Hello, Hello, 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 Loreel reimagine how scientists approach cosmetic formulation, or enabling Scuderia FERRARIHP 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.

It's 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 as revolutionary as a semiconductor.

Thank you everyone, Thank you Arvin.

You're a difficult man to schedule for one of these things, so we're enormously pleased that you could join us.

Start with a.

I have all these cousins, 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.

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.

Then 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 that's what IBM is.

So you are technology agnostic in some sense.

I'm product agnostic, productnoy I'm not technology agnostic.

Yes, But if I twenty five years from now, IBM could be doing things that would be unrecognizable to contemporary IBM, it is completely possible.

Yeah, it could be there in twenty five years from now.

The only software IBM does his open source.

It could be the only computing you do is quantum computers.

And if I add those two people today say that's not the IEM of today.

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

If you say highest technical level, yesh, Like the guy who invented the bar code.

He was solving a problem retailers wanted to scale.

Many of you wy not know it was an IBM or who invented the bar code, by the way, not somebody who was a PhD.

Not somebody who was a deep researcher.

I think it was actually a field engineer.

Oh really 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 bar code and that changed inventory management forever.

But the world needs to know that IBM invented the Parker.

You guys should do a better job.

I'm deciding that.

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

Tell me you started at Thomas Watson Research Center.

What were you doing when you first started it, IBM.

I started in nineteen ninety and that was the era in which computers and networking but 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 ninety two or ninety three, but it was clear to us that they were going to come supportable computing, and I spent my first five years building what today you would call Wi Fi.

We used to have these debates can be builded?

It's got to be small enough.

I mean, like, it can't be more than one hundred grams was kind of our thought, because if it's more than that, you're on a three thousand grand laptop.

Wold 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.

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

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 nineteen ninety that was a weird thought that some researchers held.

By the late nineties, that was obvious that it became the Internet.

I would have told you that I believe that video streaming will be the prime way people will consume video.

You would have said that in nineteen ninety.

Absolutely, Now that didn't take five years.

That took twenty.

But it happened because you could do it technically, except it just too expensive and too cumbersome.

And if you've been in technology, like in nineteen eighty five, I would have told you the Internet is old because when I went to grad school, every one of us had a those days, an Apple, Mac or Lisa on our desks.

They're all connected by a network.

We're happily sending email to people all around the country.

We were doing file transfer.

So okay, you had to be a little bit more aware of the technology.

And it didn't have a browser.

That took ten years to get the browser that took five years to be a business.

But when you see the speed and the pace of technology in usually ten or fifteen years, the cost point and the consumerization is at a scale that you couldn't imagine ten years ago until you've seen a few o those sites.

Wait, did you make the leap to Sorry, this is fascinating, I'm curious, but how far did you take?

That's a really fundamental thing you have gotten right in nineteen ninety I think.

You 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 will go.

I fundamentally believed, actually way back eighty seven, that on demand movies would become the way people would consume movies.

So those were all things that I could have predicted.

Nine didn't personally work on all those.

I mean, after networking, I moved on to doing other things.

But those were easy to predict.

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?

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 happened with Wi Fi.

So you build these wireless networks and then you say, hey, the market's 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 could 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 their routs?

How do you make it easy enough?

And that was probably I mean, I'm making it simple now.

That was probably a five to ten year evolution of myself in those days.

You know what this reminds me of when the telephone is invented in the eighteen seventies, It doesn't take off for forty years because the people running a telephone business they didn't 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, it's an exact parallel.

Yes it is, you see it again and again.

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?

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 that 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 exact buying patterns.

If you look at existing buying patterns, you forget.

All of those who have created massive value in 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 Scaler company and that imagination of making the market is how you create I think massive value.

You got to get all three pieces going.

It's not enough.

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 that's what gets you thinking along the path that leads you to this job.

Oh yeah, I'll tell you if you had met or when the nineteen ninety four and you had talked about the stock market or about a balance sheet, or looked at you like, Okay, I got though those words are, I can parse them.

I have no I 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?

Well, so you learn.

I mean, I figure, I'm willing to learn.

