’s really difficult to do on the surface, you know, on your petri dish. It’s not going to be very three dimensional. And so there’s been considerable success in building three dimensional human cell driven organisms on board the space station and protein crystal growth and that sort of thing because you just [00:09:00] can’t have a perpetually gravity free environment on earth.

So it provides it from a pure physical point of view. And there’s a lot of people thinking about how can we apply that and now that our launch costs are down, can we put up some sort of automated facility up there that creates something that is impossible or extremely difficult to build on Earth?

A bunch of people are working on that. The other is you often invent things on the frontiers of human experience. If everybody’s comfortable and everyone’s got enough food to eat and everybody’s going home satisfied every day, your desire to create something new is minimized. But if you’re out on the edge, then you recognize, man, we’re pushing the capabilities that we have.

And so let’s, let’s really try and understand how we could optimize given that a lot of our constraints are abnormal out here and the space station and space vehicles and the space environment provide that for us as well. It pushes people to rethink it because we’re in this new environment [00:10:00] and necessity becoming the mother of invention.

If you change the necessity because of the environment you’ve gotten into. Then your pace of invention comes up. And so the space station over its last, gosh, 24 years has proven to do that with the thousands of experiments that have happened on board. And there are physiological changes to the human body.

When you take away gravity, it almost mimics a lot of things to do with aging. You get a hardening of the arteries, arteriosclerosis, you get a shrinking of the heart. You get muscle wastage, you get osteoporosis, weakening of the bones. You get a suppression of the human immune system. All those things are happening.

And so it’s sort of the combination of the other two things. Here you are in this weird new environment and weird things are happening and they’re happening because of lack of gravity. You’re also get heavy radiation. And so now let’s use this as sort of a historical test bed to try and better understand how the human body works as an organism.

John Stackhouse: But then also what we can do to improve human health, not just for [00:11:00] astronauts, but for

everybody. Listening to you, Chris, you make the space station sound like Creative Destruction Lab. It’s CDL in orbit. But it also sounds kind of out of reach for a lot of people. Deep science is going on there, as it should, but not really accessible to Main Street.

How do we rethink space generally to connect with? Main Street and the mainstream of our economy and our society.

Chris Hadfield: Yeah, I mean, if you view the International Space Station as a laboratory, which it is primarily, it’s a big laboratory. We’re running like 200 experiments and everything else is just supporting the laboratory.

Well, then it’s sort of like every laboratory. I mean, there are laboratories all across Canada, and most people don’t even know they exist. And they’re doing necessary, interesting, worthwhile work, but people drive by it in their car and they don’t even know it’s there. So you need to look at it a little bit in what is the purpose of that laboratory and what is a measure of success for the [00:12:00] laboratory.

If you look at some of the agricultural research laboratories, of which there are several in Canada, if they are finding a new strain of wheat or, or growing better apples or whatever, then okay. But it doesn’t necessarily mean that everybody needs to know about it on a daily basis. You know, they’re just doing their job.

And there’s a lot of that on the space station as well. And there’s a lot of that at Creative Destruction Lab. It’s a laboratory as the third word in the name says, it’s how do you take a bunch of variables, put them under an unusual set of circumstances to produce something they didn’t use to exist. To answer your question directly, John, if people don’t know something exists, it can never affect their thinking.

It won’t affect their decision making and it won’t allow them to apply their own creative abilities to contribute to it. So even though a laboratory may be functioning well, there is definitely a huge strength in letting people know enough about it that it, sparks a [00:13:00] curiosity and maybe they then want to know not just more about that laboratory, but how to support that laboratory or shoot if there’s really cool research on understanding the earth’s ionosphere, a high upper levels of our atmosphere and how it protects us from the universe.

If we’re doing experiments on the space station, someone might have not realized that, but going, Oh, wow, well, that’s, you know, I love the northern lights and I’m really curious as to how it protects us from meteorites and high energy particles from the sun. And, oh, that’s happening on the space station.

And because you have let people know the type of things this laboratory is doing, then people might say, you know, I was looking what to study in university, or I was looking what to do my undergraduate research thesis on, or I was looking for a book to read in the library this weekend. And so definitely you need to do the core function, and that is keep a safe space station.

And have it function as a laboratory, but there’s definitely an important piece of showing people what it’s doing of the outreach of the demonstration of its [00:14:00] capabilities so that you can not just take advantage of the brains that are working on it right now, but all the brains you can borrow and everybody else that’s thinking about it remotely.

Sonia Sennik: Maybe I can ask you a bit about something we can all see, which is the moon. With the advancement of robotics and surveillance and communications, we’ve been able to see more of the moon, but we’ve still only seen a very tiny bit. How important is the moon economy as part of the space economy going to be in the next five to 10 years?

Chris Hadfield: The moon is hugely important in human culture. It’s where our months came from. It’s part of our vernacular. It’s part of werewolves and all kinds of stuff, right? And it’s omnipresent in the night sky. And when there’s a Lunar eclipse or a solar eclipse, it’s huge and Stonehenge and all the rest of it.

You know, the moon and the sun are hugely important, but it was theoretical. It was unattainable until not very long ago. You know, when I was a kid, very first robots went there and then people started going there, [00:15:00] but it was still extremely difficult to get to. But now, because of 50 or 60 years of progress, our rocket ships are a lot better and simpler, and therefore, when something’s simpler and safer, it becomes much cheaper.

Suddenly, access to the moon is no longer an extremely rare event of a superpower, but it’s going to be like a place that people can go. You know, like Antarctica. Almost impossible to get to 110 years ago, and now thousands of people live there. The moon is different than most people imagine. If you were to peel it, like an onion or an apple, and lay the peeled moon out on the world, it’d be bigger than Africa, which is a huge continent.

It kind of gets destroyed by how we draw maps, but it’s huge. So, it’s as if we suddenly not just discovered an enormous new continent on earth, but now we can get there. And the really peculiar thing about the moon that is brand new in human history is it has no life. [00:16:00] There is no biosphere. There is no biome.

There’s no life to disrupt. The moon is a pure geological resource. It’s just four and a half billion years of the evolution of rock and chemicals and untapped geology. And we have literally just scratched the surface in a few places. We really don’t know. We’ve discovered vast reserves of water on the moon in the last 10 or 15 years on the order of 400 billion liters of water in the permanently shaded parts of the moon.

And so we’re trying to figure out what to do with this new capability. If you suddenly discovered something bigger than Africa, where you don’t disrupt any life, and you don’t really know what’s there, exploration and surveying is obviously the next thing to do. That’s what’s going on. Combination of people and machines.

Doing the surveying. And there are some things that machines do way better than people, but most of the surveying we do on earth, [00:17:00] we look at all the data and then when we really want to understand it, we send some people there to go look and that’s where we are with the moon, we’re going to do a whole bunch of robotic stuff.

And then we’re going to send some people there to go look, and perhaps as a parallel to Antarctica as well, initially, we just sent the boldest of explorers and a lot of them died trying to just get there or stay there. Shackleton or Scott or whomever, but as the technology got better, as we admitted steamships, and then airplanes became safer and safer and more and more people, and then people could start to live there and then start to winter over at the South Pole, one of the least hospitable places on earth.

And that’s common now. We all just sort of accept: “oh yeah, people live at the South Pole.” Of course they do. That’s what’s happening on the moon right now. A fascinating scientific place, a poorly understood and enormous mineralogical and geological resource, and a place that people can now start to go live and stay.

We’re at that moment in history and it’s being enabled by creative technology, but also a sort of an innate unquenchable human [00:18:00] curiosity. And the opportunity that comes when you cross those two things over.

John Stackhouse: We’ve been talking about how we can use space to disrupt ourselves, creatively, constructively. How can Canada take advantage of that? What do we have? What should we have to play a leading role in this next gen?

Chris Hadfield: If you look at how space travel is going to unfold, every milligram that got to space has been sacred up until now because the rocket technology was so primitive.

There was a sort of an unattainability of it just driven by the fact of how complex and therefore how dangerous and expensive it was to get to space. But what’s happening now is the cost of access to space is radically dropping because of improvements in rocket technology and miniaturization of computers and 3D positioning through GPS satellites and all the rest of that.

They’re all feeding together. So if we can now get to space more cheaply, [00:19:00] then what does that open up for us as a species and as a planet? And what subset of that might Canada be able to take advantage of? And we’ve been asking ourselves that question since right after Sputnik in 1957, when the Soviets launched the very first satellite during the international geophysical year, some smart forward thinking Canadians said, wow, we can put Satellites in space.

Well, let’s look at some Canadian problems and try and use our best technology and build something that will take advantage of that. And so we built a little research satellite to look at Northern lights and upper atmosphere and telecommunications, the ionosphere. It’s called Alouette, third country on earth and space.

And then we thought we’re a huge country with a small population. We could use the high ground to communicate with each other. And so. We built telecommunications satellites in the 60s and early 70s that led the world in telecommunications. So it was taking advantage of a new technological capability and serving a Canadian purpose.

And then we thought, what if we could radar map our whole country? [00:20:00] I mean, think about David Thompson, you know, a couple hundred years ago, trying to map all of Canada. One guy with a compass and a piece of paper, and he did amazing things. But we can map more of Canada in eight minutes from space than he did in a lifetime, just because of the high ground and the improvements of technology.

And so putting up a radar mapping satellite is another area that Canada led with Radarsat and then the subsequent constellations. But then when people go and we’ve been living in space now on the space station for almost a quarter century, what technologies do we need and what can Canada provide? We thought early on, let’s build robots, robotics, and we’ve been the world leader in space robotics, especially for human applications since the space shuttle first flew in the early 1980s.

Canadarm1, Canadarm2, and now Canadarm3 for going to the moon. That’s also a Canadian proven space technology. And then we have done a lot of the world leading [00:21:00] work in aerospace medicine. Canada invented the G suit. Canadian researchers have done so much of the work to make human flight safer and better understood.

And pushing back what all the rules should be on how we can make flight safe for people that spend a lot of time flying airplanes. And so extending our aerospace medicine expertise into space. And we’re doing that with the deep space medical challenge and things like that. And so I think we can extrapolate all of those into the future.

And part of what we look at Creative Destruction Lab is obviously bringing in all the leading technologies of the world. But as a Canadian, I’m extremely parochial and interested in what Canadian inventors and ideas have come up with And what of those ideas can either build on the previous Canadian technologies or are trying to step into an area that we’ve never been into before that will serve the global space community, but will serve the Canadian economy and Canadian needs at the same time.

John Stackhouse: You’re not only one of the most knowledgeable people I know [00:22:00] about all sorts of things, but one of the most curious. You’re constantly learning, which is inspiring. I’m curious what you’ve learned recently about space that can maybe inspire all of us.

Chris Hadfield: Some of the great big questions in space are the ones that I’m most intrigued by.

Obviously, what happened 14 billion years ago? We know there was something cataclysmic happened. We call it the Big Bang because we have the remnants of an explosion that happened 14 billion years ago. There’s all sorts of evidence for that. Nobody knows what happened before the Big Bang. We don’t understand what drove it.

