October 29, 2025 • 53 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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Speaker 1 (00:00):
Hey everyone, it's Robert and Joe here. Today we've got
something a little bit different to share with you. It's
a new season of the Smart Talks with IBM podcast series.
Speaker 2 (00:09):
This season on Smart Talks with IBM, Malcolm Gladwell is back,
and this time he's taking the show on the road.
Malcolm is stepping outside the studio to explore how IBM
clients are using artificial intelligence to solve real world challenges
and transform the way they do business.
Speaker 1 (00:25):
From accelerating scientific breakthroughs to reimagining education. It's a fresh
look at innovation in action, where big ideas meet cutting
edge solutions.
Speaker 2 (00:34):
You'll hear from industry leaders, creative thinkers, and of course
Malcolm Gladwell himself as he guides you through each story.
Speaker 1 (00:41):
New episodes of Smart Talks with IBM drop every month
on the iHeartRadio app, Apple Podcasts, or wherever you get
your podcasts. Learn more at IBM dot com slash smart Talks.
This is a paid advertisement for IBM pushkin.
Speaker 3 (01:06):
Hello.
Speaker 4 (01:06):
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 Lreel 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
(01:28):
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
(01:49):
passion for quantum computing, which he says is as revolutionary
as a semiconductor.
Speaker 3 (01:56):
Thank you everyone, Thank you Arvin.
Speaker 4 (01:58):
You're a difficult man to scare for what they said,
so we're we're enormously pleased that you.
Speaker 3 (02:02):
Could join us.
Speaker 4 (02:04):
Let's start with a I have all these cousins, two
cousins who work for IBM their entire career.
Speaker 3 (02:08):
I would ask them what does IBM do?
Speaker 4 (02:11):
And they would always give me different, confusing, complicated answers.
Speaker 3 (02:16):
What's your answer? What's your simple answer to that question?
Speaker 5 (02:19):
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
(02:41):
hybrid cloud and artificial intelligence and a taste of quantum
coming down the road is kind.
Speaker 3 (02:47):
Of where I would take it.
Speaker 5 (02:48):
That's that's what IBM is.
Speaker 3 (02:50):
So you are technology agnostic in some sense.
Speaker 5 (02:55):
I'm product agnostic. Product I'm not technology agnostic.
Speaker 4 (02:59):
Yes, But if I twenty five years from now, IBM
could be doing things that would be unrecognizable to contemporary IBM, it.
Speaker 3 (03:07):
Is completely possible.
Speaker 5 (03:08):
Yeah, it could be that 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, WLD say that's
not the IEM of today.
Speaker 4 (03:21):
Is it even simpler to say you just IBM solves
problems at the highest technical level.
Speaker 5 (03:26):
If you say highest technical level, yes, yeah, Like the
guy who invented the bar code, he was solving a
problem retailers wanted to scale. Many of you may 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
(03:50):
out and you could use lasers to scan things, but
they could be upside down, they could be muddy, they
could be partly scraped off. And he came up with
the idea of the bar code. Yeah, and that changed
inventory management forever. But the world needs to know that
IBM invented the barcode. You guys should do a better
job ombicizing that. I am sure our CMO will listen
(04:13):
to this podcast and we'll get that idea.
Speaker 4 (04:17):
Tell me you started at the Thomas Watson Research Center.
What were you doing when you first started it, IBM.
Speaker 5 (04:24):
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
(04:48):
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. Why would anybody
put this on? And the debate used to be, why
would anybody want to walk around untethered? Won't you want
(05:10):
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 4 (05:24):
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 you? What were your predictions
at that age about where the company, where the industry
was going.
Speaker 5 (05:40):
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
(06:02):
believe that video streaming will be the primary 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
(06:22):
the Internet is old because when I went to grad school,
every one of us had a those days, an Apple,
Mac or Lisa on our desks. They're all connected by
a network. You're happily sending email to people all around
the country. We were doing file transfers. So okay, you
had to be a little bit more aware of the
(06:42):
technology and didn't have a browser that took ten years
to get the browser that took five years to be
a business. But when you see the speed and the
pace of technology in usually ten or fifteen years, the
cost point and the consumerization is at a scale that
you couldn't imagine ten years ago.
Speaker 3 (07:02):
Until you've seen a few of those cycles.
Speaker 4 (07:05):
Wait, did you make the lead to sorry, this is fascinating.
I'm curious about how far did you take that. That's
a really fundamental thing to have gotten right in nineteen
ninety I think you're that idea.
Speaker 5 (07:15):
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.
(07:38):
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 4 (07:59):
If you had a converse in those years with someone
in the television industry and you gave them those predictions,
did they see it?
Speaker 3 (08:06):
Were they convinced of this?
Speaker 5 (08:08):
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 built 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,
(08:31):
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
(08:52):
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 I mean
not making it simple? Now, that was probably a five
to ten year evolution of myself in those days.
Speaker 4 (09:08):
You know, 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 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.
Speaker 3 (09:24):
Right, It's an.
Speaker 5 (09:25):
Exact parallel, Yes, it is. You see it again and again.
Speaker 3 (09:29):
What is the source of that blindness.
Speaker 4 (09:31):
So there's a gap, in other words, between the invention,
the technological achievement, and the social understanding of the technology.
Speaker 3 (09:37):
Why is there such a gap.
Speaker 5 (09:40):
I think that the gap is fundamental and rooted in
a lot of academic disciplines. So even channeling some of
your work, though you don't intend it to be used
that way, you can say a lot of things are
data driven. If it is data driven, then by definition,
you're looking at history. If you're looking at history, that
(10:03):
means you're looking at existing buying patterns. If you look
at existing buying patterns, you forget. All of those who
have created massive value 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.
(10:24):
So I think that is the gap. And then if
you can get the technology the business acumen scaler company
and that imagination of making the market is how you
create I think massive value.
Speaker 3 (10:38):
You got to get all three pieces going. It's not enough.
Speaker 4 (10:40):
In other words, you were thinking it's not enough to
invent something new. I need to make a business case
for it. Simultaneously, and that's what gets you thinking along
the path that leads you to this job.
Speaker 5 (10:49):
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 those words are I
can parse them. I have no idea what they are.
I have no intuition on what they are. I couldn't
tell you why it's relevant or why it's not. But
(11:11):
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. Howether the
best way I read is to go to balance sheets?
Here you can read the book. It's pretty damn dry.
(11:33):
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.
Speaker 4 (11:53):
Skills you are to be a successful business leader. Do
you have to unlearn or or deviate from some of
the things that made you a successful scientist.
Speaker 5 (12:05):
I actually believe the exactly opposite.
Speaker 3 (12:06):
Yeah, but use what.
Speaker 5 (12:08):
You're really good at as a foundation, but don't make
it the only thing you use. So then how do
you add the other skills? And there's many ways. You
can have people that you trust who help you out
those skills. You can gain some intuition, maybe not the
depth of expertise. I want to be deeper on certain
areas of electrical engineering than I'm ever going to be,
(12:31):
let's say, in finance or marketing. But I want to
be curious about those. I don't want to dismiss them.
So you build on your skills, and then you have
to say, but I need a complete and holistic view.
So I'm going to be a little deep, not very deep,
in all of those. And you've also got to learn
to trust your intuition a little bit.
Speaker 4 (12:51):
Yeah, but I forgot a question that I wanted to ask,
but about the predictions of nineteen ninety arvand what did
you get wrong?
Speaker 5 (13:00):
All lots of things. I think that people were thinking
that 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
(13:21):
by let's not take the names of all of the
telecom carriers.
Speaker 3 (13:26):
Didn't turn out to.
Speaker 5 (13:27):
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.
Speaker 3 (13:34):
Minute, because they had been doing that already, because they
had been doing that for one hundred years.
Speaker 5 (13:40):
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.
Speaker 3 (13:51):
Leap for them.
Speaker 4 (13:52):
You think it's as simple. That is the most parsimonious
explanation for why you think they failed.
Speaker 5 (13:58):
No, there were a couple of hours the more technical things.
The one was written by somebody who was actually inside
one of these telecom companies, and he labeled his article
the rise of the Stupid Network. So telephone people believe
that the network should be really smart, the end device
is dumb. If you think about the telephone, telephone is dumb.
