00:20:17
These are specialty materials. They cover currently about 80% of the non-radioactive periodic table of elements. So if you had chemistry in school, you know the periodic table of elements. If you take that full picture, a couple of them are either radioactive or they have a pretty short half-life, so they’re not being used, but 80% of the rest is actually being used in making semiconductors. So most of the examples I’m currently using, they’re based on what we call a logic chip. This is a CPU, GPU, everything that does any kind of logic switching. The other big area for semiconductors is memory chips, everything used to store data on a permanent way, on a dynamic short-term way. So these are different levels of memory being used, but most of the examples I’m using here, whenever I talk about the transistors, it’s more on the logic chips, the main area that drive compute right now in the world.
Jon Krohn: 00:21:21
Very interesting. So now that I’ve asked about the business in general and we understand a little bit more about semiconductors, you’ve been working at your company since 1989. Tell us about how you got into that and how you grew into this leadership. The thing that’s interesting to me about this question, I don’t usually ask guests on air how they grew into what they’re doing today, but in your case I think it’s fascinating because it’s this highly technical field and you’re obviously very much on top of those technical aspects. You’ve been doing it for a long time. How does somebody become expert in semiconductors and then grow into a leadership position like you have?
Kai Beckmann: 00:21:58
Yeah, it wasn’t such a straight line as I probably hindsight it could be made. It was more, let’s say, a path through very different assignments. I left after I studied computer science with a very deep focus on semiconductors, already left university and so now I was working on semiconductor design. Back then, improving semiconductor design in the late ’80s, this wasn’t such a sophisticated area as compared to today, but still it gave me, of course, deep insights into what semiconductors are. After being a research assistant at that place, then I was attracted by joining industry in a very different area, more in my, let’s say, old home turf in software.
00:22:47
I worked in our corporate IT for quite a couple of years more in the database space and doing consulting for process improvement. So these kinds of things that brought me then… I skipped now probably two decades. It brought me then into the business, as well running a country organization, selling our materials and our solutions and with another steps. Then I was heading for a couple of years, HR, so more as a board member and a kind of more a general petitioner rather than a specialist in HR, but it was times of massive transformation and I think the owners wanted to have probably a person that has practical knowledge of leadership rather than just a specialist. So we drove the transformation, the company within an HR lens.
00:23:39
Then again, a couple of years later, I was asked to head our electronic materials and solutions business in our company. There was, of course, a great opportunity because it brought me back to where it all started to semiconductors and this is why over here it is the leadership part, which is exciting, as well as the deep technology where I believe I do have still quite some knowledge from the late ’80s, which I refreshed. Of course, I polished it a bit over time, but it’s exciting with the team not only to talk about the P&L and the growth plans and the strategy, but to talk about technology. I love to go to the labs and see that… I was just there a week ago and saw a new tool being used there for atomic layer deposition and talking to the R&D folks, that makes me really excited. This is what brings me to work every morning.
Jon Krohn: 00:24:34
Yeah, it’s interesting your background, blended technical aspects, as well as aspects like HR leadership and that blend over time, over decades doing those from both sides, the technical aspects, leadership aspects, it allowed you to get to a point where yeah, now the CEO of this highly technical business. So now that we’ve talked about your past a little bit, I’m going to talk about something that happened very recently, which is that your company purchased another company called Unity-SC, which is a provider of metrology and inspection instrumentation for the semiconductor industry. Now that we know about a bit about what the semiconductor industry is, what is metrology? Why does this acquisition matter?
Kai Beckmann: 00:25:18
Yeah, let me just start with the material side and then I like to share what’s the logic behind creating a broader footprint for our customers. So the materials, all these differentiated materials, highly complicated materials required to make these amazing structures possible that our customers, all the chip producers in the world need, that requires chemicals, the real chemistry knowledge. It needs physics and requires us to understand microelectronics because this is how these things are optimized to electronic properties at the end and it requires what we call a vertical integration and integration of different capabilities. You need to understand how to make these materials. You need to understand how to test the electrical properties. You need to understand the effectivities of our customer’s fabs, so what drives their yield, which is their ultimate target and what drives the performance of these tools. It needs too another capability, which is how do we deliver these chemicals to a tool that at the end deposits it on a wafer. So understanding delivery systems, another important dimension.
