Dr. Francois Laborie:
All the industries that are invisible to us, but that define what we wear, where we live, how we drive and travel around. Whether that’s energy, manufacturing, transportation, all the grid related power and utility related types of operations, which are really the fabric of our modern society.
Jeff Standridge:
If you want to drastically improve your business, learn proven growth strategies and generate sustained results for your organization, you’ve come to the right place. Welcome to the Innovation Junkies podcast.
Jeff Standridge:
Hey guys, welcome to another episode of the Innovation Junkies podcast. My name’s Jeff Standridge.
Jeff Amerine:
Hey, and this is Jeff Amerine. I’m glad to be back for another episode.
Jeff Standridge:
Yeah, good to have you, man. How you doing?
Jeff Amerine:
Not too bad. Spring is about to spring, I’m seeing green leaves on the trees. It’s all good.
Jeff Standridge:
Yeah, we had–what was it–about a 40 degree swing in temperature the other day where it was sleeting and snowing one day and then it was 80 the next.
Jeff Amerine:
Typical springtime weather in the Ozarks, right?
Jeff Standridge:
That’s right, that’s right. Well, Jeff, let me tell you who we’ve got joining us today. We have Dr. Francois Laborie. He’s the president of Cognite North America, overseeing their expansion and operations in the US, Canada, and Latin America. Francois has extensive career experience in the technology industry, serving in both research and executive roles. We’re going to learn a little more about him throughout the podcast today. Francois, great to have you with us.
Dr. Francois Laborie:
Thank you, Jeff. Thank you, Jeff. Great to be here.
Jeff Standridge:
Well, it’s great to have you. We appreciate you for taking the time to spend with us today. I suspect you haven’t had some of the temperature swings that we were just talking about.
Dr. Francois Laborie:
I was just thinking about it. Yeah, no, I have not really seen this in Austin, Texas. It’s been nice and warm. Yeah, we are certainly seeing the spring coming here. And the pollen, if you know about Texas pollen, that’s now.
Jeff Standridge:
Yeah, it’s crazy. It’s crazy.
Jeff Amerine:
It could be one of the allergy capitals, right?
Dr. Francois Laborie:
Exactly.
Jeff Standridge:
That’s right. That’s right.
Dr. Francois Laborie:
It is beautiful, but yeah, there is quite a lot of pollen going on.
Jeff Amerine:
Well, Francois, before we get into the middle of going into innovation and strategy and all those other really interesting and powerful things that we’ll talk about, we like to start with just a random musing. Today, we want to find out what was your favorite summertime vacation?
Dr. Francois Laborie:
Yeah, that’s an interesting question. With my name and my accent, I should say being in France somewhere. But I guess my favorite summertime vacation, actually the terrible epidemic that has been running through the world has allowed me to have a very different type of vacation. I have family, I have young children, and we basically packed the car and decided to drive all the way north to Montana. From Texas all the way north to Montana and we stopped in a lot of the fantastic national parks that are in the US. Coming from Europe, the breadth and size is amazing and we saw a part of the US that we would’ve never seen just driving all the way to Montana and down again through a big loop. We didn’t go to Ozark, but we did see a lot of fantastic scenery. Yeah, that was a great, unusual way to discover a country. It was really nice.
Jeff Standridge:
Yeah, that sounds like heaven to me.
Jeff Amerine:
Jeff, what about you?
Jeff Standridge:
We have invested over the years. Of course, my kids are grown now much like yours, Jeff Amerine. But we’ve invested over the years of creating experiences for our kids. We’ve had a ton of great vacations; France and Italy and South America. But I will tell you, similar to what Francois just said, we didn’t drive there, but we have taken two vacations to Alaska. Not Alaskan cruises yet, we’re going to do that with my parents this year. But we have flown into Alaska and rented an Airbnb and just day tripped out from that place to all the various experiences. We did it the first time and were blown away by how much we enjoyed it. I think we shipped home four limits of red salmon. We watched bears eating salmon out of the water while we were catching them just a few yards away. Then the next time we went, we went a little farther north and we hiked Denali and that area. I have yet to find a vacation before or after, that matched those two vacations.
