Podcast #159 Engineering What's Ahead

TRANSCRIPT

Ken: Good day, and welcome to Momenta Digital Thread podcast series. Today it is my great honor to have Prith Banerjee, Chief Technology Officer at Ansys, a leader in engineering simulation. Prith has a long track record of technology leadership roles, including EVP and CTO of Schneider Electric, Managing Director of Global Technology R&D at Accenture, EVP, CTO of ABB, and Director of HP Labs. In addition to an extensive academic background as Dean of Engineering at the University of Illinois at Chicago and Director of Computational Science and Engineering at the University of Urbana-Champaign, Prith is also an accomplished founder, founding AccelChip in 2000 and BINACHIP in 2006. He serves on the Board of Directors of Cubic and Turns Tide, and numerous technology advisory boards. Wow. That is a mouthful, Prith. So with all of that being said, welcome to our Digital Thread podcast today.

[00:01:38]

Prith: Thank you very much, and I'm looking forward to discussing this with you. Thank you.

[00:01:42]

Ken: I am as well. I must say, what an impressive record of achievements you've had, and across academia, large corporates, and certainly startups as well, so this should be a very interesting conversation. I always like to start it off by asking about your digital thread. In other words, the one or more thematic threads that define your digital industry journey.

[00:02:03]

Prith: Absolutely. So again, when people talk about digital thread in the manufacturing industry, heavy industry, like the world of ABB and Schneider, they talk about the fact that in the past, people used to build products, they design products on a theory tool. Then they prototype their part in a lab. And once they saw that kind of work, they say, okay, let's try to fabricate it at scale in a factory and then make the parts and send it out to the factories, to customers, and so on. There is no connection between what was imagined in the concept and then what was prototyped, what was manufactured, and what was operated on. In the new world of digital, with all the digital technologies of cloud mobility, IoT, analytics, etc.- essentially, people are talking about that continuous digital thread, that thread from the CAD going through the simulation, instead of physical prototyping doing hardware simulation, and then to manufacturing through 3D printing and to operations with IoT. So people are talking about the digital thread, maintaining the connection from the CAD, the manufacturing, the operations in every phase in a lifecycle. In the last 30 years, I have been working in different aspects of different digital technologies. First, in academia, then the startup world, then the current worlds in industry, both in simulation companies and in large companies like ABB Schneider. I have been part of this digital journey, and what I have observed is how personally I have become digitized. I use a lot of digital technology myself, and how different aspects of my life- my world in academia, what I used to teach, how that has transferred into the world of startups, and how that has gone into the world of large companies and so on. I am personally practicing digital threads myself.

[00:03:49]

Ken: I just love this journey you've taken and the way you kind of laid out that digital thread from academic to entrepreneurial to corporate. I'd like to call you truly a renaissance man in that regard. I also like Ansys' tagline in some sense, I think it's almost your personal one in calling engineering what's ahead in that regard. Kind of looking back on the last 30 years, as you mentioned, if you had to summarize all that experience into three observations of technology catalyzed transformation, what would those be?

[00:04:20]

Prith: Yes. I have personally gone through this journey. And I want to share my enthusiasm with all your listeners. When I was in academia, you are essentially solving futuristic problems, or I call Horizon 3 innovation. You are focused on the fundamental Discovery, basic science, and so on. And it's not product-based research. You're thinking about what the tough problems to solve are, right? For example, when I was in academia, I was working on parallel algorithms for VLSI computer design. We were thinking, 'Hey, this high-performance computing thing would come in the future, and we are designing these large electronic chips. People would run out of computing, and they would need to do those types of things. When I was publishing papers in those days, the year was 1980, the mid-1980s. Everybody says you don't need high-performance computing. But that's what an academic does. You look at the future- 10, 20, 30 years in the future and say, 'This is the problem that I need you to solve and let's try to solve it.

But what happens as an output of academic research is you end in the publications. When you get these peer-reviewed IEEE or ACM Transactions, paper, and so on- you say, 'Wow, that's great. And you graduate these Ph.D. students with like 10, 12 papers themselves, and that is the output. The output is publications, patterns, Ph.D. students, fame, recognition. You could become a fellow of IEEE, and so on. That's it, right? But then, what I realized when I was at Northwestern- I completed a DARPA-sponsored research project on the match compiler, and it had all these wonderful demonstrations and so on to DARPA. They said, 'Hey, you need to commercialize it. Now, that struck a chord. I said, 'What? Commercialize it? Transfer this to industry'? And what I found was- again, this is the valley of death that people talk about, right? In academia, you stop at the publication, stop at their pattern. It takes a long time to take those initial ideas, that proof of concept into a product, with all the user interfaces and so on that people can use.

