Nicolai Tangen: Hi everybody. I'm Nicolola Tangan, the CEO of the Norwegian Sovereign Wealth Fund and today I'm in particularly good company because I'm with Arvin Krishna in New York and Arvin is the chairman and CEO of IBM, one of the most iconic technology companies in the world. Arvin has been with IBM for over 35 years and became the CEO in 2020 and have since orchestrated one of the most striking turnarounds in big tech. when he took over IBM had been declining for years and today is growing faster than it's done for a long time. So Arin warm welcome.

Arvind Krishna: Thank you Nikolai. It's always good to talk to you.

Nicolai Tangen: Absolutely. Now a lot of people still think that IBM is a kind of company from from another era and you have changed that. So today what does IBM do?

Arvind Krishna: IBM is largely a hybrid cloud and AI software company. We made the transition to that's almost half our total revenue. We have another third that is in consulting and we try to help our clients transform for the current era of digital and AI and then we have about 20% that is hardware. I realize many people think that we are largely a hardware company but that is just a fifth of the company a very important piece but a very small piece.

Nicolai Tangen: When you took over as a as a CEO the IBM had been declining for some time. What was your diagnosis? I always like to sit back and think what are your strengths and what are your weaknesses.

Arvind Krishna: So as I talk to our own team and as I talk to clients it comes out that we were trusted but we were considered to be part of the past not necessarily the future. So my diagnosis was you have to do things that are relevant for people's future. They're not always the biggest revenue in a month or in three months or in one year but they become the big revenue over the next many years. So we began to a say what are we good at and then can we double down can we double down on helping people transition towards a hybrid cloud. We were strong believers that sovereignty would remain important for many many years to come and so you double down on the portfolio that helps them do those things and back in 2019 I was convinced that AI would be be big. It took another three years for the world to wake up to that.

Nicolai Tangen: What made you so convinced at the time?

Arvind Krishna: Data is going to overwhelm you and value is going to be derived from data. What can unlock the value from that much data? The only technology we knew was AI.

Nicolai Tangen: You made some big acquisitions and um a very large one Red Hat which is been tremendously successful. Just tell tell us about that.

Arvind Krishna: So in 2017 I came to the conclusion that public cloud is very important but that IBM becoming a big investor in public cloud is probably not good for us. It was a pretty economic conclusion. When you are that far behind you would have to spend multiple billions 5 to 10 billion a year to try and catch up. And if you think at the end of five years you're still going to be number five, that doesn't seem like a worthwhile investment. So instead, I wanted to partner with all the big cloud providers. And Red Hat, there were only two or three companies I thought that could help give us a portfolio that makes us a great partner for all of them that helps their business and helps our own. Hence, Red Hat.

Nicolai Tangen: What is the analysis that goes into such a decision?

Arvind Krishna: You got to see because in some areas you can say I can compete because I can actually build something and at the end of that time I can carve out our own niche or your own market share. I felt that the others were spending so much that they would remain ahead. So that is not purely financial analysis that's also analysis based on looking at the technology trends and at the strength of their technical teams in addition to the pure numbers. then you say okay if it's going to take a lot of capital investment then is that capital investment going to have its ROI or not that is perhaps much more analytic than not and then is there an alternate capital investment that pays off better I felt that if we put our capital into doing software M&A that's probably a better return for IBM maybe I'm lucky maybe I'm smart that's what the last six years have shown

Nicolai Tangen: yeah well I suspect you have been both well we need I mean it was a very smart decision and you've been proven right now you also spun off um the IT services business which was like a huge part of your number of people right a third of the workforce what was the thinking behind that

Arvind Krishna: look so the negative always is that these are clients that are intertwined with everything else we do so trying to separate it is painful let's acknowledge that for the employees and for the clients I was clear I mean as you began you said IBM was declining I was clear that revenue growth is essential. If you say and conclude revenue growth is essential then something which is itself declining at 5%. Is something that should not be part of it otherwise your target for growth becomes that much harder. That means everything else would have to grow at 10 not at five. So the revenue decline and I felt that that was an area that it didn't quite fit us well now though it did very well 30 years ago. We I wanted to have a company that is based on innovation that is based on high margins and that can grow. That is an area where people look for stability not innovation. It's a area where it is not going to grow because I think it is fundamentally deflationary and it is not going to be high margin because it is by its nature it's going to be decent but not high margin. Mhm. So that made it a decision to say such a company is probably much better served by being by itself and can unlock more value for its investors over time as opposed to being part of us.

