From Chip Engineering to IBM Cloud
Key Points
- Ric explains that a hardware (especially chip) background drives a meticulous, cost‑aware mindset focused on extensive planning and verification, whereas software engineers tend to iterate more freely.
- He spent 32 years at Hewlett‑Packard, evolving from hardware engineering on complex Unix servers to leading the company’s software‑defined cloud business and championing innovative business models.
- After retiring for almost two years, Ric was drawn back to the industry by IBM because he sees today’s IT sector as a “golden age,” shifting from a back‑office support role to a central driver of business transformation.
- The conversation underscores his belief that solving problems—whether in hardware or software—requires an analytical brain, but the stakes and approaches differ markedly between the two domains.
Sections
- Untitled Section
- Returning to Tech amid AI - The speaker reflects on how cloud—and now AI—are transforming all businesses, leading them to pause retirement and rejoin IBM for its talented workforce, AI and quantum initiatives, and the opportunity to ride the next major industry wave, even though the hiring process took about six months.
- Navigating Culture, Silos, and Risk - The speaker explains how IBM’s strong client‑centric, quality‑focused heritage can foster internal silos and risk‑aversion, and stresses the need to revive a growth mindset by encouraging risk‑taking and shifting investment toward software differentiation.
- Strategic Growth via Portfolio Rationalization - The speaker explains how emphasizing innovation, software add‑ons, market segmentation, and focused investment—particularly in storage—has driven a turnaround, revealing the surprising durability of legacy mainframe business despite cloud expectations.
- Hardware Thresholds Enable AI Boom - The speaker explains that the recent AI explosion is driven primarily by reaching a hardware performance threshold—especially the advent of powerful GPUs capable of massive matrix calculations—allowing large language models to become feasible.
- Moore's Law and Parallel Scaling - The speakers examine whether performance breakthroughs come from throwing more resources at problems or from technological efficiency, explaining Moore's Law’s evolution from frequency gains to adding ever‑more parallel cores on a chip and questioning how predictable that performance threshold was.
- Watson's Journey to watsonx - The speaker recounts IBM's early Watson initiatives, noting how timing, technology, and a risk‑taking culture finally aligned to make watsonx a pivotal part of the company’s AI strategy.
- Data Explosion Drives Storage & AI - The speaker describes how ever‑growing video and visual data are powering the storage market while creating a pressing need for AI solutions to search, analyze, and derive value from massive media archives.
- Hybrid Infrastructure Enables Scalable AI - The speaker outlines how adopting hybrid cloud infrastructure is essential for companies to retain data, integrate AI at scale, and seize a pivotal business opportunity.
- AI-Driven Support Time Reduction - The speaker explains how IBM’s Scale platform ingests data, fine‑tunes Granite models for cloud or on‑premise inference, and uses AI‑generated remediation recommendations to cut support‑call resolution time by roughly 30%, meeting their primary goal of faster interactions.
- AI‑Driven Workforce Amplification - The speaker explains how AI can filter high‑ROI projects, boost the productivity of thousands of technicians into “super‑powered agents,” and drive step‑function efficiency gains while alleviating fears of job loss.
- IBM's Role in Global Financial Data - The speaker stresses that IBM’s primary value is hosting the world’s critical financial transaction data rather than personal files, and explores whether IBM could quickly and affordably build AI-driven infrastructure to audit IRS returns, similar to its existing credit‑card transaction systems.
- Identifying Data for AI Solutions - The speaker explains how to choose and use existing datasets—such as customer support logs or travel expense reports—to rapidly build global, multilingual AI prototypes and consulting services.
- AI Overhead vs Customer Delight - The speaker critiques how cumbersome AI‑driven internal processes prioritize backend efficiency over genuine human interaction, emphasizing that true business value—illustrated through a coffee‑shop example—lies in enhancing the customer experience, a principle championed by IBM’s culture.
- Predictions Fail: Fit‑for‑Purpose Tech - A veteran IT professional reflects on repeatedly inaccurate industry forecasts—like hardware’s demise and a single‑cloud future—and stresses that choosing and sizing technology should always be driven by the specific use case.
- AI's Early Impact and Uncertain Future - The speakers argue that AI is still in its infancy, its future applications will unfold unpredictably—much like the VCR’s rollout—and urge us to start exploring potential changes now.
- Aim High, Leverage AI - The speaker urges setting bold, audacious goals for growth while describing how AI now streamlines everyday tasks—from faster information searches to coding assistance—boosting personal and professional productivity.