00:26:41 This is how we have built the portfolio of our company across these very different domains because our belief is and our customers confirm that’s not a stupid idea, is only if you optimize across these very different dimensions, then you are able to solve these very complex problems in the fastest possible speed because it’s always about speed. How fast can you innovate for our customers? If you drive that in a more integrated way, you save on these cycles that typically take years in order to drive new technologies. You save massively on time and you get it to our customers much, much faster. That’s a logic.
00:27:18
Now, coming to a metrology. A metrology is what is called the inspection of defects in the end product. In this case, the company acquired Unity-SC, is an expert in visual inspection. This is inspection used on more on two and a half D or 3D on layered structures where they can understand or test whether this whole system works as it’s supposed to work. So understanding are these so-called TSV, trans-silicon via, so these are holes in this silicon wafer, are they built in the right way? Is the shape exactly as desired? The inspection tool used is like a video camera in a way if you want, but of course, with a much, much better resolution that the end gives these very small structures or reproduces them in their data stream. This is where we learn how materials can be co-optimized in order to drive the performance of the end device, and this is why we are interested in this integrating this capability into our materials focus areas.
Jon Krohn: 00:28:34
It sounds like that kind of metrology, that being able to visually detect defects might itself involve an AI system, but what kind of role does high precision metrology play in enabling the next wave of AI breakthroughs?
Kai Beckmann: 00:28:48
Yeah. Of course, this metrology is specifically used for what I earlier called CoWos, so chip on wafer on substrates, these integrated structures being used for Hopper or Blackwell like chips or systems and in these areas specifically this kind of metrology is being used. Of course, as you rightly said, the data stream generated out of a metrology system by itself, of course, allows you to optimize based on AI algorithms once the technology being used to stack the devices, as well as the materials being used to drive these innovations, so on both sides. I think we call it ourselves, for our company, we call it AI for AI because we use AI to make these amazing materials happen in order to drive AI as the outcome of the chips that our customers produce. I hope that makes sense.
Jon Krohn: 00:29:50
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00:30:30
It’s AI for AI, AI enabling AI. This is a really cool concept because it’s a positive feedback loop. The better the chips get, the better the AI systems get, those better AI systems can be used to make better chips. It’s a really fortuitous cycle. Also, you’ve mentioned a couple of times on the show this idea of Blackwell or Hopper chips and many of our listeners would probably know this, but just to make clear, that’s a class. It’s a very recent class of Nvidia GPUs. So those are the kinds of chips technology like that that’s driving the most cutting-edge AI systems that we have today and fueling the generative AI boom, now the agentic AI boom in 2025.
00:31:13
I have a couple more technical questions for you before I get into some leadership stuff. So when we were doing research about you, we uncovered something called heterogeneous integration in AI chips. So what is heterogeneous integration and how does it impact performance and the packaging density of AI chips, this density thing being critical to building more and more powerful chips? Because obviously the more transistors you can get in a smaller space, the more powerful a chip can be.
Kai Beckmann: 00:31:43
Yeah, that’s an important area and I call it earlier in our conversation. So this is like what is more than Moore, so what dimension drives a performance or allows to scale performance beyond just making smaller transistors on a chip? This is the additional dimension driven by heterogeneous integration. Maybe let me just quickly with a sentence come back to the AI for AI. We have branded it the way that we call that materials intelligence. This is the use of artificial intelligence to drive the development of novel materials for applications in electronics.
00:32:27
We call that materials intelligence and this is with how our team works as a global R&D team, not just in a traditional way, sequentially improving properties of materials by using AI to replace experiments in order to avoid unnecessary experiments and going straight into where it really matters. Where can you really make a difference for the customer technology? How can you anticipate how material works in a customer setup and how does it drive the solution of their problems and not just chemicals properties in the first glance? So this is how we drive the development of novel materials. We talk about millions of different options that need to be optimized in order to drive the performance of material. So this just to give you the idea on how that blends into AI for AI.
