Columbia’s AI Playbook: Teach The Thinking, Not The Tool

Last week we ran an article, drawn from this interview, about CAiSEY, the interactive case preparation system from Columbia Business School. CAiSEY is one of a new breed of university-launched teaching systems, and a popular one, used by more than 2,000 Columbia students last year alone.

But CAiSEY was one slice of a longer conversation. The core of this series has always been to learn about, and write about, how AI is affecting business school teaching, straight from the faculty doing the teaching. To that end, we were fortunate enough to discuss the topic with Columbia professors Omar Besbes and Daniel Guetta.

Their central insight stood out: the key to teaching students isn’t any individual model but concepts. Models come and go. Guetta tells his students on the first day that a significant share of what he teaches will be irrelevant within a few years. What won’t be irrelevant is judgment. To paraphrase, what happens when we’re faced with GPT-73, a superintelligence? When the engineer at the conference table asks for millions to deploy something smarter than everyone in the room, what will the role of people be, and especially of today’s students? Columbia’s answer is to teach the thinking, not the tool: how to weigh cost against performance, open against closed, and what can go wrong, so that the decision still belongs to a person.

We also explored the history of AI at Columbia, which long predates generative AI. The school has taught AI to every first-year MBA for at least a decade, back when AI meant statistics and machine learning, and hired faculty for it years before ChatGPT made it fashionable. Guetta calls the result a loaded spring. And we discussed the impact of being based in New York City, with its thriving and eclectic business community, where, as Besbes put it, you live the market rather than imagining it.

P&Q: How are you using AI to teach? How is it being received by students, in the classroom? And how is that different from what’s happened before?

Columbia (Omar Besbes): Maybe I’ll start at a high level. Columbia Business School (CBS) has invested in tech early on. Our current dean, when he arrived almost six years ago, had two big priorities. One was the digital future, which encompasses AI, and the other was climate change, and all the new business models that will emerge out of these and how we should prepare our students for this new environment.

AI is embedded across all courses. There are courses where AI is embedded and added on, and there are new classes emerging to help MBA students think about all the stacks of AI, from energy generation all the way to the application layer and everything in between.

With the emergence of AI, the frictions MBA students encountered in the past between asking questions and getting answers have decreased dramatically. We are helping students continue to remove these barriers by helping them be builders, but also to be critical about what to build. The judgment layer is still there and is more important than ever. But we also want them to be practical and agile, ready to build when they need to build and to get their hands dirty when they need to.

P&Q: Daniel, I’d like to hear from you. And one thing to think about: Columbia is obviously in New York City. Talking to Stanford, their students are next to Sand Hill Road, back and forth to VCs, and geographically that changes how you might build a firm. New York obviously lights up the way the Sand Hill Road crowd does.

Columbia (Daniel Guetta): The idea of us being in New York dovetails with something Omar said that I’ve felt very strongly at CBS. With a new technology like AI, it would have been too late if last year we’d woken up and decided, hey, let’s bring AI into the curriculum. It is too pervasive, too broad a technology to do that. I was hired at CBS seven or eight years ago for AI. This is not something where our dean woke up one morning and realized, oh, we should do AI. We have been teaching AI to all first year MBA students since before I came to CBS, at least 10 years. The truth is, 10 years ago, what was AI? It was more machine learning, more statistical techniques. Having said that, it created a soil at CBS where a lot of my colleagues were hired for this kind of stuff very early. Now that AI has finally come into itself, we’re like a loaded spring, ready to go.

New York is that soil as well. Obviously Silicon Valley and the Bay Area are incredibly rich in VC and startups. The nice thing about New York City is, if you’re interested in data centers, we have companies building data centers. If you’re interested in people protesting data centers, we have that here. People thinking about putting data centers on the moon, people generating energy, renewable energy. There’s a real strength right now in that variety.

In terms of what’s happening, I echo everything Omar said. It’s an ecosystem of classes, not just one. One thing that strikes me, and that we’ve really taken to heart at CBS, is that AI is a very difficult topic to teach, because I can pretty much guarantee, and I tell my students this in the first class, that a significant percentage of what I’m teaching them right now will probably be irrelevant one, two, three years from now.

We make it clear to students: you are here to learn a way of thinking. You are here to learn adaptability, capabilities, how to roll with any punches AI might throw at you. When you’re sitting around a conference room table and an engineer asks you for X million dollars to deploy GPT-73 Mars, we want them to think back to the classes they took with us. Maybe we didn’t have GPT-73 Mars when we taught them, but we told them how to think critically about different models, the pros and cons, how to balance cost with performance, open versus closed models, the potential downsides. A lot of our teaching is based on adaptable skills.