Full Transcript
# From Chip Engineering to IBM Cloud **Source:** [https://www.youtube.com/watch?v=BCc29YKiOGk](https://www.youtube.com/watch?v=BCc29YKiOGk) **Duration:** 00:51:05 ## Summary - Ric explains that a hardware (especially chip) background drives a meticulous, cost‑aware mindset focused on extensive planning and verification, whereas software engineers tend to iterate more freely. - He spent 32 years at Hewlett‑Packard, evolving from hardware engineering on complex Unix servers to leading the company’s software‑defined cloud business and championing innovative business models. - After retiring for almost two years, Ric was drawn back to the industry by IBM because he sees today’s IT sector as a “golden age,” shifting from a back‑office support role to a central driver of business transformation. - The conversation underscores his belief that solving problems—whether in hardware or software—requires an analytical brain, but the stakes and approaches differ markedly between the two domains. ## Sections - [00:00:00](https://www.youtube.com/watch?v=BCc29YKiOGk&t=0s) **Untitled Section** - - [00:03:03](https://www.youtube.com/watch?v=BCc29YKiOGk&t=183s) **Returning to Tech amid AI** - The speaker reflects on how cloud—and now AI—are transforming all businesses, leading them to pause retirement and rejoin IBM for its talented workforce, AI and quantum initiatives, and the opportunity to ride the next major industry wave, even though the hiring process took about six months. - [00:06:05](https://www.youtube.com/watch?v=BCc29YKiOGk&t=365s) **Navigating Culture, Silos, and Risk** - The speaker explains how IBM’s strong client‑centric, quality‑focused heritage can foster internal silos and risk‑aversion, and stresses the need to revive a growth mindset by encouraging risk‑taking and shifting investment toward software differentiation. - [00:09:10](https://www.youtube.com/watch?v=BCc29YKiOGk&t=550s) **Strategic Growth via Portfolio Rationalization** - The speaker explains how emphasizing innovation, software add‑ons, market segmentation, and focused investment—particularly in storage—has driven a turnaround, revealing the surprising durability of legacy mainframe business despite cloud expectations. - [00:12:22](https://www.youtube.com/watch?v=BCc29YKiOGk&t=742s) **Hardware Thresholds Enable AI Boom** - The speaker explains that the recent AI explosion is driven primarily by reaching a hardware performance threshold—especially the advent of powerful GPUs capable of massive matrix calculations—allowing large language models to become feasible. - [00:15:35](https://www.youtube.com/watch?v=BCc29YKiOGk&t=935s) **Moore's Law and Parallel Scaling** - The speakers examine whether performance breakthroughs come from throwing more resources at problems or from technological efficiency, explaining Moore's Law’s evolution from frequency gains to adding ever‑more parallel cores on a chip and questioning how predictable that performance threshold was. - [00:18:36](https://www.youtube.com/watch?v=BCc29YKiOGk&t=1116s) **Watson's Journey to watsonx** - The speaker recounts IBM's early Watson initiatives, noting how timing, technology, and a risk‑taking culture finally aligned to make watsonx a pivotal part of the company’s AI strategy. - [00:21:41](https://www.youtube.com/watch?v=BCc29YKiOGk&t=1301s) **Data Explosion Drives Storage & AI** - The speaker describes how ever‑growing video and visual data are powering the storage market while creating a pressing need for AI solutions to search, analyze, and derive value from massive media archives. - [00:24:45](https://www.youtube.com/watch?v=BCc29YKiOGk&t=1485s) **Hybrid Infrastructure Enables Scalable AI** - The speaker outlines how adopting hybrid cloud infrastructure is essential for companies to retain data, integrate AI at scale, and seize a pivotal business opportunity. - [00:27:46](https://www.youtube.com/watch?v=BCc29YKiOGk&t=1666s) **AI-Driven Support Time Reduction** - The speaker explains how IBM’s Scale platform ingests data, fine‑tunes Granite models for cloud or on‑premise inference, and uses AI‑generated remediation recommendations to cut support‑call resolution time by roughly 30%, meeting their primary goal of faster interactions. - [00:30:50](https://www.youtube.com/watch?v=BCc29YKiOGk&t=1850s) **AI‑Driven Workforce Amplification** - The speaker explains how AI can filter high‑ROI projects, boost the productivity of thousands of technicians into “super‑powered agents,” and drive step‑function efficiency gains while alleviating fears of job loss. - [00:33:52](https://www.youtube.com/watch?v=BCc29YKiOGk&t=2032s) **IBM's Role in Global Financial Data** - The speaker stresses that IBM’s primary value is hosting the world’s critical financial transaction data rather than personal files, and explores whether IBM could quickly and affordably build AI-driven infrastructure to audit IRS returns, similar to its existing credit‑card transaction systems. - [00:36:56](https://www.youtube.com/watch?v=BCc29YKiOGk&t=2216s) **Identifying Data for AI Solutions** - The speaker explains how to choose and use existing datasets—such as customer support logs or travel expense reports—to rapidly build global, multilingual AI prototypes and consulting services. - [00:40:00](https://www.youtube.com/watch?v=BCc29YKiOGk&t=2400s) **AI Overhead vs Customer Delight** - The speaker critiques how cumbersome AI‑driven internal processes prioritize backend efficiency over genuine human interaction, emphasizing that true business value—illustrated through a coffee‑shop example—lies in enhancing the customer experience, a principle championed by IBM’s culture. - [00:43:01](https://www.youtube.com/watch?v=BCc29YKiOGk&t=2581s) **Predictions Fail: Fit‑for‑Purpose Tech** - A veteran IT professional reflects on repeatedly inaccurate industry forecasts—like hardware’s demise and a single‑cloud future—and stresses that choosing and sizing technology should always be driven by the specific use case. - [00:46:05](https://www.youtube.com/watch?v=BCc29YKiOGk&t=2765s) **AI's Early Impact and Uncertain Future** - The speakers argue that AI is still in its infancy, its future applications will unfold unpredictably—much like the VCR’s rollout—and urge us to start exploring potential changes now. - [00:49:08](https://www.youtube.com/watch?v=BCc29YKiOGk&t=2948s) **Aim High, Leverage AI** - The speaker urges setting bold, audacious goals for growth while describing how AI now streamlines everyday tasks—from faster information searches to coding assistance—boosting personal and professional productivity. ## Full Transcript
I'm here with Ric Lewis.
Ric, welcome.
Thank you.
Here we're here at IBM's new New York City headquarters at One Madison Avenue.
I'm going to start with, you're a hardware guy?
I'm a hardware guy.
I grew up doing hardware chip engineering.
But like I tell a lot of people, a chip engineering project is actually
a giant software project with a piece of hardware at the end of the project.