00:33:26
Second is then driving the different aspects of how our customers improve the performance of their devices. Besides shrinking the transistor, building more integrated systems and heterogeneous integration is the important area here. It started traditionally with what is called a front end process making a transistor and back end was the new wire. It somehow that at the end, the signal gets to the outside, which is then called in a product scheme packaging. Now there’s something between these two extremes that it’s called heterogeneous integration, when at the end the chip is not just one die, one single chip anymore, when you combine different chips to a system. I refer to it in this specific example as CoWos, these structures being built in the examples I’ve used. I can use different customer examples here as well. Just wanted to use one nomenclature, which is pretty common in a current conversation.
00:34:35
This is when you glue dies on top of one another in order to build memory stacks, for example, or you build a memory stack and you almost glue it next to a GPU in order to shorten the transfer of data and to make it more efficient in getting the data to the GPU. That is called heterogeneous integration to make that possible. It requires, of course, technologies well advanced from what was used in packaging historically, so much smaller structure sizes, much more complicated efforts to get your heat out of the system as one example or to optimize power consumption. The precision required then needs different technologies more front-end like technologies, which makes it an area, of course, for materials innovations and for metrology innovation as per what our company is focused on.
Jon Krohn: 00:35:34
Interesting, and I’m glad how you tied that innovation, the heterogeneous integration, integration to some other kinds of concepts like metrology and the importance that that makes in getting transistors to be smaller, to be having a higher density of transistors on a chip. In addition to transistors, another key aspect of effective computing is memory. So I have one last technical question here for you on that, and this is related to spin-on dielectric materials, SOD. How can the interplay of electronics and optical technologies and spin-on dielectric materials influence the electronics industry? What are has spin-on dielectric materials? You probably have to start there.
Kai Beckmann: 00:36:21
You picked a nice domain. Typically, what we differentiating is you got conducting materials, metals typically on… Even those metals evolve. When I was in college, aluminum was probably the most commonly used metal on a chip, and then we used copper and we used tungsten. Meanwhile, we used molybdenum and many different conducting materials and then you have insulating materials, so-called dielectrics, where you want to avoid that… You got unwanted flow of electrons on your device, so metals and dielectrics in a very simplified way. These dielectrics are specifically being used. If you want to stack functions on a chip, then you need dielectrics in between that you don’t get any short circuits, or if you want to insulate transistors from one another, then dielectrics are being used. The most probably traditionally and commonly used dielectric is silicon oxide. That is the easiest one to understand on a chip, but there’s so many more dielectrics being used and very specific dielectrics chemicals and molecules being used for insulation.
00:37:45
Then comes the way they are applied on the chip. Spin-on, that is again a pretty simple one. Going back to my record player example in the beginning, so if you remember your record player and maybe some people still do, and if you would drop some water right in the middle of that record player and then the water travels over the full record and this is a spin-on technology. Now, you put it in the middle and it spins and you cover the full surface with a thin film and which is exactly the way how a spin-on dielectric is brought to a wafer. It’s the same principle just, of course, with a bit more precision and accuracy. This is how a spin-on dielectric builds in quite nice even film on top of a wafer.
Jon Krohn: 00:38:41
If our listeners at home now get their record player out and drop water in the center of it, is that going to cause damage to their vinyl records or is that going to be a safe experiment?
Kai Beckmann: 00:38:50
I’m now talking really about my experience. There was a way to play these records with a wet surface in order to avoid any scratching and that was a pretty tricky way. This worked exactly the way I just explained.
Jon Krohn: 00:39:07
Wow, that’s wild. I had no idea. So something to look into there and maybe try safely at home if you can find a good YouTube tutorial.
Kai Beckmann: 00:39:13
If you find a record player. I don’t know how many people still find a record player at home.
Jon Krohn: 00:39:17
It’s actually pretty common in my kind of set and people I hang out with. We kind of all have record players and vinyl records. I don’t know if that’s normal, but it’s pretty common amongst people I know.
Kai Beckmann: 00:39:28
I got a couple of records in my basement though, probably exceeding 2,000, two and a half thousand records I got in my basement, which I’m not using anymore. I just stack there and wait for a moment when I retire and then I can resort to my record player and play some records, but that’s for another day.