It doesn't actually do anything. It's just about the relays.
(14:19):
And the network is smart. It routes you, it figures
out where to send it, it does echocacul 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 3 (14:40):
Yeah.
Speaker 4 (14:40):
Wait, did nineteen ninety r of end think that the
network should be dumb or smart?
Speaker 3 (14:48):
I'm not sure.
Speaker 5 (14:48):
I thought about it deeply, but everything I worked on
the network was dumb.
Speaker 3 (14:52):
The netwuck movements. That's all I did.
Speaker 5 (14:55):
Yeah, because even I in those days understood I can't
imagine all the opp loss. So if all you do
is voice, maybe the network can be smart. But if
you're doing all those other things, how could the network
possibly know all those things that be smart for it?
Speaker 3 (15:09):
Yeah, so you've been COO for five years. Five years.
Speaker 4 (15:14):
Wait, so in your five year increment, what was your
most misunderstood decision? Well, you ended up being right, but
everyone thought you were crazy.
Speaker 5 (15:22):
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 twelve 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
(15:43):
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.
Speaker 3 (16:00):
So you've got to have a view of what.
Speaker 5 (16:01):
It could be, and we drove it to a place
where I think today it stands as the leader in
its space.
Speaker 4 (16:08):
So how did you come to believe this heretical notion?
Speaker 5 (16:13):
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
(16:34):
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?
Speaker 3 (16:55):
Is there a.
Speaker 5 (16:55):
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 4 (17:15):
How hard was it to convince people needed convincing before
that acquisition.
Speaker 5 (17:21):
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 3 (17:36):
Yeah, you're very persistent.
Speaker 4 (17:39):
Oh yes, very Would you describe that as you defining trade?
Speaker 5 (17:46):
I am very persistent and I am 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.
Speaker 3 (18:03):
We will do it.
Speaker 4 (18:04):
If I got your family, put them up on stage
and ask them this exact question, is this how they
would answer?
Speaker 5 (18:09):
As well, they will tell you I'm very stubborn. They
might not agree that I don't rant and drave.
Speaker 4 (18:20):
Well, you know, one of the principal observations of psychology
is that our home self and our work self are uncorrelated.
Speaker 3 (18:27):
Once you know that, you know everything.
Speaker 4 (18:29):
Wait, I'm curious one last question about that. How long
does it take for you to be vindicated with red Hat?
Speaker 5 (18:37):
Probably took five maybe four years, I think by twenty
twenty three, So twenty eighteen we announced it, We took
the big shot crop. It took a year to close
twenty nineteen, so if O count not that I'm counting
that much, but July ninth, twenty nineteen as the day
(19:00):
that we got all the approvals. Took another few weeks
to actually transfer the money. But from there, probably twenty
twenty three, the world woke up and said, hey, you
guys deserve credit for this was actually.
Speaker 3 (19:11):
A great move, not a bad move.
Speaker 4 (19:13):
Yeah, but this is it's interesting because this is a
real gamble. If it doesn't work, you're not sitting in
the chair right now.
Speaker 3 (19:22):
Right, Oh, for sure. There were two steps.
Speaker 5 (19:24):
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 4 (19:32):
Can I ask you, sif a personal question, how much
sleep did you lose over this.
Speaker 5 (19:39):
Once we had made the decision?
Speaker 3 (19:42):
None?
Speaker 4 (19:45):
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 5 (19:53):
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 runrunning, and once it's running,
I don't even.
Speaker 3 (20:01):
Try to go back to sleep.
Speaker 5 (20:02):
I mean, okay, go get up and do work and
make yourself productive. You're going to be tired before in
the afternoon, that's fine, you'll sleep well that night. I
have actually learned a long time back. You can't do
it across. You can't do it early morning, through the
day and late at night. So an hour before I
think I want to go to bed, I will actually
(20:23):
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
spontefecating on demographics and population. But I won't read it
on leadership because that's too close. Now, twenty years ago
(20:43):
I might have that would have been different. I won't
read it on deep signs because that's too close to.
Speaker 3 (20:49):
What we do for a living.
Speaker 5 (20:50):
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.
Speaker 3 (21:01):
So shifted gears. Sorry, I want to dwell on this
just for a moment. The red hat thing.
Speaker 4 (21:06):
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 5 (21:17):
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
(21:38):
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
(21:59):
our CFO Jim Cavanaugh, he's been in that loop probably
since twenty thirteen. And the 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. You're
a patient, you go learn. If I think about many
(22:20):
of the people in the software business, they've been having
these discussions with me for always. I mean, now I'll
acknowledge I can get probably impatient and a SERVIIK, 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 going to be perfectly correct,
but I always look for if you have a strong
(22:42):
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 outside that you can call up.
Speaker 3 (23:02):
I have no hesitation.
Speaker 5 (23:04):
Somebody introduced me to a long time back, to a
CEO of the outside. I call them up all the
time and say, hey, do you have five minutes. I'm
just thinking about something. This way, the CEO of red
At who left IBM in twenty twenty one, we probably
talk every two or three months on a random topic.
One way it becomes mutual. He asked me my opinion
on some things. Now, by the way, three or four
(23:26):
times he might do something different, but he wants my opinion.
The tour the other way around.
Speaker 4 (23:32):
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 it would be
really fine to get the call.
Speaker 5 (23:38):
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 one of 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 all
(24:00):
better as a thinker of that topic, and probably most
of the people.
Speaker 4 (24:04):
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 with technology, which I one hundred percent sure
(24:25):
they did not have that in nineteen ninety, but they
their 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?
Speaker 3 (24:42):
What are we underestimating?
Speaker 5 (24:44):
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 were 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 those expectations and more?
Speaker 3 (25:06):
Yes?
Speaker 5 (25:07):
Along the way, did eight out of ten of the
companies that were invested in heavily go bankrupt.
Speaker 3 (25:14):
Yes, I actually think of that as.
Speaker 5 (25:16):
Being the huge positive of the United States capital system,
that that investment happened. Eight out of ten went broke.
By the way, those acids didn't go away. They got
consumed at ten cents in the dollar by somebody else
who could then make a lot of money. But the
two out of ten, just take two, it probably has
paid for all the capitals. If you just take Amazon
(25:40):
and Alphabet, ak, Google, just those two have probably paid
for all the capital of that time. So that's what's
going to happen this time. There will be a lot
of tears, but in aggregate, there will be a lot
of success. And I think that's the fundamental difference between
the US model and almost all other countries. In all
other countries, they're desperate to keep all the companies alive.
Speaker 3 (26:03):
But that means you're dialuting. That's a horrible thing.
Speaker 5 (26:07):
So to me, let the system works worked really effectively,
by the way, not just now, I mean all the
way back to railways and electrification, and you mentioned telephone system.
You can keep going on oil, I mean consumer goods.
It goes on and on. I think this system is
very effective. It deploys capital. Its census is a big
(26:28):
market is completely willing to over deploy capital in the short.
Speaker 3 (26:32):
Term, not the long term.
Speaker 5 (26:34):
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.
Speaker 3 (26:47):
Yeah, I saw that.
Speaker 4 (26:48):
I grew up in Waterloo and BlackBerry closs from Waterloo. Yep,
everyone used to work for BlackBerry. Ye, BlackBerry goes into
its die and that's the best thing that happened to
Waterloo because it was just capital with.
Speaker 5 (27:00):
Talents, yep, talents many other companies. So all these smart.
Speaker 4 (27:04):
People went on the next really more interesting thing. And yeah,
the 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 5 (27:17):
So I don't think AI is being underestimated because when
you look at the amount of capital and the amount
of things chasing it, I think it's incredible. I do
think that a lot of enterprises are deploying it in
the wrong place they're running after shiny experiments. There's a
lot of basic things you can do to use AI
to improve the business today. So that's really just my
(27:38):
one advice to them. Pick areas you can scale, don't
pick the shiny little toys on the side.
Speaker 3 (27:45):
Then I think, for example, that.
Speaker 5 (27:49):
If anybody has more than ten percent of what they
had for customer service ten years ago, they're already five
years behind.
Speaker 3 (28:02):
If anybody is not using.
