In this next episode of The Heidrick & Struggles Leadership Podcast, Heidrick & Struggles’ Carlos Gómez-Arnau speaks to Inaki Berenguer, managing partner at LifeX Ventures, a global fund investing at the intersection of AI and science, and the cofounder and former CEO of CoverWallet, an insurance tech start-up. Berenguer discusses the leadership challenges he’s encountered managing a fund focused on AI and science, highlighting the importance of competitive salaries and growth opportunities to retain top tech talent as well as the significant impact of AI on healthcare. He also explores the implications of AI on career longevity and productivity, and how leaders should balance scientific innovation with business scalability.

Below is a full transcript of the episode, which has been lightly edited for clarity. 

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Carlos Gómez-Arnau: Hello. I am Carlos Gómez-Arnau, principal in Heidrick & Struggles’ Madrid office and a member of the Healthcare & Life Sciences Practice. In today’s podcast, I’m excited to speak with Inaki Berenguer, managing partner at LifeX Ventures, a $100 million global fund investing at the intersection of AI and science. Inaki was also the cofounder and CEO of CoverWallet, a tech start-up reinventing insurance for businesses. After growing to 500 employees and $100 billion in revenue, CoverWallet was acquired by Aon in 2020. Inaki was also the cofounder and CEO of Pixable, a consumer internet start-up acquired in 2012 by SingTel, the second-largest telco in the world. He holds a master’s degree from MIT and a PhD from the University of Cambridge. Inaki, welcome. 

Inaki Berenguer: Thank you for the invitation. 

Carlos Gómez-Arnau: What leadership challenges have you encountered in leading a fund that operates at the intersection of AI and science, and how have you addressed them?

Inaki Berenguer: First, a little bit about what we are doing at LifeX Ventures. This is a $100 million fund, and we invest at the intersection of AI and science. And when we talk about science, it’s mainly life sciences, although we may do something in material science and other disciplines. But right now, we have invested in 35 global companies—two-thirds of them are in the US and one-third in Europe—and our thesis is that AI is accelerating innovation in pharma and biotech. So that means that with less you’re going to do more. 

The second thing that is happening, thanks to AI, is acceleration of commercialization of the best science. So that means that the average person is going to have access to very good healthcare, with the concept of AI bringing abundance to the best doctors, best nurses, best radiologists, and best surgeons.

Therefore, with all this happening, biology, health, and chemistry are becoming digital disciplines. If you think about the way you do science today, it’s all related to zeros and ones, because you are able to capture a lot of data, and you operate that data on computers. That could be genomics data, radiomics data, pathology data, and you even have cameras that can go inside of your body and capture what is happening. And once you are able to capture and digitize all that data, then you can simulate, do predictions, do experimentations. And yes, that’s why with less you are doing more. In life sciences, this means mainly tools to innovate faster. In healthcare, what you have is workflows that are automated, because today doctors don’t have the bandwidth to analyze all this data, so AI can help doctors and nurses make better decisions. 

So, when you were asking what challenges we have encountered, many of these companies were not used to having a lot of engineers and data scientists. They didn’t have a CTO [chief technology officer] or a chief data officer. And many of these companies, which are called biotech or healthtech companies for a reason, need to have more tech people. The well-established companies are making that transition, but they are not making that transition fast enough, which is good for the start-ups that we invest in, because the secret of success as an investor in a start-up is to make sure that that start-up captures a market share of the new opportunities before the large companies build innovation in-house. And because large companies are moving slowly at how quickly they are attracting that type of talent, that is good for the type of companies that we invest in. 

Carlos Gómez-Arnau: And what areas of specialized knowledge have you needed to develop or add to your leadership teams? 

Inaki Berenguer: We invest in technology companies, and these companies already have the tech background. Some of them are being started by engineers from tech companies. We invested, for example, in Betteromics in Silicon Valley. Betteromics, which is doing diagnostics like liquid biopsies, was started by the CTO of GRAIL, who spent 12 years at Google. So someone from Google, who is used to dealing with large amounts of data, goes on to this type of biotech or tech bio company. Actually, we are referred to as tech bio more than biotech, because tech is even more important than bio. So some of them come from the tech world, and they have to learn about biology and healthcare. And some of the companies are started by people from the industry, such as from biopharma health, who are frustrated with the status quo or have identified opportunities but realize that there is so much legacy that it will take forever to be reinvented inside of those companies. So they leave their companies and start a company in tech bio, in healthtech, and so on. But very quickly those people are the ones who need to complement themselves with engineers, data scientists, and these types of profiles. 