Jon Krohn: 00:39:45
Very nice. Really quickly to just get a personal sense of you, what are your favorite records?
Kai Beckmann: 00:39:52
I got lots of ’70s, so all kinds of ’70s music. I grew up with Dire Straits and Supertramp and Pink Floyd and probably I got all of those in my basement amongst so many others.
Jon Krohn: 00:40:10
I love those artists. I look forward to… Maybe someday you’ll invite me over. I can check out your 2,000 records in your basement and we can listen to them. We’ll have to do it in a room with lots of shaggy carpets on the walls.
Kai Beckmann: 00:40:24
Absolutely.
Jon Krohn: 00:40:26
Nice. So going back to my technical question about spin-on dielectrics that led to this, to this tangent on vinyl records, my understanding is that the spin-on dielectric materials are promising for high bandwidth memory in the coming years.
Kai Beckmann: 00:40:43
It’s one of many materials. There are many special materials used, especially in high bandwidth memory since high bandwidth memory requires, let’s say, the best possible DRAM performance. DRAM, dynamic RAM, is the base component of a high bandwidth memory stack, and so you need a number of very specialized materials for a high performance in DRAM. The one which we have in mind as well is there’s a so-called DRAM capacitor that need to be optimized, and this is why our high-K materials, these are specific materials required to make these capacitors are absolutely leading in order to drive the performance of a DRAM system. So there’s many different things. There’s hundreds, if not thousands of different materials being used based on 80% of the periodic table of elements and all being optimized as precursors for very different production steps. So it’s very difficult to single one out as being the most important one, but all of them are required to drive the performance of semiconductor devices.
Jon Krohn: 00:41:57
To bring the idea home to make it concrete, and actually you could speak to this better than me, but the reason why high-speed memory like DRAM is so critical to AI is because with these very large models like large language models, you have lots of different GPUs communicating with each other. By having high-speed memory, you’re able to move information between those different compute nodes more efficiently.
Kai Beckmann: 00:42:23
Absolutely. Yeah. Another important dimension that we feel we are quite well suited for is once we go beyond electrons for transferring data, we go into photons. Whenever light is used for data transfer, that gives us two advantages. One is speed. The other one is energy consumption. Just the photons don’t create the heat that are created by electrons. We have just reorganized last year the electronics sector by building an optronics unit based on our display experience and display history where we know how to manipulate light and how to generate light in a proper way. Using light for data transmission in these systems is an enormous opportunity for further improvement of performance and reduction of energy consumption.
Jon Krohn: 00:43:19
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00:44:06
Very interesting. Yeah, thank you for that. It’s been amazing to get your technical insights on semiconductors, which has been the focus entirely of this episode so far. I would also like to now to change gears a little bit and to ask you some questions about the tremendous leadership experience that you have. So we already talked about earlier in this episode how you’ve been working at Merck KGaA, Darmstadt, Germany since 1989 and in that three and a half decades, you’ve grown through the organization. We already talked about that journey a little bit earlier on. The electronics part of the company that you lead that has 8,000 employees, that used to be called performance materials. Do you want to tell us about that transition, what led to it and what the challenges were? Maybe there’s lessons that we can all learn from that kind of transition that you made at your company.
Kai Beckmann: 00:44:59
That is a very interesting part of the company’s history. Now, in the late ’60s, in the ’70s, the company started and very smart researchers found out about the properties of liquid crystals in modulating light. So this is where all the technology advancements for flat panel displays came from, initially from our work in the late ’60s and ’70s with the first calculators or watches being based on liquid crystals, and that was such a phenomenal work. It took decades to generate commercial products out of that which then has led to all the flat panel displays. We had 80% market share for all the liquid crystal materials during its peak seasons in the 2005, ’06 to ’10 when all the TVs were replaced by flat panel displays. We developed enormous experience in display materials, as well as understanding our electronics customer in this area.
