AI and the COVID-19 Vaccine: Moderna’s Dave Johnson
24:33 Share

Today we're airing an episode produced by our friends at the Modern CTO Podcast, who were kind enough to have me on recently as a guest. We talked about the rise of generative AI, what it means to be successful with technology, and some considerations for leaders to think about as they shepherd technology implementation efforts. Find the Modern CTO Podcast on Apple Podcast, Spotify, or wherever you get your podcasts. What role did artificial intelligence have in helping combat the coronavirus pandemic?

Find out today when we talk with an innovative company that used artificial intelligence to help solve the critical problems society faced in the last year. Welcome to Me, Myself, and AI, a podcast on artificial intelligence in business. Each episode, we introduce you to someone innovating with AI. I'm Sam Ransbotham, professor of information systems at Boston College. I'm also the guest editor for the AI and business strategy Big Idea program at MIT Sloan Management Review.

And I'm Sherven Kodabande, senior partner with BCG, and I co-lead BCG's AI practice in North America. And together, MIT SMR and BCG have been researching AI for five years, interviewing hundreds of practitioners and surveying thousands of companies on what it takes to build and to deploy and scale AI capabilities across the organization and really transform the way organizations operate.

Today, we're talking with Dave Johnson, Chief Data and an Artificial Intelligence Officer at Moderna. Dave, thanks for joining us. Welcome. Thanks, guys, for having me. Can you describe your current role at Moderna?

I'm Chief Data and AI Officer at Moderna. In my role, I'm responsible for all of our enterprise data functions, from data engineering to data science integration. And I also manage the software engineering team building unique custom applications to curate and create new data sets, but also to then take those AI models that are created and build them into processes. So it's kind of end-to-end, everything to actually deploy an AI model, to build, deploy, and put an AI model into production.

How did you end up in that role? I know you have physics in your background. I didn't hear any physics in what you just said. Yeah, no, it's a good point. So I have my PhD in what's called information physics, which is a field closely related to data science, actually. It's about the foundations of Bayesian statistics and information theory, a lot of what is involved in data science.

My particular research was in applying that to a framework that derives quantum mechanics from the rules of information theory. So that part, you're right, is not particularly relevant to my day-to-day job. But the information theory part and the Bayesian stats is completely on target for what I do.

In addition to that, I spent many years doing independent consulting and kind of a software engineering data science capacity. And when I finished my PhD, you know, I realized academia wasn't really for me. I wanted to do applications. And I ended up with a consulting firm doing work for large pharmaceutical companies. So I spent a number of years doing that. And it turned out to be a real great marriage of my skill sets, you know, understanding of science, understanding of data, understanding of, you know, software engineering.

And so I did one project in particular for a number of years in research at a pharmaceutical company around capturing data in a structured, useful way in the preclinical space in order to feed into kind of advanced data and advanced models. So very much what I'm doing today. And about seven years ago, I moved over to Moderna. And at the time we were, you know, preclinical stage company and the big challenge we had was producing enough small scale mRNA to run our experiments, right?

And what we're really trying to do is accelerate the pace of research so that we can get as many drugs in the clinic as quickly as possible. And one of the big bottlenecks was having this mRNA for the scientists to run tests in. And so what we did is we put in place a ton of robotic automation, put in place a lot of digital systems and process automation and AI algorithms as well. And what went from maybe like a

you know, about 30 mRNAs manually produced in a given month to a capacity of about 1000 in a month period. So without, you know, significantly more resources, and much better consistency and quality and so on. So then I just kind of from there grew with the company and grew into this role that we have now where I'm applying those same ideas to the broader enterprise.

That's great, Dave. Can you comment a bit on the spectrum of use cases that AI is being applied to here and is really making a difference?

For us, what we've seen a lot of is in the research space, and particularly at Moderna, that's been because that's where we digitized early. We see that putting in digital systems and processes to actually capture, you know, homogenous good data that can feed into that is obviously a really important first step. But it also lays the foundation of processes that are then amenable to these greater degrees of automation. So that's where we've seen a lot of that value is, you

You know, in this preclinical production, we have kind of high throughput. We have lots of data. We're able to start automating those steps and judgments that were previously done by humans. So one example is our mRNA sequence design. We're coding for some protein, which is an amino acid sequence, but there's a huge degeneracy of potential nucleotide sequences that could code for that. And so starting from an amino acid sequence, you have to figure out what's the ideal way to get there, right?

