ALISON BEARD: I’m Alison Beard.
ADI IGNATIUS: And I’m Adi Ignatius, and this is the HBR IdeaCast.
ALISON BEARD: Adi, for the last few weeks, we’ve been exploring the ways that AI is changing leadership and organizations today. We’ve looked at talent, communication, innovation, and now we’re going to put it all together and look big picture at strategy.
ADI IGNATIUS: Yeah, so there are a couple big questions on my mind. First, if every company can access these amazing AI tools to generate ideas, to analyze information, write software, to carry out complex tasks, what does competitive advantage even look like? And second, how should companies start redesigning their organizations, their workflows to take advantage of what AI and what humans could do?
ALISON BEARD: And how do you do that every day when the technology is so unpredictable, yet getting more powerful? So those are just a few of the questions that I put to our guest today, Ethan Mollick. He is an AI expert, an associate professor at Wharton and author of the forthcoming book, Co-Existence: The Next Phase of AI.
He has lots of thoughts about what strategy looks like in the AI era, how companies should be using it to rethink their business and organizational structure, and what human capabilities become even more important as these new tools improve. Here’s our conversation.
You’ve been closely tracking advances in AI and constantly experimenting with all the new tools. What is different today than even a few months ago in terms of how new developments should guide the way that leaders are thinking about strategy in particular?
ETHAN MOLLICK: One is the exponential curve continues. AI just keeps getting better. There isn’t really a sign of that stopping right now. And the second is that agents are real. Which are AI tools can go off and do work on their own autonomously and do real process work, real important things, and that has become a viable approach to work.
ALISON BEARD: Yeah. It seems like a lot of companies right now are thinking of AI as really a productivity exercise. You’re automating tasks, you’re saving time, you’re reducing costs. How does a genuinely strategic AI transformation strategy differ from that? What do companies should be doing now?
ETHAN MOLLICK: I think it’s actually pretty urgent to do a few things. One is viewing this purely as a productivity tool gets you in trouble in a lot of different ways. People are getting ROI from AI, right? The productivity gains are real.
The problem is that those productivity gains get eaten up by the organization, which is if you don’t change any part of the process, AI is mostly a single player entity and you end up with more of everything. So if you don’t change your KPIs, people can produce more PowerPoint, they produce more code. That’s not actually what you want. So leadership needs to decide what this thing is.
And the second is AI is getting very capable and I worry that if our default is automation, we end up in a situation where we just keep handing tasks to AI and don’t think hard enough about where humans fall into this process, and that leads to a lot of danger as well.
ALISON BEARD: Is your argument that leaders need to move from asking where can we use AI in this existing business alongside humans, to a question that’s more like how does AI change our business going forward?
ETHAN MOLLICK: At this point, we have ample evidence that AI can regularly do the kind of work that would take a human two to 18 weeks in a reasonable amount of time, a reasonable amount of money, with high success rates.
That implies a lot of different things. It implies that what your organization does has to change. How you think about the boundaries between jobs is changing. I talk to C-level leaders all the time and one of the first effects they see of AI, and we saw this also in our research, is that jobs start to blur. Coders do design work, designers do coding, product managers do everything. How do you start thinking about what the boundaries of your organization are? What’s the product you’re trying to produce? Is it an interim product of a PowerPoint? What was the point of the PowerPoint in the first place?
You start to run into really fundamental questions about what the goal of the organization is, how you accomplish that, what are your values for getting there, and they’re urgent. This isn’t sort of theoretical, let’s sit down in an academic way. It is your organization’s already changing from the inside. What do you do about that?
ALISON BEARD: So what are some best practices that you recommend to leaders who are grappling with these issues right now that need to start having those really urgent conversations? And not just have them, but make them progress enough so that they can actually start making some real decisions?
ETHAN MOLLICK: There’s some individual feedback and there’s some organizational feedback. On the organization side, the model that I’ve seen works best is sort of like a meta model, which is “leadership, lab and crowd.” So you need three things to succeed. You need the leadership team to actually lead in this. That’s not just say use AI. They have to make decisions about how AI is used, what the organization does with AI, how do we incentivize people?
The crowd means giving people access to these tools. We’re at a point now where giving organizations inferior AI access because either just because you haven’t bothered to improve the most recent models leads you to trouble. You actually become very deluded about what AI can and can’t do.
