IDEA Collider: Innovation & Asymmetric Learning in Pharma

Deborah Waterhouse, CEO ViiV Healthcare Deborah was appointed to GSK’s Corporate Executive Team on 8th January 2020. She became Chief Executive Officer of ViiV Healthcare on 1st April 2017. ViiV Healthcare is majority owned by GSK, with Pfizer Inc. and Shionogi Limited as shareholders. In this interview, we discuss cultures of innovation, innovation in the business model, purpose-driven innovation and more Deborah joined GSK in 1996 and was most recently the Senior Vice President of Primary Care within the company’s US business, prior to which she led the US Vaccines business. She brings a wealth of experience to GSK having lived and worked in Europe, Asia Pacific and the USA, and a strong track record of performance in both specialty and primary care. Deborah led the HIV business in the UK before heading the HIV Centre of Excellence for Pharma Europe, and held international roles as General Manager of Australia and New Zealand and Senior Vice President for Central and Eastern Europe.

For more on JLABS, go to https://jlabs.jnjinnovation.com/JLABSNavigator#/As Global Head of Johnson & Johnson Innovation, JLABS (JLABS), Melinda Richter fosters the Johnson & Johnson Family of Companies external R&D engine and supports the innovation community by creating capital-efficient commercialization models that give early stage companies a big company advantage. By providing infrastructure, services, educational programs and networks in global hotspots, JLABS is the best place to start a company working in healthcare, with a specific emphasis on Johnson & Johnson’s sectors: consumer, medical device and pharmaceuticals.Prior to joining JLABS, Melinda was Founder and CEO of Prescience International, an award-winning firm dedicated to accelerating research to the patient. Melinda founded Prescience after she had a medical emergency that left her questioning the efficiency and efficacy of the healthcare system. With the tenacity and resolve of a patient looking for a better solution, she set out to create a better model, which now forms the basis for JLABS’ operational infrastructure. Prior to starting Prescience, Melinda held posts across a variety of functional areas with a global corporation, Nortel Networks, in locations such as Research Triangle Park, New York, Toronto, London, Hong Kong and Beijing before arriving in San Francisco. She also initiated, raised capital and secured large corporate deals for several companies in both the life science and technology space. She holds a Bachelor of Commerce from the University of Saskatchewan in Canada and a MBA from INSEAD in France. Melinda is an active board member and Treasurer of the California Life Sciences Association (CLSA). 

In an archive recording, from 2015, the author of one of the best pharma-related books of the past decade, Clifton Leaf, gives us an insight into The Truth In Small Doses, Why We're Losing the War on Cancer-and How to Win It. (In this case, archive means that we thought we had lost the file, and then found it in a place we didn't expect!) Clifton, now Editor In Chief at Fortune Magazine, remains one of the most interesting voices in healthcare

Links to the papers we discussDiagnosing the Decline of Pharmaceutical R&D efficiency (2012): https://www.nature.com/articles/nrd3681When Quality Beats Quantity: Decision Theory, Drug Discovery, and the Reproducibility Crisis (2016): https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0147215Pharmaceutical Evolution: Clinical Selection versus Intelligent Design: https://www.innogen.ac.uk/sites/default/files/2019-08/Innogen-Working-Paper-115.pdfJack can be contacted at jack.scannell@btinternet.comThen the books Jack refers to:Robert Gordon: The Rise and Fall of American GrowthJames Le Fanu: The Rise and Fall of Modern MedicineDino Buzzati: The Tartar Steppe

Discussion of the new Matt Ridley book, How Innovation Works, published in May 2020. http://www.mattridley.co.uk Matt Ridley's books have sold over a million copies, been translated into 31 languages and won several awards. His books include The Red Queen, The Origins of Virtue, Genome, Nature via Nurture, Francis Crick, The Rational Optimist and The Evolution of Everything. His TED talk "When Ideas Have Sex" has been viewed more than two million times. He writes a weekly column in The Times (London) and writes regularly for the Wall Street Journal. As Viscount Ridley, he was elected to the House of Lords in February 2013. He served on the science and technology select committee 2014-2017. With BA and DPhil degrees from Oxford University, Matt Ridley worked for the Economist for nine years as science editor, Washington correspondent and American editor, before becoming a self-employed writer and businessman.

Why Boston/ Cambridge and South San Francisco?Why not New York?Cultures of innovationThe new way, scaling start-upsCritical factors for biotech hubsEurope/ The UK and the rest of the worldWhere does innovation come from? How is it defined?

Interview with Vinayak K. Prasad, author of Malignant: How Bad Policy and Bad Evidence Harm People with Cancer An essential listen for anyone involved in cancer R&D. Vinay is one of the more interesting voices on Twitter (@VPrasadMDMPH), and the book presents a wonderful exploration of what's right with oncology R&D and what's wrong. The interview reveals how positively he sees the opportunity to refocus on what matters to oncologists, and more importantly, to patients. The book is published in late April 2020, so I'd recommend a pre-order to ensure you get it on the day of release. From the book's website (http://www.vinayakkprasad.com/books) Each week, people read about new and exciting cancer drugs. Some of these drugs are truly transformative, offering major improvements in how long patients live or how they feel—but what is often missing from the popular narrative is that, far too often, these new drugs have marginal or minimal benefits. Some are even harmful. In Malignant, hematologist-oncologist Dr. Vinayak K. Prasad writes about the many sobering examples of how patients are too often failed by cancer policy and by how oncology is practiced. Throughout this work, Prasad illuminates deceptive practices which: • promote novel cancer therapies long before credible data are available to support such treatment • exaggerate the potential benefits of new therapies, many of which cost thousands and in some cases hundreds of thousands of dollars Prasad then critiques the financial conflicts of interest that pervade the oncology field, the pharmaceutical industry, and the US Food and Drug administration. This is a book about how the actions of human beings—our policies, our standards of evidence, and our drug regulation—incentivize the pursuit of marginal or unproven therapies at lofty and unsustainable prices. Prasad takes us through how cancer trials are conducted, how drugs come to market, and how pricing decisions are made, asking how we can ensure that more cancer drugs deliver both greater benefit and a lower price. Ultimately, Prasad says, • more cancer clinical trials should measure outcomes that actually matter to people with cancer; • patients on those trials should look more like actual global citizens; • we need drug regulators to raise, not perpetually lower, the bar for approval; and • we need unbiased patient advocates and experts. This well-written, opinionated, and engaging book explains what we can do differently to make serious and sustained progress against cancer—and how we can avoid repeating the policy and practice mistakes of the past.

