IBM’s $10 billion bet on what comes after AI

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0:53 Hey folks, Jeff Berman here. I am thrilled to share some of the new names who will be joining us at this year's Masters of Scale summit. This may be our biggest stage yet. Reed Hastings, Meredith Whitaker, Van Jones, Amjad Masad, and more. Will be there with us October 20th through 22nd in San Francisco. If you're building something great, or you want to build something great, We want you there with us too.

1:20 Join us at masters of scale dot com slash apply twenty six. That's mastersofscom slash apply. Twenty six. I'll say something provocative. I think foundation models are gonna become commodities.

1:42 I think that right now the token price on all of these is gonna go way up. It just has to be justified. The capital investments. That's Arvin Krishna, CEO of IBM, and he has a strong metaphor for current AI systems that are just not the right size for all uses. I guess

2:02 Good. For those in the suburbs, take your kids to school in an eighteen wheeler every morning. You could go milk shopping in an eighteen wheeler. Then you'd ask yourself, is it really the most effective vehicle for that? I think right now we're using the eighteen wheeler for everything.

2:18 This is Masters of Scale. I'm Bob Safian, your host. IBM is playing a distinctive role in the AI race, not building AI models, but betting on how best to use them and on what comes after the other. In this conversation, recorded in front of a live audience as part of New York Tech Week at IBM's Manhattan HQ, we dig into why Arvin thinks most enterprises are using an 18-wheeler for every task. Plus what kind of risk taking businesses need to take right now, how to think about costs versus benefits when implementing AI, IBM's big bet on quantum computing, and much, much more. Please welcome to the stage IBM Chairman and CEO Arvin Krishna.

3:07 Thank you. Armin, first of all, um thank you for for hosting us at your house. We're together as part of New York Tech Week. Uh IBM is a New York institution. It is a global institution. Um, as a company, it's had to

3:26 continually reinvent itself? You know, from from mainframes to PCs and consulting and cloud and now AI and uh and on the cusp quantum computing. How do you think about staying fresh? As as tech. As tech moves and are there things about IBM's legacy

3:44 Versus what isn't an advantage. Look, um The advantage is Uh

3:52 Client intimacy, knowing your clients. The advantages are around trust. Uh, I don't think if we have knowingly ever Done anything wrong with a client's IP or data. Poor people.

4:06 Those are advantages. I think our people are incredibly technically adept. I would say they're experts in the areas where they have spent time and energy. So those are all the advantages.

4:19 Now. Technology keeps changing. And I'll be the first to acknowledge Sometimes we are really good at predicting where it's going to go. And we kind of get ahead of the wave and do that.

4:32 You mentioned a few of those. Uh the mainframe certainly, but this is now sixty years ago was one of them. Uh, I would say the IBM PC may have been another one of them. I think embracing Java in the Internet era was another great one. And then you sometimes miss them.

4:49 Mm. It's not for actually lack of knowledge. You miss them because the business model doesn't align. You don't quite know how to get the investments and returns to come together. I would say public cloud is one of them. Kindly. That we missed. I actually turned around and said, Despite inventing the IBM P C, client server was another one

5:07 That we missed. So I think it comes into You could probably miss some. And you kind of know you're gonna miss some sometimes? And you know you're gonna I think because if you don't take risk, you're never gonna succeed. So part of it is, hey, I'm getting a lot from that one. I kinda wanna keep my focus there.

5:24 But uh the whole point then is Can you do enough of those where you can be with the wave as opposed to Way behind. Yeah. Because it moves so fast nowadays that if you're two, three years behind you're not going to catch up.

5:42 And so right now we are very focused on Hybrid cloud, that is sort of our answer to Uh this uh the the cloud movement. And AI. Well I think that our play is going to be much more of similar to a hybrid play.

5:55 How do we help our enterprise clients take advantage of it fully? I mean the the the first mover advantage is this sort of uh catchphrase in in in Techland. We I was thinking about um you know AI and IBM Watson and you were sort of ahead In some ways, but you didn't maybe make the splash that you wanted. Was that a m was that a missed moment or is it more that you know, you were too early or the tech wasn't quite ready, or

6:22 There's always all those things. I think When we Uh one Jeopardy with Watson. I think it woke the world up.