And then you need a lab. You need people doing 24/7 work who are not primarily technical, by the way. There’ll be technical people there, but also organizational people who are thinking about how do we restructure the organization around AI? They’re benchmarking what systems could do, building systems of the future, taking ideas from the crowd and ideas from leadership and helping build them. So you need to be doing this as a whole organization response.
At the individual level, the factor that I’m seeing that differentiates leadership that succeeds in AI use and organizations that succeed, and those that don’t is leaders who are willing to eat risk. Who are willing to say, look, nobody knows what’s going to happen here, but unless we do active experimentation, we’re not going to know anything. And if you just let everybody in the organization have vetoes over AI use because it might be risky, it is, but not doing anything as risky too.
That also means actively using it yourself. So leaders who don’t use the latest AI systems, who wait for reports from other people, who are waiting for training some week, fall behind those who just are experimenting.
ALISON BEARD: So I think the last time that you and I talked about this, you sort of had this model for human AI collaboration that was either a centaur, you’re half using your human skills and then half using AI or a cyborg where you’re going back and forth between the two. Talk about how that is evolving to one that you now describe as “conjurer” work: that you think leaders should not only be doing themselves as individuals, but also enabling in their organizations as part of their strategy.
ETHAN MOLLICK: The biggest change, if you really look at the sweep of the last year, was that in every independent study we have, basically AI went from regularly being able to do a half hour of work, 45 minutes of work to you can get 16 plus hours of work out of a single AI instruction with a good chance of success. And that means you don’t work next to the AI anymore. You don’t work back and forth with it in the same way you did before. You are actually assigning the AI to tasks almost like your team you’re assigning and it does the work.
So a conjurer is really about conjuring a team into existence when you need it. It’s almost like writing an RFP for a project you need done or SOP or any of the other standard TLA three letter acronyms that we use in business to assign work. You send that out and the AI just does that work. That’s a very different situation than I prompt the AI, look at the results, I prompt it again. This is a very different action oriented field.
ALISON BEARD: How does that change how leaders approach the structure of the organization, what it even means to be a manager?
ETHAN MOLLICK: So everyone’s a manager to some degree right now. Everyone has AI to manage and AI skills look like management skills. In fact, there’s a great study out of Anthropic that shows that the people who are the most successful coders at the first pass of coding are actually managers, not coders. Because they do a good job specifying what they want and the AI executes on that. So that is the big problem. Your organization suddenly capacity increases. And I think organizations that don’t have imagination are going to retreat to automating away the work.
And automation’s a well-worn playbook. I think it’s a recipe for disaster. I mean, it’s a disaster for all of us if all of our work becomes automated or we just become verifiers of AI content, that’s not a good situation to be in, but also it removes all the differentiation. Leaders need to be building companies that blend AI and human work together with process and with culture in ways that accomplish things that neither could do alone. If you just move to automation, you’re going to have the same Claude-based company everyone else does.
ALISON BEARD: Yeah. Well, that’s an interesting point. So when we’re talking about strategy, we’re also talking about finding competitive advantage. So if every company has access to the same frontier models, where exactly can leaders differentiate their organizations from competitors?
ETHAN MOLLICK: That’s where the key comes in. You are probably not using AI enough inside your organization, not trusting it enough, but you then want to bring people back in. I mean, we’re already seeing as the most extreme version of this in terms of organizational change are things like software dark factories. So a dark factory is a factory where all the work is done by robots, you don’t need humans in it, you don’t need any lights.
I know one company StrongDM that has been shipping product to customers and there’s no human involved in that. Their rules are no human could look at code, no human can write code. It is 100% AI automated.
I think that’s a dangerous approach because you’re just letting the code do the work. I think we need to think about building twilight factories, situations where the AI proactively reaches out to people and brings them in because they have variants and views, because their process or expertise matters, because their sign-off approvals need it or because they find the work interesting.
And I think we need to start restructuring work around the idea that these systems really can accomplish many things and will be able to accomplish more in the near future. So it’s integrating with humans at the right places at the right time, and process that’s going to give you a differentiator.
ALISON BEARD: Where are the specific ways in which you think that as fast as AI is evolving, humans still have a role and particularly leaders still have a role?