Nicolai Tangen: The latest you've done is Confluent. Uh what has that what does that add to the business you think?

Arvind Krishna: So I think that uh back to why can you get more value from data? AI is one thing but then you've got to be able to expose the data to all of AI. Confluent is the best infrastructure for beginning to move data and for exposing data for everything else. Also, there are so many companies that struggle with having real-time data. Confluent with the Scafka backbone is the best in the world at taking data and making it real time for all the purposes that you might want to use it for. So, those two things together make it like a it's a wonderful acquisition. I believe time is going to show how great it is for us.

Nicolai Tangen: It's for sure a great company and great product. But what um how to which extent do you integrate these acquisitions or you know your view on letting them do their own things and and operate on their own?

Arvind Krishna: The only one that we have not fully integrated yet and I'll emphasize the yet is Red Hat. In all the rest u I always look for three things. It should be a great capability. So to the point you're making on integration or not, you've got to make sure that even if you integrate the engineering team who's building that great capability still has freedom to build it because they are the ones who really should be in charge of what they are building. We'll try to bring them more of how they build. We'll bring them more AI, we'll bring them more tools. We'll bring them more global capability, but they should remain in charge of that. On the go to market side, I really firmly believe in full integration because in order to unlock the value of many of these companies, we can take it to more clients. We generally have a presence in far more countries and geographies than anybody we acquire. But to do that, you got to integrate otherwise you've got to grow a unique capability everywhere else. So we believe that bringing our digital capability, our geography footprint, our complete uh international global go to market capability is a big plus for all of these entities. The third part to operate you also need to worry about compliance about contracts about recruiting about HR about payroll about taxes about cash management. There is no value to me to have those as as unintegrated functions. So those my my love would be to do it on day minus one but to be fair it takes a few weeks or months to get it done.

Nicolai Tangen: You said you haven't integrated it yet. Mhm. What does yet mean?

Arvind Krishna: I believe that we are right now in the process even in Red Hat that what I call the third bucket around HR, legal, contracts, cash management, treasury, all that we did um over the last two years. So that's done. Now Red Hat definitely had more scale. So they were not going to get a advantage by being integrated with IBM let's say in the UK or Germany unlike smaller ones like Confluent. So we let the go to market uh be independent. I think that we are now discovering that even on the go to market there are areas around digital that are outside the top few markets where integration was going to help more than be a detractor. Engineering in Red Hat I actually believe given the open source nature of Red Hat is going to have to be its own function. So engineering I will likely not integrate because working at their scale of open source maybe that is one where IBM learns from them and things that are open source should belong much more in the Red Hat methodology than ours.

Nicolai Tangen: What's the best thing you've done since you became CEO?

Arvind Krishna: Um make the culture much more willing to take risk. I think that we had become a very risk averse culture. That is not the thing I would have told you if you'd asked me on the first week. But after a couple of years of observing and doing, I think that making the culture much more willing to take risk is the is the biggest thing I've done.

Nicolai Tangen: How did you do it?