6:30 Because I think for the first time in a long time AI started doing something people thought it could not do. Okay. Unfortunately it woke the world up completely in the sense that Uh a number of other companies started investing very heavily.

6:46 Now we had an advantage. We might have been able to succeed, but I'm not putting on Uh twenty twenty vision looking backwards. The mistakes we made were actually much more Of a strategic nature.

7:00 As opposed to creating building blocks. We wanted to create solutions in verticals. That I think is a mistake as technology shows. In the beginning it's quickly to the application. We went too quickly. And we wanted to make a monolithic application. Mistake number one.

7:18 Mistake number two. We picked a domain. Which is perhaps the hardest of them all, which is health. Mistake number three. How many IBMers sell to doctors and how many IBMers deal with the FDA?

7:32 Like none. So You picked the wrong solution set. In an industry you know nothing about And with a customer you know nothing about.

7:46 So as so as you um I mean as you think about IBM today and it's Roll in the in the AI ecosystem. Like what is it? I mean you're not

7:55 You're not trying to be open AI or anthropic, you're not trying to be Google or Microsoft. Like Are you ahead? Are you behind? You know, how how do you think about all of that? So We are not going to be a hyperscaler, which was two of the four you mentioned.

8:11 And we're not a foundation model provider. I'll say something provocative. I think foundation models are going to become commodities. By the way, not that far out. Is it a year, is it two years, is it three years? Commodities doesn't mean that they don't have value.

8:27 Gold to the commodity. Mm-hmm. So is it Iron. So

8:32 Commodities means that there is very little switching cost To go from one to the other. Second, I think that right now the token price on all of these is going to go way up. It just has to to justify The capital investments.

8:46 You put those two together. And there's gonna be a huge motivation then from everybody who's using them. to say I need to optimize, I need to e use each one, but in the most economic way possible. Our role is to A Let our enterprise clients do that.

9:04 Two Do it in a way that is safe. And right now there is very little demand for on premise or smaller models which are Effectively then one hundredth of the cost to run? So I'll use the analogy in a very crude way.

9:19 In the end, a automobil Which I'll include trucks into it. is an automobile at some gross level if you step back. I guess you

9:29 Could. For those in the suburbs, take your kids to school in an eighteen wheeler every morning. You could go milk shopping in an eighteen wheeler. Then you'd ask yourself, is it really the most effective vehicle for that? But if you're moving homes, which you do every seven years on average in the country

9:46 It is the most effective vehicle for that. I think right now we're using the eighteenth for everything. And this is the transition that I'll predict will happen within twenty four months. I'm not sure it'll happen within twelve months. underlying GPU pricing, which is what all of these things run on, has doubled in the last six months. on a po basis.

10:07 Uh it's getting more expensive to use these tools. And right now when you're uh prepublic, it's okay to lose money because you're gearing towards number of customers. I think you've seen this before, right? It used to be called eyeballs. And then it suddenly became the economics are important. So I think we're right. Maybe a year from that point. I mean you you said something at the IBM Think event uh a few weeks ago. You said it's day zero of the AI revolution. And and I think for a lot of folks, it feels further along than that. I mean you've got Yeah. trillion dollar AI companies, a a lot of

10:41 Business leaders worry that they're falling behind. It i does day zero mean like you're not too far behind, you don't have to rush too much, or Where did what do you mean by that? So first Let me be clear because I can sound a little bit cynical about the economics and And I actually am.

11:00 That said. I think AI is an incredible productivity tool. I think those who don't take advantage of it. will be perpetually disadvantaged compared to those who do. Let me begin by saying that

11:12 It's gonna optimize how you market, is gonna optimize how you write code, is gonna optimize enterprise operations, is gonna optimize how you sell, is gonna optimize how you get your daily work done. So there is no question about it. He's gonna make a profound and deep impact. On all of those things. By day zero, I mean

11:32 It's time to sit down. Take it seriously. You're not in the experimentation phase, so this is not like you're in high school. Day zero. The race is about to start.