Unfortunately, research funding, right? There's no research funding from NSF or DARPA, whatever- to do that additional work. You look at startups that take ideas from academic work- this is what I did in my personal life. I took leave from the university, started AccelChip, got some money from VCs, from ARCH Ventures and Greylock and Interest. It took me two, three more years to take those research ideas at Northwestern into a product. And then, once that product was actually used by customers and so on, you evolve the product, you do a lot of things with it. And ultimately, AccelChip was acquired by Xilinx Corporation, which is where it showed up that technology made it finally into a large company. And I've seen how large companies- the companies that I work for, ABB Schneider and Ansys and so on. How they do a good job with respect to the Horizon 1 innovation, the horizon two innovation. But they oftentimes struggle with the Horizon to the 3s. And that's where the best thing for these large companies to do is to partner with academia, with startups, and so on. And that has personally been my journey.

You talked about the tagline of Ansys. It talks about engineering what's ahead. And the vision that we share with our customers is- in the old world, people used to do a product innovation by imagining a product and then coming up with a physical prototype of a product which they prototype in a lab. And they try three different things, they say, 'Okay, this is the best one'. Today, with really accurate physics-based simulation, what Ansys allows customers to do is to literally say imagine. This is the possibility. If you had to do this kind of a wing shape, and the fluid will flow over that wing shape, this wing- you don't have to test it in a wind tunnel. You can do it through simulation. When Ansys Fluent says this wing will fly, it will fly. If it says at this angle, the wing will stall, it will stall. With the power of accurate simulation combined with millions of processes available in the cloud and empowered by AIML, it's enabling our customers to engineer what is ahead—innovating in various industries like aerospace and automotive, even high tech and manufacturing energy. This is how I see my personal world tie with the mission statement of Ansys.

[00:08:41]

Ken: And I can certainly see how those aligned back in to describe not only Ansys but also your career trajectory, constantly engineering what's ahead. Let me ask a slightly different question. When I looked at your digital thread, what I thought was interesting was your time in both enterprises IT and OT disciplines. And we are, of course, looking at the digital industry- always think very much on the OT side and how it connects in that IT side. Many have said that's a gap, right? The IT/OT gap. Given your time at ABB Schneider and Ansys and other companies- I guess, what's your perspective? Do you see a gap, and how has that gap changed over time?

[00:09:17]

Prith: Absolutely. There is a gap, and, it is transforming as we speak. Let me give you a specific example. When I was at ABB, ABB was a large power and automation company. It's a $40 billion company. They have different divisions serving power in high power, medium power, low powered circuits, and so on. They had these large transformers and switchgear, so very, very large assets. And also, they had things like robots and equipment. In the past, those assets- once they made those assets, they were imagined in a CAD world, right? They probably did some simulation, they manufactured it and they shipped off that switchgear or whatever, or the robot into a customer location and that's it. When that large asset failed, ABB had to send some repair people to go and fix that thing. They go in, they try to decode where the problem is, and they say, 'Oh, that left coil has failed'. So then their repair person goes back home, and they try to find a replacement part. It takes two weeks, three weeks, etc. And that equipment is down for that time. That's no good. In the world of IoT, what has happened is essentially, as soon as that breaker or robot or whatever fails, it calls home. So ABB essentially knows that that part has failed. And so not only with that, with all the diagnostics and so on, you can say, 'Hey, this is what has failed'. So by the time the repair person shows up, they actually bring the right parts to repair the thing, and it is essentially down only for a short time.

And that was- IoT has enabled services. But where people are going with this, again, it's creating digital twins and so on. Then the predictive analytics before a large part failed, gives signals that it's about to fail. Just like before we get sick, we get a fever, and so on. As you are getting abnormal signals compared to normal signals, if you can collect all the data through an IoT platform, you can do amazing things with respect to predictive analytics. And that's where companies like Schneider- when I was at Schneider, we are building the EcoStruxure IoT platform. ABB with the Ability platform, connected up all these assets, GE with the Predix platform- this is essentially what is happening. The IT/OT convergence that he talked about typically- the IT companies like HP and Dell and IBM, working in the IT world. Or Accenture, right? They were working on the IT world, the IT stack, the cloud, and mobility, and so on. And the OT companies, Schneiders, ABBs and GEs, Honeywell- they're working on the OT world, on the on-premise, in the operations, what is going on. What has happened these days is with various IoT platforms like Ability from ABB, or Predix from GE, or EcoStruxure from Schneider and Honeywell, everybody's back from- all these worlds are coming together. That's what is IT/OT convergence. Now, the Ansys angle is we do simulation, and essentially, what we allow our customers to do is to do hybrid digital twins where the prediction of that predictive analytics that you do with pure data analytics- we have found is kind of limited to about maybe 80% accuracy. If you can do it with simulation, it goes to about 90% accuracy. But you go combine physics and data analytics, you can get to 99% accuracy. This is the power of the hybrid digital twin, which is the intersection of IT/OT that I'm personally very, very excited about.

