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In Good Company with Nicolai Tangen

8 May 2026 · 11m · 6 chapters

Episode artwork

HIGHLIGHTS: Arvind Krishna - CEO of IBM

In short

Arvind Krishna (IBM CEO since 2020) discusses IBM’s turnaround and strategy around hybrid cloud and AI, why he bet on AI early, lessons from IBM Watson, comparisons of AI shifts to the internet/mobile/cloud, and IBM’s quantum computing bets.

Guest backgrounds

Arvind Krishna has been at IBM for 35+ years; Nicolai Tangen is CEO of Norway’s Sovereign Wealth Fund.

Key claims

IBM is ~half hybrid cloud and AI software revenue, ~one-third consulting, ~20% hardware; IBM was “trusted but seen as past,” so it doubled down on hybrid cloud sovereignty and AI relevance. He says AI is as transformative as the internet (not merely “10x” mobile/cloud). Watson failed because IBM built monolithic apps in the hardest vertical (health) and used bespoke, single-use-case tech; today’s AI is more industrial-scale.

Notable examples

Jeopardy-style language understanding; quantum use cases include better corrosion-resistant materials (aircraft wings/rivets/pipes), drug/molecule prediction, improved fertilizers, predicting magnetic properties for electrification/EVs, faster financial risk pricing, and optimization like reducing empty truck/container miles. He shares a “pleasure of being fired” mindset from a mentor.

Written by AI. May contain mistakes.

Chapters

0:46

IBM's Transformation Under Arvind Krishna

Discussion on IBM's transition to a hybrid cloud and AI software company.

“Now, a lot of people still think that IBM is a kind of company from another era and you have changed that.”
1:53

Diagnosing IBM's Challenges and Opportunities

Arvind Krishna shares insights on diagnosing IBM's past challenges and future opportunities.

“but they become the big revenue over the next many years.”
4:20

AI's Potential vs. Past Failures

Discussion on the potential of AI and lessons learned from past failures.

“I actually think is going to be very, very powerful, but it's in the same.”
4:39

Understanding Quantum Computing

Arvind explains quantum computing and its potential applications.

“So in 2011, we wanted to prove that AI can do and solve problems that were unimaginable.”
6:14

Practical Use Cases for Quantum Computers

Exploration of the first three use cases for quantum computers.

“But I do believe that we will succeed this time around.”
10:29

The 'Getting Fired' Mentality

Arvind shares a powerful mindset lesson about confidence and speaking up.

“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.”
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Transcript

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0:01Hi everybody, tune in to this short version of the podcast, which we do every Friday for the long version. Tune in on Wednesdays. Hi everybody, I'm Nicolai Tangen, the CEO of the Norwegian Sohren Wealth Fund, and today I'm in a particularly good company because I'm with Arvind Krishna in New York. And Arvind is the chairman and CEO of IBM, one of the most iconic technology companies in the world. Arvind 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.

0:42So, Arvind, a warm welcome. Thank you, Nikolai. It's always good to talk to you. Absolutely. Now, a lot of people still think that IBM is a kind of company from another era and you have changed that. So today, what does IBM do? IBM is largely a hybrid cloud and AI software company. We have 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.

1:20A very important piece, but a very small piece. When you took over 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? 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.

1:57So we began to 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 big. It took another three years for the world to wake up to that. What made you so convinced at the time? 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.

2:34But when you talked about these sort of technology shifts, how would you compare the AI advancements compared to those shifts? 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 was... So 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 very clever, by the way. I should mention that, not that he needs explaining, but our mutual friend Malcolm Gladwell said that you were the cleverest person he had met. I don't know whether I'm the cleverest, but I'm older than many, and I have been around a few of these.

3:16The 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.

3:53The 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. But do you think you are biased in your assessment of AI, given that you have tried before and failed? The fact that we tried, failed, but remained committed, and I made the bet on AI in 2019 again, before the advent of ChatGPT, says, no, I don't think we are biased or jaded, because you can imply that in your question. I actually think is going to be very, very powerful, but it's in the same.

4:32But tell us about, I mean, you tried with Watson, right? Just tell us about Watson. I think that Watson was the right goal at the time. What was it? 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 questions and answers on kind of a gray area using AI. Winning the game Jeopardy, which had these questions in English, but with all kinds of hidden puns and language, and that had to be interpreted, not just the black and white question, proved that AI can do those things. Then you could say, but you failed.

5:14Why 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. If we had taken it and said, let's use it to help corporations get better, let's use it to let their customers get better customer care, let's use it to 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. What I call bespoke is, yes, AI worked, but it worked when you had one use case with one corpus of data.

5:58Today'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. Well, let's spend some time on quantum computing, which is one of your big bets, right?

6:28For somebody who listens to this program who is not very technical in simple terms, what is quantum computing? 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. 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 parlance.

7:12GPUs 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, it's kind of what matrix math unlocks. So let's say now we fast forward, what do we think? 10, 20, 10, when will we have them? 2029. Right. Okay. Quantum computers. So let's say to be safe.

7:57Five years. We're now, five years from now, we have this big quantum computer in this room. Now, what are you and I going to do with it? What's the stuff we can do then? 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 coating 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.

8:46or 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 three, 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?

9:32So 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? Could we somehow attack the problem that 30 % of all truck miles and containers are empty as opposed to used?

10:14And 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. You have something called getting fired mentality. What does that mean? 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 on and told me, Arvind, you should live in the pleasure of being fired.

10:54And I looked at me and 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.

From the publisher

We've curated a special 10-minute version of the podcast for those in a hurry.  

 

Here you can listen to the full episode: https://podcasts.apple.com/no/podcast/ibm-ceo-transforming-a-tech-giant-ai-bets-and/id1614211565?i=1000766363117&l=nb

Nicolai Tangen sits down with Arvind Krishna, Chairman and CEO of IBM, for a wide-ranging conversation on technology, leadership, and reinvention. Arvind shares how he has reshaped one of the world's most iconic companies into a growing force in hybrid cloud and AI, and why he placed an early, decisive bet on AI long before it entered the mainstream. They explore the opportunities and risks of the current AI boom, IBM's push into quantum computing, and what it takes to reignite a risk-averse culture. Arvind also reflects on leading a global organization operating in more than 170 countries, and the lessons from his 35-year journey at IBM. Tune in for an insightful conversation!

In Good Company is hosted by Nicolai Tangen, CEO of Norges Bank Investment Management. New full episodes every Wednesday, and don't miss our Highlight episodes every Friday. 

 

The production team for this episode includes Isabelle Karlsson and PLAN-B's Niklas Figenschau Johansen, Sebastian Langvik-Hansen and Pål Huuse. Background research was conducted by Karoline Woie.

 


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