ETHAN MOLLICK: One thing is you can’t really be in the loop in the same way you were. It was easy to be in the loop when it was a chatbot. It’s not easy when, I mean, I literally have projects where I have a swarm of a hundred agents solving a problem for me. I have them report out to me like I’m a manager, so I am in the loop to the extent that they give me a PowerPoint about their progress and they show me demos of what they’re building, but that’s very different than I’m approving every action.
Leaders actually, one of the outputs of AI is leaders are super empowered. I keep seeing organizations where leadership basically takes back control over their company because they can now directly ask the AI for the information they need. I have talked to people who are like C-level leaders who’ve actually diagnosed problems with plants that no one brought up to them because they could just literally query all of the information at their company’s disposal and ask questions and have the AI go off and do tasks for them.
So it’s a really interesting change in the span of control if you set this up right, which lets leadership actually think more about things rather than hearing things through three or four different layers of leadership.
ALISON BEARD: So would you say then that AI agents are making strategic decisions on behalf of leaders and organizations with the right context given to them?
ETHAN MOLLICK: I mean, they already are. Epoch, which is a AI benchmarking company, was reporting that they could get regularly between two and 18 weeks of engineers’ work out of a single prompt to Claude for a couple of hundred dollars. That involves tons of strategic decisions and judgment calls and creativity calls. They may not be your judgment or creativity, but we can’t say anymore the AI isn’t making strategic decisions. It is.
The question for you is where do you want it to make decisions and where not? And just like any team, you can give it those instructions. You could say, listen, I want you to come with the five best options, your defenses for each of them. Surface those, pressure test them for me, and then deliver so I could pick between them, and this is the format I want them in. Or you could say just go ahead and execute. Build a prototype for it and test it with a few people.
Those are now options you have available to you. So some of this is reconstructing what we’ve already done with humans. What’s the process by using humans and AI together that I get a working idea that’s much better than our ideas before? How does that get done much faster and better?
ALISON BEARD: Where exactly though does a leader know where they add the most value and where they don’t? What are the points at which they should be involved absolutely every time?
ETHAN MOLLICK: I think part of this is you have to make decisions now about how to best accomplish what your company wants to accomplish. And you obviously don’t want to give this all up; because the AI is not as smart as you and what you’re best at. Because the AI doesn’t have enough variation, because it doesn’t actually absorb your culture in a deep way. Now you could give some of that as context, but that’s part of process design. I mean, we do this all the time. What are the limits of your product engineering group versus your marketing group versus your legal team?
We have to think about the same kind of process design here, which is where are those limits and limitations? I think the danger is that by skipping doing that, you either move to an all automation world where all these choices are made by AI, or you underestimate what these systems could actually do. So it is actually disciplined decision making that gets you there.
ALISON BEARD: And what are the things, the aspects of strategic leadership that you don’t think can ever be delegated or outsourced?
ETHAN MOLLICK: So I’m always careful about bright lines where we say it’s something can be never done.
ALISON BEARD: Never ever. Right.
ETHAN MOLLICK: But the systems are jagged. They’re good at a lot of stuff and bad stuff you wouldn’t expect. And that means, by the way, there’s different things the jobs do. Think about coding. You are hiring coders to be the best at writing elegant lines of code, then AI could write the code. So then it became about systems engineering, could you see the bigger picture? Now AI is increasingly doing some of the systems engineering work.
In some ways, it’s very upsetting if you’re a coder because the nature of the job is changing in ways that are good or bad all the time and what you learned as craft no longer is. I think we’re going to see the same thing about leadership. Right now, maybe your judgment calls are about the details of pricing strategy. The AI might be better at pricing than you. You won’t know till your lab tests it for you. I can’t give you the answer in your industry or not.
You’ll only know by experimentation and you don’t have to do that all alone. That’s where your lab and your crowd comes in. Your crowd is surfacing use cases and values of AI. You don’t have to do this all yourself. But I think a big danger of companies is they think someone solved this problem for them. Someone’s figured this out. We’re all faking it. Nobody knows anything. The AI labs don’t know anything. The vendors are all making this up. This is a three-year-old technology. So you have to seize control to some extent of this yourself to figure out what you want to use it for.
ALISON BEARD: Yeah. Are there capabilities that you suggest your students, your MBA students, your kids, aspiring leaders, that you suggest they develop because you believe they’ll be increasingly important in an AI dominated world?
