December 9, 2025 • 52 mins
Malcolm Gladwell sits down with IBM Chairman and CEO Arvind Krishna in a special live episode of Smart Talks with IBM. They discuss the groundbreaking potential of quantum computing, the transformative impact of AI on business, and how Krishna’s visionary predictions from the 90s continue to guide IBM’s innovations.
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Episode Transcript
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Speaker 1 (00:01):
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
(00:23):
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
(00:44):
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
(01:05):
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.
Speaker 2 (01:15):
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
(01:37):
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.
Speaker 1 (01:46):
So you are technology agnostic in some sense.
Speaker 2 (01:50):
I'm product agnostic, productnoy I'm not technology agnostic.
Speaker 1 (01:54):
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.
Speaker 2 (02:04):
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.
Speaker 1 (02:16):
Is it even simpler to say you just IBM solves
problems at the highest technical level.
Speaker 2 (02:22):
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,
(02:47):
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.
Speaker 1 (03:00):
But the world needs to know that IBM invented the Parker.
You guys should do a better job. I'm deciding that.
Speaker 2 (03:07):
I am sure our CMO will listen to this podcast
and we'll get that idea.
Speaker 1 (03:13):
Tell me you started at Thomas Watson Research Center. What
were you doing when you first started it, IBM.
Speaker 2 (03:20):
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
(03:43):
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
(04:04):
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.
Speaker 1 (04:20):
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.
Speaker 2 (04:35):
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
(04:58):
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
(05:21):
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
(05:41):
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.
Speaker 1 (06:00):
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.
Speaker 2 (06:09):
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.
(06:33):
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.
Speaker 1 (06:54):
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?
Speaker 2 (07:04):
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,
(07:26):
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
(07:47):
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.
Speaker 1 (08:04):
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.
Speaker 2 (08:22):
Yes it is, you see it again and again.
Speaker 1 (08:24):
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?
Speaker 2 (08:35):
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
(08:58):
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
(09:20):
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.
Speaker 1 (09:35):
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.
Speaker 2 (09:45):
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
(10:06):
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.
(10:29):
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.
Speaker 1 (10:50):
To be a successful business leader. Do you have to
unlearn or deviate from some of the things that made
you a successful science.
Speaker 2 (11:00):
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.
(11:22):
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
(11:42):
of those. And you've also got to learn to trust
your intuition a little bit.
Speaker 1 (11:47):
Yeah, but I forgot a question that I wanted to
ask about about the predictions of nineteen ninety arvand what
did you get wrong?
Speaker 2 (11:56):
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
(12:18):
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.
Speaker 1 (12:32):
Because they had been doing that already, because.
Speaker 2 (12:34):
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.
Speaker 1 (12:48):
I think it's as simples, that is the most parsimonious
explanation for why you think they failed.
Speaker 2 (12:54):
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
(13:15):
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.
Speaker 1 (13:35):
Yeah. Wait, did nineteen ninety r of End think that
the network should be dumb or smart?
Speaker 2 (13:43):
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
(14:04):
for it?
Speaker 1 (14:04):
Yeah, so you've been COO for five years.
Speaker 2 (14:08):
Five years.
Speaker 1 (14:09):
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.
Speaker 2 (14:18):
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
(14:39):
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
(15:01):
stands as the leader in its space.
Speaker 1 (15:04):
So how did you come to believe this heretical notion?
Speaker 2 (15:09):
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
(15:30):
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?
(15:51):
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.
Speaker 1 (16:11):
How hard was it to convince people needed convincing before
that acquisition.
Speaker 2 (16:16):
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.
Speaker 1 (16:32):
Yeah, you're very persistent, Oh yes, very Would you describe
that as you defining trade?
Speaker 2 (16:41):
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.
Speaker 1 (17:00):
If I got your family, put them up on stage
and asked them this exact question, is this how they
would answer?
Speaker 2 (17:05):
As well, they will tell you I'm very stubborn. They
might not agree that I don't try and drave.
Speaker 1 (17:15):
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?
Speaker 2 (17:32):
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
(17:56):
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.
Speaker 1 (18:08):
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.
Speaker 2 (18:18):
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.
Speaker 1 (18:28):
Can I ask you, Sif a personal question? How much
sleep did you lose over this.
Speaker 2 (18:35):
Once we had made the decision? None?
Speaker 1 (18:41):
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.
Speaker 2 (18:49):
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
(19:10):
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.