I think if you have that analytical brain, you like to solve problems.
You like to get things working.
Um, you can do that in software and hardware.
But does that, but does being someone coming from a hardware
background mean that you think about problems in a different way?
I think one thing that you do from a hardware background and
especially a chip background is a chip spin costs millions of dollars.
So you're a lot more likely to make sure everything has a great chance
of being perfect from the get go.
Where if you start kind of from a software background, your general mindset is, I
don't know, try this, see if it works.
I don't know, try that and see if it works.
And you're kind of iterate, iterate, iterate.
Chip people are a little more uptight about, okay.
If, if this first round of the chip breaks costs us for building
another new round of the chip.
So you're a little more, you, you guys are, spend more time planning and…
Planning, verifying.
Tons of time verifying
Yeah, so you began your career at Hewlett Packard.
Yes, correct.
And you were there for how many years?
I was there for 32 years.
And your last job there was?
I was leading the software defining cloud business.
I had grown up a hardware guy.
I had done all kinds of hardware projects, big complicated Unix
servers and things like that.
Um, and then came, you know, grew out of R and D and more into the business realm.
And then, uh, I'm much an innovator at heart.
I really like innovating new, uh, concepts, things like that.
And what I learned is I enjoyed innovating business models and software
projects as much as I did hardware products and projects and so getting
teams inspired toward doing that was really a deep fascination for me.
So I ended up doing a fantastic variety of experiences and had a successful
run and honestly retired, intending to retire and do some of my outside
activities and things like that.
And then how long did you stay retired before IBM came calling?
It was almost two years.
And when I first got the call, I thought, no, I'm having too much fun.
Um, but I would say three things really got me thinking hard about it.
One: the industry that we're in, the IT Industry.
I think it's the golden age.
And what I mean by that is for 20 years of that career, IT's
kind of in the back office.
"Hey, make sure that stuff doesn't crash, and can you please reduce the
cost as much as possible? Because it's not that important to the main
business. It's just a back office function.” You can see it right now.
IT is at the forefront of all business revolution.
It happened with the Internet.
Uh, it happened again with cloud and, and how that changed every ounce of business,
not just IT Business, but all business.
And I think it's happening again with AI.
So to be in that career that long and to miss the kind of this age where
it's like this is front and center.
This changes everything about all businesses, not
just technology businesses.
I was kind of feeling like, gosh, you, you train to be in these
really, uh, awesome environments.
Why wouldn't you do that for a little while longer while you still can do it?
That combined with IBM and IBM seeing the talent pool, the brilliant people at IBM.
Um, I worked with a ton of brilliant people before.
I saw a chance to work with even a larger staff of brilliant people.
And then the assets that IBM had, which is, you know, they'd already been
doing a lot of experimentation in AI.
They're working in, in quantum, the deep, rich heritage of successful projects.
I thought who wouldn't want to kind of see if they could be part
of that next great wave of IBM.
And so, um, I kind of decided, all right, I'm going to put the outside interests on
hold for a while and get back in the game.
How long between the phone call, the first phone call, and you saying yes?
It was a while.
It was probably six months.
Arvind, our CEO, teases me about that a lot.
Yeah, I don't think six months is that long.
It took a while.
You were in retirement!
I know exactly.
I mean, it's one thing to compare.
I'm working here and doing this stuff versus working there.
But it's really hard to compare.
I'm doing exactly as I want to do every single day when I wake up.
And now I'm not going to get to do that again.
Um, it took a while for me to get over it.
And I thought, I can't miss this wave.
And, and, uh, I'm really, really happy that I did because we're
doing some amazing, fun things.
And I'm getting challenged in ways that I never did.
So it's really fun.
Talk a little bit about your job here at IBM, you oversee
a kind of massive portfolio
It's a big group.
So I run the infrastructure organization.
There's three main groups of products at IBM.
There's the infrastructure group, which I run.
The software group, uh, and the consulting group.
Um, and infrastructure is built up of mainframes, which
is called our Z portfolio.
Um, our servers, which is our power portfolio.
Storage.
By the way, those businesses include the supply chain to build all of that stuff.
So that's in the group.
Uh, then I have the worldwide customer support organization.
It's called, uh, TLS Technology Lifecycle Services, which is a network of about
13,000 people around the globe that make sure that everything runs and works
when you buy IBM products and then also our IBM cloud, which is, um, how we,
uh, host applications, deliver as a service products for our client base.
So there's a lot.
It's, I think it's about 45,000 people total.
Do those, uh, components of the infrastructure group?
Are they aligned in their trajectory?
Or do they, are they on different paths?
I'm just curious about sort of navigating…
A little of both.
It's interesting you would ask that, because I think of all of the
challenges coming to the new company.
There were things I expected, things that I didn't expect, but
getting that culture right in the group, um, has been a big challenge.
Um, IBM has a great culture toward quality products, toward, um, emphasizing passion
for the client, and making sure that the client is happy, and for delivering
innovation on a scale that, that, you know, for more than a hundred years
has, has been, um, extremely powerful.
But with success comes some challenges.
And with that success, you can tend to get a little bit, um, insular, like you don't
keep an eye on the competition as well.
You can get more siloed where you know this is my business unit.
This is my business unit.
I compete with the other business unit.
That's not a good thing when you, when you're a company, um, and you can get
really risk averse, meaning you, you feel like, “Hey, this is a successful
business. I don't want to do anything to mess it up. So I don't need to try new
things.” Well, that's exactly the recipe to kind of be shrinking and infrastructure
had been shrinking for a little while.
