In university, I went into engineering because when I was growing up, I saw my dad come home from work. He was an electrical engineer, and he would set out these beautiful pieces of paper with colored pencils and make gorgeous designs that would then turn into these microchips that would go on to power things like heart pacemakers. And I grew up with this curiosity of how could this young boy from Lebanon who was orphaned at the age of five end up designing technology that looked like art that would save millions of people's lives. And that's really how I ended up in engineering — this combination of art, math, and science that could benefit people.
That's Lila Ibrahim. She's now decades into an impressive career and currently leads a powerhouse AI lab. As chief operating officer at Google DeepMind, Lila remains focused on creating technology that benefits people, developing innovative projects at scale. You'll recognize some of DeepMind's household products like Google's AI assistant Gemini, but a lot of its work isn't consumer-facing. For example, they are the Nobel Prize-winning team behind AlphaFold, an AI tool that's revolutionizing scientific research. And today, Lila is peeling back the curtain on how the lab is working on the frontiers of AI in education and science. Plus, you might get one of the best everyday AI hacks I've heard in a while.
Hi Lila, thank you so much for joining us on Pioneers of AI.
Thank you. I'm excited to be here today.
Before we dig into Google DeepMind, I want to go to your story and your background. So we're both Arab-American and we have that in common. We're also kind of one of the few women still in a very male-dominated field. So I would love to hear your story — where did you grow up? How did you end up in tech?
It's been quite a journey. So I actually am the first from both sides of my family to be born in the US. My father is Lebanese, my mother is Palestinian. They met in the US. Both had come for their education and ended up staying and eventually meeting. I grew up in Lafayette, Indiana, home of Purdue University. So it was quite an extraordinary place to grow up, to be around a university as part of my educational upbringing. It was fantastic. But when I was in elementary, middle, and high school, I was like the foreigner in my high school class. And so we kind of stuck out. I ended up going to Purdue University to study electrical engineering. And when I went into my first internship, it was with a company no one had heard of called Intel. It was quite ironic that I had this anti-computer mentality and then went on to help design the Pentium microprocessor, which was the brains of the computer — which kind of just goes to show you how much life can change, and now of course in the field of AI.
Before you joined Google DeepMind, you were at Kleiner Perkins, and Kleiner was one of Affectiva's early investors. And Mary Meeker was at Kleiner, and she was on the board of Affectiva for a couple of years. And she was one of the very first early women in VC, and she paved the path for other women. And I just remember her in these board meetings — she and I were the only women around the table, and she held her own in a very strong and powerful way. How did that experience being in VC and also overlapping with Mary Meeker shape what you do today?
Yeah. Well, I think, as I just mentioned, growing up as kind of an outsider really taught me to get more comfortable with myself from a very young age. And also, I think through the first 18 years at Intel, I really gained confidence in my ability to see things from a slightly different perspective. And that included things like when I moved to Japan in the '90s to work on this technology no one had heard of — DVD and USB — and I was willing to take that risk because I saw a lot of opportunity where maybe traditionally other people didn't. And my move to Kleiner Perkins into venture capital came immediately after Intel. I was recruited, and I realized I had been this entrepreneur my entire career of new markets, new technologies. And what was it like to go work with entrepreneurs like you who have these big crazy ideas and make things happen? I was very fortunate — again, there weren't that many women — but to have the opportunity to work with the queen of the internet, Mary Meeker, and hear the types of questions and how she thought about things, especially with all the experience she brought into that role. But it wasn't just her; it was the entrepreneurs. Right at the end of the day, these were and are people who have these crazy ideas and these ambitions and these visions of what might actually be possible in the world. And what I appreciated about that time in venture capital was seeing such a broad range of entrepreneurs and their ideas. But what I really missed was building. And you know, I'm an operator at heart, and so I actually went into one of our portfolio companies, into Coursera, where I had a chance to take the experience of learning about what the VCs — my fellow partners — the questions they were asking, how the entrepreneurs were thinking about building their companies, and apply some of that into an early-stage startup. It was about 40 people when I started.
That company, Coursera, is now a leader in online learning with more than 170 million users and a market cap of 1.38 billion. I wanted to know how and why Lila made the jump from operating Coursera to Google DeepMind.
You know, I'm laughing because I was supposed to take a year off. So I had been at Coursera for a while, and I thought, you know, I'm approaching my 50s, and I really want to be thoughtful about this next chapter of my career. And I feel like I've had this extraordinary luck of being in the right roles at the right time and making cool things happen and having a big impact in the world. And so I was going to take a year off and really focus on my nonprofit. But John Doerr from my Kleiner Perkins days said, 'Lila, I just want you to meet this one entrepreneur.' He sat on the board of Alphabet. I eventually caved in and thought, 'I'll just do this one meeting as a favor to John.' Little did I realize here I would be, you know, seven and a half years later. But I really wasn't sure because I didn't have a background in machine learning or artificial intelligence, and I was also based in Silicon Valley and the role was in London. I ended up spending 50 hours interviewing for this role before I decided it was the right role and I was the right person for this role.
Wow, that is wild. You know, people often think about these long interview processes as a negative, but actually, if you turn it on its head, it's a way to ensure that this is the right next step and adventure for you. That's pretty cool. So, what do you do as COO?
My role has evolved quite a lot over the past seven and a half years, but I oversee all of the central operations of how we organize to deliver our research and our products into the market. But I also oversee all of our responsibility and frontier safety work and our external engagement work. Now, whether that's collaborating with policymakers around the future direction of AI, to our work around impact acceleration for social good. So it's quite a broad remit. What I like about a chief operating role, a COO role, is really partnering with the organization to help achieve the mission. If I say our mission is to build AI responsibly to benefit humanity, and I get to help shape how we do this. It's really been an exciting journey so far.