Jeff Amerine:
Yeah, that’s a pretty hard one to top. I was just sitting here reflecting on some of the good ones that I’ve been on. One that was particularly good in recent memory, and there have been several, was my daughter was actually an exchange student in Italy and we dropped her off for this program. We decided, well, we’re going to stay and we ended up staying for three weeks. We rented a car, manual transmission, which thankfully I knew how to drive because I’m old. We drove all over. We started in Milan and we drove across the northern part of the country. We ended up spending a great time at Rimini Beach along the coast of the Adriatic there, when they happened to be having the World Gelato Contest there, championship, which was really cool. We spent time in Perugia, on the edge of Tuscany and we spent time in Tuscany at a place that was built in the 1100s, old castle. The whole thing was fantastic. We ended up seeing Pisa and then spending some time in Genoa, then back out in Milan.
But that couple of weeks was great. We drank too much wine, we had some great cooking lessons. If somebody dropped me in Tuscany, I wouldn’t mind. I would probably stay there. It’s really a fantastic, very livable place and the people were really great. That was good. My close favorite would’ve been Normandy, but it wasn’t in the summer. It was actually in a cooler time of year and we had a great time in Normandy as well.
Dr. Francois Laborie:
That’s a great place too, but Tuscany certainly knows how to live. It’s a beautiful area.
Jeff Amerine:
It is.
Jeff Standridge:
I’m not going to be able to finish the podcast. I’m going to head out on vacation. I’ll see you guys.
Francois, let’s talk a little bit about your company. Just briefly, tell us a little bit about what Cognite does and then talk a little bit about just this concept of industrial data operations. Let’s set the stage, understanding a little bit about the industry and how you guys operate in that industry.
Dr. Francois Laborie:
Well, diving right into it, Cognite is a software company, but it’s a software company focusing exclusively on heavy industry. You can think of all the industries that are invisible to us, but that define what we wear, where we live, how we drive and travel around. Whether that’s energy, manufacturing, transportation, all the grid related power and utility related types of operations, which are really the fabric of our modern society. You don’t notice them until either a major epidemic happens or wars happen and then you start noticing that our economy is deeply integrated, but also every one of these components is quite fragile.
We came to the space as Cognite, looking at the fact that it was a time where these industries were operating extremely well, but using very old ways of operating. Now they were having more and more data available. There were more and more insights that could be gleaned, a lot more instrumentation. So it was the right time to help them make use of this type of additional data, which is a massive amount of rather complicated data to help them transform the way they operate. It’s not a small statement. It’s not really small improvements here and there. They’re using the data to really operate very, very differently. Both in terms of their efficiency, but also when you look at the climate. I’m sure we’re going to talk sustainability.
But also, the fact that they do live in an interconnected world and they do need to understand what’s happening outside of their own parameters in order to be able to work and provide the services that we expect them to provide. Building solutions that allow them to harness the data, which is a very different type of data, much bigger volumes, and allowing them to actually put that to use to change the way they operate. Which is when you think about impact, that’s pretty interesting to think about.
Jeff Amerine:
As a follow up to that, as you work through this idea of industrial data ops, sometimes I worry that we swim in a sea of data and we have difficulty finding actionable insights. How do you think about that and how does Cognite help in some of that?
Dr. Francois Laborie:
Jeff, that’s a great point. It’s 100% a challenge. That’s exactly what the challenge is. Where we were created, I come from a computer science, computer engineering background, and the whole wave of artificial intelligence was in full swing. A lot of these industrials understood that there was a potential to be tapped there in order to derive more insights or better predict what’s going to happen on these massive, complex, expensive machines and assets. But their problem was, and everybody’s problem was, that they forgot that ultimately it’s about the data you’re going to feed these machines and algorithms. If the data is flawed, or if you do not understand where all the data is related to each other, there’s no way you’re going to be able to scale.