And so a lot of what, um, the challenge was for me was to invigorate that risk
taking and, and, uh, get to a growth mindset where you're trying new things
and seeing what works and what doesn't work and changing some of the models,
like investing a little bit less in hardware for some software differentiation
that goes into the hardware.
So, um, it's been very successful so far and it's been a good journey.
It's almost four years now.
Give me an example of what was a really hard problem that you've
dealt with in those four years.
Boy, a really hard problem.
Or an interesting.
Interesting is a better word than hard.
One of the first things that I kind of chewed on a little bit is, I talked
about how we have Z power and storage.
The Z and power product lines are well known in the industry as really fit for
purpose computing that have strengths, that, you know, Z runs, um, you know,
most of the world's economic backbone.
And, and, uh, if you use a credit card, 90 percent of credit card transactions for
the globe go through these Z mainframes.
They're in every bank there, you know, it's a big business.
It's well known in the industry.
Same with power, very tuned and optimized for, for smaller operations than, than
our giant Z mainframes, but really, mission critical workloads for retail, for
insurance, for banking, for all of that.
Our storage business - not so well known.
In fact, when I came, I thought, did they have storage?
Will I have storage when I come into IBM?
So I got online and I thought it's still hard for me to tell,
do they have storage or not?
Now I own a storage business.
So one of the things was not just to get the market perception up, but to invest
in that business, because if you look at infrastructure overall around the
globe, it's growing at 5 percent a year.
The infrastructure business had been kind of flat to declining.
And so a challenge was how do we grab onto the growth?
Well, one of the biggest growth areas, due to the explosion of
data, in the world is storage.
So what do you do to kind of get on that growth rates?
We did a lot of reinvigoration of the innovation in that, a lot
of software value add, a lot of doubling down on the things that are
working portfolio rationalization where you segment the market.
You say, “Okay, we're going to do less of this and really go big in these
areas.” And that's been probably the most dramatic turnaround inside the group is,
is our storage thing. When you say it's a hard problem, it's not just, “Oh, you
know, how do we do the math?” No, it's, it's cultural, it's, it's strategy.
And how do you get the strategy said.
It's segmentation, it's product strategy at a granular level across
a bunch of dimensions and then putting the investment behind it.
Um, it's a big challenge.
It takes a long time, but it's working.
So we're happy.
Yeah.
Tell me, give me a little bit of perspective on you've
been there four years.
Imagine we're having this conversation four years ago.
Yeah.
What sorts of things have happened over the last four years that, uh,
have surprised you that you didn't see come or at least, are we having exactly
the same conversation four years ago?
Um, no, because I didn't know what was in, I'll tell you
some of the biggest surprises.
I thought from the outside and, and you know, you, you hear from a lot of
customers, especially 10 years ago.
“We're all going to cloud.” We're all doing.
So I thought, Well, I wonder if the mainframe business is struggling
when I get inside of there.
I found the opposite to be true.
The mainframe business is actually flourishing because
transaction demand across the globe has done nothing but grow.
And even more surprising was the level of innovation that the team
was already doing in mainframes before I got here was astounding.
For example, we have AI they were building AI technology into the mainframe
processors three years before ChatGPT made everybody talk about it in the industry.
So, um, that was really pleasantly surprising.
So that was wonderful.
Um, other surprises.
I knew about the kind of the IP of IBM and the mystique in that.
And I used to joke with people, especially on the outside.
I said, I can't wait to get in there and see what's in the big blue toolbox.
Right?
What are all the things they have going on?
I way underestimated the size of the big blue toolbox and what was in there.
Meaning, um, the amount of really hardcore research that we're still doing
into how to build chips and, and how to get to things beyond two nanometer
and, and that kind of capability.
Packaging, industry leading packaging technologies, um.
That's in my hardware kind of patch.
Quantum.
The next thing that'll come after we're done talking about AI, um, you
know, all of, all of those things were surprising, but it wasn't just that.
It was then the software innovations that are going on.
Heavy investment in AI technologies before it was really popular
to be talking about that.
But as I saw that, I thought, this is going to get really fun
because I had a good feel for where the industry was going.
I just didn't.
And I knew, man, I know that talent is really good and there's brilliant people
there, but I didn't know the level of, of IP, frankly that, that IBM had
at its disposal and now you're seeing that in things like watsonx and things
like AI in, in mainframes, et cetera.
Building on that, since you brought up AI, can you walk me through what
has to happen from your perspective, from the infrastructure perspective
to make the AI explosion work?
So everyone wants to do more of this stuff.
Clearly there has to be some underpinning of it.
Yeah, I would tell you, you know I think that people feel like where we're at
right now in the AI journey had to do with one specific piece of software.
I think the inflection point for that whole thing really, at
its root was around hardware.
Meaning the algorithms needed to do large language models and
all of that had been around.
They've been talked about in the industry, but at some point you
hit a tipping point of, of hardware capability where it's like, oh, now
we can do this in a brute force way.
Massive amounts of matrix math to get weights correct so that you can do, um,
you know, the right level of predictions that enable large language models.
And once we've got to that horsepower, and that's why you hear about
giant GPUs that are driving this and the sales of those, et cetera.
It's because we just barely got over the hump where you can do
these big, hard things in terms of hardware capability to do it.
Give me a layman, give me a sense of when you say there was a
kind of threshold where suddenly these things became possible.
I don't know if there's an exact number, but, more basic question that I get
from a lot of people, you know, my friends and family outside, is why GPUs?
What does, what does a GPU graphics processor have to do?
With AI, it's not.
Well, graphics processors are really good at this thing matrix math because
they're figuring out how do I map a pixel?
And as I move an object across the screen, it's essentially
matrix math to figure out.
