October 28, 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):
Welcome to Tech Stuff, a production from iHeartRadio. This season,
non Smart Talks with IBM, Malcolm Glabwell 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. From accelerating scientific breakthroughs
(00:23):
to reimagining education. It's a fresh look at innovation in action,
where big ideas meet cutting edge solutions. You'll hear from
industry leaders, creative thinkers, and of course Malcolm Glabwell himself
as he guides you through each story. New episodes of
Smart Talks with IBM drop every month on the iHeartRadio app,
(00:43):
Apple Podcasts, or wherever you get your podcasts. Learn more
at IBM dot com, slash smart Talks.
Speaker 2 (00:58):
Pushkin Hello.
Speaker 3 (01:03):
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 Lareel 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
(01:25):
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
(01:46):
passion for quantum computing, which he says is as revolutionary
as a semiconductor. Thank you everyone, Thank you to Arvin.
You're a difficult man to schedule for one of these things,
so we're enormously please you could join us. Let's start
with a I have all these cousins, two cousins who
work for IBM their entire career. I would ask them
(02:07):
what does IBM do? And they would always give me different, confusing,
complicated answers.
Speaker 2 (02:13):
What's your answer? What's your simple answer to that question.
Speaker 4 (02:16):
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.
Speaker 5 (02:34):
Then to go to the.
Speaker 4 (02:35):
Next layer, I would say, we help them through a
mixture of hybrid cloud and artificial intelligence and a taste
of quantum coming down the road is kind of where
I would take it.
Speaker 5 (02:45):
That's that's what IBM is.
Speaker 2 (02:47):
So you are technology agnostic in some sense.
Speaker 4 (02:52):
I'm product agnostic. Product I'm not technology agnostic.
Speaker 3 (02:56):
Yes, But if I twenty five years from now, IBM
could be doing things that would be unrecognizable to contemporary IBM.
Speaker 5 (03:04):
It is completely possible.
Speaker 4 (03:05):
Yeah, it could be there in twenty five years from now.
The only software IBM does is 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 3 (03:18):
Is it even simpler to say you just IBM solves
problems at the highest technical level.
Speaker 5 (03:24):
If you say highest technical level, yes, yeah.
Speaker 4 (03:26):
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,
(03:46):
And lasers were out and you could use lasers to
scan things, but they could be upside down, they could
be muddy, they could be partly scraped off. And he
came up with the idea of the bar code. Yeah,
and that changed inventory management forever. But the world needs
to know that IBM invented the Parker. You guys should
do a better job opicizing that. I am sure our
(04:09):
CMO will listen to this podcast and we'll get that idea.
Speaker 3 (04:14):
Tell me you started at the Thomas Watson Research Center.
What were you doing when you first started it, IBM.
Speaker 4 (04:22):
I started in nineteen ninety and that was the era
in which computers and networking were beginning to converge. And
for the first five years I was actually building networks.
So let's remember this was pre laptops. Laptops came in
ninety two or ninety three, but it was clear to
us that they were going to come supportable computing, and
(04:45):
I spent my first five years building what today you
would call Wi Fi. We used to have these debates
can be built it, 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.
Speaker 5 (05:01):
Why would anybody put this on?
Speaker 4 (05:03):
And the debate used to be why would anybody want
to walk around untethered?
Speaker 5 (05:07):
Won't you want to attach a big thick cable into
it and sit down?
Speaker 4 (05:10):
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 3 (05:21):
If I had a conversation with your nineteen ninety self
about what the next thirty years were going to look like,
is it possible to reconstruct what your What were your
predictions at that age about where the company, where the
industry was going.
Speaker 4 (05:37):
It was more about where technology was going to go,
I would say, than where industry would go. I would
have told you that networking and computers would fuse in
nineteen ninety That was a weird thought that some researchers held.
By the late nineties, that was obvious that it became
the Internet. I would have told you that I believe
(05:59):
that streaming will be the primary way people will consume.
Speaker 2 (06:03):
Video you would have said that in nineteen ninety.
Speaker 5 (06:05):
Absolutely, Now, that didn't take five years. That took twenty.
Speaker 4 (06:08):
But it happened because you could do it technically, except
it just too expensive and too cumbersome. And if you've
been in technology, like in nineteen eighty five, I would
have told you the internet is old because when I
went to grad school, every one of us had a
those days, an Apple, Mac or Lisa on our desks.
(06:31):
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 technology. And it didn't have a browser.
That took ten years to get the browser. That took
five years to be a business. But when you see
the speed and the pace of technology in usually ten
or fifteen years, the cost point and the consumerization is
(06:56):
at a scale that you couldn't imagine ten years ago
until you've seen a few of those cycles.
Speaker 3 (07:02):
Wait, did you make the lead to sorry, this is fascinating,
I'm curious, but how far did you take that? That's a
really fundamental thing to have gotten right in nineteen ninety
I think that we.
Speaker 4 (07:13):
Were pretty convinced that what we used to think of
as linear television or broadcast would become digitized. That was
a given two with cable already the preponderance of how
people got it that if you put packet television over cable,
then that becomes the way it will go. I fundamentally believe,
(07:36):
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 then personally
work on all those. I mean, after networking, I moved
on to doing other things, but those were easy to predict.
Speaker 3 (07:56):
If you had a conversation in those years with someone
in the television industry and you gave them those predictions,
did they see it?
Speaker 2 (08:03):
Were they convinced of this?
Speaker 4 (08:05):
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
(08:28):
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
(08:49):
think about, now, how do you market it? To whom
do you market it? What are their routs? How do
you make it easy enough? And that was probably I mean,
I'm making it simple now. That is probably a five
to ten year evolution of myself in those days.
Speaker 3 (09:05):
You know what this reminds me of when the telephone
is invented in the eighteen seventies, it doesn't take off
for forty years because the people running a telephone business
they didn't want women using it because they were worried
that women would gossip with their friends. They didn't understand
that that's actually what telephone is, right, it's an exact parallel,
Yes it is.
Speaker 5 (09:24):
You see it again and again.
Speaker 2 (09:26):
What is the source of that blindness?
Speaker 3 (09:28):
So there's a gap, in other words, between the invention,
the technological achievement, and the social understanding of the technology.
Speaker 2 (09:35):
Why is there such a gap?
Speaker 4 (09:37):
I think that the gap is fundamental and rooted in
a lot of academic disciplines. So even channeling some of
your work, though you don't intend it to be used
that way, you can say a lot of things that
data driven. If it is data driven, then by definition,
you're looking at history. If you're looking at history, that
(10:00):
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.
Speaker 5 (10:14):
And I think that is why.
Speaker 4 (10:15):
The world is fascinated with people like Steve Jobs. For example,
he imagined a market that didn't exist. So I think
that is the gap. And then if you can get
the technology the business acumen scaler company, and that imagination
of making a market is how you create I think
massive value.
Speaker 5 (10:35):
You got to get all three pieces going.
Speaker 2 (10:37):
It's not enough.
Speaker 3 (10:37):
In other words, you were thinking it's not enough to
invent something new. I need to make a business case
for it simultaneously, and that that's what gets you thinking
along the path that leads you to this job.
Speaker 4 (10:46):
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 the those words are,
I can parse them.
Speaker 5 (11:00):
I have no idea what they are. I have no
intuition on what they are.
Speaker 4 (11:04):
I couldn't tell you why it's relevant or why it's not.
But then you began to think, Okay, why do companies
get higher values? Okay, that's the stock what does that
capture if I have to spend working capital and that's
the balance sheet? Well, so you learn. I mean, I figure,
I'm willing to learn. I'm willing to read. However, the
(11:24):
best way I read is to go to balance sheets.
Here you can read the book. It's pretty damn dry.
Much easier to go talk to a financial expert who's
around the corner, and people are if you're curious about
what they do, they're really happy to share their expertise,
and over time you learn more and more and they
actually become part of your network within the company. And
(11:45):
that's how you can both learn and evolve yourself and
actually gain the extra.
Speaker 2 (11:50):
Skills you are to be a successful business leader.
Speaker 3 (11:54):
Do you have to unlearn or deviate from some of
the things that made you as successful scientist?
Speaker 5 (12:02):
I actually believe the exactly opposite.
Speaker 4 (12:04):
Yeah, but use what you're really good at as a foundation,
but don't make it the only thing you use. So
then how do you add the other skills? 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
(12:24):
deeper on certain areas of electrical engineering than I'm ever
going to be, let's say, in finance or marketing. But
I want to be curious about those. I don't want
to dismiss them. So you build on your skills, and
then you have to say, but I need a complete
and holistic view. So I'm going to be a little deep,
not very deep, in all of those. And you've also
(12:46):
got to learn to trust your intuition a little bit.
Speaker 3 (12:48):
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 4 (12:58):
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
(13:18):
by let's not take the names of all of the
telecom carriers, didn't turn out to be the case. Actually,
I think that's the business model case. The reason is
they all had in their heads that you can charge
people by the minute.
Speaker 2 (13:34):
Because they had been doing that already.
