25 Years of IBM AI Evolution
Key Points
- Rob reflects on joining IBM Consulting straight out of school, feeling unqualified to advise clients until he was thrust into a last‑minute Vio project, forcing him to self‑teach through intensive reading and rapid hands‑on learning.
- He emphasizes that taking risks and learning faster than peers is essential in consulting, as much of the work involves figuring things out on the fly rather than following a predetermined plan.
- The discussion shifts to AI, with Rob recalling the historical context of John McCarthy coining “artificial intelligence” in the 1950s and noting how IBM’s focus on AI has grown from a peripheral curiosity to a core strategic priority over the past 25 years.
- Rob’s early experience illustrates the broader evolution at IBM, where consultants must continuously adapt to emerging technologies—now exemplified by AI—by rapidly acquiring new skills and applying them to client problems.
Full Transcript
# 25 Years of IBM AI Evolution **Source:** [https://www.youtube.com/watch?v=iaisOHeJLC4](https://www.youtube.com/watch?v=iaisOHeJLC4) **Duration:** 00:39:08 ## Summary - Rob reflects on joining IBM Consulting straight out of school, feeling unqualified to advise clients until he was thrust into a last‑minute Vio project, forcing him to self‑teach through intensive reading and rapid hands‑on learning. - He emphasizes that taking risks and learning faster than peers is essential in consulting, as much of the work involves figuring things out on the fly rather than following a predetermined plan. - The discussion shifts to AI, with Rob recalling the historical context of John McCarthy coining “artificial intelligence” in the 1950s and noting how IBM’s focus on AI has grown from a peripheral curiosity to a core strategic priority over the past 25 years. - Rob’s early experience illustrates the broader evolution at IBM, where consultants must continuously adapt to emerging technologies—now exemplified by AI—by rapidly acquiring new skills and applying them to client problems. ## Sections - [00:00:00](https://www.youtube.com/watch?v=iaisOHeJLC4&t=0s) **First-Day Consultant Reflections at IBM** - Rob recounts his early experience joining IBM Consulting, feeling unqualified and navigating unknown technologies while trying to prove his relevance as a new consultant. ## Full Transcript
[Applause]
[Music]
how we doing good Rob this is our our
second time we we did one of these in
the middle of the P pandemic but not
it's all such a blur neither of us can
figure out when it was I know it's hard
those are like a blurry years you don't
know what happened right but um um well
it's good to to see you to meet you
again um I want I wanted to start by
going back you've been in IBM 20 years
is that right 25 in July believe it or
not so you were you were a kid when you
joined I was
four so I I want to contrast present day
Rob and 25 years ago Rob when you arrive
at IBM what do you think your job is
going to be at your career is what what
do you think the kind of problems you're
going to be addressing
are well it's kind of surreal because I
I joined IBM Consulting and I'm coming
out of school
and you quickly realize wait the job of
a consultant is to tell other companies
what to do and I was like I literally
know
nothing and so you're immediately trying
to figure out so how am I going to be
relevant given that I know absolutely
nothing to advise other companies on
what they should be doing and I remember
it well like we were sitting in a room
when you're a consultant you're waiting
for somebody else to find work for you
bunch of us sitting in a room and
somebody walks in and says we need
somebody that knows Vio does anybody
know Vio I'd never heard of Vio I don't
know if anybody in the room has so
everybody's like sitting around looking
at their shoes so finally I was like I
know it so I raised my hand they're like
great we got a project for you next week
so I was like all right I have like
three days to figure out what Vio
is and I hope I can actually figure out
how to use it now luckily it wasn't like
a programming language I mean it's
pretty much a drag and drop capability
and so I literally left the office went
to a bookstore bought the first three
books on Vio I could find spent the
whole week in reading the books and
showed up and got to work on the project
and so it was a bit of a risky move
but I think that's kind of you would
caution others against doing this well
but if you don't take risk you'll never
you'll never achieve and so to some
extent everybody's making everything up
all the time it's like can you learn
faster than somebody else MH is what the
difference is in almost every part of
life and so it was not planned but it
was an accident but it kind of forced me
to figure out that you're going to have
to figure things out you know we we're
here to talk about a Ai and I'm curious
about the evolution of of your
understanding or IBM's understanding of
AI at what point in the last 25 years do
you begin to think oh this is really
going to be at the core of what we think
about and work on at this company
