AI Won't Replace Humans—But Humans With AI Will Replace Humans Without AI
[MUSIC PLAYING] ADI IGNATIUS: Hi, and welcome to Harvard Business Review's The New World of Work. I'm Adi Ignatius, Editor In Chief of HBR. And each week on this show, I interview a CEO, a thought leader, or somebody else interesting who can inspire us and educate us on the changing dynamics of the workplace. Whether you're navigating the complexities of a large corporation or the challenges of a-- excuse me-- of a small startup, whether you're based in the US or anywhere else in the world, you face your own unique set of issues. So the aim of this podcast is to inspire thought and provide insights as you seek to bolster your business and pave your own path toward career success. So on today's episode, we have a great guest, Karim Lakhani of Harvard Business School.
I'm going to come back with a proper introduction in just a moment. But first, let's hear from our good friends at KPMG, who are sponsoring this season of The New World of Work. ANNOUNCER: At KPMG, it's our people who make the difference for our clients, talented teams leveraging the right technology to uncover insights that illuminate opportunity. Ready to make the difference together? ADI IGNATIUS: All right, just a couple more things before we start.
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My guest this week, as I said, is Karim Lakhani, a professor at Harvard Business School who specializes in workplace technology and particularly AI. He's done pioneering work in identifying how digital transformation has remade the world of business. And he's the co-author of the 2020 book, which HBR published, Competing In the Age of AI. So Karim, welcome to the show. KARIM R. LAKHANI: So glad to be here with you today, Adi.
Thank you for the invitation. ADI IGNATIUS: So there's a lot to talk about, and I definitely want to talk a lot about generative AI. But maybe we'll-- once we get to it, I don't think we'll get off.
KARIM R. LAKHANI: No, exactly. ADI IGNATIUS: So let's start elsewhere. But so you co-wrote a piece for us a few years ago and it's reflected in your book, where you say, machine learning has basically changed the very rules of business.
So that's a big statement. Tell us a little bit about what you mean by that. KARIM R. LAKHANI: Yeah. So look, it's been a real pleasure working with HBR and your editors for the last 15 years of my academic career. And the book really was a partnership between Marco Iansiti and Amy Bernstein, one of the editors at HBR, as we created this book. And what Marco and I noticed in about a decade's worth of research and spending time with companies, both writing cases, as advisors, as consultants, and so forth, was that the nature of the corporation, which really was established-- the modern American corporation, which became the blueprint for the modern international corporation established in the 1920s and '30s, was changing foundationally because of technologies like AI, like machine learning.
And what we observed was that the entire business architecture in many of these AI first companies at the time, in terms of business model, how you create value, how you capture value, and your operating model, how you deliver value, how you achieve scope, the number of customers you serve, the number of products you have, scale, the number of customers you serve, and learning, these fundamental parts of a business architecture were being rewired because of machine learning and AI and digital technologies. And so if you just sort of reflect for a bit on your experience using Google, for example, much of your Google experience is fully automated, from the ads you see to the search you do to, if you're using Gmail, how you interact with them. And so it's not people that do those activities. It's the algorithms that make that happen.
Similarly, your experiences with all the large e-commerce platforms, like Amazon or Alibaba, let's say, in China, if you imagine what happens at Netflix, for example, again, all these examples, we've been using. But these companies work in a fundamentally different way than a company like General Electric, where I grew up as a right out of college in my first job were set up. And these companies, the machines and the algorithms at the center, the work is automated. The humans are actually designing the algorithms and testing them and checking them, making sure they're working within bounds, but the actual transactions and activities are being mediated through the machines. ADI IGNATIUS: So I'm guessing that-- first of all, if you're watching this, if you have your own questions for Karim about digital transformation, about generative AI, which we'll be talking about in a moment, please put them into the chat and I'll try to get to audience questions later. So that was helpful, Karim, but I would guess some of our viewers are listening to you and thinking, yeah, that's what we've done.
And others are thinking, I'm not sure we're on that journey or we're far enough along on that journey. So my question is, what's your advice to people who are listening to this who are like, yeah, I get it. I get that there's value here. I'm not quite sure if my company is right for this or I'm not quite sure what the next steps are.