Okay, what is what is what is this pixel on the screen look like?
And what's it doing?
And as you know, we've gotten more high resolution graphics, more high
resolution monitors, et cetera.
It's a lot more pixels and a lot more math and a lot more matrix
math about how you compute that.
The first big thing that kind of started to look like that, it turns
out was crypto and crypto mining.
And so you saw graphics companies starting to sell to crypto.
The technology got to a certain point and there were use cases like
Bitcoin and that, that, that kind of said, "Hey, we need to do a lot of
this matrix math to, to be able to do that." Um, so graphic chips were a
natural fit and that kind of sustained.
But meanwhile, behind the scenes, a lot of this AI.
AI is about numeric calculations having to do with weights and matrices that say,
you know, giant consolidated things that predict what's going to kind of happen
based on, what other things have happened, just like predicting where a Pixel goes,
but it's really about being able to do enough data ingest to be able to do,
and then the calculations to be able to simplify things like entire sets of
language or giant chunks of the internet to get enough weightings in there to be
able to say, okay, we can predict what you would say in this language based on all
of the volumes of stuff that we've seen that when you start talking like this,
the next word is likely, oh, it's this.
So-
But my point is, to get to that point, that threshold, we got there because,
was it because we simply threw a lot more resources at the problem?
Or is it because the underlying technology got suddenly or
gradually so much more efficient?
It's always yes and yes, but you know, uh, the industry for a lot of
years would talk about Moore's Law.
Rick, quick, will you define for us, uh, Moore's Law, for
those who've forgotten it?
Yeah, so, uh, Gordon Moore at Intel coined this thing, it was basically
that the horsepower, I'm going to translate it roughly of, uh, technology
will double every couple of years.
We’re still on Moore's Law.
Moore's Law changed a little bit.
For a while, it was always about frequency.
Things would go faster, faster, faster.
That kind of petered out.
But what happened is rather than faster, faster, faster,
we did more and more and more.
So rather than one operating unit going a lot faster on its throughput,
you put 10 operating units on a chip.
Now you put 100 operating units on a chip.
Now 1000.
Some of these problems, the matrix math problems, scale parallel extremely well.
You don't have to do something really fast.
You just have to do a lot of the similar things in parallel at the same time.
So again, that kind of that, extension of Moore's Law, more and more hardware
on a chip to be able to do more and more of those calculations in
parallel and come up with answers.
Was that threshold predictable?
In other words, did people in the industry like you sit down X number of
years ago and say, when we get here, AI is going to become much more of a?
It's funny.
The horsepower - that - very predictable.
The use cases, not always so easy to kind of figure out.
That's where the human spirit kind of gets involved.
Um, I think for some people they say, “Oh, I saw that coming.” But
people have been predicting kind of the rise of AI for 25 years.
Oh, well, then when we get to this next gen or when we get here.
It kind of hadn't happened.
There's always a magic point where you kind of get to where the technology
and the use case, and somebody does something to kind of make it catch on.
And I think we're at one of those moments in AI for sure right now.
And I don't think it's you know, people have said, Oh,
this is just the latest wave of.
You know, uh, I used to hear, I've heard this about a lot of
technologies, but AI is the technology, the future, and it always will be.
I used to hear that.
Um, you're not hearing that now, right?
It's like, no, it's prime time.
It will change everything.
Just like some of these other things changed everything.
I noticed sort of personally, when I speak somewhere where I'm
listening in an audience somewhere over the last, let's say 12 months.
There is always a whole bunch of AI questions.
Yes.
And if I go back two years ago, there were no AI questions.
Yes.
Now, my question is, so there's been this explosion on the, in popular
fascination with what's going on AI.
It seems like the last year.
I agree with you.
In your world.
When did the explosion of conversation around this start?
It's, uh, I love this question because.
IBM had a, um, fairly big effort and business, um,
called Watson before watsonx.
And this is going back kind of ten years.
I'll give you another kind of example.
I knew about a lot of tablet technology before there was an iPad.
A lot.
For 10 years, there were a lot.
But it kind of takes a magic combination of the technology, the
user experience, the software, and the need and the market ready for
it to kind of go, now it's a thing.
Now we all have either an iPad or we have the Google equivalent, And so I
think this is a little like that, meaning IBM was on the right track with watson.
Some of the hardware wasn't there.
The use cases weren't exactly figured out.
Some of the early use cases didn't pan out perfectly.
But the good news about that is it's back to that culture of risk taking.
You don't look back on that and say, “Oh, we shouldn't have done that. That
was a bad idea.” No, you look back on that and say, “What did we learn?
How should we try something new?
How would we pivot this time?” That's what we've done with watsonx and
uh, now that's a growing healthy piece of our business and very
important to our strategic future.
So we're all in.
I mean, I've just, I've always been fascinated by the gap
between insider sense of what is happening and outsider sense, like…
It absolutely is that in this case, we've all been talking about and thinking
about AI and, and is it time for that?
And what does this mean?
Et cetera.
And yet none of us really predicted that actual moment, which is kind
of, you know, early 2022 where it was like, Oh, now you have a simple
human interface of software innovation combined with large language models.
There's a moment there where you're like, Oh, unlike, you know, I think
all of us are frustrated if we ask our phone, “Hey, tell me about this.”
And it says, “I found this on the web page.” That does, you know, good.
But, you know, all of a sudden with with, um, ChatGPT and some of these
other things, you could ask a question.
It would give you a clear answer.
Sometimes it's wrong, but at least it was like I'm getting an answer
rather than, “Hey, I don't know. Here's some references. Good luck
to you.” And that's really changing.