(19:35):
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,
(19:57):
so it shift gears.
Speaker 1 (19:59):
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.
Speaker 2 (20:13):
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
(20:33):
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
(20:54):
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
(21:17):
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
(21:37):
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.
(22:00):
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
(22:22):
times he might do something different, but he wants my opinion.
Tour the other way around.
Speaker 1 (22:27):
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.
Speaker 2 (22:34):
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
(22:56):
as a thinker on that topic. And probably most of
the people.
Speaker 1 (23:00):
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
(23:20):
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?
Speaker 2 (23:40):
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?
(24:03):
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
(24:25):
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
(24:45):
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
(25:05):
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
(25:27):
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.
Speaker 1 (25:49):
Everyone used to work for BlackBerry.
Speaker 2 (25:50):
Yep.
Speaker 1 (25:51):
BlackBerry goes into its dive. And that's the best thing
that happened to Waterloo because it was not just capital
but talent.
Speaker 2 (25:57):
Yep. Challenge is to many other companies.
Speaker 1 (25:59):
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.
Speaker 2 (26:13):
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
(26:33):
one advice to them. Pick areas you can scale, don't
pick the shiny little toys on the side. Then.
Speaker 1 (26:41):
I think, for example, there.
Speaker 2 (26:44):
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
(27:06):
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
(27:27):
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,
(27:49):
But if you learn how to use it, then in
five years you'll be ready to exploit what comes.
Speaker 1 (27:54):
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,
(28:18):
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
(28:39):
about the developed world when it's actually the developing world
where the greatest ROI for this is to me.
Speaker 2 (28:45):
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
(29:07):
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
(29:29):
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.
(29:52):
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
(30:12):
to do the work if they aren't people to do
the work. So the problems are different in the places.
Speaker 1 (30:17):
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.
Speaker 2 (30:27):
Correct.
Speaker 1 (30:28):
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.
Speaker 2 (30:38):
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.
Speaker 1 (30:47):
Yeah. Yeah, what's different about IBM's version of AI versus
some of your.
Speaker 2 (30:53):
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,
(31:17):
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
(31:40):
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.
(32:01):
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
(32:25):
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,
(32:46):
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.
Speaker 1 (33:08):
Other challenges to explaining what makes you different to potential customers.
Speaker 2 (33:12):
For sure, the shiny object is always attractive. Well, I
can go and try chat GPT. Why don't you have
your GPT version?
Speaker 1 (33:21):
Do you use chat ChiPT?
Speaker 2 (33:23):
I have used it.
Speaker 1 (33:26):
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.
Speaker 2 (33:34):
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
(33:57):
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.
Speaker 1 (34:11):
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.
Speaker 2 (34:21):
Yeah, it's like dogs kind of imitating and looking. What
would you.
Speaker 1 (34:25):
Identify as the most significant bottleneck in the development of AI?
What's slowing us down right now?
Speaker 2 (34:34):
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
(34:56):
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
(35:18):
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?
Speaker 1 (35:31):
Yes?
Speaker 2 (35:32):
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.
Speaker 1 (35:56):
But why you say there is a we're in a
where people are not pursuing the the optimal strategy for
exploiting this technology.
Speaker 2 (36:06):
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.
Speaker 1 (36:21):
So is this a case where there's too much money?
Speaker 2 (36:24):
Humans have never had for more right ever?
Speaker 1 (36:26):
Yeah, but this is this a consequence of overinvestment in
the in the field.
Speaker 2 (36:33):
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.
Speaker 1 (36:48):
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?
Speaker 2 (37:01):
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
(37:23):
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
(37:47):
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
(38:07):
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
(38:27):
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.
Speaker 1 (38:43):
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.
Speaker 2 (38:54):
Of this three to five years away from shocking people?
Speaker 1 (39:01):
What does shocking people mean?
Speaker 2 (39:02):
Do something that nobody thought was possible in that timeline?
Speaker 1 (39:05):
Does an example come to mind?
Speaker 2 (39:08):
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.
Speaker 1 (39:25):
Thirty four percent.
Speaker 2 (39:26):
Thirty four percent.
Speaker 1 (39:27):
This is an industry that's used to one percent correct,
zero point five percent. Yes, that's astonishing.
Speaker 2 (39:35):
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
(39:56):
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
(40:20):
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.
Speaker 1 (40:40):
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.