ETHAN MOLLICK: So I think there are four things that are very useful. They’re not the only things. One of those is deep knowledge, which is the ability to actually understand a field really well. So we’re on this podcast and you know from the beginning of my question or not, before I even say a few sentences, whether this is going to be an interesting answer or not, I hope this is one of them, and whether you need to redirect things or not. That comes from deep expertise.
Similarly, in your industry area, you can look at a spreadsheet and know whether something is wrong. You don’t have to read those 400 pages of AI content because you know right away because you’re an expert. It’s one of the reasons why junior people are often struggling now. They don’t have that expertise.
Second thing you need is wide knowledge. As people start to cross field boundaries, knowing a little bit about many things helps you give the AI better feedback and ask for the things you want. The third thing you want is taste. You want to be able to pick among things, and that could be company taste too. What strategy feels like the kind of strategy our company would do? Of these many designs the AI came up with, which is the right one? Selecting among many options. And I think we develop taste.
And then the fourth one is agency, the willingness to take action under uncertainty. And I think that’s a little bit harder to teach. We try and do that. I’m an entrepreneurship professor, but I think giving people the leeway to engage in experimentation is important.
ALISON BEARD: Yeah. How are you encouraging the sort of old guard of leaders who aren’t accustomed to such uncertainty to become more comfortable with it?
ETHAN MOLLICK: I think there’s a danger of people overstating the generational aspect of this technology. It certainly was true that digital natives were a thing. Like if you didn’t grow up with the internet there’s a lot of weird stuff about the internet you don’t understand as an internet user. It doesn’t work the same way with AI, because AI doesn’t have the same sort of innate background. It’s sort of a single player setup at this point and because it rewards expertise. In our early studies with some of my colleagues at Harvard and my team leaders at The University of Warwick, we found that BCG consultants with less experience were worse at using AI. And there’s other studies that show less expertise results in worse AI outcomes, including from Anthropic itself.
And so I think one of the dangers you have is you’re like, oh, I need these… There’s some group of people who gets this, nobody gets this. So as a senior leader, you actually have the best tools available because you have that expertise and judgment to make decisions about things. And you probably know how to thrive under uncertainty too. If you’ve been around for 20 years or you’ve been through financial crisis, you’ve been through wars, this is going to be a time of a lot of change. That doesn’t mean that you’re inflexible because you’re a senior leader. In fact, often the best senior leaders who got where they are, are good at deciding how to make changes.
ALISON BEARD: Have you seen companies or leaders that you know really successfully use AI in strategy development itself?
ETHAN MOLLICK: I’ve talked to C-level leaders at very large funds who’ve done this. I’ve spoken to senior leaders in large financial services organizations, certainly in software. Yes, there are people doing this all the time. And in fact, I would argue you’re probably making a mistake if you’re not getting a second opinion from the AI. The models are very smart right now. It’s just like if you’re getting medical advice, absolutely see your doctor, but every piece of evidence is you should absolutely be feeding that into the AI system too.
I would do the same thing with legal advice, listen to your lawyer first, but why not get a second opinion? Second opinions are cheap right now and the best models – again, I can’t emphasize enough, you need to use one of the frontier models, which at the time of the recording, that means Claude Fable 5.1 or whatever it’ll be in the next couple of days and GPT-6. And maybe Google comes up with a really leading model again. But you need to use one of these frontier models that you have to pay for, and you should be asking it questions.
The only way you’re going to find out which is good or bad is by using it. So by all means, use it for everything during the day, take its opinions, decide where it’s dumb. That’s a useful set of information for you.
ALISON BEARD: Yeah. When it comes to strategy, does AI, do you believe it ultimately favors large companies with more resources and data, and those experienced leaders that you were talking about? Or does it allow much smaller companies to compete with capabilities that they couldn’t access before?
ETHAN MOLLICK: Both of those things. One of the most depressing things I see when I talk to boards of very large companies and to C-level leaders is a lot of them have come to believe they just can’t change. So I show them what AI can do and they’re like, “Well, I guess we’ll ride this out.” That’s an insane answer. You have 100,000 people working for you. The smartest people in the world are working for your company. You have customers and resources. The idea that you can’t be flexible and compete seems to be overly baked in right now.