Carlos Gómez-Arnau: In your opinion, what are the key strategies for managing talent in a highly disruptive sector like AI, especially in an industry that requires highly specialized expertise, such as health science? 

Inaki Berenguer: You have to pay very well, and you have to retain talent, especially tech talent. Take Silicon Valley: the best engineers, the best data scientists, and the best experts are always looking for growth, and companies have to offer a platform in which the individual feels they’re going to grow very rapidly. But you also have to pay people very well. In certain industries, such as pharma where careers don’t move very quickly, people in their late 20s are not usually paid high salaries, versus in tech, where people in their late 20s and early 30s are making $500,000 to $1 million a year. Everyone is talking about OpenAI, where the average salary is over $1 million a year. So if you need to bring in the same quality of talent, how do you feel comfortable, in, say, a big pharma company with 50,000 employees, paying someone that is 30 years old these crazy salaries? You may offer an attractive and exciting opportunity professionally, but you also have to pay big salaries. 

Twenty years ago, if you were going to a start-up, you were going there knowing that you were going to work in something exciting. You were working there for the stock options, which had some upside, but usually salaries were very low compared to incumbents and large companies. Now that is not the case. Now you get to work on interesting things, you have the upside of the stock options, but you also get paid more than in the large companies or the incumbent. So you have to be ready to pay that type of salary; otherwise, you are not going to retain the talent. 

And you need to have that type of talent at every layer of the organization. Tech people, artificial intelligence experts want to go to companies in which they see in the senior management positions experts in their categories. They don’t want to report to people who don’t understand what these guys are doing. That happened about 10 or 15 years ago in banking, when banking was being reinvented with technology thanks to fintech companies. Large banks, whether it was the large investment banks like Goldman Sachs or Morgan Stanley, or commercial banks like Bank of America or Chase, or like in Europe with HSBC, and so on, needed to recruit not only software engineers but also tech executives that were in VP positions, senior VP positions, and C-level positions, because they needed to bring this type of talent across every layer of the organization. 

You have to do this in pharma as well. And the most successful biotech companies and pharma companies in the past 10 years, companies like Moderna, understood this very well, and they started bringing in this type of talent very early on, at every single layer of the organization.

So, why is that important? Again, going back to this analogy of what happened 20 years ago in certain industries, when traditional industries were bringing in software capabilities, they would refer to them as “the IT people.” And IT was kind of a negative way of thinking about this. These people actually did understand business, and they were not being treated as an important asset inside of the companies in which they were working. And now engineering, AI, and data are becoming so important that those individuals feel that they have a seat at the table, that they are considered as equals inside of the company. This is difficult, and I have even realized this myself as an investor, because there is still a little bit of classism in health and in bio, that if you haven’t studied a medical degree or you haven’t studied biology, it doesn’t matter whether you educated yourself with years of reading books and through self-education, still if you don’t have a doctoral degree, a medical degree, then they don’t take you seriously, and those people are going to get frustrated. So, you have to make sure that the engineers, the data scientists, and the AI experts are considered heroes inside of the company, in the same way that they are heroes now in banking or, 10 or15 years ago, in Google’s Meta, Apple, and so on. 

Carlos Gómez-Arnau: What capabilities do you see on the leadership teams of the companies you invest in, and how are you finding the right leaders for those companies?

Inaki Berenguer: They have to have the right combination of understanding technology trends, but they also have to be business savvy. And by the way, in some of these companies, the original idea comes from academia, but usually academics don’t have the skills that are transferable to build a product and scale the company, like attracting talent, attracting funding, attracting partners, and some companies have been very good at acknowledging that.

The best example is Moderna, which came from research being done at the lab by company cofounder Bob Langer, the famous professor from MIT. But they brought in a CEO for Moderna who was not an academic. He came from a diagnostics company, and then he started a biotech company. He was a very well-trained, young executive with an MBA from Harvard, and Bob Langer was not afraid of saying, “I’m giving you the keys to the kingdom,” and the rest of history. Bob Langer is a billionaire despite the fact that he owned only a very small percentage of the company, proving it’s better to own a small percentage of a large company than a large percentage of a small company. 

So, you have to bring in talent who knows how to scale the company. And that doesn’t mean that you are the best expert in the world or that you have a Nobel Prize in a discipline around what you are doing in biotech. So yes, the idea may come from a Nobel Prize winner, but who is putting all the pieces together and scaling the company is going to have different types of skills. 