[00:12:35]

Ken: You've really become a thought leader, not only on digital twin but this idea of the hybrid digital twin. Maybe- just to kind of reset, what does the term digital twin mean to you? And why should it be important to our digital industry focus listening audience?

[00:12:50]

Prith: Digital twin- the definition of the digital twin, right? Ansys is a founding member of the Digital Twin Consortium, and there are other companies like GE, and Microsoft, and Dell- they're all founding members. And so the first thing that we need to do is to define, officially, what is the digital twin? Now, to get to a digital twin, you need to have a physical asset like the transformer, the switchgear, or the robot. Then you have to have a model of the asset. And oftentimes, people confuse a simulation model of an asset as a digital twin. What makes a digital twin a digital twin is the third thing, which is to weigh information for, between the physical assets and the model. So information flows from the asset to this model that makes it a little more accurate. And information flows from the model back to the asset to their IoT platform. And essentially, therefore, the Digital Twin Consortium has defined digital twin as 'a virtual model of a process or asset, which is synchronized at high fidelity, and at certain frequencies'. So it's a very short definition, but it means a lot because it's talking about the model and the model between the physical and the real. And he talks about the synchronization, the two information flow, and it says- synchronization done with a certain fidelity. You can have a very rough, low fidelity model which is also a digital twin, or a high fidelity model. And the hybrid digital twin that I'm talking about really brings it together. Just to double click, in the world of the digital twin, people used to just come up with- slap some sensors to assets and collect data on those assets, and they built a digital model of that asset. It's a very general model, but the accuracy of the predictive analytics is kind of limited to the data you have seen.

The Space Shuttle Challenger had an explosion, right? How many times did the space shuttle explode? Only once. No amount of data collection would have predicted a space shuttle would explode because you don't have enough data. However, with physics simulation, with Ansys tools, or other simulation tools, you could model the fact that here's a space shuttle, it is entering the Earth's atmosphere at 50G- that is generating a lot of fluid flow over the tiles. That is generating a lot of heat, and the heat is going to make those tiles expand. At a certain point, that tile expansion will create force stress, the tile would explode, and oops. And that's where the space shuttle may explode. Those physics-based analyses can be done only through detailed simulation. But you combine that with IoT- imagine, on that space shuttle, you had an IoT connection and say, 'Hey, this is about to crack, it's about to crack'. And you actually notice a crack, which is six inches long, then seven inches long. If you could synchronize that IoT connection, that actual connection, the real asset- with some simulation, where you say, 'Hey, I'm going to do smart propagation. You can combine the two, you can do amazing, accurate results. This is where the future of digital twins is. And we have a twin builder product in Ansys, which does this in this vision that I talked about, the hybrid digital twin.

[00:15:49]

Ken: Wow, what a great definition there. And I like the double click down, that's a relevant example. One of your peer companies, of course, in the space is PTC, which acquired ThingWorx back in 2013, which was one of our early portfolio companies. The deal rationale they had almost sounded like an early version of the digital twin, and they were calling it round trip engineering from CAD out to the asset itself, monitoring the asset. It's been almost eight years since that acquisition. Do you think we're any closer to that promise at this point?

