In this compilation episode of The Parlor Room Presents: Hello AI, host and Harvard Business School Online Creative Director Chris Linnane explores one of AI’s most intriguing qualities: its ability to surprise us.
Featuring HBS professors Nien-hê Hsieh, Christina Wallace, and Jake Cook, the conversation examines how AI is changing the way people work, create, and solve problems. From uncovering new approaches to complex challenges to empowering nontechnical professionals to build and experiment, this episode highlights unexpected lessons about innovation, creativity, and human potential in an AI-driven world.
Resources
Catch up on previous episodes of The Parlor Room, featuring faculty from this compilation episode:
You can also watch The Parlor Room Presents: Hello AI on YouTube.
Transcript
Editor's Note: The following was prepared by a machine algorithm and may not perfectly reflect the interview's audio file.
Jake Cook: It's kind of like the internet. It's going to have some...like any new technology, even when we invented fire, there were some drawbacks. Some people lit things on fire they probably shouldn't have as a result.
Voiceover: Most of us are heads down, using AI, figuring it out as we go.
Christina Wallace: A tiny bit of not being afraid of the technical side of things when you're trying to connect APIs, or in some ways, get these different functions to talk to each other, you do have to get into the code, but you don't have to be a software engineer to do this.
Voiceover: And the more we push, the more our sense of what's possible keeps expanding.
Nien-hê Hsieh: So there's a way in which artificial intelligence can approach things that we do in a way that's different, that actually could be interesting to learn from.
Voiceover: What matters is what we learn along the way. Welcome to The Parlor Room Presents: Hello AI.
Chris Linnane: Welcome to The Parlor Room Presents: Hello AI. This is a short compilation episode focused on what surprises us most about AI. We spend a lot of time talking about what AI was supposed to do. This episode is about what it actually did—three moments where AI surprised some of the very smartest people.
But before we start, I'd like to ask you for a very quick favor. We love making this podcast and it would help us out a lot if you could take a moment to like or follow the show wherever you get your podcasts. Thank you.
Okay. First, Nien-hê Hsieh on what happens when a machine starts doing things no human ever thought to try.
Nien-hê Hsieh: So the first thing is maybe we are finally at the moment where the things that are routine, boring, dangerous about work can be eliminated and done by machines. Because if you think about it, every generation of technological progress has promised that we will be freed from drudgery, for example. You go back to Oscar Wilde back in the 1850s, he was talking about how machines would liberate us from drudgery and allow us to pursue the arts and do the kinds of things that we really wanted to do. But if you think about it, that really hasn't come to pass.
If you think about kitchen appliances, laundry machines, those are supposed to take away some of the drudgery and time that people spent doing those tasks. But apparently, people still spend the same amount of time doing a lot of household work. So, maybe we're at a moment where this technological development will allow people to be freed up to do the kinds of things that they would want to do in their creative time, so that's one thing that could be good.
I think something else that's interesting is that artificial intelligence may allow us to do things a lot better. So what I mean by that are things like drug discovery is one area where there's a lot of promise, for example, and so that's another thing that's exciting. Another thing that I think is exciting is that AI can do things differently from how we do things. So here, I have in mind an example from a number of years ago, which is AlphaGo. So I don't know if you're familiar with AlphaGo or not.
Chris Linnane: No, no.
Nien-hê Hsieh: Okay, so AlphaGo is a program that was designed to play Go, the game Go. And many people thought that the game Go, because of the number of possibilities on the board, would make it impossible to have a machine doing the same way that a machine could beat a human at chess. But AlphaGo basically learned how to play Go and essentially beat some of the great world masters at Go. What was interesting was that some of these world masters actually started training themselves with AlphaGo, and what they learned was that AlphaGo was making moves that nobody had ever encountered before in the game of Go. So there's a way in which artificial intelligence can approach things that we do in a way that's different that actually could be interesting to learn from.
And I guess, if I'm allowed a moment of fantasy, I suppose, the great thing would be to be like Tony Stark and have a Jarvis, and that would be pretty cool.
