AI and the Future of Pharma with Bob Bradway, CEO of Amgen

Episode Transcript

BOB BRADWAY: When you can free up a scientist and you can take a scientist who is otherwise spending hours requisitioning materials or summarizing data or filling in forms and do that for them agentically, that's magic.

SARAH ELK: Today, we're excited to welcome Bob Bradway, the CEO of Amgen. He's been CEO for over 13 years, an unbelievable track record of shareholder returns and innovation, and one of the most digital CEOs I think I've ever interacted with.

ANDREW NG: I've been fortunate to have known Bob and worked with him on occasion for many years, and he is one of the most forward-looking, forward-thinking, technically savvy CEOs that I've ever met. So I think you have a lot to share with us about how to lead an organization forward with AI.

SARAH ELK: I'm Sarah Elk with Bain & Company.

ANDREW NG: I'm Andrew Ng with AI Aspire.

SARAH ELK: Welcome to Winning with AI. Thank you, Bob. It's great to have you.

BOB BRADWAY: Thanks, nice to be here with you.

SARAH ELK: I thought we might start in 2012. I was surprised to learn—or maybe I shouldn't have been surprised to learn—that you were so early to the digital game, sort of before the digital game became a thing, in the late teens, 2018, 2019, as you thought about your data advantage and your acquisition of deCODE. Would you talk a little bit about how you were thinking about that so early at that time?

BOB BRADWAY: Well, we were interested in human genetics as a way of selecting targets that might be relevant for human disease. So our notion was that if we could acquire a curated set of data over a large enough population of people, that we'd be able to draw insights from which we could base some of our discovery research efforts, and there were two things that were going on at that moment that were particularly attractive to us.

There existed a company, deCODE, that had been formed some number of years before and had captured the genetic information on virtually everybody on the island of Iceland. So you had a high-quality, longitudinal database linking genetics to physical and health traits, from which we thought, again, we'd be able to harvest insights that would point us to targets that would be more likely to be successful in the long run.

And so the second thing was our imagination was captured a little bit by IBM's Watson. And we knew that, at some point, artificial intelligence would enable us to make sense of large data sets like that which we were assembling in Iceland.

Now, it still [was] a little bit early then to talk about AI in the way we do today, but it was two things coming together: a belief that the sequencing technology would enable us to do it in vast numbers and at a time frame and at a cost that was reasonable and that at some point AI would change the game as well. That was behind that decision.

SARAH ELK: And you met Andrew early too.

BOB BRADWAY: Yeah, we did. In fact, I remember talking to Andrew about our genetic information in particular. And his response at the time was to say, “Well, that's interesting, but it's not tractable today.” And I'm interested in things that are tractable today, things that we can do today. Whereas there were things in that moment, image processing, for example, computer vision, that were well suited to the challenges of our business. So we began a dialogue with Andrew then about how we should think about machine learning and how we should think about artificial intelligence in the biotech industry.

ANDREW NG: I remember thinking that the genetic and genomic information you were studying was a wonderful long-term bet. But the only thing better than the wonderful long-term bet is a wonderful short-term bet.

BOB BRADWAY: That's right. And you were very clear about wanting to focus on the wonderful short-term bets.

ANDREW NG: But both are good. And I feel like, of all the CEOs I know, like Sarah was saying, you were really early to investing in AI and data. And I suspect we'd love to hear that the natural complement between AI and genetic data help[s] drive that. And then now you're doing so much AI, not just for biological data or genetic data, but for many other aspects of your business as well.

BOB BRADWAY: Well, again, we knew that if we were talking about vast sums of data that would require mathematics- and technology-processing capabilities, that would be quite profound. Of course, through the years now, we've installed a SuperPOD, an NVIDIA SuperPOD, in Iceland, and we use that technology to interrogate those data sets. But you're right that we knew that, in making a commitment to that large treasure trove of data, that we were going to need to have the processing and the technology and the mathematical capability to make sense of it over time.

But there's something else, Andrew, that may not be obvious from the outside, which is that AI scientists have been attracted to the area of protein folding for decades, catalyzed by a Nobel Prize winner who ushered [in] a challenge of sorts, suggesting that it should be possible to predict how a protein will fold in a cell based simply on the sequence of amino acids of that protein.

And in 2021, of course, DeepMind revealed what is now well known as AlphaFold and demonstrated success in being able to predict in silico what had previously been impossible to predict. Getting the crystal structure of a protein is a complicated journey. It requires very talented people, expensive technology, takes a lot of time, and you're not always successful. And so the fact that DeepMind was able to crack that nut and publish it in 2021 created a rush of excitement in our field.