KARIM R. LAKHANI: Yeah, absolutely. Look, first I would say is that I think most companies will not have a choice but to adopt AI and to adopt digital at the core functions. In many ways, your personal lives as mediated through your transactions through your smartphone, through these devices, and how you interact with consumer technology products and so on and so forth, you're already living in an AI age. And the thing I would say, Adi, which is so interesting to me, is-- and I learned this from some conversations I had with folks in India. They said, we have some folks who get mad when your Uber car or your Ola car or your DiDi car or your GrabCar doesn't show up in 3 minutes. You want this magical taxi experience.
You go on your app, you press the thing, boom, it shows up. And if it's going to be 5 or 7 minutes, you kind of get mad. And I'm reflecting on when I first moved to Boston in 1997, and it would take me a week to book a taxi in Boston.
And so now we get mad. And similarly, if there's a transaction dispute on Amazon or on Uber, automatically solved, done. But the same people, the same executives, in their own companies are completely satisfied if a customer service interaction can take two weeks, if onboarding a new vendor takes six months.
And so we're living in this disconnected world where most people, most consumers are living in this AI first world, in their experiences, with many of these platforms. And then they encounter our companies and our organizations, and they're like, what is this. And so my sense is that this is inevitable. This transition is really inevitable. And for the folks that are behind, the good news is that the cost to make the transition keeps getting lower and lower.
The playbook for this is now well known. And finally, the real challenge is not a technological challenge. I would say, that's a 30% challenge. The real challenge is 70%, which is an organizational challenge. My great colleague, Tsedal Neeley, talks about the digital mindset.
Every executive, every worker needs to have a digital mindset, which means understanding how these technologies work, but also understanding the deployment of them and then the change processes you need to do in terms of your organization to make use of them. ADI IGNATIUS: It's really interesting because we think about this. We're a relatively small publisher compared to some of the giants out there. But when people come to our site and they're searching for articles by Karim Lakhani, they're used to a Google sort of search experience.
KARIM R. LAKHANI: Exactly. ADI IGNATIUS: When they want to buy a product from us, they're used to an Amazon. And anything short of that, it's like your Uber example. There's a frustration and expectation.
So we have to find ways to lift our game without the resources, whether it's through partnership or other things, because it's table stakes. People just expect the best experience in every experience they have. KARIM R. LAKHANI: 100%. If I look at my teenage daughter, she has no patience for [INAUDIBLE] companies.
And she just gets mad and just is, like, what is this. And so absolutely. ADI IGNATIUS: So anyway, so the next big wave-- again, we're going to talk about it is, I keep teasing it, but it's generative AI. But that won't be the last wave. And quantum will hit us at some point and things we don't even can't even anticipate will hit us. So how do you prepare for that? How do you create a kind of, I don't know, culture or mindset or organization that knows that there will be unexpected ways of technology.
They'll come we'll have to figure out if they're relevant to us or not. And if they are, we need to adapt quickly. Is there a general way to think about that [INAUDIBLE]?? KARIM R. LAKHANI: Yeah, yeah. Yeah. Thank you for that question.
And in fact, I've been pondering this quite a bit. I think there are two imperatives for most executives, for most managers, for most leaders. One is a learning imperative. This is, again, Tsedal's work on digital mindset, my work. There's lots of learning you need to do and the learning has to be continuous. And the idea is that not that we want you to become AI engineers or data scientists or get a PhD from Stanford or Harvard or MIT or Tsinghua University or Oxford University, or from the IITs in machine learning.
But as executives, this is now table stakes. And the way I think about this for at the MBA program, for us, people come to the Harvard MBA program. And we have-- first year is a required curriculum. There's 10 courses, and one of them is accounting.
Now, I can tell you, accounting is a very important profession. But most MBAs that join HBS don't want to be accountants. But they need to learn accounting because that's the language of business.
That's the ways in which you think about how value is kept track of, how expenses are being tracked of, and so on and so forth, super important. And you don't take the accounting course to become an accountant, but you need to understand accounting so you can be a good business person. Same thing now with digital technologies and machine learning.
