Host Intro (Gene Kim)

Thank you, Julia. Okay, so one of the most impactful learning moments for me was taking a workshop at MIT in 2014, which has tremendously influenced my thinking. I went to this class because it was taught by Dr. Steven Spear, who I've mentioned so many times in years past. He is famous for many things, but he is probably most famous for writing the most widely downloaded Harvard Business Review article of all time, which he wrote in 1999, called "Decoding the DNA of the Toyota Production System." This was based in part on his doctoral dissertation that he did at the Harvard Business School, and in support of that, he worked on the manufacturing plant floor of a tier one Toyota supplier for 16 months. Since then, he's extended his work beyond just high repetition manufacturing to engine design at Pratt & Whitney, the building of the safety culture at Alcoa, and also how we make safe healthcare systems. And recently, he was part of an initiative to build a learning dynamic across all aspects of the US Navy enterprise.

So for nearly a year, we've been talking two or three times per week, trying to see if we can codify what we've observed in our careers. Because there is this magic dynamic that is increasingly being used to unleash human creativity in so many different domains, whether it's harnessing the atom safely, to sending a man to the moon and back, the Toyota Production System, the Alcoa safety culture that we talk about in DevOps all the time, the story of team of teams, the generative organizations as per Dr. Westrum, resilience engineering and learning from incidents as described by Nora Jones yesterday, radical delegation as per Admiral John Richardson, also described yesterday, and we are convinced that these are all parts of a larger whole. So just as there is a cohesive set of principles and practices around Taylorism and scientific management and the 100-year-old practices of the Gantt chart, centralized planning and execution, and command and control, there must be a set of cohesive practices that explains all the practice that we love so much here within the DevOps community. So here is Dr. Steven Spear to talk about an amazing experience in support of these principles and practices in a pharmaceutical development setting. Here's Steve.

Dr. Steve Spear

Hey, Eugene, thanks for that very flattering and erudite introduction. So, I'll pick the ball up with this one, is think about the situation we're trying to understand, which is we assemble all these experts with all this different specialization that they have, and we put them to work, and we think, "Oh, this is fantastic. Not only do we have this distributed intelligence, but we have this collective intelligence, and the yield out of the whole should be so much greater than the yield out of the individual parts." And then we look at this and it's like, oh my gosh, people are working so hard every day. Where's the beef? Where's the outcome of all of this? So anyway, what I want to do is share with you a case where people really tackled that problem to root cause and came up with some good explanation, which I think generalizes as to why we work so hard and get so little, and how to flip that so we work less hard and get a lot more. So here's the situation.

This is a story about a bunch of very talented, very educated, well-meaning, natural scientists, life scientists, biologists, chemists, computational biologists, and so forth. And what they're trying to do is develop therapeutics, so that other people will feel better, live longer, do more. And just as quick background, developing a new therapeutic is a very, very expensive thing to do, and it takes a lot of time, almost sometimes a decade, and upwards of a billion dollars. And if you think about the returns on this, it's an industry which has this tremendous winnowing from the ideas that get started in the pipeline at T equals zero, down to the one or two that squeak out at the end. Now, obviously, that's a huge financial hurdle for the providers of therapeutics. Now imagine the people who go into this line of work, they go to all this advanced education with the aspiration of doing something helpful that other people will appreciate in terms of therapies for disease, and if they start on something, it might be a decade till they get results, and they might not get results. Again, that winnowing thing.

So anyway, this team of natural scientists asked the question, "Why does it take us so long, and why are our yields so low?" And they looked upon this with a little bit of, not a little bit, a lot of envy, because in the hardware space, they've been bragging for, I don't know, 50, 60 years about Moore's law, which is, oh, gene, every two years, just hang on, we'll give you something even faster, even better, and it costs less money. In the pharmaceutical industry, they don't have Moore's law, they have the reverse to that. They dubbed it Eroom's law. And the thing about Eroom's law is that it seems that for every therapeutic, it takes more and more time and more and more money to get to a result. And so this is kind of what's motivating this question of why are the pieces not coming together? Or how do the pieces come together? How can the pieces come together better so that we spend less money, less time, and get more yield?