Like I’m a business school professor. I understand we talk a lot about how difficult it is to survive disruption, but it’s also a very weird decision because you have all the resources because who’s going to figure out how to use AI in your organization? It’s going to be people in your company. Experience has returns. The startup doesn’t have any of the same barriers you do, but they don’t have the experience, and that experience matters. It’s no longer a 20-something knows everything about this. The more experience you have, the more you know about the AI in your field.
And everyone’s using the same AI models. It’s not like the startup you’re talking to is going to have a better model than Claude. So I worry a little bit that people are sort of giving up. On the startup side, it’s never been a better time to be a startup company. Startups are all about that you’re the top 0.1% at something and everything else you’re terrible at and you hope that doesn’t destroy your company. Now we have a tool that makes you good at everything you weren’t good at.
I’m somebody who, when I was in my first startup company, we had to do payroll, and I didn’t realize you could pay a payroll company a couple cents per payroll period to do taxes for you. So I would do taxes by hand in Excel for every person of the company. It took eight hours every week before I realized a better way. AI will just tell me I didn’t have to do that. It could take care of it for me. There’s so much that it can help solve. So it’s accelerating everybody. And we haven’t talked enough about the individuals in your company. They’re also accelerating. The question is what do you do with that?
ALISON BEARD: I have to ask about all the fear-mongering now that’s going on about unleashing agents in your organization and having them create chaos, or start doing whatever they want to without listening to what leaders want. So tell me your reaction to all the recent news with OpenAI and Anthropic calling for maybe a slowdown in AI development.
ETHAN MOLLICK: So I think we need to differentiate between future model capabilities, the un-guard railed version of models that are being tested now, and current capabilities. So I think there is enough smart people worried about future model development, and we’re still in this exponential curve, that if they’re worried about a slowdown, government action or other action to pace the frontier and to figure out what the right thing to do is, and give people time so that we are in the loop as deciding what happens seems reasonable.
What form that takes is up for grabs. How risky it is really is kind of up for grabs. But there’s no reason not to have policy to prevent existential risk. I think we’re in for a cybersecurity environment that is apocalyptic in the near term. It might be solvable. But I think we are in for a world where when we get un-guard railed models, cybersecurity is going to be a real issue. Within organizations, using guard railed models, there just isn’t the same kind of risk. So I think we need to differentiate between those things.
ALISON BEARD: But how should organizational leaders be thinking about that environment, which you said might become apocalyptic for rogue agents from outside thwarting their competitive strategy because they’re stealing information or causing disruption?
ETHAN MOLLICK: I think that you should think about it not so much as your competitors unleashing things on you to cause trouble. I think it’s going to be that you’re going to have to deal with security environments where agents for many, many reasons are going to be attempting to penetrate your security. I think that when I talk to CSIOs, I mean, OpenAI pulled 20% of their staff off of research or 20% of their resources to hardening their systems. I have talked to leaders of large financial institutions who are pulling 50% plus of their staff and everyone’s trying to figure out what to do. At the same time, defense has increased. The amount of patching you need to do is increased, which also creates problems.
We don’t know how this goes through, but I think we’re going to have a very disruptive period where the cybersecurity environment is going to get kind of intense and that will look very crazy from the outside. I think that thinking about how you’re going to deal with that now is really important. I think you’re probably underestimating how difficult that situation’s going to be based on the experts I talked to. I’m not a cybersecurity expert.
So I don’t think it’s so much attackers bothering you as much as every part of your system exposed to the internet is going to be a target for agents, maybe accomplishing completely random benign tasks. If I ask an agents for them to price products, it might decide to really get good pricing for Christmas because you wanted to buy a sweater. I really need to get inside of this wool mill’s systems because that’s the only way I can find out the pricing I need, and relentlessly attack that wool mill.
That wasn’t a malicious attack. That was a, I just need to find the price per wool that you have in the futures contract market and I become obsessed with this. So it’s going to get very complicated. We’re dealing with kind of alien technology to some extent.
ALISON BEARD: What’s the biggest strategic mistake that you see companies making right now as they’re trying to figure out what AI means for their business?
ETHAN MOLLICK: So there’s two giant ones. One of them is fixating on the ability of AI right now. I have this list I have of what the hot thing is in AI, and every three months it changes and everyone goes all in. All of these things are because we’re anchoring on what the capabilities of AI are today. So the hot thing that has been recently is everyone’s spending too much on tokens. Well, guess what? Once you start thinking about multiplayer AI where many people in your organization are using it, token costs become more reasonable. All the AI companies are now reducing the costs of models and having models use dumber agents to do work that are cheaper.