So we are living through this crazy technological shift, right? And some people are charging full steam ahead with AI, some others are a little bit more skeptical. What's your framework for thinking about how we should be building AI and deploying it at scale in the world?
So much has changed since we started on this journey. So DeepMind was founded in 2010 here in London, in 2014 acquired by Google. And I think there's something here where we are constantly thinking about how do we be responsible stewards of the technology and how we develop, how we govern, how we roll it out. So we need to be thoughtful and responsible at the same time, making sure that while we're managing risk, also investing in the opportunity. Because at the end of the day, that's I think why many of us are here. It's not the technology for technology's sake. It's actually really wanting to make a positive impact on how people work, how they live, how they learn, on our understanding of the universe around us, helping to address some of society's biggest challenges.
Yeah. So let's talk about some of these examples. You mentioned AlphaFold. So we have talked about AlphaFold before on the show, but for people who are not familiar, can you recap what that is about and how are you making it available to other researchers to accelerate scientific discoveries?
Yeah. So AlphaFold is our advanced AI system that helps predict the 3D structure of a protein. So if you think of things like Parkinson's, Alzheimer's, malaria, these are all protein-based diseases. And this is why proteins are important. If you can understand how a protein folds, you can understand when it misfolds, what might be wrong. And that helps us understand diseases. And it also helps us deal with things like how to deal with industrial waste. Why are some crops more resilient to disease than others? There's all sorts of interesting things that we can really do in this space. So we developed an AI model specifically to predict the 3D structure of a protein. And actually that's a very challenging problem to solve. Like before AI could figure it out, we did not have a way to actually do that at scale, right?
Right. At scale is key because it used to take, let's say, a PhD student about four to five years with the right equipment, with the right experience, to just do one protein prediction.
There are 250 million known proteins. So think about it as like a billion years of research right now consolidated all within the past five years or so. And we did something that I feel very proud of, which was as we were thinking about how to actually release this, we sought outside experts to help complement and make sure that we weren't getting stuck in our own insider bias of was this safe to release? How should we release it? And the result of that led to a partnership with the European Bioinformatics Institute to publish everything in a database available freely to scientists worldwide, all 200 million plus proteins. So one of the things we said was, if we're going to give this to the world, let's make sure there's equitable access. So we've done some really interesting things. One is with a neglected disease institute.
What's an example of a neglected disease?
Leishmaniasis is an example of a protein-based disease that has actually impacted more people than COVID. It's just over a longer period of time, and because of that, it hasn't gotten the type of pharmaceutical funding that it might otherwise have gotten. So that would be one example. We also took a look at some of the usage from the database and realized that the continent of Africa had low usage. So we worked with the community across the many countries to say, how do we actually do a train-the-trainer to help with onboarding researchers? So people who might not have otherwise even had the access to the lab equipment can now advance some of the work in the fields that they're dealing with. So I think what's really exciting about AlphaFold as an example, a couple of things. One is that we went from model to impact in a very short period of time, and we're still quite early in it. So last year we got the Nobel Prize for our work in this space, two of my colleagues. Then the other thing that I think is quite significant about this is really saying, where does the human ingenuity come in? Because we may have had the model, but this is not about any one company, not any one country, not any one field. So the fact that it's being used so broadly globally, over 2.9 million researchers are using this worldwide, I think it's quite extraordinary to think of what this might do to open up our understanding of so many fields around us.
So powerful, and it changes the way we do this kind of research. Now, you also do a lot of work around weather prediction and kind of mitigating the effects of climate change. Can you tell us some more about this kind of work?
One of the areas I'm really excited about is some of the work around weather prediction, because to me it's like completely chaotic and unpredictable. But you live in the UK, you live in London, so that's part of it. I've got my umbrella over here and my wellies there. Yeah, it's completely unpredictable. So, one of the things that's been really exciting is some of our work around WeatherNext, which is our 15-day state-of-the-art forecasting. Again, we bring the AI expertise and we work with the scientists to apply it into their field. So, we've worked with meteorologists worldwide to come up with a 15-day forecasting. We've also been working with the US Hurricane Center on hurricane prediction. How can we predict 50 different potential paths that a hurricane might take? And you can imagine what this means for emergency preparedness. And I think we're still in the early stages, and imagining what might be possible in a few years and how can we avoid some of the crises that we've seen in our lifetime.
I want to put both of our investor hats on because I liked what you said, right? Like Google DeepMind is developing the underlying AI that is going to unlock all these vertical applications of AI. With an investor hat on, where do you think the opportunity is for a startup versus kind of what DeepMind's doing? Like what makes a startup competitive, sustainable, have a moat, given that you guys are building all these underlying key enabling technologies?
I think a lot of the models now are available to developers to actually add in there. Like, what are the problems that need to be solved, understanding that local context, whether it's for the communities, for the application. And I think even when we did AlphaFold, we didn't expect it to be used in agriculture in the way that it's being used. And it actually took me back to some of the early, in the late '90s, early 2000s, when we were working on the computer and the internet buildout, and there were all of these questions of like, oh, our computers are going to displace the farmers or the teachers, when in fact what it did is it changed how people worked and it opened up opportunities that they hadn't even imagined. And I think we're at the early days of AI and we're going to see the creativity and the vision that entrepreneurs have of like solving problems sometimes that we didn't even realize were a problem because we're just so used to how things work.
