IBM CEO Arvind Krishna: Creating Smarter Business with AI and Quantum

Summary

Revisionist History: IBM CEO Arvind Krishna — Creating Smarter Business with AI and Quantum

Podcast: Revisionist History
Host: Malcolm Gladwell
Guest: Arvind Krishna, CEO and Chairman of IBM
Date: November 27, 2025

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Episode Overview

In this engaging live conversation recorded at IBM’s New York office, Malcolm Gladwell interviews Arvind Krishna, IBM’s CEO and chairman. The pair discuss IBM’s unique approach to solving business problems, Krishna’s personal path from engineer to executive, his views on the evolution— and hype—of AI, and his deep passion for quantum computing. Throughout, Krishna shares candid insights into decision-making at IBM, the importance of blending technology and business strategy, and why quantum computing is poised to be as transformative as the semiconductor.

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Key Discussion Points & Insights

The Essence of IBM (01:20–02:48)

• What does IBM do?
  • Krishna positions IBM as a partner helping clients improve their businesses by deploying relevant technology, not being bound to one product or even one technological approach.
  • IBM is currently focused on hybrid cloud and artificial intelligence, with quantum computing on the horizon.

"IBM's role is to help our clients improve their business by deploying technology... But it is about improving their business, not just giving them a commodity." — Arvind Krishna (01:20)

• Product-Agnostic, Not Technology-Agnostic:
  • IBM’s solutions evolve with time; in 25 years, IBM might look unrecognizable today, perhaps being all-quantum, all-open-source.

A Culture of Innovation: Barcodes and Beyond (02:27–03:12)

• The story of the barcode’s invention (by an IBM field engineer) epitomizes how IBM solves practical problems at the highest technical level.
• The capability to transform industries (e.g., inventory management) with inventive minds at any level.

"...he came up with the idea of the barcode and that changed inventory management forever." — Arvind Krishna (02:49)

Krishna’s Early Career and Predicting the Future (03:25–07:00)

• Early work (1990s) on networking and what would become Wi-Fi.
• Predictions from the 1990s:
  • Foresaw convergence of computing and networking into the Internet.
  • Anticipated video streaming and on-demand movies (but underestimated how long adoption would take).
• The gap between invention and market adoption—how technical innovations get stymied by business imagination.

"One of the reasons I do what I do today... is because of what I saw happen with Wi-Fi... The business looks at it and says: We think the market is confined to warehouse workers doing inventory... I became convinced that I can't just help invent it. I got to think about now, how do you market it?" — Arvind Krishna (07:09)

The Blind Spot Between Tech Creation and Social Change (08:30–10:55)

• The delay in societal understanding and adoption of new technologies is fundamental; many breakthroughs require creating new markets, not just new products.
• Learning the language of business (balance sheets, stock value) was a personal evolution for Krishna, broadening his perspective beyond engineering.

What Business Leadership Requires (11:06–11:52)

• Integrate technical expertise with business intuition; be curious and build up a holistic view.
• Trust in your own evolving intuition and lean on trusted networks for skills outside one’s core discipline.

Reflecting on Past Mistakes (12:01–14:10)

• Incorrect past assumptions: believed that telecoms (comm companies) would own networking due to existing business models, but they lost out because of their pricing paradigms (charging by the minute vs. flat rate) and design philosophy (smart network, dumb devices vs. the "stupid" Internet).

"The winners in networking were those who set flat price... and the current Internet is completely dumb on the inside. All the intelligence is the computer at the end." — Arvind Krishna (12:59)

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Key Decisions as CEO: Red Hat and Strategic Persistence

The Red Hat Acquisition (14:14–18:23)

• The 2018 acquisition was initially seen as highly controversial—IBM buying a major open source company.
• The logic was to become the ‘best partner’ for cloud companies, not compete head-to-head, carving a unique space in hybrid and multi-cloud.
• Persistence was critical:
  • Took 6–9 months to convince key stakeholders.
  • Four to five years for the acquisition to be widely vindicated.

"2018, I proposed... we should buy a company called Red Hat. IBM does proprietary, but that was open source. The stock fell 15% on the day we announced it... And today most people will say this is the most successful acquisition IBM has ever done." — Arvind Krishna (14:23)

Leadership Style and Decision-Making (16:38–17:21)

• Persistent, patient, and unwilling to easily give up, but not prone to anger.
• Stubbornness as a defining personal trait; recognized both by himself and his family.

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The Nature of Hype, Investment, and Innovation Cycles

Lessons from the Internet Era (23:45–25:48)

• Comparison between the current AI hype and the dot-com bubble.
• Overinvestment is a feature: most startups fail, but the few that succeed (like Amazon, Google) create enough value for all.
• The dynamism and “creative destruction” of the US model accelerates innovation.

AI: Hype, Promise, and Where We Miss the Point (26:19–29:23)

• AI is not underestimated but is being misapplied in many enterprises—focused on “shiny objects” instead of scalable, core business improvements.
• Underutilization of AI to improve areas like customer service or software development productivity.

"There’s a lot of basic things you can do to use AI to improve the business today... I think only maybe 5% of the enterprises on both those metrics today." — Arvind Krishna (27:29)

AI's Role in the Developing World (27:59–30:32)

• The greatest ROI from AI may come in countries where it can radically leapfrog traditional solutions (e.g., environmental planning, health care, agriculture).
• In developed world, AI is essential for maintaining quality of life in the face of shrinking workforces.

IBM’s Approach to AI (30:52–33:13)

• Focused on enterprise users, not consumers—models are kept smaller, more efficient, domain-specific, and easier to customize than massive consumer LLMs.
• IBM eats its own dog food: applies AI to its own operations and shares learnings with clients.

"If you're focusing on the enterprise, that actually takes away the focus of having to go to extremely large models, which by definition are going to be computationally expensive..." — Arvind Krishna (31:44)

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AI’s Bottlenecks and Our Wrong Turn with LLMs (34:29–36:02)

• Krishna is skeptical that current LLMs alone will bring breakthroughs like AGI.
• Next advances require fusing external structured knowledge with statistical models and, crucially, improving computational and algorithmic efficiency by orders of magnitude.

"On LLMs alone... I think we can get a thousand x efficiency in power and cost and compute from today. So if you make something a thousand times cheaper, would people use more of it? Yes..." — Arvind Krishna (35:20)

• The field’s FOMO and winner-take-all mentality drives overinvestment in brute force methods, not fundamental improvements. “First win, then become efficient.”

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Quantum Computing: The Next Big Revolution

Why Krishna Loves Quantum (37:06–38:48)

• Quantum is viewed as the third great paradigm in computing:
  • Classical: operates on bits (basic arithmetic).
  • AI/GPUs: leverages linear algebra, good for perception-like problems.
  • Quantum: brings a third kind of math—abstract algebra—changing which problems are tractable.