[00:16:19]

Prith: Absolutely. PTC is one of our strongest- not just peer companies, partner companies even. I was on a call recently with Brian Thompson from PTC only last week. So you look at PTC's original product. The big thing that they do, they have this CAD tool. When I talked about product innovation, you're imagining an aircraft wing or an engine or a gas turbine blade. You design it using a CAD tool such as Creo. And then essentially, once you imagine that, you do a bunch of three, four different physical prototypes, which is how people used to do designs in the past. They do three, four different prototypes, and then they say, 'Okay, this has been fabricated. And I'm going to test it, manufacture it at scale in a factory. And that's it, it goes out in the field, and it's operating. Now, that was the old world. Then the world of simulation came in simulation-based product innovation where once you enter it into a CAD tool like Creo, you do a simulation of that. You can do a very simple quick simulation, and we have a tool called Ansys Discovery that ties in with Creo. As soon as a designer is entering a new CAD design in Creo, they can do a quick simulation through Ansys Discovery tools, and the two tools are very tied together. But Discovery is a very quick simulation called real-time simulation. But an analyst really needs to do a detailed simulation. We have Ansys Mechanical for structural simulation using finite element analysis, or fluid simulation with fluid or electromagnetic simulation with HFS. We have these different physics solvers that are tied in for doing the deep analytic simulation.

Then once you go into the manufacturing area, essentially, you can do manufacturing, with additive manufacturing- like 3D printing, and so on. Again, we have tools such as additive solutions that can simulate that 3D manufactured product. Then that product goes all the way to operations and then you connect it through an IoT platform. What PTC has, the second tool they have is this PLM Product Lifecycle Management tool called Windchill, and so that manages that digital trail. But at the end of the thread is an operation and you're collecting data through an IoT platform. And essentially that is- the thing works back from the PTC. The partnership that we have with PTC is PTC does the CAD, we do the simulation, the quick rapid simulation with Discovery and the detailed simulation with Ansys mechanical and so on, but then this is more managed by the PLM. And then at the end, you have the IoT platform which is the thing we start from. And then we essentially take the thing, it goes back from the output that PTC set up, and then use that to build a digital twin, the hybrid digital twin. This journey of this digital thread starting from concept, to design, to manufacturing, to operations, and getting your feedback to the next product innovation is the digital thread. And this is how PTC and Ansys are working hand in hand together.

[00:19:11]

Ken: At the time we were involved in the transaction, the thesis looked like this PLM, ALM, SLM all basically got you to- you'd say this lifecycle is designed to deliver. I design the product and I deliver it off the back shelf of the OEM if you will. The thing that was brought into this is this idea of monitoring that asset throughout its lifecycle, right? And so you could put connected CAD, connected PLM, ALM, SLM, etc. And it gave a whole new valuation to that full-cycle such that you could look at the lifecycle now of that equipment that was produced from design to disassembly at the end of life, really thinking long term. And it was very interesting looking at those models in those economics, in terms of the difference that it made. I agree with you, it was a pretty wise investment on their part and it seems to have really proven together with the work you're doing at Ansys, how this can truly be roundtrip. The dream for many a generation.

[00:20:07]

Prith: Absolutely. And where they're headed is- now, the world is moving to the cloud. And so essentially, they have now acquired a company on shape, which is scattered on the cloud. And again, we are doing some amazing work on simulation in the cloud. So essentially, the whole journey on the cloud is we are going to continue to partner with PTC in this area.

[00:20:25]

Ken: Perfect. You mentioned innovation earlier in this idea of Horizon 1, 2, and 3. I'd like to drill down on that a little bit, just so the audience knows- number one, Ansys was named a fast company in the list of 100 best workplaces for innovators. That's quite an interesting one. You of course, have had a lot of experience in academia, UC, Northwestern, startups, AccelChip, and BINACHIP. And large companies, HP, ABB Schneider, Ansys, etc. I guess looking at one, why Ansys got that award, and two, your broader perspective of best practices in corporate innovation outside in, I guess in some sense. What would you say are some of the interesting best practices that you've seen out there?

[00:21:09]

Prith: This topic of innovation is very, very close to me and I'm very passionate about it. As you mentioned, I've been fortunate to have worked in three completely different fields of innovation. People are thrilled if they can just work in academia, and then I was in academia for over 20 years, working for both public universities like the University of Illinois and private universities like Northwestern. That is what academics do, they do a phenomenal job in the Horizon 3 innovation- 5, 10 years, 15 years out, funded by the National Science Foundation, or DARPA, whatever. They're imagining what are the possibilities out there in the future world. For those professors and graduate students, they do all this fantastic work and they submit once they're done with the research. They publish this amazing work in a peer-reviewed conference or a journal, IEEE conference, or ACM journal. And that's it, right? Because the metrics of promotion and tenure or whatever in academia says, okay, you found the new stuff. You've innovated, you've solved the problem, this problem was how do you do a really scalable database that can scale up to 2 trillion data, etc., and that's it. You publish the paper, you're done. What I have found is, it takes a lot of work to take those ideas that were essentially written up in a publication, to convert that into a product that customers would use. And in my personal journey, when I ended the match compiler work at Northwestern- DARPA sponsored. It was amazing, really futuristic technology taking MATLAB programs and automatically generating chip designs using VHDL, and so on. They look really good, but it was not a product.