But that’s quite intriguing to try and understand the very fundamental nature of the history of life and everything that went before it. And with the greatest telescopes that we’ve just built, that Canada has been part of, like the James Webb Telescope, we are now directly seeing early stars and galaxies from first generation after the big bang from over 13 billion years ago we can see the light from those [00:23:00] galaxies so we’re getting closer and closer to truly understanding the very origins of everything that we know on board the international space station with the alpha magnetic spectrometer a great big combine magnets and sensors it samples not just little atoms, but the things that make up atoms, sub atomic particles to try and understand how does matter work?

What is dark energy and dark matter? If you look inside an atom, there’s all those little things, the muons and the leptons and the bosons and things, which we’ve given names to, but we really don’t understand. And we’re starting to figure that out. What is the combined actual physics model that allows the universe to exist?

And we don’t know where gravity comes from. We can measure gravity, but we don’t have any way to manipulate gravity. And we’re not even sure that you can. But we didn’t even discover the electron until just over a hundred years ago, 120 years ago. And think how manipulating electrons [00:24:00] has radically changed life of being able to control electricity.

And maybe it’s impossible with something like gravity, but it used to be impossible with electricity. And so to me, pushing the very edges of what we understand that has allowed us to thrive as a species and really significantly in the last few hundred years to improve the quality of life of humanity, like 000 year history, we need to be responsible about it.

When you get a new toy, you tend to play with the new toy too much. The new toy becomes part of your normal and you build a system around it. The new technologies, the industrial revolution, we’ve reaped the benefits of it. Huge increase in population quality. We now need to make that sustainable. So how do we push all of our technologies so that we can understand how all this works together so that we can make 10 billion, make that sustainable [00:25:00] for the entire planet with a good quality of life.

Yeah. And to me, so many of the things we’re working on, whether it’s at Creative Destruction Lab or generically as a species, that’s what I’m most curious about. And how can I, with my particular set of experiences and ideas, how can I be part of a team of people that are doing that? And to me, that’s one of the greatest draws of organizations like Creative Destruction Lab and the people that it attracts.

Because it’s a bunch of folks trying to work on those great big problems with curious minds, but also with the drive and the unquenchable desire to get to those answers and incorporate them into our new normal. To me, that’s the most exciting thing going on in the world, and I’m super happy to be part of that.

John Stackhouse: I love it. I’ve got chills going up my spine. But I think what I’m taking away is that if you’re curious, there is no final frontier, even in space.

Chris Hadfield: Oh, no, curiosity is, here’s an interesting thing I read recently, we have taught, uh, some of the higher apes to do sign language, [00:26:00] and they’ve learned, some of the brighter ones, they’ve learned like 200 words, but they’ve never asked a question, and I found that really interesting.

They have an existential existence, and they’ve never had sort of a philosophical question. And I think that’s what truly separates us is our curiosity and our recognition that I want to take care of my hierarchical needs and I need to be fed and you know, all those things. But what really gives me not only a different advantage and perspective, but what really drives us and gives me satisfaction.

is my ability to imagine and therefore my ability to be curious and to try and understand things even better than we’ve understood them so far. That’s what gets me up every morning. I’m just burning with curiosity. How does everything work and how can I maybe help contribute to us all understanding it better?

Sonia Sennik: So imagination and curiosity is the uniquely human compass.

Chris Hadfield: It’s definitely what floats our boat. Whether it always gives us a direction, I don’t know. [00:27:00] But when I’m talking to a new person, as soon as I can get to our shared mutual curiosity about things, to me, that’s where the conversation always deepens.

The things we already understand, that’s just the platform that you’re standing on. But the stuff you’re looking at and wondering about, that’s the very essence of joyful discovery and recognition and progress forwards. And so, yeah, I think curiosity and wonder. Are the most childlike and also the absolutely most necessary of the human traits.

John Stackhouse: Chris, thank you for the conversation. Thank you for your curiosity. Never, never let it go.

Chris Hadfield: Great. Well, I was wondering what you were going to ask me about. Good to talk to you both.

Sonia Sennik: Thanks, Chris.

John Stackhouse: That was an extraordinary conversation. Sonia, what was one of the big takeaways for you?

Sonia Sennik: The biggest takeaway for me was just how much we have to learn about the moon and how important it’s going to be in this next phase of space exploration.

And the way Commander Hadfield framed it as, you know, imagine if you discovered a [00:28:00] new continent with no pre-existing biosphere or life to disrupt, but incredible resources. And to think that we’ve only explored a sliver of it? There’s just so much for us to learn.

John Stackhouse: I was reminded how the Big Bang is not a theory.

We know a lot about it and we’re learning lots and lots more and we’re getting close to answering some of those truly existential questions, which for us as a species driven by curiosity is so animating.

Sonia Sennik: And here on planet Earth, we have so many imaginative creators building new technologies for the next generation of space and the space economy. We’re going to meet two of them in part two. So get your space suits and set your phasers to fun. We’ll see you next time.

John Stackhouse: So join us for part two of our special series on space, the next frontier of innovation. I’m John Stackhouse.

Sonia Sennik: And I’m Sonia Sennik.

John Stackhouse: And this is Disruptors, an RBC podcast. Talk to you [00:29:00] soon.

John Stackhouse: [00:00:00] Hi, it’s John here. Welcome back to Disruptors and CDL: The Innovation Era. A big welcome back to my co host, Sonia Sennik, who’s CEO of Creative Destruction Lab.

Sonia Sennik: Great to see you, John. In this special series, we’re exploring the transformative ideas and technologies shaping Canada’s future and the people leading that change.

And if you’ve been listening to The Innovation Era, you know we’ve been especially focused on AI.

John Stackhouse: If you’re on a college or university campus, or just happen to know someone who is, you know it’s midterm season. That’s a good time to pause and assess what’s been learned. And you could say the same about the Gen AI revolution.

Most of us have probably tried ChatGPT, but if you’re like me, you probably feel you’re nowhere near ready for final exams.

Sonia Sennik: Nowhere is the Gen AI revolution having more impact and with it potentially more controversy than in our schools. And that’s what we’re focusing on today.

John Stackhouse: I came across a really interesting new research report, Sonia, from KPMG [00:01:00] Canada that shows a majority of students are now using Gen AI in their schoolwork.

Now, it’s largely for generating ideas, researching, and editing. Yet few are willing to tell their teachers that that’s what they’re doing.

Sonia Sennik: Two thirds of the students that were surveyed felt that using Gen AI was actually a form of cheating and they were reported feeling kind of ashamed that they were using it.

John Stackhouse: This is so fascinating. Well, there is that guilt factor. A majority of students say they want their teachers and their schools to incorporate Gen AI more and more in everything on campus.

Sonia Sennik: One of the things I found was very curious was that faculty reported being less inclined to use it, but a few are trying to catch up.

They’re seeing its value in not just tracking students. But reducing the often mindless administrative inefficiencies of the teaching process itself.

John Stackhouse: Now, before we get to our first guest, maybe a last point from the KPMG study, which I found fascinating [00:02:00] that almost three quarters of professors and instructors are adjusting their curriculum because of AI.

So to make sense of what’s happening out there, we’re now joined by one of Canada’s most respected and outspoken academics, Janice Stein. Janice is the founding director of the Munk School of Global Affairs at the University of Toronto, where she’s also Belzberg Professor of Conflict Management. She holds honorary doctorates from five universities around the world.

She’s written eight books and hundreds of articles rooted in her research. That sits at the intersection of cognitive science, psychology and international politics. Janice was also in some ways an inspiration for this series. She’s helping shape a new AI strategy for Canada with a lot more emphasis on adoption.

Janice, welcome to the podcast.

Janice Stein: Pleasure to be with you, John, and with Sonia.

John Stackhouse: You’re renowned in the field of conflict studies, so what got you interested in AI?

Janice Stein: AI [00:03:00] is the future of conflict studies. If you’re thinking about the future battlefield, every weapon of any significance is going to be powered by AI.

AI, space, satellite, target identification. It is impossible to think even five years out of a battlefield that is not transformed by AI. And I needed to understand the technology. It’s not good enough just to read about it. You actually have to understand.

John Stackhouse: That makes you a great teacher if you are always a student.

Sonia Sennik: And Janice, speaking of the classroom where you’ve been teaching for nearly six decades, what are some of the more enduring lessons that you’ve learned about the intersection of technology and education?

Janice Stein: People learn best by doing. They don’t learn best by listening. Despite 800 years, faculty standing at the front of the classroom, that is not the ideal learning environment.

So, if you can transfer the leadership to students, if [00:04:00] they can feel it, if they can touch it, if they can experience it, if they can imagine it, it doesn’t matter what we use, that is what I would call deep learning, as opposed to just listening.

John Stackhouse: I love that definition or redefinition of deep learning. Take us further, Janice, into the deep learning, the real learning experience.

Sonia and I were talking in the introduction about some new KPMG research, which shows, and maybe this is not a surprise, the majority of students are using ChatGPT. And maybe that’s not new. Students tend to innovate first with technology. You’ve probably seen this in a few go arounds in technology.

What can we learn from past experiences that we can and maybe should apply to AI in the classroom and around the classroom?

Janice Stein: I think the easiest analogy here, John, would be to a calculator. When calculators first came on the scene, you had a whole bunch of elementary school teachers [00:05:00] panic about what this was going to do.

to people’s capacity to multiply, right? Well, nobody would take that argument seriously right now. I think we’re in the same place with AI. There was, in the early days, right after ChatGPT was first released, all the university staff turned to issues that are very familiar to both of you. Privacy, safety, things that administrators love to talk about.

And there were seminars and workshops right from the beginning. We had several serious sessions with students, how to use it. And the first assignment in the course was, do an essay on this subject, ask GPT first, and then you rewrite it and tell us how much time it took. How did you verify whether anything in it was accurate?

Now that’s, relatively speaking, time consuming, but the easier question. The harder question for them was, how did GPT build the argument? Are you okay with that? Would you construct the argument differently? [00:06:00] Because that’s a skill. It doesn’t matter what age we’re in. If people can’t do that, they are not going to be leaders in their field.

John Stackhouse: Early days, Janice. But how is this changing the nature of students? Are they different coming out of your courses?

Janice Stein: To some extent, students are freed up, but some of the more routine things, ChatGPT is really helping them, and that’s great. But when we come to the kinds of challenges that we’re talking about, I get two quite different reactions from students.

Oh darn, I only read the summaries, I missed the point, I gotta go back, right? If they haven’t read the argument along the way, they feel at a disadvantage. And that’s what the early data on students are telling us. They want to use it. We need to take away, by the way, any discussion of penalties or ethics.

We have to take that off the table entirely. But at a [00:07:00] deeper level, they’re worried that they’re missing out on some of the higher level learning if they are overly reliant on AI.

John Stackhouse: So you’re saying take away the penalties. I think I get the logic of that, yeah. But are you okay with a student just asking ChatGPT to write a paper for them and they submit it?

Janice Stein: Yeah, as long as they tell me that they have and that’s where it gets really interesting for them. And, you know, this is harder than writing the paper, John, right? ChatGPT wrote this paper. I am confident in the validity of the data. I am confident that this is a well constructed argument, and you should have to tell me why you can answer those two questions, which is not unreasonable.