ANDREW NG: And, by the way, I remember shortly after AlphaFold, I think you and your team called me up, and we chatted. And one thing I really appreciated was there was all the PR about AlphaFold, exciting stuff, but beyond that, even though AlphaFold was good at predicting the static protein structure, your team, you and your team, didn't just bind to the PR hype. So I actually really appreciated that deep, technical perspective that goes well beyond what the newspapers are reporting to then try to translate this in a precise way to what your business could then use or not use it for.

BOB BRADWAY: And since then, we've developed our own models. We continue to absorb and test other protein-folding models and have moved beyond that to try now to actually design drugs. For example, zero-shot antibodies is the current exciting topic in the field. But you're right that we're not relying on any single approach to this but rather seeking to bring our expertise, our data, evolve the models that are available in ways that make them useful to us.

In our business, we need to know the structure of a protein, so we need to know how it folds in order to gain insights into what its function might be or gain insights into what the problems in manufacturing it and delivering it to patients might be.

But our interest is, of course, in designing medicines that are going to be effective in humans, so we've learned through decades of doing this that there are certain attributes of a molecule which make it either difficult to manufacture or create a risk of immunogenicity, and both of those can be liabilities which show up at the end of a 10-year, $2.5 billion journey. So if you can use technology and data and insight upfront to make the changes necessary to improve your likelihood of success, that's obviously a big deal for us. So, again, we were very fortunate that we had this notion that protein folding would be possible someday with the tools of artificial intelligence and that if artificial intelligence did crack that nut, we would do some things differently. And we had begun assembling some of those things that we would want to do differently in anticipation of that moment.

Now, in fairness, it came much more quickly, and it was probably more profound even than what we had expected. And then, like everyone, I think we realized that AI was going to be a big deal, and we needed to figure out where it was going to be a big deal in our business and then invest around those areas.

SARAH ELK: One of the other unique aspects about your approach, Bob—and certainly from an R&D perspective—you have a very enviable pipeline, but it's a whole company approach, and it goes beyond R&D, into manufacturing, sales, and other aspects of your business. Could you talk a little bit about the full suite of things that you're trying to address from an AI standpoint? Because I do think it's quite differentiated in how holistic it is.

BOB BRADWAY: Well, I think our business does start with innovation. So the core of our strategy is innovation. We don't have an innovative, new idea; an innovative, new molecule. We really don't have a business now. So our initial effort in artificial intelligence was around the things that happened very early on that journey. So I mentioned protein folding a moment ago, but it starts at that early stage. And what we've tried to do is work downstream from that and say, “OK, once we have a molecule, what are the other processes, what are the other things that we do in order to get it developed and get it registered and then make it available to patients and then supported in manufacturing and so forth?”

So we've tried to understand, end to end, the processes that we engage in and then to define or identify those that might be amenable to the tools of artificial intelligence and then to say, “OK, of those places where we could intervene, in which places should we intervene?” And, of course, that changes from one project to the next, and it changes as new technologies have been introduced along the way. So as an example, using AI today, we're able to select the molecules that we want to move into clinical development much more quickly. Let's say we're saving 50% of the time that it once took us to get a molecule—to make the choice of which of the molecules that we think will bind the relevant target is the right one—to move into preclinical and then clinical experiments. So we're moving more quickly there.

So then we say, “OK, well, what's the next step in the process? And is there something that technology would enable us to accelerate?” And so the process goes, and then again, right through the value chain. Manufacturing in our environment includes a lot of visual inspection, and that, of course, is an area that's been ripe for AI intervention for some time. And so there are capacity and modeling and so forth that, over time, we'll do more of with AI.

And then sales and marketing, obviously, we're excited about what might be available down the road in the way of sales and marketing. There are some AI tools at the moment which clearly are changing how our customers consume information about either our medicines or the diseases medicines treat. So literally, each of the different stages of the way in which we engage in business are—we have groups trying to figure out, Are there things we could do differently with AI? And, again, if we could do it differently, should we do it differently?

ANDREW NG: Can you say a bit more about how you lead your organization through this strategic thinking?