Chris Linnane: Yeah. Now, we often hear that AI in the large language models and all these things are based off of things that already exist and it's reconfiguring, taking pieces, putting them together. So in that AlphaGo example, how was it doing things that people hadn't thought of before?
Nien-hê Hsieh: So that's a great question. I think part of it was that it actually trained against itself.
Chris Linnane: Okay.
Nien-hê Hsieh: And so that's one way in which it might have learned new things. Another way is that some models also aren't just purely engaged in pattern recognition, but they're actually engaged in a kind of form of basic reasoning as it were. So it's not just simply based on a prediction model, the parrots and the machined kind of LLM, but actually thinking in terms of patterns and then learning from that to think more abstractly.
Chris Linnane: Next, Christina Wallace, who, as well as being an HBS professor, runs a Broadway production company. She also, it turns out, builds AI agents.
Christina Wallace: I think the number one personality is instead of throwing money at the problem, which is what especially venture-backed startups might do in a previous era, this is someone whose default reaction is, "Is there a way I can build this through a series of AI tools? How do I check that first and tinker with that and try to build it myself before I go to the point where I bring in an expert or I go in and hire a full-time person for it?" So it's just this fundamental shift of, "Let me try to do it first," which has always been in the DNA of founders, so I don't think it's materially different, but it doesn't require you to be a technical founder to build any of these. A lot of these tools are really quite user-friendly.
I mean, I was building some agents for my Broadway production company. I have two partners, but we don't have any operating staff, we don't have any full-time employees, and we're all three of us busy with full-time jobs. I was like, "How could I build out my investor onboarding flow through a Zapier agent that connects to the APIs of my bank account and my document signing processes?" And the work is twofold. One, you have to really think through, step-by-step in an algorithmic way, what is the work to be done? What does the input look like? What do the outputs need to look like? How does that connect to the next step of the process? Because the outputs from the one step become the inputs to the next step, so it's a very logistical way of breaking down a problem or a process into these really concrete steps.
And then a tiny bit of not being afraid of the technical side of things. When you're trying to connect APIs or, in some ways, get these different functions to talk to each other, you do have to get into the code, but you don't have to be a software engineer to do this. Claude Code now allows very non-technical people to build entire prototypes of apps, of websites, without ever having to tinker with the code themselves. So I think there's just, if you have that mindset of, "Let me figure it out first," and I'm not afraid to have to tinker and onboard to a bunch of different new ways of working. It's just, it materially changes your cost structure and the speed with which you can experiment.
Chris Linnane: And now, Jake Cook on what happens when a tool gives you something you didn't know you were missing.
Jake Cook: There are certain folks that are, "It's going to be amazing and it's idyllic and utopian." I think it's kind of like the internet. It's going to have some, like any new technology, even when we invented fire, there were some drawbacks, some people lit things on fire they probably shouldn't have as a result. But what has me excited is, I think a lot of how people approach their careers and their craft could be stemmed with, maybe we all have insecurities around things that—for example, "I'm not creative." Well, if you ask, "Why aren't you creative?" "Well, I was never really good at drawing." "Oh, so you never had the feedback that you're good at taking a tool and creating an artifact visually."
But, you probably in your mind expressed, maybe you couldn't get it from your brain to your hand to draw and shade this charcoal drawing or whatever, but you knew what that should look like. You had some sort of a sense of what that could be or you could iterate through that. That's what I think is exciting about AI.
So it provides this new interface for us to express ourselves, and people will do nefarious things with that. That's on a whole spectrum of things, but I've seen, even with students, one of the best things that comes out of working with them is you watch them start the class and they have anxieties or they're nervous, or, "I'm a computer science undergrad, I'm not creative." And then we'll do some exercises and they find out that it turns out, lo and behold, it was an interface challenge and a tool challenge, but they had ideas and they just couldn't—and conversely, "I'm not technical. I don't know how to do code and all this stuff." And all of a sudden, they're building an app, and I think that's what's really cool about it, similar to what the PC did.
Chris Linnane: The common thread here isn't the technology. It's the moment after when something shifts and you can't quite go back to thinking the way you did before. As always, thank you for listening, and please follow the show wherever you get your podcasts. Thank you.