IDEA Pharma: IDEA Collider Jackie Hunter  https://www.youtube.com/watch?v=K59qefVIsbA  Interviewer: So welcome to another series of IDEA Collider with leaders in innovation. I'm delighted to be joined by Jackie Hunter of Benevolent AI. First of all, would you like to tell us more about you and about Benevolent? Jackie Hunter: Yeah, I'm a board director of Benevolent AI. I've been with the company since 2016, where I came to set up the drop discovery and development arm of the business. It was founded in 2013 and the vision of the founder, Ken Mulvaney, was really to harness the power of all the information, that it's unfair to make better decisions, come up with improved targets, and reduce the cost and time of drug discovery and development. [inaudible] big and small pharma. You know, there are big issues, big pharma is slow to make decisions, because of just big organizations usually are, but has a lot of money. And the smaller companies could be nimble and quick but didn't have the resources and the cash of the larger companies. And we thought there was a real opportunity to be able to improve the way we did drug discovery. I mean, it costs 2.5 billion to bring the new product into it to the market, there's no way that all the products that come to the market will report that. And the reason it costs 2.5 billion, is because you're paying for the failure. And I think it's probably the only industry I know that has a 97% failure rate. Interviewer: Yeah. Interesting. I literally wrote a paper about that last week on the cost of failure and what you can do about it, so I'm going to dive into that, if that's ok. Jackie Hunter: Yeah. Interviewer: So even unpacking what you just said there, so there's an interesting thing that you said about better decisions. Decision making is typically poorly understood as a science in this industry. Can you tell me more about your thoughts? Jackie Hunter: Well my thoughts are - I remember [Ken Keaton] Titan from [Thoughs] came to talk to, I was at JSK, and said, the one thing the pharmaceutical industry can do is make better decisions. Now that sort of sounds trite and a bit obvious. But I think the issue is that there's so much information around, that unless you use this new technology, you can't harness that information. And so, you're making your decisions on a very small portion of the evidence. And so, I've sat in senior meetings, in pharmaceutical companies, the person who was pushing that particular idea, that particular product comes along, they have to be passionate about it, because you need to have a champion. But you also need to have the evidence before you, to make a judgment to say actually, if what they are telling me that some of the whole picture. And you can use this technology to go for example, dive into past clinical trials, pull out more information about why a drug failed, or whether there are potential particular subset of patients that have responded in ways that, you know, just haven't been able to do. So, I think that for me, it's making better decisions, because you got more evidence, you have believed that if you got a better evidence base, the decisions you make, are going to be more likely to be correct. Interviewer: And is there a sense of anonymizing or dehumanizing that process as well, because people tend to come with their biases and priors? Jackie Hunter: That’s a really good example. Absolutely. We came up with a set of hypothesis in glioblastoma, a very horrible, nasty disease, unfortunate, I have lost two friends to it. And one of those particular targets was a class of drugs that I had worked on, for pain and other indications in the past. And they haven't worked. Now my bias, even though I know zip the bag clear of glioblastoma, or quality in generally would have been too damn prioritize that target, just because I have a body experience with it in the past. And yet the machine had surfaced it as being potentially very interesting. And long behold the person who was working on that project, took the set of hypothesis to a collaborator, who independently had screened the drugs and guess what? One of the gnomes that worked targeted that class that I just have downplayed in my mind and brought my own bias to it. So, I do think this lack of bias, certainly, when surfacing and evaluating the bulk of the information is very powerful. Of course, bias comes from insight and experience. And so, then you can bring the insight and the experience on that much more unbiased set of hypotheses. Interviewer: And you mentioned that you think large organizations are slow, maybe old fashioned in some of the ways they think, and some smaller companies are too small to have that. Would you see what your vision is part of, is closing that gap between the large and capable, but the poor making decisions and the small potentially... Jackie Hunter: Yes, definitely. I think one of our advantages is that we're the only really company doing what we are doing, going all the way from really early hypothesis generation through to sort of phase two clinical trial. So, we've got that capability. And it's important because the people we have on the clinical side, can think about inputting into the types of target the work or the diseases we work on and the data we get can feedback in. And we can bring the translational medicine further into the pipeline, but also some of the earlier thinking to stay forward into selecting and structuring patients. So, you need to have kind of circular rather than a linear process. Interviewer: It feels like a process that people need to move towards that instead of away from because it's hard to argue with what you just said. Jackie Hunter: Yeah, I agree. That's one thing. Interviewer: Excellent. So, I wanted to get back to this definitions of innovation, because clearly you will be identified as in the inventive kind of world, and your own background in innovation gives you a lot of insight. How do you define innovation? Jackie Hunter: I think for me innovation is not just having an idea about doing something differently or making something new. It's about taking that idea. And then actually delivering on it actually applying it. It could be a social innovation, it could be something like an impact on micro finance, that have a huge impact societally. Or it could be a product service. Or it could be a business model innovation. One of the challenges, I think we found is initially when we had discussions with larger companies, is they were very used to just operating a service provider model, especially with tech companies. Actually, what we bought is because of the investment that we've put into developing our technology, we wanted to have a collaborative model where we could bring our disease, if you like, knowledge graph and our predictive chemistry, and the company could bring their expertise in that disease area and data. And you know, that's a really good collaboration, but it took some companies a bit of time to get their head around it. Interviewer: Why is that do you think? Is there a certain problem they have in decision making? Jackie Hunter: Well, I think it's just it's doing things a bit differently. And I think if you think about the farmer business models, they tend to be quite stereotyped. And it's actually really interesting. There's been a lot of talk over the last decade about open innovation in the pharmaceutical industry, but I would say it's only in the last five years that companies have really become to adopt some of those open innovation principles. There's been a lot of in-licensing into a company, but a very little spinning stuff out, and that's starting to change. So, innovation is not only about having control of your idea to make it happen. It's about saying, this is the best way that we could make our idea create value for us or society or patients. And it may be that it actually goes through partnership with somebody else. I mean, we have a huge knowledge graph with billions of facts. And we can use it to generate hypotheses for any disease, but we can only work on a few diseases ourselves. So, to optimize the value for the company, you know, we don't just sit on diseases that we are not going to work on, we try to actively partner. So, we saw recently, this week, we announced with Miracle that we're going to collaborate on [inaudible 00:09:54] on chronic kidney disease and idiopathic  pulmonary fibrosis. So, you know, that's a language we can put our technology out there, if you will work collaboratively, to be able to get value for patients, get value for big collaborative companies and for ourselves. Interviewer: So, there's some companies who are thinking about doing things differently, whilst doing the same thing, harder and bigger?  Jackie Hunter: Yeah.  