11:43 Put yourself in the blocks. And start sprinting. But By that I mean Take three, four, five things.

11:50 A hundred. and learn how to do them at scale. Because that'll teach you How do you get uh all your change management done? How do you get your data organized? How do you really Get people motivated to change a process.

12:04 So do a few things at scale. Learn how to do that really well. Now do ten. And then give yourself the confidence to do the next twenty. I mean there's um There's this expression that's used to talk about the economy these days, that's a K shaped economy. You know some households do great and and some don't as well. Sometimes I get the sense that when it comes to AI, we're sort of having

12:25 K shaped businesses that like The tech companies, folks like you are super excited, and then there are a bunch of other companies and and these may be clients of yours, I don't know. That are like Falling behind. Yeah.

12:38 So uh unfortunately I think corporate performance is even more of a Um Differentiated K. If you look at corporate uh performance Actually it tends to be a twenty eighty rule, more of a power law.

12:52 Then the K is really more of a fifty fifty, I think if I follow the economists correctly. Here is more of a twenty eighty. And so twenty percent get it. They go forward, they're jumping into it. They kind of are going to get their returns.

13:06 And eighty percent are either not getting a return or don't quite know what to do. If you're in that eighty Figure out what is it that you should do. And it probably doesn't matter where you start as long as you're starting to try to do it at scale. To make a real difference to your bottom line.

13:22 So y what you're saying though is like You don't have to know what to do. J like p it's better to pick something and go than to just be like, I'm not sure what to do. Like I have a client walk up to me and say, look, I got it that I need to do it, but I don't have the right people in my team. Can you give me a deep AI expert, somebody who's kinda done their PhD in AI? I looked at the man and I said. Actually I recommend we give you somebody from a domain who doesn't really know the depth of AI.

13:49 But to understand the difference AI could make to your domain. So you don't need to know what to do because those domain experts exist in every company. Find that twenty or thirty percent of them who are motivated to say, I want to learn a new way to do things. So I think curiosity a willingness to adapt is more important

14:08 And think we're getting hung up on I need to know AI. Like a PhD in computer science. I think that's the wrong Thing. Because that's for the inventors of AI.

14:18 That's not needed for the deployers of AI. There's also um You know, this idea that AI's gonna save me a lot of money. It's gonna be very efficient for me. And I think for a lot of businesses when they start implementing Those results don't necessarily come. На you you guys have talked about that you've You know, unlock four, four and a half billion dollars of efficiency from AI. So you're doing something.

14:41 Um But there are also these like hidden costs, as you me as you mentioned, tokens. Like how do you balance what Are the cost versus the efficiency and what you should be expecting. Yeah. So

14:55 This was my point of scaling. I would probably turn around and say that for our first six months to a year We were probably spending more than we were saving. Because if you think about you putting a couple of hundred engineers to work at it, that's an incremental cost. The underlying infrastructure, AK the tokens if you're doing it on public, is a

15:15 added cost. There's opportunity costs also of not doing other things. With these people that could have resulted in revenue, that's a cost. No. Once we learn a rinse and repeat method that you're not doing it across two or three but across ten or twenty.

15:30 When you're saving a billion dollars a year. Well, that's a lot more than the cost of a couple of hundred After year two. We were definitely getting a return that was ten X compared to what we were spending. And now at uh year four

15:44 Uh we'll be I think all five billion. From a baseline of twenty two spent. So that's not an incremental five over last year. That's a incremental saving compared to uh year and twenty two spending.

15:57 So that is tremendous. That is more than enough to offset any extra expense. We were talking about the inexact nature of some of the outputs you get from AI, right? I don't want to call them hallucinations, but you know, the things that don't go the way you want. Well what he was saying was that the money that he was saving by having his engineers use AI. That on the Few cases where it was

16:30 Wrong. Yeah, to spend so much trying to find what was wrong and fix it. That he wasn't actually coming out ahead. Yeah. So I actually think that that is In edge case of how you use AI. And I think is actually wrong.

16:44 I think that you should try to use AI in a case where you're not going to have to go undo Six months of work or undo Having spent hundreds of millions. Take customer service. If it gives a wrong answer.