It’s what you would expect in your editorial role. You would want to know that the evidence is reliable and that the arguments are valid. That’s really hard work. You gotta go back. You gotta look. You gotta read something. And you can’t do that with ChatGPT itself. You can go some way, but [00:08:00] you can’t go all the way with it.

And so my view, there’s a contract between somebody in front of the classroom and somebody in the classroom. It’s not about cheating, and it’s not about catching cheating. So we reached an agreement right at the beginning. This is how we’re doing things. I’m going to use chat GPT. So bring it out in the open.

Sonia Sennik: From a teaching perspective, one of the most common questions I’m guessing a professor or a teacher asks themself is, okay, what do I teach next? What does this group one to many? What do I need to guide them to next? And large language models and generative AI is taking away that one to many. A student can have an engagement with a large language model and ask it questions.

As you said, get it to coach them through some things they may not understand yet. So with that utility on a personal level for students, we’re still seeing that faculty and admin at post secondary education institutions seem to be a bit behind the curve on adopting it or embracing it. What do you think they need to do to catch up?

Janice Stein: So I’m going to answer this question this way, Sonia. They’re behind the [00:09:00] curve, there’s no question, and they’re ahead of a curve. In other words, for coaching, for tutorials, for individual customized pathways to learning, AI is great. And I have no doubt that within five years, the classroom will look very, very different.

But here’s one way the classroom is probably not yet looking good enough, that as we move through higher and higher levels of responsibility, team based decision making is absolutely critical. And in the university, in the classroom, I’m getting more pushbacks from students about doing their work as part of a group, in a team, than I am about technology, frankly.

That’s the real hill to climb. Students cannot accept that their grade, their performance, is partly dependent on the performance of others. And they don’t understand that part of what they have to learn is not only the what, but how to get the [00:10:00] best of everybody and not to worry about that person is not doing as much as I am, but that person has skills that I don’t have.

They’re studying high level decision making. They see that happens to teams and groups all the time, but they can’t get themselves there.

John Stackhouse: I think you’ve just nailed a critical point that we’re living in a hybrid world. And the more technology we have, we also need a lot more humanity and those human skills of working with others, collaborating and communicating as people.

How are you finding that balance? And where in the education system do we have to really focus on creating that balance?

Janice Stein: You have to be very intentional in the way you design the learning environment. So you have to build in that teamwork and it’s got to be part of the requirements. What good is it if you do great research and you have great ideas but you can’t communicate?

These to me are the critical skills that we’re going to need going [00:11:00] forward. The capacity to de silo and to really work in a larger group and to get the best ideas from everybody, and then to communicate over those barriers. University has to prepare people to live in that world. It has to. So how did we do this?

First of all, it’s going to be slower than we like. We’re going to try some things that are going to fail, great. That’s another thing we need to model, that it’s okay. Because if you can’t fail in the classroom, you’re sure not going to fail when you take your first job in the private sector or anywhere else.

We have to build a culture of experimentation. Try it, see what works, adopt what works, and students are partners. And the best universities do that, they allow you to experiment. You know, I noticed in reading some of the research on this, that there’s a big focus on what university policies should be. I wouldn’t invest a [00:12:00] huge amount of effort there.

What you want them to do is have an enabling environment. Ultimately, let the faculty on the ground do the work, partner with students and see how much AI will change the learning environment. We will get the best results that way.

Sonia Sennik: So Janice, what do you see is that right balance between having these structures and institutions, places curious people can go, and the agility and flexibility for these institutions to keep up with the constant change?

Janice Stein: You know, I’m not big into binaries. It just doesn’t work. It’s and, right? I think universities can do everything they’re doing now and so I push our alumni all the time. We need you back for a weekend. And we have some in place and we have some all over the world and we can do hybrid and we could do all forms of learning, but they have to come back.

And that’s, I think, the biggest message. We need leaders like you, John, you know, to send that, you know, your [00:13:00] own teams that you lead, but more generically, as you look out at Canadian society, we need you back. And it’s a message that if you’re not learning in this world, where the pace of change is probably faster than it ever has been, how can you lead?

John Stackhouse: That’s a great message Janice. Gen AI is not about replacement, it’s about addition. I wonder as we move to close Janice, you mentioned how the classroom is going to be different five years out. Take us out to the 2030s. What in your imagination and maybe your vision does the learning experience as well as the classroom look like in that age?

Janice Stein: 2035 is an eternity away where we will have technologies, I think, that you and I can’t even think of right now. So how is the classroom going to be different in a fundamental aspect? I think it’s going to be the same, that it is the place where people come to argue and to think. And [00:14:00] to refine what they’re arguing, to walk out thinking differently than they walked in.

That, to me, is the essence of a classroom. Now, everything else I think we can design differently, depending on the technologies that are available to us. But that probably can’t go away, nor should it go away, actually. Because we’re social beings. We think better on some issues in a group, some issues we think better alone.

We want to be able to preserve that flexibility for people, but that social component of learning is so important and communication. That’s not going to change.

John Stackhouse: Wow. Listening to you speak, I’m just thinking Socrates had it right.

Janice Stein: It’s about dialogue. It’s about dialogue.

John Stackhouse: Exactly. It’s about dialogue. So we have the Socratic approach and dialogue.

But a heck of a lot more information thanks to the internet and then ways to synthesize, to understand, to curate, and maybe organize that [00:15:00] information in ways that were kind of overwhelming just a few years ago. And that’s one of the advantages of Gen AI. It’s an organizational tool, yes. Not necessarily a thinking tool.

Janice Stein: That’s right. It’s a huge organizational assist, and in an ideal world that I can imagine, it frees up time for more thinking.

John Stackhouse: Let’s free up time for more thinking. I can’t think of a better summation of this wonderful conversation, Janice. That should be a motto on all our walls. How can I free up more time for more thinking?

Janice, thank you so much for being on the podcast.

Sonia Sennik: Well, great to be with you and Sonia. That’s a great perspective on what AI is doing to our classrooms. But that’s just the classroom. Learning takes place in all sorts of ways, in all sorts of places, and few Canadians have done more to advance digitally enabled learning than John Baker.

John is founder of Desire2Learn, or D2L, one of Canada’s most successful and enduring edtech companies. He created the company in 1999 while studying at the University of [00:16:00] Waterloo. Today, D2L is a global software company and John is one of Canada’s most respected tech entrepreneurs. John Baker, welcome to the podcast.

John Baker: Thank you very much. I’m looking forward to the conversation.

Sonia Sennik: You founded D2L over 20 years ago with a vision to transform education through technology. Can you share a bit about what inspired you to start the company and how the landscape of digital learning has changed over the last 25 years?

John Baker: So I was in my third year of university, and I was wrestling with one key question.

What’s the most important problem that I could solve that would have the biggest impact in the world? And I couldn’t think of anything bigger than transforming the way the world learns. And we’re at a stage today where, because of the technologies in place, enables us to do that at scale, where in the past we wouldn’t have been able to do it.

Things like competency based learning, where instead of just simply passing or failing an exam, you get the ability to demonstrate mastery on the specific learning outcomes that you’re striving towards. So probably a good example to really understand that is if you’re going to go in for a heart surgery, you want the surgeon that’s demonstrated mastery of that procedure many [00:17:00] times, not the one that just passed their medical exam.

But what I’m probably most excited about is AI. AI is very much like the internet in the early days, a new way of doing things that’s going to really have a big impact on education globally.

John Stackhouse: And John, that’s a perfect segue into the theme of this episode. How is AI changing and challenging the way we learn?

So maybe give us a sense of how it’s changing D2L and where you see AI taking the company.

John Baker: The first thing is a lot of people get hung up on the risks attached to AI. That’s a natural tendency. If you’re a skier and you’re skiing down through the glades on a big mountain, what you want to avoid is looking at the trees for too long.

Otherwise, you wind up in one. So I break it down into sort of like five key paths that universities and schools and companies all over the world need to follow. One is, yes, working on the risks, understanding academic integrity issues, understanding if students are using these technologies to just have a shortcut or are they, Are they using it as a productivity tool?

Second is doing the research around this new [00:18:00] intersection point between AI and the scholarship of teaching and learning. AI is going to change how people learn. It’s going to change how people get assessed. It’s going to change how we do tutoring. And so there’s a lot of work that needs to be done in terms of, well, exactly how do we embrace this new technology?

Just like we did with the internet in the past. It was another disruptive technology that came in and changed how we taught, assessed, and tutored folks. Third is, now that we know that, how do we change our curriculum? How do we change how we teach students computer science or engineering or nursing now that this new technology is in place?

Fourth is how do we upskill the workforce that’s already there, not just prepare the current generation of students, but the workforce? And then the fifth, how do I personally start to use this technology to improve my own workflows and get better at the things that I do each and every day?

John Stackhouse: I wonder if you can also take us a bit deeper into how this is working at D2L.

You’ve got a new platform, D2L Lumi, maybe give us a sense of what that is seeking to do.

John Baker: So with Lumi, what we’re doing is really starting with the productivity enhancers for educators. So how do I take [00:19:00] your course content and now turn that into interactive content that would engage and inspire people?

How do I then take that interactive content and turn it into assessments that could really help address whether the students have actually learned what they’re being taught? Virtual tutors is another example of that. The application of AI to support the student experience so they can query just like they would a chatbot, if you will, to understand the material that they’re being taught.

Sonia Sennik: Folks that are listening may be thinking, okay, these AI tools seem great, but when I was in a classroom, my connection with my teacher mattered so much, or my connection with my tutor mattered so much. And you can think back to that person who made the difference in your learning journey. What would you say to folks who are thinking through whether or not AI erodes that human connection aspect of learning?

John Baker: I think that is at the heart of what we’re doing. AI is being used to support giving you more time to build that human connection. More time to build a connection with your classmates, with your faculty member, your teacher. It is going to free you up to be able to build better relationships, get [00:20:00] better feedback, and be more inspired.

That’s why we’re doing it. We’re not leveraging this technology for the sake of the technology. We’re using it to actually build a better educational experience, and at the heart of a better educational experience is more human connection.

Sonia Sennik: So how are your teachers and educators adjusting to this Gen AI revolution?

John Baker: So we’ve been using Lumi now just for a few months, but so far it’s been a home run. Faculty that are using it are loving the fact that it can help them to do things that would normally take them hours or days or weeks and do it within minutes. So the idea of taking a PowerPoint or a PDF or Word document and convert that into beautiful, engaging, interactive, inspiring content used to take a long time.

Now we’re doing it within, in some cases, seconds or minutes. And then being able to craft really good assessments of learning is also hard work. And many educators were never trained on how to do that. And so this starts to fix those issues. And what we’re seeing is More students getting A’s and B’s, more students completing, but more time for giving feedback.

It really is liberating in terms of looking at it as a [00:21:00] productivity tool versus as a replacement for the educator. I think it elevates the opportunity for the educator to do more group work, problem based learning, case studies, all kinds of other things that are going to be more engaging in that class experience.