So anyway, for the purpose of our case, we're looking at a phase of drug discovery called hit-to-lead. Now, where this fits in is that there's a group of biologists primarily who do something called target selection. They figure out, for a particular disease, what protein seems to be the one that's misbehaving inside a cell that's causing them the disease. And then that gets passed over to another group of people who try and figure out where can we attach onto that protein a compound, a molecule which will get to the protein to behave differently than it otherwise is behaving. It behaves nicely rather than badly. And so that's called a hit. And if you want to think about what a hit is, it's like you have a plot and a basic floor plan.

It's like, yeah, we'll put a building here, and that'll add value to this plot of land. That's kind of what it is, where the protein in this picture is the orange and the void that it's trying to fill in is the white. And the hit is the rough sketch, is that little purple blue thing. And the job of chemists and biologists in this phase called hit to lead is to develop that further so they have a lead which is actually worthy of being passed over to people who do something called lead development that it just might have a chance of going into clinical trials. So that's the phase of the process that we're looking at here. Now, the way these scientists do their work is they go through a cycle, which is probably familiar to everybody. There may be different names, right, which is they think a lot, right, and they come up with a design.

And then they actually have to physically synthesize that. So, for the software engineer, maybe that second phase is like coding. Then they have to figure out how well the thing that's been synthesized behaves. So that's kind of like debugging. And they go through a couple of these, not a couple, but they go through several of these thinking, making, debugging, testing loops to try and converge down to a compound and parts of a compound that seem to perform better than not. So they start around this question, why does it take us so long, and why do we yield so little? They said, "Well, how long, really, does it take to go through several cycles of design, make, test?" And they figured convergence is maybe through three cycles of this thing.

So they looked at data and said, "Well, how much time do we spend designing?" Boom. "How much time do we spend making?" Boom. "How much time do we spend testing?" And they said, "Probably a reasonable estimate of touch time here, process time is about 50, 51 days to get some kind of meaningful convergence." Well, when they looked at the actual data, what they found was not process time, touch time, but transit time for a compound through all of this was double to quadruple. And they're like, "What the heck? How can it possibly double to quadruple?" So then the lead chemist on this program, on this pilot, she started looking at where ideas resided in this system of very smart people in chemistry labs and very smart people in biology labs. Where were the ideas? Now, you would like to think that someone has a good idea.

Let's say Gene has a good idea. He hands it to Steve. Steve works on the idea. He hands it to Erin. Erin works on it. She hands it to Anne, to Marguerite. Da, da, da.

Right? Boom, it goes around. What they found instead is Gene works on something, and it goes up on a shelf. And then Steve is working on something else. It goes on a shelf. And Erin walks over to the shelves and picks whatever the heck she wants and puts her stuff on a shelf. And when they looked at the ratio of things being worked on versus ideas which are sitting around for the next step, the ratios was all sorts of out of whack.

And they started getting into the question then, why is it that we thought we had these processes which flow right on through, and instead we have-- So they take a look, and they say, "Well, what did we think we were doing?" So what they thought they were doing is they thought that chemists would be collaborating with other chemists on the designing and making things, and biologists would be collaborating with biologists on the development of these assays, the development of these tests, and running the tests. They'd be collaborating back and forth with each other. And what they found instead that no one was talking to anybody else. That's an extreme, but almost nobody was talking to almost nobody else. They said, "Well, why is that?" And I think now we'll start getting into their diagnosis, which I hope rings true elsewhere, is that they were organized around specialty. And let me-- Hold on. I'm not saying that every organization should be flat and cross this thing and cross that thing.

There's a lot of reason to have silos because you get critical mass of the molecular chemist talking to other molecular chemists and saying, "Oh, what do you think?" But when things start to fragment, it's just Steve working away over here, and maybe Marguerite is working away over there, and you got Anna working away, and no one's talking to anybody else. So the lead chemist, she says, "Wait a second. Why are we not having these collaborative conversations that tap into collective intelligence?" And she realized that-- And maybe it once existed, that someone had actually drawn out the flow of work, who depended on whom for what, that when Steve is doing his work, he's depending on Gene to inform that work. When Steve is doing his work, he's doing it because Erin depends on him to inform her work and so on and so forth. Even if it once existed, though, that shared sense of the system decayed, and the only sense we have is very local, what's in front of us. So the very first step for this lead chemist, what she did is she started making people aware not only of their role, that's kind of like a title, not only of their responsibility, you're working on this compound or that part of a compound, but relationships. Back to this example of Steve is dependent on Gene, and Erin is dependent on Steve and so forth.