The whole concept behind Cognite and data ops is to actually help make sense of the data. It’s such a large volume of data that the only way to do that is to actually turn the technology on the data itself and say, “Look for patterns. Tell me where all my compressors are and tell me everything that you can know that is related to it. Who was the last person touching it? What’s the current production? How was it operating last year?” All of this information is floating around. The latest pictures I have and all of this information. When you think about the challenge of transforming industry, it’s going to be able to look for this massive amount of data. Some of it is structured and nicely structured in database. Most of it is not, most of it is lying around. It’s very complex, very big time series or images. Looking for these patterns and being able to put it together so that you can do artificial intelligence, you can get inside and derive insights. That’s really the power.
You were talking about data ops. The next step of it is when you look at how humans are currently operating there, the operators, the people on the floor, the engineers, the way they operate is interesting because they’re building on knowledge. It’s like they’re building a mold of knowledge. They take little pieces of information from multiple different sources, and then little by little, they create assumptions that they validate based on that assumption. They’re going to create another assumption about what should be done next. If you try to decompose that from a computer software perspective, you want to capture all of these incremental insights by looking at temperature and current production. Oh, I’m actually running hot, but my production is a little bit higher. What is my schedule? That’s combining all of these sources and creating incremental insights. Data ops is the art of providing that type of information, capturing these insights so that somebody else can exploit them.
Jeff Standridge:
Very good. Talk to us a little bit about the role that you see data playing, and then perhaps how Cognite is involved in that in the sustainable energy future. You mentioned sustainability a few moments ago, so as we transition to a more sustainable future from an energy perspective, what’s the role of data and where does Cognite play in that?
Dr. Francois Laborie:
It’s a fascinating question. There’s a lot of elements to sustainability and what we mean by sustainability as a society. A lot of it is around de-carbonization, whether that’s the grid or the industry. Then how you get there will be a mix of looking at the way you’re currently operating and trying to lower the carbon intensity of what you do, whether you’re producing cement or you’re producing energy.
On the other hand, they need to invest in new ways of producing, especially energy. But producing things differently, completely, radically differently. In the middle, you will have the whole infrastructure, whether that’s grids that will transport more and more electricity, or that’s the typical ways that we are having our supply chain working and looking at how we could improve that, shorten the cycles and everything.
If I look at the role of data, well, data is really going to be central to all of it. If we start with existing operations and you have the big pledge moments where all the big manufacturers, the big companies have been pledging net zero by 2030, 2050, Scope 1, Scope 2, Scope 3 for some of them. If you want to go there and you already are operating, you are already producing, you’re established and you need to continue to produce using your current assets and the world continues to need the products that we are producing. Then you’re talking about two things mostly. The first one is, do I actually know what my current carbon intensity is? The answer is most of the time yeah, not quite. So the first step is, okay, I want to go to net zero, but where am I today? What is my baseline? I can start creating assumptions and scenarios. That’s not as simple as it sounds.
You have the SEC now saying by 2023, 2024, by the way, all publicly traded companies, we’re going to ask you to have auditable carbon reports. You’re going to have to assure us where you make all of these assumptions for us, so that we can all as a society agree whether or not we are going in the right direction. This is a pure data problem and it’s a massive data problem, because we are talking about all the ugly data that is hidden in the operation, which is hard to get by again.
The next step is okay, so now I know I need to optimize. I need to decide what are my bad actors, I need to make capital investments and decide that I’m going to change a big part of my assets. Or I’m going to stop doing specific products, or I’m going to change the way I operate them. This is also a data issue. All of these optimizations tend to be too complex for humans so you will want to have algorithms to help you make the decision and then operate them. That’s for the first big bucket.