Speaker 5 (13:35):
Because they'd been doing that for one hundred years.
Speaker 4 (13:37):
Yeah, and in the end, the winners the networking were
those who sat flat price thirty bucks a month or
fifty bucks a month or whatever, and that was just
too much of.
Speaker 5 (13:48):
A leap for them.
Speaker 3 (13:50):
You think it's as simples, That is the most parsimonious
explanation for why you think they failed.
Speaker 4 (13:56):
No, there were a couple of other more technical things.
The one was written by somebody who was 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. And
(14:17):
the network is smart. It routes you, it figures out
where to send it. It does echocaculation backwards, and the
current Internet is completely dumb with the inside. It just
takes the bits and shoves them out the other end.
All the intelligence is the computer at the end. That's
probably a bit more of a found explanation. But business
model didn't help them either.
Speaker 2 (14:37):
Yeah.
Speaker 3 (14:37):
Wait, did nineteen ninety r end think that the network
should be dumb or smart?
Speaker 4 (14:45):
I'm not sure I thought about it deeply, but everything
I worked on the network was dumb.
Speaker 5 (14:50):
The network movements, That's all I did.
Speaker 4 (14:52):
Yeah, because even I in those days understood I can't
imagine all the applications.
Speaker 5 (14:58):
So if all you do is voice me, maybe the
network can be smart.
Speaker 4 (15:01):
But if you're doing all those other things, how could
the network possibly know all those things and be smart
for it?
Speaker 2 (15:06):
Yeah, so you've been COO for five years.
Speaker 5 (15:10):
Five years?
Speaker 3 (15:11):
Wait, so in your five year increment, what was your
most misunderstood decision?
Speaker 2 (15:16):
Well, you ended up being right, but everyone thought you
were crazy.
Speaker 4 (15:19):
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
(15:40):
the most successful software acquisition in history. So it was
completely misunderstood because people didn't see that you actually did
need a platform that could make it agnostic across multiple
cloud platforms, across on premise environments. So you've got to
have a view of what it could be, and we
drove it to a place where I think today it
(16:03):
stands as the leader in its space.
Speaker 3 (16:05):
So how did you come to believe this heretical notion?
Speaker 4 (16:10):
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
(16:32):
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?
(16:52):
Is there a different space you can occupy instead of
competing with them? Can you become their best partner? In
which case you right there 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 3 (17:12):
How hard was it to convince people needed convincing before
that acquisition.
Speaker 5 (17:18):
Probably six to nine months of.
Speaker 4 (17:22):
Breaking my head with no success, and then six months
of building the momentum once a couple of people began
to see it.
Speaker 2 (17:34):
Yeah, you're very persistent.
Speaker 3 (17:36):
Oh yes, very Would you describe that as you defining trade?
Speaker 5 (17:43):
I am very persistent and I'm very patient.
Speaker 4 (17:46):
I'm also probably very impatient, But I'm not a yeller
and screamer.
Speaker 5 (17:50):
I don't rant and rave.
Speaker 4 (17:53):
But as I say, if I think we're going to
do something, I can be remarkably stop about it.
Speaker 5 (18:00):
We will do it.
Speaker 3 (18:01):
If I got your family, put them up on stage
and ask them this exact question, is this how they
would answer?
Speaker 2 (18:06):
As well?
Speaker 4 (18:07):
They will tell you I'm very stubborn. They might not
agree that I don't rant and drave.
Speaker 3 (18:17):
Well, you know, one of the principal observations of psychology
is that our home self and our work self are uncorrelated.
Speaker 2 (18:25):
Once you know that, you know everything.
Speaker 3 (18:26):
Wait, I'm curious one last question about that. How long
does it take for you to be vindicated with red Hat?
Speaker 4 (18:34):
Probably took five maybe four years, I think by twenty
twenty three, So twenty eighteen we announced it, We took
the big shot croft. It took a year to close
twenty nineteen, so if I count, not that I'm counting
that much, but July ninth, twenty nineteen as the day
(18:57):
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 5 (19:08):
A great move, not a bad move.
Speaker 3 (19:10):
Yeah, but this is it's interesting because this is a
real gamble. If it doesn't work, you're not sitting in
this chair right now.
Speaker 2 (19:19):
Right.
Speaker 5 (19:19):
Oh, for sure. There were two steps.
Speaker 4 (19:21):
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 2 (19:30):
Can I ask you a sort of a personal question.
How much sleep did you lose over this?
Speaker 5 (19:36):
Once we had made the decision, none.
Speaker 3 (19:43):
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 (19:50):
Once a week, I'll probably wake up at two or
three in the morning.
Speaker 4 (19:53):
I acknowledge it because I wake up and my brain
is running, and once it's running, I don't even.
Speaker 5 (19:58):
Try to go back to sleep.
Speaker 4 (19:59):
I mean, go get up and do work and make
yourself productive. You're gonna be tired before in the afternoon,
that's fine, you'll sleep well that night. I have actually
learned a long time back. You can't do it across.
You can't do it early morning, through the day and
late at night. So an hour before I think I
want to go to bed, I will actually change what
(20:20):
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 I might
have that would have been different. I won't read it
(20:44):
on deep signs because that's too close to what we
do for a living. So it's got to be outside
the things that will make my brain churn about work.
But it's got to be something that is dense enough
to occupy your brain, so it shift gears.
Speaker 2 (21:00):
Sorry, I want to dwell on this just for a moment.
The red hat thing.
Speaker 3 (21:03):
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 4 (21:14):
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
(21:35):
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 Nicol who
introduced us, she has been in that loop since at
least twenty fifteen. For me, if I look at our CFO,
(21:57):
Jim Cavanaugh.
Speaker 5 (21:58):
He's been in that.
Speaker 4 (21:58):
Loop probably since twenty thirteen, and the IBM's will probably wonder,
what the hell intersection did you guys have? It didn't
when I talked about learning finance. I will go to
him and say, hey, explain this to me. I don't
understand why it's like this, and to me it's okay.
Speaker 5 (22:14):
A patient you go learn.
Speaker 4 (22:16):
If I think about many of the people in the
software business, they've been having these discussions with me for always.
I mean, now I'll acknowledge I can get probably impatient
and a serbic, 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
(22:38):
for if you have a strong point of view, that
means it's from a different perspective than mine. So what
do I learn from that is the question which helps
to improve my point of view?
Speaker 5 (22:48):
That makes sense.
Speaker 4 (22:51):
I actually think that each person should try to build
a community of a one hundred people inside your enterprise
and a hundred outside that you can call up. I
have no hesitation. Somebody introduced me to a long time
back to a CEO on the outside. I called them
up all the time and say, hey, do you have
five minutes. I'm just thinking about something this way, the
(23:12):
CEO of red At who left IBM in twenty twenty one,
we probably talk every two or three months on a
random topic.
Speaker 5 (23:18):
One way, it becomes mutual. He'll asks me my opinion
on some things.
Speaker 4 (23:22):
Now by the way, three or four times he might
do something different, but he wants my opinion to the
other way around.
Speaker 3 (23:29):
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 fun to get the call.
Speaker 5 (23:36):
Sure you can.
Speaker 4 (23:36):
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.
Speaker 5 (23:51):
And what moment may make it much more.
Speaker 4 (23:53):
Attractive to a large audience. Sure you would. You'd be
far better as a thinker on that topic. Probably most
of the people.
Speaker 3 (24:02):
I was thinking, you know, when you were making your
comments about your nineteen ninety self and streaming that the
rational thing would have been for there have been a
reserved board seat for every television network from someone from
the world of technology, which I one hundred percent sure
(24:22):
they did not have that in nineteen ninety but they
they're board was probably composed of people like them. Let's
talk a little bit about technology now. There's so much,
so much of the changes going on right now are
accompanied by a great deal of hype. What are we overestimating?
Speaker 2 (24:40):
What are we underestimating?
Speaker 4 (24:41):
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 5 (25:03):
Yes.
Speaker 4 (25:04):
Along the way, they'd eight out of ten of the
companies that were invested in heavily go bankrupt.
Speaker 2 (25:11):
Yes.
Speaker 4 (25:12):
I actually think of that as 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.
Speaker 5 (25:34):
If you just.
Speaker 4 (25:35):
Take Amazon and Alphabet aka Google, just those two have
probably paid for all the capital of that time. So
that's what's going to happen this time. There will be
a lot of tears, but in aggregate, there will be
a lot of success. And I think that's the fundamental
difference between the US model and almost.
Speaker 5 (25:55):
All other countries.
Speaker 4 (25:57):
On all other countries, they're desperate to keep all the
companies alone.
Speaker 5 (26:00):
So that means you're dialuting. But that's a horrible thing.
Speaker 4 (26:04):
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
(26:26):
market is completely willing to over deploy capital in the short.
Speaker 5 (26:29):
Term, not the long term.
Speaker 4 (26:31):
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 2 (26:44):
Yeah, I saw that.
