My teaching mentality is simple: when students come to research universities, especially those that claim to be among the top in the world, every class should have a base in frontier research. I do not teach with textbooks. Feel free to reach out if you would like to build on any of the materials not shared here.
In 2023, I developed a series of AI-based "Virtual TAs" for my classes, to amazing success. We added a number of features like AI-based adaptive question banks, and spun this out into a company in 2024. I truly believe what we're building is the future of university courses - we can now know what every student knows every week of the term, and ensure they have the help they need to learn exactly what we are teaching. It is now used in universities in over 30 countries: reach out at Alldayta.com if you want to sign up!
In Fall 2024, I produced a "new PhD student Tech Stack" document which may be of interest. New students should know how and why to write replicable code, use AI tools, and present their work professionally! We updated this in September 2025 and now require this for Rotman PhD students across fields. My 2025 "short reading list" for folks who want to know what is going on with AI: The Bitter Lesson by Sutton, Situational Awareness Intro and Ch 1 by Aschenbrenner, The Unreasonable Effectiveness of Mathematics by Wigner, and Scaling Laws by Kaplan et al, and Early Science Acceleration by Bubeck et al. If you want more detail, read Why Machines Learn by Ananthaswamy.
This is a brand new course in Fall 2025. I cannot express how excited I am teach this one - to my knowledge, one of the first courses of its type in the world. I'll open-source teaching materials as we go, but the basic idea here is economic theory + history of technology + philosophy + history of thought on why a few organizations in a few places at a few times accomplish very important things. Is it because of Great Men and Women? Is basic science a necessary precursor? Is liberty? What role do cities play? What organizational forms work better for "big things" vs day-to-day effectiveness?
You may also use the All Day TA for this course, including our AI-driven adaptive quizzes meant to ensure learning by requiring students to explain when they misunderstand and adaptive quizzes on a topic-by-topic basis. Both the quizzes and the general All Day TA for this course will be updated weekly throughout the fall.
Syllabus/Reading List from a fourth iteration of this PhD course (previously taught at Rotman, and jointly at Duke Fuqua with Wes Cohen).
NBER Innovation Boot Camp, jointly organized with Benjamin Jones, Heidi Williams, Ina Ganguli and Kyle Myers.
A series of video lectures and slides produced in 2021 with Heidi Williams as part of the AEA Continuing Education series. These are intended for use both in graduate courses on innovation and for one-off lectures in courses on IO, labor, and growth.
A novel way to teaching MBA-level strategy. Everything we teach has a formal economic theory behind it, and the goal is give students a "toolbox" of methods for generating supernormal profits even when others can see what you are doing and can react. The basic idea is that it is unusual for any such strategy to work: if you are making money doing something, then either others should do the same thing and compete your price down, or buyers and suppliers should squeeze any margin you have remaining. So it needs to be the case that other actors either can't or don't want to do so even when they see what you are doing. The course gives nine strategies that work: strategic fit, differentiation, firm boundaries others can't or don't want to match, formal incentives other can't or don't want to match, relational incentives others can't or don't want to match, economies of scale and scope, network effects, collusive mechanisms, and learning curves. We also discuss what these strategies mean for firms who want to enter and compete with strong incumbents. Please contact me for teaching notes and slides.
Graduate level course in entrepreneurship, taught with Ajay Agarwal as part of Rotman's famed Creative Destruction Lab. Slides and lecture notes available on request; occasional topics class for 2nd year MBA students in an intensive course working with science-based startups. Individual classes cover the basics of practical startup finance, testing/experimentation, scaling, and pricing (especially in 2 sided, enterprise, and other unusual settings). The guiding principle of the class is that startups are different from advanced firms because of uncertainty with highly-skewed payoffs, which must be investigated despite highly constrained firm resources. We make no claim that creativity or diligence or cleverness can be taught - but it is possible to teach students how to properly think about the option value of various paths to a final business, and what that means for how the startup should grow. That is, this is a formal class on Bayesian entrepreneurship, which we have been teaching since 2019. For slides and teaching material, some of which is also available in French, please contact me directly.