BOB BRADWAY: Well, I think part of what we do is we brainstorm, and we try to think to the future and say, “Not now, but what's possible 5 or 10 years down the road? What do we see coming that may have a big impact on our business?” So a current example would be quantum. I think by now we all recognize at some point there will be quantum computing, and quantum will enable us to tackle some challenges that are difficult to break down with classical computing, and in our field, there are some specific areas where that's true. You mentioned the three-dimensional and the movement of proteins, for example. Some of the movement and the way in which proteins fold and then react in different environments may be more possible to model with quantum than what is feasible with classical computing, so we have begun, with respect to quantum, to begin to identify the quantum equations that are relevant for our business, the quantum experiments or the quantum technology solutions that may be possible, and begun to think, “OK, if it were available, what would we do with it?”

So we try to think about strategy in that fashion, to say, “What are the big changes that might be coming our way?” How would we want to get in front of those big changes to harness them for the benefit of what we try to do, which is discover, develop advanced medicines that make a big difference for patients that are struggling with serious disease? And so, in that way, we're not trying to say it will happen by a certain date and we need to be ready, but rather, if it happens, what would we do? And then try to line up some capabilities so we're not surprised and we're ready to move quickly.

ANDREW NG: So I think you probably realized that the way you talk about AI and the way that Amgen has embraced it sounds very different than the way many CEOs talk about AI and the way their organizations have not yet come to embrace this very pervasive usage. At AI Aspire, Kirsty Tan and I often think about the change management aspects of moving organizations to embrace AI. And it seems like you started early, which is fantastic, but it seems like you've really moved the organization. Can you say a bit more about lessons learned for the change management, bringing your whole company along with you?

BOB BRADWAY: Yeah, it's interesting that you frame it around change management. I think that's right. I think this is a huge change management process that we're all at the early stages of.

ANDREW NG: But compared to others, it feels like you are a lot less early than many businesses. But maybe it also feels early to many of us. I don't know.

BOB BRADWAY: I hope you're right because I think being late to this party is going to be expensive. I think being early to the party has the potential for real benefit.

ANDREW NG: That’s true.

BOB BRADWAY: So I hope you're right that we're ahead of some of the competition. But for different reasons, unrelated to AI, we needed to make some profound changes to our organization in 2013. And we decided at the time that the high fruit of that would be learning how to lead change management in the organization. And Amgen had been such a successful company for so long. We'd had decades of success and, for many years, uninterrupted success. But as the patents on our first sets of molecules approached the date of expiry—in our business, when a patent expires, revenues plummet and profits plummet with it. And as our first set of molecules were approaching that point, we realized we needed to operate differently.

And so we focused not just on what had to change for the benefit of the loss of those patents, but we focused on how would we lead company-wide change processes. How could we teach the organization and learn ourselves, as leaders, what's required to get lots of people to move together and make change? And that's hard. But fortunately, we created some leaders along the way who know how to do it and do it well. And those same leaders are being useful in the context of AI. So, for example, Dave Reese, who has served as our chief technology officer for the past couple of years, was one of the instrumental leaders in helping us make the changes that I referred to beginning in 2013, leading, in particular, changes that we needed to make in research development and at the intersection of research development with process development and business development and so forth. So leaders like Dave cut some important teeth on change management many years before this AI moment arrived and can leverage that skill now.

ANDREW NG: What lessons would you have to share with other CEOs, anything nonobvious, anything surprising, nonintuitive?

BOB BRADWAY: Well, I don't know about nonobvious, but I think if you're not leading it from the top, I think it's easy for people to ignore it. I think if the CEO is enthusiastic about AI, after a while, people will start to figure out whether they're enthusiastic, too, or whether they want to move on and work elsewhere. But I think at Amgen, for better or worse, people know that the senior leadership team believes that the suite of tools that fall under the umbrella term artificial intelligence are a big deal and are changing the way we compete and the way we operate and the way we run the business.

ANDREW NG: Two things I thought were remarkable: I remember you got all of your senior executive team to take AI courses, and then, something that not many people know, you write code yourself.

BOB BRADWAY: That's a little bit of an overstatement, but I can get in front of the computer and do some useful work.

SARAH ELK: I've heard lots of stories about how you lead by example. What is a demo that you've built for your team, if you had an example, just to illustrate how you lead?

BOB BRADWAY: Well, I tend to use it in the form of self-service. So, for example, over the weekend, there was something that I was interested in, and it required—manually, it would have required a lot of work. It probably would have taken me hours and hours and hours to do. I could have asked somebody in the organization to do it for me, but it would have taken them hours and hours and hours. So instead, I sat down and worked it out pretty quickly and got what I wanted.