You need to understand the machine learning stuff and the AI stuff, not because you're going to become an engineer or an AI scientist, but because that is now going to be a critical table stakes for you to understand how business works. So there's a learning imperative and I don't think we can take away the learning imperative anymore. Now, look, I'm self-centered about this. I'm self-interested. I'm in the learning profession. That's what I do, so I want to caveat that.
But I want to just insist that. I think the learning journey does not stop. And you have to invest in your own personal learning, and I think companies need to invest in the learning for their own employees as well. It's a two-phased conversation. Companies have to embrace this and so do individuals. But the second bit, I think, Adi, is equally important, which I think is completely underrated, which is change and change management.
And change becomes a skill for managers and for leaders and for executives. How you change, how you continue to change, how you build the DNA for changing becomes very important. At a time, by chance, I was in Asia a month ago and had a chance to spend some time with Mickey Mikitani at Rakuten. And one of the things that's amazing is what he has thought about as change as a core competency for all workers and for all employees. Right now, most change programs are viewed with skepticism, as flavor of the month, blah, blah, blah, and people resist.
People resist. I think the best companies will be the ones that can understand how change becomes a skill. And if you think about change as a skill, what does that mean? Skills require acquisition of the skills. You've got to invest in learning. What does it mean to change? It requires practice.
You've got to keep changing as well. And it requires adjustment. Once you've learned how to change, how do you change that-- how do you project that to everybody else? So those elements, I think, will become a key part of the ways in which leaders need to adapt to this world. ADI IGNATIUS: Yeah. I think that's great. It could be generational and it should not be generational.
I always think that when you come of age, when you join the workforce, there's a certain suite of technology that it's just you grew up with it and you're comfortable with it and you are part of figuring out how to use it. And then subsequent ways, a lot of us hit a point where, yeah, that one seems stupid. And for my father, who's still alive at 102, that was email.
So you can't-- you've got to keep trying. You got to keep experimenting. You have to keep current. KARIM R. LAKHANI: Adi, such a good point. And I have to tell you, around COVID I had this experience with my mother, who lives in Toronto, and my in-laws, who also live in Toronto, when the things sort of eased up a bit and in November of 2022 when Thanksgiving was on we were able to fly them back to have a reunion with them and-- in 2020, sorry, November 2020, when things eased up a bit with the vaccines and so on and so forth, we were able to bring them over. And [INAUDIBLE] dates, but around that time, one Thanksgiving in COVID.
And if you recall, traveling, even just crossing the border from Canada to the US was very difficult because you had QR codes and apps galore. Canada needed all this stuff to exit, US needed these stuff to enter, and the same thing for entry as well. And I looked at, sadly, how helpless my in-laws my parents were with these technologies. They were just lost. And my wife and my daughter and I had to spend a ton of time with them, holding their hands to go through these things.
Now, of course, the UX was terrible and all that kind of stuff. But we figured out how to do it ourselves, but they were stuck. They couldn't adjust.
And then as I reflected on that experience, I said, oh, this is what's happening to most executives. This is what's happening to most companies. It's like they're the senior citizens, the elderly who have resisted the technology, have not really embraced it, and now have no choice but to deal with it and are frozen and need a ton of help. And that's what the thing that we have to get over as we think about this.
ADI IGNATIUS: Yeah, that's great. I think we can throw out our QCADs. But otherwise-- KARIM R. LAKHANI: Yes.
I remember, and I have a QCAD still. Old jokes for those in publishing. ADI IGNATIUS: Yeah, I know. That's definitely-- this dates us. So let's talk about generative AI. So people who, if that sounds jargon-- oh, by the way, you referred to earlier to Tsedal, and I just want to fill out of that's Tsedal Neeley, who is another-- KARIM R. LAKHANI: Professor Tsedal Neeley, Senior Associate
Dean of the Harvard Business School, my good friend, and author of another great book called The Digital Mindset. ADI IGNATIUS: Absolutely. So all right, generative AI, for people who aren't conversant, and probably most of you are now, but this is large language model learning, predictive, ChatGPT, Bard, Bing, all these things. And I like to say that there were three waves. The first was we played with this technology when it came out in whatever it was, November of last year. And we tried to break it.