Interviewer: And your distinction was between invention, you know, the kind of ideas and that kind of delivery of value, about the organization? Jackie Hunter: Yes. I mean, anybody can have an idea. But if you don't move it, that's not innovation. And actually, I've seen some small startups where the founders are so worried about losing control that they don't take in the investment that they need. And so, the whole thing just drags on and on and on. Whereas if they've just thought, actually, we're going to do this, to do it quickly, we need this amount of money, we're going to have to give up some control, the whole project would have moved ahead much more rapidly. Interviewer: So, do you see yourself here as being in that learning process as well? Because you're not fixed on your model either? Jackie Hunter: Yeah, we're learning all the time, because what we're doing is so new. And one of the reasons that it's very good [inaudible 00:11:15], because her background is scaling up in tech brings to the Benevolent group of companies, a great wealth of experience. So, it's a learning environment for us all. And the other thing that we would really have to learn about is how to work together. So internally, a data scientists views the world very differently from a biologists or a chemists. And, you know, the biologists and chemists have to focus down on what questions they really want answering. And the data scientists have to be able to deliver all that, working with actually quite messy data in many cases. Biology data is messy. Interviewer: Yes, apparently. And that was part of the belief of five years ago, was the farmer was missing a whole bunch of data scientists, and even interested in learning about how to play with those folks. So, you are kind of pushing that edge. Jackie Hunter: Yeah. And I would say it took a couple of years before that really embedded. And, you know, now we have cross functional teams, we take the best of both worlds, really, we do have startups and stand ups and you know, sprints and cycles, but overlaying with that to some of the project management expertise that you see in pharmaceutical companies, so that we can, you know, both look to the longer term, because programs are long. But also maximize the kind of waste working that agile startups. Interviewer: Yeah. Just to loop back to something you said earlier about your kind of cost of R&D and [March]. Is that part of your active vision to lower the cost of doing this? Interviewer: Yes. And we've already shown we can because we can get to - and it's not us actually, other companies have shown that you can reduce the time from a chemical starting point to a candidate down to somewhere between 12 to 14 months. Traditionally, it's usually about three years, sometimes it's two, that straight away, you know, you're probably making [temp set] molecules, you are cutting the time by third, two thirds. And so, what you're actually going to see is, even in the early phase, you can cut costs quite considerably. And then if you look at some of the wok in patient stratification, if you can pick the right patient, then you should be able to do smaller and more effective clinical trials. And we've already mold some of that on publicly available data, for example. So, I'm very confident that we will increase the success. But I know we can cut the cost of failure. Interviewer: And the paper I mentioned last week was on the failures and phase three, which is where we'd expect not to be losing drugs for advocacy, for example, you expect not to be losing them for, you know, those kind of variable things, like finance and strategic businesses. People are taking drugs in the face cream and stopping once they've bought the problem there is that they get the money back when they do launch another drug, and it becomes an expensive one. So, this kind of upward spiral as part of Jackie Hunter: Yeah. I mean, the statistics show that you're still seeing 50% of jobs in phase three, failing, and that's just not good.  Interviewer: Absolutely. This is what you would have to regard as predictable parameters. Jackie Hunter: Yeah, so one of the things I think, by better understand the patient population, and this is I think why [inaudible 00:14:52], they have such a wealth of patient data, they could look to see how you could decide a face to try to mimic or even phase three to mimic your real world prescribing practice, as opposed to you've got very narrow criteria in phase two, you expanded a bit in phase three, and then it goes out into the wild west, when you put it out into the hands of your general practitioner.  Interviewer: Yeah. And that is this kind of unspoken problem for the industry, which is we looked at was pure signal in the clinic, and then sent it to a very dirty world, where people are taking all kinds of things, or they are not taking them. Jackie Hunter: Exactly. I mean, you know, people are on combinations of drugs. And we give exactly the same dose to a 50 kilos lady, as we do to a 120-kilo bodybuilder, you know, like sumo wrestlers. So, there's a lot of variation in general practice awaiting in specialty is that it just not accounted for at the moment. But hopefully, if we can look at the data in a much higher automated way, we'll be able to pick up the patterns of the signals that might allow us to cut that phase three failure rate. Interviewer: How does it begin that feedback... because a lot of people don't want all that data once it goes into the real world, to come back. So, innovation itself, is that the kind of active process here? Do you actively manage innovation? Jackie Hunter: Well, I think any company should be actively managing innovation, but it actually matters much more in a company like ours where you know, you need to get to the next value inflection point. So, we've got to make sure that we are innovating. And, frankly, if you're not measuring it, then that is bad for your business, because Einstein said the surest way of going backwards is standing still. So, we've got to be constantly innovating. Now, if you're looking at something like pharmaceutical industry, we can measure that in the number of patents filed. But in the tech world, patenting is less common, because it's really you will know how, and then we can measure our innovation, by the number of quality hypotheses we generate, as a surrogate for progression into things. And also validate. So importantly, the ones that we validate in our disease models, either internally or externally. And one of the things that I think we've been very good at is also designing the system so that when we make changes, we can check whether or not we have actually improved the system. Now I don't know, I think that's something that is routinely done. But certainly, one of the things and one of the benefits, I think about having clinical studies within our company is right from the get-go. We're thinking about version control, regulatory submissions, so that we have to make sure that everything is really very well documented. Interviewer: So, decision quality is part of your metric, which is interesting, because you know, you look at kind of archaic approach with TPPs and everything else in the kind of total gates the [pharmaceutical] industry .There's actually very little reexamination of whether any of that is useful. And is that, like what you're saying here, is this embedded? Jackie Hunter: Absolutely. It's embedded in our company. And I think, I mean, I remember when I left for SmithKline [Beach], and we went to look at some upside-down antidepressant, and postpartum depression. And I said, why? And then we should come back and say, why? They are being excluded, there's no documentation. Again, somebody's V point, well, and another classic one actually. When [Blackstone] and SmithKline merged, JP Guardian got migraine. And he didn't believe that you could tell that you could - he didn't get symptoms alerting him to the fact that he was going to get a migrate. And so, he didn't believe in migraine prophylaxis. But migraine prophylaxis has a huge market. Again, it shows have one person's view that actually shaped the decisions and the organization.  Interviewer: And there's a lot of that, right, which is the people usually have one kind of views of disease to inform them. There are these strategies. Ok. So, you know, constant measurement, with internal references, external references as well? Jackie Hunter: Well, we teach you benchmarking. Yeah. So, we have our internal benchmarks, and external benchmarks as well, saying the publicly available data sets, etc. So, I think all of that is really, really important. And we can also go back to the source of the information as well. So, when we generate hypotheses, we can look to see where that information surfaced, where the highest - you can go back through them, sort of 400,000 pieces of information if we wanted to, but it's really the key pieces of information. So, if, for example, it turns out that looking at that paper or that patented, it had flaws in it, we can signal down the system, so that it gets down, as a source of information later. Interviewer: So, waiting is the hardest part. So, you can contrast large pharma, from your own experience with, you know, with the way things are today. So, what have you learned, over that sort of time period? Jackie Hunter: Well, I mean, clearly, the importance of evidence-based decision making, it's really important. And actually, funnily enough, you know, I think that the whole thing about turning down to the cause analysis is really important. The trouble with us a scientist is, when we say problem, we jump into solution mode. And actually, quite often, the solution we're proposing doesn't tackle the root cause of the problem. And if we think about diseases, you know, finding what is the critical node in a particular disease pathway is really essential. But the other thing that's really important is people, you are only as good as the people in your team. And you need to have diverse teams. And I don't just mean in terms of protective characteristics, I mean, in terms of thinking. Quite often, the person who is the most innovative is not necessarily the person who's going to be actually inducing it to practice or managing the people who are going to run the project. And you've got to find a way to balance that and make room for those people, I should say this is one guys used to have, called it a group discovery leprechaun sitting on his shoulder, because he's always somehow just managed to pull out the best targets and come up with some really interesting ideas. But he wasn't necessarily the best manager of people. So, he couldn't be rising up in the organization, to be a leader, that is more senior level. But you had to find a way of making that person feel valued and wanted and giving them the space to do so. Interviewer: So, do you think it's likely that you will, as you grow, you will end up looking like an organization, that we currently recognize what you think because