SARAH ELK: I don't think there are many CEOs illustrating their thinking through what they've built to show their teams. I think that's quite exceptional.

BOB BRADWAY: Well, it’s useful. I mean, I think there'll be more of that. I think the beauty of it is, the so-called vibe coding, it's able to take what I describe my desire to be and turn it into useful code. So the first time I showed somebody inside the organization who knew how to write code well, my first attempt at it was a little bit less than overwhelming or thrilled.

SARAH ELK: But certainly that has to have had a cascading effect on your team, as you think about, down through the middle management ranks to the enthusiasm Andrew's describing that you exude for AI in the direction of the company. That must fuel, as a very successful company, the case for change.

BOB BRADWAY: Sure. Yeah, I think those who, like me, believe that this is a tool that can help us do our jobs better, see that enthusiasm, and it probably helps amplify for them their confidence to pick up a new tool and try it. I get a kick out of receiving input from colleagues, day and night, weekends, ways in which they're using agentic tools now in the business. And there's some pretty ambitious, impressive things underway from people that have never coded before. So I think it's, again, proof that we're in an era where we can leverage new technologies and bring together processes at a scale and speed that we never would have imagined before.

I know we've got to be careful. I listened to Andrew quite carefully in his courses on not getting ahead of ourselves about what agentic AI can do. It will be a journey, but

what we believe and what I've said in our organization is that 2026 is the year we all need to figure out, What is agentic AI? What does it mean? How are we going to be using it? How are people going to be using it in business? And over the next couple of years, I think we will see wide-scale adoption and use of genetic processes in our business.

ANDREW NG: I think the fact that you code—I find a lot of executives still have a slightly fuzzier understanding of AI, and this translates into fuzzy business decision-making. But when you personally are hands-on-keyboard, seeing exactly what AI can and cannot do, it drives really much more precise decision-making.

BOB BRADWAY: Yeah, I think I have benefited from that. But again, Andrew, I wouldn't call myself a coder. So be careful. You're going to get me in trouble with real coders.

SARAH ELK: I think that's an important insight, though, Bob. One of the challenges we see at Bain is a lot of leaders and CEOs are talking about AI. But when you look at where their time goes, they aren't actually prioritizing AI, whether they're delegating to [sic] that to somebody on their team—I don't know if you're seeing the same thing from an AI Aspire standpoint, Andrew. But it seems like there could be a different way to lead and probably spending more hands-on time on [the] keyboard to understand what the tools can and can't do. I guess, reflecting on that, I'd be curious how your time spent with AI has changed your leadership approach or leadership style.

BOB BRADWAY: Yeah, well, that's interesting. So there are some things that we've just made available to everyone. So, for example, the Microsoft Copilot and OpenAI chat-related tools we made available to all of our staff. So there are some things—and then we did provide some training so that people could leverage that early on the journey. But we also made it clear that this is an exercise in self-learning. We're living in an amazing moment in time for people who are autodidacts and curious and want to learn. It's changing so fast. You have to learn as the new developments occur. And so some set[s] of people, I think, have been pretty quickly adapting or adopting those tools and learning how to leverage them. Some people are struggling with it. And we'll see where that winds up. But I think what we've encouraged everybody is to recognize it is going to be hard to compete externally, and therefore, it's going to be hard to succeed internally if you're not aware of the tools that are available to help you do that.

SARAH ELK: Bob, I love your description of learning and sort of the first wave of this and broad access of tools and what I'll affectionately describe as your “go forth and tinker” message to your employees.

BOB BRADWAY: Yeah, that's right, yeah.

SARAH ELK: How are you thinking about agentic?

BOB BRADWAY: Well, I think, because the problem is it is going to present challenges, and we are going to have to do it in a more coordinated way than what we have had to do with the large language models. Large language models, people can do quite a lot. We have obviously—we're ring-fenced, so we dealt with the data leakage issue right up front. And we feel pretty confident that we have that in a position where we can have people using it without intellectual property or other risk to the business. But when it comes to agentic, I think that's going to require a whole other level of security and coordinated engineering and so forth. So we have people that can do simple things, like what I described I did over the weekend. And we have people doing things that are a little bit more complex than that. But the large-process, large-scale reorganizations of things that we do, I think, will require hardcore technology supervision still.

ANDREW NG: And it sounds like rather than taking existing processes of 20 or 50 or 100 steps or whatever and automating a few of them, you are seeing successes already with rethinking, What are the steps? And if you find a lot of brainstorming exercises, possible innovation ends up with point solutions that fits [sic] in existing process, which is good. That's very valuable. By rethinking the process, it's a different role.