I'm willing to read either.

The best way I read is to go to balance sheets.

Here you can read the book.

It's pretty dawn 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 you nere.

To be a successful business leader.

Do you have to unlearn or deviate from some of the things that made you a successful science.

I actually believe the exactly opposite.

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?

And there's many ways.

You can have people that you trust who help you out 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.

Yeah, but I forgot a question that I wanted to ask about about the predictions of nineteen ninety arvand what did you get wrong?

All lots of things.

I think that people were thinking that but in those days, and it started 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 carried.

If you all think through the nineties 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.

Because they had been doing that already, because.

They'd been doing that for one hundred years.

Yeah, And in the end, the winners the networking were those who said flat price thirty bucks a month or fifty bucks a month or whatever, and that was just too much of a leap for them.

I think it's as simples, that is the most parsimonious explanation for why you think they failed.

No, there were a couple of other more technical things.

One was written by somebody who was actually inside one of these outcom companies, and he leveled 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 about the relays.

And the network is smart.

It routes you, it figures out where to send it, it does echocaculation backwards.

And the current Internet is completely dumb with 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 found explanation, but business model didn't help them either.

Yeah.

Wait, did nineteen ninety r of End think that the network should be dumb or smart?

I'm not sure I thought about it deeply.

But everything I worked on the network was dumb.

The network movements.

That's all I did.

Yeah, because even I in those days understood I couldn't imagine all the applications.

So if all you do 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 that be smart for it?

Yeah, so you've been COO for five years.

Five years.

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.

Twenty and eighteen, I proposed to our boat that we should buy a company called rad Hat.

IBM does proprietary, but that was open source.

The stock fell fifteen percent of 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 you agnostic across multiple cloud platforms, across on premise environments.

So you've 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.

So how did you come to believe this heretical notion?

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

Okay, you're five years to be generous, maybe longer behind at that point the two leaders, So you could spend maybe ten billion a year and a lot of 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 write their success.

If I want to be the best partner, then what are the set of technologies that would be useful so you can flip The problem is how I thought about it.

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

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.

Yeah, you're very persistent, Oh yes, very Would you describe that as you defining trade?

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.

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

As well, they will tell you I'm very stubborn.

They might not agree that I don't try and drave.

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?

Probably took five maybe four years, I think by twenty twenty three.

So twenty eighteen we announced it, we took the big start crop, it took a year to close twenty nineteen, so if our count not that I'm counting that much, but July ninth, twenty nineteen as the day that we got all the approvals.

Took another a few weeks to actually transfer them.

But from there, probably twenty twenty three, the world woke up and said, hey, you guys deserve credit for this was actually a great move, not a bad move.

Yeah, but this is it's interesting because this is a real gamble.

If it doesn't work, you're not sitting in this chair right now.

Right.

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.

Can I ask you, Sif a personal question?

How much sleep did you lose over this.

Once we had made the decision?

None?

Can you give me pointers on how you do this?

Because I wake up at two am every morning and I over much more trivial things than this.

Once a week, I'll probably wake up at two or three 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, get up and do work and make yourself productive.

You're gonna be tired before 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 spontificating on demographics and population.

But I won't read it on leadership because that's too close.

Now twenty years ago I might have that would have been different.

I won't read it on deep signs 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 shift gears.

I want to do.

It's 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 made a big difference to you.

Getting their support made a big difference.

You'd be surprised.

I'm remarkably open inside.

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 the other 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 O Nicol who introduced us, she has been in that loop since at least twenty fifteen.

For me, if I look at our CFO Jim Cavanaugh, he's been in that loop probably since twenty thirteen.

And IBM's will probably wonder, what the hell intersection do 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, use a 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 a servig, 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 will be 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?

That makes sense.

I actually think that each person should try to build a community of one hundred people inside your enterprise and a hundred 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 called 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 At who left IBM in twenty twenty one, we probably talk every two or three months on a random topic.

Ony way, it becomes mutual.

He'll asks me my opinion on some things.

Now, by the way, three or four times he might do something different, but he wants my opinion.

Tour the other way around.

If 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 fine to get the call.