16:57 You got to undo one customer service answer. Then You can put all kind of evaluations and checks. So AI can check itself to make sure that you're not like way off in the wild. Mm. Okay, it may be slightly off. But you're not way off.

17:13 So you can put checks and balances and this is the sophistication of how you use it. So when we use it for example for our software developers to help them code I don't think they realise it. We actually have checks built in. To make sure that what it's suggesting is not absolutely crazy. Right. But do you don't you I I mean, as with

17:33 A human worker, you have to expect that sometimes It will go wrong. It will go wrong. I kinda turn around. If I look at customer service, I think the start which would be a good one is eighty five percent of the time the human people get it right. And fifteen percent of the time and I say, of course humans get angry, humans get pissed off, humans may not like the tone of the person on the other end, if it's a call, humans are sometimes um overconfident. I'm sure we all remember things perfectly, right? I mean perfect recognition. You and I do. I've not. So humans have all those two.

18:07 Mm. I think AI is at least if you keep it constrained to some extent, is probably ninety five percent correct. So the evals I'm talking about is more like saying when you think your way off Pointed to a human. Don't try to venture into the underconfident

18:25 uh range. But I mean but the AI is always confident. No it's not actually. You'd be surprised. If you tell it, don't pretend to be confident, you will get from it that hey I'm not quite sure that this is ours, this thing. And you can put uh something to check it. Who

18:41 Who's rewarded on actually spawning out the other's mistakes. So now you have So this is the this is the advantage of having multiple agents, mu multiple Multiple models. Multiple models working working at the at the same time.

18:54 Just like humans. You put four eyes. We call it four eyes in software development. You put two people. One is coding, one is checking their work. Just like the models. One is doing something, the other is checking its work. And and when you but when you have Two models. Instead of two people. Does that mean that you need

19:10 fewer people. I mean that that is one of the you know, you you got some grief a few years back for saying y you know, your your back office was gonna get smaller, which is seems like it's Like small change compared to the things that some other CEOs are saying right now. Do you think there's gonna be a lot of jobs? So I'll address both parts of the question because it's a and it's not a

19:35 So Our software developers are probably forty percent more productive today than they were two years ago. So I'm not saying it's one month, over two years, they're forty percent more productive. So you could turn around and say That means you need forty percent for your developers.

19:50 We actually tripled our college level entry hiring this year. Tripled. Compared to last year. Mm. So you said w.

19:58 That seems off. No, because this is what people are missing. If my cost of software development is going down That means we can make products that were not economically affordable three years ago. If we can do those, we can get more revenue at an appropriate margin.

20:16 So why wouldn't I get more people? Because these are value creating. Then there is the twenty percent I'll call it is what you need to run the operation. So is it compliance? Is it accounts payable? Is it procurement? Is it all those things? It's not gonna go down to zero.

20:33 But I would not be surprised if about thirty percent of the total headcount in those areas It's not needed. Within a few years. That's the statement I had made and I'm actually still consistent. Note, I just said we tripled our entry level hiring. So while there is some decrease on this side,

20:50 In about twenty percent of the enterprise. There is a big increase in the remaining eighty percent of the enterprise. So I actually think net will have increased demand for jobs. But There is some displacement which is always a little bit painful.

21:03 And and always happens with new technology. And always has happened with new technology. What kind of responsibility Do you feel like you have do you think other CEOs have? helping to ameliorate the the displacement that's inevitable. You know, tech folks are very excited about the future. Because they're beneficiaries of it. But not everyone is a beneficiary in that way. Well, actually I think that if we can get five to ten points of productivity in every enterprise around the planet,

21:29 Everybody's a beneficiary. That may be the early, early beneficiary. But I will note. Everybody in tech right now is losing money on it. So we can claim Is it going to be a long term beneficiary or not? I think there's open questions on that. I think everybody is going to be a beneficiary. I think that in our societies, at least in the West,

21:47 The responsibility of business leaders is to provide an opportunity So we want to help our people get upskilled, we want to help our people get reskilled. We want to open up that there are other opportunities of jobs. We can't force them to do any of that. But then

22:03 It's on them. Do they want to take advantage of those opportunities? And step up to do it. And I would say that answer has always been about Fifty fifty. Some do. But a lot of people say

22:15 I don't want to get retrained. I want my old job. Okay, I'm sorry, that's not gonna happen. And and those folks That's ends up being the responsibility of government and society, not necessarily of the business. Correct. We try to be compassionate. We don't force people out like in a day. But if over six months or nine months they are not willing to learn the appropriate skills where they're needed.