00:46:08
The unfortunate part of that is every of these enormous innovations has a life cycle. They start slow. They ramp nicely, and then they plateau and then it’s getting really difficult. The question is how can you switch gears once you were so enormously successful in those days to find something else? That something else was how could we apply the same way of improving our customer’s product performance in a different domain using as much of what we learned in the display arena? This is where we entered the semiconductor world in 2014. Basically, it’s just about 10 years ago.
00:46:56
Then we started venturing into adjacencies from our display experience into semiconductor. In semiconductor, that was an opportunity which got bigger and bigger the more we dealt with it, and this is where we acquired then quite a number of companies, from AZ Materials where it all started from, to Sigma-Aldrich, the high-tech business, a part of our Sigma-Aldrich acquisition was focused on semiconductors, to Versum Materials, to Intermolecular to M Chemicals and a number of companies acquired to form a market leading portfolio around semiconductor technologies based on this steep chemistry history legacy that our company has. We drive chemistry for three and a half centuries and understanding quality challenges since the middle of the 19th century was the first industry’s quality promise that was given to customers to already in 1850. So there’s such a deep legacy and we conquered a new market with semiconductors and this was like perfect fit going forward, since chemistry was needed, physics were needed, electronics needed and all this together has formed a business which is now called the electronic sector.
Jon Krohn: 00:48:22
It’s pretty wild to think how 350 years ago this company was founded and there’s no way… I mean, they would never ever imagine the kinds of innovations that 350 years later you would be doing as a company and the capabilities that would be allowing. It’s wild to think that when you think back 350 years ago, what were the leading technologies of the day? A horse and buggy. It’s wild.
Kai Beckmann: 00:48:50
Yeah, it was even more severe 1668, the year when our company was founded that it was shortly after the 30 years war in Europe ended, devastating, devastating war. Of course, healthcare was probably the most important need in order to improve people’s lives and this is where it all started. Then scaling from there into how can we support other pharmacies, not just our own? How can we support other companies in the 19th century and make other companies successful? This is why we call ourselves in the electronic sector the company behind the companies advancing digital living. So we pride ourselves that we help our customers to drive the latest and greatest innovations, not only in the industry, one could say in the world right now. So this is how it all comes together. To your point, innovating an existing company is far more complicated than driving innovations out of a startup of a new company. Typically, you are in your own way whenever you drive innovation. Within an existing company, typically you believe what I did yesterday probably is successful tomorrow as well, which is probably the most fatal mistake you can do in business.
Jon Krohn: 00:50:09
Speaking of business in semiconductors, you earlier this year at the World Economic Forum in Davos, which by the way, before starting recording, I mentioned to Kai how I had a ski injury. So for our listeners, due to a skiing accident that I had, I can’t currently flex my elbow or move my left shoulder, so my left arm is kind of this weird… Luckily, the fingers work so I can still type and still grab things and the neurologists say that because my fingers work, everything else should eventually start to work as well, but that actually the skiing accident happened in Davos last week. Yeah.
00:50:53
So anyway, Davos hosts the World Economic Forum every year. That town name has almost become synonymous with WEF. In an interview at WEF, you mentioned that AI is fueling growth in leading edge semiconductors while other parts of the industry remain in a cyclical downturn. I thought that this was really interesting because we hear so much about things like Nvidia’s share price or TSMC. We’ve heard about all the innovations that your company has today and things seem to be moving along really well. It seems like at the cutting edge there is a huge amount of demand, yet you say that other parts of the industry remain in a cyclical downturn. Could you tell us a bit more about that?
Kai Beckmann: 00:51:31
The semiconductor industry serves very different markets. There’s an industrial market. If you take all semiconductors required for automation in the industry, then there is the typical consumer electronics-related market, TVs and what have you, and then you have mobile phones, desktop and laptop computers, and then you have the big area of data center as well. If you take these very different markets, still a lot of volume is driven by all of us replacing our smartphones and replacing our desktop and laptop computers. This is where a lot of volume is generated from.
00:52:12
If you look into the placement rates of smartphones and computers on the consumer, as well as on the industry side, it’s still low. It’s still low, so people try to hold onto what they bought during COVID now for year number four, probably year number five already, and that is still dragging a bit the recovery cycle for the semiconductor industry. The growth is driven predominantly from data center applications right now and data center applications related to AI in the first place. This doesn’t bail everything else out. This is the situation right now. It’s not compensating completely, but of course, it drives an important high-end segment of the market and this is why of course, for us, it was driving our growth last year and gave us quite some upside from a very nice high-end application.