Now, what starts happening with that is that once this lead chemist started making people more aware of these mutual interdependencies, it became both trigger inspiration license to start having conversations rather than just being stuck at the bench top. And as they started having conversations, it became possible not only to focus on the work directly in front of you, but to start having conversations with the person with whom you with whom you have a relationship and say, "Hey, Erin, I'm working on this. What you working on?" "Oh, I'm working on this thing." "Oh, very interesting. Can you tell me more about the thing you're working on?" "Oh, I didn't know about that. Let me tell you about what I'm working on. Let's compare notes. How do these things compare?

What should we do first? What should we do second?" And all of a sudden, you start getting this critical mass. Because whatever Erin is working on, on her bench top, and Steve, and Gene, and Marguerite, and Anna, and Anne, now all of a sudden, the ideas are starting to collide with each other, and mix with each other, and synthesize with each other. So one of the things that came out of starting to have these much richer conversations within a silo is that when I show up with my work and I say, "Oh, Erin, let me explain my work to you because I've got this data behind it." She says, "Oh, let me take a look at that data. Can you explain it to me?" I discover, wait a second, where is that data? Why am I having this conversation? That data's been sitting around, or I'm having this conversation before the data arrived.

And so then just by having the conversation within the silo, it raises awareness that there was another silo with a whole bunch of other very talented people upon whom this silo, the chemistry silo, the chemistry lab depended on. And so they started trying to synchronize their conversations within chemistry with the timing of the arrival of information from biology. And now they're starting to look at the data, and if the data shows up on a Tuesday afternoon, they're having conversation on Wednesday. And so they're starting to get more in sync, one laboratory with the other. But then when they're looking at the data, they start realizing, "Son of a gun. I'm not exactly sure I fully understand it. Wait, Jeff the biologist, can you come over here?" He says, "Oh, I'd be delighted to explain the context, the nuance, the subtlety of what the data means." And he says, "And actually while I'm here, can you explain back to me what exactly you were looking for when you wanted to test that compound?

Because maybe I can be more precise and more thoughtful about the set of tests that I construct for that." So what you end up getting with is this first conversation, which is sort of liberated by showing who's in a relationship. It becomes a more frequent, richer, wider bandwidth conversation. And then it leads to other conversations which are not only more frequent, but better synchronized, better harmonized, so that the work over there is now synchronized with the work going on over here. Now, where this continues is that as the chemists start having this collaborative collective intelligence conversation within their silo, then it becomes a collaborative collective intelligence conversation across silos with biology. Then they start realizing, "Oh, wait a second. There are other people with other specialties where we're not necessarily well synchronized, harmonized with what they're doing for us and what we're doing for them." And so wouldn't you know, this very simple, I say, but simple act of the lead chemist mapping out relationships led to this cascade of conversations and this increase in awareness of who's in relationship to whom. And who consequently, because they're in a relationship, has to have rich conversation.

Anyway, where's this land? Is when these chemists and biologists started thinking, "What's our starting point?" They benchmarked against previous programs. And again, to emphasize previous successful programs. And this phase of hit to lead sort of rough idea to something worthy of further development, the benchmark said took over a year. And of these design, make, test, think, code, debug cycles, up to 60 of them. And the pilot, as a consequence of having these better, richer, more expansive collective intelligence conversations, they got done the pilot in six months with far fewer cycles. Now, let's think about this.

Think about getting a therapy to market six months earlier and the amount of alleviation that would mean. Now imagine you start picking up these six months here. And then you're getting therapies into the marketplace years earlier, and the amount of suffering that alleviates. Now think about the enormous financial rewards to anyone who could actually do this on a consistent basis. It's simply off the charts. Now, just to put this in some present context is when COVID hit start of 2020, and we realized how bad it was. At first when we hit it, we went, "Ah, well, be dismissive." But when we first realized it, we said, "Oh my gosh, vaccines can take five, 10 years." And we were being told by CDC and others like, "Oh look, we'll be lucky, it'll be a miracle if we get something within two years." And here we are, vaccine developed within a year through enough trials to get sort of emergency certification, whatever the term is.

And here about a year and a half, and we're going to be vaccinated. It's freaking miraculous. Now here's my theory about that. Is that because this was a pandemic global level crisis, that the folks who developed vaccines, they discarded their old, isolated, their legacy silos within silos with ideas being worked on and on a shelf. And they too streamlined the flow of conceptual work through the system. So anyway, that's my theory as to how we got so many vaccines, like a handful of them in so much less time than anyone predicted. Anyway, Gene, let me just finish off where I started, is that we're desperate for our... collaborative work to tap well into our collective intelligence.