Carlos Gómez-Arnau: How do you see the integration of AI and science transforming the healthcare industry as a whole, and what implications does this have on its talent? 

Inaki Berenguer: When we talk about healthcare at LifeX, we differentiate what is happening in life sciences and pharma and biotech, like the tools that are being used for doing discovery. This includes all the things that you do preclinical, like in silico experiments instead of in vivo and robotics in wet labs. So you don’t need people with a white coat in a lab running the experiments and looking at the microscope. You’re going to have a lot of robotics intersecting AI, and those experiments are going to be running 24/7, so you are going to be able to accelerate the number of experiments. 

So, we differentiate what is happening in life sciences, but we also differentiate what is happening in the clinical phase. Clinical trials are being accelerated thanks to AI and technology, but also how do you find CROs [contract research organizations] that are going to help you to run them more efficiently, with tools or project management to coordinate multiple patients participating in the clinical trials at multiple sites over a long period of time? Also all the tools that you’re going to be able to use once you get approved by the FDA, all the market access tools to negotiate pricing between pharma and the payers, all the clinical evidence that you need for that market access. All of that is being accelerated by AI.

So again, like I said at the beginning, with less you are going to do more. Before, to bring a drug to market, you needed to invest $1 billion from preclinical to clinical until you went to market. Now, with a fraction of that budget, you are going to come up with new molecules, new therapies, and new cell therapies. And while, yes, with less you do more, it also means that diseases that were not being addressed in the past, because it was too expensive to try to bring a drug to market for a small market opportunity, are now going to have new start-ups focusing on those corner diseases that, before, nobody was paying attention to, because you can now have a small team finding a new therapy. So this is in life sciences. 

We also differentiate what is going to happen in healthcare. And healthcare is about the payers, the providers, and the patients. You’re going to have a lot of tools going into that. The way we look at healthcare, in particular, is with a two-by-two metric. There are going to be tools for the providers to better cure people: more personalized medicine, better diagnostics, better anticipation of what is going to happen, more monitoring of patients. That is going to help cure people, but you are also going to have tools to make the system more efficient. 

When people think about healthcare, they think that the industry is so expensive because you have a lot of doctors and nurses, but the reality is that it is the result of back-office processes. If we think about the cost of running a hospital in the US, it’s about $1 billion a year, and people think that, of course, there is a lot of equipment, there are a lot of doctors, a lot of nurses. But actually, more than 50% of the people who work in a hospital system are not doctors or nurses but back-office people doing reauthorizations, customer support, payment reconciliations, and so on. 

Payment reconciliations, which is in a category called revenue cycle management, is trying to understand how much the patient has to pay as a copayment, trying to see whether and what is going to be covered, whether the doctors are in network or out of network, filing the claim to the payer, and so on. The payer analyzes this and comes back to the provider, which could be a clinic, a doctor, or a hospital, asking for additional documentation and more documents from the patient. With AI, all that back and forth is going to become much more efficient, and everything is going to be automated. Payment reconciliations is currently 15% of the cost of running a hospital. Imagine if you can save 15%, how many more doctor you can hire with that money. 

So, in the two-by-two matrix, one is the tools to cure people and the other is tools for the back office. And then we also differentiate tools based on access: tools for the patient versus tools for the providers, doctors, and nurses. And with the latter, we are seeing a lot of innovation. Today there is a scarcity of good doctors and good nurses. With AI, you are going to create abundance. Abundance means that one doctor is going to do the work of 10 or 20 doctors because of the number of good tools, or AI copilots. So, every doctor is going to do the work of 10 or 20 doctors—and with better quality. For example, as soon as a patient enters the door for a consultation, the doctor will have in front of them on their computer screen all the information about that patient, all their history. And not only their current blood test but their blood test from six months ago, twelve months ago, three years ago, five years ago; all the images from CT scans, MRIs; all the genomic data; and all the drugs that have been prescribed to that patient. AI gives insights for the doctor to provide a more personalized consultation to that particular patient, including when prescribing a new drug, knowing whether that drug is going to have a side effect, whether it is compatible with any allergies, or whether it will interact with other drugs the patient is taking. This is the whole concept of personalized medicine. So if you have migraines, out of the 30 different drugs that exist for migraines, AI can help determine which one is going to work better for you based on all the biomarkers that have been seen in your images, radiomics, genomics, proteomics, blood tests, and so on. 

So, abundance in good healthcare, good nurses, good monitoring. And everyone in the world is going to benefit from that, and, eventually, people are going to live longer and better. This is not only about living 120 years but about making sure that in the last 30 or 40 years of your life you have good vitality and good productive years. 