And so what we have is algorithms that can do that translation in an optimal way. And then we have algorithms that can take one and then optimize it even further to make it better for production or to avoid things that we know are bad for this mRNA in production or for expression. And so we can integrate those into these live systems that we have so that scientists just press a button and the work is done for them. And they don't know what's going on behind the scenes, but then poof, out comes this better sequence for them.

And then we've seen it with quality control steps as well. We're also doing some work right now with our clinical partners in the clinical operations space in terms of like optimal trial planning. We're doing some work right now around our call center planning, right? Now that we're rolling our vaccine out across the whole world.

More and more phone calls are coming in. And as we look to launching in new countries, we have to start planning our resources for that. So we're looking at machine learning models to help predict the forecast of these calls so we can then staff up appropriately. So we do see it across a variety of different areas. You mentioned pressing the button. Scientists press the button and some trials happen. So what do these scientists think? I mean, you've suddenly taken away something that used to be something that they did and you're having AI do it.

What's the reaction? Are they thrilled? Are they despondent? Somewhere in between? I would say closer to the thrilled side. Usually how it works, we're a company that

believes in giving people a lot of responsibility and people work really hard. And what that leads to is people doing a lot of work, right? And so what often happens is folks will come to us and say, look, I'm doing this activity over and over. I would really love some help to automate this process. And so in that case, they're thrilled, right? They don't want to be looking at some screen of data over and over and over again. They want to be doing something insightful and creative. And so that's where we really partner with them and take off that component of what they do.

Dave, I want to build on that because I think you're putting your finger on something quite interesting. And in addition to the financial impact that many get from AI, productivity, efficiency, and all of that, you talked about some of those, Dave. There is an impact in overall organizational culture and teams being more efficient.

collaborative, higher morale, happier, more confident, et cetera. Are those some of the things that you guys are seeing as well?

Yeah, for sure. I think one of the sure signs of that is we get a lot of repeat customers. You know, if we do some particular algorithm for somebody, that person comes back with the next one or, you know, that their team comes back time and time again. We don't think about AI in the context of replacing humans. Like we always think about it in terms of this human machine collaboration because they're good at different things. You know, humans are really good at creativity and flexibility and insight.

Whereas machines are really good at precision and giving the exact same result every single time and doing it at scale and speed. What we find the most successful projects are where we kind of put the two together, have the machine do the parts of the job that it's good at, let the humans take over for the rest of that. So with this freedom, what have people done?

You've opened up this time. What kind of new... I got two shots of that. What people have done. Yeah, actually, there's at least one product that's in the market now, isn't there? I think I've heard something in the news. There's one, yeah.

Yeah. You know, I always like to joke that work is like a gas that always expands to fill the container. So if you take something off somebody's plate, there's all this mountain of work that they didn't even realize just wasn't being done. And so people are always relieved to then go on and find, you know, the next mountain to climb and the next thing to do. But what are these kinds of things? Like, you know, how are people choosing how to, you know, expand to fill that space? Well,

Well, if you think of the examples, you know, like the preclinical quality control steps that we've automated,

The reality is, you know, one operator stretched over a huge amount of work is it's really hard for them to really do really depth inspection of these samples. And so by taking off, you know, a bulk of that work, 80, 90% for the algorithm to do that, what they're able to do is just do a better, more thorough job of inspecting the samples that are left. It also means we're not hiring a whole bunch of other people just to go look at, you know, screens of data. It's a bit of an immediate gain for the people who are there and then kind of this longer term gain on our headcount plans.

Some folks talk about AI in the pharma space being like, I just want an algorithm that can predict from the structure of a small molecule, the efficacy in humans, right? Like that's the entire drug discovery process. Like that's just not going to happen. That's completely unrealistic. So, you know, we just think about the fact that, you know, there are countless processes. It's a very complicated process to bring something to market. And there are just numerous opportunities along the way.