BOB BRADWAY: That's right. No, the rethinking the process is the big moment, the big opportunity. And, particularly, think about something like drug development, where 10 to 15 years, [the] average molecule costs $2.5 billion. You fail 90% of the time. So there are huge amounts of activity in there, which, if you were starting with a blank slate, you might think about very differently. And so that's what we and others are going to have to stare into.

SARAH ELK: Bob, how do you think about human capital in that context? I feel like there's a lot of flippant arguments in the media around job loss, et cetera. And I think part of what that misses is the knowledge and context that these employees have inside of your company and their ability to lead through that transformation and then foster growth in new roles or other opportunities that come as we rethink over time how agentic impacts our organizations. And certainly that's not going to happen overnight, and there's plenty of time for that transition. I'm curious what your thoughts are on the human capital question.

BOB BRADWAY: Oh, yeah, there's no question that it's far more subtle than what we see in the media. I think we all understand that our data are valuable, that where we have proprietary data, there's value there. Our human capital is the same thing. We have decades of accumulated experience, particularly for us in the area of protein-related therapies, so large molecules. The hallways are full of people who have the experience of understanding if you make a substitution of one amino acid for another, there's a consequence there that you're going to regret down the road. Or there are little things that are picked up through the years that travel through the hallways. And we want to be very careful not to lose that.

SARAH ELK: Absolutely.

BOB BRADWAY: So there's no question that in a business like ours, the artisans still have a lot of value. Hopefully they have more time to use the essence of their skill and less time devoted to menial things that, frankly, can be automated or may not even be necessary in the first place.

So when you can free up a scientist and you can take a scientist who is otherwise spending hours requisitioning materials or summarizing data or filling in forms and do that for them agentically, that's magic. To free up their time to think about interesting things is far more rewarding than having to fight to create time in your day in order to be able to do the interesting experiments. So I'm not pessimistic about what this means for our staff in the long term.

I do think people will work differently. I do think, over time, the character of the people that are drawn to our business may change. But I do believe it will skew heavily in the direction of people that are comfortable with biology and with technology.

ANDREW NG: And how do you bring all these people with you? So according to many surveys, America broadly does not like and does not trust AI, which I think is a very unfortunate state of affairs. But when people, including potentially employees, have all sorts of AI worries . . .

BOB BRADWAY: Well, I don't know that I have an answer to that. I think you're right. There is a lot of misinformation, and there's a lot of uncertainty. And humans hate the unknown, right? Humans don't like to be kept in the dark. And so the more we can share transparently what we're doing, I think the better. But I don't have the answer to that, although, as you can see, I'm an optimist, and I'm optimistic for the future of our company and for our people.

SARAH ELK: Maybe a final question: What haven't we asked you that you would want to share that you think would be beneficial to other leaders?

BOB BRADWAY: I read a lot and hear a lot about people expressing frustration that they're not earning a return from the investments that they're making. I'm a little bit puzzled by that. And again, I would say we're pretty early on this journey now. Maybe my bewilderment reflects the fact that, in our industry, again, we invest 10 or 15 years in a molecule and $2.5 billion before we learn whether it's successful or not. So we may have tolerance for the uncertainty in a way that shorter-life-cycle leaders struggle with.

But when I see the improvements and I see the things that can be unlocked with AI, I'm not at all worried about whether we're going to earn a return from the investments we're making overall. We are getting to a point where the cost of compute is noticeable. And again, if you're used to the idea that it takes years before I know whether the investments I'm making are wise or not, then maybe we're tolerating the cost of compute in a way that others are struggling with. Now, hopefully, that cost of compute for us translates into fewer mistakes, translates into months and even years of time saved, in which case that will add to the potential for our returns.

ANDREW NG: Intellectually, I know you're a biotech CEO, but a lot of times, you sound more like an AI person than a biotech CEO.

BOB BRADWAY: I'm in real trouble now. It's all those courses.

SARAH ELK: That's a compliment.

BOB BRADWAY: It's all those Andrew courses I've taken.

SARAH ELK: Started early.

ANDREW NG: That was great. Thank you so much, Bob.

BOB BRADWAY: Thank you, Andrew. I enjoyed it. Thank you, Sarah.

SARAH ELK: Thank you so much for being here, Bob.

BOB BRADWAY: Thank you.