Jim Scapa Podcast Transcript
Jim Scapa joins host Brian Thomas on The Digital Executive Podcast.
Welcome to Coruzant Technologies, Home of The Digital Executive Podcast.
Brian Thomas: Welcome to The Digital Executive. Today’s guest is Jim Scapa. Jim Scapa brings nearly 40 years of business growth, innovation, and cultural stewardship to his role as founder, chairman, and CEO of Altair, a global leader in computational intelligence.
In 1985, Jim and two partners identified a need and formed Altair with a focus on the then new field of simulation using high performance computing. Today, through Jim’s leadership, the company serves more than 16,000 customers and employs more than 3, 000 employees with 81 offices across 29 countries.
Altera software, which includes simulation, high performance computing, and data analytics and AI, is used across a broad range of industries, including automotive, aerospace, government and defense, banking and finance, technology, energy, electronics, life sciences, architecture, and heavy equipment.
Well, good afternoon, Jim, welcome to the show!
Jim Scapa: Thanks very much. Pleasure to be here!
Brian Thomas: Absolutely. I appreciate you making the time hailing out of the great state of California there in the Bay area. Appreciate you making the time. We’re a two-hour difference, but it all works out when we’re doing these podcasts around the world. So, Jim jumping into your first question.
You founded Altair in 1985 with a focus on simulation using high performance computing. Can you share the initial challenges you faced, in this then new field and how you overcame them?
Jim Scapa: Sure. I mean, we were a very small company when, when we formed the company, we were based in Detroit. So not in, in the world of, of Silicon Valley and software.
And we started with 1,500. So not, not heavily financed. And we were playing in a, in a field that you know, had some public companies playing in it. We started primarily as a consulting company working with the auto industry. And it was the early days of doing simulation and, and you know, computers were relatively smaller, but, you know, through the years.
As computers continue to evolve larger memory, all that we began developing some software and, we essentially disrupted the market for modeling and visualization by taking a new direction where we had much, much larger memory. What brought the entire model into memory, and we disrupted by having about 30 times faster graphics performance with our 1st software product.
So, the challenges were, you know, getting the distribution, getting out to customers. Being unknown, being small, and we just had to simply overcome those with better technology and agility and hard
Brian Thomas: work. Thank you for sharing. And I know, you know, starting out and 1,500 dollars is probably a lot. More back then than it is today to just to give our audience some perspective.
I remember those days and the green screens and so forth, but no, I appreciate you sharing kind of some of the struggles and challenges you did start out, but you’re still in this space. And obviously you’ve moved along, or at least moved ahead with technology because you’re still in business and you’ve grown quite a bit.
So, thank you. And Jim Altair has grown to serve over 16,000 customers with 3,000 employees across 81 offices in 29 countries. What key leadership principles have you followed to achieve and sustain this growth?
Jim Scapa: For me, it’s largely about building a culture in the company that is sort of the guiding light and, and it’s a culture of a lot of innovation, frankly. Very broad communications looking forward to where, where the market and where technology is going and making decisions in the context of that longer, longer future that we see. And we’ve been very experimental list. Through the years, and, and you know, trying, trying new things from a technology standpoint from a business model standpoint.
And you know, it’s worked well for us. We were a private company for 32 years, and I IPO in late 2017 and we’ve obviously had a great run. And in these last 6 years, we IPO to 13 dollars a share. And today we’re, we’re close to 100 a share. So, yeah, it’s been, it’s been quite a ride. And again, it’s, it’s very driven by you know, a, a strong culture in the company of, of mutual respect and, and great, great technology and innovation.
Brian Thomas: Thank you. I do appreciate you highlighting that. That’s nearly a 40-year span. Obviously if you’re. Company from its inception to today. And, and it’s really a testament to your leadership. With the culture and being able to move a private company to IPO and that’s, that’s just simply awesome. So, thank you for sharing that.
And Jim, the landscape of computational science and AI has changed significantly over the last few decades. How has Alt Alter adapted its technologies and services to stay ahead of the curve?
Jim Scapa: You know, I think once again, we’ve been looking forward and I think we’ve been often leaders in, in terms of embracing.
Where technology is going, so we started mechanical simulation primarily with software for, you know, structural analysis and fluid mechanics and motion and those sorts of things. We’ve added lots of electronics. And so, we have a full portfolio of electronic simulation technology. Which is an area we’re continuing to pour investment into in the early 2000s, we could see that that computing for high performance computing.
Is really moving from a world of very specialized. Computers craze and convex is and floating-point systems and Apollo has lots of dead computer companies actually. And it all moved to commodity clusters, which is what you see today. And we saw the need for managing where and when jobs are running and scheduling them and moving the data around.
And so, we began investing in that direction. And today we’re the leading player for. Workload and workflow management on high performance computing. And it took us out of just manufacturing into life science and pharma, you know, banking and financial services, energy. Lots of markets that were not served by engineering software and by coming into the HPC world, we began to see, we would go to supercomputing conferences, and we were seeing data science really starting to rise.