And I just want to offer that it's not just an empty hope, it's a reality if we do what these folks did. Anyway, thank you. Back to you.

Gene Kim

Thank you, Steve. So Steve and I have been thinking about this problem a lot, trying to piece together the common principles and patterns in transformations like this. And so here's something that we wrote. Sometimes it just doesn't seem fair. You've done everything right in your career. You've won all the tournaments required to get you where you are. You've been tasked with solving the most challenging problems in your organization so that you can win in the marketplace.

Everyone in your organization is working hard to solve problems that no one could solve individually, to develop, design great products and services to beat the competition. And yet competitors keep beating you, arriving earlier and faster with solutions that customers love, generated with seemingly less effort, and they keep pulling away from the pack. And so one asks, how do they do that when you share the same starting line and allegedly a level playing field, using the same science and technology, the same talent pool, and the same market information? And sometimes you may even feel that you and your organization are unable to respond effectively, that you feel as though your organization's actually fighting you. And so Steve and I are now wondering that maybe it actually is because you are structured to be slow when you should actually be structured to be fast. And so I've talked in years past about our quest to understand how organizations work the way they do, both in the ideal and not ideal. And we're starting to come to believe that you can predict whether an organization is high-performing or low-performing just by looking at the communication paths within the organization and what is the frequency and intensity of those communications.

And so we assert that there is this slower integrated problem-solving style, where that in order for two individuals from two different functional silos to actually work together, requiring vast escalations up and down the org chart, maybe up eight and then down eight. And so this has a couple of problems. Is that when things escalate, leaders are getting incomplete information, often too late. And the result is that teams don't have access to the expertise they need, deprived of their full creative problem-solving potential. And the reason is that everyone's trapped in these functional silos, these cones where they're not allowed to talk to people in other silos because that is just not allowed. And so what Steve described in the pharmaceutical development example is that they change the structure so that the majority of communications, the majority of integrated problem-solving, is happening at the edges. And when things escalate, they don't escalate up eight levels.

Instead, they escalate up one level. Steve described how by making these very explicit value streams, where the relationships are enabled by sanctioned interfaces, where integrated problem-solving can happen at the edges, magical things happen. And as he mentioned also, it's actually easier to change the system in this mode because the organization can dynamically change itself because the structure is simpler. And so if you haven't picked this up, we're obviously borrowing the language from the amazing book, "Thinking, Fast and Slow," based on the work by Dr. Daniel Kahneman and Dr. Amos Tversky. And so structuring for fast integrated problem-solving and slow integrated problem-solving is really a proxy for four measures.

And so in control theory, there are really four axes. There's frequency. There's latency. So in other words, are we reacting to old information or are we reacting to near real-time present conditions? And then the granularity or detail information, as well as the accuracy and fidelity. And so in operations, we very much favor the second two, frequency and latency, whereas for planning, we very much favor the first two. In other words, we don't want to make plans based on categorically false information.

And so what I find so amazing about that slide that Steve showed about Eroom's law is that as we increase the number of functional specialties that must work together to solve tough problems, we feel the effects of that in the amount of difficulty and expense of creating solutions. So I love this graph that shows for every given billion dollars, the number of pharmaceutical therapeutics being able to be generated is actually going down logarithmically, the exact opposite of Moore's law. And so we see the limitations of the slower integrated problem-solving style in so many domains. This is the story of why in "Team of Teams," they weren't able to successfully dismantle the Al-Qaeda terrorist network because despite being larger and having better technology. Steve described what's happening in pharmaceutical development. This is what led to the creation of Agile and DevOps within software development and delivery, and is often hampering vaccine rollouts, and is exactly the symptomology, in my opinion, of why so many organizations are having difficulties in deploying OKRs, objectives and key results, as described by John Smart and Dr. Mik Kersten.

In other words, we are clinging firmly to the slower integrated problem-solving style. And so really, if we take a look at the domains of planning operation improvements, we can start to bifurcate which activities should be in the slower mode and which ones should be in the fast modes. In planning, so these are the slower cognitive activities of setting system-level goals, designing the organization, how we organize teams, the relationships between them. Also defining and deploying objectives and key results. And then leadership should stay out of operations because that's mostly in the business of expediting. Instead, we come back to the slower problem-solving style in improvement of the system, where we ask, is the system achieving the system-level goals? So this, in the book "Team of Teams," this is when General Stanley McChrystal asks, "Despite all the tactical wins, are we achieving our strategic objectives?" And his answer was, "Absolutely not." Which led to the breakthroughs described in the book.