Jon Krohn: 00:53:07
Very cool. How do you, as the CEO of a big company that has to navigate these different kinds of situations where there’s, “Okay. There might be a downturn in this sector, an upturn in this sector,” how do you navigate that in this semiconductor industry?
Kai Beckmann: 00:53:20
Semiconductor industry is cyclical by nature. This is ever since it started to exist because of its huge capacities being built that typically only know two different modes. One is running full power with full capacity and probably not running as the other option. In between doesn’t make economic sense, and this is why this industry, of course, tends to have cyclicality because of that kind of demand and supply situation I just explained. Now, having worked in this industry for quite a couple of years, you get a bit more relaxed as it comes to cycles. You don’t freak out whenever things go up and down. In times when we had gross rates for our semiconductor business, north of 20% for semiconductor materials, that was not long ago, we tried to be still humble and try to keep our feet on the ground and not get too excited because we know few years later, then you look into a shrinking market and you have to manage then cost and you have to manage idle capacity.
00:54:28
If you manage both extremes well, then you can be a successful company in a cyclical environment. Our customers know how to deal with that, and I believe our peers, as well as we ourselves, we know how to deal with the cyclicality. For the team, I think it’s a nice humbling situation. So nobody freaks out if we have 20% gross rates nor anybody panics if we have a year of decline. We try to manage our performance across these different cycles and manage our cost, manage our R&D spent well that we don’t get any negative disruption.
Jon Krohn: 00:55:06
Nice. Great explanation. You’re clearly a pro at this kind of thing. Something else related to your industry or the AI industry in general in Europe, you hear a lot of people complain about regulations in Europe and that potentially slowing things down. But in a panel discussion recently, you highlighted that while regulation can be a constraint, clear regulatory frameworks also provide investment certainty, particularly in things like chemical and pharmaceutical industries. What kind of AI regulations would strike the right balance in your view between fostering innovation and ensuring compliance?
Kai Beckmann: 00:55:45
Just take an example if you provide data in whatever form or shape for being used by the public, then you want to be sure that you get, of course, the returns for making your data available. If you provide data and everyone then can exploit that data in large language models or whatever AI-based application without linking it back to the originator, that definitely is not helpful for generating that data. Everything related to protecting the contribution of those who make data available is certainly important in the current AI boom. We need that. We need that, and this stability helps.
00:56:30
Of course, then on the other end, there can be regulations that limit the close of new technologies when things get too complicated. If you have to have a lot of regulatory burden of applying for new technologies, if it takes you, for example, if you make a new material here in Darmstadt in Germany, make a new material and it requires us to get permit, it requires us a year to get the permit to make that new material, that definitely doesn’t help the industry to innovate. So speed of applications for new technologies, novel technologies is an essential area where Europe has to work on. This is where Europe certainly is not leading. So it’s both sides, providing a framework which creates more a long-term understanding of how to invest, as well as not harming the industry by slowing down innovation cycles. That’s the balance to keep.
Jon Krohn: 00:57:24
Nice. Well stated. Well stated like everything else that you’ve said in this interview. My last big question for you before I get to my usual conclusion questions for you, this is something that is I think really exciting, something that our researcher, Serg Masís, pulled out about some recent statements that you’d made. In discussions with Josef Aschbacher at the Munich Security Conference, you emphasize the importance of collaboration between your company and the European Space Agency in advancing AI applications. I thought that was really cool. I don’t talk about AI and space very often, so what kind of specific projects or areas of AI do you think hold promise in space?
Kai Beckmann: 00:58:04
It has quite a number of different perspectives on that. Maybe the first of all, which is the least obvious one, Darmstadt here in Germany is a bit like Houston. So we have the Space Operations Center right in Darmstadt. This is almost walking distance from here. It’s a 20-minute walk and you get there. This is where all the space missions of ESA are being operated from is here in Darmstadt in Germany. This is why we have such a proximity to the technology folks from ESA here in Darmstadt is one dimension. Both organizations, ESA, as well as our company is highly tech and science focused. We are so much focused on driving technology advancements, so there’s a lot of similarities in the mindset of people. People click easily from both organizations.