Speaker 3 (26:46):
I grew up in Waterloo and BlackBerry closest from Waterloo.
Speaker 2 (26:50):
Yep, everyone used to work for BlackBerry.
Speaker 1 (26:52):
Ye.
Speaker 3 (26:52):
BlackBerry goes into its dive and that's the best thing
that happened to Waterloo because it was not just capital
but talent.
Speaker 5 (26:58):
Yep. Talent is way any other companies.
Speaker 3 (27:00):
So all these smart people went on the next really
more interesting thing. And yeah, the wait, you haven't answered,
So what is you an idea that we are underestimating
at the moment that's in the current kind of suite
of innovations.
Speaker 4 (27:15):
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.
Speaker 5 (27:34):
So that's really just my one advice to them.
Speaker 4 (27:36):
Pick areas you can scale, don't pick the shiny little
toys on the side.
Speaker 2 (27:42):
Then I think, for example, that.
Speaker 4 (27:46):
If anybody has more than ten percent of what they
had for customer service ten years ago, they're already five
years behind. If anybody is not using AI to make
their developers who write software thirty percent more productive today
(28:07):
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 5 (28:15):
Then they're not.
Speaker 4 (28:17):
And I would turn around and tell you I think
only maybe five percent of the enterprises on both.
Speaker 5 (28:22):
Those metrics today.
Speaker 4 (28:23):
Yeah, yeah, and the one that is completely underestimated. I
kind of put it like this, Quantum today is where
GPUs a 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,
(28:46):
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 (28:55):
Yeah, we're gonna get to quantum in a moment. But
I have a couple other AI questions you know I
as you know where This conversation is part of this
thing that we do with IBM Smart Talks, and I've
been The last episode I did was on Kenya, which
has a massive deforestation problem, and they got together IBM
took all the NASA satellite data, ran it through an LLM,
(29:19):
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 much thinking
(29:41):
about the developed world when it's actually the developing world
where the greatest ROI for this is to me.
Speaker 4 (29:47):
Look, software technologies are wonderful and the sense they can
scale and they can be an ad so you don't
have to do one or the other. You use deforestation.
How about the use of pesticides and fertilizers, We overuse it.
We tend to for irrigation, we tend to just flood everything,
as opposed to say, okay, only that one needs it.
(30:07):
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 of people. I know that
nobody likes to hear it. Most of the Far East
(30:29):
is going to have half the number of people by
twenty seventy compared to 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 look at, is either
one point six births per women.
Speaker 5 (30:46):
Or two or two point one. Why are the three numbers?
Speaker 4 (30:50):
One point six is to women who were born in
the United States, It becomes two point two if you
include immigrant women. It becomes two point one if you
include children who to make getting in.
Speaker 5 (31:01):
So you got to decide where the trend is obvious.
This is going down.
Speaker 4 (31:05):
So AI in the developed world is going to be
essential because to keep our current quality of life, you
need more work done or what's going to do the
work if they're people to do the work.
Speaker 5 (31:16):
So the problems are different in the places.
Speaker 3 (31:19):
Yeah, it gives you In the developing world, you get
access to a suite of technologies and things at a
price that you could never been able to afford.
Speaker 5 (31:29):
Correct.
Speaker 2 (31:29):
That was my in talking to the Kenyon thing.
Speaker 3 (31:31):
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.
Speaker 4 (31:39):
And that is one country that might get it done
because they do take a lot of pride in their
ecology and the sort of returning to the land and
giving back.
Speaker 2 (31:48):
Yeah.
Speaker 3 (31:48):
Yeah, what's different about IBM's version of AI versus some of.
Speaker 4 (31:54):
Your So we are not a consumer company, so we
have no focus on a B two C chat pot.
And the reason I say that is, if you're making
a B two C chat bot, does it help you
to make it even bigger and more computationally inefficient? And
the short answer is yes, because you have a certain
number of users, and you kind of say, I kind
(32:14):
of say this jokingly. If I add finished to French capabilities,
I can probably add five million users. If I add
writing a high coup I might be able to add
another five million users. If I add writing an email
in the voice of Steinbeck, I can probably add another
five million users. Do all those things if my goal
(32:35):
is to get help a company summarize the legal documents
in English.
Speaker 5 (32:40):
That can be a model.
Speaker 4 (32:41):
That's one hundred size as effective, probably higher quality, but
I don't need to go wide. So if you're focusing
on the enterprise, that actually takes away the focus of
having to go to extremely large models, which but definition
are going to be computationally expensive, power hungry, and demand
(33:01):
lots and lots of data.
Speaker 5 (33:02):
So I can turn ontell the enterprise.
Speaker 4 (33:04):
You don't need to worry about copyright issues, about all
those because you can train on a much smaller amount
of data.
Speaker 5 (33:10):
And now, by.
Speaker 4 (33:11):
The way, turning it for you yourself is a weekend exercise,
it's not a six month on a big super computer
cluster somewhere out there.
Speaker 5 (33:19):
That's one big difference of what we do.
Speaker 4 (33:21):
Second, we are very focused on helping those problems that
can give people immediate benefit.
Speaker 5 (33:29):
Where we have domain knowledge.
Speaker 4 (33:30):
So our domain knowledge is around operations, is 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
(33:51):
and so we are not asking our clients to be
the first experiment on it.
Speaker 5 (33:55):
We say you can leverage what we did. We're happy to.
Speaker 4 (33:59):
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 3 (34:09):
Other challenges to explaining what makes you different to potential customers.
Speaker 4 (34:14):
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 2 (34:23):
Do you use chat gipt?
Speaker 5 (34:25):
I have used it.
Speaker 3 (34:27):
I asked it a question recently which I thought was
really simple, and it made up about ten people.
Speaker 2 (34:34):
Anyway, I had a bad experience.
Speaker 4 (34:35):
I should think that that's the fundamental issue with all
lms as they get larger. Yeah, because you had to
ask what was the original insight that led to these
It was a reward function with intent. So if it
has learned by using a reward function, it's reward function
comes from giving an answer that satisfies you. So if
(34:59):
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.
Speaker 5 (35:09):
What bullshits the way to an answer. Well, it's exactly
the same.
Speaker 3 (35:13):
It's like the example of clever hands in that story
the horse that they thought could speak, then all it.
Speaker 2 (35:19):
Was doing was pleasing it. It's master. Yes, it is
a little bit of clever hands.
Speaker 5 (35:23):
Yeah, it's like dogs kind of imitating and looking.
Speaker 3 (35:26):
What would you identify as the most significant bottleneck in
the development of AI? What's slowing us down right now?
Speaker 4 (35:36):
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
(35:58):
a way to fuse 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 LMS alone, my view is, I think we
(36:20):
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 2 (36:32):
Yes?
Speaker 4 (36:33):
And I think those answers lie as is usually in compute.
So advances in semiconductors, advances in software, and advances in
agorthic techniques all three. But how come we're not working
in any of those three. We're just taking the current
sevic conductor and going more. We're taking the current algorithmic
techniques and not really trying to invent new ones.
Speaker 5 (36:53):
So I think those are all happen less than five years.
Speaker 3 (36:58):
But why say there is a we're in a moment
where people are not pursuing the the optimal strategy for
exploiting this technology.
Speaker 4 (37:08):
Why because when you see a few people running really
hard and they're willing to invest any amount of money,
so efficiency is not the focus. People feel if you
don't do the same, you'll get left behind.
Speaker 2 (37:23):
So is this a case where there's too much money
ones have never had for more?
Speaker 5 (37:27):
Right? Ever?
Speaker 3 (37:28):
Yeah, but this is this a consequence of overinvestment in
the in the field.
Speaker 4 (37:34):
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 5 (37:47):
So first you win, then you become efficient.
Speaker 3 (37:50):
Yeah, let's talk about what is I was told your
favorite topic, it's quantum. It is what boy even go
to further?
Speaker 2 (38:00):
Why is quantum your favorite topic?
Speaker 4 (38:03):
We've only had two kinds of compute in the history,
so nineteen forty five was to use that year for
anyac all the way till twenty twenty, we had one
kind of 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 of bits,
(38:25):
which is algebra or high school algebra, to including neurons,
which is captured in linear algebra. But that gives you
a different kind But it can do problems that are
really hard to do. I don't say impossible, just hard
to do on normal computers. Quantum adds a third kind
of math. Yes, the physics properties which really get people
(38:49):
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
a third kind of math to make it really simple,
a third kind of math that comes from the field
of abstract algebra.
Speaker 5 (39:08):
It does the math.
Speaker 4 (39:10):
You can use Hamiltonians for those who like physics, or
you can use the word Lee algebras for those who
like abstract mathematics. If you can do a third kind
of math, which algorithms are suited to that third kind
of math? So it excites me because we can now
approach algorithms that you just could never do on the
other two it's impossible. Now it's different than AI. It's
(39:32):
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 5 (39:42):
That's quantum.
Speaker 3 (39:44):
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.
Speaker 2 (39:51):
There's incredibly exciting looking machines. But where are we in
the timeline of this.