22:37 That's actually bad for the other ninety percent who are around them. Still ahead? Why Arvin thinks the math behind today's AI bubble just doesn't add up? The cybersecurity question keeping his team up at night? And IBM's$10 billion bet on quantum. Stay with us. The Wired Newsroom is known for award-winning reporting on how technology shapes our world. On Wire's Uncanny Valley, we take that curiosity even further.

23:07 Each week, journalists from Wired break down the biggest stories in tech while speaking directly with the people building, challenging, and reshaping the future. Is the AI boom sustainable? How do you protect your privacy in an age of constant surveillance? Uncanny Valley tackles the questions driving today's tech debates and lighting up your group chats. Listen to new episodes every Thursday.

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24:07 Humans will never be more intelligent than AI. There's gonna be two types of companies. Those who were great at AI and those that went out of business because they weren't. How do we build a future? That is human centered. I'm Rana Al Chayubi. And on my podcast Pioneers of AI, we answer that question and so many more. As an AI scientist, entrepreneur, and investor, I know what it takes to build AI that works for everyone.

24:36 Every week, I sit down with the pioneers shaping our future. And we take you behind the scenes of the AI that's transforming our lives. Find pioneers of AI wherever you tune in. Yeah. Welcome back to Masters of Scale. You can find this conversation and much more on our YouTube channel, and be sure to check out the link in the show notes to subscribe to our newsletter. Ар signs of a bubble that you see in different places?

25:08 If I take All the verbal promises And if we say there's a hundred and twenty five gigawatts of AI data centers that are gonna come online in the next two to three years. That's the eight to twelve trillion of CapEx in total. Not in one year, in total. That is where I come to I don't see the economics of that at all.

25:31 Because that would imply close to a trillion dollars of profit, which is in the best case that means four trillion of more revenue. Okay, where exactly is that gonna come from? The math doesn't do it doesn't work for you. No. Will it really, really work out well for at least half of them?

25:48 Yes. Some I'm going to thrive. But some will disappoint. So

25:55 And I don't think in a commodity world There probably isn't space for a dozen Foundation models. Is there space for three or four? Probably. Mm-hmm. But since there's a dozen being run after globally. It tells you that

26:09 They're not all going to work out. So I'm going to ask you a super basic um technology question, which uh which will maybe reveal something about me, but may help those in the in the room. What is the difference between a data center And a mainframe? I mean, aren't they both buildings with a lot of boxes in them? So um First, for the ge few geeks in the room. The main frame is actually one box.

26:35 Yeah. But a mainframe is designed differently. When we say data centers nowadays What people are intuitively implying

26:45 is there is a collection of similar boxes Hundreds, thousands, tens of thousands, maybe hundreds of thousands of them in a single data center. And The work is such that you can divide it up amongst all of these. And they can talk to each other if they need to collaborate. That's the network or the optics that does all that. That's a data center.

27:07 A main framework. Well it could be used in that context really is. A mainframe is really useful. When you have one piece of work that has

27:17 An incredible volume. Example airline reservations. If we sell you the seed. You probably don't want to sell the same exact seat on the same flight to somebody else. That would be inconvenient. So that is a different kind of workload. Then you asking an AI model a question and Joe asking it a question and me asking it a question, that can be divided up.

27:40 Because it doesn't need to know all the three answers. So the work is inherently can be divided or parallelized. Part of the reason I I asked is because, you know, w IBM was s is sort of the the mainframe shop, the OG mainframe shop, right? And and there was a time where mainframes were sort of seemed like they were I don't know, passe everything was going to the cloud, and that has like

28:03 shifted, like suddenly they're back. I'm sure the mainframe people don't like the idea of saying they're back, but But so I remember in nineteen ninety three there was I think it was Time Magazine where they were showing a Miami frame dressed up as a dinosaur And it was called The Death of the Mainframe. That was only thirty four years ago. And then every ten years people talk about the death of it. I think you got to be a bit more astute.