John Stackhouse: D2L is a global company and you get to travel the world and meet educators in all sorts of countries. I think you’ve just come back from Asia. What’s most exciting out there in the world in terms of AI applications in education?

John Baker: I’ve been at this for 25 years. John, and I’ve never seen more willingness to embrace a new technology coming in than I am seeing it today.

I’m on a Strive AI task force with the State University of New York, where the whole university system’s rethinking how they teach and how they support workforce upskilling and changing almost everything. If I go to Singapore or Hong Kong, or I was also just in South Africa, or I was even just with one of the top universities here in Canada this morning.

Everybody is talking about it. What they don’t know is how to actually go through the transformation. And that’s where we’re trying to come in and be a partner on that journey. We’ve been working on machine learning and [00:22:00] AI for over a decade. So it’s bringing that expertise to the market now and bringing to life real technology that really has a big impact.

And then working with our clients almost as design partners. Let’s test this out. Is virtual tutoring going to work for you or not? Why not? How do we get it to be tuned such that you will want to deploy it for everybody? I think, unlike internet, I think we’ll see a transformation here much, much faster.

Internet really broke down time and place. You could take learning from anywhere, get it at any time of the day. So that was huge in terms of impact. But with AI, all of a sudden, we can maybe do a four year degree in six months. Because we can adapt learning to you. There’s another dimension. Maybe we can become better at the profession we’re pursuing so we can build better mastery.

Like I gave you that as an example with doctors being better because they’re able to to demonstrate mastery of every learning outcome because they have more time to actually perfect their profession. And then there’s a third dimension to this too, which is maybe we can teach things differently. So instead of just teaching how to remember or understand something, we can now do things like create something [00:23:00] or code something.

or analyze a whole bunch of data that we could have never imagined analyzing if we didn’t have AI superpowers, if you will. And so it allows us to not only do what we were always doing in the past with greater efficiency, but allows us to redefine what higher education, what learning could look like in the future.

And that to me is probably the most exciting part of this.

Sonia Sennik: So John, in the context of reimagining education and giving educators or students superpowers, what do we need to most protect in the next two decades of the development of education?

John Baker: Protect? Oh, that’s an interesting question. I think at the heart there, you’re getting at the risks.

The way I think about it is almost like a design challenge. And so we can design our AI to be used in education, to know everything. Even including the answers to the exam questions, or we can choose to limit its knowledge. We can design AI to just be responsive to your demands, or we can design AI to give you a grade for how you interact with it.

We can design AI to have more authority or, or to [00:24:00] care more about you as a student. And so I’m most concerned about making sure we get that design right. To me, that’s critical. I’m also really concerned about shortcuts. The risks are real. And I think the more that we could leverage this as a productivity tool, but without students just taking the simple shortcut.

So, for example, there are AIs that could give you the answer to every question on an exam almost instantly. Well, clearly we don’t want students using those. So, how do we redesign assessment, uh, is going to be a big question for the next few years to support authentic experiences for students, while at the same time not slowing down the educational journey.

John Stackhouse: So John, one of the design opportunities is to rethink the academic calendar and our schools are still, some people like to say they’re designed around the agriculture calendar still, even though very few students have to work on the farm. You mentioned that maybe we could take a four year program and do it in a few months.

AI would allow that. How much do we need to think about the business model and the operating model [00:25:00] of education, both secondary, but particularly post secondary?

John Baker: Well, I don’t think every university is going to support every type of experience, all with lower price points. I think what you’re going to see is more variety of types of education that we can receive. There’s no question these technologies can help make us more productive, which should help address the cost concerns, especially if you look at some of the universities where costs are really skyrocketing. This would be a way to save, in some cases, millions, maybe tens of millions for a university in their overall cost model, which would help them relieve some of that pressure.

But I think at the core, it’s like, well, what do you want that education experience to be? If you want to have the best nurses and best doctors and best engineers coming out of the program, you’re probably not going to get them through the program within six months. You’re probably going to want to spend that time to help them become better researchers, better scientists, better entrepreneurs, better nurses, better engineers.

And so, that will be a choice you make as a university and the cost for those types of programs will probably go higher because you’re spending more time building that better experience for those students. But for others, like let’s say for example you’re a working professional [00:26:00] and you’ve been in the industry for a long time and you just want to go back and get a degree.

Well, you have a lot of experience, so why can’t we just quickly assess what skills you already have and then provide you a personalized pathway that gets you through a four year program in six months. So the ability for us to support a wide variety. of circumstances and needs for the market, that’s going to be exciting versus everyone kind of doing it the one size fits all kind of approach, which is what we’ve been used to for the last few hundred years.

I think that will start to address some of the economic pressures that folks are under.

Sonia Sennik: Fantastic. John, thank you so much for joining us on the podcast.

John Baker: You’re very welcome Sonia.

John Stackhouse: What a fascinating and frankly inspiring conversation. I’ve known John Baker for years. He was an early guest on Disruptors. I don’t think I’ve ever heard him not excited about something.

Here we are in frankly, a post secondary education crisis in the country. It’s really challenging right now to run a university or a college. And along comes an innovator who sees [00:27:00] opportunity here with AI to transform the business model, to improve the education experience.

Sonia Sennik: These conversations were, dare I say it, educational and reinforced how the future of education is at such a pivotal moment.

And as you mentioned, John, post secondary crisis, but also massive opportunity for post secondary innovation and transformation with technology playing a critical role in equipping students and educators with the skills they need to thrive in our very rapidly changing world.

John Stackhouse: I also love how AI is changing the competitive playing field.

This is a chance for small schools, big schools to rethink what they’re doing and loss can go to first pretty quickly.

Sonia Sennik: And what I love is when Janice mentioned, learn by doing, and how important it is for students to get practical experience in the classroom. That practical learning and human connection truly is at the heart of innovating our education systems and processes and both Janice and John Baker reinforced that human connection.

John Stackhouse: What great words to [00:28:00] attach to AI. That’s all for today’s episode of Disruptors and CDL: The Innovation Era. A big thank you to our guests, Janice Stein and John Baker, for their incredible insights into the future of education. And how innovation is reshaping, not just our institutions, but also how we prepare for the challenges ahead.

Sonia Sennik: If you like this episode, leave us a review wherever you get your podcasts. And be sure to subscribe to Disruptors and CDL: The Innovation Era, for more conversations with industry disruptors, innovators, and thought leaders.

John Stackhouse: I’m John Stackhouse.

Sonia Sennik: And I’m Sonia Sennik.

John Stackhouse: This is Disruptors, an RBC podcast. Talk to you soon.

John Stackhouse: [00:00:00] Hi, it’s John here and welcome to Disruptors x CDL: The Innovation Era. I’m joined by my co host Sonia Sennik, who’s CEO of Creative Destruction Lab. And in this special series, we’re exploring the future of Canada’s economy through the lens of cutting edge technologies and the visionaries who are at the forefront of so many breakthroughs.

Sonia Sennik: In today’s episode, we’re diving into the life sciences sector. The pandemic showcased the incredible speed and power of scientific Breakthroughs when paired with new technologies like artificial intelligence. When the pandemic hit in 2020, Moderna’s AI systems allowed them to prepare the vaccine for human trials in just 42 days compared to a typical vaccine that takes between five and 10 years.

But what’s next? What will it take for Canada to lead the world in new drug development and life saving treatments as we move into the 2030s?

John Stackhouse: That’s such a great way to frame this opportunity Sonia. The pandemic was brutal for so many people but [00:01:00] if something good came out of it, it was that innovation story around vaccines.

We all rely in different ways on pharmaceuticals and drugs and yet as Canadians probably don’t appreciate all that goes into their creation and so much of it right here in Canada. We actually have a storied history. Insulin was developed in Canada in the 1920s. In the 1980s, Montreal based scientists developed life saving treatments for HIV AIDS.

And just four years ago in 2020, Michael Houghton, a professor at the University of Alberta, was awarded a Nobel Prize for co-discovering a Hep C vaccine. Those discoveries don’t happen in some isolated lab. They’ve come out of Canada because of our remarkably strong network of universities, research labs, and the 2,000 companies in the life sciences space, some of whom we’re going to meet today.

But for all our creations, we seem to be lagging in terms of commercial development. Canada’s trade deficit in pharmaceuticals is big and it’s growing. And the amount we’re spending on everything to do with healthcare is getting pretty much unsustainable. So can [00:02:00] we innovate rather than spend our way out of this?

Canada has an opportunity to build on the momentum coming out of the pandemic. And establish ourselves as a global leader in drug discovery and life science innovation. Today, we’re joined by three leaders who are at the forefront of this revolution. Our first guest is Anne Woods. She’s the Managing Director of Life Sciences at RBCx, our innovation banking arm.

And she brings with her more than 25 years of experience in life sciences and capital markets. Anne’s a passionate advocate for innovation and innovation. And a trusted advisor for the next gen of life sciences founders.

Sonia Sennik: Anne, welcome to the podcast. Maybe you could share with our listeners a little bit about the focus of your work.

Anne Woods: Sure. I’m part of the RBCx division of RBC, so you can think of us as really being a team that focuses on the unique needs of the innovation economy, and that’s even more exaggerated when it comes to life sciences because there’s just even more unique needs with companies that are so heavily [00:03:00] research based.

So I joined last year to really launch a coordinated life sciences strategy across the country.

Sonia Sennik: What are some of the unique strengths and weaknesses of the Canadian life sciences ecosystem?

Anne Woods: Often I think some of the things that are our weaknesses are also our strengths. Someone said to me, and I wish I remembered who it was because it’s such a great quote, is that Canada has a reputation of being really good at digging natural resources out of the ground, sending them overseas to be refined, and then buying them back.

And we do that with our intellectual property too, whether it’s artificial intelligence or even the COVID vaccines, they wouldn’t have been possible without the lipid nanoparticle technology that came out of UBC, but all of that really, it took those partnerships with the United States to really bring them to market.

And so I think it’s a little bit of a mindset that Canadians have that potentially holds us back.

John Stackhouse: This is not a new problem. And I’m thinking back to the great John Evans and the creation of the MaRS Discovery District in Toronto [00:04:00] that was meant to address the very challenges you’re speaking of to make Toronto a bit more like Boston.

And there has been progress made. I’m not underestimating that, but what do you think we’re missing?

Anne Woods: I think we’re maybe where Boston was 10, 15 years ago. Right now we’ve got the talent and we’ve got the research, but we maybe don’t have that critical mass or critical concentration that exists in Boston today.

And so I think we need to really have a more coordinated strategy to take that research and make sure that we are finding what’s most compelling and that we translate it into commercial opportunity.

Sonia Sennik: I’m curious if you have some thoughts on what Canada can learn from the United States in terms of funding and supporting early stage life science ventures, because it’s a very different path than a traditional early stage startup when they’re in the life science sphere.

What can we learn and what are some of the gaps we may need to fill?

Anne Woods: It might even be worth defining what we mean when we say early [00:05:00] stage in life sciences. Because I think when people think about early stage tech companies, they’re the ones that have maybe got a bit of commercial traction and are just about to be widely adopted.