"Quantum adds a third kind of math... If you can do a third kind of math, which algorithms are suited to that third kind of math? So it excites me because we can now approach algorithms that you just could never do on the other two." — Arvind Krishna (37:06)

Quantum is Close to “Its Internet Moment” (39:01–45:13)

• Three-to-five years from a breakthrough that will "shock people."
  • Example: HSBC achieved 34% better accuracy in bond trading using quantum computation (vs. prior methods, where 1% improvements are huge).
  • Potential in material science: e.g., modeling solid-state batteries in seconds versus years.
  • Logistic optimization: Quantum could save millions of gallons of fuel with slightly improved routing, with knock-on climate impacts.

Quantum’s Transformational Potential (44:31–45:52)

• Quantum computing could be as significant as the semiconductor for civilization.
• The field is under-discussed because its advances are abstract and difficult for most people to grasp—waiting for its "Netscape browser" moment to popularize it.

"Quantum... equal to semiconductor. And I think that if semiconductors vanished, modern life would stop. Like just stop." — Arvind Krishna (44:38)

IBM’s Internal Journey on Quantum (46:08–49:16)

• Krishna began investing IBM research resources in quantum back in 2015; it was nurtured over years before betting big as CEO.
• Deciding how much to invest relies on:
  • Scarcity of specialized talent.
  • Balancing ambitious goals with realistic timelines to avoid failure through excessive pressure.
  • Open, candid debate between leaders and technical experts—calibrating the “Goldilocks pressure” for innovation.

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Notable Quotes & Memorable Moments

• On Innovation and Market Creation:

  "Many have created massive value and time have all created markets, meaning they've all created new markets... you've got to get all three pieces going." — Arvind Krishna (08:41)

• On the Red Hat Deal:

  "The stock fell 15% on the day we announced it. And today... the most successful acquisition IBM has done in all time..." — Arvind Krishna (14:23)

• On Quantum’s Promise:

  "Quantum today is where GPUs and AI were in 2015. And I bet you every AI person is thinking and hoping, 'I wish I had started doing more in 2015...'" — Arvind Krishna (27:29)

• On Transformational Impact:

  "Quantum... equal to semiconductor. And I think that if semiconductors vanished, modern life would stop..." — Arvind Krishna (44:38)

• On Leadership Style:

  "I'm very persistent and I'm very patient. I'm also probably very impatient, but I'm not a yeller and screamer... if I think we're going to do something, I can be remarkably stubborn..." — Arvind Krishna (16:47)

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Important Timestamps & Segments

• 01:20 — What is IBM? Krishna’s vision
• 03:25 — Early IBM career, predictions for networking/internet
• 07:09 — Lessons from Wi-Fi invention and importance of business sense
• 14:23 — The Red Hat acquisition: logic and vindication
• 23:45 — Investment cycles: Parallels with the Internet age
• 27:29 — AI: Misapplications, wasted potential, and untapped opportunity
• 29:23 — AI’s impact in developing vs. developed world
• 31:44 — IBM’s approach to AI versus the “shiny” consumer LLMs
• 34:29 — The LLM problem: hallucination and limitations to progress
• 37:06 — Why Quantum is the next big deal—and how it differs from AI
• 39:13 — Real-world impact of quantum: HSBC bond trading example
• 44:38 — Quantum’s significance compared to the semiconductor
• 46:08 — IBM’s internal path and decision-making in nurturing quantum
• 48:42 — Leadership, technical pressure, and open disagreement

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Tone and Flow

The conversation is intellectually lively, candid, sometimes irreverent, with Gladwell blending curiosity and humor. Krishna is direct, humble about past mistakes, confident in convictions, and passionate about both technological potential and business realities.

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Summary Takeaways

• IBM defines itself not by a product, but by helping clients solve complex business problems using the right tech mix.
• True leadership in innovation comes from blending technical vision, business sense, and relentless curiosity—not just invention, but market creation.
• Enormous hype (and overinvestment) is necessary to create world-changing tech, but learning from failure and scaling the right bets is key.
• Quantum computing is poised—within a few years—to spark a transformation as profound as the original Internet moment.
• Krishna’s story exemplifies how persistent, open-minded leadership and willingness to challenge both oneself and one's organization can steer a technological giant into the future.

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Transcript

[Arvind Krishna]

This is an iHeart podcast. Guaranteed Human. Hello. Hello.

[Malcolm Gladwell]

I'm Malcolm Gladwell, and you're listening to Smart Talks with IBM. This season we've been bringing you stories of how IBM works with its clients to solve complex problems, like helping l' Oreal reimagine how scientists approach cosmetic formulation or enabling Scuderia Ferrari HP to connect with fans in new ways. But in this episode, we're going to zoom out and look at the bigger picture. Earlier this month, I had the chance to meet the person who's shaping IBM's future. Its CEO and chairman, Arvind Krishna. We sat down in front of an intimate live audience at IBM's New York City office and talked about his uncanny ability to anticipate where technology is heading, the future of AI, and his passion for quantum computing, which he says is. Is as revolutionary as a semiconductor.

[Interviewer (possibly a journalist or podcast host)]

Thank you, everyone. Thank you to Arvind. You're a difficult man to schedule for one of these things, so we're enormously pleased that you could join us. Let's start with a. I have all these cousins. I have two cousins who work for IBM their entire career. I would ask them, what does IBM do? And they would always give me different, confusing, complicated answers. What's your answer? What's your simple answer to that question?

[Arvind Krishna]

IBM's role is to help our clients improve their business by deploying technology. That means you're not ever gated to one product. It is what makes sense at that time. But it is about improving their business, not just giving them a commodity. And to go to the next layer, I would say we help them through a mixture of hybrid cloud and artificial intelligence. And a taste of quantum. Coming down the road is kind of where I would take it. That's what IBM is.

[Interviewer (possibly a journalist or podcast host)]

So you are technology agnostic in some sense?

[Arvind Krishna]

I'm product agnostic.

[Interviewer (possibly a journalist or podcast host)]

Product agnostic.

[Arvind Krishna]

I am not technology agnostic.

[Interviewer (possibly a journalist or podcast host)]

Yes. But if I. 25 years from now, IBM could be doing things that would be unrecognizable to contemporary IBM.

[Arvind Krishna]

It is completely possible. It could be that in 25 years from now, the only software IBM does is open source. It could be that the only computing we do is quantum computers. And if I add those two people today would say, that's not the IBM of today.

[Interviewer (possibly a journalist or podcast host)]

Is it even simpler to say you just. IBM solves problems at the highest technical level?

[Arvind Krishna]

If you say highest technical level, yes. Like the guy who invented the barcode, he was solving a problem retailers wanted to scale. Many of you may not know it was an IBM who invented the barcode. By the way, not somebody who was a PhD, not somebody who was a deep researcher. I think it was actually a field engineer.