Even within a specific use case, you're rarely using one AI algorithm. It's often, you know, for this part of the problem, I need to use this algorithm for this and you need to use another. Dave, I want to ask you something about the talent base and people you commented that

Moderna is a kind of company that likes to give people a lot of freedom, you know, highly motivated, smart, ambitious team working to do the best they can. How do you bring and cultivate that talent? And what are you finding to be some of the lessons learned in terms of how to build a high performing team?

It's a good question. I don't know that, you know, if we look across the company as a whole, there is one particular place where we hire people. We get people from biotechs, you know, five people to pharmas of 100,000 people and everywhere in between inside the industry and outside the industry. I think for us, it's always about finding the right person for the job, regardless of where they come from and their background.

I think the important thing for us is to make sure that we set expectations appropriately as we bring them in and we say, look, this is a digital company. We're really bold. We're really ambitious. We have really high quality standards. And if we set those expectations really high, you know, it does start to self-select a lot of the people who want to come through that process. So I want to flip over and talk, you know, you mentioned some of the infrastructure, I would call it, that you put in place, right?

that suddenly the world benefited from a few months ago. How do people know to get those things set up in the first place? You mentioned being able to scale from, I think, you know, 30 to a thousand different. How did you know that was the direction or that was the vision to get those things set up?

Yeah, it's a great point. The whole COVID vaccine development, you know, we're immensely proud of the work that we've done there. And we're immensely proud of the superhuman effort that our people went through to bring it to market so quickly. But a lot of it was built on just what you said, this infrastructure that we had put in place where we didn't build algorithms specifically for COVID. We just put them through the same pipeline of activity that we've been doing. We just turned it as fast as we could. When we think about

Everything we do at Moderna, we think about this platform capability. You know, we were never going to make one drug. That was never the plan. The plan was always to make a whole platform around mRNA. Because since it's an information-based product, all you do is change the information encoded in the molecule and you have a completely different drug.

We knew that if you can get one to the market, you can get any number of them to the market. And so all the decisions we made around how we designed the company and how we designed the digital infrastructure was all around this platform notion that we're not going to build this for one thing. We're going to build a solution that services this whole platform. So that's exactly why we built, you know, this early preclinical stuff where we're

We can just crank through quite a few of these. It's why we built these algorithms to automate activities. Anytime where we see something where we know that scale and making it parallel is going to improve things, we put in place this process. The proof is certainly in the pudding. One thing that I'm kind of finding fascinating is how normal this all is. I guess I'm just surprised at how much that seems to be part of your...

Can I use the word DNA here in this? RNA. It's part of our RNA. Yeah, no, it's true. You know, we were founded as a digital biotech and like a lot of companies say things and put taglines on stuff, but we really meant it. And we have pushed on this for many years and we've built out this for many years. It's the platform we built and now it's running. Yeah, and it's the platform approach we take to our data science and AI projects as well.

I hear a lot of struggles from folks around like, great, I built a model in a Jupyter notebook. Now what do I do with it? Right. Because there's all this data cleansing and data curation to even get it to be in a useful state. And then they don't know where to go from it to deploy it. Right. And we took the same platform approach to our data science activities, right? We spent a lot of time on the data curation, the data ingestion to make sure the data is good to be used right away.

And then we put a lot of tooling and infrastructure in place to get those models into production and integrated. This platform mentality is just so ingrained into how we think.

Take us back to, you know, early in the COVID race for a vaccine. You know, what was it like being part of that team and a part of that process? I mean, what were the emotions like when the algorithms or when the people find something that seems to work or it seems promising? Does that lead to a massive appetite for more artificial intelligence and more algorithms? I guess, tell us a little bit about that story.

I think if you look at how people felt in general at the time, I mean, it was a real sense of honor and pride, right? We felt very uniquely positioned. We've been spent a decade getting to this point and putting all of this infrastructure in place and putting things in the clinic before this to get to this moment. And so we just felt truly honored to be in that position.

And for those of us on the digital side who have kind of contributed to this and built it, you know, this is why we did it. This is why we're here is to help bring as many patients as fastly and as quickly and safely as possible to the world. But there was always the question of would this thing work in the real world? You know, and that's where the proof came in the clinical data. And we're all anxiously waiting like everybody else to see that readout. Was AI always front and center at Moderna?