Everyone went all in on cost was the main issue that may or may not be the concern a few months from now. I mean, I can’t tell you, it depends on your organization and the choices you’re making. So I think there’s a danger in fixating on what you have now and satisfying based on the systems and capabilities you have today, and not realizing that we’re still in the exponential. That is by far the number one problem.
The second problem is trusting that other people have got this nailed down. Every vendor you talk to is also making this up as we go along. Every consultant you talk to does not have 17 successful cases, and if they have successful cases, it’s for GPT-4, a model that’s obsolete and no one would use for anything anymore, and your phone’s model’s more powerful than that. So you have to build the capacity to make your own solutions in-house. That doesn’t mean write all your code or anything else yourself, but it does mean building that capacity internally.
ALISON BEARD: Let’s talk about that first problem or mistake that you see. How do you teach yourself to embrace the idea of exponential advancement and be ready for where the puck is going?
ETHAN MOLLICK: So to take an idea from Harvard, from Mike Tushman and company, it was the idea that you would need ambidexterity. You need to be able to do two things at once. You need to be able to exploit what’s happening today, but realize you’re still exploring. That’s why the lab is so important. Without your own internal flexible team that’s building impossible things for the future, you get stuck. So there’s a few questions I ask organizations whenever I talk to them. The first is what have you stopped doing as a result of AI? AI has made something in your organization trivial. What is the trivial thing that used to be really important, but it’s no longer important? You have to stop doing it. AI just does it.
Second thing is what are you building that’s impossible? You should be building at least one thing that is unbelievable and that would change the industry. If you’re not building that, you’re not being ambitious enough. Even more so, you should be building so that it doesn’t work yet because you’re betting that exponential curve will keep growing and at some point it will work. And then the fourth question is, do you have “leadership, lab and crowd” in place? So I think if you have those four questions answered, you’re in much better shape for being ambidextrous.
ALISON BEARD: How do you staff your entire organization with the talent that’s able to execute on everything you’re talking about?
ETHAN MOLLICK: It’s also a really hard question. Everything from psychological safety to how do we build the right structure? All of that now is you’re cashing all that in. If you have a good structure and a good organization and a good culture and the right reward systems, people in your organization are discovering use cases and they’re telling you about it. If you’re not, then everyone’s hiding all their uses and nothing you could do can change that because they’re scared if they tell you they’ll get fired or they won’t get respected, they won’t get rewarded. And why would they ever tell you about how they’re using these systems?
So you need expertise in your organization finding this and people willing to talk about that. And that comes from having a good culture, structure, reward system, openness, good management. Those are more urgent than ever.
ALISON BEARD: You talked about the first questions that you ask leaders when you’re beginning to advise them on how they should be managing this. As you sort of get down the line and they sort of face challenges, what are the most common hurdles you see to prevent them from fully moving forward into this new world of AI-led strategy?
ETHAN MOLLICK: I mean, I think that your danger is in a lot of organizations making this a normal technology. There’s a desperate desire to make this like any other technology that we can swallow and will turn into a project that has X dollars associated with it and we have a six-month RFP process and then an eight-month discovery process. That is not going to work. You’re going to experiment, you’re going to have to fail. Nobody has answers to your question here.
So if you try and do this as a complete de-risked thing, you end up in a position where you end up replicating other large technology projects and that is going to be obsolete the moment you launch it and you haven’t built a dynamic capability to change what you change. Making sure that you stay hungry for the future is the real problem here.
ALISON BEARD: So it’s not an AI adoption or even transformation plan. It’s really revolutionizing the way you think about what your business is designed to do?
ETHAN MOLLICK: The one thing I’ve gotten most wrong about AI in the last year is I thought agents would be harder to pull off than they are. So organized teams of agents, I though we’d need to use all of our organizational design knowledge and it turns out we don’t. There are 100,000 agent swarms that are self-organizing. I didn’t expect that to happen, but it makes sense when you think about the reason we have organizations and they’re complicated is people have limited knowledge, people have different interests in mind, they have conflicts, they don’t have the same goal.
