Rapid Response: Make AI your co-pilot, w/Microsoft VP of Design & AI, John Maeda

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1:49When you understand how the machine really works, you're going to fear it less. This triangle of technology, design, and business, I can hear the technology folks, this is amazing, let's do more of it. I can hear the design, social science part of it saying we have to ask questions. and I can hear on the business product side, well, how is this going to lead to more profitable business, happier customers? Navigating those three points is so critical for AI to make a true difference. Building with AI is important so you'll know where the proverbial puck is bouncing. It isn't just like moving, it's like bouncing off of everything and it's moving at light speed.

2:38Unless you're there near the models that are evolving, you're not going to know where this is going to end up.

2:48That's John Maeda, VP of Design and Artificial Intelligence at Microsoft. John is working at the center of AI development, including a new programming tool called Semantic Kernel, and is also author of the book, How to Speak Machine. I'm Bob Safian, former editor of Fast Company, founder of the Flux Group, and host of Masters of Scale Rapid Response. I wanted to talk to John because understanding AI and where it's going has become an essential task for all of us, and John has been deep in the area throughout its evolution. I first met John when he was president of the Rhode Island School of Design.

3:26He's since worked at a string of Silicon Valley VC and tech firms, and as an advisor and board member for both consumer and B2B enterprises. John has a master's from MIT and an MBA, as well as a doctorate in design, and has written books about leadership and about AI. For the next few months, this podcast is going to lean into the rapidly evolving impact of AI for individuals, for businesses, and for society. John provides an ideal place to start by offering an accessible foundation for understanding the complexities we face and why and how to respond to the changes swirling around us.

4:17I'm Bob Safian. I'm here with John Maeda, the VP of Design and Artificial Intelligence at Microsoft and author of the book, How to Speak Machine. John, thanks for joining us. Bob, thanks for having me. I'm so excited. So you have been engaged with AI at various levels for many years as a technologist, as a designer, as an executive. You've described our current phase for AI as spring break in Fort Lauderdale. And I'm curious what you mean by that. Are you talking about like bad behavior, like a party destined for a hangover? Or is there something else that's going to grow from this party? Bob, you must know a lot about spring break and Fort Lauderdale.

5:03A little bit too much, yes. It's the fact that this AI winter phrase, most people aren't aware that it comes from the AI nuclear winter. And the fact that much of science in the late 1900s was born of war, which is pretty dark and dim. So I like to contrast that darkness to, oh my gosh, AI Spring Break in Fort Lauderdale. The drinks are free. They're blue. It's all okay. It's awesome. It is a switch. It's a switch from something very dark to something very bright. And to your point, could it be irresponsible? Absolutely. Could it be overdone? Absolutely. Because we're starting from AI Nuclear Winter.

5:51The people who are still afraid, because there is a lot of still fear around AI. Absolutely. Are they still living in the nuclear winter? Or is that always part of a new technology? There was a time where we were all around a fire. We were hungry. We were thirsty. It was dark. And then there's something rustling out there in the bushes. And one person says, oh, I wonder what's out there. And like wanders out and didn't come back versus the bushes were rustling for someone out and like, look, we have something to eat now. I think that story is ingrained in our DNA. You authored a book called How to Speak Machines, explaining for those who aren't engineers and maybe some of those who are, what's involved in computing and AI.

6:45the recent blossoming of generative AI seems to be about the machine speaking our language, that it speaks human. Do we not need to speak machine anymore? In this day and age where we can give prompts, natural language prompts, it is extremely powerful to understand what's going on underneath all of that. And that's the world of people who can speak machine. And I think that when you understand how the machine really works, you're going to fear it less. And if you don't understand it, it's going to feel even more scary. So yes, we want to speak more natural language without speaking machine. But if you don't know what's happening behind the scenes, it's always going to be mystifying.

7:34And once you do see what's happening underneath the machine, it's not scary the same way. It's not scary the same way is the point. Can it be scary? Yes. It's kind of like looking inside your car. Do you remember when cars, you open the hood, it's like, whoa. If you stick your hand in there, it's not good. So when you understand what's going on underneath the hood, you understand how powerful it is. And something that's powerful, we tend to respect and treat a little differently. The term AI is used to cover so many things right now. There's enthusiasm for chat GPT, really, that's catalyzed the whole tech sector, the stock market, billions and billions of dollars in value.