Speaker 5 (28:04):
AI to make their developers who write software thirty percent
more productive today with the goal of being seventy percent
more productive, that's not to say you will need less,
you'll just get more software done.
Speaker 3 (28:18):
Then they're not. And I would turn.
Speaker 5 (28:20):
Around and tell you I think only maybe five percent
of the enterprises on both.
Speaker 3 (28:25):
Those metrics today.
Speaker 5 (28:26):
Yeah, yeah, and the one that is completely underestimated. I
kind of put it like this, Quantum today is where
GPUs and AI war in twenty fifteen, and I bet
you every AI person is thinking and hoping I wish
I had started doing more in twenty fifteen as opposed
to wait until twenty twenty two. Quantum today is there.
(28:49):
So it's not good enough that you can get a
big advantage. But if you learn how to use it,
then in five years you'll be ready to exploit what comes.
Speaker 3 (28:59):
We're going to get to contum in a moment.
Speaker 4 (29:00):
But I had a couple other AI questions, you know,
I as you know where this conversation is part of
this thing that we do with IBM Smart Talks, and
I've been The last episode I did was on Kenya,
which has a massive deforestation problem, and they got together.
IBM took all the NASA satellite data, ran it through
(29:21):
an LLM, and gave them this incredibly precise ten meter
by ten meter analysis of what trees to 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
(29:43):
much thinking about the developed world when it's actually the
developing world where the greatest ROI for this.
Speaker 3 (29:49):
Is to me.
Speaker 5 (29:49):
Look, software technology is 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 defrautestation,
how about the use of pesticides and fertilizers, We overuse it.
Speaker 3 (30:04):
We tend to for.
Speaker 5 (30:05):
Irrigation, we tend to just flood everything, as opposed to say, okay,
only that one needs it. You could do all those
things to get a 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
(30:26):
of people. I know that nobody likes to hear it.
Most of the Far East is going to have half
the number of people by 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. How
the US, also, depending on which number you want to
(30:46):
look at, is either one point six births per women
or two or two point one.
Speaker 3 (30:52):
Why are the three numbers?
Speaker 5 (30:53):
One point six is to women who were born in
the United States, It becomes two point two. If you
include immigrant women, it becomes two point one if you
include children or remigrating in So you got to decide
where the trend is obvious, This is going down. So
AI in the developed world is going to be essential
because to keep our current quality of life you need
(31:14):
more work done, or what's going to do the work
if there aren't people to do the work. So the
problems are different in the places.
Speaker 4 (31:22):
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 3 (31:31):
Correct. That was my in talking to the Kenyon thing.
Speaker 4 (31:34):
It was like the whole it's it's maybe one of
the largest ecological projects of its kind at fifteen billion
trees they want to plan, and.
Speaker 5 (31:42):
That is one country that might get it done because
they do take a lot of pride in their ecology
and in sort of returning to the land and giving back.
Speaker 3 (31:51):
Yeah.
Speaker 4 (31:51):
Yeah, what's different about IBM's version of AI versus some
of your.
Speaker 5 (31:57):
So we are not a consumer company, so we have
no focus on a B two C chatbot. 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 competitionally inefficient? And the short
answer is yes, because you have a certain number of
users and you kind of say, I kind of say
(32:18):
this jokingly. If I add finished to French capabilities, I
can probably add five million users.
Speaker 3 (32:24):
If I add.
Speaker 5 (32:25):
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.
Speaker 3 (32:35):
Do all those things.
Speaker 5 (32:36):
If my goal is to get help a company summarize
the legal documents in English, that can be a model
that's one hundred size as effective, probably higher quality.
Speaker 3 (32:48):
But I don't need to go wide.
Speaker 5 (32:50):
So if you're focusing on the enterprise, that actually takes
away the focus of having to go to extremely large models,
which but definition going to be computationally expensive, power hungry,
and demand lots and lots of data. So I can
turn onto the enterprise. You don't need to worry about
copyright issues, about all those because you can train on
(33:11):
a much smaller amount of data.
Speaker 3 (33:12):
And now, by the.
Speaker 5 (33:14):
Way, tuning it for you yourself is a weekend exercise,
it's not a six month on a big super computer
cluster somewhere out there. That's one big difference of what
we do. Second, we are very focused on helping those
problems that can give people immediate benefit where we have
domain knowledge. So our domain knowledge is around operations, is
(33:37):
around programming, encoding, is around customer service, is around customer experience, logistics, procurement,
let's change the areas where we have a lot of expertise,
and then three we kind of apply it to ourselves,
and so we are not asking our clients to be
(33:57):
the first experiment on it. We say you can have
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 4 (34:12):
Other challenges to explaining what makes you different to potential customers.
Speaker 5 (34:16):
For sure, the shining object is always attractive. Oh, I
can go and try chat GPT. Why don't you have
your GPT version?
Speaker 3 (34:25):
Do you use chat ChiPT?
Speaker 5 (34:28):
I have used it.
Speaker 4 (34:30):
I asked it a question recently which I thought was
really simple, and it made up about ten people.
Speaker 3 (34:37):
Anyway, I had a bad experience.
Speaker 5 (34:38):
I actually think that that's the fundamental issue with all
lms as they get larger, 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
(34:58):
from giving an answer that satisfies you. So if it
thinks that if it makes up an answer that will
satisfy you, how will you stop it? Why do we
think this is different than the clever college kid who
doesn't know an answer? What bullships the way to an answer? Well,
it's exactly the same.
Speaker 4 (35:16):
It's like the example of clever Hands during that story
the horse that they thought could speak, then all it.
Speaker 3 (35:21):
Was doing was pleasing it. It's master. Yes, it is
a little bit of clever hands. Yeah, it's like dogs
kind of imitating and looking.
Speaker 4 (35:29):
What would you identify as the most significant bottleneck in
the development of AI?
Speaker 3 (35:35):
What's slowing us down right now?
Speaker 5 (35:38):
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
(36:00):
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 leaves and beyond.
Today on LLMS alone, my view is I think we
(36:23):
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 3 (36:35):
Yes?
Speaker 5 (36:36):
And I think those answers lie as is usually in
compute through advances in semiconductors, advances in software, and advances
in algorithmic techniques, all three. But how come we're not
working in any of those three? Were just taking the
current semic 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 3 (37:00):
Mm hmm.
Speaker 4 (37:01):
But why you say there is a we're in a
moment where people are not pursuing the the optimal strategy
for exploiting this technology.
Speaker 5 (37:11):
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 we
don't do the same, you'll get left behind.
Speaker 4 (37:25):
So is this a case where there's too much money
humans have never had for more?
Speaker 2 (37:30):
Right?
Speaker 3 (37:30):
Ever?
Speaker 4 (37:31):
Yeah, but this is this a consequence of overinvestment in
the in the field.
Speaker 5 (37:37):
Going back to my internet allology, if two out of
ten are going to succeed. How do you guarantee or
how do you improve the orders that you are one
of those two. So if you pause to say I
want to become more efficient, that's not the way to win.
Speaker 3 (37:50):
So first you win, then you become efficient.
Speaker 4 (37:53):
Yeah, let's talk about what is I was told your
favorite topic it's quantum?
Speaker 3 (38:00):
Is what? Boy even go any further? Why is quantum
your favorite topic?
Speaker 5 (38:06):
We've only had two kinds of compute in the history,
so nineteen forty five was to use that year for
any act. All the way till twenty twenty, we had
one kind of computerlassical 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
(38:27):
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
(38:51):
get people energized and the imagination going. And we use
all these words about entanglement and silver position, but maybe
because I'm a bit of a math guy. The real
thing is that 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
(39:11):
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
(39:32):
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, it require lots of compute.
Speaker 3 (39:45):
That's quantum.
Speaker 4 (39:47):
A couple months ago was at the to watch some
research center and they have you know, on the ground floor,
they have those behind the glass. There's incredibly exciting looking machines.
But where are we in the timeline of this.
Speaker 5 (40:00):
Three to five years away from shocking people? What does
shocking people mean do something that nobody thought was possible
in that timeline.
Speaker 3 (40:10):
Does an example come to mind?
Speaker 5 (40:12):
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 3 (40:29):
Thirty four percent. Thirty four percent.