Arvind Krishna: It becomes a question of you have to first ask yourself that if if I don't like the culture taking risk, why is that? You got to begin to understand that. And I think these lessons are drawn from whether it's biology or whether it's history. You said we were declining. When a culture begins to decline, the focus becomes inward. And I think it's a natural I don't actually call it malicious. Human beings are very good at saying how do I survive? If the culture isn't declined, then people begin to say I survive by not raising my head, by not looking like an outlier. And so it becomes the thing which becomes self-reinforcing, not necessarily by design. So then you have to say how do you unlock that capability to take risk? You've got to hold up examples. You've got to tell people, "I want you to take risk." I tell people, "Don't give me your 90% confidence. Give me your 50% confidence." And then you lean into it recognizing they're probably not going to meet the timelines of the quality you want at 50. So you build a bit of a buffer, but jeweling them to go down that path is a big unlock then in total productivity and in how delighted clients feel.

Nicolai Tangen: Do you think also there is a factor of um you know a fact that when you when you decline a lot of the risktakers leave the company and so you are in a way stuck with the most riskaverse people

Arvind Krishna: that is definitely a big piece of it but then if you can unlock risk takingaking even amongst those that are left of course you got to get new people I'll completely acknowledge that I think a 10 to 15% refreshment rate per year is a great one but you can actually unlock even that because my point being if the ones or risk averse. It's a learned behavior as opposed to inherent in them. Then they can unlearn it. If it's truly inherent, then that's different.

Nicolai Tangen: What's the worst thing you've done?

Arvind Krishna: I think I've been slow so far in terms of client expansion. I think that we are very good at dealing with large clients and many of our people and probably myself turn that you're B2B into thinking that you're B to large B. You need to be good for B2B to everybody and I think that those are things that we are yet to unlock.

Nicolai Tangen: And how will you unlock that?

Arvind Krishna: Uh a lot of focus on it. You got to say the very the longer tale is not going to necessarily want to buy everything that we sell. We tend to have a habit right now of everybody should buy everything. So you got to then get more focused in what you do there. Uh those people also are not making large decisions. They're not viewing IBM as their partner. They're typically purchasing a capability. So you got to be really good at saying okay if you all you want to buy is that capability I'm going to give it to you at a great price at a great quality. So you've got to begin to do those things but those are changing some of the plumbing of the company in terms of how you sell how you price uh how you go to market all of that.

Nicolai Tangen: Moving to AI, which part of AI is a bubble?

Arvind Krishna: I think that some of the infrastructure buildout is probably a bit ahead of what the world can tolerate for the next few years. The way I would phrase it as because I have been accused sometimes by saying that it's a it's a not a bubble, but I believe that

Nicolai Tangen: which is kind of why I pose the question a bit carefully here.

Arvind Krishna: Yeah. Some will disappoint, many will thrive. Yeah. But not all will thrive is the way I would uh phrase it.

Nicolai Tangen: So when you say the uh infrastructure billard is a bit ahead what does that mean?

Arvind Krishna: Look by by the math that I have done about a gawatt of power you can debate but cost you 60 to 80 billion dollars worth of semiconductors to go populate it. So if you look at people have committed over a 100 uh gawatt of uh AI data center buildout that points to 6 to 8 trillion worth of a buildout. If you say that that's got a 5 to sevenyear payback you are going to need an extra 1 to2 trillion a year of revenue because inside that 1 to2 even if it's high margin that high margin would be 20 to 30%. So that much incremental revenue I don't believe is there and so that's why I think it's a bet ahead. I also believe on a second one that many of the largest models are going to be a commodity. Commodities can have a lot of value but there is low switching cost usually between commodities. If there is low switching cost that means you can have a margin but it's not going to be a margin with a massive moat around it. So, so those two make me believe that perhaps there aren't going to be a half dozen to a dozen companies who can build the largest models and survive. Maybe two or three. And that then tells you the second side of it is how much can be the total uh capital expense that goes into the data centers. If you had said it was half as much as today, I would have said that completely makes sense. I mean that aligns. But when it's double of that, then maybe some some of those are not going to be able to get a great return.

Nicolai Tangen: So who are going to be the losers?