Talk about the kind of macro trends that are going to shape
your infrastructure battle
Yeah.
Um, we've talked about a few already, but I'm actually going to
go a little different direction.
So macro trends first, and this one has been before even,
even this AI conversation that we've had: explosion of data.
Uh, as humans, we don't think exponentially very well.
We really struggle with exponential thinking.
We think linearly.
Oh, there'll be more.
There'll be more.
There'll be more.
But we don't think, well, when it's like, no, there'll be more and
there'll be 10 times more and then there'll be 10 times that more.
That's what's going on with data right now in our industry.
It's one of the reasons that that storage business is doing so well, is
there’s just more and more and more data.
Um, you know, you'd say, well, how can there be more data?
It's just life and that thing.
The things that we care about: video capture, video
images, you know, the thing.
I don't know.
For my parents, you needed a drawer with all your family photos.
Now we need gigabytes and gigabytes.
If you knew how many pictures my wife has taken of our children, you would.
Exactly, exactly so that, so that's your case.
Now think of companies who used to just think about their transaction data.
What's the ledger say?
That now have video assets of all of their campaigns and their marketing.
They're trying to figure out, you know what campaigns are working the best.
It's just an explosion of data.
And that's not going to stop.
Dealing with that, and more importantly, getting value from that data is,
is a massive trend in the industry.
Second trend, AI.
And this is the AI, not like we were just talking about, about
how it changes how I search for things or how I learn about things.
But I would argue dealing with that data.
How do I figure out what's in all those video streams?
How do I figure out - Okay, I want all of the chunks of my corporate video
that have to do with, uh, client buying some specific product or something.
That's a different problem.
It's not just, okay we'll look it up in a spreadsheet, and here's
the math associated with that.
That is a huge trend in the industry.
You're seeing it play out in this regard.
It's a little different bent on AI.
Fraud detection is the one that we cite in our main frames.
It's a similar problem where it was kind of a traditional AI problem.
Look up a rule, you know, if somebody does too small transactions, then a
massive one, it might be fraud, right?
Because they were seeing whether it worked.
Now to detect fraud, you might be saying, okay, two transactions, then a huge one.
Plus, does this entity have a real address?
Second, is there any web traffic on, you know, better business bureau kind
of things that says this is a bad business that could help you with fraud.
So it's a lot more of a, it's an exponential problem.
It's a holistic problem that takes a lot more than just, you know, little
chunks of rules, etc. And then the third one, you know, after AI is the
nature of hybrid IT or hybrid computing.
For a while, 10 years ago, when cloud was on the rise, I think, the notion
of hybrid computing basically having to do with things in the cloud versus
things that people still have, um, on the premises inside a business.
It was almost a religious argument.
Now it's no, it's the reality.
And the reason is because that data that I talked about is the lifeblood of these
companies, particularly IBM's companies.
Our clients that usually that data has to be secure.
They have to be able to get value from it.
It is the lifeblood of the company.
If you go to an A. T. M. And you can't get your money out, you know,
to our financial transactions.
If that lasts a day, you're probably going to change banks immediate.
So it's like life or death to these companies.
So having that hybrid infrastructure so that they can still hold their
data yet still interact with clouds and still get value from it from AI.
That's a kind of the magic where we play, and it's a huge business opportunity.
It is a true inflection point for the industry.
Mhmm.
I'm gonna go back.
I interrupted you when you were in the middle of a really, we were talking about
what has to happen for, um, for AI to scale from the infrastructure standpoint.
You gave one example then I got you off on a tangent.
Can you go back and talk?
Very sort of practically, like.
So I'm, you know, I'm a big company.
I have all these dreams of AI of how I'm going to use this dramatically.
So give me a very granular sense of the works you have to
do to make that dream possible.
So let me first say what the company has to do.
And then maybe I'll say then how do I help them, if that makes sense.
So if I'm a company and I want to do that.
So it turns out I am a company, meaning I want to use AI in my processes.
I mentioned that I have a global network of 13,000 employees that support
our infrastructure around the world.
That challenge is a great challenge for AI.
That means I have data for every customer situation for 13,000
employees globally around the world on what was their problem?
How did we fix it?
Um, what next steps did they have to do?
How did they remediate that?
That data is extremely valuable to me because if I can get better at
doing that than anybody else in the world, that brings my cost down.
I sell more products.
I sell more service.
I sell more anything.
So what I have to do to get there is I have to figure out,
okay, what's my objective?
I have a couple objectives.
One, I want customers to be able to support themselves without even calling
me first off, and I don't want when they, when they call for the first answer to
come back to be, did you try rebooting?
Because I think that irritates every single one of us.
Did you try?
“Of course, I tried rebooting. I've had a laptop my entire life. Of course.” Well,
okay, well, then tell me, okay, what firmware version, all that other stuff.
Okay, we know this interaction.
So, you know, So that's kind of the problem set.
Do I want that to be customers solving their own problems?
Well, even for my support agents, I want something in their pocket on their
phone where they say I'm seeing these symptoms and says, “Oh, this happened
in around the globe. Here's here's kind of specific.” So there's my problem.
And what does it mean for for infrastructure on the back end?
So first, I got to get all that data together, right?
All of those customer law, all that customer support around the globe,
et cetera, that needs to be stored.
That's a big set of data.
And some of it's not just fix and, and that kind of thing.
Some of it is okay.
You know, what was the firmware version?
Who was the tech?
Cause it can matter.
Is this their first time fixing this problem?
Is it their 150th time?
What's their level?
It's a very complicated problem.
Ingesting all that data takes an architecture.
We have a product called Scale, um, which is one of our storage
projects that actually makes it easy to ingest all that data, get it
organized, etc. And then have a model.