The last bucket, which is okay, new energy sources, renewables. If we take these examples, we already know carbon captures will try to sequestrate and capture carbon from the atmosphere. All of these new technologies coming to play cannot be operated like the old ones. We know that already. At scale, it’s going to be a challenge for us to operate them cost effectively. Right now we’re taking the best parts for wind, for solar, there’s room to grow. But if you look at offshore winds, we know that it’s a huge opportunity for us. It’s a lot more reliable as a source of energy because the winds are a lot more predictable offshore. But we also know that right now it’s too expensive. The way to do these types of projects in a cost effective way, a lot of it is about planning them, choosing the right concepts, but also developing them to be autonomous from the start.
You cannot spend as much time and effort to build them and operate them as you do for traditional industries. All of that is data driven. To autonomous operations, to autonomous planning, all of that is data driven. Then if you look at the problem in the middle, which is the whole infrastructure piece, this is also a lot about predicting, optimizing and trying to get these closed circles working better. Whether you’re going to distribute energy, or you’re going to try to be a lot more efficient in your supply chain. Long story short, data is everywhere in that story.
Jeff Standridge:
You’re bringing an innovative way of analyzing a set of industries that perhaps have not always been as innovative in the way they look at their business model and their production processes and what have you. What challenges have you seen as you guys have tried to penetrate what I would call a legacy heavy industry set, with new innovative products and services that are particularly AI driven, software driven, analytics driven, what have you. Can you talk a little bit about that?
Dr. Francois Laborie:
Yeah, it’s a great question. The whole question about innovation, sometimes when I’m thinking about innovation I have this bias of thinking it’s a big thing that comes out of nowhere. Nobody had thought about it and boom, it transforms our life. The fact is, in my experience of innovation, it’s usually more like a buildup over a long period of time and it’s multiple factors that allow you at that precise time to be able to scale a new way of thinking or a new way of doing things. You find this everywhere. If you think about again, the COVID epidemic and the vaccines that we were able to do with MRNA. This has come from decades of research, from our ability to sequence the genome and the fact that we can now have much more interconnected ways of development and production. All of these things make it possible now to have a vaccine done in a record time. And it feels like it’s a massive innovation, but it’s been years in the making.
In industry, I would claim it’s a little bit about the same thing. We’ve had a wave of instrumentation, cheaper sensors, better sensors, better cameras. We’ve now had interconnectivity and connection and clouds coming, allowing us to actually be able to pool that data and analyze and take decisions out of it. We’ve had tools like artificial intelligence or neural network approaches that allow us to handle these volumes of data and look for patterns and train on them. In a way, it’s about being there at the right time, and then being able to ask the right question. In our case, it’s to say, well, it’s not about the magic AI power that I’m going to put on it and all of a sudden the industry is going to transform. It’s about actually making sure that we understand and trust the data, all of this new data, so that I can give you the ability to make decisions differently.
You usually start by simple things like visualization. The engineers I was talking about before, give them the data they need to make decisions differently. Because right now they’re looking at seven systems, when they’re out in the field they have paper. Well, they do carry phones in their pocket, they’re ready to use digital if only you can give them the right data. It’s incremental and then you’re changing the mindset because they will start trusting the data. They will start building insight. And yes, then we do extremely cool things. You start optimizing the full assets, production based on what the market is saying or what your raw materials are, which is fantastic. But it all builds on small, incremental along the way. That’s what I’m seeing really and this adoption really is multiple small innovations.
Jeff Amerine:
Yeah, I mean, it’s great insight and we know that talent plays a big part in all that. We’ve seen in some industries that one of the pacing items for the adoption of AI and ML and robotics and other things that are very data intensive, is there’s just not enough skilled data scientists to go around. Maybe this is a role that Cognite plays, but how is it that you make it approachable so that a data analyst or a business user can actually engage with the tools and be able to take some of this into their own hands? Because we hear this repeatedly, there just aren’t enough data scientists. They are the choke point for adoption of some of these. How do you think about that?