Essentially at that point, I took leave from the university- from Northwestern and founded AccelChip, raising money from VCs and so on, and building that product. Building that product took about two years. I was very focused on just building their product. MATLAB in, register, transferable VHDL out for the mach on FPGAs. And then you work with customers- well, this doesn't work, etc. So there's a lot of work that is needed. And VCs come in at that point, taking that idea, a very focused product and building their product and disrupted the industry in the time around 2000. And then AccelChip after four or five years was acquired by Xilinx Corporation. And that became part of the portfolio, Xilinx makes FPGAs, and they were building design tools for FPGAs so it was kind of wasn't part of this thing. Now, the observation that I have made is that large companies do a fantastic job in the Horizon 1 innovation, which is they have a current set of products, they're building FPGAs, or breakers or panels or whatever from ABB. Ansys has simulation tools like HFSS and Maxwell. They do a good job there because they have an existing product, they are interfacing with customers.

The field tells us, 'Oh we need these features, your competition is doing this'. They do a fantastic job in the Horizon 1 innovation. Horizon 2 is the adjacency. You had these things- okay, now I need to go on the cloud, maybe I should do this, etc. Maybe this thing is working for the North American market. I have a breaker for North America, 410 volts. I now need to go to China for 220 volts, they also do a good job. Where large companies struggle is in the disruptive innovation, the Horizon 3 innovation. I don't have a product in that space, and I'm just imagining a product. I worked at HP Labs, and when I was at HP Labs, I was helping HP build those disruptive innovations, the Horizon 3, and so on. But even if you build a really interesting product in your lab, the large companies' sales forces really don't know how to sell the product. So they say, 'Oh, I don't know what to do'. There is a challenge. Essentially, the observation I'm making is that these large companies, in order to do disruptive innovation, they need to have a central arm for R&D, and then that central arm needs to do partnerships with universities and startups and bring those startups' technology as part of this. Now, what am I doing at Ansys? At Ansys, I am leading efforts in AI machine learning applied to simulation. So we have an organic group, we are doing some amazing work with AIML. But in addition to the organic work, we are also working with academia. We are working with professors at Stanford, at Carnegie Mellon, at Princeton, at Brown, and so on- and taking those really research innovative ideas, and working, cooking it jointly with the work that is going on at Ansys.

And then we are also working with various startups. Now, I don't want to name the startups here, but we are working with a set of startups. We are taking those startups' ideas, and combining that with our stuff, and doing some amazing stuff. And it's just that- for example, the work that we do in AI now, we are doing similar work in digital twins, similar work with HPC. The observation is that large companies will be successful with Horizon 3 innovation if they know how to work with startups and so on. And through the startups, what we have done is we initially work with a startup, then we invest in their startup, then we acquire their startup. For example, two years ago we worked with a startup called Dynardo, they had a tool called OptiSLang, that was allowing our customers to use our simulation tools and do design optimization around it. We initially had an OEM agreement with that startup, then we invested in the startup, then we bought the startup Dynardo in 2019. And over the last 15 years, Ansys has acquired 26 companies. This is how we are essentially doing this Horizon 3 kind of innovation, where we see all these possibilities, bring those technologies, and integrate the technology and then essentially bring them to our customers. And that is what has allowed Ansys to win this Fast Company Award for Best Innovators. And one of the things that we do internally at Ansys is to have these CEO Innovation Awards. We have three sets of awards, awards for the most innovative product, the most innovative solution, the most innovative technology- and that has changed the culture at Ansys. We're very happy to be talking about innovation at Ansys with all of your listeners.

[00:27:03]

Ken: You clearly have a lot of passion around this space and clearly, a track record that says you know what you're talking about. Maybe we can expect a book from you in the future on this great topic because it would be very, very timely.

[00:27:14]

Prith: Actually Ken, since you mentioned this, I'm writing a book called "Innovation Factory". It should come out hopefully by the end of this year or early next year. And essentially, it is about how large companies can innovate in the 21st century by better partnering with universities and startups. And every large company, they say, 'Oh yeah, I work with a university. I do some funding. Oh, I work with startups'. But the thing is, I mean, you can do those kinds of work as a checkbox, or you can actually do it. So my book is going to talk about practical guidelines as to what I have learned and being on the receiving end of company funding at the University of Illinois and Northwestern, and funded research when I was HP Labs, and I worked with startups. So I have seen what works, what doesn't. And that's my contribution to society. Hopefully, your listeners will read my book on "Innovation Factory" six months from now. Thank you.