It used to be all about simulation, but data science was starting to rise. And so, we began to invest in data analytics and data science. And so today there’s really those 3 legs to our stool and they’re all converging, you know, very, very dramatically. You know, today you can run a series of simulations, run it through a neural net you know, model and basically end up with you know, a neural net that that can now run the next simulation and give you a very accurate results.
Or complete a simulation run and run the, you know, the last few milliseconds of that simulation by projecting it using data science. And that continued to expand our world. Most of our data analytics and data science customers. Are actually in banking and financial services. But there’s an explosion right now of AI and engineering, and we see just every, every customer wanting to embrace that.
So, we’re finding ourselves sort of in the center of, you know, where all this technology is, is going and, you know, it’s, it’s just been very, very exciting for us.
Brian Thomas: That’s amazing. Again, just highlighting some of the shifts. You’re always trying to stay a step ahead of the, you know, evolving landscape as far as technology.
And I do love working in that analytic space, right? You talked a little bit about predictive modeling while using some of the machine learning. And I think that’s just awesome. And I appreciate you highlighting some of that for our audience.
And Jim last question of the day, what are some of the emerging trends in computational science and AI that excites you the most? How is Altair preparing to address these trends and continue its legacy of innovation?
Jim Scapa: Yeah, well, there’s two main areas that we’re probably most focused investing in right now. 1 is electronics. And even though you didn’t mention it, I’ll mention it and data science is playing a big role there as well either way.
But in electronics, you’re, you’re seeing this big move from sort of 2 D chip integrated circuit design to chiplets where you’re stacking chiplets and it’s becoming much more of a 3-D. World, it’s called 3-D. I say, and that’s creating a, you know, the opportunity to disrupt that market. And I love coming in and disrupting markets. And so that’s an area of a lot of focus for us. And we’ve been a very acquisitive company. We’ve acquired 58. Mostly founder led technology companies. And more recently, it’s all been around electronics and data science. In data science we find ourselves in just a really great spot. It’s taken, you know, 5, 6 years, but we just were selected to be in the leader quadrant of the magic quadrant for Gardner, which is not an easy place to be.
And it took them a couple of months to release the results. And I think it’s because there was a lot of infighting about how the results came out by some other players that thought they should be in the leader. Community, but I think we have a really unique portfolio. Now we have a very strong platform for data science.
And the areas we’ve been investing in most recently, obviously, is a huge part of what we’re doing, but we also acquired what we think is the best technology for analytical graph. Database technology. Which lets you kind of build this fabric across your enterprise. If you have. You know, many, many disparate databases all around the enterprise, especially large enterprises.
And you want to connect that and find all the relationships between all the data and this database over here of customers and this database over your products. And, you know, and whatever marketing databases and sales databases and engineering databases and manufacturing databases, and we build the semantic layer above all of that.
That connects all that data and now you can query for what you want. Analytically, and you can use large language models, for example, to do natural language questions. And it will basically search the entire universe of your data across the entire enterprise. We think that’s going to be a huge and dramatic shift.
As everyone’s been trying to find, how do we, how do we solve that problem? Do we bring all the data into a giant data lake and, you know, and do it that way? I think this solution. And it’s massively parallel processing, so it’s extremely, extremely fast. Very scalable, I think, is going to be really, really fundamental to sort of transforming how you get to data, reducing hallucinations when you’re doing these queries.
So, that’s an area where we’re absolutely investing in heavily. And, you know, the other 1 is, is just trying to understand where goes. You know, the, the current hype cycle, I think is pretty substantial and the investments I think that are being made are, are probably. Not aligned with the return today, but that doesn’t mean I don’t think.
You know, there’s a huge future for all this technology. I just think there’s going to be a big sort of comeback here in the next couple of years. And then the typical slow, slow rise on the hype cycle. I think a lot of what’s going to start to happen is. Techniques like quantification, which are, you know, sort of reducing the size, if you will of these models so that they’re more feasible.
They run faster. And you know, just using lots of new technologies to be able to bring down the scale of these models, retain the accuracy. So that the cost to run them becomes more in line. It’s absolutely an area that that’s really just at the beginning. I think. And I think is going to be transformative in engineering.
You know, in I see design and mechanical engineering in business and much more. So, yeah, those are pretty obvious, but I think clearly the big areas that we see change in.
Brian Thomas: Thank you and you highlighted quite a bit. Obviously, you started out with exploring other technologies, diversifying in the technologies, including electronics, but.
I do like the tail end of your conversation about how you can make things faster when you have so many different disparate data systems across your enterprise. It is hard sometimes to bring that all together and scaling can be hard when you’ve got that much data. So, I appreciate you sharing some of those insights. Really do. Our audience will certainly appreciate it and Jim.
It was certainly a pleasure having you on today and I look forward to speaking with you real soon.
Jim Scapa: Well, it’s my pleasure as well. Thanks for the interest in talking with me. So, appreciate the visibility that you bring to my company. So, thank you.
Brian Thomas: Bye for now.
Jim Scapa Podcast Transcript. Listen to the audio on the guest’s podcast page.
