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.

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.

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.