Speaker 4 (39:57):
Three to five years away from shocking people?
Speaker 2 (40:02):
What does shocking people mean?
Speaker 5 (40:04):
Do something that nobody thought was possible in that timeline?
Speaker 2 (40:07):
Does an example come to mind?
Speaker 4 (40:09):
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 2 (40:26):
Thirty four percent.
Speaker 5 (40:27):
Thirty four percent.
Speaker 3 (40:29):
This is an industry that's used to one percent correct,
zero point five percent.
Speaker 2 (40:34):
Yes, that's astonishing.
Speaker 4 (40:37):
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.
Speaker 5 (40:46):
Now. Can you imagine when will somebody so you.
Speaker 4 (40:50):
Were correct, you talk about an industry where one basis point,
if I remember I may be wrong, like thirteen trillion
dollars of money kind of moves around in the financial
industr each day, right, So basis point would be thirteen
billion something like that, right, one over ten thousand. So
(41:11):
when you think about the kind of profit that people
can make, if you can tell somebody that you can
come up with a better price than your competition by
just one basis point, they would actually gain the dire
market share. Yeah, so I think something around there or
something in the world of materials.
Speaker 5 (41:31):
Can we make a better battery? Could we make a
solid state battery?
Speaker 4 (41:37):
Which means your risk of fires heating decrease dramatically.
Speaker 3 (41:42):
And the reason, sorry to ask a really nice question.
Why is it that a quantum computer would be better
at solving a battery problem than our existing methods of computing.
Speaker 4 (41:52):
So the equations of quantum mechanics and chemistry and how
things interact are well known. To solve them, that are
no known techniques. So these are not like closed form,
you know, it's not like the square root of a
qualtertic equation. So the only way to solve them is
to explore the state space. So if you have a
few hundred electrons, you need two to the one hundred states. Well,
(42:17):
I'm sorry, you don't have that much memory. It's impossible.
So it takes a really really long time on a
normal computer to solve those problems. Right, But 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.
(42:39):
So in a couple of seconds, it can tell you
this is how that material will be here.
Speaker 2 (42:45):
Oh, I see, so you've.
Speaker 4 (42:46):
Taken what could take years to a few seconds. Yeah,
that's a pretty big change.
Speaker 2 (42:51):
Yeah. Yeah, it's speaking a different language.
Speaker 5 (42:54):
Different.
Speaker 2 (42:54):
So any kind of problem that comes along.
Speaker 3 (42:56):
That's specific to that language correctly, which is not all problems.
Speaker 2 (43:00):
Yeah, just as I call it.
Speaker 5 (43:02):
It's one more kind of math.
Speaker 3 (43:03):
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 4 (43:15):
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 size 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
(43:37):
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
(43:58):
that I think is one hundred million gallons of my
man others 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
(44:19):
go after. So if I look at the interest recently,
New York 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
(44:41):
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 5 (44:49):
This is a new kind of man. What are the
new problems we can solve?
Speaker 4 (44:52):
And the fact that we have about roughly two hundred
clients who worked with us very early stage, small experiments
is because they're in is I could do something here
that I couldn't do in other places.
Speaker 2 (45:04):
Three to four years is not a long time.
Speaker 5 (45:06):
No.
Speaker 2 (45:08):
But if I'm in the battery business.
Speaker 3 (45:10):
And I don't have a line out to a quantum
computing experiment.
Speaker 2 (45:17):
I have a problem. Don't have a problem.
Speaker 4 (45:19):
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 3 (45:27):
You had to what is quantum of rank in the
kind of great inventions of the last one hundred and
fifty years.
Speaker 4 (45:34):
Equal to something conductor? And I think that if sem
Conductor's vanished, modern life.
Speaker 5 (45:42):
Would stop, like just stop.
Speaker 4 (45:44):
Yeah, no electricity, no automobile, no streaming. You can imagine
the yells from all the kids who ever hear that
no streaming?
Speaker 3 (45:57):
The and is that it's funny because don't. As someone
who's outside this world, I feel like quantum is underdiscussed
relative to its potential for transforming society.
Speaker 4 (46:09):
Because I use my Internet example. Ninety five was the
moment with Netscape that Internet came on people's consciousness. I said,
when eighty five I considered it to be this is
a solve problem because it needs something that makes it
accessible easy.
Speaker 5 (46:26):
That was the browser. The Netscape browser.
Speaker 4 (46:28):
Is what brought it easy to understand. We have probably,
as I said, about four to five years from that moment.
That's why it's under discussed, because the moment I say,
and you 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 3 (46:48):
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 this, CEO, was this your first priority.
Speaker 4 (47:04):
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 it. And
I've had a i'll call it a weird career.
Speaker 5 (47:17):
I was a researcher at some point.
Speaker 4 (47:19):
If he had asked me out, I said, I'm one
of those people, you know, throw a pizza under our
door and like leave me alone.
Speaker 5 (47:23):
I don't want to talk to people.
Speaker 4 (47:25):
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.
Speaker 5 (47:46):
At that time.
Speaker 4 (47:46):
It was hey, can we make a computer not just
a science experiment? Can it run by itself all night?
Can you think about software so that even people who
are not deeper quantum mechanics can begin to use it?
Speaker 5 (47:59):
And they began to do those things.
Speaker 4 (48:01):
So over three four years, did I get enough confidence, Yeah, okay,
this is something that can really work.
Speaker 5 (48:07):
And then you've got to nurture it to.
Speaker 4 (48:10):
Where it gets bigger and bigger until you get the
confidence that okay, now it's a big bet.
Speaker 2 (48:15):
And what was the moment when you when you realize now.
Speaker 5 (48:17):
It's a big bet, probably two or three years ago.
Speaker 3 (48:22):
How do you decide, as the head of a company
like this, how much money, how many resources, and how
many people and how what kind of prominence to give
to an idea like that?
Speaker 4 (48:33):
So three layers the set of people who actually have
the knowledge and the intensity to fundamentally advance the technology.
Speaker 5 (48:42):
If I could find more out higher then.
Speaker 4 (48:44):
So I'm constrained on people on that one because normally
there's only so many people who can do these things.
Speaker 5 (48:50):
Two, you got to be careful.
Speaker 4 (48:53):
If you push too hard on timing, you will get
people to take so much risk that actually the thing
will fail. So that's the art of 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 2 (49:14):
Yeah, how do you This is fascinating.
Speaker 3 (49:17):
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 4 (49:38):
So one you got to have what I call and
this is channeling a word from one of my favorite books,
The Geekway, How open can you be? So I want
to press hard, but the team knows that they're allowed
to push back and really argue back hard. That means
you'll get to probably that correct goldilocks pressure do the
(50:02):
people themselves should want to go as hard as possible,
but not harder than possible. So that is then personality
of leadership that makes sense.
Speaker 2 (50:12):
But you have to be someone who people feel comfortable
being honest with.
Speaker 5 (50:15):
Yes, absolutely, and people feel.
Speaker 2 (50:18):
Comfortable being honest with you.
Speaker 5 (50:20):
I believe so.
Speaker 2 (50:21):
Yeah. When has there been a moment in this path
with quantum where you did think you were pushing too hard?
Speaker 4 (50:30):
No, because I think that the leadership there will argue
back with me any day of the week. I don't
think that they feel that they have to forward.
Speaker 3 (50:41):
Do you drop by at sort of Saturday night at
ten pm to see if people are working?
Speaker 4 (50:47):
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 don't 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,
(51:09):
then me I can answer my question. I'll say thoughtfully,
not always completely accurately. You're thinking about it on their own.
I don't need to push Yeah.
Speaker 3 (51:18):
One last question I wanted to ask you, do you
have the most interesting job in America?
Speaker 4 (51:23):
I believe that it's the most interesting job, which I
won't give up anything.
Speaker 2 (51:27):
It also sounds like you're enjoying yourself.
Speaker 4 (51:31):
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 5 (51:43):
I love it. If I don't, somebody else should do it.
Speaker 3 (51:47):
Harvin, this has been so much fun. Thank you so
much taking the time and a fascinating, completely fascinating conversation.
I wish I was one of those people who could
help you out with quantum, but I'm afraid I'm not
in a few years.
Speaker 2 (52:03):
Good. Thank you so much.
Speaker 3 (52:14):
Smart Talks with IBM is produced by Matt Ramano, Amy Gaines, 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,
(52:37):
listen on the iHeartRadio app, Apple Podcasts, or wherever you
listen to podcasts. I'm Malcolm Glabo. This is a paid
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represent IBM's positions, strategies, or opinions.
Welcome to Tech Stuff, a production from iHeartRadio. This season,
non Smart Talks with IBM, Malcolm Glabwell 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. From accelerating scientific breakthroughs
(00:23):
to reimagining education. It's a fresh look at innovation in action,
where big ideas meet cutting edge solutions. You'll hear from
industry leaders, creative thinkers, and of course Malcolm Glabwell himself
as he guides you through each story. New episodes of
Smart Talks with IBM drop every month on the iHeartRadio app,
(00:43):
Apple Podcasts, or wherever you get your podcasts. Learn more
at IBM dot com, slash smart Talks.