Whereas an early stage life science company is probably still in the lab. And really needs to de risk their technology before they’re able to attract institutional investment. And so I think if you compare Canada to the U. S., there’s really two things that kind of jump into my mind. The lack of capital for early stage funding is, of course, important.

But it’s also that even when there is capital available for companies, it’s often spread out amongst all the different organizations that really want to help and do good things. And then these researchers and entrepreneurs end up spending so much of their time writing grants to get 500,000 a year and 500,000 a year to really get to that 2 to10 million that they need to de [00:06:00] risk their technology.

Whereas in the U.S. The NIH and the SBIR grants are kind of a one stop shop, and so that concentration allows the entrepreneurs to go back to doing what they do best, which is developing the science into a business.

John Stackhouse: As we’ve discussed, Anne, in the past, in addition to that financing, procurement is also important, and there are many who argue we need more flagship companies.

That startups can feed off of. Is it that simple?

Anne Woods: It’s not that simple, but you can see how having that concentration and that, anchor company can create both talent and capital that’s. Can spin out new companies. So even if things go badly, what you have is the infrastructure and the talent that can then go on and start new companies.

And so we’ve seen that happening, particularly in BC That BC is much more mature and really has like almost a self sustaining kind of ecosystem now where they’ve got that critical [00:07:00] mass. People move from one company to the other. I mean, as an Ontario resident, I look at them with envy.

John Stackhouse: What did BC and Vancouver specifically do to get there?

Anne Woods: I think a little bit of it is Western entrepreneurial culture. That’s something that I’ve always said BC has. If we can’t attract the big med tech and the big biotech, big pharma, we’re going to have to build it ourselves. So there’s a real united voice in BC to build an ecosystem. If you think about Quebec and Ontario, it gets a bit more complicated because you’re talking to policymakers about decisions that are great for foreign investment, but maybe not so great for local companies.

And when you’re looking at early stage life science companies and you say the commercial success is 12 years from now. Because it’s going to take hundreds of millions of dollars to invest to get there. It’s hard for policy makers to take that long term view.

Sonia Sennik: Are there Canadian life science companies that [00:08:00] you see right now are making all the right moves to scale globally?

And what are some learnings you could share with us about their journey?

Anne Woods: So if you think about Fusion Pharmaceuticals, radio pharmaceutical company based in Hamilton, it was acquired by AstraZeneca. But we all feel pretty good about that because the infrastructure and the expertise is in Hamilton. And because they put down roots there, even though it’s no longer Canadian owned, It still is going to be a Canadian company.

It is now going to be a center of excellence for AstraZeneca and radio pharmaceuticals. And so I think that’s a great success story that putting down the infrastructure really can start to create an ecosystem or a cluster. And then I think if you look to the West, companies like Abcelera and Aspect Biosystem that have done the same thing.

And so the mindset and the ambition is really important because it is fairly easy to say, I’m going to build something and we’re all backed by venture capital firms who want that exit. So I think it does take [00:09:00] public private partnerships and ambition to say, we need a reason to put down roots here and stay.

Sonia Sennik: I do love that McMaster University example of fusion. Professor John Valiant. building the roots. So after that purchase was completed, the lab, the center of excellence will continue to be at McMaster growing in Hamilton with that Canadian talent engaged. And international talent too.

Anne Woods: I mean, it’s attracting international talent, which I think is also important.

John Stackhouse: And listening to you speak, I’m reminded of how important universities are, and we’ve got some of the world’s best in our country, but they are real anchors for this kind of innovation, whether it’s U of T McMaster, UBC, the list goes on and on. I wonder before we wrap up, if you can give us a sense of where you see the next big waves of innovation coming.

Anne Woods: In my world, it’s always going to come out of the universities because in life sciences, that’s where that basic research is done. So you’ve got the biologists and the chemists and the physicists and the computer scientists [00:10:00] all working together. And I feel like that is a uniquely Canadian opportunity because we are small still.

And so there’s a real desire to innovate and I’m really excited about that. The final piece that I’m really excited about is maybe not so much the innovation, but the innovators. I look at someone like Clarissa Desjardins, who’s a serial entrepreneur based in Montreal, who’s created her fourth company.

We just celebrated at the Bloom Burton Gala, three amazing entrepreneurs that are now reinvesting and moving on to their next projects. And so I think the innovators should be celebrated as much as the innovations.

John Stackhouse: What a great message to end on, celebrate the innovators as well as the innovations. And thanks for being on the podcast.

My pleasure. Thanks for having me. Our next guest is Sue Paish, the CEO of DIGITAL. That’s Canada’s global innovation cluster for digital technology. Sue is at the forefront of commercializing digital health solutions and leading projects that are setting new global standards [00:11:00] for healthcare.

Sonia Sennik: Sue, welcome to the podcast.

Sue Paish: Thank you, Sonia. Delighted to be here.

Sonia Sennik: Maybe we’ll kick it off with some conversation about digital. So digital is working on a range of projects, advancing the development and deployment of health technologies, including AI driven solutions. Can you share with us a bit more about some of the initiatives that have the potential to be game changers in the healthcare industry?

Sue Paish: I’ll give you a couple of examples. One is, we don’t think about wounds very much in health, but wounds actually comprise 30 to 50 percent of all health care spend. Usually, the care approach is a person comes to your bedside or comes to your home to care for your wound. You can imagine in 2020, with the onset of COVID, that created a problem.

Through our model, we brought together a number of organizations, researchers and academics, and they created a device that goes on your phone. It allows the patient or caregiver to take a 3D medical grade image of the wound, transmit that [00:12:00] to a specialist, and receive guidance in their home. either as the patient or as a caregiver, and care for that wound.

It’s proven to have 95 percent accuracy, 35 percent faster wound healing, and a 50 percent reduction on having patients with wounds admitted to hospital. Moving to another area of health data is something that we debate a lot in Canada. With our demographic diversity, and we have some of the richest health data in the world, and leveraging that health data in a secure, safe way could be a game changer in terms of reducing the costs and improving the effectiveness of our health system.

John Stackhouse: So, listening to your talk, I was thinking, I suspect Apple knows more about my health than my doctor does. They have more health data on me. Why can’t we move faster? Why can’t we move at the speed of technology when it comes to these health opportunities?

Sue Paish: That’s the 344 billion question because that’s what we spent on healthcare in [00:13:00] Canada in 2023.

8,470 per person. Do the math. We can’t sustain this. In Canada, we have a very high degree of respect for health information. It’s our most important personal information. And the curators of a lot of that health data have been resistant to sharing that data for fear that personal information would leak somehow into a public domain.

That fear is unfounded now. There’s absolutely nothing. Technologically or operationally preventing us from leveraging our health data for the betterment of individual and community health. It’s all mindset. The second thing that is blocking us is the structure of our health system that is delivered provincially and then each province divides that down into a multiplicity of health regions and those health regions or sometimes institutions believe that they own our health data.

And what we [00:14:00] need is a mindset change so that we as individuals are given the right to decide who can share our health data, how, and in what context. Nothing technologically is blocking that.

Sonia Sennik: Sue, what do you think is going to build confidence? There’s two innovations you’ve talked about. One is the mindset shift on how we approach data and privacy.

And two is a mindset shift or a systems wide shift on how we actually deliver care. What are the things that you think are going to build confidence in Canadians to cross that bridge?

Sue Paish: Well, I think there’s three things. One is the generation younger than me and the generations coming behind me are far more open to sharing data.

But I will say Canadians are quite comfortable sharing private data. The second thing is, If we don’t address this situation with our health system and leverage the data to improve the quality of care and reduce costs, we will bankrupt the country. It’s 23 [00:15:00] percent of tax revenue, and it’s increasing almost exponentially because our population is aging.

So we’re going to have a combination of mindset shift, forcing providers to change and systems to change. So we’ve got some early adopters, we’ve got the technology, we’re gonna get a mindset shift, we just have to move faster.

John Stackhouse: So you keep coming back to data, and data is the fuel of AI. How much of an opportunity or a challenge is AI in everything that you’re talking about?

Sue Paish: Well, AI is both the opportunity and the challenge. The opportunity is AI can actually transcribe the conversation that you’re having with your physician, interpret it and give guidance to the physician in real time on potential issues or care paths. The point that we need to make sure we’re comfortable with AI, and I think, we’ve still got a little ways to go, is the [00:16:00] accuracy of that guidance and to make sure that AI is seen as a supportive tool, not as a cure all.

We should never think of AI replacing the judgment or the expertise of a health care practitioner. AI supports the exercise of judgment and compassion by providing data driven decisions and guidance that the practitioner will then decide whether they’re going to deploy or change or not deploy.

Sonia Sennik: There’s really interesting underlying themes of trust in all the comments you’ve given us. Trust in how the data is going to be used, trust in how to make a systems wide change, trust that the AI will be supportive and augment as opposed to replace. So what advice would you give folks that are in that conversation right now trying to make those innovations, trying to adopt These new technologies on building trust with these systems.

Sue Paish: Well, the individuals and the organizations that are building these systems have built them on the basis of human interaction, [00:17:00] thousands and thousands of conversations or interactions or gathering data from real live doctors or nurses or wound care specialists. And so building trust comes from getting your data and building your models, not in a lab with your door closed, but by being.

in the community so that when the platform or the technology emerges, you actually have physicians or caregivers or nurses speaking up saying this works. This is helpful.

John Stackhouse: So we’ve talked about the challenges that Canada is up against. Where do you see the opportunities? And what do you think are great strengths that can give us a competitive edge in the world?

Sue Paish: The strengths that we have is Canada has one of the most demographically diverse populations in the world, which makes the data in the health system extremely valuable. No one has the kind of health data that we have. So that’s a real opportunity. In [00:18:00] terms of the next steps, I’ll be blunt here, we have to get out of our own way.

We have to look at the opportunities and the imperatives that our health data presents to us, that hasn’t been available in the past because we didn’t have these technologies, we didn’t have the protections that we now have around health data, and we didn’t have the ability to leverage the data the way we have now.

But we need the public policy makers to celebrate this. Not to make Canadians fearful and that’s what concerns me is that we drive a fear mindset that somehow leveraging your health data or population health data for the benefit of you personally and your family is not a good thing. It’s a very good thing.

John Stackhouse: So what a great note to end the conversation on fear. Fear can be the enemy of innovation, but mindset can be so liberating. So thank you for using that word in an inspiring way. Thanks for being on the podcast, Sue.

Sue Paish: [00:19:00] Thanks, John. Thanks, Sonia. And thank you for doing this series. It’s good for Canada.

Sonia Sennik: Our final guest is Dr. Christine Allen. She’s a world renowned researcher and leader in drug development. She’s a professor at the University of Toronto and CEO of Intrepid Labs, just one of the companies that Christine has co-founded that are pushing the boundaries of drug formulation and precision therapies. Christine, welcome to Disruptors.

Christine Allen: Thank you. I’m delighted to be here.

Sonia Sennik: So I was surprised to hear that 90 percent of drugs fail in clinical trials. What key factors do you believe are responsible for these high failure rates?

Christine Allen: Yeah, it’s actually a staggering number. I would say that it’s a multifactorial problem, but one of the key reasons is really that in many cases we’re using off the shelf formulations of drugs that are quick, they’re cheap, but they’re also in many cases ineffective.