[Interviewer (possibly a journalist or podcast host)]

Oh, really?

[Arvind Krishna]

Yeah. And lasers were out and you could use lasers to scan things, but they could be upside down, they could be muddy, they could be partly scraped off. And he came up with the idea of the barcode and that changed inventory management forever.

[Interviewer (possibly a journalist or podcast host)]

Marvin, the world needs to know that IBM invented the barcode. You guys should do a better job publicizing this.

[Arvind Krishna]

I am sure our CMO will listen to this podcast and we'll get that idea.

[Interviewer (possibly a journalist or podcast host)]

Tell me, you started at the Thomas Watson Research Center. What were you doing when you first started at IBM?

[Arvind Krishna]

I started in 1990 and that was the era in which computers and networking were beginning to converge. And for the first five years I was actually building networks. So let's remember this was pre laptops. Laptops came in 92 or 93. But it was clear to us that they were going to come. So portable computing. And I spent my first five years building what today you would call WI fi. We used to have these debates, can we build it? It's got to be small enough. I mean, like it can't be more than 100 grams was kind of our thought. Because if it's more than that, you're on a 3,000 gram laptop. Why would anybody put this on? And the debate used to be, why would anybody want to walk around untethered? Won't you want to attach a big thick cable into it and sit down? Because that was the thought. That's how terminals worked. And I spent five years having a lot of fun building many iterations of those and making progress on that.

[Interviewer (possibly a journalist or podcast host)]

If I had a conversation with your 1990 self about what the next 30 years were going to look like, is it possible to reconstruct what you are? What were your predictions at that age about where the company, where the industry was going?

[Arvind Krishna]

It was more about where technology was going to go, I would say, than where industry would go. I would have told you that networking and computers would fuse in 1990. That was a weird thought that some researchers held. By the late 90s, that was obvious that it became the Internet. I would have told you that I believe that video streaming will be the primary way people will consume video.

[Interviewer (possibly a journalist or podcast host)]

You would have said that in 1990?

[Arvind Krishna]

Absolutely. Now that didn't take five years, that took 20. But it happened because you could do it technically, except it was just too expensive and too cumbersome. And if you've been in technology, like in 1985, I would have told you the Internet is old because when I went to grad school, every one of us had those days. An Apple Mac, a Lisa on our desks, they were all connected by a network. We were happily sending email to people all around the country. We were doing file transfers. Okay, you had to be a little bit more aware of the technology. And it didn't have a browser that took 10 years to get the browser that took 5 years to be a business. But when you see the speed and the pace of technology in usually 10 or 15 years, the cost point and the consumerization is at a scale that you couldn't imagine 10 years ago until you've seen a few of those cycles.

[Interviewer (possibly a journalist or podcast host)]

Wait, did you. Did you make the leap to sort of. This is fascinating. I'm curious about how far did you take. That's a really fundamental thing to have gotten right in 1990. How far did you run with that idea?

[Arvind Krishna]

We were pretty convinced that what we used to think of as linear television or broadcast would become digitized. That was a given two with cable already, the preponderance of how people got it, that if you put packet television over cable, then that becomes the way it'll go. I fundamentally believed, actually way back 87 that on demand movies would become the way people would consume movies. So those were all things that I could have predicted nine and personally work on all those. I mean, after networking, I moved on to doing other things. But those were easy to predict.

[Interviewer (possibly a journalist or podcast host)]

If you had a conversation in those years with someone in the television industry and you gave them those predictions, did they see it? Were they convinced of this?

[Arvind Krishna]

I'm actually going to take it back to wireless networking. I think one of the reasons I do what I do today, which is at the intersection of business and technology, is because of what I saw happen with WI Fi. So you build these wireless networks and then you say, hey, the market is going to be millions, tens of millions, billions of users. And the business looks at it and says, we think the market is confined to warehouse workers doing inventory. You can look at them and say, why not people in their homes? Because they couldn't imagine outside how people bought things at that time. And so I became convinced that I can't just help invent it. I got to think about now, how do you market it? To whom do you market it? What are the routes? How do you make it easy enough? And that was. I mean, I'm making it simple now. That was probably a 5 to 10 year evolution of myself in those days.

[Interviewer (possibly a journalist or podcast host)]

You know what this reminds me of when the telephone is invented in the 1870s, it doesn't take off for 40 years because the people running a telephone business and they didn't want women using it because they were worried that women would gossip with their friends. They didn't understand that that's actually what telephone is. Right, but it's an exact parallel.

[Arvind Krishna]

Yes, it is. You see it again and again.

[Interviewer (possibly a journalist or podcast host)]

What is the source of that blindness? So there's a gap, in other words, between the invention, the technological achievement, and the social understanding of the technology. Why is there such a gap?

[Arvind Krishna]

I think that the gap is fundamental and rooted in a lot of academic disciplines. So even channeling some of your work, though you don't intend it to be used that way, you can say a lot of things are data driven. If it is data driven, then by definition you're looking at history. If you're looking at history, that means you're looking at existing buying patterns. If you look at existing buying patterns, you forget all of those who have created massive value and time have all created markets, meaning they've all created new markets. And I think that is why the world is fascinated with people like Steve Jobs, for example. He imagined a market that didn't exist. So I think that is the gap. And then if you can get the technology, the business acumen to scale a company and that imagination of making the market is how you create, I think, massive value. You got to get all three pieces going.

[Malcolm Gladwell]

It's not enough.

[Interviewer (possibly a journalist or podcast host)]

In other words, you were thinking, it's not enough to invent something new. I need to make a business case for it simultaneously. And that's what gets you thinking along the path that leads you to this job.

[Arvind Krishna]

Oh, yeah. I would tell you, if you had met or when the 1994 and you had talked about the stock market or about a balance sheet, I would have looked at you like, okay, I got all those words on. I can pass them. I have no idea what they are. I have no intuition on what they are. I couldn't tell you why it's relevant or why it's not. But then you began to think, okay, why do companies get higher values? Okay, that's the stock. What does that capture if I have to spend working capital? And that's the balance sheet. So you learn. I mean, I figure I'm willing to learn. I'm willing to read. However, the best way I read is like to go to balance sheets. Here, you can read the book. It's pretty damn dry. Much easier to go talk to a financial expert who's around the corner and people are, if you're curious about what they do, they're really happy to share their expertise. And over time you learn more and more and they actually become part of your network within the company. And that's how you can both learn and evolve yourself and actually gain the extra skills.

[Interviewer (possibly a journalist or podcast host)]

In order to be a successful business leader, do you have to unlearn or deviate from some of the things that made you a successful scientist?