the um computer scientist John McCarthy
he was the he's the person that's
credited with coining the phrase
artificial intelligence it was like in
the
50s and he made an interesting comment
he said he said once it works it's no
longer called AI
MH and that then became it's called like
the AI effect which is it seems very
difficult very mysterious but once it
becomes commonplace it's just no longer
what what it is and so if you put that
frame on it I think we've always been
doing AI at some level I I even think
back to when I joined IBM in 99 at that
point there was work on rules-based
engines analytics all of this was
happening so it all depends on how you
really Define that term You could argue
that you know elements of
Statistics probability it's not exactly
AI but it certainly feeds into it and so
I feel like we've been working on this
topic of how do we deliver better
insights better automation since IBM was
formed if you read about what Thomas
Watson Jr did that was all about
automating tasks yeah is that AI well
probably certainly not by today's
definition but it's in the same zip code
so from your perspective it feels a lot
more like an evolution Than A revolution
is that a fair statement yes yeah which
I think most great things in techn ology
tend to happen that way yeah many of the
the revolutions if you will tend to
fizzle out but even given that is there
I guess what I'm asking is I'm curious
about whether there was a a moment in
that Evolution when you had to readjust
your expectations about
what AI was going to be capable of I
mean was there you know was there a
particular Innovation or a particular
problem that was solved that made you
think oh this is different than what I
thought I would say the moments that
caught our attention
certainly casprov winning the chess
tournament nobody or deep blue beating
CASRO I should say nobody really thought
that was possible before that and then
it was Watson winning Jeopardy these
were moments that said maybe there's
more here than we even thought was
possible and so I I do think there's
there's points in time where we realized
maybe way more could be done than we had
even imagined but I do think it's
consistent progress every month and
every year versus some simal moment now
certainly large language models as of
recent have caught everybody's attention
because it has a direct consumer
application but I would almost think of
that as what Netscape was for the for
the web browser yeah it brought the
internet to everybody but that didn't
become the internet per se yeah I have a
cousin who worked for IBM for 41 years I
saw him this weekend he's in Toronto by
the way I said you work for Rob Thomas
he went he went like this he
goes he said I'm five layers
down but so I always whenever I see my
cousin I ask him can you tell me again
what you do because it's always changing
right I this is a function of working at
IBM So eventually he just gives up and
says you know we're just solving
problems that's all we're doing which I
sort of loved as a kind of frame and I
was curious what's the coolest problem
you ever worked on not biggest not most
important but the coolest the one that's
just like that sort of makes you smile
when you think back on it probably when
I was in
microelectronics because it was a world
I had no exposure to I hadn't studied
computer science
and we were building a lot of high
performance semiconductor technology so
just chips that do a really great job of
processing something or other and we
figured out that there was a market in
consumer gaming that was starting to
happen and we got to the point where we
became the chip inside the Nintendo Wii
the Microsoft Xbox Sony PlayStation so
we basically had the the entire gaming
Market running on IBM chips and so
you're the every parent basically is
pointing at you and saying you're the
culprit probably yeah well they would
have found it from anybody but was it
was the first time I could explain my
job to my kids who were quite young at
that time like what I did yeah like it
was more tangible for them than saying
we solve problems or do you know build
Solutions like it it would became very
tangible for them and I think that's you
know a rewarding part of the job is when
you can help your family actually
understand what you do most people can't
do that it's probably easier for you
they can they can see the books yeah um
but for for some of us in the the
businesses to business world it's not
always as obvious so that was like one
example where the dots really connected
yeah the um there were a couple there's
a couple let's talk about a little bit
of this in the context of of AI I
because I love the frame of problem
solving as a way of understanding what
the function of the technology is so I
know that you guys did something did did
some work with um I never know how to
pronounce it is it Seeva Seeva Seeva
with the football club Seeva in Spain
tell me about tell me a little bit about
that what problem were they trying to
solve and why did they call you
in every sports franchise is trying to
get an advantage right let's just be
that clear everybody's how can I use