This is what we do in agile retrospectives. This is what we do in halftime in an American football game. And so, the domain of operations is where we are using this faster, integrated problem-solving style. This is where the majority of communications and interactions are happening within the teams, or between teams using sanctioned interfaces. So just as Steve described how they changed the relationship of how biologists and chemists work together. Dave Silverman, one of the co-authors of the book "Team of Teams," as he presented last year, he had this wonderful language for it, is that when leaders can radically delegate leadership, what does that do for those teams? It says it is this amazing, magical feeling of when their leadership is eyes on, but hands off.

And the inverse is that when leadership reaches down into daily operations too much, he had a term for that, too. It is when your leaders have pulled your decision space up. In other words, there are decisions that you were making that are now no longer yours to make. Which is actually a terrible feeling because that is now depriving you of your full creative problem-solving capabilities. In Steve's story, he described how Beth, the head of chemistry, changed how chemists and biologists work. They enabled chemists and biologists to change the way they work so they can better jointly co-create new knowledge. So there's one other case study that I'd like to bring up just because in my mind, it demonstrates so vividly these two different types of problem-solving styles, and that is United Airlines Flight 232.

And so this was a regularly scheduled United Airlines flight in around 1989, and it was a DC-10 that suffered the loss of all three hydraulic systems. It was the first time that that had happened, and that was a condition that was arguably one that should never happen. The captain of the flight gave a lecture at NASA Ames. And incidentally, this is a famous story for many reasons, one of which is that it was the first disaster that fully utilized what they called crew resource management. They changed the dynamics of how people acted and reacted within the airplane cockpit. And so I remember reading this in 1995, and he is describing what happened. And so I'm reading from the transcript.

The link is in the slides. He said, I'm going to dramatically reenact his talk. He said, "On July 19th, Murphy's Law caught up with us, and we lost all three hydraulic systems. As a result, we had no ailerons to bank the plane, we had no rudder to turn it, no elevators to control the pitch, no leading edges to land, no slats to slow the plane down, no trailing edge flaps for landing, and we had no spoilers on the wing to help us get us down. Once we were on the ground, we had no steering, no nose wheel or tail, and no brakes." And so, apparently, they had a simulation, I think, that they ran before his talk. And so he said, "As you saw, the number one and number three engine controls were frozen, and that was the only means of controlling the plane." Another really unusual aspect of United Flight 232 was the fact that there just happened to be, in the passenger cabin of the plane, Captain Fitch. Here's how Captain Haynes talked about that.

"We learned that there was a DC-10 captain in the back. He was an instructor, and we like to think that instructors know more than we do. So I figured that Captain Denny Fitch might know something that we didn't, so we asked him to come up. And he came into the cockpit, took one look around, and that's his knowledge. And it is sort of funny listening to the transcripts because he is 15 minutes behind us now. He's trying to catch up with where we are, and everything he says to do, we've already done. And after about five minutes, that's now 20 minutes into this emergency, he says, 'We're in trouble.' So we thought, that's an amazing observation, Denny." There's laughter in the audience.

"And we kid him about it now, but he's just trying to catch up with our thinking. We're 15 minutes ahead of him. After that, he asks, 'What can I do now?' So I said, 'You can take these throttles and help us steer.' He took one throttle in one hand, another in the other, and with the number two throttle frozen, this is something that the pilot and the co-pilot could not do themselves. So we said, 'Give us the right bank, bring the wing up. That's too much bank. Try to stop the altitude.' He tried to respond. After a few minutes of doing this, everything we'd do with the yoke, he would correspond with the throttle.

So it was a synchronized thing between the three of us with Dudley," I think that's the flight engineer, "still being able to do all his communications with air traffic control. And so that is how we operated the airplane. That is how we got it on the ground." So there's another interesting thing that happened. He describes his communications with the San Francisco area maintenance crew. He said, "The area maintenance crew are those experts sitting in San Francisco for each type of equipment that United flies. All the history of the aircraft, all the information that they could draw upon to help a crew that is having a problem. But unfortunately, in our case, there was nothing that they could help us with.