It's a whole different process to kind of say, did we train our model?
We train our own models inside of IBM.
We have a Granite set of models.
Those models we fine tune and then we inference based on those models.
So we can do that inferencing in our cloud.
I have a cloud set of infrastructure or in my power servers.
We can do inferencing with our capabilities and say, OK, based on what
I'm saying, here's what the remediation that you should do for that customer.
We already are doing that today.
We've seen, um, over a third of our support calls have had significant
reduction in the amount of time that it takes to resolve that support
call just by what I said right there.
Does that I've really been curious about this.
If I had reduced something like AI into that equation, as you just did, and you
said, we've already seen a 30 percent, you say, did you say 30 percent reduction?
30 percent of our interactions have seen significant reduction in the time
Was that your primary goal to reduce the time of the interaction?
In other words, if you, if everything else was the same, but all, but what you
were doing was shrinking the amount of time that, would you declare victory?
One of the primary goals.
So, to us in that business, uh, net promoter score, kind of the satisfaction
of a client is the supreme goal.
What makes them satisfied?
Doesn't cost me a fortune, happens really quickly.
And if I can do it myself, I'd be thrilled.
It affects all of those, right?
It kind of says it got resolved faster.
It didn't cost me an arm and a leg because the tech was barely here
because it's a common problem.
Or I solved it myself without even calling one.
So, um, all of those objectives we kind of hit across all so that now you see it.
So that's a little microcosm.
That's just me and my customer support business.
Now think of how many problems for businesses around the
world there are like that.
It's not a, it's not like a new AI application that changes
the entire user experience.
That's, those will come, but right now it's kind of practical, which is, I
just want to do what I'm doing better and faster, and I can get immediate
economic return from those things.
How long, how long did it take you to just stick with that example of
the customer interaction, reducing 30 percent of the time, how long from the
very beginning of that project to that 30 percent reduction was how long?
Uh, less than a year.
And.
Yeah.
So we, one of the challenges, and this is interesting with a very large
organization, as you can imagine, just like you're seeing in the industry,
we don't have a problem of generating ideas for how AI could help us.
We actually have a problem filtering the thousands of ideas from our
employees and from, from everywhere.
It's like, Hey, we could use AI to solve and filtering down and saying, okay, which
of these will have a return on investment quickly and at a level that sustains that
that's worth kind of going and investing in the infrastructure and and, uh, the
software and kind of making that happen.
Is that unusual?
If I talked to you 25 years ago and said, do you have a problem
of too many good ideas or too few?
What would you have said?
Um, in this specific area, probably too few because at some point
you reach diminishing return.
So, for example, let's use this exact same example.
Can those 13, 000 technicians go faster?
Can they spend less time driving to the site?
I mean, there's only so much you can kind of do on those things.
But if you can get them an answer to the problem and maybe even avoid
them having to visit at all because the client helped themselves.
That's a step function.
So this, that's why people are kind of talking about.
There's a, there's a business revolution coming with AI where there's some step
function changes that can be there.
And notice I didn't say I'm going to have less of those agents.
That's not my objective.
My objective.
And I think that's the fear in the industry about AI is going
to eliminate all the jobs.
No, I just created 13,000 super powered agents that can do more.
Right?
And so I'm not just going to support IBM products.
I'm going to go out and support other people's products because I
know how to do that really well.
And once I have the data on how to fix their problems, I may just
have a customer support business that's independent of my boxes.
So, you know, I think that's where people sometimes get it wrong in the AI thing
is, it's like, you know, did, did word processing eliminate the need for writers?
No, it enabled writing instead of mucking around with mimeograph machines
and clickety click typewriters.
It may have enabled too much writing.
Yeah, maybe, maybe.
Wait, can I give you a hypothetical?
I ask this because I ran.
I was at some conference and I ran to some guy from the IRS.
He was really, really, really, really excited about AI So let's suppose
they call you up and they say…
You're gonna talk, to ask me when the IRS. calls me up
I'm the commissioner of the IRS. I call you up and I say, “Rick, uh,
clearly there's something that we could do for the IRS. if we work together.”
Yeah. What would your answer be?
Of course.
No, I think, we sell to a lot of government agencies, as you can imagine
in, in the business that we're in.
We enable a lot of social security transactions and things like
that through our mainframes.
And I think, you know, we're in the business of helping whatever client
get the most out of their data and be able to secure it and and be
able to do analytics with this.
And IRS. has a heck of a lot of data.
So yes, we would help them.
Do you know how the amount of data they have compares to some
of the corporate clients you have?
I don't know specifically for the IRS. how much data they have,
but I would assume it's a whole lot.
It's mountains.
But, but that's our business.
I mean, it's interesting.
Sometimes, people have asked what's the most, you know, what is it
that IBM has that's of great value?
Is it a server?
Is it a storage array?
Is it, you know, software and all that?
What we have is the most important entities in the world
have their data on our stuff.
The most important data in the world.
It's not, you know, You know, pictures of your grandkids and
things like that, generally for us.
It's all of the financial transactions that happen globally, right?
It's all of the it's the world's economy is kind of running through our systems.
And so we take that really seriously.
You know, you would be distraught if you lost one photo on your laptop, whatever.
But, you know, if we lose a transaction, like somebody moves a
big amount of money, and it's like, well, don't know what happened there.
It is a massive deal, right?
So that doesn't happen.
I wanna go back to my IRS. example for a moment.
Yes.
So, one, is it reasonable to assume that, uh, you could, that somebody,
IBM or somebody else, could in a short period of time put together, not just
the AI capability to audit returns, but also this, the infrastructure support
for that in a reasonable amount of time for a reasonable amount of cost?