Speaker 2 (40:51):
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,
(41:16):
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
(41:38):
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.
Speaker 1 (41:50):
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.
Speaker 2 (41:56):
Language correctly, which is not all problems. Yeah, just that's
I called it. It's one more kind of math.
Speaker 1 (42:02):
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.
Speaker 2 (42:13):
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
(42:36):
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,
(42:56):
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.
(43:19):
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
(43:39):
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.
Speaker 1 (44:02):
Three to four years is not a long time.
Speaker 2 (44:04):
No.
Speaker 1 (44:07):
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.
Speaker 2 (44:18):
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.
Speaker 1 (44:27):
What is quantum rank in the kind of great inventions
of the last one hundred and fifty years.
Speaker 2 (44:33):
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?
Speaker 1 (44:56):
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.
Speaker 2 (45:08):
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,
(45:31):
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.
Speaker 1 (45:47):
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.
Speaker 2 (46:02):
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
(46:24):
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
(46:46):
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
(47:06):
got to nurture it to where it gets bigger and
bigger until you get the confidence that, okay, now it's
a big bet.
Speaker 1 (47:13):
And what was the moment when you when you realize
now it's a big bet?
Speaker 2 (47:19):
Probably two or three years ago.
Speaker 1 (47:21):
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?
Speaker 2 (47:31):
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
(47:53):
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.
Speaker 1 (48:13):
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
(48:35):
at this moment.
Speaker 2 (48:37):
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.
(49:00):
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.
Speaker 1 (49:10):
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?
Speaker 2 (49:28):
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.
Speaker 1 (49:40):
Do you drop by at Saturday night at ten pm
to see if people are working?
Speaker 2 (49:45):
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
(50:07):
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.
Speaker 1 (50:15):
I don't need to push Yeah. One last question I
wanted to ask you, do you have the most interesting
job in America.
Speaker 2 (50:21):
I believe that it's the most impressing job, which I
won't give up for anything.
Speaker 1 (50:26):
It also sounds like you're enjoying yourself.
Speaker 2 (50:29):
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.
Speaker 1 (50:46):
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.
Speaker 2 (51:01):
Good.
Speaker 1 (51:02):
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,
(51:25):
Cassidy Meyer and Sophia Derlong. 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 Glawell.
This is a paid advertisement from IBM. The conversations on
(51:46):
this podcast don't necessarily represent IBM's positions, strategies, or opinions.
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
(00:23):
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
(00:44):
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
(01:05):
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.
Speaker 2 (01:15):
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
(01:37):
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.
Speaker 1 (01:46):
So you are technology agnostic in some sense.
Speaker 2 (01:50):
I'm product agnostic, productnoy I'm not technology agnostic.
Speaker 1 (01:54):
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.
Speaker 2 (02:04):
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.
Speaker 1 (02:16):
Is it even simpler to say you just IBM solves
problems at the highest technical level.
Speaker 2 (02:22):
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,
(02:47):
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.
Speaker 1 (03:00):
But the world needs to know that IBM invented the Parker.
You guys should do a better job. I'm deciding that.
Speaker 2 (03:07):
I am sure our CMO will listen to this podcast
and we'll get that idea.
Speaker 1 (03:13):
Tell me you started at Thomas Watson Research Center. What
were you doing when you first started it, IBM.
Speaker 2 (03:20):
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
(03:43):
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
(04:04):
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.
Speaker 1 (04:20):
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.
Speaker 2 (04:35):
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
(04:58):
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
(05:21):
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
(05:41):
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.
Speaker 1 (06:00):
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.
Speaker 2 (06:09):
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.
(06:33):
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.
Speaker 1 (06:54):
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?
Speaker 2 (07:04):
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,
(07:26):
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
(07:47):
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.
Speaker 1 (08:04):
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.
Speaker 2 (08:22):
Yes it is, you see it again and again.
Speaker 1 (08:24):
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?
Speaker 2 (08:35):
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
(08:58):
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
(09:20):
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.
Speaker 1 (09:35):
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.
Speaker 2 (09:45):
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
(10:06):
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.
(10:29):
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.
Speaker 1 (10:50):
To be a successful business leader. Do you have to
unlearn or deviate from some of the things that made
you a successful science.
Speaker 2 (11:00):
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.
(11:22):
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
(11:42):
of those. And you've also got to learn to trust
your intuition a little bit.