These drugs are not entering clinical development in optimal formulations and so we’re not setting them up for success.

Sonia Sennik: Does this mean that there’s a window here [00:20:00] where new technology can enable innovations on formulation?

Christine Allen: Absolutely. I mean, there’s the real potential there to identify fit for purpose formulations for each drug.

How do we exploit the full therapeutic potential of the drug while managing toxicity?

Sonia Sennik: Christine, you’ve often compared drug formulation to a plane carrying the drug as its passenger. Can you walk us through a real world example of where the formulation played a critical role in a treatment success?

Christine Allen: The one that we’re probably all familiar with are the COVID 19 vaccines, right? Where lipid nanoparticles were used to deliver the mRNA. Without the lipid nanoparticles, the mRNA would not have been stable and it would not have been able to reach its site of action. Maybe another great example is Doxil.

Doxorubicin is a chemotherapeutic that was originally available in a conventional formulation administered to patients and would result in cardiotoxicity. You would treat the patient of the cancer and years later they [00:21:00] would have cardiovascular disease. And so it was reformulated then in lipid nanoparticles and liposomes, and this then addressed the cardiotoxicity so it could actually exert its chemotherapeutic effect without having any damaging effects on the heart.

Sonia Sennik: Fantastic. Speaking about emerging technology, I know that In your role as CEO, you’re leveraging artificial intelligence in your drug formulation process. And we know that AI has accelerated the timeline for drug development significantly. What are some of the most promising AI driven innovations that you see on the horizon for drug discovery?

Christine Allen: I think we’re still determining the highest and best uses of AI in drug development. We’ve certainly seen a lot of investment, also some in clinical development, much less so in formulation. There’s a great discussion paper that was put out by the FDA that looks at the extent to which AI and machine learning have been used in drug development, talks about discovery and clinical development and manufacturing, but actually nothing on formulation.

So a lot of running room [00:22:00] or white space there. But there’s actually some just really interesting, low hanging fruit type examples of the kind of impact that AI is making. I was at a talk with the head of machine learning of a large multinational pharmaceutical company, and they have this large language model that they’re now using to write the first draft of clinical trial reports.

And this is saving them two to three hundred million dollars a year and enabling them to run an additional phase three clinical trial. Great example, right? Low risk use of AI. It’s the first draft. It’s not the last draft. And enabling technology that’s making an impact. That’s just a simple example.

There are others, of course, but I think we’re still seeing where AI makes the most sense and the most sense right now. And of course, there’s always new advancements and that may then open kind of the extent of usage and or the applications that it’s appropriate for.

Sonia Sennik: So lots of room for innovation and creativity.

Christine Allen: Absolutely. But I do think that we need to be looking at AI critically, ensuring that the models [00:23:00] that we’re using are accurate, that we have evidence of the accuracy of those models, and that they’re able to be interpreted. So accuracy, evidence, interpretability, very key to ensuring effective use.

Sonia Sennik: At the core of the innovation engine are universities.

What role does the collaboration between universities, industry, and policymakers play in turning academic research into market ready treatments?

Christine Allen: That’s a great question. If you look at the University of Toronto, I mean, second to none in terms of the research outputs, publications, and so on. And I just think that it’s so important to take some of that creativity and those innovations and ensure that they are made available for translation and commercialization. And that’s where those partnerships make so much sense. I love to see academic researchers working closely or in discussions with policymakers to ensure that the technologies that we are developing are available for successful commercializations, that our policies are in line with that and enabling of that. Certainly [00:24:00] right now, this is an exciting time in Toronto because we are seeing multinationals, Unilever, Sanofi, Roche, and so on, investing in AI. And this is what we really need. We need small biotechs and companies. We need medium scale companies. We need multinationals as well as academics and government really working together to create this successful innovation ecosystem that will ensure these innovations that are based on AI are able to move forward for the benefit of humans, of people.

Sonia Sennik: And it sounds like the interdisciplinary approach. It’s going to take many people with many different skill sets, as you mentioned, all across the ecosystem, but what will it take for Canada to lead the world in new drug development and life saving treatments as we move to the 2030s?

Christine Allen: One of the things I think about is when we started developing the technology that Intrepid is based on, it was really built through a collaboration between my lab and Alan Aspuru-Guzik ‘s lab.

So you’ve got kind of best in class drug formulation, drug development expertise with best in class AI and robotics expertise, and that was [00:25:00] necessary to develop this technology. Then we have these young people from both labs working together, and I call some of them now unicorns, because they’re not just experts in drug formulation, or AI and robotics.

They’re experts in both and they’re working at that interface and they are needed to drive this next wave of developments, innovations, advancement. And we need to retain them in Canada. We need to retain that talent and provide them with opportunities here so they can drive that growth and they can be leaders in this space.

Sonia Sennik: But the heart of this, it would be obviously a very deep tech health related technology company. And you have those very specialized skills. The surrounding complimentary skills are also absolutely critical to help these scale. So for example, procurement or customer engagement, operations, managing teams, building processes to scale.

So what have you seen, Christine, in those types of skill sets and talent in Canada?

Christine Allen: I think we actually have a lot of talent in that space, and I would say that there are some great programs. [00:26:00] Biotalent Canada is a great program that provides support for young people that have just graduated. I know my company has tapped into their programs quite extensively to provide positions or support for young people that want to gain new skills through working with companies.

I just think we’ve got so many different experts in different areas, and it’s just about bringing them all together, sure. I’m not concerned with a lack of expertise in those areas and certainly we have excellence just with this Nobel Prize being won by Geoffrey Hinton last week. I don’t think we need to explain to anyone anymore why Toronto is the place to be for AI.

Sonia Sennik: One of the challenges in drug development also is personalization. How can new technologies make it possible to create more personalized and targeted treatments?

Christine Allen: One of the things I know that we’re doing at Intrepid is, we’re able to identify fit for purpose formulations for each drug, and you can imagine if that drug is to target a very specific patient population, then that’s absolutely critical to ensure we can fully [00:27:00] exploit the therapeutic potential of that drug while managing toxicity.

And so I see some of this AI and as well robotics and the combination of the two as enabling technologies to finally be able to implement. precision medicine or personalized medicine.

Sonia Sennik: Christine, thanks so much for being on the podcast.

Christine Allen: Thank you so much for having me. This has been awesome.

John Stackhouse: Sonia, that was a great conversation with Christine.

Both inspiring and in some ways challenging to think about all that we need to do to ensure that AI helps improve what we’re doing in life sciences and ultimately make all of our lives better. Much of that can be done right here in Canada. We’ve got all the ingredients for success in life sciences, world class research institutions, cutting edge technologies, and a strong foundation of collaboration.

But to truly lead, as we heard from our guests, we need to turn these strengths into a cohesive strategy.

Sonia Sennik: Absolutely, John. The innovation potential is enormous. But realizing that potential requires interdisciplinary collaboration across sectors and industries. It was [00:28:00] inspiring to learn from Christine about how emerging technology is creating better, fit for purpose outcomes for patients.

But what really stuck with me was Sue’s call to action. Canada needs growth, and growth is on the other side of discomfort. Thanks for joining us today, and a special thank you to Sue, Anne, and Christine for sharing their insights.

John Stackhouse: If you’re interested in how AI will continue to shape our world, From the opportunities to the challenges, stay tuned for more episodes.

Be sure to subscribe, leave a review, and tell us what topics you want us to explore next. This has been Disruptors, an RBC podcast. I’m John Stackhouse.

Sonia Sennik: And I’m Sonia Sennik.

John Stackhouse: Thanks for listening. Talk to you soon.

John Stackhouse: [00:00:00] Hi, it’s John here and welcome to Disruptors and CDL: The Innovation Era.

I’m joined by my co host, Sonia Sennik, who’s CEO of Creative Destruction Lab. And in this special series, we’ll be exploring the future of Canada’s economy through the lens of cutting edge technologies, and the visionaries who are at the forefront of these breakthroughs.

Sonia Sennik: Today’s episode is all about artificial intelligence, a technology that’s shaping everything from healthcare, to finance, to energy.

AI has the potential to transform our economy and redefine Canada’s role on the global stage. But there are also risks and challenges that come with these advancements.

John Stackhouse: If there’s one thing we’ve learned over the past year, it’s that AI is no longer just the stuff of Silicon Valley. It’s here, it’s now, and it’s transforming Canadian industries at a pace we haven’t seen before.

I was actually just in Silicon Valley. And the momentum has not let up. In fact, just a year ago when I was [00:01:00] last there, the innovations that many of the companies thought might take a few years are now already here. But another thing I heard in the Valley was that Canadians are not moving at the same pace as many other leading countries.

So how do we change this?

Sonia Sennik: Maybe we can first start with assessing how are we doing in Canada. We have world class research and academic excellence. Canada is home to several prominent AI research hubs, notably MILA, Montreal Institute for Learning Algorithms, Vector Institute in Toronto, and the Alberta Machine Intelligence Institute, or Amii, in Edmonton.

The University of Toronto is another key institution with Geoffrey Hinton’s contribution to AI significantly impacting the global development of deep learning technologies. At Creator Destruction Lab, we launched our AI focus stream in 2015. We have since seen a huge expansion of AI into every industry and technology area.

But John, as we’ve talked about before, Canada currently has a productivity challenge. We are a leader in AI research, but a laggard in AI adoption. It is really important for Canada to enable investment in new technologies to maintain [00:02:00] global competitiveness and improve things like the efficiency of our complicated project approval systems or reducing the complexity of the tax system, for example.

John Stackhouse: So Sonia, that’s a lot to figure out in this episode. And fortunately, we’re joined by three remarkable leaders. First up will be Sheldon Fernandez. He’s the CEO of DarwinAI, a company that’s pioneering AI driven solutions for a range of industries. Darwin actually came out of the Creative Destruction Lab in its AI stream in 2017.

And Sheldon is going to give us an inside look at how AI is already reshaping Canadian business and what the future might hold.

Sonia Sennik: We’ve also got Kory Mathewson, a senior research scientist at Google DeepMind, whose groundbreaking work is pushing the boundaries of what AI can do. Kory’s at the forefront of exploring how AI can augment human creativity and decision making.

And I can’t wait to hear his perspective on where this technology is taking us.

John Stackhouse: And rounding out our discussion is Gillian Hadfield, who’s a professor at the University of Toronto and at Johns Hopkins University in the United [00:03:00] States. She’s also been named a Schmidt Sciences AI 2050 Senior Fellow, which in AI circles is a really big deal.

Sonia Sennik: Let’s dive in.

Sheldon Fernandez: Sheldon, welcome to Disruptors. Thank you for having me.

Sonia Sennik: So Sheldon, can you tell us a bit about your background and your work at Darwin?

Sheldon Fernandez: So I am first a reluctant entrepreneur and then a serial entrepreneur, if that makes any sense. I went to the University of Waterloo and had the unique privilege as a co op student of doing a couple of my work terms in the United States in Silicon Valley in New York.

And that’s where I would say the entrepreneurial spirit of our neighbors down South really made an imprint on me. So I’ve actually started two companies right out of school. I started a company called Infusion with some classmates and some partners in the US. We grew that from the original six people to a company of 700.