[Arvind Krishna]

I actually believe the exact opposite. Yeah, but use what you're really good at as a foundation, but don't make it the only thing you use. So then how do you add the other skills? There's many ways. You can have people that you trust who help you add those skills. You can gain some intuition, maybe not the depth of expertise. I want to be deeper on certain areas of electrical engineering than I'm ever going to be, let's say in finance or marketing. But I want to be curious about those. I don't want to dismiss them. So you build on your skills and then you have to say, but I need a complete and holistic view. So I'm going to be a little deep, not very deep in all of those. And you've also got to learn to trust your intuition a little bit.

[Interviewer (possibly a journalist or podcast host)]

Yeah. When I forgot a question that I wanted to ask about the predictions of 1990, Arvind, what did you get wrong?

[Arvind Krishna]

Well, lots of things. I think that people were thinking that in those days, and it's not my phrase, but I'll come back to it. I think most people thought that the communication companies would turn out to be the winners of how networking got guided. If you all think through the 90s of the investments that were being done by, let's not take the names of all of the telecom carriers, didn't turn out to be the case. Actually. I think that's the business model case. The reason is they all had in their heads that you can charge people by the minute.

[Interviewer (possibly a journalist or podcast host)]

Because they had been doing that already because they had been.

[Arvind Krishna]

Doing that for 100 years. In the end, the winners in networking were those who set flat price 30 bucks a month or 50 bucks a month or whatever. That was just too much of a leap for them.

[Interviewer (possibly a journalist or podcast host)]

You think it's as simple. That's, that's, that is the most parsimonious explanation for why you think they fail.

[Arvind Krishna]

No, there were a couple of other more technical things. One was written by somebody who was actually inside one of these telecom companies and he labeled his article the rise of the stupid network. So Telephone people believe that the network should be really smart. The end device is dumb. If you think about the telephone, telephone is dumb. It doesn't actually do anything. It's just a bunch of relays. And the network is smart. It routes you, it figures out where to send it. It does echo cancellation and they had it backwards. And the current Internet is completely dumb on the inside. It just takes the bits and shoves them out the other end. All the intelligence is the computer at the end. That's probably a bit more of a profound explanation, but business model didn't help them either.

[Interviewer (possibly a journalist or podcast host)]

Yeah. Wait, did 1990 Arvind think that the network should be dumb or smart?

[Arvind Krishna]

I'm not sure I thought about it deeply, but everything I worked on, the network was dumb. The network moved bits. That's all it did. Because even I in those days understood I couldn't imagine all the applications. So if all you're doing is voice, maybe the network can be smart. But if you're doing all those other things, how could the network possibly know all those things and be smart for it?

[Interviewer (possibly a journalist or podcast host)]

Yeah. So you've been CEO for what, five years?

[Arvind Krishna]

Five years.

[Interviewer (possibly a journalist or podcast host)]

Wait, so in your five year increment, what was your most misunderstood decision where you ended up being right, but everyone thought you were crazy?

[Arvind Krishna]

2018, I proposed to our board that we should buy a company called Red Hat. IBM does proprietary, but that was open source. The stock fell 15% on the day we announced it. And today most people will turn around and say this is the most successful acquisition that IBM has done in all time and probably the most successful software acquisition in history. So it was completely misunderstood because people didn't see that you actually did need a platform that could make it agnostic across multiple cloud platforms, across on premise environments. So you got to have a view of what it could be. And we drove it to a place where I think today it stands as the leader in its space.

[Interviewer (possibly a journalist or podcast host)]

So how did you come to believe this heretical notion.

[Arvind Krishna]

So cloud was happening? You could ask yourself the question, should we spend a lot of capital and chase cloud?

[Interviewer (possibly a journalist or podcast host)]

Okay.

[Arvind Krishna]

You're five years, to be generous, maybe longer behind at that point the two leaders. So you could spend maybe 10 billion a year. And a lot of business, businesses tend to do that. Okay. It's so important, it's going to be half the market. I can't not. My view was we'll always be five years behind. They're not dumb and they're not slow. So if you're going to be there, you're going to be Best case, a distant third. Worst case, maybe a fourth or a fifth. Because there's Chinese also in the mix. Why would you do that? Instead, is there a different space you can occupy? Instead of competing with them, can you become their best partner, in which case you ride their success? If I want to be their best partner, then what are the set of technologies that would be useful? So you kind of flip. The problem is how I thought about it.

[Interviewer (possibly a journalist or podcast host)]

How hard was it to convince people who needed convincing before that acquisition?

[Arvind Krishna]

Probably six to nine months of breaking my head with no success. And then six months of building the momentum once a couple of people began to see it.

[Interviewer (possibly a journalist or podcast host)]

Yeah, you're very persistent.

[Arvind Krishna]

Oh, yes, very.

[Interviewer (possibly a journalist or podcast host)]

Would you describe that as your defining trait?

[Arvind Krishna]

I am very persistent and I'm very patient. I'm also probably very impatient, but I'm not a yeller and screamer. I don't rant and rave. But as I say, if I think we're going to do something, I can be remarkably stubborn about it. We will do it.

[Interviewer (possibly a journalist or podcast host)]

If I got your family, put them up on stage and ask them this exact question, is this how they would answer as well?

[Arvind Krishna]

They will tell you I'm very stubborn. They might not agree that I don't rant and rave.

[Interviewer (possibly a journalist or podcast host)]

Well, you know, one of the principal observations of psychology is that our home self and our work self are uncorrelated. Once you know that, you know everything. Wait, I'm curious, one last question about that. How long does it take for you to be vindicated with Red hat?

[Arvind Krishna]

Probably took five, maybe four years. I think by 2023. So 2018, we announced it. We took the big StarCraft. Took a year to close 2019, if I count. Not that I'm counting that much, but July 9, 2019 as the day that we got all the approvals, took another few weeks to actually transfer the money. But from there, probably 2023, the world woke up and said, hey, you guys deserve credit for this was actually a great move, not a bad move.

[Interviewer (possibly a journalist or podcast host)]

Yeah, but this is interesting because this is a real gamble. If it doesn't work, you're not sitting in this chair right now, right?

[Arvind Krishna]

Oh, for sure. There were two steps. One, if it was obviously not going to work, I wouldn't have been selected. And two, if it hadn't worked after that. That's why CEOs can be short lived.

[Interviewer (possibly a journalist or podcast host)]

Can I ask you a sort of a personal question? How much sleep did you lose over this?

[Arvind Krishna]

Once we had made the decision, None.

[Interviewer (possibly a journalist or podcast host)]

Can you give me pointers on how you do this? Because I wake up at 2am every morning and I over much more trivial things than this once a week.