I am so grateful I’m at a university. If you’re a new investment banking analyst given a one-week course on AI, you can only teach stuff that is immediately useful. The nice thing about the university framework is, absolutely, we focus on very useful stuff right now, to build and to code and to use the tools, but we also get to step back and think holistically. Omar is about to start teaching a class on AI deployment: what does it mean to actually put an AI model out there in practice? Are students going to use it tomorrow? Probably not. But two or three years from now, when they have to deploy a model, they have the skill to pick up those modalities quickly.

P&Q: I see Columbia Business School future-proofing your students. That sounds like what I’m hearing.

Besbes: Yes, absolutely. And to add on to the New York point, the number of tech jobs in New York City is one of the highest in the US. A lot of people want to live in New York despite the challenges of the city and how expensive it is. Anthropic is opening in New York. Meta has opened offices in New York. Google has had a huge presence for a long time.

Guetta: Ironically, Airbnb, which is banned in New York, is opening an office in New York.

Besbes: I was speaking to some VCs recently, and they said they specialize in investing in New York-based companies because New York is where you have the customers. You have customers across all areas, from entertainment to media to everything. You live the market. You don’t just imagine the market.

Guetta: Future-proofing is correct, but I think it’s beyond future-proofing. Future-proofing almost has this implication that the future is scary and we should make sure we don’t get eaten by it. It’s also incredibly exciting.

P&Q: What are you hearing from industry and from your students? You’re there with so many businesses, and they’re so eclectic. The Bay Area is more tech-focused. New York is a little bit of everything, Broadway to movies to music to strategy firms. What’s coming back?

Besbes: One thing across the board, in every industry, is a definite movement towards testing. In interview processes there’s now an ability to test students on very concrete things. It’s not impossible to imagine that a few months or years from now students will go out with a resume but also a portfolio of things they’ve built. People want to look not just at credentials and what people know, but how they can translate what they know into something concrete, how agile they are. It’s a new form of assessment across different industries. Our students are prepared through the classes, but they’re also preparing themselves.

Guetta: One thing that has really struck me: I teach AI classes, but I also teach core classes with every student, and I have not felt much of a “people feeling down about it, it’s going to take our jobs.” Excitement is the preponderance of the feeling around CBS. It’s possible they just don’t come to me with it. And I don’t want to minimize that there are big questions. What jobs is it going to replace? What’s going to happen? No one knows the answer.

The main feeling is just overwhelmed. It’s a lot, and it changes very quickly. For what it’s worth, the AI labs are not doing themselves a favor. It’s gotten a little better, but I still remember how every time a new GPT model came out, like clockwork, somebody would be like, oh, I’m worried for humanity now, this is it, this is the end. Every time. GPT-3, GPT-4, GPT-4.1. So there’s a certain feeling of, what do we do? Where do we go? That’s something we try to help with by having all these classes and keeping students up to date.

P&Q: Thinking back to (Anthropic CEO) Dario (Amodei) and those pronouncements he made, I went back through the history of innovation, and the same fears, almost verbatim, came up with every breakthrough. Luddites and their weaving machines, electricity was going to burn our houses down, cars were going to run us over. Every tech breakthrough was going to destroy everything. But we’re living longer, healthier lives, flying higher than any bird. Did the machines take away a lot of jobs? Yes. One job, at one point, was switchboard operator, almost entirely women. That’s gone. But their daughters are instead professors and doctors and lawyers, and I don’t think anybody would be complaining about that.

Guetta: I think you’re right. There is no need to shy away from the potential consequences. In the core business analytics class, the core AI class we teach every incoming MBA student, we have a lecture about the pitfalls, things that can go wrong, things to be worried about, things to be on the lookout for. We don’t shy away from it by any means.

P&Q: Omar, your thoughts?

Besbes: We spoke a lot about how we prepare our students. Another dimension that has been exciting at CBS is not just how entrepreneurial the students are, but the faculty are quite entrepreneurial, not just in developing new classes but in developing new tools to teach with AI, not just about AI. A great example is the tool my colleague Dan Wang developed, CAiSEY, a voice-interactive tool students use to prepare for cases. It was essentially developed by former MBA students at Columbia Business School together with Professor Wang, who teaches strategy. That tool was rolled out across something like 27 sections last year, and more than 2,000 students used it to prepare for class. There’s a lot of thinking and experimentation going into how we can leverage AI to improve learning outcomes, and this is an example of leveraging AI to improve student engagement and preparation for cases.