Speaker 2 (00:58):
Pushkin Hello.
Speaker 3 (01:03):
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 Lareel 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
(01:25):
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
(01:46):
passion for quantum computing, which he says is as revolutionary
as a semiconductor. Thank you everyone, Thank you to Arvin.
You're a difficult man to schedule for one of these things,
so we're enormously please you could join us. Let's start
with a I have all these cousins, two cousins who
work for IBM their entire career. I would ask them
(02:07):
what does IBM do? And they would always give me different, confusing,
complicated answers.
Speaker 2 (02:13):
What's your answer? What's your simple answer to that question.
Speaker 4 (02:16):
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.
Speaker 5 (02:34):
Then to go to the.
Speaker 4 (02:35):
Next layer, I would say, we help them through a
mixture of hybrid cloud and artificial intelligence and a taste
of quantum coming down the road is kind of where
I would take it.
Speaker 5 (02:45):
That's that's what IBM is.
Speaker 2 (02:47):
So you are technology agnostic in some sense.
Speaker 4 (02:52):
I'm product agnostic. Product I'm not technology agnostic.
Speaker 3 (02:56):
Yes, But if I twenty five years from now, IBM
could be doing things that would be unrecognizable to contemporary IBM.
Speaker 5 (03:04):
It is completely possible.
Speaker 4 (03:05):
Yeah, it could be there in twenty five years from now.
The only software IBM does is 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 3 (03:18):
Is it even simpler to say you just IBM solves
problems at the highest technical level.
Speaker 5 (03:24):
If you say highest technical level, yes, yeah.
Speaker 4 (03:26):
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,
(03:46):
And lasers were out and you could use lasers to
scan things, but they could be upside down, they could
be muddy, they could be partly scraped off. And he
came up with the idea of the bar code. Yeah,
and that changed inventory management forever. But the world needs
to know that IBM invented the Parker. You guys should
do a better job opicizing that. I am sure our
(04:09):
CMO will listen to this podcast and we'll get that idea.
Speaker 3 (04:14):
Tell me you started at the Thomas Watson Research Center.
What were you doing when you first started it, IBM.
Speaker 4 (04:22):
I started in nineteen ninety and that was the era
in which computers and networking were beginning to converge. And
for the first five years I was actually building networks.
So let's remember this was pre laptops. Laptops came in
ninety two or ninety three, but it was clear to
us that they were going to come supportable computing, and
(04:45):
I spent my first five years building what today you
would call Wi Fi. We used to have these debates
can be built it, 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.
Speaker 5 (05:01):
Why would anybody put this on?
Speaker 4 (05:03):
And the debate used to be why would anybody want
to walk around untethered?
Speaker 5 (05:07):
Won't you want to attach a big thick cable into
it and sit down?
Speaker 4 (05:10):
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 3 (05:21):
If I had a conversation with your nineteen ninety self
about what the next thirty years were going to look like,
is it possible to reconstruct what your What were your
predictions at that age about where the company, where the
industry was going.
Speaker 4 (05:37):
It was more about where technology was going to go,
I would say, than where industry would go. I would
have told you that networking and computers would fuse in
nineteen ninety That was a weird thought that some researchers held.
By the late nineties, that was obvious that it became
the Internet. I would have told you that I believe
(05:59):
that streaming will be the primary way people will consume.
Speaker 2 (06:03):
Video you would have said that in nineteen ninety.
Speaker 5 (06:05):
Absolutely, Now, that didn't take five years. That took twenty.
Speaker 4 (06:08):
But it happened because you could do it technically, except
it just too expensive and too cumbersome. And if you've
been in technology, like in nineteen eighty five, I would
have told you the internet is old because when I
went to grad school, every one of us had a
those days, an Apple, Mac or Lisa on our desks.
(06:31):
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 technology. And it didn't have a browser.
That took ten years to get the browser. That took
five years to be a business. But when you see
the speed and the pace of technology in usually ten
or fifteen years, the cost point and the consumerization is
(06:56):
at a scale that you couldn't imagine ten years ago
until you've seen a few of those cycles.
Speaker 3 (07:02):
Wait, did you make the lead to sorry, this is fascinating,
I'm curious, but how far did you take that? That's a
really fundamental thing to have gotten right in nineteen ninety
I think that we.
Speaker 4 (07:13):
Were pretty convinced that what we used to think of
as linear television or broadcast would become digitized. That was
a given two with cable already the preponderance of how
people got it that if you put packet television over cable,
then that becomes the way it will go. I fundamentally believe,
(07:36):
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 then personally
work on all those. I mean, after networking, I moved
on to doing other things, but those were easy to predict.
Speaker 3 (07:56):
If you had a conversation in those years with someone
in the television industry and you gave them those predictions,
did they see it?
Speaker 2 (08:03):
Were they convinced of this?
Speaker 4 (08:05):
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
(08:28):
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
(08:49):
think about, now, how do you market it? To whom
do you market it? What are their routs? How do
you make it easy enough? And that was probably I mean,
I'm making it simple now. That is probably a five
to ten year evolution of myself in those days.
Speaker 3 (09:05):
You know what this reminds me of when the telephone
is invented in the eighteen seventies, it doesn't take off
for forty years because the people running a telephone business
they didn't want women using it because they were worried
that women would gossip with their friends. They didn't understand
that that's actually what telephone is, right, it's an exact parallel,
Yes it is.
Speaker 5 (09:24):
You see it again and again.
Speaker 2 (09:26):
What is the source of that blindness?
Speaker 3 (09:28):
So there's a gap, in other words, between the invention,
the technological achievement, and the social understanding of the technology.
Speaker 2 (09:35):
Why is there such a gap?
Speaker 4 (09:37):
I think that the gap is fundamental and rooted in
a lot of academic disciplines. So even channeling some of
your work, though you don't intend it to be used
that way, you can say a lot of things that
data driven. If it is data driven, then by definition,
you're looking at history. If you're looking at history, that
(10:00):
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.
Speaker 5 (10:14):
And I think that is why.
Speaker 4 (10:15):
The world is fascinated with people like Steve Jobs. For example,
he imagined a market that didn't exist. So I think
that is the gap. And then if you can get
the technology the business acumen scaler company, and that imagination
of making a market is how you create I think
massive value.
Speaker 5 (10:35):
You got to get all three pieces going.
Speaker 2 (10:37):
It's not enough.
Speaker 3 (10:37):
In other words, you were thinking it's not enough to
invent something new. I need to make a business case
for it simultaneously, and that that's what gets you thinking
along the path that leads you to this job.
Speaker 4 (10:46):
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 the those words are,
I can parse them.
Speaker 5 (11:00):
I have no idea what they are. I have no
intuition on what they are.
Speaker 4 (11:04):
I couldn't tell you why it's relevant or why it's not.
But then you began to think, Okay, why do companies
get higher values? Okay, that's the stock what does that
capture if I have to spend working capital and that's
the balance sheet? Well, so you learn. I mean, I figure,
I'm willing to learn. I'm willing to read. However, the
(11:24):
best way I read is to go to balance sheets.
Here you can read the book. It's pretty damn dry.
Much easier to go talk to a financial expert who's
around the corner, and people are if you're curious about
what they do, they're really happy to share their expertise,
and over time you learn more and more and they
actually become part of your network within the company. And
(11:45):
that's how you can both learn and evolve yourself and
actually gain the extra.
Speaker 2 (11:50):
Skills you are to be a successful business leader.
Speaker 3 (11:54):
Do you have to unlearn or deviate from some of
the things that made you as successful scientist?
Speaker 5 (12:02):
I actually believe the exactly opposite.
Speaker 4 (12:04):
Yeah, but use what you're really good at as a foundation,
but don't make it the only thing you use. So
then how do you add the other skills? 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
(12:24):
deeper on certain areas of electrical engineering than I'm ever
going to be, let's say, in finance or marketing. But
I want to be curious about those. I don't want
to dismiss them. So you build on your skills, and
then you have to say, but I need a complete
and holistic view. So I'm going to be a little deep,
not very deep, in all of those. And you've also
(12:46):
got to learn to trust your intuition a little bit.
Speaker 3 (12:48):
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 4 (12:58):
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
(13:18):
by let's not take the names of all of the
telecom carriers, didn't turn out to be the case. Actually,
I think that's the business model case. The reason is
they all had in their heads that you can charge
people by the minute.
Speaker 2 (13:34):
Because they had been doing that already.
Speaker 5 (13:35):
Because they'd been doing that for one hundred years.
Speaker 4 (13:37):
Yeah, and in the end, the winners the networking were
those who sat flat price thirty bucks a month or
fifty bucks a month or whatever, and that was just
too much of.
Speaker 5 (13:48):
A leap for them.
Speaker 3 (13:50):
You think it's as simples, That is the most parsimonious
explanation for why you think they failed.