of those things, that you look different? Jackie Hunter: I don't hope we would be different. I would hope that, you know, we will change and evolve, and our business will change and evolve as we scale up. And I don't think you can be prescriptive about what that's going to look like, technology is changing everything so fast, it's how we work. You know, even now, we have cross-functional teams, we've been doing a chemistry design program with somebody in New York or Antwerp or Cambridge and London, virtually with a machine? Yes, it's just a different way of working. Interviewer: Because it's a significant contrast between, say the tech careers grow, which is encouraging diversity, encouraging team-based collaboration, but not having you progress in your career to the point where you get to make all the decisions, that you've only been preparing for before. Jackie Hunter: Yes. For me, it's been a bit of a learning as well, sort of seeing how cross functional teams that self-organize, can work together so well, it's not a structure that I'm used to in the pharmaceutical industry, where it's much more kind of regimented and rigid, but it's good feeling comfortable sometimes. Interviewer: Well, that's one of the things that get kind of crossing of experienced, and the willingness to be wrong. So that doesn't occur very often. So, one of the things that matter, you mentioned culture, diversity, the ecosystem, decision making, what are the things that you're trying to model as you grow? Jackie Hunter: Well, obviously, to grow, you need to have funding. And so, you know, we've been very successful in raising money, but longer term, we want to be able to show more proof points as a technology. And that's really why I came to work at Benevolent, because I knew we had to do drug discovery differently. And it's important for me, that actually we do discover that sense for patients, we're working on diseases, [inaudible 00:25:09] really underserved. I mean, you know, they are, unless you're someone like Stephen Hawking with death sentence, within [inaudible 00:25:21] are frequently a year. So, I think this technology is the only way that we will be able to solve those problems. And I'm really looking forward to the day when Benevolent can say we've got a drug in the clinic for ANS, we've got a drug in the clinic for [inaudible 00:25:41].  Interviewer: You are not far away.  Jackie Hunter: What have we got? We've got a drug program in both those areas. So, I hope it's not that far away. Interviewer: Not too far away. And what drives you personally? What's you motivation on Monday morning? Jackie Hunter: My motivation is exactly that, is for this company to show we can do things differently and to produce effective medicines. And hopefully, through our collaborations with pharma partners, allow them to be more successful too. So, we can reach a greater range of diseases. And actually, I think it's this kind of thing that's going to save the industry really. Interviewer: It's hard to see that it hasn't future in industry. What are the barriers to this becoming more part of things quickly? Jackie Hunter: Well, I think there are - there are a couple of big barriers, I think the first is the availability of the correct talent, because data scientists are in high demand. And then having the environment that allows them to thrive and have the impact on the organization. And one of the things I love about [Henna], is it kind of puts the information in the hands of the scientists, whereas you've brought organizational setups in big companies where you have your chem informatics, your bio informatics, and they're almost gatekeepers to the data from to the scientist. And so, finding a way in which those expertise and skills can be utilized and still work, but at the same time, allow the move free flow and input the scientists onto those processes. That's what a lot of established organizational structures that I think could be an impediment to the adoption of this technology. And it's not just in companies, I think it's the same in universities as well, they organized a lot of very traditional lines, especially medical schools, I think we might have to think about Interviewer: Blurring those lines? Jackie Hunter: Yeah. Interviewer: Some people are going to not do well in that, in the new setup.  Jackie Hunter: Yeah, we desperately need the people who can straddle both the data science and the domain expertise, because they can facilitate the interaction and collaboration. Interviewer: Ok. And I realize that we're using our time really quickly here. But there's a few things I always ask everyone, which is books that you would recommend, that you've loved, that have made a big difference to you Jackie Hunter: The best book on innovation is Making Innovation Work by David Davila, Epstein and Shelton I think it is. I am impressed, super book. And they eventually sent me a copy of it while at GSK. And then I think Startup Nation is really interesting study of how Israel and its ecosystem, how it works and anything, and has been since successful innovation. Interviewer: What was the key takeaway from that? Jackie Hunter: Oh, I think the key takeaway from that actually - there are two. One was that because everybody went into the military, they were superbly well networked when they came out of the military, with not necessarily people from their own specialism. And secondly, that Israel has a lot of French money. And that allowed the flow of ideas to happen much more easily. Interviewer: Very interesting. And you in your next five years? Where's Benevolent? Where are you? Jackie Hunter: Well as I said, I hope to have a couple of drugs in the clinic for these serious diseases. And I'm hoping to move from making [side] to learning how to make capital, I think.  Interviewer: Ok. I was waiting for the metaphor. Ok. Fantastic. And one is easy, [inaudible 00:30:14] at home?  Jackie Hunter: I think so, there is a limit, it's only for personal consumption. Interviewer: Ok. And in terms of Benevolent, this is obviously a scalable situation. Is it all going to be Benevolent or is there going to be multiple collaboration kind of? Jackie Hunter: We already collaborate a lot with people, and I'm sure you will see announcements over the next 12 months about new units. As I said, there is a big collaboration. We are developing the time new relationships, because as I said earlier, we can use our technology in any disease, and finding creative ways of making it as available as possible. Working collaboratively with big companies, large and small. And another organizations will be really good, I think.  Interviewer: Ok. And this is one thing that you sign of, as a sort of lesson of innovation within our industry, what would you say to everyone? Jackie Hunter: Oh, that's a difficult one. The [wordless] in innovation, it has to be about the people, pick the right people and get the right mentality to not only have the ideas, but drive them forward.  Interviewer: Perfect. Thank you so much, Jackie. Jackie Hunter: My pleasure. It's been really great talking to you.  Interviewer: Time went so fast. Thank you.

0:00 Defining innovation01:00 Incremental innovation vs big changes01:45 On designing back from the unmet need, and introducing innovation(Interruption by a phone call �)02:54 Problem backwards vs solution forwards03:55 On the ‘guided random walk’ and adoption of agility/ serendipity (low validity environments in pharma)04:45 Prediction, hubris and certainty in process06:50 The stopping rule in drug development (07:30 interruption by a phone call �)07:50 Zombie projects08:00 The ‘Keytruda story’ as ‘the biggest poison in our industry’08:50 On ‘busters’ vs blockbusters09:20 On breadth of exploration in Discovery/ ‘pick the winners’/ ‘kill the losers’11:05 The misaligned incentives that lead to decisions to continue - the ‘legions of zombies’11:50 Spreading resource too broadly without good filters12:20 On the development of better filters, and too much resource in the ecosystem13:00 Does constraining resource lead to better outcomes?15:00 On ‘Follow the Science’16:30 On giving people the benefit of the doubt… Now what…?(17:40 One more phone interruption - sorry! � Leads to some audio spiking from here…)19:00 On a disease like Alzheimer’s - pinning a tail on a large donkey19:50 On ‘value signals’ in development20:45 On hubris in selection of models22:15 On allowing ‘the whole market’ to distort clinical development25:00 How important are measures of innovation? The role of the incentive structure25:30 On decision quality (and the distraction of ‘resources’)26:30 How does more data improve decision quality?27:00 On being successful or not being blamed for failure29:00 On the feedback loop and its utility in pharma30:20 What would a better incentive structure look like?31:00 What do we mean by failure?32:00 On the misattribution of error33:30 The way we misuse language, biases, and the impact of language on ‘failure’34:40 What are the most important lessons you’ve learned over time?34:55 On the power of dissociating asset from infrastucture, idea from process37:20 On the ‘organisation’ problem - separate nodes with a ‘project pilot’38:20 On the translation of success in one therapeutic area into another - ‘process structures are not transferable’39:15 On ‘retrenchment’ in major pharma, into fewer therapeutic areas40:50 On the ‘nonsense’ of product profiling too early42:30 ‘Instead of recognising you’re pinning the tail on a donkey, you think you’re aiming’42:50 What drives David Grainger?44:30 What is the role of tech and AI in early development?45:00 What problem is AI solving?45:30 Better predictions in a low validity environment46:30 What kind of ‘training data’ would we use?47:00 Unknown vs unknowable data48:30 On which books David would recommend50:30 What are David’s ambitions?52:30 Does 2019 look very different than 1999?