And we were acquired in 2017 by a company called Avanade. They’re coined by Microsoft and Accenture. My plan was to take a break after [00:04:00] that 17 year journey and, watch hockey and eat Tim Hortons and do all the wonderful things that I think Canadians do when they have free time. But of course, the artificial intelligence revolution was happening around that time.

And through a series of chance events, I met a really gifted academic team at the university of Waterloo and just couldn’t walk away from this team and the potential of this IP. So in 2017, we started Darwin AI. And it really got going in 2018, about four months after starting Darwin, AI, my wife got pregnant with our first child.

So I often joke for the last five and a half years, I’ve really had two startups. I’ve had an artificial intelligence startup called Darwin AI and a biological intelligence startup called Max Fernandez, and they are magical and exhausting equal measure.

John Stackhouse: Sheldon, I’m sure there’s a Darwinian joke in there about survival of the fittest, but maybe we’ll leave that to later in the conversation.

One of the things I find fascinating about your background is just the interdisciplinary nature of it. You’ve studied neuroscience and metaethics. I’m not even sure what metaethics is, but it sounds impressive. [00:05:00] And you’ve pursued creative writing at Oxford, no less. I’m curious how the combination of those fields and all that it does to your brain helps you and therefore might signal how it can help all of us.

In this new age of AI.

Sheldon Fernandez: Yeah, I did engineering as an undergrad. Then I did a master’s degree in theology and philosophy where my thesis was on basically asking the question, what could the latest neuroscience tell us about our foundational morality? And I did it purely out of interest, not knowing that Almost 10 years later, neuroscience would directly connect to neural networks because of the conceptual overlap.

And of course, the question of morality would become very important as we think about the ethical implications of this pervasive and ubiquitous technology. So I think it just gave me a very holistic appreciation for How technology is not just in a box and it touches literally, so many different things.

And certainly we brought that holistic perspective to Darwin when we thought about the implications of our technology, societal and otherwise.

Sonia Sennik: And Sheldon, [00:06:00] just on the topic of deep tech and AI, Many companies see challenges and barriers on new technology adoption. What are the biggest barriers you’re seeing right now for AI adoption and how would you recommend companies overcome them?

Sheldon Fernandez: I think one of them is just being overwhelmed with the different applications of this really powerful technology and the many areas it can be used in your business. And what I often say is when we’re advising companies, start with low hanging fruit, start with obvious processes where, basic AI can help you, where you can measure the uptick in performance, where you have a lot of data and use that project as a means to familiarize yourself with this world a bit before tackling more ambitious undertakings.

So it’s a combination of fear, conceptual overload, and a little bit of, new technology syndrome that we see with any new transformative technology.

John Stackhouse: Sheldon, you deal with a lot of companies. When I talk to Canadian business people right across the economy, I [00:07:00] sense there’s still a bit of hesitation around AI.

Maybe that’s wrong, but curious what your perspective is and what you read into a bit of that Canadian mindset when it comes to this new opportunity.

Sheldon Fernandez: Yeah, I can tell you that of the dozen or so clients that we had at Darwin, all but one were outside Canada, which is a real shame to me as a proud Canadian, there’s so much innovation around the fundamental IP and technology and deep tech that’s happening here, Vector, University of Waterloo, the Creative Destruction Lab.

I mean, we were a child of that program, yet the corporate clients we engage are just very risk averse. It was much easier to engage in an experimental project with our companies and partners in the United States and Europe and even Asia than here in Canada. So that’s a culture that we’ve known about and I’ve dealt with it, in my previous business, but it is something that I think is limiting the aggressive adoption of AI transformation in this country.

John Stackhouse: When you think of those other clients, the non Canadians who are embracing this, what [00:08:00] kind of questions are they asking of you and of the technology that Canadians should be asking?

Sheldon Fernandez: I think they’re asking, first of all, what are the areas where this can transform our business? But one question we get a lot of is what are our competitors doing?

Or what are the small startups doing that could be threats to our business? To give you an example, financial institutions, the United States are looking very aggressively at fraud detection, and it was harder to get that conversation going with companies that we advised here. When you look across the Canadian economy, Sheldon, where do you think the biggest opportunities are?

One of the things I’ve noticed is that there’s some very seemingly mundane industries that really have been untouched by AI. I think of natural resources, I think of mining, I think of, things that we traditionally have strengths with, where there hasn’t been a lot of injection of this technology because it doesn’t seem exciting at the beginning, right?

So I think it’s those kind of underserved areas where we have traditional [00:09:00] industrial strengths, where there’s a real opportunity for Canada to show leadership.

Sonia Sennik: How is Canada doing? The hook for our conversation here on AI is, what does Canada need to do to stay ahead in the global AI race? What would be your grade or estimation of how Canada is performing right now?

Sheldon Fernandez: I don’t want to be too critical of my own country because I’m such a proud Canadian, so I would give us like a B minus. I think there’s so much wonderful innovation that happens here. I see it at the University of Waterloo where I’m a regular guest and speaker. I see it, of course, at CDL, I see it at Vector.

So on the innovation side, we’re not doing too badly where there’s a lot of room for improvement is with the larger scale corporate adoption of AI. The other advice I give a lot of entrepreneurs who are starting the journey and younger than myself is that the knock on Canadian entrepreneurs is that we don’t think ambitiously enough.

Many of the Canadian entrepreneurs that I speak to, they’d be comfortable with a 250 million market. In the United States, I [00:10:00] want a 10 billion market, I want to change the world. I would encourage everybody listening to this, including entrepreneurs, think bigger. This is going to be a transformative technology without question.

It reminds me a little bit of the internet in the early 90s when we didn’t know that this rudimentary technology that was sending signals over telephone lines would completely transform the world. We had no idea. The same thing is true of artificial intelligence. And so there’s an incredible opportunity here that I would really urge a lot of my fellow Canadians to take advantage of.

Sonia Sennik: I love that Sheldon. Think bigger, get creative. Thank you so much for your time and for joining us.

Sheldon Fernandez: My pleasure. Thank you for having me.

John Stackhouse: Kory, welcome to Disruptors. Happy to be here. Thanks for having me. Kory, I was just saying on the introduction that I was in Silicon Valley recently and got to go back to Googleplex, hadn’t been there in a few years, and saw for the first time the new gigantic building, it’s stunning, architecturally, [00:11:00] dedicated to DeepMind, and pardon the expression, it was mind blowing, it just was a big statement on the ambition.

when it comes to AI that we see from Google, but lots of other companies in the valley. And I wonder if we can start off with your perspective, because you get to see the world through DeepMind and the world as it is today, but also the world as it is becoming. Where does Canada fit into that picture?

And how do Canadians need to see ourselves in that bigger global picture?

Kory Mathewson: There’s a lot to unpack there. So first off, Canada occupies a pretty unique place on the world stage when it comes to the leadership in AI. We have an incredibly open and collaborative ecosystem with layers of academic, non profit, and private sector cooperations, and that’s really helped to position Canada as a leading player in this space.

We were, I think, the first nation to have a Pan Canadian AI strategy. There has been significant investment on a federal level for a long time. The National Research Council, a lot of the tech incubation, a lot of the universities, a lot of great [00:12:00] faculty and students that come out of Canada, and some fantastic representation in Canada.

in the private sector, including myself and the amazing colleagues I have at Google DeepMind in Montreal and in Toronto. I think that there’s a lot of exciting people that are working in it, a lot of great trainees and a great like system of development in Canada. And that has really driven a lot of knowledge sharing and a lot of innovation.

But as you say, there’s always more to do.

Sonia Sennik: Kory, your research focuses on the human machine interaction, most recently in domains of interactive conversational systems or creative applications of AI. As I like to call it, whose line of code is it anyway? So what potential do you see for AI to enhance human creativity further?

Kory Mathewson: I love that. So Colin Mockery loves that bit. I told it to him and he said he was very fond of it. He’s seen a lot of my technology brought to the stage and is pretty excited about the I love it. possibilities for these technologies to challenge creative people on the theater stage. So yeah, I like to work at the [00:13:00] intersection of AI and artistic creativity.

I think that there’s so much that can be done by listening and engaging with and empathizing with the creative professionals because they’re really the people that are going to push these models past the frontier. And their opinions are important because we have to do this together. It’s really critical that we do this together because.

Some of these people will be the ones that will be most impacted by the technologies that we’re building, right? To make the next era of art, like generative AI, is the storytelling technology of our generation. And that means that how we build it has to be done in collaboration and alongside creative people and technical people.

John Stackhouse: How do you apply that to businesses, and I’m thinking of healthcare as well as education, not just big corporations. How do you apply that idea of a technology that is enabling the storytelling of our times? That’s a poetic description.

Kory Mathewson: I aim for a bit of a poetic description every once in a while, but [00:14:00] humanity is built around storytelling.

knowledge transfer and a lot of what we would describe as culture is built around storytelling and the way that we share our principles and values comes down to the way that we can communicate and we’ll leverage the technology of our time to do that sort of communication. So I’m thinking about Educators, I think about students also, every single student in post secondary education, the TAs, the professors that are trying to communicate these classes and curriculums, they have the capacity to leverage these generative AI technologies so that they can communicate their messages more effectively, but also personalize that learning experience, that learning journey.

In the healthcare industry, there’s a lot that Google’s doing to understand the medical research domains and assist medical research. I think personalization has a real place here. Everyone is different. And with these powerful tools, we have the capacity to appreciate that context and appreciate the individual and to really build future alongside [00:15:00] them as they build the best version of themselves.

Sonia Sennik: Thinking about the creative community, Canada has some incredible musicians. Some of the work you’re focused on right now is transforming the future of music creation. And I’d love for you to share a bit about Dream Track or any of the other music AI tools that you’re starting to develop. How do they work and how do creators leverage them in their music or art creation process?

Kory Mathewson: Dream Track is powered by our most advanced music generation model. It’s called Lyria and Dream Track in YouTube shorts is a technology that’s going to allow creators to express themselves in new and interesting and different ways. It’s a model. It’s a generative AI model that’s been trained on a whole bunch of information to generate new original content.

And that content can either be used directly in Dream Track or in a lot of our different Music AI Sandbox tools. And that can be used directly in your own creative workflows if you want to come up with new idea generation, smash two ideas together, play something [00:16:00] on one instrument and hear it in a different voice.

We’ve seen artists like Wyclef Jean and Dan Deacon many more that have asked for certain things. that are doable, and we implement them, and then we see the sort of like fruit of the collision of the arts and the

John Stackhouse: technology. Kory, that’s so exciting to hear. When I talk to a lot of companies, though, they’re doing some pretty rudimentary things with AI.

How do organizations need to think about these big ambitions while also focusing on the plumbing? And using AI as kind of an enhancement tool for efficiency purposes.

Kory Mathewson: So this, I think, will happen slowly, and then progressively quicker, and quicker, and then rather quickly. Google Canada put out an economic report just recently, and it said that this sort of technology has an amazing potential to boost Canada’s economy.