Speaker 1 (11:47):
Yeah, but I forgot a question that I wanted to
ask about about the predictions of nineteen ninety arvand what
did you get wrong?
Speaker 2 (11:56):
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
(12:18):
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.
Speaker 1 (12:32):
Because they had been doing that already, because.
Speaker 2 (12:34):
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.
Speaker 1 (12:48):
I think it's as simples, that is the most parsimonious
explanation for why you think they failed.
Speaker 2 (12:54):
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
(13:15):
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.
Speaker 1 (13:35):
Yeah. Wait, did nineteen ninety r of End think that
the network should be dumb or smart?
Speaker 2 (13:43):
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
(14:04):
for it?
Speaker 1 (14:04):
Yeah, so you've been COO for five years.
Speaker 2 (14:08):
Five years.
Speaker 1 (14:09):
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.
Speaker 2 (14:18):
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
(14:39):
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
(15:01):
stands as the leader in its space.
Speaker 1 (15:04):
So how did you come to believe this heretical notion?
Speaker 2 (15:09):
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
(15:30):
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?
(15:51):
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.
Speaker 1 (16:11):
How hard was it to convince people needed convincing before
that acquisition.
Speaker 2 (16:16):
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.
Speaker 1 (16:32):
Yeah, you're very persistent, Oh yes, very Would you describe
that as you defining trade?
Speaker 2 (16:41):
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.
Speaker 1 (17:00):
If I got your family, put them up on stage
and asked them this exact question, is this how they
would answer?
Speaker 2 (17:05):
As well, they will tell you I'm very stubborn. They
might not agree that I don't try and drave.
Speaker 1 (17:15):
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?
Speaker 2 (17:32):
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
(17:56):
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.
Speaker 1 (18:08):
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.
Speaker 2 (18:18):
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.
Speaker 1 (18:28):
Can I ask you, Sif a personal question? How much
sleep did you lose over this.
Speaker 2 (18:35):
Once we had made the decision? None?
Speaker 1 (18:41):
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.
Speaker 2 (18:49):
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
(19:10):
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.
(19:35):
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,
(19:57):
so it shift gears.
Speaker 1 (19:59):
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.
Speaker 2 (20:13):
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
(20:33):
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
(20:54):
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
(21:17):
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
(21:37):
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.
(22:00):
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
(22:22):
times he might do something different, but he wants my opinion.
Tour the other way around.
Speaker 1 (22:27):
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.
Speaker 2 (22:34):
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
(22:56):
as a thinker on that topic. And probably most of
the people.
Speaker 1 (23:00):
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
(23:20):
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?
Speaker 2 (23:40):
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?
(24:03):
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
(24:25):
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
(24:45):
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
(25:05):
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
(25:27):
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.
Speaker 1 (25:49):
Everyone used to work for BlackBerry.
Speaker 2 (25:50):
Yep.
Speaker 1 (25:51):
BlackBerry goes into its dive. And that's the best thing
that happened to Waterloo because it was not just capital
but talent.
Speaker 2 (25:57):
Yep. Challenge is to many other companies.
Speaker 1 (25:59):
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.
Speaker 2 (26:13):
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
(26:33):
one advice to them. Pick areas you can scale, don't
pick the shiny little toys on the side. Then.
Speaker 1 (26:41):
I think, for example, there.
Speaker 2 (26:44):
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
(27:06):
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
(27:27):
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,
(27:49):
But if you learn how to use it, then in
five years you'll be ready to exploit what comes.
Speaker 1 (27:54):
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,
(28:18):
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
(28:39):
about the developed world when it's actually the developing world
where the greatest ROI for this is to me.
Speaker 2 (28:45):
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
(29:07):
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
(29:29):
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.
(29:52):
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
(30:12):
to do the work if they aren't people to do
the work. So the problems are different in the places.
Speaker 1 (30:17):
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.
Speaker 2 (30:27):
Correct.
Speaker 1 (30:28):
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.
Speaker 2 (30:38):
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.
Speaker 1 (30:47):
Yeah. Yeah, what's different about IBM's version of AI versus
some of your.
Speaker 2 (30:53):
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,
(31:17):
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
(31:40):
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.
(32:01):
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
(32:25):
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,
(32:46):
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.
Speaker 1 (33:08):
Other challenges to explaining what makes you different to potential customers.
Speaker 2 (33:12):
For sure, the shiny object is always attractive. Well, I
can go and try chat GPT. Why don't you have
your GPT version?