Carlos Gómez-Arnau: As you think about the mission of LifeX, which is precisely to extend the life of people and planet, how do you envision that affecting how people think about their careers? 

Inaki Berenguer: In general terms of people, not only people working in pharma, I think that we’re going to live much longer. And it’s not only about extending the years that you are alive but also about extending the productive years of your life. So, more professionals are going to work until they are 70, 75, 80, 85, 90. I’m not a sociologist or economist, so I can’t talk about what the implications for pensions and retirement might be, but the reality is that, for the knowledge economy, people are going to be able to do the best job in their lives when they are 70 or 80. 

And that also has some other implications. For example, if what you can do today is so different from what you could do 10 years ago, and if what you will be able to do in 10 years is going to be completely different from today, then you will have to keep reinventing yourself. You may study a new degree when you are 50, and you may change careers when you are 65. Historically, you would study something when you were 18 until you were 22 or 25 or, if you were doing an advanced degree, until you were 30. And based on that knowledge, with just some updates during your career, you were able to continue performing the same profession for the next 40 years of your life. Now, you are going to be reinventing yourself every 10 years because everything is changing so rapidly. I tell everyone that I know, whether they are in their 30s, 40s, or 50s, that they have to jump into what is happening with AI. And jumping into AI is not only becoming a software engineer. Many of the tools are going to be at your fingertips, and not only ChatGPT, which is more of a consumer product, but all the tools that you can use inside your organization that are low-code or no-code tools. So without knowing anything about software engineering, you are going to become 10 times more productive, and with those tools you are going to become a much better professional. 

Carlos Gómez-Arnau: And from a leadership perspective, how will this be affected?

Inaki Berenguer: Leaders are going to become tech leaders. This has happened in every industry during the past 20 years. Twenty years ago, technology companies were a subsector of the economy. You would have tech companies like Cisco, Hewlett-Packard, IBM, Google, or Intel, and then you would have other industries, such as logistics, banking, insurance, pharma, construction, and logistics. Now every company is a tech company. Whether it influences or impacts the products that you offer or it impacts how you run your organization—HR, finance, sales, business development, customer support—everything is impacted by technology. So you had to understand what you could do with technology, and that is what has happened during the past 20 years, how software and tools are going to make the company competitive. 

With AI, it’s going to be the same in every single industry. It has to start with the people from the top, at the board level, at the C-level, at the senior executive level, and then to the managers and the individual contributors. So, you need to bring this type of talent into the boardroom, but you also have to educate the senior executives, because otherwise people will continue talking about how AI is going to change the industry, but there is no depth with those comments. You have to go one or two levels down to what the implications are for their organization, and people have to be aware of this, because you have to know what you don’t know. In the knowledge economy, you have what you know, then you have what you know that you don’t know, and then what you don’t know that you don’t know. So it’s very important that you know what you don’t know,. even if you don’t know how to do the work itself but you know that there is something that you could do leveraging the technology and the tools.

So, AI is changing everything in life and the work environment, and it’s going to happen faster than ever. We are seeing that many companies that you would consider very traditional, incumbents, old school are already designing 10 or 15 pilots of AI across the whole organization, the core product and also the back-office function. 

Carlos Gómez-Arnau: Final question. Looking ahead, what do you think is next for the healthcare and life sciences industry, and what can leaders do today to be prepared for that?

Inaki Berenguer: What these companies look like is going to change dramatically. If you picture a biotech or a pharma company in your head today, you think of a biologist in a white coat, in a lab with a microscope with cell cultures, some Illumina machines, and a freezer running experiments, and then there are some mice there in order to run the experiments. I think that is going to change dramatically. The pharma company or the biotech company of the future is going to be a lot of people in front of computers running experiments at the speed of AI instead of the speed of biology. You run experiments on models of AI with billions of parameters, with models of a mouse, and it’s going to be very different and is going to happen much faster. And the company itself is going to look different. For example, when you enter a hospital, what you see there and your experience are going to be dramatically different in 10 years. And this is going to happen much faster than people think. 

Carlos Gómez-Arnau: Inaki, thank you for taking the time to speak with us today. 

Inaki Berenguer: Thank you, Carlos.

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About the interviewer

Carlos Gómez-Arnau (cgomezarnau@heidrick.com) is a principal in Heidrick & Struggles’ Madrid office and a member of the Healthcare & and Life Sciences Practice.