Sure you can.

Do 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.

And probably most of the people.

I was thinking, you know, when you were making your comments about your nineteen ninety 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 one hundred percent sure they did not have that in nineteen ninety but they they're board was probably composed of people like them.

Let's talk a little bit about technology now.

There's so much, 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?

Okay, let's go back to ninety ninety five 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 or much smaller Internet was the major moment.

If you remember back to ninety nine and two thousand people claimed there was a lot of hype.

Would we say that the Internet of today has more than fulfilled all the expectations and more?

Yes?

Along the way, did eight out of ten 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, eight out of ten went broke.

By the way, those acids didn't go away.

They got consumed at ten cents in the dollar by somebody else who could then make a lot of money.

But the two out of ten, just take two, it probably has paid for all the capitals.

If we 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 the fundamental difference between the US model and almost all other countries.

On all other countries, they're desperate to keep all the companies alive.

So that means your dialute.

But that's a horrible thing.

So to me, let the system works 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.

Its census is a 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 a rate of innovation.

That means what might have taken twenty years takes five and the winners emerge exactly the same is going to happen this time.

Yeah, I saw that.

I grew up in Waterloo and BlackBerry curses from Waterloo.

Yep.

Everyone used to work for BlackBerry.

Yep.

BlackBerry goes into its dive.

And that's the best thing that happened to Waterloo because it was not just capital but talent.

Yep.

Challenge is to many other companies.

It's all the smart people went on the next really more interesting thing.

And yeah, but wait, you haven't answered.

So what is your an idea that we are underestimating at the moment that's in the current kind of suite of innovations.

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 shining experiments.

There's a lot of basic things you can do to use AI to improve the business today.

So that's really 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, there.

If anybody has more than ten percent of what they had for customer service ten years ago, they're already five years behind.

If anybody is not using AI to make their developers who write software thirty percent more productive today with the goal of being seventy percent more productive, that's not to say you will need less, you'll just get more software done.

Then they're not.

And I would turn around and tell you I think only maybe five percent of the enterprises on both those metrics today Yeah wow Yeah.

And the one that is completely underestimated.

I kind of put it like this, quantum today is where GPUs and AI war in twenty fifteen, and I bet you every AI person is thinking and hoping.

I wish I had started doing more in twenty fifteen, as opposed to wait until twenty twenty two.

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.

Yeah, we're gonna 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 to call the NASA satellite data, ran it through an LLM, and gave them this incredibly precise ten meter by ten meter analysis of what trees the plant, where to plant them exactly where the you know, 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 ROI for this is to me.

Look, software technologies are wonderful and the sense they can scare and they can be an ad so you don't have to do one or the other.

You use defraortestation.

How about the use of pesticides and fertilizers.

We always use 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 it ten times effectiveness, and that all would apply to the developing world.

How about remote healthcare or telehealth using an AI agent.

So the examples are numerous 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, But twenty seventy competed 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 one point six births per women or two or two point one.

Why are the three numbers?

One point six is to women who were born in the United States.

It becomes two point two.

If you include immigrant women, it becomes two point one.

If you include children who immigranting in 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 or what's going to do the work if they aren't people to do the work.

So the problems are different in the places.

Yeah, it gives you in the developing world, you get access to a suite of technologies and things at a price you could never been able to afford.

Correct.

That was my in talking to the Kenyon thing.

It was like the whole it's this.

It's maybe one of the largest ecological projects of its kind, A fifteen billion trees they want to plan, and.

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

Yeah.

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

So we are not a consumer company, so we have no focus on a B two C chat pot.

And the reason I say that is if you're making a B two C chat bot, does it help you to make it even bigger and more computationally inefficient?

And the short answer is yes, because you have a certain number of users, and you kind of say, I kind of say this jokingly.

If I add finished to French capabilities, I can probably add five million users.

If I add writing a high coup I might be able to add another five million users.

If I add writing an email in the voice of Steinbeck, I can probably add another five million users.

Do all those things.

If my goal is to get help a company summarize the legal documents in English, that can be a model that's one hundred 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 and lots of data.