ChatGPT, are you in love with me? And now we're trying to figure out how to use it, and the use cases are happening. So first question for you, where are you in the hype cycle? Because technologies come and go. And there was a sense with this one that everyone-- everyone-- basically said, OK, this is different. This is important. This is transformational in ways that few technological innovations are. But where are you on that? KARIM R. LAKHANI: I'm on the, holy crap,
this is transformational. Yeah. So look, the way I think about this is it's actually worth to pause and look at history a bit. And since I studied technology in business, something transformational happened 30 years ago, approximately, as well, which was the browser.
The browser got invented. And if you think about the browser, there was 30 years of the internet. The browser gets invented, and people were like, oh, my goodness, look at this.
And I remember-- I can still see as clear as day when I first encountered the browser. And I was working at General Electric. I was at a conference for radiology, and one of my clients, a radiologist at Saint Paul's Hospital in Vancouver, showed me the browser and the thing he showed me was the Oxford coffee pot.
I'm like, interesting. All of a sudden, the Oxford coffee pot has global distribution. Anybody that has a web browser and an internet connection can use it. So there's 30 years of internet in the basement in the bowels of companies. We didn't understand it. We saw it was coming, it was coming.
It was USENET, there was Gopher, there was Telnet, there was FTP, all these kinds of things. The browser showed what the world would look like. And the initial applications were cute applications. And people were, like, this is nothing. This is whatever. But fundamentally, from an economics point of view, what the browser did is that it lowered the cost of information transmission dramatically.
And then the last 30 years, we've been living through the buildout of the internet and waves and waves of the internet changing more and more and more industries over and over again. We've all living through that. Just imagine this right now. We are broadcasting live to, I don't know, thousands of people, and more people will be looking at this broadcast at relatively zero marginal cost to us to do this. It seems unbelievable compared to 1993, where you needed a massive TV studio, massive broadcast studio, satellite dishes to be able to do what we're doing right now.
And so the cost of information transmission went to zero. And then new companies formed, Google, Amazon, Facebook, you name it. E-commerce got invented and so on and so forth. And so that is the world that we are coming out of.
The internet era is we're coming out of. And the same thing has happened with generative AI. There's been 20 years of AI being deployed at scale inside of many tech companies, the ones we use in our examples in our book. And that was in the basement, so Netflix movie recommendations, your Google search results, your Amazon recommendations, your Spotify music results, your car access. All of that was being-- your Waze access, your directions, all that was being empowered by AI tools, even your spam killers.
Remember how bad spam used to be for a while, and then overnight it went away? Because people deployed machine learning systems to those things, early machine learning systems to those things. Now-- ADI IGNATIUS: Go ahead. KARIM R. LAKHANI: Now, how do we think about generative AI? So my view is, generative AI is a drop in the power in the cost of cognition, in how we think. So if the internet was one of cost of information dropping to zero, my sense is that the cost of cognition, how we think, who we think with, is dropping to zero or lowering significantly with this. And that has significant ramifications for this.
And I have to tell you, I had to do a major pivot even in my research side on what to do with this. I was doing a lot of stuff around AI adoption and so on and so forth, a lot of research, a lot of nerdy research that only, like, three people ever read. But we have gone-- my whole Institute and my labs have gone big time into figuring out what this means for knowledge workers, for managers with generative AI.
ADI IGNATIUS: Yeah, well, let's talk about that because I think, again, for our viewers, there are probably some of you are well conversant or using it, whether it's for fun or to try to figure out work applications. There are products available that are using this that rolled out pretty quickly. How to think-- is there a way to think about this generically? What is, for a generic company, if there is such a thing, how should they think about using generative AI? I mean, we've seen the amazing things that can do. We've seen the hallucinations that they can create, that confidently provide errors.
So how does a business think about, is there a generative AI application that is significant for my company and how do I figure that out? KARIM R. LAKHANI: Yeah. So first of all, I think we're at the super early stages of this hype, of this cycle. And if you think about it, the first web browser was Mosaic, and then Netscape and then Explorer and Mozilla and so on and so forth came about. So I think we should just think-- and then all the applications on top. So I think we are at the early stages.