00:59:00
Third, and this is when the application comes into play, is under low-gravity environment, you can work on biological experiments quite a fair bit. So pharmaceutical research in space makes a lot of sense. Many companies investing in that. Materials research in space an important area. So how do we drive our R&D in space could be an important part, as well as new materials needed in order to make space missions more safe, more affordable, and more efficient. These are things where materials are being used as well with certain R&D institutes of ESA.
00:59:51
In general, collaborations are required across the different areas of the value chain, and ESA is a good example for good collaboration. Lastly, data generated from space missions is an important source for optimization later on. It’s in geo data, weather data, but as well as research data, materials data and other areas where of course, there’s huge amount of data being generated can be fed into platforms that we have built for the industry, such as Athinia being a semiconductor industry platform in order to drive innovation for devices, as well as for materials.
Jon Krohn: 01:00:34
Super cool. In case people were wondering, this might be obvious, but Kai said this abbreviation ESA, that’s European Space Agency, ESA. Fantastic. This has been an amazing episode, Kai. I’ve really enjoyed learning so much about semiconductors, the industry, as well as potential future innovations like AI and space that semiconductors will play a key role in. Before I let you go, do you have a book recommendation for us?
Kai Beckmann: 01:01:01
It’s not that you gave me a hint. It’s why I have two books in front of me and now I’m taking now tech books. I’m taking very recently I wouldn’t say acquired because I got them as a present, two books, and I’m kind of holding these into the camera. The one is Pivot or Die from Gary Shapiro. He is running the Consumer Electronics Show and he gave it to me in January at the CES in Vegas. So he’s writing about driving changes as a leader. So that’s an interesting read. I’m not completely done. I’m reading two now concurrently. The other one is Leadership in Management. This is maybe less known. This is a book written by a leader from NVIDIA, John Chen. So he wrote that book.
01:01:56
I was just with my team in the Silicon Valley. Was it three weeks ago or two and a half weeks ago? We were meeting at NVIDIA as well because we drive jointly on materials innovations projects with NVIDIA and John Chen, he wrote that book. So two books on leadership. They give you always new perspectives on what drives successes of companies, what makes leaders more effective in today’s world. I think leadership is an important dimension of understanding how do you enable a team to do even greater stuff going forward than it did in the past, an important part, where it’s worth reading at sometimes. It is an analog. So these, I’m reading analog while I’m reading a lot of stuff, of course, on the iPad, so digital as well, but sometimes we need the old-fashioned way.
Jon Krohn: 01:02:47
For people listening in our audio-only format, Kai has the books with him. He can prove that he is reading these analog. It’s also my preferred way of reading. I find that because my phone has so many other things in it, just my mind starts to think, “I wonder if I got any…” I don’t have notifications on my phone except a few people. My mom can phone me, that I’ll come through, but there’s almost no way for me to get an active notification. But even then, if I’m trying to read on my phone, I think, “Oh, I wonder if that email came through. I should just have a look,” or “You know what would be easier than getting through this tough paragraph is seeing if today’s puzzle on Chess.com is easy.” Yeah.
Kai Beckmann: 01:03:32
I can fully relate to that. A book doesn’t have these notifications. That’s a good thing. This is why a book is a book.
Jon Krohn: 01:03:39
Although I do do enough reading on my phone now, especially, I read The Economist on my phone every day for probably 20, 30 minutes, and I get so used to being able to hover over a word and get the definition. We’ve got to get that into books. I mean, you need to make semiconductors small enough and cheap enough. The books can do that.
Kai Beckmann: 01:04:00
We get kind of all kind of reader e-ink devices and then you can do all that stuff on a book, but then we are back to an iPad, so maybe this kind of closes the circle. So maybe sometimes it’s good to have something which is completely undistracted and just using it, no battery life. It just works
Jon Krohn: 01:04:19
For sure. 100%. All right. Kai, for our listeners who want to follow you after today’s episode, how should they do that?