Or has it become more critical as a pillar of growth and innovation over time? I think it's always been there that we probably didn't call it that in the early days. It's become obviously much more of a hot marketing term than it used to be. But the notion of algorithms taking over decision making and data science capabilities is absolutely always there.

We were very thoughtful about how we built this digital landscape such that we're collecting structured data across all these steps, knowing full well that what we wanted to do is then turn those into algorithms to do things. So it was very purposeful for that. But I do think it's also come into a greater focus, right? Because we've seen the power of it. Very recently, obviously, we've seen how this digital infrastructure and how these algorithms can really help push things forward. And so it's gotten a kind of a renewed focus and importance in the company.

We tend not to be a company of half measures. So when we decide we're going to do something, we're going to do it. And so it's been very strong message from our senior leadership about this is the future of the company is injecting digital and AI into everything we do. And under non-certain terms, this is happening to the point that, you know, as we think about the fact that we're growing really fast as a company, you know, we just doubled, we're probably going to double again.

We're bringing in a lot of new folks from outside the company to grow who are not necessarily familiar with this digital culture that we've had. And so what we're working on right now is actually developing what we're calling an AI Academy, which we intend to be a very thorough, in-depth training for our company. So from people who would use and interact with AI models in their daily basis to senior leaders who would be responsible, kind of a portfolio of potential projects in their areas. And that just shows the level of serious commitment we have about this.

We were built on this concept of having a smaller company that's very agile and can move fast. So we see digital as a key enabler for that and AI as a key enabler for that. And so the hope is that it helps us to compete in ways that other companies can't. And that is certainly the intention here.

Well, Dave, thanks so much for talking with us today. We really enjoyed it. I mean, you mentioned Moderna, how smart people, and we know that from a sample size of one. That's clearly true. Thanks for taking the time to talk with us today. Thank you so much. Absolutely, guys. I really appreciate it. Sam, that was an awesome conversation with Dave. What do you think? Yep. Impressive. I'm glad he's around.

Glad Moderna's around. That's right. Yeah. He's not giving lip service to buzzwords and this, that, the other. He's just like, yeah, we started this way. That's what we're doing it. We would not have existed without digital and AI and data analytics.

Of course it's real. Like, that's what we are. I mean, he said Moderna is a digital company, is what he said. It's just part of their process. I mean, some of the questions that didn't even occur to him that it was artificial intelligence and that's just the way they do things. I wonder if that's like the new industry after industry. Are we going to see the Moderna-type approaches come into industries and just be the dominant? You know, the vestiges of historical, oh, we've been around for 100 years, are almost a liability versus a plus.

I think this sort of contrast, you know, Sam, that you were sort of trying to get at, which is like, how come it's so easy for you guys? And what about the pre-post and the transformation? I mean, these guys, you know, he's like, well, we actually started this way. You know, we said we want to be a small company. They started post. Yeah, we started post. We wanted to be agile. We wanted to be small. We wanted to do a lot more.

with everything that we had. And so that had to be platform centric, data centric, AI centric, and that's how we built. And so AI is everywhere. Yeah. Why are you surprised, Sam, that AI is everywhere? Of course it's everywhere. We do it for planning and trials and sequencing and it's quite energizing and intriguing how it's just like a very different mind towards AI.

Right. And I know that we don't want to make everything AI. There's a lot that's going on there that's not artificial intelligence. You know, I don't want to paint it as entirely. But that certainly was a big chunk of this of the speed story here. And it's pretty fascinating. Thanks for joining us for this bonus episode of Me, Myself and AI. We'll be back in the fall with new episodes for season three.

In the meantime, stay in touch with us on LinkedIn. We've created a group called AI for Leaders, specifically for audience members like you. You can catch up on back episodes of the show, meet show creators and hosts, tell us what you want to hear about in season three, and discuss key issues about AI implementation with other like-minded people.

Find the group at mitsmr.com forward slash AI for Leaders, which will redirect you to LinkedIn where you can request to join. We'll put that link in the show notes as well. And we hope to see you there.