Arvind Krishna: that is very hard to predict. I mean like having gone through a few technology cycles um uh pretty pretty different who's going to be the winners generally from AI I think like uh look some of them that already have a very large consumer business that means you have a natural distribution advantage on the consumer side on the enterprise side I think it's wide open to decide uh who's going to win I don't think that that is predetermined on the consumer side. I think history has shown us if you have distribution and if the distribution is aligned to AI, there's a pretty good chance you will be one of the winners.

Nicolai Tangen: Are you surprised that the uh that there is such a tech overhang? I that we are so slow in utilizing the full capability of this technology.

Arvind Krishna: No, there's a human time scale always. Technology can move at its rate and pace, but people take time to because you get into the questions always of is there a risk? Am I going to lose something? Always you get both sides of those voices. Whether you go back to the 1700s industrial revolution or whether you look at the last one which was the internet era or you look right now at the AI one, you get those voices that makes some people run and embrace. It makes many kind of watch cautiously on the sidelines and it makes some dislike and hate it. So as you look across those three each one I'll just observe has gone faster. So if I look at computers and semiconductors from the 50s to the 70s probably took 20 years to get fully embraced. If you look at PCs that was circa 1980 probably took 10 years to get fully embraced. If you look at internet that probably took five years 95 to 2000. So this one but it's still years. It's not yet months.

Nicolai Tangen: So how long will this take?

Arvind Krishna: I think we are right now I used to say if I do a baseball analogy and our global audience may not always love baseball and know baseball but about innings and stuff innings it has nine innings. I used to say AI was in its first innings. I would say maybe it's in its second innings now. So if we are in the second innings, it probably is going to take three four years to get enough to embrace it, but probably not definitely not 10, but not not it's not already done.

Nicolai Tangen: But when you talk you talked about these other technology shifts, how would you compare the AI uh advancements compared to those shifts?

Arvind Krishna: I think it's bigger than mobile and it's bigger than cloud. It's probably in the same category as internet if you go back to 1995. I use 95 because that's

Nicolai Tangen: you are much more I mean many clever people say it's like 10 times the internet but you don't think it's that. No. And you are and you are very clever by the way. Uh I should mention that. Not that he needs explaining but our mutual friend Malcolm Gladwell um said that you were the cleverest person he had met.

Arvind Krishna: I don't know whether I'm the cleverest, but I'm older than many and I have been around a few of these. The internet to me was fundamental. If you think about it, because many people don't realize the scale of global business that happens today is because of the internet. The ability to move work in technology from country to country is because of the internet. The fact that a small producer be it in Europe or be it in Africa or in China can sell and be part of the supply chain for a global corporation anywhere in the world is because of the internet. If I look at that the internet has had a massive impact. The direct revenue even to the technology providers is measured in the trillions today. The rise of all the social media companies could not have happened without the internet. The rise of cloud could not happen without the internet. If I include all those as the impact of the internet then AI is going to be in that category.

Nicolai Tangen: But do you think you are uh biased in your assessment of AI given that you have tried before and failed?

Arvind Krishna: The fact that we tried failed but remained committed and I made the bet on AI in 2019 again before the advent of chat GPT says no I don't think we are biased or jaded because you can imply that in your question. I actually think it's going to be very very powerful but it's in the same

Nicolai Tangen: but but tell us about I mean you tried with with Watson right just tell us about Watson

Arvind Krishna: I think that Watson was the right goal. so in 2011 we wanted to prove that AI can do and solve problems that were unimaginable at that time people couldn't imagine that you could do natural language question and answers on kind of a gray area using AI winning the game Jeopardy which had these uh questions in English but with all kinds of hidden puns and language and that had to be interpreted not just uh the black and white question proved that AI can do those things then you could say but you failed why did you fail the reason is we took that technology and we said we want to construct monolithic applications and we unfortunately picked the vertical that is the hardest health that was the mistake thick if we had taken it and said let's use it to help corporations get better. Let's use it to let their customers uh get better customer care. Let's use it to uh digest all the documents that enterprises have. I think we would have been five years ahead of where we are today on AI. But it would have been a long journey because the technology then was still what I call bespoke. Mhm. What I call bespoke is yes AI worked but it worked when you had one use case with one corpus of data. Today's AI actually can do many use cases. So that's a big advantage. Two, you don't have to relearn if some of the data changes. You can add the new data and add to it. Those two things make it much more industrial scale today than the one from 10 years ago. And that is why we didn't succeed then. But I do believe that we will succeed this time around.