Dr. Francois Laborie:
That’s a great question and I’ll start again by deconstructing what do I need a data scientist for? Because ultimately the people who understand the best what I’m doing are probably my engineers. They have the competence to understand the chemical process or the physical process that is happening. I’ll go back to the data science question, but there’s a lot of value to be gained by giving them the tools and making it easy enough. People talk about locals, people talk about citizen data scientists. But taking them with these competencies that are actually years in the making around processes that are pretty well understood from a physics perspective, but they have a layer of experience on top of that and try to empower them to codify some of their knowledge so that they can do their job better. There is a lot of value there.
Now, I’m not saying you don’t need data scientists or programmers or automation specialists. But very often and that’s been also the bias of the industry. If I look at it, they’ve gone through very specialized data scientists and mathematicians, which are brilliant people, but they don’t understand the process and they don’t have the data right now. So in a way you’re trying to run before you walk.
Jeff Amerine:
That’s great.
Dr. Francois Laborie:
I would also say that, yes, you do need for some of the advanced optimization, advanced data machine learning models, you will want to build. Of course, it takes specific skills. Up scaling is only going to take you so far and the citizen data scientists or local approaches. So yes, there is a recruiting challenge, but maybe you don’t need 100 people. Maybe it’s a team of 20 and the rest is actually your existing engineering workforce.
Jeff Standridge:
In our experience, we have found that one of the dependencies of building an innovative company is certainly talent, leadership, and culture and all of those things intertwined. Do you see any major differences between your highly technical software company and the culture that’s needed to attract and retain and innovate in that culture, versus the culture of the vast number of clients that you work with in a completely different industry?
Dr. Francois Laborie:
It’s a great question. When we started, it was a lot different. We started six years ago, there were certainly major differences and the innovation team of our clients were in a way, very separate and removed from the actual operation, which was a challenge for them as well, because of this difference of culture. I think by approaching the problem, not saying you don’t understand anything, even though you have 20 years, 30 years experience, we will figure it out because data science is here. That was very arrogant as an approach. You will not succeed like that. It’s not just the culture, it’s also an approach.
The fact that yes, we do work agile. We come with concepts that are different. We’re pushing the boundaries a little bit by telling them why can’t you make that data shared? Why don’t you trust the algorithm to actually tell you what’s possible? But looking at it from a more domain perspective and getting that credibility actually helps us gel quite well overall. But yes, there are still challenges with very bureaucratic approaches to processes which don’t match the reality. That’s not how innovation happens. Yes, it is a challenge in places, but this credibility helps.
Jeff Standridge:
How do you all approach innovation within Cognite?
Dr. Francois Laborie:
Oh, that’s a big question, huh? How do we approach? There’s a couple of things. There’s how do we approach innovation? There’s the indirect innovation, the one where we ask our customers to think differently. We don’t quite know what the outcome should be, but we ask them to think differently because we are going to give them completely new capacity and capabilities. There, it’s a lot about empathy, it’s a lot about guiding with examples, listening and very much a centric design by throwing ideas, trying to listen and iterate around what these new capabilities could give you.
You also have the innovation from Cognite on its own, and there you will have the classical prongs. One of them is actually what we learn by engaging closely with these operators, these people who are experiencing the day to day life and trying to bring some of these ideas back and being open to that. But also making sure that you put some guard rails, you decide selectively this is something that we think will drive us further, will make a difference, and therefore we adopt it. Then we also leave some room for the intel creativity, the things that are a lot harder to control, but you can create the environment for these things to happen.
One of them is making sure that the engineering and product and the operational people have time for every now and again, to do hackathons, to come up with crazy ideas, to merge and mix teams and come up with ideas. Yes, some of them will be cool for one evening and they may win a prize and they’ll not go further and some of them actually make their way to the product. Leaving room for a little bit of experiment and then a lot more specific teams that are there to disrupt ourselves. We have a team, their goal is to disrupt us. And they should work, we were talking autonomous and robotics.