The rate of innovation and the rate of improvement is increasing rapidly, and it keeps increasing. And the rate of application development is also increasing rapidly. So the first thing I would say is that the places where you can apply it is in many ways like, well, where do you apply thinking? Well, where else could you apply this, with all the caveats about hallucination and bias and so forth.
So the first thing I would say is that I think, if you step back and say, what should leaders do, what should managers do, what should executives do around this thing, one is to start thinking about and start practice in their own sandboxes what the use cases may be. We're seeing tremendous use cases, for example, just in sort of content generation, like, our work. Us as knowledge producers, that's changing rapidly. Now I use ChatGPT as an amazing research associate, thought partner, copy editor, idea generator. I can tell you one thing.
I was in Asia. My wife was with me on my trip and I wanted to actually have some time for break as well. So I went to ChatGPT and I said, this is me. This is my wife. Here's the kind of vacations we like. Can you please give us ideas of a place that would be about three hours from Singapore that we could go to? And I prefer beach, blah, blah, blah, blah.
Boom. In microseconds, I got many recommendations. And then through a conversational setup, I found the place that we wanted to go to. It was a hidden place in the South China Sea off Indonesia, and it was incredible.
It was incredible. And that I would not have discovered, even with my travel agent. So just even in that activity, just imagine what we can now start to do with this.
And so what I would say is that the thing that managers and leaders need to do is, step 1, start using it. I think the bans on ChatGPT and on these things are misguided in many companies. It's already on my phone. It's already people-- there's 100 million users. It's already there.
So I think executives and IT departments and legal departments are fooling themselves. They don't think their workers are already not using these tools. And instead of pushing against it and saying, no, you need to embrace it and run boot camps, run use case analysis, figure out where it's hallucinating in your use cases and figure out where it's actually going to be very helpful.
And what I say to people, for managers, leaders, and workers, is AI is not going to replace humans, but humans with AI are going to replace humans without AI. And this is definitely the case for generative AI. And so the first step is begin, start experimentation, create the sandboxes, run internal boot camps. And don't just run boot camps for technology workers. Run boot camps for everybody.
Give them access to tools. Figure out what use cases they develop, and then use that as a basis to rank and stack them and put them into play. ADI IGNATIUS: Yeah, I agree with that. We have to think about that as a publisher.
There are some publishers who say, we will not take articles, papers where generative AI has been involved. That, similarly to me, doesn't make sense. It's like saying, don't use-- KARIM R. LAKHANI: How will they know? How will they know? ADI IGNATIUS: How will they know? And then it would be like saying, don't use Google.
It's a tool. What we're saying, though, is that the responsibility more than ever is on the person with the byline on this piece. That was true-- you didn't want to just use Google search results or just use Wikipedia results. You need to verify and do a little bit more than that, now more than ever because you may be relying on-- KARIM R. LAKHANI: No, absolutely. And as scholars, we publish, again, these nerdy papers that very few people read, and we're in the same crisis because, well, if I use an RA to come up with ideas, do I have to acknowledge the RA? Is the RA the co-author? If I use a copy editor, I typically don't acknowledge a copy editor for my article, but they're super helpful.
Should that be-- attribution becomes interesting. There's so many important questions at play, just as writers and producers. But my advice to everybody is-- and the best place to learn, Adi, is YouTube. YouTube has, oh my God, so many tutorials and so many domains. And very soon, you'll be down the rabbit hole. Is learn and adopt and practice and practice and see.
ADI IGNATIUS: Yeah. And I think I think there's a trap that sometimes people feel like, if they don't jump on the wave immediately, somehow it's too late. KARIM R. LAKHANI: No, no, gosh, no. ADI IGNATIUS: And I think it's really important-- it's early.
KARIM R. LAKHANI: Oh my gosh. ADI IGNATIUS: Karim, if you're right that this is truly transformative, it's early. If you feel like, wow, everybody's moving faster than I am, catch up, whether it's YouTube or just doing some reading and figure out how to applies. So let me go to some questions from the audience because there are a lot coming in.
And so this is from [? Vena ?] from somewhere in the US. And it's really [? Vena ?] is saying that AI, machine learning, this is somebody's code. It comes with biases and assumptions built in. It's a topic we think about a lot.