Speaker 1 (33:21):
Do you use chat ChiPT?
Speaker 2 (33:23):
I have used it.
Speaker 1 (33:26):
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.
Speaker 2 (33:34):
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
(33:57):
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.
Speaker 1 (34:11):
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.
Speaker 2 (34:21):
Yeah, it's like dogs kind of imitating and looking. What
would you.
Speaker 1 (34:25):
Identify as the most significant bottleneck in the development of AI?
What's slowing us down right now?
Speaker 2 (34:34):
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
(34:56):
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
(35:18):
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?
Speaker 1 (35:31):
Yes?
Speaker 2 (35:32):
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.
Speaker 1 (35:56):
But why you say there is a we're in a
where people are not pursuing the the optimal strategy for
exploiting this technology.
Speaker 2 (36:06):
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.
Speaker 1 (36:21):
So is this a case where there's too much money?
Speaker 2 (36:24):
Humans have never had for more right ever?
Speaker 1 (36:26):
Yeah, but this is this a consequence of overinvestment in
the in the field.
Speaker 2 (36:33):
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.
Speaker 1 (36:48):
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?
Speaker 2 (37:01):
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
(37:23):
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
(37:47):
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
(38:07):
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
(38:27):
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.
Speaker 1 (38:43):
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.
Speaker 2 (38:54):
Of this three to five years away from shocking people?
Speaker 1 (39:01):
What does shocking people mean?
Speaker 2 (39:02):
Do something that nobody thought was possible in that timeline?
Speaker 1 (39:05):
Does an example come to mind?
Speaker 2 (39:08):
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.
Speaker 1 (39:25):
Thirty four percent.
Speaker 2 (39:26):
Thirty four percent.
Speaker 1 (39:27):
This is an industry that's used to one percent correct,
zero point five percent. Yes, that's astonishing.
Speaker 2 (39:35):
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
(39:56):
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
(40:20):
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.
Speaker 1 (40:40):
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.
Speaker 2 (40:51):
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,
(41:16):
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
(41:38):
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.
Speaker 1 (41:50):
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.
Speaker 2 (41:56):
Language correctly, which is not all problems. Yeah, just that's
I called it. It's one more kind of math.
Speaker 1 (42:02):
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.
Speaker 2 (42:13):
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
(42:36):
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,
(42:56):
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.
(43:19):
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
(43:39):
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.
Speaker 1 (44:02):
Three to four years is not a long time.
Speaker 2 (44:04):
No.
Speaker 1 (44:07):
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.
Speaker 2 (44:18):
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.
Speaker 1 (44:27):
What is quantum rank in the kind of great inventions
of the last one hundred and fifty years.
Speaker 2 (44:33):
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?
Speaker 1 (44:56):
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.
Speaker 2 (45:08):
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,
(45:31):
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.
Speaker 1 (45:47):
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.
Speaker 2 (46:02):
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
(46:24):
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
(46:46):
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
(47:06):
got to nurture it to where it gets bigger and
bigger until you get the confidence that, okay, now it's
a big bet.
Speaker 1 (47:13):
And what was the moment when you when you realize
now it's a big bet?
Speaker 2 (47:19):
Probably two or three years ago.
Speaker 1 (47:21):
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?
Speaker 2 (47:31):
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
(47:53):
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.
Speaker 1 (48:13):
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
(48:35):
at this moment.
Speaker 2 (48:37):
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.
(49:00):
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.
Speaker 1 (49:10):
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?
Speaker 2 (49:28):
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.
Speaker 1 (49:40):
Do you drop by at Saturday night at ten pm
to see if people are working?
Speaker 2 (49:45):
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
(50:07):
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.
Speaker 1 (50:15):
I don't need to push Yeah. One last question I
wanted to ask you, do you have the most interesting
job in America.
Speaker 2 (50:21):
I believe that it's the most impressing job, which I
won't give up for anything.
Speaker 1 (50:26):
It also sounds like you're enjoying yourself.
Speaker 2 (50:29):
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.
Speaker 1 (50:46):
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.
Speaker 2 (51:01):
Good.
Speaker 1 (51:02):
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,
(51:25):
Cassidy Meyer and Sophia Derlong. 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 Glawell.
This is a paid advertisement from IBM. The conversations on
(51:46):
this podcast don't necessarily represent IBM's positions, strategies, or opinions.
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