[Arvind Krishna]

I'll probably wake up at 2 or 3 in the morning. I acknowledge it because I wake up and my brain is running. And once it's running, I don't even try to go back to sleep. I mean, okay, it's. It's the go get up and do work and make yourself productive. You're going to be tired by 4 in the afternoon. That's fine. You'll sleep well that night. I have actually learned a long time back, you can't do it across. You can't do it early morning through the day and late at night. So an hour before I think I want to go to bed, I will actually change what I'm doing. Meaning I will start reading something interesting to me but completely outside the scope of work. I may read a biography, I might read somebody who's modificating on demographics and population. But I won't read it on leadership because that's too close. Now 20 years ago I might have. That would have been different. I won't read it on deep science because that's too close to what we do for a living. So it's got to be outside the things that will make my brain churn about work. But it's got to be something that is dense enough to occupy your brain so it shifts gears.

[Interviewer (possibly a journalist or podcast host)]

Sorry, I want to dwell on this just for a moment. The red hat thing. Was there someone or is there someone who you went to and explained the logic of this and they saw the logic of this and that meant a big difference to you?

[Arvind Krishna]

Getting their support made a big difference? You'd be surprised. I'm remarkably open insight. I mean when I have. Are there probably a half dozen to a dozen people inside that I'll talk to and I'll be completely open about. Hey, this is what I'm thinking. I don't know. Here are the risks. I'm open about those also. It's not just the benefits. I think these are the risks. But I think the benefits outweigh the risks. I talk about that to people all the time. So whether for example, I mean I'll take names. I think our current chro, Nicole, who introduced us, she has been in that loop since at least 2015. For me, if I look at our CFO, Jim Kavanaugh, he's been in that loop probably since 2013. And IBM is really wonder what the hell intersection did you guys have? It didn't when I talked about learning finance I will go to him and say, hey, explain this to me. I don't understand why it's like this. And to me it's okay, you're patient. You go learn. If I think about many of the people in the software business, they've been having these discussions with me for always. I mean, now I'll acknowledge I can get probably impatient and acerbic, but it's meant to be a discussion. I mean like, let's have the discussion. If you have a strong point of view. I got it. Nobody has a perfectly correct. But I always look for if you have a strong point of view, that means it's from a different perspective than mine. So what do I learn from that? Is the question which helps to improve my point of view, if that makes sense. I actually think that each person should try to build a community of 100 people inside your enterprise and 100 outside that you can call up. I have no hesitation. Somebody introduced me to a long time back to a CEO on the outside. I call them up all the time and say, hey, do you have five minutes? I'm just thinking about something this way. The CEO of Red Hat who left IBM in 2021. We probably talk every two or three months on a random topic, by the way. It becomes mutual. He'll ask me my opinion on some things now, by the way, three out of four times he might do something different, but he wants my opinion. To the other way around, it's if.

[Interviewer (possibly a journalist or podcast host)]

I gave you my phone number, can I be on that list? I would just be fascinating. I don't know if I can help you, but I would be really fun to get the call.

[Arvind Krishna]

Sure you can. You think that we can ever succeed unless people who influence opinions say things about us? So you may not think deeply about maybe the physics of quantum computing, but would you think deeply about why and what moment may make it much more attractive to a large audience? Sure you would. You'd be far better as a thinker on that topic than probably most of the people.

[Interviewer (possibly a journalist or podcast host)]

I was thinking when you were making your comments about your 1990 self and streaming that the rational thing would have been for there have been a reserved board seat for every television network from someone from the world of technology, which I 100% sure they did not have that in 1990, but their board was probably composed of people like them. Let's talk a little bit about technology now. There's so much of the changes going on right now are accompanied by a great deal of hype. What are we overestimating? What are we underestimating.

[Arvind Krishna]

Look, let's go back to 1995, the Internet, because I think that the current moment is very much like the Internet moment. Actually all the moments in the middle were much smaller. I think mobile streaming were much smaller. Internet was the major moment. If you remember back to 99 and 2000 people claimed there was a lot of hype. Would we say that the Internet of today has more than fulfilled all those expectations and more? Yes. Along the way, did 8 out of 10 of the companies that were invested in heavily go bankrupt? Yes, I actually think of that as being the huge positive of the United States capital system. That investment happened. 8 out of 10 went broke by those assets, didn't go away. They got consumed at 10 cents of the dollar by somebody else who could then make a lot of money. But the 2 out of 10 just take 2. It probably has paid for all the capital. If you just take Amazon and Alphabet, AKA Google, just those two have probably paid for all the capital of that time. So that's what's going to happen this time. There will be a lot of tears, but in aggregate there will be a lot of success. And I think that's a fundamental difference between the US model and almost all other countries. On all other countries they're desperate to keep all the companies alive. That means you're diluting. That's a horrible thing. So to me, let the system work. It's worked really effectively by the way, not just now, I mean all the way back to railways and electrification. And you mentioned telephone system. You can keep going on oil, I mean consumer goods. It goes on and on. I think this system is very effective. It deploys capital. It senses the big market is completely willing to over deploy capital in the short term, not the long term. That results in more competition. So it actually improves the rate of innovation. That means what might have taken 20 years takes five and the winners emerge. Exactly. The same is going to happen this time.

[Interviewer (possibly a journalist or podcast host)]

Yeah, I saw that. I grew up in Waterloo and BlackBerry of course is from Waterloo. Yep, everyone used to work for BlackBerry.

[Arvind Krishna]

Yep.

[Interviewer (possibly a journalist or podcast host)]

BlackBerry goes into its dive and that's the best thing that happened to Waterloo because it was not just capital, but talent.

[Arvind Krishna]

Yep, talent just went many other companies.

[Interviewer (possibly a journalist or podcast host)]

So all these smart people went on did the next really more interesting thing and. Yeah, but wait, that you haven't answered. So what is there an idea that we are underestimating at the moment that's in the current kind of suite of innovations?

[Arvind Krishna]

So I don't think AI is being underestimated because when you look at the amount of capital and the amount of things chasing it. I think it's incredible. I do think that a lot of enterprises are deploying it in the wrong place. They're running after shiny experiments. There's a lot of basic things you can do to use AI to improve the business today. So that would be just my one advice to them. Pick areas you can scale. Don't pick the shiny little toys on the side then. I think, for example, if anybody has more than 10% of what they had for customer service 10 years ago, they're already five years behind. If anybody is not using AI to make their developers who write software 30% more productive today with the goal of being 70% more productive, that's not to say you will need less. You just get more software done than. And I would turn around and tell you I think only maybe 5% of the enterprises on both those metrics today.

[Interviewer (possibly a journalist or podcast host)]

Yeah, yeah.

[Arvind Krishna]

And the one that is completely underestimated. I kind of put it like this. Quantum today is where GPUs and I were in 2015. And I bet you every AI person is thinking and hoping, I wish I had started doing more in 2015 as opposed to wait until 2022. Quantum today is there. So it's not good enough that you can get a big advantage, but if you learn how to use it, then in five years you'll be ready to exploit what comes.