Speaker 4 (13:56):
No, there were a couple of other more technical things.
The one was written by somebody who was 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. And
(14:17):
the network is smart. It routes you, it figures out
where to send it. It does echocaculation backwards, and the
current Internet is completely dumb with the inside. It just
takes the bits and shoves them out the other end.
All the intelligence is the computer at the end. That's
probably a bit more of a found explanation. But business
model didn't help them either.
Speaker 2 (14:37):
Yeah.
Speaker 3 (14:37):
Wait, did nineteen ninety r end think that the network
should be dumb or smart?
Speaker 4 (14:45):
I'm not sure I thought about it deeply, but everything
I worked on the network was dumb.
Speaker 5 (14:50):
The network movements, That's all I did.
Speaker 4 (14:52):
Yeah, because even I in those days understood I can't
imagine all the applications.
Speaker 5 (14:58):
So if all you do is voice me, maybe the
network can be smart.
Speaker 4 (15:01):
But if you're doing all those other things, how could
the network possibly know all those things and be smart
for it?
Speaker 2 (15:06):
Yeah, so you've been COO for five years.
Speaker 5 (15:10):
Five years?
Speaker 3 (15:11):
Wait, so in your five year increment, what was your
most misunderstood decision?
Speaker 2 (15:16):
Well, you ended up being right, but everyone thought you
were crazy.
Speaker 4 (15:19):
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
(15:40):
the most successful software acquisition in history. So it was
completely misunderstood because people didn't see that you actually did
need a platform that could make it agnostic across multiple
cloud platforms, across on premise environments. So you've got to
have a view of what it could be, and we
drove it to a place where I think today it
(16:03):
stands as the leader in its space.
Speaker 3 (16:05):
So how did you come to believe this heretical notion?
Speaker 4 (16:10):
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
(16:32):
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?
(16:52):
Is there a different space you can occupy instead of
competing with them? Can you become their best partner? In
which case you right there 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 3 (17:12):
How hard was it to convince people needed convincing before
that acquisition.
Speaker 5 (17:18):
Probably six to nine months of.
Speaker 4 (17:22):
Breaking my head with no success, and then six months
of building the momentum once a couple of people began
to see it.
Speaker 2 (17:34):
Yeah, you're very persistent.
Speaker 3 (17:36):
Oh yes, very Would you describe that as you defining trade?
Speaker 5 (17:43):
I am very persistent and I'm very patient.
Speaker 4 (17:46):
I'm also probably very impatient, But I'm not a yeller
and screamer.
Speaker 5 (17:50):
I don't rant and rave.
Speaker 4 (17:53):
But as I say, if I think we're going to
do something, I can be remarkably stop about it.
Speaker 5 (18:00):
We will do it.
Speaker 3 (18:01):
If I got your family, put them up on stage
and ask them this exact question, is this how they
would answer?
Speaker 2 (18:06):
As well?
Speaker 4 (18:07):
They will tell you I'm very stubborn. They might not
agree that I don't rant and drave.
Speaker 3 (18:17):
Well, you know, one of the principal observations of psychology
is that our home self and our work self are uncorrelated.
Speaker 2 (18:25):
Once you know that, you know everything.
Speaker 3 (18:26):
Wait, I'm curious one last question about that. How long
does it take for you to be vindicated with red Hat?
Speaker 4 (18:34):
Probably took five maybe four years, I think by twenty
twenty three, So twenty eighteen we announced it, We took
the big shot croft. It took a year to close
twenty nineteen, so if I count, not that I'm counting
that much, but July ninth, twenty nineteen as the day
(18:57):
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 5 (19:08):
A great move, not a bad move.
Speaker 3 (19:10):
Yeah, but this is it's interesting because this is a
real gamble. If it doesn't work, you're not sitting in
this chair right now.
Speaker 2 (19:19):
Right.
Speaker 5 (19:19):
Oh, for sure. There were two steps.
Speaker 4 (19:21):
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 2 (19:30):
Can I ask you a sort of a personal question.
How much sleep did you lose over this?
Speaker 5 (19:36):
Once we had made the decision, none.
Speaker 3 (19:43):
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 (19:50):
Once a week, I'll probably wake up at two or
three in the morning.
Speaker 4 (19:53):
I acknowledge it because I wake up and my brain
is running, and once it's running, I don't even.
Speaker 5 (19:58):
Try to go back to sleep.
Speaker 4 (19:59):
I mean, go get up and do work and make
yourself productive. You're gonna be tired before in the afternoon,
that's fine, you'll sleep well that night. I have actually
learned a long time back. You can't do it across.
You can't do it early morning, through the day and
late at night. So an hour before I think I
want to go to bed, I will actually change what
(20:20):
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 I might
have that would have been different. I won't read it
(20:44):
on deep signs because that's too close to what we
do for a living. So it's got to be outside
the things that will make my brain churn about work.
But it's got to be something that is dense enough
to occupy your brain, so it shift gears.
Speaker 2 (21:00):
Sorry, I want to dwell on this just for a moment.
The red hat thing.
Speaker 3 (21:03):
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 4 (21:14):
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
(21:35):
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 Nicol who
introduced us, she has been in that loop since at
least twenty fifteen. For me, if I look at our CFO,
(21:57):
Jim Cavanaugh.
Speaker 5 (21:58):
He's been in that.
Speaker 4 (21:58):
Loop probably since twenty thirteen, and the IBM's will probably wonder,
what the hell intersection did you guys have? It didn't
when I talked about learning finance. I will go to
him and say, hey, explain this to me. I don't
understand why it's like this, and to me it's okay.
Speaker 5 (22:14):
A patient you go learn.
Speaker 4 (22:16):
If I think about many of the people in the
software business, they've been having these discussions with me for always.
I mean, now I'll acknowledge I can get probably impatient
and a serbic, 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
(22:38):
for if you have a strong point of view, that
means it's from a different perspective than mine. So what
do I learn from that is the question which helps
to improve my point of view?
Speaker 5 (22:48):
That makes sense.
Speaker 4 (22:51):
I actually think that each person should try to build
a community of a one hundred people inside your enterprise
and a hundred outside that you can call up. I
have no hesitation. Somebody introduced me to a long time
back to a CEO on the outside. I called them
up all the time and say, hey, do you have
five minutes. I'm just thinking about something this way, the
(23:12):
CEO of red At who left IBM in twenty twenty one,
we probably talk every two or three months on a
random topic.
Speaker 5 (23:18):
One way, it becomes mutual. He'll asks me my opinion
on some things.
Speaker 4 (23:22):
Now by the way, three or four times he might
do something different, but he wants my opinion to the
other way around.
Speaker 3 (23:29):
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 fun to get the call.
Speaker 5 (23:36):
Sure you can.
Speaker 4 (23:36):
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.
Speaker 5 (23:51):
And what moment may make it much more.
Speaker 4 (23:53):
Attractive to a large audience. Sure you would. You'd be
far better as a thinker on that topic. Probably most
of the people.
Speaker 3 (24:02):
I was thinking, you know, when you were making your
comments about your nineteen ninety self and streaming that the
rational thing would have been for there have been a
reserved board seat for every television network from someone from
the world of technology, which I one hundred percent sure
(24:22):
they did not have that in nineteen ninety but they
they're board was probably composed of people like them. Let's
talk a little bit about technology now. There's so much,
so much of the changes going on right now are
accompanied by a great deal of hype. What are we overestimating?
Speaker 2 (24:40):
What are we underestimating?
Speaker 4 (24:41):
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 5 (25:03):
Yes.
Speaker 4 (25:04):
Along the way, they'd eight out of ten of the
companies that were invested in heavily go bankrupt.
Speaker 2 (25:11):
Yes.
Speaker 4 (25:12):
I actually think of that as 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.
Speaker 5 (25:34):
If you just.
Speaker 4 (25:35):
Take Amazon and Alphabet aka Google, just those two have
probably paid for all the capital of that time. So
that's what's going to happen this time. There will be
a lot of tears, but in aggregate, there will be
a lot of success. And I think that's the fundamental
difference between the US model and almost.
Speaker 5 (25:55):
All other countries.
Speaker 4 (25:57):
On all other countries, they're desperate to keep all the
companies alone.
Speaker 5 (26:00):
So that means you're dialuting. But that's a horrible thing.
Speaker 4 (26:04):
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
(26:26):
market is completely willing to over deploy capital in the short.
Speaker 5 (26:29):
Term, not the long term.
Speaker 4 (26:31):
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 2 (26:44):
Yeah, I saw that.
Speaker 3 (26:46):
I grew up in Waterloo and BlackBerry closest from Waterloo.
Speaker 2 (26:50):
Yep, everyone used to work for BlackBerry.
Speaker 1 (26:52):
Ye.
Speaker 3 (26:52):
BlackBerry goes into its dive and that's the best thing
that happened to Waterloo because it was not just capital
but talent.
Speaker 5 (26:58):
Yep. Talent is way any other companies.