8:19But there's other sorts of AI and machine learning and automation and robotics that's been implemented in a lot of areas for a while. How much of what we're talking about now are new things? How much of what we're doing is talking about old things in a new way? first of all we've had ai for so long it's called a computer a smart engineer could make a machine do things that you couldn't tell your cat to do like imagine telling your cat i want you to move this ball from point a to point b you can write a computer program to do that if your cat did that you'd say it's intelligent there is a phenomenon that was known since the 1960s by the inventor of this kind of AI chat, Dr.

9:08Joseph Weisenbaum, my AI professor at MIT, who recognized that any human being, when faced with a machine, typing at it, is going to think a human is behind it. He discovered that any above average human will have the delusion that a human's behind it. Why? Because we call our car Lola. We anthropomorphize, we can't help doing that. These models are, so much more powerful than we ever could have imagined that the inferensibility of it is next level. We think, oh, there's a person behind there, but there isn't a person behind there. That's a delusion. And it's just that computation got a lot better.

9:51Our listeners are entrepreneurs and business leaders. How did they adjust to this spring break that we're in. Is there a first mover advantage for every business in getting engaged with AI? Or is it just if you're building AI products versus using AI products? Building with AI is important. So you'll know where the proverbial puck is bouncing. It isn't just like moving. It's like bouncing off of everything and it's moving at light speed. unless you're there near the models that are evolving, you're not going to know where this is going to end up. If you're end-usering AI via the tools, that's awesome because you get a speed up and you're competitive.

10:39But to be truly competitive, being near this stuff, building with this stuff is, I don't know if it's a first move or advantage anymore. I think everyone's moving. I know you've been working on a tool at Microsoft called Semantic Kernel. And I'm not sure I'm going to describe it correctly, but it's an open source tool for creating your own AI or getting the most out of AI. You use this phrase, better inputs, better outputs. I wasn't sure, like, is a guide for engineers or is a guide for non-engineers to be able to code and get the benefits of AI better? Where does this sit? Am I explaining this at all the right way?

11:19Oh, you have walked into the living room perfectly. So, Semantic Kernel does something called AI orchestration, which is basically the core piece of technology needed to go from point A to point B and do the steps along the way. So, like, I want to plan a party. Well, you have to do these 10 steps. And it figures out how to do each 10 steps. And it does it. And Semantic Kernel will then reach out and interact with other software and other tools to be able to create what it is you're trying to create. Yes, and classically, programmers have been doing this by hand. This lets you do something that took me a while to understand.

12:06This kind of computation is, as we know, non-deterministic. It makes things up. The old way of programming would never make anything up. It was truly Vulcan. When you do both together, it does much more than you expect. So think of those 10 steps from A to B. A few of them might be done with large language model AI, but a few may be done by conventional computation. Think of like a hybrid car drawing from two sources of energy. So what it lets you do is do much more complex computational tasks with AI. I've heard you talk about how there are already chatbots like the GitHub bot that increases engineer productivity by something like 20%.

12:55And that sort of makes it sound like the advantages right now are most useful for people who are already technologists. Is that roughly right where the tech is now or not necessarily? So if you look at OpenAI's different models, there's Ada, Babbage, Curie, and DaVinci. You may have heard one of these names used in sort of passing. It's very simple. Ada is A, Babbage is B, Curie is C, DaVinci is D. Ada came first. DaVinci is the newest one. So why that's important is it helps you understand that these models have been evolving, but they're all valid for different tasks. There are code models in the ABCD hierarchy because who's making these models developers?

13:41And so the first cool trick that was found was writing code. Not saying it's by accident, but it's like, oh, wait, it can write code. Wait, it can write papers? So yes, the first gen coding, but basically 1.5 gen, oh, it can write copy for us. It can write this entire podcast. That is what the average non-developer is feeling right now and is perplexed, right? Like, wait, do you need us anymore? And even as you're inviting me into this living room that you're in, like, how much do I need to understand the coding and what's going on and which generation it is versus like, oh, I just want to do better user research?