[Interviewer (possibly a journalist or podcast host)]

Yeah, we're going to get to Quantum in a moment, but I have a couple other AI questions. You know, I as you know where this conversation is part of this thing that we do with IBM Smart talks and I've been. The last episode I did was on Kenya, which has a massive deforestation problem. And they got together, IBM took all of the NASA satellite data, ran it through an LLM and gave them this incredibly precise 10 meter by 10 meter analysis of what trees to plant, where to plant them, exactly where the an astonishing kind of blueprint about how to fix their country ecologically. And it made me think, when we analyze the potential of AI, are we making a mistake by spending too much thinking about the developed world, when it's actually the developing world where the greatest.

[Arvind Krishna]

ROI for this is to me, look, software technologies are wonderful in the sense they can scale and they can be an end. So you don't have to do one or the other. You use deforestation. How about the use of pesticides and fertilizers? We overuse it. We tend to. For irrigation, we tend to just flood everything as opposed to say okay, only that One needs it. You could do all those things to get a 10 times effectiveness. And that all would apply to the developing world. How about remote health care or telehealth using an agent? So the examples are numerous.

[Interviewer (possibly a journalist or podcast host)]

Yeah.

[Arvind Krishna]

In the developed world, I believe we are running out of people. I know that nobody likes to hear it. Most of the Far east is going to have half the number of people by 2070 compared to today. That's not that far away. If I look at Europe, birth rates are far under sustaining or keeping population flat. The US also, depending on which number you want to look at, is either 1.6 births per women or 2 or 2.1. Why are the three numbers. 1.6 is to women who are born in the United states. It becomes 2.0 if you include immigrant women. It becomes 2.1 if you include children who are immigrating in. So you got to decide where the trend is. Obvious this is going down. So AI in the developed world is going to be essential because to keep our current quality of life, you need more work done. Well, what's going to do the work if there aren't people to do the work? So the problems are different.

[Interviewer (possibly a journalist or podcast host)]

Yeah.

[Arvind Krishna]

It gives you.

[Interviewer (possibly a journalist or podcast host)]

In the developing world, you get access to a suite of technologies and things at a price you could never have been able to afford.

[Arvind Krishna]

Correct.

[Interviewer (possibly a journalist or podcast host)]

That was my in talking to the Kenyan thing. It was like the whole.

[Arvind Krishna]

It's this.

[Interviewer (possibly a journalist or podcast host)]

It's maybe one of the largest ecological projects of its kind at 15 billion trees they want to plant.

[Arvind Krishna]

And that is one country that might get it done because they do take a lot of pride in their ecology and in sort of returning to the land and giving back.

[Interviewer (possibly a journalist or podcast host)]

Yeah, yeah. What's different about IBM's version of AI versus some of your.

[Arvind Krishna]

So we are not a consumer company. So we have no focus on a B2C chat bot. And the reason I say that is if you're making a B2C chatbot, does it help you to make it even bigger and more computationally inefficient? The short answer is yes, because you have a certain number of users. And you say this jokingly. If I add finish to French capabilities, I can probably add 5 million users. If I add riding a haiku, I might be able to add another 5 million users. If I add writing an email in the voice of Steinbeck, I could probably add another 5 million users. Do all those things. If my goal is to help a company summarize their legal documents in English, that can be a model. That's 100 the size as effective, probably higher quality, but I don't need to go wide. So if you're focusing on the enterprise, that actually takes away the focus of having to go to extremely large models, which by definition are going to be computationally expensive, power hungry and demand lots and lots of data. So I can turn and tell the enterprise you don't need to worry about copyright issues about all those because you can train on a much smaller amount of data. And now, by the way, tuning it for you yourself is a weekend exercise. It's not a six month on a big supercomputer cluster somewhere out there. That's one big difference of what we do.

[Interviewer (possibly a journalist or podcast host)]

Yeah.

[Arvind Krishna]

Second, we are very focused on helping those problems that can give people immediate benefit where we have domain knowledge. So automate knowledge is around operations is around programming and coding is around, customer service is around, customer experience, logistics, procurement. Let's stay in the areas where we have a lot of expertise and then three, we kind of apply it to ourselves. And so we are not asking our clients to be the first experiment on it. We say you can leverage what we did. We are happy to bring out all our learnings, including what needs to change in the process. Because the biggest change is not technology. It's getting people to accept that there's a different way to do things.

[Interviewer (possibly a journalist or podcast host)]

Other challenges to explaining what makes you different to potential customers.

[Arvind Krishna]

For sure, the shiny object is always attractive. Well, I can go and try ChatGPT. Why don't you have your GPT version?

[Interviewer (possibly a journalist or podcast host)]

Do you use ChatGPT?

[Arvind Krishna]

I have used it.

[Interviewer (possibly a journalist or podcast host)]

I asked it a question recently which I thought was really simple and it made up about 10 people. Anyway, I had a bad experience.

[Arvind Krishna]

I actually think that that's the fundamental issue with all LLMs as they get larger.

[Interviewer (possibly a journalist or podcast host)]

Yeah.

[Arvind Krishna]

Because you have to ask, what was the original insight that led to these? It was a reward function with intent. So if it has learned by using a reward function, its reward function comes from giving an answer that satisfies you. So if it thinks that, if it makes up an answer that will satisfy you, how will you stop it? Why do we think this is different than the clever college kid who doesn't know an answer, but who bullshits their way to an answer? Well, it's exactly the same.

[Interviewer (possibly a journalist or podcast host)]

It's like the example of clever hands. Do you know that story? The horse that they thought could speak and all it was doing was pleasing its master? Yes, it is a little bit of clever hands.

[Arvind Krishna]

Yeah. It's like dogs kind of imitating and looking.

[Interviewer (possibly a journalist or podcast host)]

Yeah. What would you identify as the most significant bottleneck in the development of AI. What's slowing us down right now?

[Arvind Krishna]

I am not convinced that alarms is the way to get much beyond where we'll get incremental improvements. But I for one don't believe that LMS are going to get us to super intelligence or AGI. So I'll park that on the side and simply say we have to find a way to feed use knowledge and how do you represent knowledge as opposed to have to statistically rediscover it each time I ask a question. And how do we fuse knowledge with lm? Maybe then we'll get to leaps and bounds beyond today.

[Interviewer (possibly a journalist or podcast host)]

Yeah.

[Arvind Krishna]

On LMS alone, my view is I think we can get a thousand x efficiency in power and cost and compute from today. So if you make something 1,000 times cheaper, would people use a lot more of it? Yes, and I think those answers lie, as is usual in compute through advances in semiconductors, advances in software, and advances in algorithmic techniques. All three. But how come we're not working on any of those three? We're just taking the current semiconductor and going more. We're taking the current algorithmic techniques and not really trying to invent new ones. So I think those are all happened less than five years.

[Interviewer (possibly a journalist or podcast host)]

But why? You see, there is a. We're in a moment where people are not pursuing the, the optimal strategy for exploiting this technology. Why?