Speaker 3 (27:00):
So all these smart people went on the next really
more interesting thing. And yeah, the wait, you haven't answered,
So what is you an idea that we are underestimating
at the moment that's in the current kind of suite
of innovations.
Speaker 4 (27:15):
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.
Speaker 5 (27:34):
So that's really just my one advice to them.
Speaker 4 (27:36):
Pick areas you can scale, don't pick the shiny little
toys on the side.
Speaker 2 (27:42):
Then I think, for example, that.
Speaker 4 (27:46):
If anybody has more than ten percent of what they
had for customer service ten years ago, they're already five
years behind. If anybody is not using AI to make
their developers who write software thirty percent more productive today
(28:07):
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 5 (28:15):
Then they're not.
Speaker 4 (28:17):
And I would turn around and tell you I think
only maybe five percent of the enterprises on both.
Speaker 5 (28:22):
Those metrics today.
Speaker 4 (28:23):
Yeah, yeah, and the one that is completely underestimated. I
kind of put it like this, Quantum today is where
GPUs a 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,
(28:46):
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 (28:55):
Yeah, we're gonna get to quantum in a moment. But
I have a couple other AI questions you know I
as you know where This conversation is part of this
thing that we do with IBM Smart Talks, and I've
been The last episode I did was on Kenya, which
has a massive deforestation problem, and they got together IBM
took all the NASA satellite data, ran it through an LLM,
(29:19):
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 much thinking
(29:41):
about the developed world when it's actually the developing world
where the greatest ROI for this is to me.
Speaker 4 (29:47):
Look, software technologies are wonderful and the sense they can
scale and they can be an ad so you don't
have to do one or the other. You use deforestation.
How about the use of pesticides and fertilizers, We overuse it.
We tend to for irrigation, we tend to just flood everything,
as opposed to say, okay, only that one needs it.
(30:07):
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 of people. I know that
nobody likes to hear it. Most of the Far East
(30:29):
is going to have half the number of people by
twenty seventy compared to 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 look at, is either
one point six births per women.
Speaker 5 (30:46):
Or two or two point one. Why are the three numbers?
Speaker 4 (30:50):
One point six is to women who were born in
the United States, It becomes two point two if you
include immigrant women. It becomes two point one if you
include children who to make getting in.
Speaker 5 (31:01):
So you got to decide where the trend is obvious.
This is going down.
Speaker 4 (31:05):
So AI in the developed world is going to be
essential because to keep our current quality of life, you
need more work done or what's going to do the
work if they're people to do the work.
Speaker 5 (31:16):
So the problems are different in the places.
Speaker 3 (31:19):
Yeah, it gives you In the developing world, you get
access to a suite of technologies and things at a
price that you could never been able to afford.
Speaker 5 (31:29):
Correct.
Speaker 2 (31:29):
That was my in talking to the Kenyon thing.
Speaker 3 (31:31):
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.
Speaker 4 (31:39):
And that is one country that might get it done
because they do take a lot of pride in their
ecology and the sort of returning to the land and
giving back.
Speaker 2 (31:48):
Yeah.
Speaker 3 (31:48):
Yeah, what's different about IBM's version of AI versus some of.
Speaker 4 (31:54):
Your So we are not a consumer company, so we
have no focus on a B two C chat pot.
And the reason I say that is, if you're making
a B two C chat bot, does it help you
to make it even bigger and more computationally inefficient? And
the short answer is yes, because you have a certain
number of users, and you kind of say, I kind
(32:14):
of say this jokingly. If I add finished to French capabilities,
I can probably add five million users. If I add
writing a high coup I might be able to add
another five million users. If I add writing an email
in the voice of Steinbeck, I can probably add another
five million users. Do all those things if my goal
(32:35):
is to get help a company summarize the legal documents
in English.
Speaker 5 (32:40):
That can be a model.
Speaker 4 (32:41):
That's one hundred size as effective, probably higher quality, but
I don't need to go wide. So if you're focusing
on the enterprise, that actually takes away the focus of
having to go to extremely large models, which but definition
are going to be computationally expensive, power hungry, and demand
(33:01):
lots and lots of data.
Speaker 5 (33:02):
So I can turn ontell the enterprise.
Speaker 4 (33:04):
You don't need to worry about copyright issues, about all
those because you can train on a much smaller amount
of data.
Speaker 5 (33:10):
And now, by.
Speaker 4 (33:11):
The way, turning it for you yourself is a weekend exercise,
it's not a six month on a big super computer
cluster somewhere out there.
Speaker 5 (33:19):
That's one big difference of what we do.
Speaker 4 (33:21):
Second, we are very focused on helping those problems that
can give people immediate benefit.
Speaker 5 (33:29):
Where we have domain knowledge.
Speaker 4 (33:30):
So our domain knowledge is around operations, is 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
(33:51):
and so we are not asking our clients to be
the first experiment on it.
Speaker 5 (33:55):
We say you can leverage what we did. We're happy to.
Speaker 4 (33:59):
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 3 (34:09):
Other challenges to explaining what makes you different to potential customers.
Speaker 4 (34:14):
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 2 (34:23):
Do you use chat gipt?
Speaker 5 (34:25):
I have used it.
Speaker 3 (34:27):
I asked it a question recently which I thought was
really simple, and it made up about ten people.
Speaker 2 (34:34):
Anyway, I had a bad experience.
Speaker 4 (34:35):
I should think that that's the fundamental issue with all
lms as they get larger. Yeah, because you had to
ask what was the original insight that led to these
It was a reward function with intent. So if it
has learned by using a reward function, it's reward function
comes from giving an answer that satisfies you. So if
(34:59):
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.
Speaker 5 (35:09):
What bullshits the way to an answer. Well, it's exactly
the same.
Speaker 3 (35:13):
It's like the example of clever hands in that story
the horse that they thought could speak, then all it.
Speaker 2 (35:19):
Was doing was pleasing it. It's master. Yes, it is
a little bit of clever hands.
Speaker 5 (35:23):
Yeah, it's like dogs kind of imitating and looking.
Speaker 3 (35:26):
What would you identify as the most significant bottleneck in
the development of AI? What's slowing us down right now?
Speaker 4 (35:36):
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
(35:58):
a way to fuse 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 LMS alone, my view is, I think we
(36:20):
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 2 (36:32):
Yes?
Speaker 4 (36:33):
And I think those answers lie as is usually in compute.
So advances in semiconductors, advances in software, and advances in
agorthic techniques all three. But how come we're not working
in any of those three. We're just taking the current
sevic conductor and going more. We're taking the current algorithmic
techniques and not really trying to invent new ones.
Speaker 5 (36:53):
So I think those are all happen less than five years.
Speaker 3 (36:58):
But why say there is a we're in a moment
where people are not pursuing the the optimal strategy for
exploiting this technology.
Speaker 4 (37:08):
Why because when you see a few people running really
hard and they're willing to invest any amount of money,
so efficiency is not the focus. People feel if you
don't do the same, you'll get left behind.
Speaker 2 (37:23):
So is this a case where there's too much money
ones have never had for more?
Speaker 5 (37:27):
Right? Ever?
Speaker 3 (37:28):
Yeah, but this is this a consequence of overinvestment in
the in the field.
Speaker 4 (37:34):
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 5 (37:47):
So first you win, then you become efficient.
Speaker 3 (37:50):
Yeah, let's talk about what is I was told your
favorite topic, it's quantum. It is what boy even go
to further?
Speaker 2 (38:00):
Why is quantum your favorite topic?
Speaker 4 (38:03):
We've only had two kinds of compute in the history,
so nineteen forty five was to use that year for
anyac all the way till twenty twenty, we had one
kind of 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 of bits,
(38:25):
which is algebra or high school algebra, to including neurons,
which is captured in linear algebra. But that gives you
a different kind But it can do problems that are
really hard to do. I don't say impossible, just hard
to do on normal computers. Quantum adds a third kind
of math. Yes, the physics properties which really get people
(38:49):
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
a third kind of math to make it really simple,
a third kind of math that comes from the field
of abstract algebra.
Speaker 5 (39:08):
It does the math.
Speaker 4 (39:10):
You can use Hamiltonians for those who like physics, or
you can use the word Lee algebras for those who
like abstract mathematics. If you can do a third kind
of math, which algorithms are suited to that third kind
of math? So it excites me because we can now
approach algorithms that you just could never do on the
other two it's impossible. Now it's different than AI. It's
(39:32):
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 5 (39:42):
That's quantum.
Speaker 3 (39:44):
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.
Speaker 2 (39:51):
There's incredibly exciting looking machines. But where are we in
the timeline of this.
Speaker 4 (39:57):
Three to five years away from shocking people?
Speaker 2 (40:02):
What does shocking people mean?
Speaker 5 (40:04):
Do something that nobody thought was possible in that timeline?
Speaker 2 (40:07):
Does an example come to mind?
Speaker 4 (40:09):
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 2 (40:26):
Thirty four percent.
Speaker 5 (40:27):
Thirty four percent.
Speaker 3 (40:29):
This is an industry that's used to one percent correct,
zero point five percent.