14:23The reason why I wrote How to Speak Machine is even in the era pre-Amazing AI, I just saw so many people being left behind. And you think of tech and those who understand How to Speak Machine and those who don't. And the barrier has been programming. But at the end of the day, if you're in business, you have to understand it conceptually. So, you know, semantic kernel is made for developers. but I was working with whatever head of PR comms. And within five minutes, they were able to walk through the kind of like thorny bush of the install of VS Code. He's like, wait, I just generated an embedding?

15:02This is what I'm talking about? It's that kind of moment that I think is not bad to have. I did hear you talk somewhere about organizations can tap into and parse data now using AI in ways that maybe they're not aware of. I'm sure you remember in your past lives when you wanted data, you're like, we're going to need someone to be able to gather the data. We're going to need someone to be able to interpret it. We don't have like a data science person. I think we need someone to actually understand these problems from a social science perspective. So this is like a year and a half has passed. And it's like, wow, I wish I had that data.

15:40The competitor had data. So this kind of AI is unusually good at gathering data that is unstructured. It's good at giving it structure so you can use it. It's good at testing some general assumptions around how data could be used. And it's able to present that to you in a way that is useful. I know that having a volume of data helps make your AI more effective. So if I'm a bigger organization, that maybe gives me an advantage. On the other hand, if I can access data somehow and interpret it much faster, maybe it levels the playing field a little bit for a smaller player to get in. Now with these kind of new models, we're seeing democratization.

16:27How many meetings have you met at, Bob, where you're like, can someone get some data? And then someone at the meeting says, wait, that data is wrong. So I think organizations of every scale have off-sites to collect data across the organization, and they can never do it. What is different is that these kinds of models can work with imperfect pictures of data, and they can fill it in with plausible kinds of data. This is Mike Nicholas, Capital One business customer and co-founder of Ansett Uncles, a plant-based restaurant and community space in Brooklyn, New York. And he's telling us how they started a product line.

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18:57Let's create smarter business, IBM. Before the break, we heard Microsoft VP of Design and Artificial Intelligence, John Maeda, explain how AI fits into the trajectory of computing and the advantages for those who stay close to where the puck is moving. Now he talks about how AI can be a tool in leadership decisions, as well as the positive impact of adding friction in an AI world. Plus, he shares lessons about why we should approach AI as a player rather than a victim and the work that will be required to guide this tech to its best outcomes. Many of us have an emotional relationship with our machines, our phone, our computer, our TikTok account.

19:40We're beholden to them almost. Does AI make that worse? How do we have to recalibrate our relationship to our machines, to our technology? I use my phone for timers and reminders, but I am the pilot. So I gave it the instructions. I don't feel it telling me what to do. I have a few colleagues at Microsoft that are calling this collaborative UX. You're collaborating with your AI. You have to be a good boss. If you don't give it a smart goal, it's not going to know what to do. If you let AI do everything in an automatic way and it's making mistakes, it didn't hallucinate. It's your fault for letting it do things that you have to actually think harder around.

20:31We're now in a new math. Make AI think harder. You want to tell AI, hold it. I don't think that data is right because the co-pilot is controlled by you, the pilot. So if you trust a co-pilot too much, it really is on us now. Before you wrote a book about speaking machines, you wrote a book about leadership, reflecting on your transition to being president of a university, the Rhode Island School of Design. What does AI change about leadership? AI in that era could have been extremely useful for me to think of more what-if scenarios. because when you're in the middle of crisis, it's hard to think.

21:18Fast thinking is on fleek because you're like desperate, but you know you got to turn on slow thinking. So I think AI can be a partner when a leader is doing slow thinking, trying to figure it out. Being a leader is lonely. Like, who can you trust? Like, oh my gosh, you're going to trust an AI? No, I'm just using a calculator that does inference in a very efficient way so I can ask myself, am I doing the right things and I can game out those things? It's giving you perspective. I mean, that's what the idea of what a copilot is. It's not necessarily saying this is the answer you must use. It's giving you options.

21:55It's a weird design principle. It's add friction, which makes no sense because you're like, no, we're supposed to be frictionless, right? That's what simplicity is. You have to add friction to remind the executive, hey, boss, don't forget, this is just an AI. You know, it's up to you to use critical thinking. We have to teach how to use these things better. And a way to use these things well means asking critical questions. So I think critical thinking is going to be the skill that we're going to have to teach a lot to make AI think harder. Because AI does not naturally critically think. You've straddled these realms of design and technology.