But what's your view? How can the industry ensure that there isn't a monopoly on how we think and how we're biased and the assumptions that we make? KARIM R. LAKHANI: Yeah. So look, I as an individual, I'm part of Mozilla, mozilla.com. We made the Firefox browser owned by an open source foundation, Mozilla Foundation.
If you love Fire-- if you haven't used Firefox in a while, go back and use it again. But we just set up Mozilla.ai, and the idea is that we want to create open source large language models as well and create the tooling that enables many people around the world to also have large language models suited for them. And our view is that we can build tools to, A, detect bias and to fix bias and to fix all the craziness that these large language models can do. So I'm actively working and trying to create and support organizations that do that. I would say that the first thing that we need to think, though, is to step back and say, the world is biased.
We had bias before there was AI. AI is just amplifying it and making it apparent. The world is biased. You look at the unbelievably bad treatment African-Americans receive in our health care system and the financial system and so on and so forth in the US, or if you go to some other country, there's always been discrimination without AI.
AI is helping to amplify it. And so the response-- the ethical responsibility for us as leaders has to be that we have to understand what is biased today in our systems. And then let's translate to AI.
Well, certainly, how representative is your data? How representative is your training? How representative is your labeling? Those are essential, essential questions that need to be now part of, again, the executive conversation. That's why the learning mandate doesn't stop, because you have to understand how these machine learning systems are built for you to understand what the biases are and how you might get sued or be put in jail, for God sakes, if you don't follow through on these things. And so this is super important. But I want us to also be aware that we need to think counterfactually.
There's always this bias in the world. And now let's imagine a world with AI. And is it going to take the biased world and amplify it? Or can we correct for it? Can we recognize it for it? So that's going to be very important.
ADI IGNATIUS: Yeah. And it depends, maybe, on ultimately if you're a techno optimist or a techno pessimist. KARIM R. LAKHANI: Yes. I tend to be on the optimist side.
ADI IGNATIUS: Yeah. So there are a million questions coming in on this topic, and we're not going to be able to get to them all or even close to all. But this is sort of a different question that's come in, but I think it's kind of interesting. This is from Janelle in Washington, DC. So when we're talking about dealing with waves of technology and changing and adapting, so the question from Janelle is-- we've been talking about how your employees can learn and adapt.
But how do you help the customer learn? Because sometimes that's the learning arc that you need to accelerate for your business to go to where it wants [INAUDIBLE]. Do you have thoughts on how to [INAUDIBLE]?? KARIM R. LAKHANI: That is a great question. I think customers tend to be ahead, I think. I think what happens, what's interesting-- I was in sales and marketing for four years at General Electric.
And what would happen, what would be so interesting is, because my customers knew what I had and what I didn't have and what we're good at and what we're not good at, they wouldn't talk to me about things that we were not good at or we weren't exploring. So I never got that message until much, much later. I discovered, like, oh, you're interested in this? Oh, we've got some nascent product, whatever. But they said, no, we knew GE was not going to be good here, so we didn't talk to you. So I think you'd be surprised, especially today's customers because, again, as I mentioned, all of them are living, including yourselves, Janelle, are living in a digital age with our smartphones and our capabilities and so forth.
So you'd be surprised at how fast they adapt. And in many situations, with other companies they're already further down the pike than with you. And so we get the wrong signals from our sales teams, from our marketing teams, even from our focus groups, because we actually don't observe customers in situ and see what's going on. And again, you think about the median user of Facebook, I think they're, like, 50 or something right now. So I think adoption is no longer as big a deal that I think we think it is. ADI IGNATIUS: My generation ruined Myspace and now we're going to ruin [INAUDIBLE].
KARIM R. LAKHANI: I know. Look at that. Now we're aging out Facebook, too. ADI IGNATIUS: We'll get TikTok next.
So last question. And again, I wish we had more time. But maybe the new wave of how people are thinking about generative AI is, and at first with an explanation, it's really like that technology that predicts the next word-- KARIM R. LAKHANI: Yes, totally.