Speaker 4 (40:31):
This is an industry that's used to one percent correct,
zero point five percent.
Speaker 3 (40:37):
Yes, that's astonishing.
Speaker 5 (40:40):
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
(41:00):
of money kind of moves around in the financial industry
each day, right, so basis point would be thirteen billion
something like that, right, one over ten thousand. So when
you think about the kind of profit that people can make,
if you can tell somebody that you can come up
with a better price than your competition by just one
(41:25):
basis point, they would actually gain the market share. Yeah,
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 4 (41:45):
And the reason, sorry to ask a really nive question,
why is it that a quantum computer would be better
at solving a battery problem than our existing methods of computing?
Speaker 5 (41:55):
So the equations of quantum mechanics and chemistry and how
things interact or 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
quarter 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.
Speaker 3 (42:20):
Well, I'm sorry you don't have that much memory. It's impossible.
Speaker 5 (42:22):
So it takes a really really long time on a
normal computer to solve those problems, right, that's simple a problem.
If a quantum computer operates in the equation domain, it
doesn't need to explore the state space. It can actually
solve it. That's why I call it a different kind
of math. That's the kind of math it does. So
(42:43):
in a couple of seconds it can tell you this
is how that material will be here.
Speaker 3 (42:48):
Oh, I see, so you've.
Speaker 5 (42:49):
Taken what could take years to a few seconds. Yeah,
that's a pretty big change.
Speaker 4 (42:54):
Yeah. Yeah, it's speaking a different language, different line. So
any kind of problem that comes along that's specific to
that language correctly.
Speaker 5 (43:01):
Which is not all problems. Yeah, just as I call it,
it's one more kind of math.
Speaker 4 (43:06):
Yeah, what's an example? So so many questions. L give
me another example of a of a of a kind
of problem that a quantum computer would love.
Speaker 5 (43:18):
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 posts and advanced country says
every house, every address, each day. The way to optimize
(43:40):
this is we can formulate the problem. It's called the
proveling 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
(44:01):
that I think is one hundred million gallans 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 carbon footprint
climate change were in't less mileage on vague. I'm not
even counting all that. These are pretty attractive problems to
(44:22):
go after. So if I look at the interest recently,
New York has started a whole program in some places.
Illinois stood up a quantum algorithm center between a number
of the universities. The governor there was heavily behind it, etc.
So I wouldn't say that this is widespread. This is
(44:43):
why I'm saying three to four years for that moment.
But there's enough people who are deeply cognizant who are saying,
wait a moment, we kind of get it.
Speaker 3 (44:52):
This is a new kind of man. What are the
new problems we can solve?
Speaker 5 (44:55):
And the fact that we have bought roughly two hundred
clients who worked with us early stage small experiments is
because the intuition is I can do something here that
I couldn't do in other places.
Speaker 4 (45:07):
Three to four years is not a long time, no,
But if I'm in the battery business, and I don't
have a line out to a quantum computing experiment.
Speaker 3 (45:20):
I have a problem, don't have a problem.
Speaker 5 (45:22):
Yeah, you'll probably be out of business in ten years.
Well maybe you could write a big check and buy
the technology from somebody else.
Speaker 4 (45:30):
You had to What is quantum rank in the kind
of great inventions of the last one hundred and fifty years.
Speaker 5 (45:37):
Equal to semic conductor? And I think that if semiconductor's vanished,
modern life.
Speaker 3 (45:44):
Would stop, like just stop.
Speaker 5 (45:47):
Yeah, no electricity, no automobile, no streaming. You can imagine
the yells from all the kids. Who ever hear that
no streaming?
Speaker 4 (46:01):
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 5 (46:12):
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 easy to understand. We have probably,
(46:35):
as I said, about four to five years from that moment.
That's why it's under discussed because the moment I say
you 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 4 (46:51):
So you, as c 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 a CEO, was this your first priority.
Speaker 5 (47:07):
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 I'll call it a weird career. I was a
researcher at some point. If it had asked me out,
I said, I'm one of those people, you know, throw
a pizza under.
Speaker 3 (47:25):
Our door and like leave me alone. I don't want
to talk to people.
Speaker 5 (47:28):
Then I decided I was interested in the business. Then
I went and started acquiring companies and doing that. Then
somebody told me, hey, why did 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
(47:49):
was hey, can we make a computer not just a
science experiment? Can it run by itself all night? Can
you think about software so that even people who are
not deep of quantum mechanic can begin to use it?
And they begin to do those things. So over three
four years, did they get enough confidence Yeah, Okay, this
is something that can really work. And then you've got
(48:11):
to nurture it to where it gets bigger and bigger
until you get the confidence that okay, now it's a
big bet.
Speaker 3 (48:18):
And what was the moment when you when you realize
now it's a big.
Speaker 5 (48:21):
Bet, probably two or three years ago.
Speaker 4 (48:25):
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 promise to
give to an idea like that?
Speaker 5 (48:35):
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 out higher then So I'm
constrained of people on that one, because normally there's only
so many people who can do these things. Two, you
got to be careful if you push too hard on timing,
(48:58):
you'll get people to take so much risk that actually
the thing will fail. So that's the art of between
the leadership on the project and me to say, Okay,
how hard can you push? But not so hard that
you cause it to fail because then they get compelled
to commit timelines that are just impossible.
Speaker 3 (49:17):
Yeah, how do you This is fascinating.
Speaker 4 (49:20):
So it's ultimately a question of judgment trying to figure
out what's the sweet spot between enough pressure to keep
them ahead of the pack, but not too much pressure
so that they start taking risks. How do you calibrate
whether you're hitting that sweet spot? I mean, do you
reassess every few months and say I think I'm over
correcting or undercorrecting at this moment.
Speaker 5 (49:41):
So one you got to have what I call and
this is channeling a word from one of my favorite books,
The Geek Away, How open can you be?
Speaker 3 (49:51):
So I want to press hard?
Speaker 5 (49:53):
But the team knows that they're allowed to push back
and really argue back hard. That means you'd get do
probably that correct goldilocks pressure. Do the people themselves should
want to go as hard as possible, but not harder
than possible. So that is then personality of leadership that
(50:14):
makes sense.
Speaker 4 (50:15):
But you have to be someone who people feel comfortable
being honest with.
Speaker 3 (50:18):
Yes, absolutely, and people feel comfortable being honest with you,
I believe so. Yeah.
Speaker 4 (50:25):
When has there been a moment in this path with
quantum where you did think you were pushing too hard?
Speaker 3 (50:33):
No?
Speaker 5 (50:34):
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 ford.
Speaker 4 (50:44):
Do you drop by at sort of Saturday night at
ten pm to see if people are working.
Speaker 5 (50:49):
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're already watching the competition, They're
watching the literature, they're watching the science. I don't need
to push hard. If they are already ahead of it,
(51:12):
then me I can answer my question. I'll say thoughtfully,
not always completely accurately, you're thinking about it on their own.
Speaker 3 (51:19):
I don't need to push. One last question I wanted
to ask you, do you have the most interesting job
in America?
Speaker 5 (51:26):
I believe that it's the most interesting job, which I
won't give up for anything.
Speaker 3 (51:30):
It also sounds like you're enjoying yourself.
Speaker 5 (51:34):
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?
Speaker 3 (51:46):
I love it.
Speaker 5 (51:47):
If I don't, somebody else.
Speaker 3 (51:48):
Should do it. Harvin, this has been so much fun.
Speaker 4 (51:53):
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 3 (52:03):
In a few years. Good Thank you so much.
Speaker 4 (52:17):
Smart Talks with IBM is produced by Matt Ramano, Amy Gains, McQuaid,
Trina Menino, and Jake Harper. Mastering by Sarah Bugerer, Music
by Gramoscope, Strategy by Tatiana Lieberman, Cassidy Meyer and Sophia Derlong.
Smart Talks with IBM is a production of Pushkin Industries
and Ruby Studio at iHeartMedia. To find more Pushkin podcasts,
(52:40):
listen on the iHeartRadio app, Apple Podcasts, or wherever you
listen to podcasts. I'm Malcolm Glawe. This is a paid
advertisement from IBM. The conversations on this podcast don't necessarily
represent IBM's positions, strategies, or opinions.