28:26 What is the workload that is great for a mainframe? What is the workload that's not good for a mainframe? I really am a believer in fit for purpose. The same way as a GPU is probably not ideal for running a smartphone because you kinda want your battery to last all day. Not be over in three minutes? So there is a fit for purpose that is underneath

28:45 These things. Where do you do AI training, that's one kind, where do you do inferencing, that's the second kind. Where do you do web serving or streaming? And well, and if you want to keep things secure, you wanna have them on your own Sovereignty also comes into play because especially if you're outside of the US, people care deeply about which government has control over the tech stack. So all of those things come into play. For where you want to run things.

29:10 IBM recently announced a$5 billion initiative called Project Lightwell to identify and fix AI vulnerabilities. І на оп сорс ворол. And that was Reportedly triggered by Anthropic's um release of Mythos. What did what did you see? That that sparked this at the at that time? And and and what do you think people sort of misunderstand about cybersecurity overall?

29:34 First for the good news. Um At least in our case from the things that we've been running for the last few months Mythos didn't find anything that other models Didn't and couldn't find.

29:48 I'll call that the good news. Here's the bad news. We have a lot of people Tens of thousands who are experts in using these models to try to find Vulnerabilities and then go fix them.

30:01 Okay. So for the expert, they could already do all this using other models. We will completely acknowledge That Mythos is way easier to use. So what it did do was it opens up the attack surface To where I don't need one of those hundred experts to go do it.

30:20 I can now do it with somebody with average skills. So that is definitely uh Something to be wanted about. So When Mythos came along

30:30 It's not just methods. The ability of these foundation models to actually help you write code, to understand code, is also there at the same time. Could be used to exploit. We could turn around and say

30:45 Can I use it to fix at least all open source? And that answer became a very quick we can. So we said As opposed to only worrying about Oh my God, I got all these things and like okay, I have my list of ten thousand of them.

31:00 We said Can we do something? But it's not Altruistic. It is good for society. But we do intend to charge

31:08 Uh people a fair price, not a not a usurious price for it, to say, can we instead turn this into a utility where people can come to us So that they can get their fix.

31:22 After giving us the vulnerability, but we can share Th to others who are inside the closed set also that hey, your friend here found a waterbury, we're not gonna tell you which friend. I'm not going to tell you where they're using it, so that that information is anonymized and protected, but you can actually get the same fix if you want. Mm. And

31:42 Yes, we are throwing a lot of people at it. But despite throwing that many people, without using the current AI tools, it would have been impossible. For us to go about saying That if you give us a piece of open source, we can actually give you What is in our belief a very well constructed patch of fix.

32:01 Against that vulnerability. S cybersecurity is like My AI's gotta be better than your AI. Right. Like the l then the attackers are are using. It's the old uh I think it was very sudden, right? Why do you rob banks? Well that's where the money is. Uh-huh. Why are you attacking cyber infrastructure? Well today that's where the data is, which is where the money is.

32:24 That's why I began by saying the good news is That it's not really A brand new. The bad news is simply it'll be done faster. So nation states have been doing this for decades.

32:35 But you would say three or four nation states were capable of doing it, maybe that opens up to a couple of dozen. That means more. And I guess it means organizations that might otherwise not have been targets. Smaller, mid size, it's the the bars. Right. So we all have to be a little bit more prepared, even if uh even before we reach The scale of I would turn around and say if you don't think you're protecting yourself, it is only a matter of time.

33:06 Mm. It will come. Um Earlier this year, the the IBM Institute for Business Value released uh a provocative report called Enterprise in in twenty thirty, and citing the big bets that CEOs needed to be making, and one of those bets was about quantum computing.

33:25 Um Now we've talked here, most business leaders are struggling to adapt to AI. You partnered with the US government on a new quantum foundry, investing$10 billion in a large scale commercial quantum computer. Why go all in on something even harder to understand? And control than AI.