data analytics insights anything that
will make us 1% better on the
field uh at some point in the
future and Sevilla reached out to us
because they had seen some of the we've
done some work with the Toronto Raptors
in the past and others
and their thought was maybe there's
something we could do they'd heard all
about generative AI that heard about
large language models and the problem
back to your point on solving problems
was we want to do a way better job of
assessing talent because really the the
lifeblood of a sports franchise is can
you continue to cultivate Talent can you
find talent that others don't find can
you see something in somebody that they
don't see in themselves or maybe no
other team sees in them and we ended up
building something with them called
Scout advisor which is built on Watson X
which basically just
ingest tons and tons of data and we like
to think of it as finding you know the
the needle and the hay stack of you know
here's three players that aren't being
considered they're not on the top teams
today and I think working with them
together we found some pretty good
insights that's helped them out and how
what was intriguing to me was we're not
just talking about uh quantitative data
we're also talking about qualitative
data but that's the puzzle part of the
thing thing that fascinates me how does
one incorporate qualitative analysis
into that sort of so you're feeding in
Scout scouting reports and things like
that I gotta real think about how much I
can actually
disclose but if you think about so
quantitative is relatively easy yeah
every team collects that you know what's
the 40 yard dash I don't think they use
that term certainly not in Spain uh
that's all quantitative qualitative is
what's happening off the field mhm it
could be diet it could be habits it
could be Behavior you can imagine a
range of things that would all feed into
an athlete's performance yeah and so
relationships there's many different
aspects and so it's trying to figure out
the right blend of quantitative and
qualitative that gives you a unique
Insight how transparent is that kind of
system I mean is it telling you it it's
saying pick this guy not this guy but is
it telling you why it prefers this guy
to this guy is that I think for anything
in the realm of AI you have to answer
the why question yeah otherwise you fall
into the Trap of the you know the
proverbial black box and then wait I
made this decision I never understood
why it didn't work out so you always
have to answer why without a doubt M and
how is why
answered sources of data the reasoning
that went into it and so it's basically
just tracing back the chain of how you
got to the
answer and in the case of what we do in
Watson X is we have IBM models we also
use some other open source models so it
would be which model was used what was
the data set that was fed into that
model how is it making decisions how is
it
performing is it robust meaning is it
reliable in terms of if you feed it to
with the same data set do you get the
same answer yeah these are all the you
know the technical aspects of
understanding the why how quickly do you
expect um all professional sports
franchises to adopt some kind of are
they already there if I went out and
pulled the general managers of the 100
most valuable sports franchises in the
world how many of them would be using
some kind of AI system to assist in
their
efforts uh 120% would meaning that
everybody's doing it and some think
they're doing way more than they
probably actually are so everybody's
doing it I think what's weird about
sports
is everybody's so convinced that what
they're doing is unique that they
generally speaking don't want to work
with the third party to do it because
they're afraid that that would expose
them but in reality I think most are
doing 80 to 90% of the same
things uh so but without a doubt
everybody's doing it yeah yeah the um
the other C that I love was there was
one about a a shipping line Trion on the
Mississippi River um tell me a little
bit about that project what problem were
they trying to
solve think about the the problem that I
would say everybody noticed if you go
back to 2020 was things are getting H up
held up in ports there was actually an
article in the paper this morning kind
of tracing the history of what happened
in 2020 2021 and why ships were
basically sitting at Seas for months at
a time and at that stage we just we had
a massive throughput issue
but moving even beyond the pandemic you
can see it now with ships getting
through like Panama Canal there's like
there's like a narrow window where you
can get through and if you don't have
your paperwork done you don't have the
right approvals you're not going through
and it may cost you a day or two and
that's a lot of money in the shipping
industry and the tricon example it's
really just about when you're pulling
into a port if you have the right
paperwork done you can get Goods off the
ship very quickly they ship um a lot of
food which by definition since it's not
packaged food it's fresh food there is
an expiration period and so if it takes