Speaker 2 (53:00):
The take UNT
Hey everyone, it's Robert and Joe here. Today we've got
something a little bit different to share with you. It's
a new season of the Smart Talks with IBM podcast series.
Speaker 2 (00:09):
This season on Smart Talks with IBM, Malcolm Gladwell is back,
and this time he's taking the show on the road.
Malcolm is stepping outside the studio to explore how IBM
clients are using artificial intelligence to solve real world challenges
and transform the way they do business.
Speaker 1 (00:25):
From accelerating scientific breakthroughs to reimagining education. It's a fresh
look at innovation in action, where big ideas meet cutting
edge solutions.
Speaker 2 (00:34):
You'll hear from industry leaders, creative thinkers, and of course
Malcolm Gladwell himself as he guides you through each story.
Speaker 1 (00:41):
New episodes of Smart Talks with IBM drop every month
on the iHeartRadio app, Apple Podcasts, or wherever you get
your podcasts. Learn more at IBM dot com slash smart Talks.
This is a paid advertisement for IBM pushkin.
Speaker 3 (01:06):
Hello.
Speaker 4 (01:06):
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 Lreel 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
(01:28):
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
(01:49):
passion for quantum computing, which he says is as revolutionary
as a semiconductor.
Speaker 3 (01:56):
Thank you everyone, Thank you Arvin.
Speaker 4 (01:58):
You're a difficult man to scare for what they said,
so we're we're enormously pleased that you.
Speaker 3 (02:02):
Could join us.
Speaker 4 (02:04):
Let's start with a I have all these cousins, two
cousins who work for IBM their entire career.
Speaker 3 (02:08):
I would ask them what does IBM do?
Speaker 4 (02:11):
And they would always give me different, confusing, complicated answers.
Speaker 3 (02:16):
What's your answer? What's your simple answer to that question?
Speaker 5 (02:19):
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
(02:41):
hybrid cloud and artificial intelligence and a taste of quantum
coming down the road is kind.
Speaker 3 (02:47):
Of where I would take it.
Speaker 5 (02:48):
That's that's what IBM is.
Speaker 3 (02:50):
So you are technology agnostic in some sense.
Speaker 5 (02:55):
I'm product agnostic. Product I'm not technology agnostic.
Speaker 4 (02:59):
Yes, But if I twenty five years from now, IBM
could be doing things that would be unrecognizable to contemporary IBM, it.
Speaker 3 (03:07):
Is completely possible.
Speaker 5 (03:08):
Yeah, it could be that 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, WLD say that's
not the IEM of today.
Speaker 4 (03:21):
Is it even simpler to say you just IBM solves
problems at the highest technical level.
Speaker 5 (03:26):
If you say highest technical level, yes, yeah, Like the
guy who invented the bar code, he was solving a
problem retailers wanted to scale. Many of you may 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
(03:50):
out and you could use lasers to scan things, but
they could be upside down, they could be muddy, they
could be partly scraped off. And he came up with
the idea of the bar code. Yeah, and that changed
inventory management forever. But the world needs to know that
IBM invented the barcode. You guys should do a better
job ombicizing that. I am sure our CMO will listen
(04:13):
to this podcast and we'll get that idea.
Speaker 4 (04:17):
Tell me you started at the Thomas Watson Research Center.
What were you doing when you first started it, IBM.
Speaker 5 (04:24):
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
(04:48):
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. Why would anybody
put this on? And the debate used to be, why
would anybody want to walk around untethered? Won't you want
(05:10):
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 4 (05:24):
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 you? What were your predictions
at that age about where the company, where the industry
was going.
Speaker 5 (05:40):
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
(06:02):
believe that video streaming will be the primary 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
(06:22):
the Internet is old because when I went to grad school,
every one of us had a those days, an Apple,
Mac or Lisa on our desks. They're all connected by
a network. You're happily sending email to people all around
the country. We were doing file transfers. So okay, you
had to be a little bit more aware of the
(06:42):
technology and didn't have a browser that took ten years
to get the browser that took five years to be
a business. But when you see the speed and the
pace of technology in usually ten or fifteen years, the
cost point and the consumerization is at a scale that
you couldn't imagine ten years ago.
Speaker 3 (07:02):
Until you've seen a few of those cycles.
Speaker 4 (07:05):
Wait, did you make the lead to sorry, this is fascinating.
I'm curious about how far did you take that. That's
a really fundamental thing to have gotten right in nineteen
ninety I think you're that idea.
Speaker 5 (07:15):
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.
(07:38):
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 4 (07:59):
If you had a converse in those years with someone
in the television industry and you gave them those predictions,
did they see it?
Speaker 3 (08:06):
Were they convinced of this?
Speaker 5 (08:08):
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 built 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,
(08:31):
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
(08:52):
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 I mean
not making it simple? Now, that was probably a five
to ten year evolution of myself in those days.
Speaker 4 (09:08):
You know, 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 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.
Speaker 3 (09:24):
Right, It's an.
Speaker 5 (09:25):
Exact parallel, Yes, it is. You see it again and again.
Speaker 3 (09:29):
What is the source of that blindness.
Speaker 4 (09:31):
So there's a gap, in other words, between the invention,
the technological achievement, and the social understanding of the technology.
Speaker 3 (09:37):
Why is there such a gap.
Speaker 5 (09:40):
I think that the gap is fundamental and rooted in
a lot of academic disciplines. So even channeling some of
your work, though you don't intend it to be used
that way, you can say a lot of things are
data driven. If it is data driven, then by definition,
you're looking at history. If you're looking at history, that
(10:03):
means you're looking at existing buying patterns. If you look
at existing buying patterns, you forget. All of those who
have created massive value 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.
(10:24):
So I think that is the gap. And then if
you can get the technology the business acumen scaler company
and that imagination of making the market is how you
create I think massive value.
Speaker 3 (10:38):
You got to get all three pieces going. It's not enough.
Speaker 4 (10:40):
In other words, you were thinking it's not enough to
invent something new. I need to make a business case
for it. Simultaneously, and that's what gets you thinking along
the path that leads you to this job.
Speaker 5 (10:49):
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 those words are I
can parse them. I have no idea what they are.
I have no intuition on what they are. I couldn't
tell you why it's relevant or why it's not. But
(11:11):
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. Howether the
best way I read is to go to balance sheets?
Here you can read the book. It's pretty damn dry.
(11:33):
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.
Speaker 4 (11:53):
Skills you are to be a successful business leader. Do
you have to unlearn or or deviate from some of
the things that made you a successful scientist.
Speaker 5 (12:05):
I actually believe the exactly opposite.
Speaker 3 (12:06):
Yeah, but use what.
Speaker 5 (12:08):
You're really good at as a foundation, but don't make
it the only thing you use. So then how do
you add the other skills? And there's many ways. You
can have people that you trust who help you out
those skills. You can gain some intuition, maybe not the
depth of expertise. I want to be deeper on certain
areas of electrical engineering than I'm ever going to be,
(12:31):
let's say, in finance or marketing. But I want to
be curious about those. I don't want to dismiss them.
So you build on your skills, and then you have
to say, but I need a complete and holistic view.
So I'm going to be a little deep, not very deep,
in all of those. And you've also got to learn
to trust your intuition a little bit.
Speaker 4 (12:51):
Yeah, but I forgot a question that I wanted to ask,
but about the predictions of nineteen ninety arvand what did
you get wrong?
Speaker 5 (13:00):
All lots of things. I think that people were thinking
that 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
(13:21):
by let's not take the names of all of the
telecom carriers.
Speaker 3 (13:26):
Didn't turn out to.
Speaker 5 (13:27):
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.
Speaker 3 (13:34):
Minute, because they had been doing that already, because they
had been doing that for one hundred years.
Speaker 5 (13:40):
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.
Speaker 3 (13:51):
Leap for them.
Speaker 4 (13:52):
You think it's as simple. That is the most parsimonious
explanation for why you think they failed.
Speaker 5 (13:58):
No, there were a couple of hours the more technical things.
The one was written by somebody who was actually inside
one of these telecom companies, and he labeled his article
the rise of the Stupid Network. So telephone people believe
that the network should be really smart, the end device
is dumb. If you think about the telephone, telephone is dumb.