[Arvind Krishna]

Because when you see a few people running really hard and they're willing to invest any amount of money. So efficiency is not the focus. People feel efficient. If we don't do the same, you'll get left behind.

[Interviewer (possibly a journalist or podcast host)]

So is this a case where there's too much money?

[Arvind Krishna]

Humans have never had fomo, right? Ever.

[Interviewer (possibly a journalist or podcast host)]

Yeah. But is this, is this a consequence of. Of overinvestment in the, in the field?

[Arvind Krishna]

Going back to my Internet analogy, if 2 out of 10 are going to succeed.

[Interviewer (possibly a journalist or podcast host)]

Yeah.

[Arvind Krishna]

How do you guarantee or how do you improve the odds that you are one of those two? So if you pause to say, I want to become more efficient, that's not the way to win. So first you win, then you become efficient.

[Interviewer (possibly a journalist or podcast host)]

Yeah. Yeah. Let's talk about what is. I was told your favorite topic was quantum. It is what? Before you even go any further, why is quantum your favorite topic?

[Arvind Krishna]

We only had two kinds of compute in the history. So 1945, I was to use that year for ENIAC all the way till 2020. We had one kind of compute, classical. What today you would call a classical computer. Then GPUs and AI came around. So you would say the intuition there is. You went from sort of bits, which is algebra or high school algebra, to including neurons, which is captured in linear algebra. But that gives you a different kind. But it can do problems that are really hard to do. I don't say impossible, just hard to do on normal computers. Quantum adds a third kind of math. Yes, the physics properties, which really get people energized and their imagination going. And we use all these words about entanglement and superposition. But maybe because I'm a bit of a math guy, the real thing is it does a third kind of math to make it really simple. A third kind of math that comes from the field of abstract algebra. It does the math. You can use Hamiltonians for those who like physics, or you can use the word lie algebras for those who like abstract mathematics. If you can do a third kind of math, which algorithms are suited to that third kind of math. So it excites me because we can now approach algorithms that you just could never do on the other two. It's impossible. Now it's different than AI. It's not data intensive, it's compute intensive. So, so we kind of had compute and supercomputers. Then we went to data, which is AI. And now if you say there's another class of problems which require lots of compute, that's quantum.

[Interviewer (possibly a journalist or podcast host)]

A couple months ago it was at the Watson Research center and they have, on the ground floor, they have those behind the glass, those incredibly exciting looking machines. But where are we in the timeline of this?

[Arvind Krishna]

Three to five years away from shocking people.

[Interviewer (possibly a journalist or podcast host)]

What does shocking people mean?

[Arvind Krishna]

Do something that nobody thought was possible in that timeline.

[Interviewer (possibly a journalist or podcast host)]

Does an example come to mind?

[Arvind Krishna]

I was actually pleasantly surprised. So one of our clients, hsbc last week published a result that using a quantum computer bond trading was 34% more accurate than their prior technique.

[Interviewer (possibly a journalist or podcast host)]

34%.

[Arvind Krishna]

34%.

[Interviewer (possibly a journalist or podcast host)]

This is an industry that's used to 1% correct. Or 0.5%.

[Arvind Krishna]

Yes.

[Interviewer (possibly a journalist or podcast host)]

That's astonishing.

[Arvind Krishna]

Now that was not at a scale where they could turn it into production today, but that was sort of their original thought experiment and that's what they did. Now, can you imagine when will somebody. So you are correct. You talk about an industry where 1 basis points, if I remember, I may be wrong. Like $13 trillion of money kind of moves around in the financial industry each day. So basis point would be 13 billion, something like that one over 10,000. So when you think about the kind of profit that people can make, if you can tell somebody that you can Come up with a better price than your competition by just one basis point, they would actually gain the market share.

[Interviewer (possibly a journalist or podcast host)]

Yeah.

[Arvind Krishna]

So I think something around there or something in the world of materials, can we make a better battery? Could we make a solid state battery? Which means your risk of fires heating decrease dramatically.

[Interviewer (possibly a journalist or podcast host)]

And the reason, Sorry to ask a really naive question. Why is it that a quantum computer would be better at solving a battery problem than our existing methods of computing?

[Arvind Krishna]

So the equations of quantum mechanics and chemistry and how things interact are well known to solve them. There are no known techniques. These are not like closed form, you know, it's not like the square root of a quadratic equation. So the only way to solve them is to explode the state space. So if you have a few hundred electrons, you need two to the 100 states. Well, I'm sorry, you don't have that much memory. It's impossible. So it takes a really, really long time on a normal computer to solve those problems. Right. With that simple a problem, if a quantum computer operates in the equation domain, it doesn't need to explore the state space, it can actually solve it. That's why I call it a different kind of math. That's the kind of math it does. So in a couple of seconds it can tell you this is how that material will behave.

[Interviewer (possibly a journalist or podcast host)]

Oh, I see.

[Arvind Krishna]

So you've taken what could take years to a few seconds. Yeah, That's a pretty big change.

[Interviewer (possibly a journalist or podcast host)]

Yeah, yeah. It's speaking a different language.

[Arvind Krishna]

Speaking a different language.

[Interviewer (possibly a journalist or podcast host)]

So any kind of problem that comes along that's specific to that language.

[Arvind Krishna]

Correct. Which is not all problems. Yeah, just one more kind of math.

[Interviewer (possibly a journalist or podcast host)]

Yeah. What's an example? So, so many questions. Give me another example of a, of a, of a, of a kind of problem that a quantum computer would love.

[Arvind Krishna]

This one is a bit more speculative, but and I'm going to use a little bit of poetic license. So let's take a post office in a mid sized country. They probably burn a billion gallons of fuel per year delivering packages and letters. Because most posted in advanced countries says every house, every address, each day. The way to optimize this is we can formulate the problem. It's called the traveling sales problem. Solving it is really hard. So people have heuristics. Let's suppose today our heuristics get us to within 20% of the optimal answer. Let's suppose a quantum computer can get you the next 10%. Well, if I can get 10% of a billion gallons, that I think is 100 million gallons. If my math is Right. And in the country I'm thinking about that could be 800 million pounds of saving to one entity in one year and the associated carbon footprint, climate change, wear and tear, less mileage on vehicles. I'm not even counting all that. These are pretty attractive problems to go after. So if I look at the interest, recently New York has started a whole program in some places. Illinois stood up a quantum algorithm center between a number of the universities. The governor there was heavily behind it, et cetera. So I wouldn't say that this is widespread. This is why I'm saying three to four years for that moment. But there's enough people who are deeply cognizant who are saying, wait a moment, we kind of get it. This is a new kind of map. What are the new problems we can solve? And the fact that we have about roughly 200 clients who work with us very early stage small experiments is because their intuition is I can do something here that I couldn't do in other places.