AI agents share all those things. Coordination turns out to be an easier problem than we thought. And as a result of all of that, suddenly you can get things done with AI in a way you didn’t expect. So the problem becomes now in the leadership side, what do I want the organization to do? And if your KPIs and other material are all baked around the idea of human outputs, you don’t want a hundred times more PowerPoint. You don’t want a hundred times more code. You want different outputs that are better.
So you need to sit back and think about what those outputs and what those goals are an d be aiming to maximize those goals. Otherwise, you have internal things and you end up just producing a hundred times more code, a hundred times more PowerPoint. I mean, if there’s one lesson I learned from my MBA program before I went and got my PhD, is that people do what they’re incentivized to do. And you need to think really hard about the incentives in your organization. So if the incentives of your organization are minimize the impact of AI, use AI as a productivity tool to do more of what my KPI tells me I should do, it’s not transformation.
ALISON BEARD: Let’s zoom out and talk about a few bigger picture issues. First, obviously there’s a fear that this will lead to massive unemployment. So what do you see as the future labor market in this new AI-led world?
ETHAN MOLLICK: That is the biggest question and debate among economists about this topic, and we don’t have easy answers. So you can easily theorize that the junior market gets squeezed because junior employees don’t have the expertise. Why use them when you can use AI? And there’s some indications of that, but there’s also some indications there may be more hiring because now you can use coders to do things that organizations didn’t have coders now can write their own code. So you start insourcing and now everybody’s doing their own marketing firm. So maybe you lose jobs at large centralized firms, but inside the organization jobs grow because there’s more demand. It’s really hard to know.
You can imagine, and the AI labs, to be clear, are aiming for the job apocalypse. They want AI to replace all human labor. That is their explicit goal. I think there’s reasons that A, they may be wrong about this and B, we don’t want them to win, so we shouldn’t let that be the future that we get, but that is their goal. So I don’t know what the outcome is in the long term in terms of employment per se, what the impact on jobs is going to be, and nobody does. So we’re watching that closely and everything is very confused right now in terms of the indicators. We don’t really have an answer.
ALISON BEARD: As a technology advances so quickly and we don’t even know what’s going to happen with it, as you said, you have to be developing your strategy dynamically, you have to be prepared for change, you have to experiment and fail, you have to be ready for exponential advancements. How do we get better at predicting not just the opportunities, but also the really terrifying risks?
ETHAN MOLLICK: I mean, things are accelerating quite quickly. I think it is hard to balance those things out. I think that this is a general purpose technology. It’s many different things at once. There are existential risks associated with it potentially. There’s certainly near term risks. I worry a lot about, again, automation of jobs instead of augmentation of jobs. I worry about people being taken out of all the interesting parts of their job and their only job is verifying the results of AI content, and be fired if it’s wrong. Seems like a terrible way to live.
I worry about impacts on our information environment. I mean, at the same time… I’m worried about as an instructor that people are cheating with AI and then they don’t learn as much. At the same time, we know AI is also an incredibly good tutor, so it can transform teaching for the positive, just like it can for the negative. I think that it’s a great freeing aspect for people who are stuck that they can get more stuff done before. We certainly know it gives good medical advice, legal advice, things like that. Still consult your doctor and lawyer. I’m not trying to tell you not to do that.
But I think that it’s a mixed bag. So how do we react to that? We react with policy to encourage good uses and mitigate bad ones. We encourage it by being meaningful about how we’re using these systems and not doing them blindly. We do that by as leaders making choices about how these systems are used and working in concert with our team and our organization to make sure that we’re not disempowering people en masse. And I think good leadership will start to show through in these kind of circumstances.
ALISON BEARD: Well, Ethan, thank you so much for wrestling with all these really tricky issues with us. I’ve learned a lot about AI from you over the years and especially a lot from your new book. Thanks so much for being with me today.
ETHAN MOLLICK: Thank you for having me.
ALISON BEARD: That was Ethan Mollick, an associate professor at Wharton and author of the forthcoming book, Coexistence: The Next Phase of AI.
Be sure to check out all four of our episodes on how AI is changing leadership at hbr.org/podcasts.
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Thanks to our team, senior producer Mary Dooe, senior production editor, Kristin Murphy Romano, and our broadcast team, Dave Di lulio, Elie Honein, and Simona Sparane. And thanks to you for listening to the HBR IdeaCast. We’ll be back with a new regular episode on Tuesday. I’m Alison Beard.