them an extra two hours certainly
multiple hours or a day they have a
massive problem because then you're
going to deal with spoilage and so it's
going to set you back and what we worked
with them on is using assistant that
we've built in Watson X called
orchestrate which basically is just AI
doing digital labor so we can replicate
nearly any repetitive task and do that
with software instead of humans so as
you may imagine shipping industry still
has a lot of paperwork that goes on and
so being able to take forms that
normally would be multiple hours of
filling it out this isn't right send it
back we've basically built that as a
digital skill inside of Watson X
orchestrate and so now it's done in
minutes now did they real did they
realize that they could have that kind
of efficiency by teaming up with you or
is that something you came to them and
said guys you could we can do this way
better than you think what's
the I'd say it's always it's always both
sides coming together at a moment that
for some reason makes sense mhm cuz it
you could say why didn't this happen
like 5 years ago like seems so obvious
well technology wasn't quite ready then
I would say but they knew they had a
need because I forget what the precise
number is but you know reduction of
spoilage has massive impact on their
bottom line
M and so they knew they had a need we
thought we could solve it and the two
together um who did you guys go to them
though my point did they come to you I
recall that this one was an inbound
meaning they had reached out to
IBM say we'd like to solve this problem
I think it went into one of our digital
centers if I if I recall it's a
literally phone call yeah but so the the
other the reverse is more interesting to
me because there seems to be a very very
large Universe of people who have
problems that could be solved this way
and they don't realize it what's
your is there a shining example of this
of some
you just can't you just think could
benefit so much and isn't benefiting
right
now maybe I'll answer it slightly
differently I'm I'm surprised by how
many people can benefit that you
wouldn't even logically think of first
let me give you an example there's
a franchiser of hair salons M Sport
Clips is the name my My Sons used to go
there for haircuts because they have
like TVs and you can watch so so they
loved that they got entertained while
they would get their haircut I think the
last place that you would think is using
AI today would be a franchiser of hair
salons
yeah but just follow it through the
biggest part of how they run their
business is can I get people to cut
hair and this is a high turnover
industry because there's a lot of
different places you can work if you
want to cut hair people actually get
injured cutting hair because you're on
your feet all day that type of thing and
they're using same technology
orchestrate as part of their recruiting
process how can they automate a lot of
people submitting resumés who they speak
to how they qualify them for the
position and so reason I give that
example is the the opportunity for AI
which is unlike other Technologies is
truly
unlimited it will touch every single
business it's not the realm of the
Fortune 500 or or the Fortune 1,000 this
is the fortune any size and I think that
may be one thing that people
underestimate about AI yeah what about I
mean I was thinking about education as
as a kind of I mean education is the
perennial uh Whipping Boy for you guys
are living the 19th century right I'm
just curious about if a if a
superintendent of a public school system
or the president of a university sat
down and had lunch with you and
said do the University first my costs
are out of control my my uh enrollment
is down my students hate me and my board
is revolting
help how would you s how would you think
about helping someone in that
situation I spend some time with
universities I like to go back and
visit Alma moders where I went to school
and so I do that every year MH the the
challenge I've Hol of universities is
there has to be a will yeah and I'm not
sure the incentives are quite right
today because bringing in new technology
let's say we want to go after we can
help you figure out student
recruiting or how you automate more of
your
education everybody suddenly feels
threatened at a university hold on
that's my job I'm the one that decides
that or I'm the one that wants to
dictate the course so there has to be a
will so I think it's very possible and I
do think over the next decade you will
see some universities that jump all over
this and they will move ahead and you
see others that do not because it's very
possible where how does when you say
there has to be a will um is that the
kind is that a kind of thing that you
that people at IBM think about like when
in this conversation you hypothetical
conversation you have with the
University president would you give
advice on on where the will comes
from I don't do that as much in a
university context I do that every day
in a business
context because if you can find the
right person in a business that wants to
focus on growth or the bottom line or