IDEA Pharma: IDEA Collider Mike Rea talks with Joseph Owens from Google X https://vimeo.com/269261531  Joseph Owens: Good to see you.  Mike Rea: Thanks for coming over. So, this is just for the benefit of everyone who has seen the previous live streams with me talking [in the camera]. This is hopefully the start of a new series of live streams and recordings where we're going to interview people that I find most interesting among the folks that I come across. Joseph Owens: Well I hope you find some interesting folks. Mike Rea: Well you're about as interesting as it gets. So, for those of you who don't know Joseph - Do you want to do a quick introduction? Joseph Owens: Yes. I'm Joseph Owens. I am a Neuroscientist at Google X, which is now actually just called X. It's the R&D factory for Alphabet, which is the parent company for Google. I am a Neuroscientist and a Management Consultant by training, by way of McKinsey and Northwestern. And right now, I'm on a team we call The Early Pipeline and we're looking for big ideas that would eventually be companies that would rival Google, basically. So, for Alphabet, we are de risking to bet on Google by creating other bets. Before that I was a Consultant at Google actually, in the Ad side. And then way back when, I mentioned my PhD was in neuroscience of sleep. So back when I was a Consultant, I was one of the experts on why the job was not very good for you. Mike Rea: And just for everyone who knows my background, Joe and I got over the McKinsey thing quite quickly. We've settled that conversation. So, one of the things that was most interesting actually in the conversation was really -- one of the things that pharmaceuticals struggle with is scaling innovation. And I know that you've had thoughts on that before you joined Google, and clearly since you've joined Google. It'll be interesting to hear whether you think pharma's going the wrong way, in terms of its approach, or do you think that there's a different approach possible? Joseph Owens: Well, I don't know if I can speak so well towards what pharma is doing specifically, but I can speak towards some of the things that happened in Google that are good and some of the things that I think we're improving. One of the things that Google was blessed with was, and I think it was really funny because we both knew this analogy, which was, it was a windless tree. And so, it had so much revenue for quite a long time that basically it made sense to plant as many flowers as you could. And so, by spreading bets as widely and sometimes even duplicative, you have the opportunity to let things bloom and let things figure out. As you have businesses that are more related to each other -- a great example is DoubleClick, which is programmatic advertising. The pipes for that are so complex. Having three different versions of that doesn't really work. And in some cases, we've made smart acquisitions -- DoubleClick was actually an acquisition -- and in others, we've built our own from the ground up. I think for innovation to be learned from Google, I would say it's knowing when to pull -- it's giving the engineering directors -- so Google is an engineering-led company and so the equivalent in pharma would be like the scientists or the people closest to it -- some leeway to make a call on whether they're going to let their [directs] just sort of experiment. From what I do know from you and others from pharma, that experimentation is probably not -- the degree of experimentation is probably radically different. And it is software, so you have to remember that some of that experimentation is a little bit cheaper from an opportunity [inaudible 03:51] point of view. But Google engineers are pretty well paid. Mike Rea: That metaphor of the windless tree, I think I wrote something about that like two or three years ago. It was based on the observation and the biased biome or biosphere or whatever the name is -- the trees grow to a certain height without wind but they fall over quickly because they need distress of the wind to grow. And I think that was an appropriate metaphor for companies and pharma’s that are doing very well despite much pressure from anywhere else. They haven't really needed to think about that innovation thing. But I wonder whether in pharma we spend too much time -- make it a quick call, "Right, well we've done the science already, now let's go to market with this thing." We stop experimenting at that point. So, I wonder whether that's a lesson to be learned.  Joseph Owens: So, Google has made a lot of changes around how it proceeds to launch, and specifically, how it measures that. Because of its size, it's pretty hard, just statistically, to figure out whether something's successful because it's got the Google brand with it. So, it's like, what is the adjustment factor for Google [to] launch this. And I think that's been something teams have been figuring out -- how to actually [re-weight] the metrics to see whether this would have been a success on its own. And there's some interesting programs in Google right now. There's a program where they're actually encouraging entrepreneurship within Google. So, people have great ideas and they might want to leave. They're allowing them to form their own teams and startup and pitch them to internal sort of VC-like group. Not necessarily with upside for the individual, except for just being able to pursue this thing that they see is really important. And so, it's a way to catch some of those folks that might otherwise leave and start other things. Because everybody has that entrepreneurial spirit. Mike Rea: We spoke with that a little bit [inaudible 05:54]. We covered that. It was one of the things that I thought about it over time. You look at [inaudible 06:00] with a lot of people who've left Genentech because they had to, to go and pursue their other interests. Interesting that you mention that there's no actual incentive for folks internally other than the progression of their careers. Joseph Owens: Yeah. I think it's interesting. If you look at -- I think it's Maslow's Hierarchy of Needs or whatever -- once you're paid a decent rate -- the monetary incentive, and you're comfortable -- If you're engaged with your work and you feel like it's doing something worthwhile, I think the monetary upside can be a bit overvalued in that scenario for a lot of people I've seen. There's great engineers who have families and are comfortable and would be great on startups, but they're not going to do that. And so, I think it actually taps into, maybe it's a slightly different slice of who would do that, but they're willing to do it because if they can keep their Google job and go for it. There's other people who want to strike it rich -- go [inaudible 07:04]. Mike Rea: But that spirit of innovation is encouraged within people that join Google? Joseph Owens: Oh yeah, definitely. So, Google still has, and I'm a product of this, the 20 Percent Program. I was a Strategy Consultant and I came into Google. I wanted to learn the main core business, how ads work. It's a lot more complicated than you think is.  Mike Rea: It's become a big issue recently. Joseph Owens: Google touches a lot of surfaces that you might not be aware of. Like how ads get populated across all of these different elements of the Internet, how they're sold, how they're traded in real time. All of these things. And like we talked about with the windless tree, they hire a lot of consultants, specifically from McKinsey, to come in and help them make those calls. While I was doing that, using my core McKinsey skillset, I started a 20 Percent Project. And so, for those that want to and have the inclination and [inaudible 08:01] and we formed a team, were running for six months or so, we had engineers, businesspeople, marketers -- our own little thing -- pitched it to all sorts of people. And that's actually what landed me at X. We had that drive because we saw an opportunity that Google should be working on this thing, and we can't talk about the thing. But we said we want to make sure that Google or Alphabet, actually, is working on this thing. And we pushed it until they took recognition of it.  Mike Rea: So just let me walk through the basics of the culture -- they enabled you to put together a bunch of people to pitch it to someone else. What's that look like? Joseph Owens: First off, to do a 20 Percent Project you need to be doing well in your role. But then the idea is -- It's based on good psychology, which is, you can't focus on one problem all the time. It's sometimes switching over to a different problem that actually helps you. And you can pull things over from that. Maybe you're going through a lull where you're bored with the implementation of your project and you're waiting for that next interesting part of your project, but you're still the right person to do that thing. Use some of that spare mental energy, connecting energy, whatever it is, on that 20 Percent piece. And instead of it being seen as lost time away from core work, it's more of an acknowledgement that you can only do core work very, very well for four or five hours a day. The idea that we can do more than that -- my background in psychology, you can't. You check your email, check the stocks, you check the news, go have coffee. And then when you look at your time across the day, whatever. But if you have something that was really driving you on the side, and you can keep up that -- The other thing is momentum. You're keeping up the momentum with the one thing that carries over the first.  Mike Rea: So, the 20 percent time isn't like Friday, it's spread across.  Joseph Owens: It's spread across, yeah. I don't think it would be effective if it was like everybody takes one day and puts on a different hat. There might be teams that do that. The other 20 percent thing I've done in my time is, I teach a mindfulness course. And that's actually a two-and-a-half-day course called Search Inside Yourself. And there's an org that runs that, a not for profit that now runs that. And we teach it to Googlers. And so, once a quarter I go and do that. So that sort of is a different day. Now that I've turned my 20 percent job into my full-time job, I have a different 20 percent job.  