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'We will have raised Canada’s game': U of T's Ajay Agrawal on the Creative Destruction Lab's past, present and future

Ajay Agrawal, the founder of the Creative Destruction Lab, is the Geoffrey Taber Chair in Entrepreneurship and Innovation and a professor of strategic management at U of T's Rotman School of Management (photo by Yana Kaz)

The Creative Destruction Lab – a seed-stage accelerator affiliated with the University of Toronto's Rotman School of Management – is a startup success story in its own right. 

Since its founding in 2012, CDL has undergone a massive expansion, boasting multiple streams of startups and partnerships with business schools across Canada and into the United States.

It has also emerged as an early leader when it comes to supporting startups in the field of artificial intelligence – technologies many believe are poised to revolutionize everything from transportation to medicine.

“To our knowledge, the CDL is home to the greatest concentration of AI-based companies of any program on Earth,” says CDL founder and Rotman Professor of Strategic Management Ajay Agrawal.

In an interview with Rotman's Karen Christensen that originally appeared in the winter issue of Rotman Management, Agrawal discusses the origins of CDL's focus on AI, how the lab benefits business school students and why it decided to make a “bold” move into the quantum realm. 

Note: This interview has been edited and condensed. The full interview can be accessed here.

Three years ago, CDL made a huge bet on artificial intelligence and machine learning. What prompted that?

In our first year of operation, one of the startups that came to us was Chematria, now called Atomwise. Its founder, Abe Heifets – a U of T PhD in computer science and biology – was applying a new AI technique to drug discovery. What Abe was doing represented not just a marginal improvement, but a potentially transformative change to the way drugs are discovered, which represents a multibillion-dollar problem for the pharmaceutical industry.

While we were working with Abe, a team of graduate students from U of T computer science won a high-profile competition at Stanford called ImageNet. It’s basically an image-recognition competition, whereby a computer is given a bunch of pictures and has to identify the image, whether it’s a ball, a horse or a wheelbarrow. This team from Toronto participated, and not only did they win – using a machine learning technique called deep learning, largely developed at U of T – but they won by such a margin that the following year, all of the finalist teams were using their technique.

Those are just two examples of events that inspired us to bet on machine intelligence. Overall, we saw mounting evidence that AI was a general-purpose technology that could be applied to a wide range of problems across a vast array of industries, and that’s what prompted us to dedicate a new stream of the Lab’s activity to AI.

At first, you faced resistance. Why?

People said we were being too narrow, that there weren’t enough startups to fill an AI stream and that there wasn’t enough interest from investors. At the same time, we had believers. One such believer who herself had written a highly influential blog post describing the “landscape” of companies emerging in the machine learning world was Shivon Zilis – a Canadian based in San Francisco and a partner at the venture investing firm Bloomberg Beta, where she led the firm’s investments in machine intelligence. I invited her to the Rotman School to present her insightful analysis to our MBA students, and the CDL team – quickly realizing she is a star – recruited her to join forces on our AI initiatives. Elon Musk subsequently saw the same potential and recruited her to help him build his empire.

So, we moved forward with the new stream, but to address these concerns, in 2015 we also launched an annual conference – with Shivon as co-chair – called Machine Learning and the Market for Intelligence. The goal was to educate the Canadian business community about the importance of this emerging field. Leaders in the field – from organizations like Google, Uber, Apple, Stanford, Carnegie Mellon and MIT – came to Rotman to discuss and debate how AI is and will impact a variety of fields, from life sciences to manufacturing to retail. We held our third annual conference in October 2017.

Talk a bit about the CDL’s results to date.

The launch of our AI stream transformed the lab from a Canadian enterprise into a global one. In our first year, our startups were all from Ontario, but they now come from around the world. Similarly, in our first year, our fellows were all from Canada, and that, too, changed when we launched the AI stream. Our ML7 – Machine Learning Seven – includes William Tunstall-Pedoe, who flies in from Cambridge, England, every eight weeks. He has a PhD in machine learning and founded Evi, which was acquired by Amazon in 2012. Evi’s technology powers the AI engine in Amazon’s Alexa, which, to my knowledge, is still the top-selling consumer AI hardware product in the world.

The ML7 also includes Barney Pell, who flies in every eight weeks from San Francisco. Barney also has a PhD in machine learning and led an 85-person team at NASA that flew the first AI into deep space. He then built an AI company called Powerset that was acquired by Microsoft, and now he’s the co-founder of Moon Express, which is essentially building a Federal Express-type service to the moon, because Barney believes the moon is going to be an important gateway for commercial space travel.