how do you create more productivity yes
it's going to create a lot of
organizational resistance potentially
but you can find somebody that will
figure out how to push that through I
think for
universities I think that's also
possible I'm not sure there's there's
there's a return on investment for us to
do that yeah yeah yeah let's let's
define some terms um uh AI years a term
I told you like to use what does that
mean we just started using this term
literally in the last three months
and it was a it was what we observed in
ter
which is most technology you build you
say all right what's going to happen in
year one year 2 year three and it's you
know largely by by a calendar AI years
are the idea that what used to be a year
is now like a
week and that is how fast the technology
is moving and to give you an example we
had one client we're working with
they're using one of our Granite models
and the results there we getting were
not very good accuracy was not there
their performance was not there so I was
like scratching my head I was like what
is going on what business were they in
they were Financial Services the bank so
I'm scratching my head like what is
going on everybody else is getting this
and like these results are horrible and
I said to the team which version of the
model are you using this was in February
like we're using the one from
October I was like all right now we know
precisely the problem because the model
from October is effectively useless now
since we're here in February are you
serious you you actually useless ABS
completely useless yeah that is how fast
this is changing and so the minute same
use case same data you give them the
model from late January instead of
October the results are off the charts
yeah wait so what exactly happened
between October and January the model
got way better but dig into that like
what do you mean by the we are con we
have built large compute infrastructure
where we're doing model training and to
be clear model training is the realm of
probably in the world my guess is five
to 10 companies MH and
so you build a model you're constantly
training it you're doing fine-tuning
you're doing more training you're adding
data every day every hour it gets
better and so how does it do that you're
feeding it more data you're feeding it
more live examples
using things like synthetic data at this
point which is we're basically creating
data to do the training as well all of
this feeds into how useful the model is
and so using the October model those
were the results in October just a fact
that's how good it was then but back to
the concept of AI years two weeks is a
long time and is that are we had a in a
steep part of the model learning curve
or do you expect this to continue along
this at this
Pace I think that is the big
question and don't have an answer yet by
definition at some point you would think
it would have to slow down a bit but
it's not obvious that that is on the
horizon still speeding up yes how fast
can it
get we've debated can you actually have
better results in the afternoon than you
did in the
morning really it's nuts yeah I know but
that's that's why we came up with this
term because I think you also have to
think of like Concepts
that gets people's attention so you
you're basically turning into a bakery
you're like the bread from yesterday you
know you can have it for 25 cents but I
mean you you do preferential pricing you
could say We'll charge you X for
yesterday's model 2 x for today's
model I think that's dangerous um as a
merchandising strategy but I get your
point yeah but that's crazy and this by
the way so this model is the same true
for all models you're talking
specifically about a model that was
created to help some aspect of a
financial
services so is that kind of model
accelerating faster and learning faster
than other models for other kinds of
problems so this domain was code yeah
and so by definition if you're feeling
feeding in more data so so more code you
get those kind of results um it does
depend on the model type yeah there's a
lot of code in the world and so we can
find that we can create it like I said
um there's other aspects where there's
probably less inputs available which
means you probably won't get the same
level of iteration yeah but for code
that's certainly the cycle times that
we're seeing yeah and how do you know
that let's stick with this one example
of this model you have how do you know
that year model is better
than big Company B down the street a
client asked you why would I go with IBM
as opposed to some there's some firm in
the valley that says they have a model
on this what's your how do you how do
you frame your
advantage well we Benchmark all of this
and I think the most important is metric
is price
performance not price not performance
but the combination of the two and we're
super competitive there well for what we
just released with what we've done in
open source we know that nobody's close
to us right now on code now to be clear
that will probably change yeah because
it's like Lea frog people will jump
ahead then we jump back ahead but we're
very confident that with everything