Mike Rea: So, people internally, they have the permission to spend that 20 percent time. Are they looking for each other? Joseph Owens: Oh yes. There's so many ways in which people find people. I just happened to be really crazy interested in this one topic. And someone introduced me to somebody else who's interested in that, and then found one more person, and then steamrolled from there. I said, "Google has to be doing this." And we just pushed it.  Mike Rea: And then you mentioned X as a special place. What's the special sauce about X that's different from Google itself? Joseph Owens: So, X is meant to build new companies. There is [triad] of criteria. One is that it would affect enough people. So, we think of a billion people, which means it needs to not be just U.S. So, it can't be just a U.S. business. Mike Rea: Basic rule of thumb. Joseph Owens: Basic rule of thumb. And it needs to be some sort of radical advancement of technology that has some real breakthrough way of solving a problem. And so, that's the three criteria, and you put X marks the spot in the middle. And it's always in the eye of the beholder, obviously, how breakthrough something is. But it has to be for good and it has to be a self-sustaining business. So, it's not [inaudible 12:07] Mike Rea: So, the really interesting thing about the business side is that those rules of thumb are not market size. The problem [inaudible 12:17] and the benefit, those are interesting rules of thumb.  Joseph Owens: Yeah. And all of the things that happen at X will touch regulation because they're [inaudible 12:28] business model plays, they aren't regulatory plays. We have a rule, you can't break the laws of physics but you might, for a little while, break the laws of man maybe, at least as they currently exist. So, for example, we have a project that literally launched drones into the air, called Wing, in New Zealand. And because the laws of man there were a little more friendly towards flying things, that was a good place to literally launch. And so, you have to figure those things out. Same thing with [inaudible 13:05] which is the inertial project at X, which was driverless cars -- figuring out how to get those safely on the road and get enough miles driven to train the AI. Obviously had [inaudible 13:17]. Mike Rea: Someone was telling me, actually yesterday, internal [betting] that happens at Google. Is there a market internally on which projects are going to succeed and which ones aren't? Joseph Owens: I don't know of one. But I've only been there for three years. I've been at Alphabet for three years, about a year and a little at Google and a year and a little at X. Mike Rea: Again, someone from pharma had heard this and felt, "This is an interesting way to see which projects are likely to succeed and which ones aren't." Because internally there's a culture of -- you know stuff internally that maybe senior management don't know about.  Joseph Owens: Yeah. I would be interested in that. There's a company called Steam; they do video for gaming. So, it's a very engineering -ed company somewhat. It's much smaller than Google. I think what they do are sort of a platform for online games, but then they also I think, sort of video. Could get that wrong. But an engineer friend of mine told me that they vote with their feet. So literally, their desks are attached to their chairs and people just move their desks together to work together on whatever the project is. I like that model. You can see what's working and what isn't, based on where people are moving. But the betting on things, we have a different version of that at X which is, before we kick off and get really running at speed on a project or even an idea in our early pipeline, we create kill criteria. And so, these kill criteria are what would be convincing reasons to stop working on this. Because the most valuable thing is our time. And those are easier to make before you spend a lot of time on a project because you're not as invested. You haven't hired as many people and all of these things. And you try to make them as objective as possible. And the way we do it is we just [sense] test with other people. "Is this significantly better than what exists?" or "Will this out compete the current thing on the market?" And that allows you as you get further down, if you realize you're not meeting that -- and you choose when you're going to check in with your ill criteria beforehand. So, it's like good statistics, it's a priority bet. And that allows for a more objective decision later on down the road. So, it's a way to manage your bets. I guess.  Mike Rea: And then one of the things I was really keen to recover was you mentioned the "Thank god its Thursday" and I described to you this  environment where in pharma that we spent so much time moving towards this six-monthly review with senior management of very polished, carefully curated slides that they're allowed to see. Can you describe a little bit more about this? Joseph Owens: Yeah. So, speaking completely for myself, and I think it's well known that this exists out in the world, but the company is -- Steven Levitt, the guy from Freakonomics wrote about this in the early days of Google. Larry and Sergei and others of the founding team decided to have a meeting every Thursday with the company. And I think the first meetings were around a ping pong table, which is also like their boardroom. And that tradition has carried on. That was on Fridays. As the company grew to have enough [inaudible 16:47] components they moved "Thank god it’s Friday" to Thursday. And crazily enough, Larry, Sergei, now Sundar, Susan Wojcicki -- all these folks get up there and talk about the state of the company, weekly. Mike Rea: Every week. Joseph Owens: Weekly. And it's kind of funny because Sergei also does a lot of things at X, and so he is often out of breath from making the one mile, mile and a half, from X to doing the same thing at X -- going over to main campus. And I think I said this to you. That's really good, but if you're a product manager, your product's coming up this week. You're going up in front of the CEO and chairman of the board and whatever, and they'll tell you what they think. I think that level of transparency is something Google obviously has struggled with this last year, because of the leaks. And I won't talk about that. But maintaining that transparency, it's amazing. I came into the company, my first day and they give you a computer and you're on the Intranet and you're like, "I can see this?" In any other company I wouldn't be allowed to see that. And that trust in a first day Googler -- Well, maybe I'd go look at that, I'm like, "Wow!" In my last business, we were doing that a different way. "Maybe I should let that person know," And I often do. When I get launch notices from people and they haven't -- from PMs and they'd go out to all of Google -- and I see something that I have a point of view on, I'll let them know. I'll just reply to that launch notice. Not to everyone, but to the PM and say, "Hey, I noticed that you guys did something here." And I think there's a lot of people that do that. And it's not liked a trolling sort of way. It's like there's something I really care about, that maybe you should know about it. And they might ignore you. They might not. But sometimes you get really long responses. They're like, "Oh I'm so glad you pointed out that. I was really struggling with how to weight that decision. And I'd love to have coffee." whatever exactly.  Mike Rea: And that was what struck me about that idea of Larry and Sergei and their [comfort] to do it. I'm the type to do it as well. I think we spoke about the [inaudible 19:13] book about the beginnings of Pixar and pulsing and the way that -- Pulsing sounds nice and gentle but sounds like there it's also not. You do get your animations ripped apart by everyone -- the magazine, then Pixar. That's not a destructive thing but it's a constructive, enabling, empowering way to -- Joseph Owens: As an employee, you can get an answer. If there's an issue that you believe is important enough, you can stand up at the mic and ask the heads of the company, from the beginning. You might face social feedback on that. I've never heard of anything of someone's manager getting mad at them for saying something like that. I think I would have heard that if -- Someone would tell you, "Hey, don't get up to the mic." And then they take internet questions from around company. And then they take my questions and they alternate. Mike Rea: Okay. I've spoken to a few people in pharma about whether they could imagine a pharma CEO standing there every day, every month, every week.  Joseph Owens: It goes with overall cultural transparency though. So, if they get at -- Mike Rea: Is it just transparency or is it something about the connection to the product or the ideas or the -- Joseph Owens: Yeah. I think you've got to be willing to go both ways. You have to defend your project, the people getting up there and talking about whatever they're launching or whatever or the bad news cycle on their project, whatever it is. That's one side of it. But then them asking like, "Hey, we did this launch and --" The thing that was in the way might have been you. Can you tell the audience why you made that decision? I don't know if I would go up and there do that, but people in the audience will. They'll say like, "Why did you make that decision?"  