We believe that autonomy is the future of the majority of the industries we currently work with. That’s going to completely change our current approach and our current products, because we believe that both the data acquisition, finding the patterns and execution is going to be closed loops across the board. Their job is to make sure that works and they already started actually three years ago. They started with something I didn’t believe was going to stick, which was computer vision and robotics. I love that. I come from that background. I think it’s super exciting science, but I was saying, “It’s not going to work. I mean, this is too far ahead of where we are.” But they managed to bring it to a level that now most of the projects that we are doing involve imagery and computer vision, and some level of already autonomy, but more as a mobile sensor.
They’re thinking about the next generation. That’s one way of saying, “Okay, is the innovation driven by let’s listen to the market.” It may not be as disruptive, but it certainly helps understand where the needs are and some of them we will take. There’s the innovation driven by our own team and the talent we have and giving them room to cross pollinate and breathe and come up with ideas and see what sticks and accept that some of them will not necessarily stick. Then the last piece, which is a lot more thoughtful to say, well, actually, your job is to disrupt what we are doing right now and create something that could put the current job, the current product offering out of a job. That’s how we are approaching it. Is that resonating with what you hear?
Jeff Standridge:
Yeah, yeah. Very much, very much.
Jeff Amerine:
No, it definitely is. Given that you deal with heavy industries, critical industries in terms of what they do, given the world events today, all the concern around cybersecurity has grown, especially as it relates to critical infrastructure. As you’re going through one of these implementations and dealing with these large scale industries that are really crucial to the economy and can have really serious impacts if they’re shut down or if they have any malware, denial of service, whatever it may be. How do you approach that as a supplier, a software provider? How is that integrated into your approach?
Dr. Francois Laborie:
That’s a fantastic question. From the start we’ve been working with critical infrastructure, companies that are falling under the critical infrastructure denomination. We are a target, of course. I mean, our product is a target from the start. There are some things that you do from the start, both in the way you work, in the way you build the product, in the way where you actually accept to hear and you invite feedback and audits and white hat attacks on a regular basis. But also throughout the company very early on, we were maybe two years old, two and a half years old when we were seeking already the ISO certification. Honestly, that’s not a fun thing to do when you’re two and a half years old. I mean, if we are really going to change industry we have to be ready to play by the rules, because indeed there are consequences for as an organization being conscious of the risk that we would make our customers take and the society take.
The other side of that conversation is interesting. Knowing what we know and why Cognite is having the success we have, which is basically we give visibility to a lot of data that nobody knew about. And that data is actually explaining what’s really going on, on the operation side. Then you have to ask yourself, “If there was not that visibility, how were we sure that we were not hacked?” There was no way to have a holistic view of the process. You could secure each small component, but you could not be sure on the holistic approach that your operation was actually being secure.
Now, if you think about it and I mean, we are working there, the results of the insights that we are allowing to create are also feeding cybersecurity. At some point an algorithm cannot just tell you we believe there’s a breach or this packet seems to be corrupted, or somebody’s been tampering with it, but also telling you something is happening with your process. It’s not normal, this shouldn’t be happening right now. Right now that visibility is very hard, nigh impossible. That’s also interesting to see, it’s like with everything, it opens up new possibilities that we didn’t even think about when we started.
Jeff Amerine:
Yeah, very interesting. Another follow on to that is, there’s obviously lots of concern over infrastructure in general, in the developed world in maintaining the infrastructure and building the infrastructure for the next 100 years. Given the position that you sit in, what would be your recommendations? How should people be thinking about what are the smart infrastructure investments that countries could make?
Dr. Francois Laborie:
I have been worried ever since I started working with Cognite, by the lack of investment on the grid in itself and the lack of realization that this is a pretty important piece of our society. It’s hard to direct investments to our infrastructure. We know it more than anything here in the US, but it’s true also in Europe. The fact that we will want to go towards more renewables, and we are going towards more renewables, even more stress, especially on the power infrastructure. There’s a few things that are happening that are interesting.