we've done in the last few months we've
taken a huge leap forward here yeah this
it's I mean this goes back to the point
I was making in the beginning so about
the difference between your 20-some self
and 99 and yourself today but this time
compression is has to be a crazy
adjustment so your the concept of what
you're working on and how you make
decisions internally and things has to
undergo this kind of Revolution if
you're if you're switching from I mean
back in the day a model might be useful
for how long years years I think about
you know statistical models that sit
inside things like SPSS which is a
product that a lot of students use
around the world I mean those have been
the same models for 20 years yeah and
they're still very good at what they do
and so yes it's a completely it's a
completely different moment for how fast
this is moving and I think it just
raises the bar for everybody whether
you're a technology provider like us or
you're a bank or an insurance company or
or a shipping company to say how do you
really change your culture to be way
more
aggressive than you normally would
be does this mean this is a weird
question but does this mean a different
set of kind of personality or character
traits are necessary for a decision
maker in Tech now than 25 years
ago there's a there's a book I saw
recently uh was called The Geek way
which talked about how technology
companies have started to operate in
different ways maybe than many you know
traditional
companies and more about being data
driven
more about delegation are you willing to
have the smartest person in the room
make decision as opposed to the highest
paid person in the room I think these
are all different aspects that every
company is going to face yeah yeah next
term talk about open when you use that
word open what do you
mean I think there's really only one
definition of open which is for
technology is open source MH and open
source means the code is freely
available anybody can see it access it
contribute to it and what is tell me
about why that's an important
principle when you take a topic like AI
I think it would be really bad for the
world if this was in the hands of one or
two
companies or three or four doesn't
matter the number some small number
think about like in history sometime
early
1900s the Interstate Commerce Commission
was created and the whole idea was to
protect Farmers from railroads meaning
they wanted to allow free trade but they
knew that well there's only so many
railroad tracks so we need to protect
Farmers from the shipping cost that
railroads could impose so good idea but
over time that got completely overtaken
by the railroad
Lobby and then they used that to
basically just increase prices yeah and
it made the lives of farmers way more
difficult I think you could play the
same analogy through with AI if you
allow a handful of companies to have the
technology you regulate around the
principles of those one or two companies
then you've trapped the entire world I
think that would be very
bad so is there a danger of that happen
for sure I mean there's companies in
Watson in Washington every week trying
to
achieve that outcome MH and so the
opposite of that is to say it's going to
be an open
source because nobody can dispute open
source yeah because it's right there
everybody can see it yeah and so I'm a
strong believer that open source will
win for AI it has to win it's not just
important for business but it's
important for
humans on the I'm curious about on the
sort of list of things you worry about
actually let me before
let me ask this question very generally
what is the list of things you worry
about what's your top five business
related worries right
now top those are your first question we
could be here for hours for me to
answer I didn't say business related we
can leave you know your kids haircuts
got it out of
the number one is always it's the thing
that's probably always been true which
is just people
MH do we have the right skills are we
doing a good job of training our people
are our people doing a good job of
working with clients like that's number
one number two is
innovation are we pushing the envelope
enough or are we staying
ahead number three is which kind of
feeds into the Innovation one is
risk-taking are we taking enough risk we
without risk there is no growth and I
think the Trap that every larger company
inevitably falls into is is conservatism
yeah things are good enough and so it's
are we pushing the envelope are we
taking enough risk to really have an
impact I'd say those are probably the
top three that I spend most of my time
talk about the last term to Define
productivity Paradox something I know
you've thought a lot about what does
that
mean so I started thinking hard about
this because all I saw and read every
day was Was Fear about AI
and I studied economics and so I kind of
went back to like basic economics and
there's been like a macro
investing formula I guess I would say
it's been around forever that says
growth comes
from productivity growth plus population
growth plus debt
growth so if those three things are