So I can turn ontell 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, turning it for you yourself is a weekend exercise, It's not a six month on a big super computer cluster somewhere out there.

That's one big difference of what we do.

Second, we are very focused on helping those problems that can give people immediate benefit where we have domain knowledge.

So our domain knowledge is around operations, is around programming, encoding, is around customer service, is around customer experience, logistics, procurement.

Let's stage 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're happy to bring out all our learnings, including what needs to change in the process, because the biggest change is not technology, is getting people to accept that there's a different way to do things.

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

For sure, the shiny object is always attractive.

Well, I can go and try chat GPT.

Why don't you have your GPT version?

Do you use chat ChiPT?

I have used it.

I asked you a question recently which I thought was really simple, and it made up about ten people.

Anyway, I had a bad experience.

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

Yeah, because you had 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, it's 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?

What a bullships the way to an answer?

Well, it's exactly the same.

It's like the example of clever Hands.

You know that story the horse that they thought could speak, and all it was doing was pleasing it.

It's master.

Yes, it is a little bit of clever hands.

Yeah, it's like dogs kind of imitating and looking.

What would you.

Identify as the most significant bottleneck in the development of AI?

What's slowing us down right now?

I am not convinced that LLMS 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 fuse 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 LLM.

Maybe then we'll get to leafs and beyond beyond today 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 a thousand times cheaper, would people use a lot more of it?

Yes?

And I think those answers lie as is usually in compute.

So advances in semiconductors, advances in software, and advances in agorthic techniques all three.

But how come we're not working in any of those three.

We're just taking the current sevic conductor and going more.

We're taking the current algorithmic techniques and not really trying to invent new ones.

So I think those are all happen less than five years.

But why you say there is a we're in a where people are not pursuing the the optimal strategy for exploiting this technology.

Why 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 if you don't do the same, you'll get left behind.

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

Humans have never had for more right ever?

Yeah, but this is this a consequence of overinvestment in the in the field.

Going back to my internet allology, if two out of ten are going to succeed, yeah, 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 make a more efficient, that's not the way to win.

So first you win, then you become efficient.

Yeah, let's talk about what is I was told your favorite topic it's quantum?

It is what?

Boy even go any further?

Why is quantum your favorite topic?

We've only had two kinds of compute in the history, so nineteen forty five was to use that year for anyac all the way till twenty twenty 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 the imagination going.

And we use all these words about entanglement and silver position, but maybe because I'm a better 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 Lee 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 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 that require lots of compute, that's quantum.

A couple months ago was at the to watch some research center and they have you know, on the ground floor, they have those behind the glass.

There's incredibly exciting looking machines.

But where are we in the timeline.

Of this three to five years away from shocking people?

What does shocking people mean?

Do something that nobody thought was possible in that timeline?

Does an example come to mind?

I was actually pleasantly surprised.

So one of our clients, HSBC, last week published a result that using a quantum computer bond trading was thirty four percent more accurate than their prior technique.

Thirty four percent.

Thirty four percent.

This is an industry that's used to one percent correct, zero point five percent.

Yes, that's astonishing.

Now, that was not at a scale when 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 were correct.

You talk about an industry where one basis point, if I remember, I may be wrong, like thirteen trillion dollars of money kind of moves around in the financial industry each day, right, so basis point would be thirteen billion something like that, right, one over ten thousand.

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.

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.

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

So the equations of quantum mechanics and chemistry and how things interact are well known.

To solve them, that are no known techniques, So these are not like closed form, you know, it's not like the square root of a quarterly equation.

So the only way to solve them is to explore the state space.

So if you have a few hundred electrons, you need two to the one hundred 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, that's simpler 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 be here.

Oh I see, so you've taken what could take years to a few seconds.

Yeah, that's a pretty big change.

Yeah.

Yeah, it's speaking a different language, taking a different line.

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

Language correctly, which is not all problems.

Yeah, just that's I called it.

It's one more kind of math.

Yeah, what's an example?

So so many questions, Eric, give me another example of a of a of a kind of problem that a quantum computer would love.