Mike Rea: Right. Okay. It's interesting because part of that same conversation that we had around whether they could imagine pharma CEOs doing that, people tend to go with the ones that they've worked for, that they could imagine being that. And actually, at the same time, those people also seemed to be the most empowering and best leaders -- the people talk I'm talking about in pharma -- people like Bob Levinson at Genentech have a [proof ability] but also deep -- you'd follow them anywhere with the science. So, I wondered whether that was a -- Joseph Owens: Well you have to remember that Larry and Sergei were grad students at Stanford, in information sciences. So, the transparency piece is there. The depth of engagement is there. These are future academics made into CEOs. And I think Larry's written about this bunch, about what that transition was like for him. What they're gifted with is all these great people who can teach them these things. And so, as they were going through -- I think I've seen this written in a number of books about Google -- One of the things I did before I applied to Google was, I read all the books about Google, at least the ones that are available. When I was at McKinsey what I did was a lot of reorgs. And so, I worked on helping organizations be more effective, because I liked the novelty of that problem every time. And reading about their early history and seeing the problems they faced in changing their worldview -- I was a PhD student. That is a very different [person] to being an executive. And so, the attitude there is you have journal club. And I got to say, TGIF is not that dissimilar from journal club. Journal club, you get up, you talk about some data, you beat it up. The goal is everybody gives their opinion. And if someone is silent then you're losing out on something useful. Because all the researchers in the room are going to have different takes on that data, or maybe they have statistics or genetics or whatever it is. It's not that dissimilar. It's bringing a little bit of that academic culture into corporate; I think. Mike Rea: There is something about pharma which I think we [could] change. We've got [inaudible 23:30] with people with project teams to say, "Well, what are all the things that could go wrong here?" Remarkably, it's the first time we've ever been asked, typically. And then they have this long list of things that could go wrong. They're not just about the product succeeding or failing on its basic parameters, but everything else that needs to be thought about it to get it there. If they're not being asked, those things will still happen and we're just going to ignore it until they do. Is there something that's enabled -- Let me describe it perfectly just from the beginning -- it always was that way. Joseph Owens: Yeah. If you're not working [in] your culture at the beginning then you're going to have whatever culture you get. The changing it though, is that what you're asking? Mike Rea: Well, I was wondering because one of the approaches that you have, clearly, is that you stop other cultures that are separate -- that you've created companies within Google that are different. Joseph Owens: Yeah. That was one of the things that kept me awake the most when I was working on the 20 Percent Project. I said, "Okay, we've got five people on this. Whatever we do right now that's the beginning of the whole proto-companies culture. And those are big weighty problems to think about. So how are you making decisions as a group? How are you choosing the direction? Are you going to be monolithic based on that one engineer or are you going to be consensus driven? Those decisions are made on those teams as they form, and a lot of big projects in Google started out that way. I'd say there's an example that teams can learn from, which is what's happened at Google and maybe what's happening on their own teams. And then when they make these new teams -- like the 20 percent ones for example, or the new bets at X, or the acquisitions -- there's a lot of freedom given to them to make those calls. I think it's an experiment that keeps happening over and over. Mike Rea: And we also discussed the accidental versus on purpose nature of the organization within Google. Which you can say about the way that it's organized and your observations on how controlled that is versus uncontrolled.  Joseph Owens: I think Google, last couple of years, they made the switch to be a holding company, I think quite wisely, while I was there. And the reformation that happened because of that has objectively been good for at least the short-term stock price. And starting to compare some of these projects against each other, and to make some of these calls. I think those things happen in cycles. And so, they're on that cycle of it. I think the culture probably still has this exploratory way. And so, if you go through one cycle of comparing things and choosing which ones of the best ones, you'll go through a growth phase. I think the inertia is clearly there for it to be a [inaudible 26:54] thing, not like a, "It was doing this and now it's doing that." Mike Rea: And there was an observation that you mentioned along the way about how much people want to work for Google, as opposed to somebody else. Joseph Owens: Yeah. So, Google maintains a pretty amazing reputation, at least as a place to work, in the world. I always saw it on lists with McKinsey and other consulting companies. And I feel like those are pretty different jobs, which is interesting. Mike Rea: Those rankings are usually done by [inaudible 27:24] Joseph Owens: Yeah. [inaudible 27:26] does rankings too. If you want to be a world class software engineer and you want to have some of the best tools at your disposal, and obviously the [inaudible 27:43] places to work, and smart people -- I think I've got a little bit off the question -- but the attractiveness to do that, I think it's quite high. What was the question again? Mike Rea: Well,  it's linked to that. Because we had the conversation around the pharmaceutical innovation index, on whether that leads to retention of people over time or the ability to recruit.  Joseph Owens: Yeah, I think there's everything at Google. So, there's enterprise businesses at Google, there's consumer businesses at Google. With the cloud bet, that's a very different business than the hardware bet. And one of the things Google has is a lot of ability to move around. I think that's what I was mentioning. And so, you might work two or three years in one role and then you might change ladders as I did. I went from a strategy consulting ladder -- I'm actually on the engineering ladder now. I don't know that that happens that frequently, but I definitely see people who might go from, say, a sales ladder to PM ladder or a program management to product management, or one type of engineering to another type of engineering, as they change their skill set. One of the things I do as a 20 Percent Project is, I work on what's called G to G which is Googler to Googler training. And we have loads of that. We have an engineering school. If you want to get ML training, there's weeks of training you can go take to start teaching yourself to be an ML engineer. There's Python 101. There's everything you can imagine if you want to spend that effort to train yourself. Now there are tools that are available for online training and any person training, you can literally change your career while you're at Google. And I've seen a lot of people do that. Mike Rea: And you can start your 20 Percent Project from any one of those ladders? You don't have to be on -- Joseph Owens: Yeah. You can be a salesperson and be the PM on your 20 Percent Project. Or like me, you can be a strategist on your normal ladder and you can be a scientist on your 20 Percent Project.  Mike Rea: Because one of the things that we haven't spoken to anyone yet about is about the rankings that lead to companies being perceived as more innovative, and whether that leads to the ability to attract and retain all the time. Joseph Owens: Google made a big bet on hiring ML engineers and that looks like it's paying off. Mike Rea: That's machine learning? Joseph Owens: Machine learning, yes. Sorry. Everything where you teach a computer to label things. That's all ML is. So, it's saying, "I give you a lot of data --" and then computers are very good at saying, "That is A and that is B." Assuming that you have good enough examples of A and B. That is all machine learning is. And Google made a big bet on that because they get a lot of -- it's an information technology. We're categorizing and making available the internet. And so, all that tagging, that's kind of the grass of machine learning. You have videos on YouTube that are labeled, and voice recognition and all these things. These were the data we were taking in. And so, not being [inaudible 30:39] ML was pretty obvious, you're not going to work. And then we happen to have servers. So, the other thing that's happened to make machine learning capable these days is something called deep learning. And that's only possible with the amount of server space, basically. The amount of little, literally, processors to throw at the problem to run these iterative models. And without that you can't do the kind of machine learning that we do. And so, we had both of those things and then we are where we are. Mike Rea: Which is interesting, the ability to understand and deconstruct at the same time, is important. And then clearly within the health space, I know we spoke a lot about the essential problem of hundred-year-old disease definition still being part of the fabric against which we're developing new drugs and new ideas.  