Nicolai Tangen: Are you bu basically building the infrastructure layer that the AI systems need?

Arvind Krishna: We are not. We rent the semiconductors from other people as appropriate. So you can imagine all the big cloud players. I won't name them on this call but we tend to use many of them. Uh we tend to not build frontier models. Those we will uh uh get from those. The point I had made earlier I believe that there will be low switching cost. So this will be based much more on what are the T's and C's, how can you have a business relationship with our providers. We will build small domain models when necessary but not because we necessarily want to but because very few people are building the smaller models that can then augment the bigger models. So we will actually we have a belief that most people are going to be multimodel down the road and so we want to help enable uh that for our clients.

Nicolai Tangen: Yeah. And you got like granite and so on, right?

Arvind Krishna: We have a openw weight models called the granite family. But in there I'll note we have not built a single model that's over a 100red billion parameters and the biggest ones now are in the trillions. So we don't want to go there because we believe those are good enough. We don't uh we don't have an advantage in coming there. But we build we will build many because smaller models are going to be much more power effective to operate or much more cost effective to operate and where people care deeply about where is the data they could operate them on premise or at the edge which is an advantage for some workloads and some clients.

Nicolai Tangen: When you see uh things like the latest entropic model which is so powerful they can't even release it. What what kind of reflections do you have?

Arvind Krishna: I have two at a very deep computer science level. This is not new. Let me observe really clever people with extremely sophisticated tools have been able to find vulnerabilities and have been able to exploit them for decades. Yeah, I remember when I was in graduate school, this is 35 years ago. Uh we had the Morris worm that came out of a really clever uh kid at Carnegie Melon. Okay. But if AI can inherently use some of those capabilities and you can harness or harvest it into that, that means you're now letting a person with a high school education able to exploit these models to do what those really well-trained, really clever people did. That opens up the speed aperture, unfortunately. So these models may well be able to exploit things in seconds that used to take really clever people months to get done. So the intensity and the speed is what we have to worry about. It's not the fact that it exists. It it has always existed. But that means that you have to be able to have layered defenses and you need to pick your right partners to help protect the enterprise because I actually think that uh some of the smaller vendors may not have the capability to defend against these models.

Nicolai Tangen: Do you think there'll be a do you think it'll be a requirement to pre-release models to some kind of regulator or oversight function to make sure it's not too powerful before it's released to the public?

Arvind Krishna: I think that that sounds good on paper. So in practice, how do you do this globally? Because you can say that here. So do you stop the Kodians from doing it? You stop the Chinese from doing it? How do you get to being able to control these things globally?

Nicolai Tangen: Can AI be regulated?

Arvind Krishna: I am skeptical that the technology can be regulated. I believe the use cases can be because it's embedded in all the races and it's it's physical goods that are tangible or possible to regulate because you can put border conditions because there is a weight to them. You can control how much and where a digital good that can cross a boundary I think is really hard to regulate. If you think about some countries in the world that some or the others don't always love the regime and that side tries to control internet access. You tell me how effectively they can or cannot do it.

Nicolai Tangen: Is software going to die? You think? I mean your stock was down 13% when clawed code was released.