ADI IGNATIUS: --sort of on steroids. But then it sort of evolved into this almost feels like a sentient-- the machine is developing this kind of emotional intelligence or that we may be on the path to that. Is that-- what's your view? Is that a pure illusion or are we heading toward something that will at least feel like an intelligence and an emotional intelligence [INAUDIBLE]?? KARIM R. LAKHANI: What a good question. The first thing the first thing I always say is, be kind to your robots. So always say please and thank you when you're using ChatGPT or Bard. I do that as a principle.
I tell everybody, be kind to your robots, because if the sentience moment shows up, all the data will be there. All the history of our records with these systems will be there. And you don't want them to get pissed off because, hey, Karim was a bad actor for us.
So always be-- like, I'm an ardent atheist, but I still say inshallah. ADI IGNATIUS: Just in case. KARIM R. LAKHANI: Who knows?
Just in case. Hedge your bets. Hedge your bets. So like I say, inshallah, we should always say please and thank you to your robots. That's the first thing.
The second thing is, right now the human-like responses are a statistical illusion. They absolutely are. They've just been well trained by humans to respond like humans. And they've used all of our texts and all of our videos to be human like in many ways.
But in the end, it's a statistical or computational illusion. But I can tell you, I got a little bit of a wake up call on this. I felt like this stuff, like, the strong AI stuff that has been talked about this-- all this stuff is what we call weak AI. The strong AI stuff is decades away, multi decades away. But in conversations with leaders at Harvard at the Kempner Institute, which is the new Institute for Natural Intelligence and Artificial Intelligence, so marrying biology with AI and AI with biology, so two amazing scholars, amazing world leaders both in neuroscience and machine learning, said, hey guys-- this is pre-ChatGPT as well, by the way. Said, hey, guys, what do you guys think? How far away is this strong AI world? They said, 20 years.
And I was like, whoa. I'm not ready for that. But the world experts, people that know better than I do on this thing, are saying 20 years. It might even be faster. The thing that's interesting to me, Adi, is we may not even know when it has sentience, because it's like we assume human-like forms on alien intelligence, on intelligence.
But if you read a lot of science fiction, like I do, maybe alien life is going to be carbon-based, but maybe not. Maybe they'll have a different metabolism, maybe different neural systems. And you need to be ready for that.
So we may not even know it, that's the thing. And I think-- there was a paper that was at Harvard recently called "First Contact." Some machine learning expert at Microsoft wrote this paper. And I was like, wow, that's pretty radical.
ADI IGNATIUS: Yeah. This is fabulous. We're going to have to get you back on the show because there's a lot more to talk about. KARIM R. LAKHANI: Yeah [INAUDIBLE].. ADI IGNATIUS: We didn't even get into the Congressional hearings on aliens.
KARIM R. LAKHANI: Oh, gosh yes. ADI IGNATIUS: We're just a half step away from that. So this has been Karim Lakhani, Harvard Business School Professor. Karim, thank you very much for being on the show.
KARIM R. LAKHANI: Great to be here with you, Adi. ADI IGNATIUS: OK. See you soon. All right. So I want to thank you all for joining us today.
Just a reminder, you can watch previous episodes of the show on YouTube or right here on LinkedIn and Facebook. Now, be sure to join us next week on Wednesday, August 9 at 12:00 noon Eastern time, when my guest will be Andrew Liveris he's the former CEO of Dow Chemical, who in that job got credit for pushing an ambitious sustainability agenda at the company. He's also the author of a new book, Leading Through Disruption-- a Changemakers Guide to 21st Century Leadership, and we'll be talking about how inspired leaders can best adapt to the challenges that keep getting thrown their way.
Again, if you're an HBR subscriber watching this, you can head to hbr.org/newsletters to sign up for The New World of Work newsletter, where I offer an inside look at each of these interviews each week and talk about some of the ideas that came out of them. And again, if you like content like this, Why not subscribe to our magazine and website. The address is hbr.org/subscriptions. And finally, we want to thank our friends at KPMG, who are our sponsors, for this season of The New World of Work.
And I want to thank all of you for tuning in today. I'm Adi Ignatius, and this is The New World of Work. [MUSIC PLAYING]
2023-08-05 07:07