22:39and sometimes technologists create what's possible without necessarily focusing on the human implications. Designers tend to start from the human side. And I'm curious how you try to navigate that. This triangle of technology, design, and business, I'm awkwardly placed across these three points. I can hear the technology folks. This is amazing. Let's do more of it. AI Fort Lauderdale for the win. It's going to be incredible, right? I can hear the design, social science part of it saying we have to ask questions, critical thinking. How does it impact everything? How does it lead to more unfairness?

23:26Really important, humanity questions. And I can hear on the business product side, well, how is this going to lead to more profitable business, happier customers, a whole different kind of dimension? They're all related. I feel like navigating those three points is so critical for AI to make a true difference to business and to the culture and to advanced technology. You've been part of this AI arc for a long time. So I'm going to ask you this question, like how far along the curve are we? In the development and execution of what's possible, is chat GPT 50 % of the way there? Is it 15 %? Is it 5 %?

24:08Or is there no way for us to really know? Well, having been there in the 80s, working on something called a Lisp machine, which was the AI workstation, Lamborghini of AI, which really a lot of computation owes to it. Things like Python are largely merged from things that we did in Lisp and on Lisp machines. But that said, it couldn't do much. I think this kind of AI will do a lot more for corporations and individuals. This mental model built out of the machine learning world is going to take us seven months. And they built a model that identifies a cat in a forest with 72 % likelihood. And you're like, well, can it identify dogs in a forest?

24:55No, no, Bob. We're going to go back seven months from now. We're going to make another model. That's the way we positioned machine learning over the last five years. This new AI is just warm boot. You just show up with your data, you sprinkle it on the foundation model, and it produces heat. This is like a whole new kind of AI that's more in the application side versus machine learning side. How to Speak Machine teaches you how machine learning works, which resulted in these foundation models. But everything after is brand new. There's going to be a new kind of AI app developer. They need new kind of tools, which don't exist yet.

25:38Wow, I am deep into your living room now. What's at stake with all this? How do we parse out the good and the bad? The stakes against AI and people understanding it, it's so easy to vilify versus what it can do to accelerate you in your career and change your career. That sounds too Pollyanna happy for many people, but it's more about being a player versus a victim. This stuff has challenges. There's issues with it. And so you're going to question whether it's the right thing. That's why critical thinking, boosting that vitamin in your brain while you approach this and looking for the opportunities both for your business and for humanity and understanding the technology.

26:27But we should want to be players in this, right? But be players with our eyes open. Yeah, it's not simple. I just think of that mentality, the true player mentality, where they are thinking extremely critically about what they're doing. That kind of player on the field of AI, seeing more of them, whether in pure tech or pure business or pure design-ish, that's going to be interesting. But it requires understanding how to speak machine, how to speak AI, but speak really good human. If you're going to be a player, you shouldn't just be a casual player. You have to straddle all of these things in the play that you're doing.

27:08And it's different than buying the gear. You know, people who buy the gear, it's like, oh my gosh, I've got such and such sweatpants. You see this like headband? This headband is authentic. And you're like, okay, but can you really play? It means work, which in this AI world, you think, oh, I can lean back and have to do everything. No, you have to work really hard because AI will not think harder on its own. Well, John, this is great. Oh, thanks for the chance to hang out again, Bob. It means a lot to me. After talking with John Maeda, a key takeaway for me is the importance of both a technical understanding of AI, at least conceptually, and the very human task of managing its tools and evolution.

27:48So many times in life, what seems too good to be true deserves further inspection. I know that my education about AI is only beginning. Maybe it will need to go on indefinitely, but that's okay. Learning about the new helps me to clarify my uncertainties. I hope you'll join in as I continue to explore AI's intriguing possibilities and evolving impacts in the episodes ahead. I'm Bob Safian. Thanks for listening. Meet Emily Warden, Capital One business customer and owner of Emily Warden Designs, a handcrafted fine jewelry store in Richmond, Virginia that got its start in a dorm room. I said I was going to try it for a year.