It doesn't actually do anything. It's just about the relays.
(14:19):
And the network is smart. It routes you, it figures
out where to send it, it does echocacul 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 3 (14:40):
Yeah.
Speaker 4 (14:40):
Wait, did nineteen ninety r of end think that the
network should be dumb or smart?
Speaker 3 (14:48):
I'm not sure.
Speaker 5 (14:48):
I thought about it deeply, but everything I worked on
the network was dumb.
Speaker 3 (14:52):
The netwuck movements. That's all I did.
Speaker 5 (14:55):
Yeah, because even I in those days understood I can't
imagine all the opp loss. So if all you do
is voice, maybe the network can be smart. But if
you're doing all those other things, how could the network
possibly know all those things that be smart for it?
Speaker 3 (15:09):
Yeah, so you've been COO for five years. Five years.
Speaker 4 (15:14):
Wait, so in your five year increment, what was your
most misunderstood decision? Well, you ended up being right, but
everyone thought you were crazy.
Speaker 5 (15:22):
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 twelve 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
(15:43):
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.
Speaker 3 (16:00):
So you've got to have a view of what.
Speaker 5 (16:01):
It could be, and we drove it to a place
where I think today it stands as the leader in
its space.
Speaker 4 (16:08):
So how did you come to believe this heretical notion?
Speaker 5 (16:13):
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
(16:34):
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?
Speaker 3 (16:55):
Is there a.
Speaker 5 (16:55):
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 4 (17:15):
How hard was it to convince people needed convincing before
that acquisition.
Speaker 5 (17:21):
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 3 (17:36):
Yeah, you're very persistent.
Speaker 4 (17:39):
Oh yes, very Would you describe that as you defining trade?
Speaker 5 (17:46):
I am very persistent and I am 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.
Speaker 3 (18:03):
We will do it.
Speaker 4 (18:04):
If I got your family, put them up on stage
and ask them this exact question, is this how they
would answer?
Speaker 5 (18:09):
As well, they will tell you I'm very stubborn. They
might not agree that I don't rant and drave.
Speaker 4 (18:20):
Well, you know, one of the principal observations of psychology
is that our home self and our work self are uncorrelated.
Speaker 3 (18:27):
Once you know that, you know everything.
Speaker 4 (18:29):
Wait, I'm curious one last question about that. How long
does it take for you to be vindicated with red Hat?
Speaker 5 (18:37):
Probably took five maybe four years, I think by twenty
twenty three, So twenty eighteen we announced it, We took
the big shot crop. It took a year to close
twenty nineteen, so if O count not that I'm counting
that much, but July ninth, twenty nineteen as the day
(19:00):
that we got all the approvals. Took another few weeks
to actually transfer the money. But from there, probably twenty
twenty three, the world woke up and said, hey, you
guys deserve credit for this was actually.
Speaker 3 (19:11):
A great move, not a bad move.
Speaker 4 (19:13):
Yeah, but this is it's interesting because this is a
real gamble. If it doesn't work, you're not sitting in
the chair right now.
Speaker 3 (19:22):
Right, Oh, for sure. There were two steps.
Speaker 5 (19:24):
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 4 (19:32):
Can I ask you, sif a personal question, how much
sleep did you lose over this.
Speaker 5 (19:39):
Once we had made the decision?
Speaker 3 (19:42):
None?
Speaker 4 (19:45):
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 5 (19:53):
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 runrunning, and once it's running,
I don't even.
Speaker 3 (20:01):
Try to go back to sleep.
Speaker 5 (20:02):
I mean, okay, go get up and do work and
make yourself productive. You're going to be tired before in
the afternoon, that's fine, you'll sleep well that night. I
have actually learned a long time back. You can't do
it across. You can't do it early morning, through the
day and late at night. So an hour before I
think I want to go to bed, I will actually
(20:23):
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
spontefecating on demographics and population. But I won't read it
on leadership because that's too close. Now, twenty years ago
(20:43):
I might have that would have been different. I won't
read it on deep signs because that's too close to.
Speaker 3 (20:49):
What we do for a living.
Speaker 5 (20:50):
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.
Speaker 3 (21:01):
So shifted gears. Sorry, I want to dwell on this
just for a moment. The red hat thing.
Speaker 4 (21:06):
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 5 (21:17):
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
(21:38):
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
(21:59):
our CFO Jim Cavanaugh, he's been in that loop probably
since twenty thirteen. And the 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. You're
a patient, you go learn. If I think about many
(22:20):
of the people in the software business, they've been having
these discussions with me for always. I mean, now I'll
acknowledge I can get probably impatient and a SERVIIK, 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 going to be perfectly correct,
but I always look for if you have a strong
(22:42):
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 outside that you can call up.
Speaker 3 (23:02):
I have no hesitation.
Speaker 5 (23:04):
Somebody introduced me to a long time back, to a
CEO of the outside. I call them up all the
time and say, hey, do you have five minutes. I'm
just thinking about something. This way, the CEO of red
At who left IBM in twenty twenty one, we probably
talk every two or three months on a random topic.
One way it becomes mutual. He asked me my opinion
on some things. Now, by the way, three or four
(23:26):
times he might do something different, but he wants my opinion.
The tour the other way around.
Speaker 4 (23:32):
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 it would be
really fine to get the call.
Speaker 5 (23:38):
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 one of 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 all
(24:00):
better as a thinker of that topic, and probably most
of the people.
Speaker 4 (24:04):
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 with technology, which I one hundred percent sure
(24:25):
they did not have that in nineteen ninety, but they
their 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?
Speaker 3 (24:42):
What are we underestimating?
Speaker 5 (24:44):
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 were 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 those expectations and more?
Speaker 3 (25:06):
Yes?
Speaker 5 (25:07):
Along the way, did eight out of ten of the
companies that were invested in heavily go bankrupt.
Speaker 3 (25:14):
Yes, I actually think of that as.
Speaker 5 (25:16):
Being the huge positive of the United States capital system,
that that investment happened. Eight out of ten went broke.
By the way, those acids didn't go away. They got
consumed at ten cents in the dollar by somebody else
who could then make a lot of money. But the
two out of ten, just take two, it probably has
paid for all the capitals. If you just take Amazon
(25:40):
and Alphabet, ak, Google, just those two have probably paid
for all the capital of that time. So that's what's
going to happen this time. There will be a lot
of tears, but in aggregate, there will be a lot
of success. And I think that's the fundamental difference between
the US model and almost all other countries. In all
other countries, they're desperate to keep all the companies alive.
Speaker 3 (26:03):
But that means you're dialuting. That's a horrible thing.
Speaker 5 (26:07):
So to me, let the system works worked really effectively,
by the way, not just now, I mean all the
way back to railways and electrification, and you mentioned telephone system.
You can keep going on oil, I mean consumer goods.
It goes on and on. I think this system is
very effective. It deploys capital. Its census is a big
(26:28):
market is completely willing to over deploy capital in the short.
Speaker 3 (26:32):
Term, not the long term.
Speaker 5 (26:34):
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.
Speaker 3 (26:47):
Yeah, I saw that.
Speaker 4 (26:48):
I grew up in Waterloo and BlackBerry closs from Waterloo. Yep,
everyone used to work for BlackBerry. Ye, BlackBerry goes into
its die and that's the best thing that happened to
Waterloo because it was just capital with.
Speaker 5 (27:00):
Talents, yep, talents many other companies. So all these smart.
Speaker 4 (27:04):
People went on the next really more interesting thing. And yeah,
the 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 5 (27:17):
So I don't think AI is being underestimated because when
you look at the amount of capital and the amount
of things chasing it, I think it's incredible. I do
think that a lot of enterprises are deploying it in
the wrong place they're running after shiny experiments. There's a
lot of basic things you can do to use AI
to improve the business today. So that's really just my
(27:38):
one advice to them. Pick areas you can scale, don't
pick the shiny little toys on the side.
Speaker 3 (27:45):
Then I think, for example, that.
Speaker 5 (27:49):
If anybody has more than ten percent of what they
had for customer service ten years ago, they're already five
years behind.
Speaker 3 (28:02):
If anybody is not using.