33:45 So let's go back to your very first question. If you can get ahead of the curve. And if what you're doing is hard enough that you actually have A couple of years advantage. Our industry, the tech industry has shown

34:01 that you can create outsize returns for yourself and outsize returns for your clients in doing that. We felt That quantum is going to be one of those. We actually came to that recognition many years ago. Then the question became

34:15 Can we do the hard science it takes to be able to make progress? I would say earlier this year We convinced ourselves of that. The evidence of that is both in our ten billion dollar investment Because that means we expect to see a real return on it.

34:30 As well as In the government agreeing to invest, because that is a sign that they did their homework. and agreed it is now time to scale this as an industry. So I think you should think of quantum as doing the following. CPUs, we've had them for sixty or seventy years, do a lot of great problems, right?

34:50 GPUs came around and did a different kind of problem. They did matrix math that allowed AI and other things to happen. But in some sense it's not that CPUs couldn't do it. They were ten thousand times slower to do it. So last summer some out of twenty five. They could simulate

35:08 A five atom molecule. I'll be honest. A five atom molecule, a really good competitional chemist, if it's a simple molecule, could probably solve by hand. Mm. An expert, but they could do it by hand.

35:20 So you'd say, okay, your quantum can do it, but who cares? Лас Uh winter, November, December. They could do a 300-atom molecule. But

35:30 Good progress. Now you're getting beyond what you can do by hand? But you could do that on a Normal supercomputer. Pretty easily.

35:39 So you say Okay, you still haven't told me that this is interesting. In April They did. Twelve thousand items.

35:47 They're now getting into the protein realm. When you're in the ten to thirty to forty thousand atom range, you're in the protein realm. So this was a piece of a protein called trypsin. We're pretty sure that in another month or two we'll be at double that range. Which means you can solve trips in.

36:03 If you can understand the properties of a protein Using a few minutes of computation. You can now understand Which molecule

36:15 Aka your drug may bind to it. To stop its bad behavior. Mm. We've now opened up a new pathway. Possibly for health.

36:24 Which didn't even exist. I mean when you when you give that example like Part of the um amazing part of AI has been the exponential pace that it keeps improving. And as you give that example about quantum You're implying that it that is moving at that

36:40 similar kind of pace. It's we're getting that much closer to not being science fiction. Correct. So I think we're solving problems now whether it's in I talked about biology and molecules because I think most people intuitively get That that's a hard problem. But there are problems.

36:57 Things like uh fluid dynamics Aerodynamics, all of these are problems that are now coming Right about now. Within the range

37:06 of quantum computers to solve. Hm. Understand. where quantum may be in two or three years. what kind of algorithms you may need to develop So when it is there, you don't then spend two years doing all that.

37:20 That's what I would recommend. But that makes it an easy and That's not a A choice then. It's not a dilemma to say which of the two do you do. And this uh th this this uh report from the IBV uh the Enterprise in twenty thirty.

37:37 Do you know what IBM will look like in twenty thirty? We want to be known for not just being technologically innovative. I think that we had that reputation. We weren't always great at making that.

37:51 uh easily accessible to clients. So I think we want to be in the position where we are bringing all of our innovation to clients in a way that they can easily consume. That's one big piece. Two We were

38:05 About twenty percent software in twenty nineteen, we're now about forty five percent. I think that number will keep going up at a few percent a year. So we'll be much more In that space. Uh than anything else.

38:17 I think we'll become known as one of the exemplars of how we are deploying AI and agents To not just improve our own business. But To help improve our clients' business.

38:29 I wanted to ask you one uh one last thing here. Um a big focus For you is making IBMs culture more willing to take risks. For the business leaders who are here and and who are listening Or watching at home, like what advice do you have on

38:46 How you make that adjustment? So I would tell everybody The most risky route Is taking zero risk. Mm.

38:56 What happens in any business that takes no risk? That means that you're kind of Trying to extract Prophet. Or what an economist would call rent from what you already have.

39:09 But that means you're giving everybody else the opportunity to clone you or copy you? Be innovative from the bottom. So they will pick off the most profitable parts of your business. So now you have a declining profit pool.