So far, the results have surpassed our expectations. Back in 2012, we accepted 25 companies into our general high-tech stream. Last year, we doubled that by adding the second cohort focused on AI, so we had 50 startups. This year, we doubled our intake again by accepting 100 AI-focused startups and adding a new stream: The world’s first program focused on launching startups predicated on quantum machine learning (QML). To our knowledge, the CDL is home to the greatest concentration of AI-based companies of any program on Earth.

The Lab is one of the most popular second-year MBA courses at the Rotman School. Why does it resonate so much with students?

For two reasons: First, it combines the traditional mode of learning from lectures with learning-by-doing; and second, it links academic work with a sense of ownership. The traditional approach to learning at CDL is led by our chief economist, Professor Joshua Gans, who developed a structure for teaching entrepreneurial strategy along with MIT’s Scott Stern. This provides students with an academic framework and context for what they’re going to experience next. Then comes the learning-by-doing part. Normally, business schools use Harvard Business School cases to provide examples in the classroom. We replaced those with real companies. Working with founders, fellows and associates provides students with an opportunity to roll up their sleeves. Instead of reading a 30-page case that comes with a fact set, they have to find the facts themselves and figure out – of the infinite information out there – which bits are the most valuable for their needs. They experience the messiness of the real world and the reality of having to make decisions without having full information.

The second piece is ownership. When our students work with these startups, every decision matters, so they have a real sense of ownership. It’s a powerful learning experience to feel ownership over the results because the consequences are so tangible.

Universities rarely adopt programs developed elsewhere. What motivated UBC, NYU and others to adopt the Creative Destruction Lab program?

Every university has a program or course on entrepreneurship and startups, but I think the CDL stands out due to its significant results. The calibre of investors from the business community who have rallied around the CDL is unprecedented. Naturally, other universities would love for that to happen at their own business schools.

When UBC indicated interest in adopting the program, the big question was, ‘Is this replicable?’ But a very competent team, under the direction of UBC Professor Paul Cubbon, was able to reproduce it. When CDL-West completed its first year, the results on all dimensions were impressive, and we had evidence that, yes, this program is replicable. We have since launched CDL at the University of Calgary, Dalhousie University and Université de Montreal, and in October, we announced a partnership with New York University’s Stern School of Business.

CDL Toronto’s competition is not Vancouver, Calgary, Montreal, New York or Atlantic Canada: It’s Silicon Valley. Each of the CDLs has attracted some of the top business people from its region. Our challenge now is to cross-pollinate, so that the Montreal fellows are connecting with companies in the Toronto program and the Toronto fellows are connecting with companies at CDL Atlantic, and so on. One of the things that makes the Bay Area so effective is that everything moves so fast. If we can accelerate the velocity of business development here, we will have raised Canada’s game as a whole.

You mentioned earlier that CDL launched the world’s first program focused on quantum machine learning. What is your vision for this initiative?

It’s a bold one: By 2022, the QML Initiative will have produced more well-capitalized, revenue-generating quantum machine learning-based software companies than the rest of the world combined, with the majority based in Canada.

Why QML? First, we can leverage the leadership that CDL currently has in the commercial application of machine learning. Second, we can leverage Canada’s leadership in quantum computing at places like the Perimeter Institute and the Institute for Quantum Computing in Waterloo, Université de Sherbrooke in Quebec, and D-Wave in Vancouver, among others. Third, we can leverage the network of investors, entrepreneurs, scientists, and corporations that have rallied around the CDL and our mission of commercializing science for the benefit of humankind.

Clearly, the CDL is leading the way in this arena.

I believe so. Three years ago, it felt like we were moving early on AI, but we realize now that – if we could turn back the clock – we actually should have started even sooner and moved faster. We were roughly a year ahead of everyone else, but now a number of programs in other countries are focused on AI startups – so we’re running fast just to keep our position.

In terms of QML, so far we’re the only ones doing it, and that’s because the technology is so embryonic. We might go for two or three years without a significant success, because we
might be too early. The point is, once there’s a hit, places like MIT, Stanford and Silicon Valley will all double down in this field. Our approach is to get ahead, make the investments now, and attract all the elements of the ecosystem to Canada.

We basically want to do in Toronto with QML what Silicon Valley did with semiconductors in the 1960s. There’s nothing inherently magical about Silicon Valley. The semiconductor industry happened to start there due to the pioneering efforts of a handful of people, and once that community grew big enough, it became very hard for other regions to compete. Our view is, if we can seed it here and if the industry takes off five years from now, by that time, Canada will have such a critical mass that it will be hard for the whole community to move somewhere else. We’re trying to plant the seeds now.

Already, three top Silicon Valley venture capitalists are sufficiently optimistic about this program that they offered to invest in every one of the companies that gets into it – sight unseen. Most of these companies won’t make it – and they know that – but they want to be involved because along the way, they will get an education in QML, and there is some positive probability that one or two of these companies will figure out a commercial application.