Speaker 5 (28:04):
AI to make their developers who write software thirty percent
more productive today with the goal of being seventy percent
more productive, that's not to say you will need less,
you'll just get more software done.
Speaker 3 (28:18):
Then they're not. And I would turn.
Speaker 5 (28:20):
Around and tell you I think only maybe five percent
of the enterprises on both.
Speaker 3 (28:25):
Those metrics today.
Speaker 5 (28:26):
Yeah, yeah, and the one that is completely underestimated. I
kind of put it like this, Quantum today is where
GPUs and AI war in twenty fifteen, and I bet
you every AI person is thinking and hoping I wish
I had started doing more in twenty fifteen as opposed
to wait until twenty twenty two. Quantum today is there.
(28:49):
So it's not good enough that you can get a
big advantage. But if you learn how to use it,
then in five years you'll be ready to exploit what comes.
Speaker 3 (28:59):
We're going to get to contum in a moment.
Speaker 4 (29:00):
But I had a couple other AI questions, you know,
I as you know where this conversation is part of
this thing that we do with IBM Smart Talks, and
I've been The last episode I did was on Kenya,
which has a massive deforestation problem, and they got together.
IBM took all the NASA satellite data, ran it through
(29:21):
an LLM, and gave them this incredibly precise ten meter
by ten meter analysis of what trees to 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
(29:43):
much thinking about the developed world when it's actually the
developing world where the greatest ROI for this.
Speaker 3 (29:49):
Is to me.
Speaker 5 (29:49):
Look, software technology is 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 defrautestation,
how about the use of pesticides and fertilizers, We overuse it.
Speaker 3 (30:04):
We tend to for.
Speaker 5 (30:05):
Irrigation, we tend to just flood everything, as opposed to say, okay,
only that one needs it. You could do all those
things to get a 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
(30:26):
of people. I know that nobody likes to hear it.
Most of the Far East is going to have half
the number of people by 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. How
the US, also, depending on which number you want to
(30:46):
look at, is either one point six births per women
or two or two point one.
Speaker 3 (30:52):
Why are the three numbers?
Speaker 5 (30:53):
One point six is to women who were born in
the United States, It becomes two point two. If you
include immigrant women, it becomes two point one if you
include children or remigrating in So you got to decide
where the trend is obvious, This is going down. So
AI in the developed world is going to be essential
because to keep our current quality of life you need
(31:14):
more work done, or what's going to do the work
if there aren't people to do the work. So the
problems are different in the places.
Speaker 4 (31:22):
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 3 (31:31):
Correct. That was my in talking to the Kenyon thing.
Speaker 4 (31:34):
It was like the whole it's it's maybe one of
the largest ecological projects of its kind at fifteen billion
trees they want to plan, and.
Speaker 5 (31:42):
That is one country that might get it done because
they do take a lot of pride in their ecology
and in sort of returning to the land and giving back.
Speaker 3 (31:51):
Yeah.
Speaker 4 (31:51):
Yeah, what's different about IBM's version of AI versus some
of your.
Speaker 5 (31:57):
So we are not a consumer company, so we have
no focus on a B two C chatbot. 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 competitionally inefficient? And the short
answer is yes, because you have a certain number of
users and you kind of say, I kind of say
(32:18):
this jokingly. If I add finished to French capabilities, I
can probably add five million users.
Speaker 3 (32:24):
If I add.
Speaker 5 (32:25):
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.
Speaker 3 (32:35):
Do all those things.
Speaker 5 (32:36):
If my goal is to get help a company summarize
the legal documents in English, that can be a model
that's one hundred size as effective, probably higher quality.
Speaker 3 (32:48):
But I don't need to go wide.
Speaker 5 (32:50):
So if you're focusing on the enterprise, that actually takes
away the focus of having to go to extremely large models,
which but definition going to be computationally expensive, power hungry,
and demand lots and lots of data. So I can
turn onto the enterprise. You don't need to worry about
copyright issues, about all those because you can train on
(33:11):
a much smaller amount of data.
Speaker 3 (33:12):
And now, by the.
Speaker 5 (33:14):
Way, tuning it for you yourself is a weekend exercise,
it's not a six month on a big super computer
cluster somewhere out there. That's one big difference of what
we do. Second, we are very focused on helping those
problems that can give people immediate benefit where we have
domain knowledge. So our domain knowledge is around operations, is
(33:37):
around programming, encoding, is around customer service, is around customer experience, logistics, procurement,
let's change the areas where we have a lot of expertise,
and then three we kind of apply it to ourselves,
and so we are not asking our clients to be
(33:57):
the first experiment on it. We say you can have
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 4 (34:12):
Other challenges to explaining what makes you different to potential customers.
Speaker 5 (34:16):
For sure, the shining object is always attractive. Oh, I
can go and try chat GPT. Why don't you have
your GPT version?
Speaker 3 (34:25):
Do you use chat ChiPT?
Speaker 5 (34:28):
I have used it.
Speaker 4 (34:30):
I asked it a question recently which I thought was
really simple, and it made up about ten people.
Speaker 3 (34:37):
Anyway, I had a bad experience.
Speaker 5 (34:38):
I actually think that that's the fundamental issue with all
lms as they get larger, 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
(34:58):
from giving an answer that satisfies you. So if it
thinks that if it makes up an answer that will
satisfy you, how will you stop it? Why do we
think this is different than the clever college kid who
doesn't know an answer? What bullships the way to an answer? Well,
it's exactly the same.
Speaker 4 (35:16):
It's like the example of clever Hands during that story
the horse that they thought could speak, then all it.
Speaker 3 (35:21):
Was doing was pleasing it. It's master. Yes, it is
a little bit of clever hands. Yeah, it's like dogs
kind of imitating and looking.
Speaker 4 (35:29):
What would you identify as the most significant bottleneck in
the development of AI?
Speaker 3 (35:35):
What's slowing us down right now?
Speaker 5 (35:38):
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
(36:00):
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 leaves and beyond.
Today on LLMS alone, my view is I think we
(36:23):
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 3 (36:35):
Yes?
Speaker 5 (36:36):
And I think those answers lie as is usually in
compute through advances in semiconductors, advances in software, and advances
in algorithmic techniques, all three. But how come we're not
working in any of those three? Were just taking the
current semic 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 3 (37:00):
Mm hmm.
Speaker 4 (37:01):
But why you say there is a we're in a
moment where people are not pursuing the the optimal strategy
for exploiting this technology.
Speaker 5 (37:11):
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 we
don't do the same, you'll get left behind.
Speaker 4 (37:25):
So is this a case where there's too much money
humans have never had for more?
Speaker 2 (37:30):
Right?
Speaker 3 (37:30):
Ever?
Speaker 4 (37:31):
Yeah, but this is this a consequence of overinvestment in
the in the field.
Speaker 5 (37:37):
Going back to my internet allology, if two out of
ten are going to succeed. How do you guarantee or
how do you improve the orders that you are one
of those two. So if you pause to say I
want to become more efficient, that's not the way to win.
Speaker 3 (37:50):
So first you win, then you become efficient.
Speaker 4 (37:53):
Yeah, let's talk about what is I was told your
favorite topic it's quantum?
Speaker 3 (38:00):
Is what? Boy even go any further? Why is quantum
your favorite topic?
Speaker 5 (38:06):
We've only had two kinds of compute in the history,
so nineteen forty five was to use that year for
any act. All the way till twenty twenty, we had
one kind of computerlassical 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
(38:27):
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
(38:51):
get people energized and the imagination going. And we use
all these words about entanglement and silver position, but maybe
because I'm a bit of a math guy. The real
thing is that 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
(39:11):
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
(39:32):
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, it require lots of compute.
Speaker 3 (39:45):
That's quantum.
Speaker 4 (39:47):
A couple months ago was at the to watch some
research center and they have you know, on the ground floor,
they have those behind the glass. There's incredibly exciting looking machines.
But where are we in the timeline of this.
Speaker 5 (40:00):
Three to five years away from shocking people? What does
shocking people mean do something that nobody thought was possible
in that timeline.
Speaker 3 (40:10):
Does an example come to mind?
Speaker 5 (40:12):
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 3 (40:29):
Thirty four percent. Thirty four percent.
Speaker 4 (40:31):
This is an industry that's used to one percent correct,
zero point five percent.