Kai Beckmann: 01:04:26
I’m very active on LinkedIn, though this is the way I try to stay connected with sharing some of my thoughts on LinkedIn and maybe amongst a few CEOs who really operate their own LinkedIn account. I must say I’m a LinkedIn user since 2008 already, and so I’m the only one who has the credentials. My credentials are my credentials, so what actually is done is physically done by me on the account. Just tell me whether you like what I’m writing there, and I like to stay connected. I cannot reply to every service offered on LinkedIn. I must admit that there’s so many, even personal health and fitness programs being offered to me. I must admit, I do not reply to all of them and maybe to none, but that’s a different story though. But I like to write and if there’s anything more constructive in any of the people’s replies, I’m active even in replying to those. Hope that makes sense.
Jon Krohn: 01:05:27
Yeah, it makes perfect sense to me. The key thing for me that I do is I actually have an auto response when people send me a DM that says, “If you can write this as a comment, I’ll respond to basically all of those.” You’ll at least get a reaction, and if appropriate, I’ll write something as a follow-on comment response. But yeah, there’s just too many service offerings in those DMs to possibly stay on top of it all. But yeah, when I read something post about an episode just like you, I’m delighted for people to comment and I will definitely read it and I will reply.
01:06:05
Kai, it’s been so awesome having you on the show. Thank you for taking the time out of your no doubt extremely busy schedule to do this episode with us. Yeah, maybe catch up with you again in a couple of years and we can see how the semiconductor industry is coming along.
Kai Beckmann: 01:06:17
Jon, thank you. It was a great conversation. I really appreciate it. Thank you.
Jon Krohn: 01:06:26
What an honor to have such a renowned technical leader as Kai Beckmann on the show. In today’s episode, he filled us in on the intricate process of semiconductor manufacturing, which involves 1,400 steps from a blank silicon wafer to a functioning chip using materials that cover 80% of the stable, non-reactive periodic table. It’s wild to me. He also talked about the concept of materials intelligence, which is using AI to develop innovative materials that power the next generation of AI technologies. He talked about the development of heterogeneous integration such as a chip on wafer on substrate that allows for more efficient data transfer between memory and compute processors. He also talked about how technologies like quantum computing, neuromorphic computing, and photonics could dramatically accelerate society’s technological capabilities in the coming years.
01:07:17
As always, you can get all the show notes, including the transcript for this episode, the video recording, any materials mentioned on the show, the URLs for Kai’s social media profiles, as well as my own at www.superdatascience.com/875. If you’d like to engage with me in person as opposed to just through social media, I’d love to meet you in real life at the Open Data Science Conference, ODSC East, which is running from May 13th to 15th in Boston. I’ll be hosting the keynote sessions, and along with my long-time friend and colleague, the extraordinary Ed Donner, I’ll be delivering a four-hour hands-on training in Python to demonstrate how you can design, train, and deploy cutting-edge multi-agent AI systems for real-life applications. Hopefully, see you there.
01:08:02
Thanks, of course, to everyone on the SuperDataScience Podcast team, our podcast manager, Sonja Brajovic, media editor, Mario Pombo, partnerships manager, Natalie Ziajski, our researcher, Serg Masís, writer Dr. Zara Karshay, and our founder, Kirill Eremenko. Thanks to all of them for producing another scintillating episode for us today.
01:08:20
For enabling that super team to create this free podcast for you, we are deeply grateful to our sponsors. You can support this show by checking out our sponsor’s links in the show notes. If you’d ever be interested in sponsoring a podcast episode yourself, you can find out how to do that at jonkrohn.com/podcast. Otherwise, help us out by sharing this episode with people who’d like to hear it, review it, review the show on your favorite podcasting app or on YouTube, subscribe to the show, edit the show, our YouTube videos into shorts if you feel like it, but most importantly, just keep on tuning in. I’m so grateful to have you listening and hope I can continue to make episodes you love for years and years to come. Until next time, keep on rocking it out there and I’m looking forward to enjoying another round at the SuperDataScience Podcast with you very soon.