working you'll get GDP growth
and so then you think about that and you
say well debt growth we're probably not
going back to 0% interest rates so to
some extent there's going to be a
ceiling on
that and then you look at population
growth there are shockingly few
countries or places in the world that
will see population growth over the next
30 to 50 years in fact most places are
not even at replacement rates yeah and
so I'm like all right so population
growth is not going to be there
so that that would mean if you just take
it to EXT to the extreme the only chance
of continued GDP growth is
productivity and the best
way to solve productivity is AI so
that's why I say it's a paradox on one
hand Everybody's scared half to death
it's going to take over the world take
all of our jobs ruin
us but in reality maybe it's the other
way which is it's the only thing that
can save us yeah and if you believe that
economic equation which I think has
proven quite true over hundreds of years
I do think it's probably the only thing
that can save
us actually looked at the numbers
yesterday for a totally random reason on
population growth in Europe I me see
this a special bonus question we'll see
how smart you are which country in
Europe Continental Europe has the
highest population
growth it's small Continental Europe um
probably one of the nordics I would
guess
close Luxembourg okay something is going
on in
Luxembourg I feel like we all of us need
to investigate they're at 1.49 which in
the day by the way would be a relatively
that's the best performing country is
one I mean in the day you'd be countries
had routinely had 2 point something you
know percent growth in a in a given year
um last question you're writing a book
now we were talk chat about it backstage
um and now I appreciate the Paradox of
this book which is in a universe where
the model is better in the afternoon
than it is in the morning how do you
write a book that's like printed on
paper and expect it to be
useful this is the challenge and I uh
I'm an incredible author of useless
books meaning most of what I've spent
time on in the last decade is stuff
that's completely useless like a year
after it's written and so when um we
were talking about I was like i' would
like to do something around AI That's
Timeless yeah that would be useful 10 or
20 years from
now but then to your point so how do you
how is that even remotely
possible if the model's better in the
afternoon than in the morning so that's
the challenge in front of us but the
book is around AI value creation so kind
of links to this productivity Paradox
and how do you actually get sustained
value out of AI out of automation out of
data science and so the biggest
challenge in front of us is can we make
this
relevant past the day that it's
published how are you setting out to do
that I think you have to to some extent
level it up to bigger Concepts which is
kind of why I go to things like
macroeconomics population geography as
opposed to going into the the weeds of
the technology itself if you write about
this is how you get better performance
out of a
model we can agree that will be
completely useless two years from now
maybe even two months from now yeah and
so it will be less in the technical
detail and more of what is sustained
value creation for AI which if you think
on what is hopefully a 10 or a 20 year
period it's probably we're kind of
substituting AI for Tech technology now
I've realized because I think this has
always been true for technology it's
just now ai is the thing that everybody
wants to talk about um but let's see if
we can do it time will tell did you have
any inkling that the pace that this AI
years phenomenon was going to that
things the pace of change was going to
accelerate so much because you had Moors
law right you had a model yeah in the
technology world for this kind of
exponential increase in so were you
were you thinking about that kind of
acceler similar kind of acceleration in
the I think anybody that said they
expected what we're seeing today is
probably exaggerating I think it's way
faster than anybody expected yeah but
technology back to your point of Mo's
law has always accelerated through the
years so I wouldn't say it's a shock but
it is surprising
yeah you've had a kind of
extraordinary uh privileged position to
watch and participate in this revolution
right I mean how many other people have
been in that have
ridden this wave like you
have I I do wonder is is this really
that much different or does it feel
different just because we're here
meaning I do think on one level yes so
in the time I've been in IBM mobile
happened social Network happened
blockchain happened AI so a lot has
happened but then you go back and say
well but if i' had been here
between 1970 and
'95 there were a lot of things that are
pretty fundamental then to so I wonder
almost do we do we always exaggerate the
time frame that we're
inh I don't know yeah but it's a good
idea
though I think the ending with the
phrase I don't know it's a good idea
though it's probably a great way to wrap
this up thank you so much thank you Mal
[Applause]