Speaker 3 (40:37):
Yes, that's astonishing.
Speaker 5 (40:40):
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
(41:00):
of money kind of moves around in the financial industry
each day, right, so basis point would be thirteen billion
something like that, right, one over ten thousand. So when
you think about the kind of profit that people can make,
if you can tell somebody that you can come up
with a better price than your competition by just one
(41:25):
basis point, they would actually gain the market share. Yeah,
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 4 (41:45):
And the reason, sorry to ask a really nive question,
why is it that a quantum computer would be better
at solving a battery problem than our existing methods of computing?
Speaker 5 (41:55):
So the equations of quantum mechanics and chemistry and how
things interact or 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
quarter 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.
Speaker 3 (42:20):
Well, I'm sorry you don't have that much memory. It's impossible.
Speaker 5 (42:22):
So it takes a really really long time on a
normal computer to solve those problems, right, that's simple a problem.
If a quantum computer operates in the equation domain, it
doesn't need to explore the state space. It can actually
solve it. That's why I call it a different kind
of math. That's the kind of math it does. So
(42:43):
in a couple of seconds it can tell you this
is how that material will be here.
Speaker 3 (42:48):
Oh, I see, so you've.
Speaker 5 (42:49):
Taken what could take years to a few seconds. Yeah,
that's a pretty big change.
Speaker 4 (42:54):
Yeah. Yeah, it's speaking a different language, different line. So
any kind of problem that comes along that's specific to
that language correctly.
Speaker 5 (43:01):
Which is not all problems. Yeah, just as I call it,
it's one more kind of math.
Speaker 4 (43:06):
Yeah, what's an example? So so many questions. L give
me another example of a of a of a kind
of problem that a quantum computer would love.
Speaker 5 (43:18):
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 posts and advanced country says
every house, every address, each day. The way to optimize
(43:40):
this is we can formulate the problem. It's called the
proveling 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
(44:01):
that I think is one hundred million gallans 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 carbon footprint
climate change were in't less mileage on vague. I'm not
even counting all that. These are pretty attractive problems to
(44:22):
go after. So if I look at the interest recently,
New York has started a whole program in some places.
Illinois stood up a quantum algorithm center between a number
of the universities. The governor there was heavily behind it, etc.
So I wouldn't say that this is widespread. This is
(44:43):
why I'm saying three to four years for that moment.
But there's enough people who are deeply cognizant who are saying,
wait a moment, we kind of get it.
Speaker 3 (44:52):
This is a new kind of man. What are the
new problems we can solve?
Speaker 5 (44:55):
And the fact that we have bought roughly two hundred
clients who worked with us early stage small experiments is
because the intuition is I can do something here that
I couldn't do in other places.
Speaker 4 (45:07):
Three to four years is not a long time, no,
But if I'm in the battery business, and I don't
have a line out to a quantum computing experiment.
Speaker 3 (45:20):
I have a problem, don't have a problem.
Speaker 5 (45:22):
Yeah, you'll probably be out of business in ten years.
Well maybe you could write a big check and buy
the technology from somebody else.
Speaker 4 (45:30):
You had to What is quantum rank in the kind
of great inventions of the last one hundred and fifty years.
Speaker 5 (45:37):
Equal to semic conductor? And I think that if semiconductor's vanished,
modern life.
Speaker 3 (45:44):
Would stop, like just stop.
Speaker 5 (45:47):
Yeah, no electricity, no automobile, no streaming. You can imagine
the yells from all the kids. Who ever hear that
no streaming?
Speaker 4 (46:01):
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 5 (46:12):
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 easy to understand. We have probably,
(46:35):
as I said, about four to five years from that moment.
That's why it's under discussed because the moment I say
you 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 4 (46:51):
So you, as c 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 a CEO, was this your first priority.
Speaker 5 (47:07):
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 I'll call it a weird career. I was a
researcher at some point. If it had asked me out,
I said, I'm one of those people, you know, throw
a pizza under.
Speaker 3 (47:25):
Our door and like leave me alone. I don't want
to talk to people.
Speaker 5 (47:28):
Then I decided I was interested in the business. Then
I went and started acquiring companies and doing that. Then
somebody told me, hey, why did 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
(47:49):
was hey, can we make a computer not just a
science experiment? Can it run by itself all night? Can
you think about software so that even people who are
not deep of quantum mechanic can begin to use it?
And they begin to do those things. So over three
four years, did they get enough confidence Yeah, Okay, this
is something that can really work. And then you've got
(48:11):
to nurture it to where it gets bigger and bigger
until you get the confidence that okay, now it's a
big bet.
Speaker 3 (48:18):
And what was the moment when you when you realize
now it's a big.
Speaker 5 (48:21):
Bet, probably two or three years ago.
Speaker 4 (48:25):
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 promise to
give to an idea like that?
Speaker 5 (48:35):
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 out higher then So I'm
constrained of people on that one, because normally there's only
so many people who can do these things. Two, you
got to be careful if you push too hard on timing,
(48:58):
you'll get people to take so much risk that actually
the thing will fail. So that's the art of between
the leadership on the project and me to say, Okay,
how hard can you push? But not so hard that
you cause it to fail because then they get compelled
to commit timelines that are just impossible.
Speaker 3 (49:17):
Yeah, how do you This is fascinating.
Speaker 4 (49:20):
So it's ultimately a question of judgment trying to figure
out what's the sweet spot between enough pressure to keep
them ahead of the pack, but not too much pressure
so that they start taking risks. How do you calibrate
whether you're hitting that sweet spot? I mean, do you
reassess every few months and say I think I'm over
correcting or undercorrecting at this moment.
Speaker 5 (49:41):
So one you got to have what I call and
this is channeling a word from one of my favorite books,
The Geek Away, How open can you be?
Speaker 3 (49:51):
So I want to press hard?
Speaker 5 (49:53):
But the team knows that they're allowed to push back
and really argue back hard. That means you'd get do
probably that correct goldilocks pressure. Do the people themselves should
want to go as hard as possible, but not harder
than possible. So that is then personality of leadership that
(50:14):
makes sense.
Speaker 4 (50:15):
But you have to be someone who people feel comfortable
being honest with.
Speaker 3 (50:18):
Yes, absolutely, and people feel comfortable being honest with you,
I believe so. Yeah.
Speaker 4 (50:25):
When has there been a moment in this path with
quantum where you did think you were pushing too hard?
Speaker 3 (50:33):
No?
Speaker 5 (50:34):
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 ford.
Speaker 4 (50:44):
Do you drop by at sort of Saturday night at
ten pm to see if people are working.
Speaker 5 (50:49):
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're already watching the competition, They're
watching the literature, they're watching the science. I don't need
to push hard. If they are already ahead of it,
(51:12):
then me I can answer my question. I'll say thoughtfully,
not always completely accurately, you're thinking about it on their own.
Speaker 3 (51:19):
I don't need to push. One last question I wanted
to ask you, do you have the most interesting job
in America?
Speaker 5 (51:26):
I believe that it's the most interesting job, which I
won't give up for anything.
Speaker 3 (51:30):
It also sounds like you're enjoying yourself.
Speaker 5 (51:34):
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?
Speaker 3 (51:46):
I love it.
Speaker 5 (51:47):
If I don't, somebody else.
Speaker 3 (51:48):
Should do it. Harvin, this has been so much fun.
Speaker 4 (51:53):
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 3 (52:03):
In a few years. Good Thank you so much.
Speaker 4 (52:17):
Smart Talks with IBM is produced by Matt Ramano, Amy Gains, McQuaid,
Trina Menino, and Jake Harper. Mastering by Sarah Bugerer, Music
by Gramoscope, Strategy by Tatiana Lieberman, Cassidy Meyer and Sophia Derlong.
Smart Talks with IBM is a production of Pushkin Industries
and Ruby Studio at iHeartMedia. To find more Pushkin podcasts,
(52:40):
listen on the iHeartRadio app, Apple Podcasts, or wherever you
listen to podcasts. I'm Malcolm Glawe. This is a paid
advertisement from IBM. The conversations on this podcast don't necessarily
represent IBM's positions, strategies, or opinions.
Speaker 2 (53:00):
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