Arvind Krishna: No, I'll give you my reason for explaining why. So we didn't talk about demographics but if the number of people in the world is going to decrease then seat seatbased software by definition has a smaller market 10 or 20 years down the road. I think investors are quite smart at recognizing long-term trends. So I think one thing is how well will seedbased software do. Let me fully acknowledge to you I think AI and agents will replace some of the front end of mud software but if you replace the front end then by definition it has less total value. So you combine fewer seats less value for some things. That said, the system of record, the database that contains the business function, the business logic, that is still important, but I do think that the value for some of the software where the front end was the prime value that is going to decrease and then to give full credit to the investors, they're saying, look, I can't decide today who falls into that camp, who are the few who might benefit. And then let's acknowledge maybe half are not going to have a help or a hurt. If I can't determine that, I'll take the sector down and then over time that'll determine itself based on the numbers that you print. So I think that I could see that a fourth of people where the value was largely the front end could actually have a long-term impact. That would kind of be where I would finish in this. So I would look at you and say no I actually think that we were hit in a way that was unfair but look the market depending on who you talk to is down from anywhere to 40 to 60% in software for many companies and we are down about 25 so so in some sense we are taking a hit but I think that means there are enough people who also think wait a moment you may not get that full hit and so that's kind of where we are

Nicolai Tangen: talking about expected hit uh your mainframe business is uh thriving right and uh a lot of people didn't think it would. So the thing which cloud core thought that it is going to replace is the thing that is thriving the most. That's interesting isn't it?

Arvind Krishna: Yeah.

Nicolai Tangen: So so why is mainframe still thriving?

Arvind Krishna: Because the mainframe workloads tend to be workloads in critical industries. They're doing workloads like retail banking. They're doing workloads like credit card transaction authorizations. They're doing workloads like airline reservations. They're doing workloads around warranty and maintenance. It's systems of record. It's places where that full 6 to9 of availability. I want to make sure that the transaction is not corrupt. I need to make sure I can get the batch workload done in 20 minutes at 5:00. Those workloads are only increasing.

Nicolai Tangen: But should those workloads have been moved to cloud already?

Arvind Krishna: If you want to pay three times as much, right? So economics would dictate that that's the more expensive answer.

Nicolai Tangen: Did you think when you became CEO that mainframe would continue to grow?

Arvind Krishna: I was 100% convinced of it. Really? Wow. So the 10 years before I became mainframe was declining. It's surprising, isn't it, that in the last six years mainframe has grown every single year.

Nicolai Tangen: It's incredible what one person can do.

Arvind Krishna: It's not one person. It is unlocking back to risk takingaking. It's unlocking the enterprise. many people there believed it could and so you had to unlock them and give them the the environment which allowed them to succeed and thrive. We have built AI into the mainframe that was not being done a decade ago. We built more and more capability. We built and encouraged our partners to build more software. Uh actually we built our own cobalt conversion code leveraging large language models three years ago. So it's harnessing innovation and putting it into the platform that then allows it to thrive.

Nicolai Tangen: And you put AI on the platform before Chatt existed.

Arvind Krishna: That is correct. We put it on the platform in 2021. Then we put more in 2024 and then we put even more uh this year

Nicolai Tangen: and you have tell us about this new version the Z7.

Arvind Krishna: So in the Z7 we decided to do three things. one we put a it's a mini GPU is the best way to think about it right on the main processor. So you could actually do I'll call it smaller models not very large models right in line. So if you're doing a credit card approval and you want to approve your transaction previously you would do sampling. You would take some transactions off the platform see whether there's fraud and then if there's a transaction like it you would block it. Now you can run that model right in line. Then we said if that's useful, should we put more? And then we added our what we call the spire card that lets you in a fully populated mainframe do 450 billion inferences per day on the platform at zero latency and right in line. So no extra cost. That means you don't have to move all the data. You don't have to live with the seconds of latency of taking it off platform. And those are very powerful in terms of the capability. And the third one which people have begun to wake up to is we have postquantum cryptography built right into the platform. So you can for the data you think could get attacked by quantum computers down the road allow it to be pretty safe on the mainframe.