230 billion, save the average Canadian worker 175 hours a year. That’s not [00:17:00] nothing. You think about a lot of the jobs that are being done and how those jobs can be done more efficiently, more effectively with these generative AI tools. Obviously, there’s going to be a lot of re skilling, on ramping that’s going to take time and energy and effort.

investment, but the payoff, the dividend that comes once you build out the workflow and how the workflow can be augmented by generative AI is starting to be measured and will pay off as the models get better, which is a bet I’m willing to make.

John Stackhouse: I’m glad you raised the point about skills and we all need to think pretty aggressively and ambitiously about the skills we need.

For that augmentation, as you described it, we also need to think about the talent that is critical to all of our organizations as well as our country. We’ve seen a lot of that talent leave. It goes to Silicon Valley. How do we do better? Kory, keeping our best talent. Especially when it comes to AI.

Kory Mathewson: So venture investment from previous founders is critical.

Now I do a lot of work with the Creative Destruction Lab [00:18:00] adjudicating the science behind early stage startups, not just in Canada, but companies that come to Canada to connect with our venture ecosystem and our science ecosystem. So I think it’s not just a matter of reducing the brain drain, but also attracting talent here and saying, hey, we have an incredible amount of scientific expertise and mentorship for.

These early stage startups and that can be fostered through the acceleration of the creative destruction lab through the incubation at the Mila or the Vector or Alberta’s Machine Intelligence Institute. These ecosystems are inviting and they want to support people. Canada is a great place to build a company.

Canada is a great place to do your early stage research to be a graduate student. There’s an incredible amount of funding that’s available provincially, federally, and at each of these institutions. So, would I like to see more? For sure. Am I happy to mentor early stage researchers, students, so that they do consider Canada as a place to build what they want to build?

For sure.

Sonia Sennik: Thank you so much, Kory. This was fantastic. Thanks for your time.

Kory Mathewson: Always a pleasure to talk with you, Sonia, and nice to meet you and to chat with you, John. [00:19:00] Thanks, Kory.

Sonia Sennik: Gillian, welcome to the podcast.

Gillian Hadfield: Hey, very glad to be here.

Sonia Sennik: So, Gillian, we met earlier this year, just before you started as a Schmidt Sciences AI 2050 Senior Fellow. Can you tell us a bit more about your work in this role so far?

Gillian Hadfield: Sure. The theme that’s informing that work is how can we build AI systems that what I call normatively competent and how can we build the normative infrastructure for AI alignment?

So this is a different take on how do you get computer machines, AI to do what you want them to do. One approach, which is the dominant approach today, is well just figure out what values and norms you want them to follow and stuff them in there. And I think it’s not going to work very well. It’s going to be very brittle, not very adaptive.

So I’m working with some fantastic computer scientists on how do you build AI systems that can go into a context or a setting and figure out what they [00:20:00] should be doing in this environment.

Sonia Sennik: The first way you were talking about is very policy driven or rules based, and what you’re researching is these AI models that need to do off policy learning to have the ability to innovate on policy.

How do those two things work?

Gillian Hadfield: So I think of normative competence as what describes humans. We don’t actually just come like pre programmed with a bunch of rules and norms that we should follow. You could drop any of us down in an unusual new environment, and we could kind of figure out, oh, I know there must be rules around here, and I know what to look at to go and figure out what the rules are, and I know what’s expected of me, both in terms of complying with rules, and also helping to enforce rules.

If you’re going to get AI systems that follow the rules, it’s much more complex than just say, well, give them the rules and they’ll follow the rules. Rules are actually really complex things and we use all kinds of institutions and ways of signaling and so on to figure out what is the right thing to do here.

Sonia Sennik: So given your experience on advising governments and tech companies on [00:21:00] AI policy, how do you think Canada can develop a regulatory framework that promotes both trust and innovation at the same time?

Gillian Hadfield: A really important place to start is to recognize that we don’t really even have the basic legal and regulatory infrastructure in place that would allow us to figure out when and how we want to regulate what AI does.

So everybody’s very focused on, we should come up with the rules and standards of behavior. But I think about things like, well, we need to figure out how we’re going to register. AI systems so that we can learn about them and keep track of them. We need to figure out how do we give them durable ID. Maybe we need to be figuring out how to make them directly accountable, just like we make corporations, which are also artificial entities, directly accountable through the legal system.

Sonia Sennik: So it sounds like Gillian, you’re thinking on very much a systems level for these types of innovations and that the recommendation for Canada is to think system wide. So how [00:22:00] can these regulatory or legal frameworks remain flexible? Because it sounds like they need to have some structure and standards, but also flexible at the same time, just like building a building or a bridge, it needs to stay standing tall, but it also needs to survive the weather and sway.

Yes. So what are some recommendations or what are you seeing that’s proving effective

Gillian Hadfield: I think the most important move we need to make to be able to get that balance between stability and adaptability between, having reasonable protections against harms, but also allowing innovation and evolution is an idea that I’ve put forward with Jack Clark called regulatory markets.

Rather than government coming up with very specific requirements, here’s the kind of data you should train on, here’s the particular tests you need to be able to pass. Government should be setting what the outcomes are. How safe does that autonomous vehicle have to be? How fair does your credit approval algorithm have to be?

Then you recruit the private sector to [00:23:00] invest and innovate in designing the systems that will implement those outcomes, and what.

Sonia Sennik: Are you seeing, Gillian, right now as a barrier for that to flow?

Gillian Hadfield: So I’ve actually been talking about this idea for close to eight or 10 years. So it’s been a long slog to get people to not think this is crazy or this is like turning it over to corporations to regulate.

But we are actually starting to see much more uptake because I think it’s becoming clear to people that it. Governments are not going to have the capacity to respond fast enough and the level of complexity and technological complexity here. So I will say I’m actually much more optimistic that this is going to happen than I was even like three or four years ago.

I think the release of ChatGPT and the sort of the language model explosion has gotten everybody to realize, Oh, my gosh, we may need to be doing things quite differently. But the kinds of obstacles that are there is it is a really different way of thinking about regulating, and you have [00:24:00] to get past this idea that it’s handing it over to the private sector.

Now, I do point out, we’ve already handed it over to the private sector because we actually don’t have very much AI regulation in place, and companies are regulating themselves. So we need to do things like, let’s pick some domains. And say, okay, how would we know that we’re achieving the outcomes we want?

Let’s identify some companies that are startups that are already in the space or could soon be in the space to license them to be a licensed regulator in this domain. Let’s think about how we create the incentive to adopt that regulatory system, like by giving like a safe harbor. It says, you can’t get sued in tort law.

If you’ve adopted this regulatory regime, like, oh your algorithm started discriminating against a group of people or it started behaving in ways that were unpredictable, like in the medical space or something like that. So I think I’d really be focused on [00:25:00] help those companies get a market foothold.

So they’ve got serious demand and I think governments could do that in a really straightforward way.

Sonia Sennik: There’s a lot of companies and enterprises that struggle with adopting new technologies like AI. And we’re seeing Canada right now, extremely low on our productivity and AI adoption comparative to the G7 or G20.

What are some of the barriers Gillian, that we’re experiencing and what are some recommendations on how companies can overcome them?

Gillian Hadfield: I think there are barriers that are coming from risk aversion in companies about, could we get sued? We don’t know. I’ve been reading scary stories about chatbots that go crazy or predictions about what might happen with AI.

So I actually think that the lack of sensible regulatory infrastructure that’s attuned to the current risks and, I mean, there are current risks, but it’s nothing we couldn’t handle. But I think it’s terra incognita for a lot of the people who are managing [00:26:00] risk and liability and compliance in organizations.

So I do think that building that regulatory infrastructure, trying to keep it simple, that’s why I keep coming back to the safe harbor idea. Right? Like the idea that, oh, we can actually put a straight line through our risk calculation because somebody said, take these steps or, enter into a contract, a regulatory contract with this organization, and you won’t face those kinds of downsides.

There are economic barriers, but this is certainly a factor.

Sonia Sennik: So de risking is critical. Thank you so much, Gillian, for your time. And thank you for joining the podcast.

Gillian Hadfield: Yeah, happy to, Sonia. Thank you.

John Stackhouse: That was a fascinating conversation, Sonia. And a really good reminder of the complexities of AI. We can all get excited about the technology and the opportunity for innovation.

As we should, but there’s so many more considerations that we’re hearing from right around the world.

Sonia Sennik: And what I love is talking to folks who are expanding AI into [00:27:00] areas that you wouldn’t typically think about, like Kory talking about the creation of music and the creative process, augmenting that with new innovative tools, and how essential it is to hear back from that community to shape those tools and build things that they’re excited to use.

John Stackhouse: That’s such an important word, tool. AI is a tool, and I fear that too much of the public debate around it is ascribing superpowers to AI that we may see one day, but right now it’s a tool that’s in the hands of humanity and we can all use these tools, whether we’re music creators or code writers to improve what we do. And how we do it.

Sonia Sennik: To quote Ani DiFranco, every tool is a weapon if you hold it right. And so now is the time to figure out how do we want to manage this tool? How do we want to shape the way we use it, the way we integrate it into our lives? And that’s why it was so inspiring to hear Gillian talk about the work she’s doing at the Schmidt Foundation to have a global conversation about how this evolves.

This is a moment in time as well, John, where I feel [00:28:00] companies and people can really have profound influence on how we use this technology. So now’s the time to get involved, test it out, try it in your company, in your industry, in your life, and make it work for you.

John Stackhouse: What a great message to end the show on.

Make it work for you. Sonia, it’s been great sharing the episode with you.

Sonia Sennik: Always a pleasure, John. And a special thank you to Sheldon, Kory, and Gillian for sharing their insights.

John Stackhouse: This has been Disruptors, an RBC podcast. And if you liked what you heard, be sure to subscribe, leave a review, and tell us what topics you want us to explore next.

I’m John Stackhouse.

Sonia Sennik: And I’m Sonia Sennik.

John Stackhouse: Thanks for listening.

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.

Sonia Sennik: How do we reimagine an economy in a time of unprecedented change?

John Stackhouse: And who are the disruptors leading that change? Welcome to Disruptors and CDL, the Innovation Era, a limited series where we dive deep into the technologies and innovators reshaping our world. I’m John Stackhouse, Senior Vice President at RBC.

Sonia Sennik: And I’m Sonia Sennik, CEO of Creative Destruction Lab. Together, we’re creating change. We’ll explore how Canada can harness innovation to stay globally competitive in this time of disruption.

John Stackhouse: So, here’s the problem. We’re lagging. Canada’s adoption rates for transformative technology, like AI, are far behind where they should be.

Sonia Sennik: And this is not a tech issue. It’s an economic one. From artificial intelligence to biotech, to the future of energy, to post secondary innovation, this collaborative series between RBC and Creative Destruction Lab explores the breakthroughs and ideas that can unlock Canada’s economic potential.

John Stackhouse: We’ll speak to the innovators at the forefront, those changing the way we think about productivity, sustainability, and the future of work.

Sonia Sennik: Subscribe now to The Innovation Era and join us as we explore critical insights into Canada’s economic challenges

John Stackhouse: and what we can all do to build a brighter future.