39:24 Now you begin to have declining profit pools and if you're conservative by nature You go to invest even less because you're getting smaller? So you got to Accelerate your decline. And you'll be approaching a cliff.

39:37 Without realizing it. Go read history and see how many companies behave like that. When you said it watch them. Right. It takes about five years and you begin to hit a decline and then you're r going along and then five or ten years later somebody comes along and either Bites you up and chews you up and spits you out for parts?

39:56 Or you actually just go and fall over Into oblivion. So I call it that's the most risky. So how do you maintain enough innovation that you're actually growing?

40:08 All innovation does not pay off. Innovation is risky by nature. So You got to say, how do I manage it when I'm generating enough profit to pay for innovation? recognizing that not all of it will have a long term return. But enough of it should.

40:25 They will pick off the most profitable parts of your business. So now you have a declining profit pool. And if you're conservative by nature You go to invest even less because you're getting smaller? So you're going to

40:38 Accelerate your decline. And you'll be approaching a cliff. Without realizing it. Go read history and see how many companies behave like that. It takes about five years and you begin to hit a decline and then five or ten years later somebody comes along and either

40:53 Bites you up and chews you up and spits you out for parts? Or you actually just go and fall over Into oblivion. So I call that that's the most risky. So how do you maintain enough innovation that you're actually growing?

41:09 All innovation does not pay off. Innovation is risky by nature. So You got to say, how do I manage it when I'm generating enough profit to pay for innovation, recognizing that not all of it will have a long term return. But enough of it should. Enough? That's the nature of

41:28 I mean people saying like I yeah, I'm fine with taking a risk as long as I don't lose anything. Well this is Humans are incredibly loss of us. If you're given the option of you can gain ten dollars but lose one. Nobody wants to take that bet because they'd rather not lose one.

41:43 Even though you can gain ten. Okay, so we recognize that that's human nature point of leadership in at least Corporations is

41:51 How do you counteract that base human nature because people are not going to take risk if they think their jobs are on the line or if you chastise them in public. So what I always go to is I wanna hear fifty percent uh probability win. I don't want the ninety percent because if you tell people I need to be ninety percent certain That's no risk.

42:09 Mm. So you have to tell people, hey, it's okay, but one out of two times you won't meet the deadline. So you start building a bit of a buffer and to say, okay, I got it, that they won't meet the deadline, but if you're doing six things and four work out. Fine. And I guess you have to yourself own up to the things that maybe didn't work out the way you want as you were talking about

42:27 You know, uh Watson maybe not working out the way IBM might have ideally wanted. Plenty of things. I mean I can think about our digital sales channel, something we're still working on. We're probably on our fourth try in my tenure. But you're not gonna give up. It it did work out. I mean you got it Not just say go harder at it.

42:45 Not working out means think deeply about what is there structurally inside. That didn't make it work. Or is it that the market is different than what he presumed? You gotta sort of Pressure test all those things and keep trying. Well, um Erwin, this has been great. Thank you so much for doing it. It's my pleasure.

43:02 Look, talking on these topics I could probably go on all day. Yeah. Thank you, Bob. Thank you. Thank you. I found Arvind unexpectedly candid from acknowledging IBM's strategic lapses with Watson to a looming AI data center bubble, and I appreciated that he could explain quantum computing's impact without getting too deep into the complex science of it all. What sticks with me most is his emphasis on risk.

43:36 as he puts it, that the biggest risk is taking no risk. That's particularly true in times of tech transition like right now. Thanks again to Arvin for joining us. I'm Bob Safian. Thanks for listening. Masters of Scale is a Wait What original. Our executive producer is Eve Tro. Senior Supervising Producer is Trisha Bobeda.

44:10 Associate producer is Mashimaku Tonina. Video editor is Noah Wolstein. Senior town executive is Stefani Stern. Mixing and Mastering by Aaron Bastanelli and Brian Pugh. Original music by Ryan Holiday. Our head of podcast is Lital Malad.

44:28 Special thanks to Jodine Dorsay, VP of Live Events. Visit masters of scale.com to find the transcript for this episode and to subscribe to our newsletter. And be sure to check out our YouTube channel.

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