Nicolai Tangen: Well, let's spend some time on quantum computing which is one of your big bets, right? um for somebody who listens to this program who is not um very technical in in simple terms what is quantum computing

Arvind Krishna: so quantum computers at one level and I'll just geek on the science for 10 seconds and get off are trying to harness properties of quantum mechanics to do a new kind of math that's the simplest way to explain it so if I think about normal computers they do arithmetic IC you know think of high school arithmetic and algebra that's what they do but they do it at incredible speed so that looks remarkable then we can say what do GPUs do because with the advent of AI GPUs have come into the pollins GPUs do matrix mathematics let's just put it that simply but matrix math unlocks a lot of problems that would be 10,000 times slower to do on normal computers such as for example if I want to do an LLM to do an LLM on a CPU would probably be so slow none of us would care. But you can do these large language models. You can recognize is this a cat or is this a dog or is this a human being in a photograph is kind of what matrix math unlocks.

Nicolai Tangen: So let's say now we are we fast forward uh what do we think 10 10 when when will we have them?

Arvind Krishna: 2029

Nicolai Tangen: right? Okay. So let's say to be safe, we're now we're now uh 5 years from now we have this big uh quantum computer in this room. Now what are you and I going to do with it? What what's the stuff we can do then?

Arvind Krishna: I think the first three use cases the first one I think nobody debates is going to be in the world of materials. So you look at me and say materials. What do you mean by materials? Would I like a better quoting so that things don't corrode? example, aircraft wings or rivets or pipes that carry oil. That's pretty important. Or could I design a better pharmaceutical drug because we can do things with molecules and be able to predict the properties as opposed to have to do a wet lab experiment? Or can I come up with a better fertilizer than the current very energy inefficient fertilizer that is there? or one that I'm getting excited by because our team just showed that you can predict magnetic properties of materials using quantum computers. Well, if I can do that, is there a possibility and I'm calling it a possibility because we're still 3 four years away to have a better magnet and as we know you need magnets for electrification, for EVs, for lots of things. So those are the first category. The second category I think is going to be around financial risk. How can we price something better knowing what we know? So the way we do it today, we kind of use data that's a week old or a day old and then we try to guess during the day what it should be. But if a quantum computer could price some of those things in milliseconds, then that gives you an advantage in the financial world to be able to price complex instruments or derivatives or bonds as some of our clients have begun to do during the day. And the third is going to be in the area of optimization. And by optimization, I mean things like can we do a better route plan? uh could we somehow attack the problem that 30% of all truck miles and containers are empty as opposed to used and that's because we use very simple routes because it's just too hard to do a complex route for all these things. So those are problems that I think quantum computers are going to unlock in the first few years.

Nicolai Tangen: What's the relationship between quantum computers and AI?

Arvind Krishna: I think that that relationship is going to be more in the long term than in the short term. If I begin to look not at the first five but the next five years, quantum computers to make it very simple are great at finding hidden patterns in data. Okay, what is AI trying to do? In the end, a large model is trying to find those patterns in the data that it sees. So could quantum computers in the first instance be used to help create these models in a much more energyefficient way than could be done otherwise is one example of how a quantum computer is going to help on AI. But for the first five years I believe the two will complement each other. You will do a problem on AI. You might discover that I need a property of a material that AI could not figure out. Quantum computer does goes and does that. Then AI says knowing everything else here's the prediction it can make. Then it can say there's a gap in the knowledge. Maybe a quantum computer can help on that.

Nicolai Tangen: Does AI help you develop the quantum computer?

Arvind Krishna: Absolutely. Today already it's a new form of programming. We call it circuits but let's call it programming for the sake of being simple. How do you help people understand how to write these programs? AI is going to help you do that. How do you begin to make some of the normal electronics around a quantum computer? AI is going to do that. So AI is going to accelerate the development of quantum computers.

Nicolai Tangen: From 1 to 100, how confident are you that you'll have it by 29?

Arvind Krishna: 100.

Nicolai Tangen: There is no such thing as 100% certainty.

Arvind Krishna: There is a certainty in we'll be able to have it. Now we can debate how useful will it be.

Nicolai Tangen: When will it be uh like properly in production and and useful?