Every time they tried to find something that we could do, we had either already done it or we couldn't do it because we had no hydraulics." In other words, there was nothing in their procedures to handle the contingency where they had lost all three hydraulic systems. So, going to this model. The planning part, the slower cognitive problem-solving included setting up the CRM protocols, establishing training of CRM across all US airlines, setting up those area maintenance crews in San Francisco. So the faster integrated problem-solving was happening in that cockpit, trying to figure out how to fly the plane with the loss of all three hydraulic systems, assessing whether the maintenance area team in San Francisco could actually help, and the answer was no. Assessing whether Captain Fitch had some sort of revelatory knowledge that could help save the airplane, and the answer is no. But they did assess that he actually had skills that could help them land the plane, and establishing the new cockpit roles and responsibilities within the cockpit. All of that happened within the fast integrated problem-solving mode.

In this lecture, Captain Al Haynes talks about how much of the success was due to crew resource management. One of my favorite lines in his talk was this. He said, "In that cockpit, we had over 100 years of flying experience, none of which included flying with all hydraulic systems down." So he said, "Why would I know any more than my colleagues about how to land that plane? It required tapping the collective problem-solving capabilities of everyone in order to get that plane on the ground." To me, it's so interesting that the only experts that had the expertise relevant to landing that plane were in that cockpit. There's another example that I really love, where it was the opposite, and that's one of my favorite scenes in "Apollo 13," and that is when they are trying to figure out how to change the carbon dioxide filters to save the astronauts. And the problem is that this requires fitting a square peg into a round hole, which then leads to this scene, which is where the engineers all get together and try to figure out how to fit this square peg into a round hole using only these parts, which appear to be a spacesuit, duct tape, and a bunch of tubing. So in this case, the astronauts who were much able to tap the collective experience and integrated problem-solving of hundreds of engineers that were able to describe how to change those CO2 filters.

So just as Steve mentioned, I think the COVID global pandemic is showing how much potential there is to incredibly increase our cognitive, creative problem-solving skills. Steve mentioned how we have not one vaccine within a year of first being identified, but five that have been approved for emergency use. Steve and I had the privilege of talking with the chief operating officer of one of the healthcare systems here in Portland, Oregon, where they increased the number of vaccinations per day from 2,000 to 8,000. And so here's a picture of Dr. Chris Streer, Trent, and me here at the convention center in Portland. And so that is what Steve and I have been working on. Steve, I sure hope this resonates with you and that this represents our growing understanding of why organizations work the way they do, both in the ideal and not ideal.

Dr. Steve Spear

Yeah, absolutely, Gene. Look, I think we've made a couple of strong assertions, and I'd be delighted if we could get feedback from the people watching and listening as to whether what we're saying resonates or doesn't at all. And those assertions are, one, we have a cause and consequence that this loss of critical mass by people being disconnected across boundaries and consequently isolated in silos, and then not only isolated silo to silo, but within silos, that's a common experience. I think it is. That's why we offered it. But is it or not? And that being a cause and then the consequence being that people working very hard, but not necessarily in a way that's harmonized, synchronized, synthesized, so that they're working very hard, but they're getting output less than they hope and want.

So anyway, true or false? That's our question. And then- Right? And then the other kind of validation, refutation we want is we offer a corrective action, which is making clear not only roles and responsibilities, which is a very egocentric view of the world. "This is my title and this is what I do," but relationships, which is now a very expansive view. That our recommendation or our suggestion is that that as a corrective action will liberate the opportunity for collaborative conversation and expansive collaborative conversation, which will help tap into collective intelligence. And again, the question back to viewers and listeners is true or false?

Makes sense or doesn't make sense? Those are our asks.

Gene Kim

And by the way, I'm reminded of this amazing part of the H&M presentation this morning where Daniel Claussen said when he saw what happens when you have a merchandiser sitting next to the developer, that magic happens, as opposed to stuck in their respective silos. Bingo. So, as so much of this is being pieced together in this podcast that I've been doing called "The Ideal Cast," of which Steve has been involved in so much as well. In fact, Steve and I, in two weeks, will be releasing an episode where we actually interview Trent Green, the chief operating officer who is responsible for the mass vaccination clinic in the Portland Convention Center. So, so great. And Steve, any help you're looking for?

Dr. Steve Spear

Yeah, no. Feedback on this stuff, and we've created some tools to make that mapping of relationships easier. If you're interested, you can find us.

Gene Kim

Thank you so much. Over and out.