Joseph Owens: I just read the outgoing NCI directors book on cancer, which was I think, Curing Cancer. And it's a labeling problem. Initially, when you go into labels, if the label's too general, well, the machine can't learn to label below that. At least, it can't learn on its own. There are machine learning techniques called clustering and unsupervised learning, and those can begin to do some of that. And we're not -- Google is not the only person doing this. Unsupervised learning without the gold standard labels with it, and clustering these things out and then saying, "Hey, this is a cluster. Let's go study that." And yeah, these were all what we were calling cancer. But [now in terms of] mass childhood lymphoma -- and this is sarcoidosis or something, whatever it is --  Mike Rea: But we're getting there, or we're starting to get towards that in cancer. I think probably because it's had a molecular target for such a long time and people have explored the genetic mutation mode and so forth within the tumors. My concern is that you get into areas like mental health, that we're still using broad categories like schizophrenia or major depression -- Joseph Owens: Now you're getting into my wheelhouse. I'm not going to begrudge the people who hammer these things out in committee to make the DSM. That is exceptionally hard, based on what we have. Because we don't have data. We have an empirical wisdom and we have research going in lots of different directions. Because we just don't know very much. We don't know very much about the brain. We have to admit it. We don't know very much. And I won't compare neuroscience to cancer or anything like that but taking one of these labels and deconstructing it. And then, we have loads of studies. I was doing genetics too, where we say, "Wait, why does one disease and another disease and another disease, all radically different labels, run of the same family? Are these normal curves and we're just picking out the ends of the curves? Are these bimodal curves under certain environments?" Picking that stuff out, I think computers will be very good. But we need more labeling data. So, the move right now -- and there's a lot of folks doing this -- is to get passive monitoring. One interview in a doctor's office is not enough. And if you can move towards passive monitoring and long-range continuous datasets -- And then folks are very wary of doing that. Mike Rea: It starts to feel healthier as a way of -- if you take something like schizophrenia, we know there's genetic components, we know that there's typically socioeconomic components as well, and then the family environment components. But then also, the interventions that we've had are pretty broad brush and pretty crude measures, in terms of their effect. And if you look at the construct that you're describing of an appetite, to want to break it down into micro subsets -- Joseph Owens: I mean, it's been variously called personalized medicine, lots of different titles for it. But subcategorizing disease for neurological -- I mean, all of this -- is the next wave, I think. And hopefully we'll destigmatize it.  Mike Rea: And then you put together, what I see from the outside, as a kind of long bet that someone like Google is prepared to take on. If you look at mapping the roads and self-driving cars, there was no business in that for a long time. There's a long-time bet. Parmer is in that same sort of 20 or 40-year cycle of discovery to development to revenue. Do you see any parallels or any differences between them? Joseph Owens: Oh yes. I think pharma is interesting because it starts, and at the early stage if you can kill something and save you a lot of money down the road -- because the last trials were the most expensive, theoretically. We're at a point which is very different, where we say, "Let's take in as much data because we don't know what it's going to lead to." And that's a very different decision to make at the beginning. So, to take the mapping example, "Let's go out and put cameras on backpacks and on cars and take in this data." I don't think they knew exactly what product that would turn into. But when I did my interview at Google they said, "What product at Google do you admire?" And I said, "Apps. It changes my life every day." Every day I set out with confidence. I can get to where I want to go. Every day I can take a request from somebody to go meet somewhere I've never been. And I [inaudible 36:16] take that request. "Hey, come meet Mike in this building you've never been to." Didn't bat an eyelash. Before maps -- get on the internet, look up where I can find it, find a map, whatever it is. That's a radically different decision multiple times a day. I think when they first sent the cars out -- there's no way they're foreseeing that everyone would be making different decisions. At least that has, the luxury of having Google Maps.  Mike Rea: Yeah. And that's one of the things that we talk a lot about. That idea is that exploration and value early. Because no one knew where the iPod would lead, in terms iPhones and apps and a bunch of other things. And certainly, if you try to do what pharma tends to do, which is to try to put like five decimal place forecasts around a Phase 1 asset, you're already limiting -- Joseph Owens:  False precision is -- Mike Rea: Yeah. And how does that get approached at Google? Do use those rules of thumb all the way through or is it something that someone else [inaudible 37:14] Joseph Owens: I don't think I've been there long enough to see things from genesis to multiwave implementation at scale. So, mine would be snapshots across different projects. The decisioning that happens to kick something off at Google is, I think, laxer. So, it's experimentation. If you're really excited about, "Well, I believe in you because I hired you." or "we hired you" and you're coming to me and saying, "I really [inaudible 37:50] this." Your energy is the voting factor for a little while. At the point that you start needing additional resources, then you start to make decisions. So, then you're making prioritization. Some of the similar, "Let's make a business case for these things." pops up and you say, "Here's a design brief, here's a PRD -- product requirements document -- and here's the case." At the beginning of the product requirement document it would be, "Here's why we need the thing. And here's what the thing has to look like." From the ones I've seen, they're not trying to get to decimal places of precision. And that's probably a little bit because of the luxury of resources. I think things are allowed to flourish a little bit.  Mike Rea:  That's an interesting word -- flourish and thrive. Because one of the things that pharma tries to do is to project ten years into the future and then bring it back to today with a huge degree of accuracy, despite us all knowing whether it's wrong every time we do it. And then the project's not allowed to flourish, despite all the evidence that most of the great drugs have got where they are through serendipity. They've pivoted at some point in their life cycle.  Joseph Owens: My example for that is -- basic research is -- Carrie Mullins goes and works on a project which, its title, would every time get defunded. He's going to go measure proteins and enzymes in hot geysers. No one cares. No one cares about those organisms. But then you get PCR. His project, if it came up for a vote, everyone would, "De-fund." And then he's driving down the highway and he thinks -- So we have some core principles; more data, better; diverse data. So, try not to just collect data in Silicon Valley, these sorts of things. Build for scale down the road. Because everything we want to do is going to serve Google's customers. And so, things probably move more slowly than they would at a startup because we're building for scale early. That could be a headwind of saying, "We'll go get a bigger dataset than maybe a startup would want to launch their thing." Or,  "Build your pipes a little bit stronger than a startup might." Mike Rea: So, there's some value to their being Google? Joseph Owens: Right. But then you're slower. I mean, those are tradeoffs.  Mike Rea: And probably the last question, because we could carry on for another few hours, would be just really around how you personally see health -- the intersections -- health and Googling -- the kind of technology that sits behind those. Joseph Owens: So, health for me,  for Joe Owens, II vacillate between extreme jaded [inaudible 40:54]-- like I said, we don't know anything about the brain. And we're at that moment of Newton where we can't see Einstein. When you're Newton, you can't see Einstein. [inaudible 41:05] physics, you can't move into relativity. And I feel like we're -- that moment on brain -- But in the same sentence I have to say, there's so much science sitting on the table that hasn't been brought to people's lives. We have doctors that have no time to do the thousand things that have been recommended for them to do. Well that just sounds like a platform and it's fixing the issues that happened. If that's an operations problem, that's a more McKinsey [hat] problem. So, if we can take all of these recommendations that we have for our health, and figure out a way to [massage out] the way we live to meet them, well then maybe we don't need -- we don't actually have to know all those things about the brain to actually operationalize some of that. So, I think science [hat] kind of terrified, consulting [hat], I feel like we just need to do some stuff.  Mike Rea: So, some systems thinking? Joseph Owens: Systems thinking, yeah. And design thinking. Changing the way, we live to be healthier. We know what to do, we just haven't done it.  Mike Rea: Yeah. Some of those things are [inaudible 42:09]. If you've got socio economic problems -- Joseph Owens: Google, it changes every day of my life with maps. It could change every day of my life with my health. And I think there's people at Google who are seeing that. They have been thinking about it.  Mike Rea: And actually, [inaudible 42:29] is really around the ethics with that as well. I'm aware that there's a kind of internal ethics, people looking at whether you can do harm as well as good. Joseph Owens: Oh, yeah. First do no harm, is the first rule of medicine. So, if an IT company wants to get into medicine, they've got to follow that. One of the things we have going right now is a lot of people thinking about machine learning fairness. Do you collect first data sets? Things that work on one population won't work on another, unless you figure out the little bits. And so again, that'll be a tax for speed but it'll be in the effort of fairness. And ultimately, scale. So, the ethics of that -- I would say you probably don't see people out in the world talking about this, but teams talk about fairness a whole lot. [inaudible 43:23] would be a great example. And the computers are sometimes really good at this. They have an example where there's a woman out in the street shooting a duck with a broom. There's no way you're going to train your data [to solve] that because you could even conceive that the car would ever see that? And so, figuring out ways to end all of the niche cases via getting really diverse data sets and really good transfer learning, that's -- Computers actually may have a better shot at scale -- oh, sorry, in fairness.  Mike Rea: Excellent. So, I think I've promised everyone that this would be phenomenal, and it has been, Joe. It's probably obvious, we could carry on for another couple of hours and debate this. And I hope you get a chance to [inaudible 44:16].  Joseph Owens: Yeah. That would be great. Mike Rea: Thank you. Joseph Owens: Of course. Have a good day. Mike Rea: Thank you very much.