Arvind Krishna: Probably in the 28 to 30 range. So will we have it? The reason I have 100% confidence we have them at a scale of hundreds to low thousands today. So that's not a future statement. So we got to get up and scale by an factor of 10. And we got to improve error correction by a factor of 10. That's what has to happen between now and 2029 to make it just really very simple.

Nicolai Tangen: And what does it mean? what does it mean for the geopolitical race for sovereigntity um national security?

Arvind Krishna: So these things always have a number of different lenses. So first if it is going to unlock extreme economic value let's stick to the main one that is extremely important for national security because if you're economically way superior to somebody else the world has shown that that is a big national security advantage. Second, some of those problems have incredible applications to direct defense applications, better munitions, uh uh way to navigate without having to rely upon uh low earth orbit satellites. All those are examples of quantum applications. Then the third one which we have not talked about, we know that quantum computers can do what is called shores algorithm. What does it do? It helps you decrypt most of today's encryption. So the ability to be able to read somebody else's encrypted communications in the clear is an offensive uh military application. So for national security all those three are important which makes it imperative that we work on these to be able to solve all of those applications.

Nicolai Tangen: Arvin let's move on to leadership and just how you lead. Now, not many CEOs have a PhD and 15 patents to to his or her name. Uh, and a and a kind of stupid question, of course, is it is it helpful to be so clever as a leader?

Arvind Krishna: I think it's really important to know where your strengths are, leverage your strengths, but also try to be very, very self-aware of where you don't have strengths. I actually don't think about it in that lens at all. I always think about it in terms of how do you want to empower the team to do their best. That really is the first job of every leader. The next is where can I help them? So for those who may not be very technical, it may be helpful for them to get a sense of where the world can go and as I call it look around the corner a little bit. I think my depth of knowledge more than cleverness is on the technology dimension but there are so many other dimensions where others are going to bring much more um of a skill set to me and I think that actually my strength has been much more and trying to build around a team. I know far less about politics and the financial markets than many others. So I try to bring in people from both those dimensions into the team to give us that. I'm not ever going to know much about the law. So I got to have a great GC and a team around that for doing M&A. I bring in people who were ex-investment bankers.

Nicolai Tangen: What kind of credibility does it give you with your scientists that you have a scientific background?

Arvind Krishna: It gives me the ability to argue with them. I mostly lose the argument but it allows them to have fun in their argument.

Nicolai Tangen: You have something called getting fired mentality. What does that what does that mean?

Arvind Krishna: 15 years ago, I walked into the office of a mentor of mine at 6:30 in the morning and I was kind of frustrated. I said like, "Look, you know, I really believe that I need to say these things, but I'm afraid that if I say them, somebody will want to try to fire me." And this person turned around and told me, Arvin, you should live in the pleasure of being fired. And I looked at me, he said, "That doesn't mean that you always pick a fight. It doesn't mean that you just make disruptive things for the sake of it. But if you are living in the pleasure of being fired, that means you're not afraid of being fired. That means you'll do the right thing and if you have confidence in your abilities, why does it matter? And that was truly freeing. And I encourage people to think like that and to be like that. And I think it really has been very powerful.

Nicolai Tangen: Have you been close to being fired?

Arvind Krishna: Couple of times. Uh the last time was probably in 2014. What happened? I was trying to make a decision that was popular with some people and was extremely unpopular with some others. So, they decided that they were going to try and get me fired. They almost succeeded. Yeah. Not not quite. Not quite. No, not quite.

Nicolai Tangen: finally, what is your advice to young people?

Arvind Krishna: Number one, do something you have a passion for and interest in. It's really important to wake up enthused about the day. Do it with people that you respect and can learn from. I don't say so that's not necessarily your friends and popular but do it with people that you like working with do not ever focus on the title or the compensation I believe if you have the first two those will come I'm not saying don't focus on that at all but don't make it your criteria for doing something

Nicolai Tangen: good advice big thank you

Arvind Krishna: thank you Nikolai tremendous