[Interviewer (possibly a journalist or podcast host)]

Three to four years is not a long time.

[Arvind Krishna]

No.

[Interviewer (possibly a journalist or podcast host)]

But if I'm in the battery business and I don't have a line out to a quantum computing experiment, I have a problem.

[Arvind Krishna]

Don't have a problem. Yeah, you'll probably be out of business in 10 years. Or maybe you could write a big check and buy the technology from somebody else.

[Interviewer (possibly a journalist or podcast host)]

You had to. What is quantum rank in the kind of great inventions of the last 150.

[Arvind Krishna]

Years equal to semiconductor. And I think that if semiconductors vanished, modern life would stop. Like just stop.

[Interviewer (possibly a journalist or podcast host)]

Yeah.

[Arvind Krishna]

No electricity, no automobile, no screaming. You can imagine the yells from all the kids who ever hear that. No screaming.

[Interviewer (possibly a journalist or podcast host)]

The. And is that it's funny because. Don't. As someone who's outside this world, I feel like quantum is under discussed relative to its potential for transforming society.

[Arvind Krishna]

Because I use my Internet example. 95 was the moment with Netscape that Internet came on people's consciousness. I said in 85, I consider it to be this is a solved problem because it needs something that makes it accessible, easy. That was the browser. The Netscape browser is what brought it, made it easy to understand. We are probably, as I said, about four to five years from that moment. That's why it's under discussed, because the moment I say a new kind of math, I probably lost 99% of the audience. If I go to quantum mechanics, I probably lost 99.99% of the audience.

[Interviewer (possibly a journalist or podcast host)]

So you, as CEO for the last five years have been really the birth mother for a lot of the quantum computing work. I'M curious. So you come in. When you started as CEO, was this your first priority?

[Arvind Krishna]

I had already started investing in it back in 2015 when I was leading IBM research. So let me acknowledge. And, like, nobody should try to copy it. I had a. I'll call it a weird career. I was a researcher. At some point, if he'd asked me, I would have said, I'm one of those people, you know, throw pizza under a door and, like, leave me alone. I don't want to talk to people. Then I decided I was interested in the business. Then I went and started acquiring companies and doing that. Then somebody told me, hey, why don't you start doing some business strategy? Then I went back to research and led our research division for a couple of years. And when the people described it to me, I asked some questions. So it wasn't a big investment at that time. It was, hey, can we make a computer not just a science experiment? Can it run by itself all night? Can you think about software so that even people are not deep in quantum mechanics, can begin to use it and they begin to do those things? So over three, four years, did I get enough confidence? Yeah, okay, this is something that can really work. And then you got to nurture it to where it gets bigger and bigger until you get the confidence that, okay, now it's a big bet.

[Interviewer (possibly a journalist or podcast host)]

And what was the moment when you. When you realize now it's a big bet?

[Arvind Krishna]

Probably two or three years ago.

[Interviewer (possibly a journalist or podcast host)]

And how do you decide as the head of a company like this, how many. How much money, how many resources and how many people and how. What kind of prominence to give to an idea like that?

[Arvind Krishna]

So three layers. The set of people who actually have the knowledge and the intensity to fundamentally advance the technology. If I could find more, I would hire them. So I'm constrained on people on that. One, because normally there's only so many people who can do these things. Two, you got to be careful. If you push too hard on timing, you'll get people to take so much risk that actually the thing will fail. So that's the art between the leadership on the project and me to say, okay, how hard can you push? But not so hard that you cause it to fail, because then they get compelled to commit timelines that are just impossible.

[Interviewer (possibly a journalist or podcast host)]

Yeah. How do you. This is fascinating, though. So it's ultimately a question of judgment, of trying to figure out what's the sweet spot between enough pressure to keep them ahead of the pack, but not too much pressure so that they start taking risks. How do you Calibrate whether you're hitting that sweet spot. I mean, do you, do you reassess every few months and say, I think I'm overcorrecting or under correcting at this moment.

[Arvind Krishna]

So one, you got to have what I call. And this is channeling a word from one of my favorite books, the Geek Way. How open can you be? So I want to press hard, but the team knows that they're allowed to push back and really argue back hard. That means you'll get to probably that correct Goldilocks pressure. Two, the people themselves should want to go as hard as possible, but not harder than possible. So that is then personality of leadership. Yeah, that makes sense.

[Interviewer (possibly a journalist or podcast host)]

But you have to be someone who people feel comfortable being honest with.

[Arvind Krishna]

Yes, absolutely.

[Interviewer (possibly a journalist or podcast host)]

And people feel comfortable being honest with you?

[Arvind Krishna]

I believe so, yeah.

[Interviewer (possibly a journalist or podcast host)]

Has there been a moment in this path with Quantum where you did think you were pushing too hard?

[Arvind Krishna]

No, because I think that the leadership there will argue back with me any day of the week. I don't think that they feel that they have to fold.

[Interviewer (possibly a journalist or podcast host)]

Do you drop by sort of Saturday night at 10pm to see if people are working?

[Arvind Krishna]

I tend to text people and ask questions and like I'll read something and say, hey, are these people doing this? And if they can answer me in reasonable terms, I actually then say, great. They are already watching the competition, they are watching the literature, they are watching the science. I don't need to push hard. If they're already ahead of it, then me can answer my question. I'll say thoughtfully, not always completely accurately. You're thinking about it on their own. I don't need to push.

[Interviewer (possibly a journalist or podcast host)]

Yeah. One last question I wanted to ask you. Do you have the most interesting job in America?

[Arvind Krishna]

I believe that it's the most interesting job, which I wouldn't give up for anything.

[Interviewer (possibly a journalist or podcast host)]

It also sounds like you're enjoying yourself.

[Arvind Krishna]

I enjoy it as long as. Look, my role and goal should be to make the enterprise thrive. As long as I'm making the enterprise thrive and our clients delighted. I love it. If I don't, somebody else should do it.

[Interviewer (possibly a journalist or podcast host)]

Arvind, this has been so much fun. Thank you so much for taking your time and a fascinating, completely fascinating conversation. I wish I was one of those people who could help you out with Quantum, but I'm afraid I'm not in a few years. Good. Thank you so much.

[Malcolm Gladwell]

Smart Talks with IBM is produced by Matt Romano, Amy Gaines McQuaid, Trina Menino and and Jake Harper. Mastering by Sarah Bruguer Music by Grammascope Strategy by Tatiana Lieberman, Cassidy Meyer and Sophia Durlon. Smart Talks with IBM is a production of Pushkin Industries and ruby studio at iHeartMedia. To find more Pushkin podcasts, listen on the iHeartRadio app, Apple Podcasts, or wherever you listen to podcasts. I'm Malcolm Gladwell. This is a paid advertisement from IBM. The conversations on this podcast don't necessarily represent IBM's positions, strategies or opinions.