If you look at the existing infrastructure, it’s not operated optimally right now. We should also realize that there is a significant investment that has been made and we don’t need to scrap it and put ten hundred billions of dollars there to build a new infrastructure. But we can help optimize the use of the current infrastructure, starting to understand based on patterns and projections, based on the line rating, on time of the day, there are things that we can be a lot smarter about on how we use existing infrastructure. Directing some of the attention to the existing infrastructure is critical and that’s going to save us billions of investment.
Then yes, we need to acknowledge that there is going to be a buildup of additional infrastructure. Then we need to think about how fast and when we can make sure that this is happening because there’s a lot of investment being poured into renewables, which is fantastic, which is needed. But there’s a dark side of that story not everybody’s realizing. Is that one: they’re not always in locations that are very convenient to get connected to the grid. They may be far from where the energy is actually needed. And two: the process for planning the connection itself is cumbersome even if they’re in an ideal location. There’s a lot of things we can do and we should focus on saying, “We need to build a new infrastructure, but also the new connections and the new renewables that we’re putting in, we need to think about how we can actually accelerate the efficiency and how are we going to be able to tap into it?” If you look at China, they have a massive problem around that today and in the US, it’s the same thing.
The last piece is what is called distributed energy resources, where you actually think about also a future network, a future grid where some of the energy creation and consumption is actually a lot more distributed on the centralized system, which is a paradigm shift in Europe and here in the US, as well. So a lot of exciting things coming there. I’m the data guy, I’ll make sure that they have the data in order to make this decision, but there’s a lot of things that are happening that are really fascinating in that field.
Jeff Amerine:
Yeah, it is interesting if you study networks and my background was telecommunications. They go from centralized, and same with computer networks, to decentralized, to centralized. And you realize for resilience, you need some of all of it. I mean, you need decentralized, you need some centralization for efficiency. One other little tidbit I’d throw in there as it relates to the grid, which was a shocking statistic to me, something like 40% plus of all the energy consumption, at least in the United States, is through HVAC, it’s heating and cooling. If you attack that problem at the source, then your generation needs can be significantly less. But there’s not always a lot of conversation around how do we do that much more efficiently than what we do today? If you focus on that, you’re attacking a real root cause of consumption.
Dr. Francois Laborie:
Living in Texas has been a shock. I used to live 13 years in Norway, which they understand weather and they build and operate according to weather. I come from the south of France as well, which is also very, very hot. That was a shock for me to see that we have not been building in full understanding of mother nature and which goes back to isolation and use of air conditioning, but also the way we plan our cities. There is indeed an element of sustainability that needs to be taken into account in everything we do.
Jeff Standridge:
We’re talking with Dr. Francois Laborie, he’s the president of Cognite Americas. Francois, great to have you with us today. Fascinating stuff, we appreciate you for taking the time to share it with us.
Dr. Francois Laborie:
Thank you, Jeff. Thank you, Jeff. It was great speaking and now I’m craving a trip to Tuscany. It was really great having a chat. Thank you very much.
Jeff Standridge:
I thought you were going to say you craving a trip to Arkansas.
Jeff Amerine:
Yeah, come on up anytime. We’re not that far away. If you like to cycle, this is a great place to come for sure.
Dr. Francois Laborie:
And we have a new CEO that is not too far, I believe so, yes.
Jeff Standridge:
Yeah, tell our listeners where they can find you and learn more about Cognite, the company.
Dr. Francois Laborie:
Oh, just head to cognite.com. I’m based in Austin with the team here, but we have colleagues all over North America and Latin America.
Jeff Standridge:
Well, once again, thank you for taking the time to spend with us. We appreciate it very much.
Dr. Francois Laborie:
Likewise, thank you, gentlemen.
Jeff Standridge:
Take care.
This has been another episode of the Innovation Junkies podcast. Thank you for joining.
Jeff Amerine Outro:
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