Speaker 2 (40:34):
Yes, that's astonishing.
Speaker 4 (40:37):
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.
Speaker 5 (40:46):
Now. Can you imagine when will somebody so you.
Speaker 4 (40:50):
Were correct, you talk about an industry where one basis point,
if I remember I may be wrong, like thirteen trillion
dollars of money kind of moves around in the financial
industr each day, right, So basis point would be thirteen
billion something like that, right, one over ten thousand. So
(41:11):
when you think about the kind of profit that people
can make, if you can tell somebody that you can
come up with a better price than your competition by
just one basis point, they would actually gain the dire
market share. Yeah, so I think something around there or
something in the world of materials.
Speaker 5 (41:31):
Can we make a better battery? Could we make a
solid state battery?
Speaker 4 (41:37):
Which means your risk of fires heating decrease dramatically.
Speaker 3 (41:42):
And the reason, sorry to ask a really nice question.
Why is it that a quantum computer would be better
at solving a battery problem than our existing methods of computing.
Speaker 4 (41:52):
So the equations of quantum mechanics and chemistry and how
things interact are well known. To solve them, that are
no known techniques. So these are not like closed form,
you know, it's not like the square root of a
qualtertic equation. So the only way to solve them is
to explore the state space. So if you have a
few hundred electrons, you need two to the one hundred states. Well,
(42:17):
I'm sorry, you don't have that much memory. It's impossible.
So it takes a really really long time on a
normal computer to solve those problems. Right, But 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.
(42:39):
So in a couple of seconds, it can tell you
this is how that material will be here.
Speaker 2 (42:45):
Oh, I see, so you've.
Speaker 4 (42:46):
Taken what could take years to a few seconds. Yeah,
that's a pretty big change.
Speaker 2 (42:51):
Yeah. Yeah, it's speaking a different language.
Speaker 5 (42:54):
Different.
Speaker 2 (42:54):
So any kind of problem that comes along.
Speaker 3 (42:56):
That's specific to that language correctly, which is not all problems.
Speaker 2 (43:00):
Yeah, just as I call it.
Speaker 5 (43:02):
It's one more kind of math.
Speaker 3 (43:03):
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 4 (43:15):
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 size 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
(43:37):
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
(43:58):
that I think is one hundred million gallons of my
man others 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
(44:19):
go after. So if I look at the interest recently,
New York 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
(44:41):
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 5 (44:49):
This is a new kind of man. What are the
new problems we can solve?
Speaker 4 (44:52):
And the fact that we have about roughly two hundred
clients who worked with us very early stage, small experiments
is because they're in is I could do something here
that I couldn't do in other places.
Speaker 2 (45:04):
Three to four years is not a long time.
Speaker 5 (45:06):
No.
Speaker 2 (45:08):
But if I'm in the battery business.
Speaker 3 (45:10):
And I don't have a line out to a quantum
computing experiment.
Speaker 2 (45:17):
I have a problem. Don't have a problem.
Speaker 4 (45:19):
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 3 (45:27):
You had to what is quantum of rank in the
kind of great inventions of the last one hundred and
fifty years.
Speaker 4 (45:34):
Equal to something conductor? And I think that if sem
Conductor's vanished, modern life.
Speaker 5 (45:42):
Would stop, like just stop.
Speaker 4 (45:44):
Yeah, no electricity, no automobile, no streaming. You can imagine
the yells from all the kids who ever hear that
no streaming?
Speaker 3 (45:57):
The and is that it's funny because don't. As someone
who's outside this world, I feel like quantum is underdiscussed
relative to its potential for transforming society.
Speaker 4 (46:09):
Because I use my Internet example. Ninety five was the
moment with Netscape that Internet came on people's consciousness. I said,
when eighty five I considered it to be this is
a solve problem because it needs something that makes it
accessible easy.
Speaker 5 (46:26):
That was the browser. The Netscape browser.
Speaker 4 (46:28):
Is what brought it easy to understand. We have probably,
as I said, about four to five years from that moment.
That's why it's under discussed, because the moment I say,
and you 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 3 (46:48):
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 this, CEO, was this your first priority.
Speaker 4 (47:04):
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 it. And
I've had a i'll call it a weird career.
Speaker 5 (47:17):
I was a researcher at some point.
Speaker 4 (47:19):
If he had asked me out, I said, I'm one
of those people, you know, throw a pizza under our
door and like leave me alone.
Speaker 5 (47:23):
I don't want to talk to people.
Speaker 4 (47:25):
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.
Speaker 5 (47:46):
At that time.
Speaker 4 (47:46):
It was hey, can we make a computer not just
a science experiment? Can it run by itself all night?
Can you think about software so that even people who
are not deeper quantum mechanics can begin to use it?
Speaker 5 (47:59):
And they began to do those things.
Speaker 4 (48:01):
So over three four years, did I get enough confidence, Yeah, okay,
this is something that can really work.
Speaker 5 (48:07):
And then you've got to nurture it to.
Speaker 4 (48:10):
Where it gets bigger and bigger until you get the
confidence that okay, now it's a big bet.
Speaker 2 (48:15):
And what was the moment when you when you realize now.
Speaker 5 (48:17):
It's a big bet, probably two or three years ago.
Speaker 3 (48:22):
How do you decide, as the head of a company
like this, how much money, how many resources, and how
many people and how what kind of prominence to give
to an idea like that?
Speaker 4 (48:33):
So three layers the set of people who actually have
the knowledge and the intensity to fundamentally advance the technology.
Speaker 5 (48:42):
If I could find more out higher then.
Speaker 4 (48:44):
So I'm constrained on people on that one because normally
there's only so many people who can do these things.
Speaker 5 (48:50):
Two, you got to be careful.
Speaker 4 (48:53):
If you push too hard on timing, you will get
people to take so much risk that actually the thing
will fail. So that's the art of 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 2 (49:14):
Yeah, how do you This is fascinating.
Speaker 3 (49:17):
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 4 (49:38):
So one you got to have what I call and
this is channeling a word from one of my favorite books,
The Geekway, How open can you be? So I want
to press hard, but the team knows that they're allowed
to push back and really argue back hard. That means
you'll get to probably that correct goldilocks pressure do the
(50:02):
people themselves should want to go as hard as possible,
but not harder than possible. So that is then personality
of leadership that makes sense.
Speaker 2 (50:12):
But you have to be someone who people feel comfortable
being honest with.
Speaker 5 (50:15):
Yes, absolutely, and people feel.
Speaker 2 (50:18):
Comfortable being honest with you.
Speaker 5 (50:20):
I believe so.
Speaker 2 (50:21):
Yeah. When has there been a moment in this path
with quantum where you did think you were pushing too hard?
Speaker 4 (50:30):
No, because I think that the leadership there will argue
back with me any day of the week. I don't
think that they feel that they have to forward.
Speaker 3 (50:41):
Do you drop by at sort of Saturday night at
ten pm to see if people are working?
Speaker 4 (50:47):
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 don't 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,
(51:09):
then me I can answer my question. I'll say thoughtfully,
not always completely accurately. You're thinking about it on their own.
I don't need to push Yeah.
Speaker 3 (51:18):
One last question I wanted to ask you, do you
have the most interesting job in America?
Speaker 4 (51:23):
I believe that it's the most interesting job, which I
won't give up anything.
Speaker 2 (51:27):
It also sounds like you're enjoying yourself.
Speaker 4 (51:31):
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 5 (51:43):
I love it. If I don't, somebody else should do it.
Speaker 3 (51:47):
Harvin, this has been so much fun. Thank you so
much taking the time and a fascinating, completely fascinating conversation.
I wish I was one of those people who could
help you out with quantum, but I'm afraid I'm not
in a few years.
Speaker 2 (52:03):
Good. Thank you so much.
Speaker 3 (52:14):
Smart Talks with IBM is produced by Matt Ramano, Amy Gaines, 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,
(52:37):
listen on the iHeartRadio app, Apple Podcasts, or wherever you
listen to podcasts. I'm Malcolm Glabo. This is a paid
advertisement from IBM. The conversations on this podcast don't necessarily
represent IBM's positions, strategies, or opinions.
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Crime Junkie
Does hearing about a true crime case always leave you scouring the internet for the truth behind the story? Dive into your next mystery with Crime Junkie. Every Monday, join your host Ashley Flowers as she unravels all the details of infamous and underreported true crime cases with her best friend Brit Prawat. From cold cases to missing persons and heroes in our community who seek justice, Crime Junkie is your destination for theories and stories you won’t hear anywhere else. Whether you're a seasoned true crime enthusiast or new to the genre, you'll find yourself on the edge of your seat awaiting a new episode every Monday. If you can never get enough true crime... Congratulations, you’ve found your people. Follow to join a community of Crime Junkies! Crime Junkie is presented by audiochuck Media Company.
The Breakfast Club
The World's Most Dangerous Morning Show, The Breakfast Club, With DJ Envy, Jess Hilarious, And Charlamagne Tha God!