[00:28:01]

Ken: Excellent. Wow, I will be one of your first readers. Let us know when that is published. Given all this discussion around innovation, and again, this tagline of engineering what's ahead, I'm gonna ask you to put your prognosticator hat on and tell us what three trends do you think will define the next five years for the industry?

[00:28:22]

Prith: Obviously, it's very hard to predict the future, but a few things that I am seeing is AI and machine learning is going to be extremely, extremely important to the world in general. And we at Ansys, really looking at the use of AIML applied to simulation. How are we doing this? Well, we are trying to use it to improve the customer productivity, use of our tools- I mean, augmented simulation, how do you simulate things so much faster, etc. AIML is clearly disrupting a lot of the things in various industries with recommendation engines, and so on, so forth. But it will completely change the way that the world goes and so on. The second thing is access to tremendous amounts of computing and storage. Essentially, people do these things with- on workstations, they have HPC clusters, and so on, so forth. But you look at all the cloud providers from Amazon, AWS, and Azure, and Google Cloud Platform and IBM and Oracle. All these cloud providers are providing a tremendous amount of computing on their resources. Essentially, this is going to change, transform the world of simulation-based product innovation. Because in the past, you were limited. Well, I want to do a finite element analysis. I don't have as much computing power, so I'm limited to only a million mesh points. If I had a million processes, I could go to a billion mesh points, I could do a trillion mesh points. And don't worry about the accuracy, I could be as accurate, more accurate than the physical world.

Essentially, a cloud and the HPC on the cloud is going to be a very, very big disrupter. And you can already see all of this. And the third thing that I see is the world around the ecosystem. And that ties in with this whole concept of open innovation that I was talking about. A company by itself cannot do all the work itself. You look at the automotive industry, right? There's fundamental things happening in electrification, autonomy, and whatever. But the car companies, the Fords and the GMCs and BMWs- they cannot do all that innovation themselves, so they rely on the suppliers. The Boschs, the Continentals, and so on. They will do this really interesting thing to feed into that process. The question is, how will innovation happen in the future? And I believe it will happen through an ecosystem where you enable third-party developers, suppliers, developers, and so on to co-innovate for you some really interesting parts. And within our own little field of simulation, we are thinking about how we enable third-party developers in innovating very, very interesting things by using Ansys solvers in the back end, and so on. Anyway, those three things that I can talk about- AIML, cloud-enabled with HPC, and this whole world of APIs, and third-party developers and so an ecosystem. Those are three I think, very, very interesting trends to watch for.

[00:31:03]

Ken: I would fully agree, I like the fact that you've coupled two technologies with one social pattern and we are seeing a lot more of this, even if you look at the EU's Industry 5 initiative. It's coupling the technologies of Industry 4 with resilience, social good environmental, etc. It's pretty interesting to see in terms of how we go about doing something that may be important as what we do in the end. And I like this ecosystem concept. So finally, in closing, where do you find your inspiration? Think books, people, etc?

[00:31:37]

Prith: I read a lot of books. Fortunately, with all the digital things that are out there, you don't have to read physical books. You can go online and get amazing readings and so on. I listen to the radio, listen to podcasts, such as yours. And just looking at some amazing leaders, innovators- wonderful people that I get inspired with. So it is a combination of all three. The podcasts or online readings, the books, and the people that I see. And yes, obviously people like Elon Musk and Jeff Bezos- just absolutely wonderful role models for all of us to kind of emulate and follow.

[00:32:12]

Ken: Clearly. Speaking of ecosystem, it sounds like you've got a wonderful ecosystem of inspiration. Well Prith, thank you for sharing this time and these wonderful insights with us today.

[00:32:23]

Prith: Thank you very much, Ken. Really appreciate it.

[00:32:25]

Ken: Oh, as well. This has been Prith Banerjee, CTO of Ansys, a man and a company focused on engineering what's ahead. Thank you for listening, and please join us next week for the next episode of our Digital Thread podcast series. Thank you, and have a great day. You've been listening to the Momenta Digital Thread podcast series. We hope you've enjoyed the discussion, and as always, we welcome your comments and suggestions. Please check our website at momenta.one for archived versions of podcasts as well as resources to help with your digital industry journey. Thank you for listening.

Thank you, and have a great day.

[The End]