P&Q: People say, look what AI is disrupting in education. (Harvard Professor Clayton) Christensen wrote a whole book about that years before we knew what generative AI was. We’ve been grappling with disruption in education a lot longer than generative AI. It seems inevitable, and it sounds like you’re facing it head on. Thoughts?

Besbes: The only way to face it head on is to be adaptable, continuously experiment, and try to be agile for the future. The advent of AI commoditizes certain skills but also makes others more important. Deep institutional knowledge, together with the ability to ask the right questions, to know what to build, will still be a differentiator in the market. The judgment it takes to ask those right questions is what we want to prepare our students for, and that’s not going away. What AI brings is it removes a lot of frictions, so you can experiment with a lot of ideas before converging on the right ones. It changes certain processes. It’s going to change parts of certain professions, and we want to prepare students to be the most effective within whatever profession they choose.

P&Q: There are courses specifically about AI, and there are ways you’re designing courses that deploy AI. Any examples?

Guetta: It’s a useful distinction: teaching with AI and teaching about AI. They often intersect but are not the same thing. On teaching with AI, Omar gave great examples: Dan Wang and CAiSEY, our colleague using AI to grade students on how well they understand what’s happening, various AI tutors, which work for some classes and don’t for others. A side point: there is a very delicate balance between standardizing things across classes, which stifles individual professors’ creativity, and letting everyone run loose. It’s a real tightrope walk, and CBS does a very good job of letting people do their own thing while really sharing what’s happening.

On teaching about AI, we have Generative AI for Business, which teaches students how to get acquainted with LLMs, what they do, how they work, where they apply, the general LLM ecosystem. Then I teach two classes, Business Analytics 2 and Business Analytics 3, with a bunch of colleagues on the teaching team. These are probably the most technical AI classes at CBS. They teach how the AI works. On curricular innovation, we designed a tool that lets students touch the backbone of these models in Excel. Without needing to know how to code, they can see what it means to build with AI in a very granular way. That class is not about building useful things. No one’s going to build an AI agent in Excel. It’s about understanding what actually goes on behind these tools. Separately, we have classes on vibe coding, on AI agents, on AI deployment, which are very much about what it takes to implement AI in practice.

Pretty much every class I’ve mentioned, BA2, BA3, vibe coding, Generative AI for Business, we are talking massive waitlists, massive enrollments. This is not one section once in a while. We can’t create enough capacity to keep up. Fifteen years ago stats was the class people had to survive to get their MBA. Now AI and stats and business analytics and machine learning are things people are excited about.

P&Q: Omar, anything to wrap up?

Besbes: It’s an exciting ecosystem, with both my colleagues and my students, and there’s a lot to learn from everyone. One thing I realized early on, and it applies even more with AI, is that students have their pulse on the market and we can learn a lot from that. When I go into the classroom, I always view it as an exchange with the students. It’s not just delivering material. That’s the beauty of teaching in a business school. Every year I learn quite a bit from the students, and that’s even more true given how fast things are changing in industry. We need to have our pulse on the market to prepare our students in the best way.

One more thing we think hard about: given how fast the space is changing, we want to serve not just our own students but our former students. We have a relationship for life, and lifelong learning is a priority. We’re in the process of designing sessions for alumni to make sure they’re able to stay up to date, independent of what stage of career they’re at.

P&Q: Daniel, Omar, thank you for your time.

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Michael Olenick, JD, is a former INSEAD research fellow and founder of VSTRAT.ai. He has spent four decades building AI and expert systems; his research has been used at INSEAD and, before then, cited by Congress and media on how to resolve the 2008 era financial crisis. Michael currently lives with his partner, Anastassia, in Austin, Texas.

Latest in an ongoing series exploring how business schools are integrating AI into teaching and research, one school and one faculty member at a time. See also At Duke Fuqua, Using AI To Strengthen The Bonds Between Humans, At Indiana Kelley, “AI Is,” Not “AI Will,” Training Students To Leverage Increased Cognition (Michigan Ross), Rebuilding The Role Play (Kellogg), Learning Requires Friction (Wharton), The Deck Is Dead (Stanford), and We Teach MBAs To Apologize About The Best Tool They Have (INSEAD).

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