This one is a bit more speculative, 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 post and advanced country says every house, every address, each day.

The way to optimize this is we can formulate the problem.

It's called the traveling sales and problem solving it is really hard, so people have heuristics.

Let's suppose today our heuristics get us to within twenty percent of the optimal answer.

Let's suppose a quantum computer can get you the next ten percent.

Well, if I can get ten percent of a billion gallons, that I think is one hundred million gallons of my math is right, and in the country i'm thinking about, that could be eight hundred million pounds of saving to one entity in one year.

And the associated common footprint climate change weren't less mileage on vanging.

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, etc.

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 man.

What are the new problems we can solve?

And the fact that we have about roughly two hundred clients who worked with us very early stage small experiments.

Is because the intuition is I can do something that I couldn't do in other places.

Three to four years is not a long time.

No.

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.

Don't have a problem.

Yeah, you'd probably be out of business in ten years.

Well maybe you could write a big check and buy the technology from somebody else.

You had to.

What is quantum rank in the kind of great inventions of the last one hundred and fifty years.

Equal to some conductor?

And I think that if semi conductor's vanished, modern life would stop, like just stop.

Yeah, no electricity, no automobile, no streaming.

You can imagine the yells from all the kids who ever hear that no streaming?

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

Because I use my Internet example.

Ninety five was the moment with Netscape that Internet came on people's consciousness.

I said in eighty five I considered 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 easy to understand.

We have probably, as I said, about four to five years from that moment.

That's why it's under discussed because the moment I say and you've got a math, I've probably lost ninety nine percent of the audience.

If I go to quantum mechanics, I've probably lost nine percent of the audience.

So you, as CEO, over 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.

I had already started investing in it back in twenty fifteen when I was leading IBM research.

So let me acknowledge and like nobody should try to copy.

And I've had a I'll call it a weird career.

I was a researcher at some point.

If he'd asked me out, I said, I'm one of those people, you know, throw a pizza under our 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 didn'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 who are not deeper quantum mechanics can begin to use it?

And they began to do those things three four years.

Did they get enough confidence?

Yeah, okay, this is something that can really work.

And then you've got to nurture it to where it gets bigger and bigger until you get the confidence that, okay, now it's a big bet.

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

Probably two or three years ago.

And how do you decide, as the head of a company like this, how much money, how many resources, and how many people?

And how what kind of prominence to give to an idea like that?

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 higher them.

So I'm constrained of people on that one because normally there's only so many people who could do these things.

Two, you got to be careful.

If you push too hard on timing, you will get people to take so much risk that actually the thing will fail.

So that's the art of it in 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.

Yeah, how do you This is fascinating.

So it's ultimately a question of judgment 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 reassess every few months and say, I think I'm over correcting or undercorrecting at this moment.

So one, you got to have what I call and this is channeling a word from one of my favorite books to geek away, 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'd get to probably that correct Goldilocks pressure.

The people themselves should want to go as hard as possible, but not harder than possible.

So that is then personality of leadership that makes sense.

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

Yes, absolutely, and people feel comfortable being honest with you, I believe so.

Yeah.

When has there been a moment in this path with quantum where you did think you were pushing too hard?

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 forward.

Do you drop by at Saturday night at ten pm to see if people are working?

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're watching the science.

I don't need to push hard.

If they are already ahead of it, then me I 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 Yeah.

One last question I wanted to ask you, do you have the most interesting job in America.

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

It also sounds like you're enjoying yourself.

I enjoy it as long as.

Look my role and goal should be to make the enterprise thrive.

As long as than making the enterprise thrive, and are clients delighted?

I love it.

If I don't, somebody else should do it.

Harvin, this has been so much fun.

Thank you so much taking the 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.

Good.

Thank you so much.

Smart Talks with IBM is produced by Matt Ramano, Amy Gains, McQuaid, Trina Menino, and Jake Harper.

Mastering by Sarah Buger, music by Gramoscope, Strategy by Tatiana Lieberman, Cassidy Meyer and Sophia Derlong.

Smart Talks with IBM is a production of Pushkin Industries and Ruby Studio at iHeartMedia.

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