Or is it a, over, is it going to the moon?
Or is it….
It definitely, I mean, So we're already doing that kind of thing right across
a network of, of banks and others.
Yeah
Essentially all credit card transactions for all of the
world go through our systems.
So that in some ways is more volume than the tax returns of the U.S. people
and their you know, W2s and all that.
Yeah, and we do that stuff, too.
I try not to describe it too much in detail, but we
definitely do a lot of that.
Um, in fact, I think, um, most, if you think, okay, what is super
critical data, who would be doing the business transaction processing?
It is most likely us, in almost all cases, whether it's government things or, or
private or banks or that kind of thing.
That's what we do.
Ric, we're gonna end with, uh, the way we always end with a
couple of quick fire questions.
Okay.
Here we go.
Um, what single piece of advice would you give to businesses trying
to use AI in an effective way?
The simple version is get started.
By get started, I mean, think of what is something that I want to improve.
The things that we have traction on right now in the market are around
business process automation, digital labor, uh, uh, those kinds of things.
But my other little piece of advice there is keep it simple to begin with.
You're going to learn a lot, but getting started means you'll
start that learning curve.
I even advise, you know, my friends like, "Hey, should I be playing around
with some of this AI stuff?" And I say, yeah, because I think it will help you
start to be more comfortable and you may find a use case personally for that.
I think the same is true for businesses.
The first step in that journey is always.
With what data?
Notice when I talked about our customer support people, I thought
about, okay, what's the data?
The data is all of those logs, of all of those service
engagements around the world.
And what could I do with that?
Well, I could use that to get to a knowledge base that really helps.
Um, and hopefully that I can do it in multiple languages because it's global
and I can, you know, all of those things.
That was kind of my data set.
That one's not super simple, but we've had a lot of experience in AI.
For other people that might just be: How do I automate filling out travel
expense reports for my company?
We can help people that we have consulting.
We have watsonx tools.
We can do that like this and we're doing it globally for people around the world.
Pick that thing.
What's the data you have in that case?
It's data of expense reports.
And it's like, okay, we can help you automate that for people
where they could do it just by, you know, a verbal interface.
What did you spend?
Where did you go?
Who are you with?
Okay, we filled out your travel expense report for you.
And you don't have to mess around with it.
We were playing with this idea, where we would pick a business.
And go in there and do, it would be AI makeover.
Yeah, I love that idea.
Okay, what's the, what is the ideal business to do?
We only have a couple months.
We don't want to spend a kajillion dollars.
We want to be able to show tangibly and quickly what AI can do.
What's an ideal business to do that in?
It can be a small business, but we're not talking.
This isn't a grand corporate thing here.
Boy, small business that we could do an AI makeover.
Customer support is one of my favorites because it's a, it's, it's, I have it
on the business side where I provide customer support, but I have it on the
consumer side where it drives me nuts when I have to go through 30 layers
of phone menus and speak to an agent, speak to an agent, speak to an agent.
That for any business, I think is just ripe to be able to kind of say, Why do I
have to click through these menus message?
I just need to tell you in human language.
Here's the issue.
And I'll be really good about telling you details about, you know, I tried
to set up this thing for my bank and I did... They can go through all
the menus, automate that process.
I think it would change everything because all that frustration as a
consumer would go down dramatically.
And it's all, you know, you know, why are you making me the beep, boop,
press one, press five, press four?
They’ve offloaded.
Exactly.
Well, don't offload to me.
Offload to AI.
We can help you with that.
Here's my version of that.
Drives me crazy.
Every morning, I go to the same coffee shop, and I get
a cup of tea and a croissant.
And here's what happens.
A person has their screen, and they go, I go, cup of tea,
croissant, sparkling water.
Do, do, do, do, do like at least 20 keystrokes, right?
Yes, yes.
And then like, then the screen is turned around like at this point we're like
45 seconds in and I'm like, why is this, first of all, it's not for me.
All those keystrokes.
Yes. It's for their internal
Yes, yes.
Correct, correct.
So they're burdening me in order to service their backend.
You should be able to walk in, go up and they go, “Hi, Malcolm. Same thing?”
Yeah. And you just go, yes.
And then…
Boom, we're done.
Can we do AI makeover of my coffee shop?
You notice I quickly jumped more to banks than your coffee shop because
I think I'm a business person.
I'm not trying to kind of do a deal on one coffee shop.
No, but this is interesting because it takes me back to something you said
that I thought was really important.
When you were talking about, when you were using AI in your customer service
thing, it was clear that your goal, you could have any number of goals going in.
It could be to cut costs.
It could be to dramatically improves profits.
Your goal quite specifically was to improve the experience
of your customer, right?
So you were using it to that.
All the other things come from that come from that.
And that is actually one of the beautiful pillars of the IBM culture
is delighting clients is actually where all of the good stuff comes from.
Always.
So my coffee shop thing is the same principle right now.
Right now, they're making my customer experience worse, and they don't want to,
but their eyes are glued to the screen.
At a moment when I walk in and I want to say, “Hi, how are you
doing?” We could have a conversation.
They’re busy beeping and booping.
They're too busy, beeping and booping.
So like, this is the same thing.
If they had their, Oh, we, this is if they understood they had
an opportunity to improve the experience, their customer experience.
I would not be surprised if a chain comes along where that
is their value proposition, I would not be surprised at all.
Right?
So, I mean, and, and when those things kind of catch
hold, it becomes a revolution.
You know, when, um, the guy comes to do like to redo your roof and they put a sign
out front like, you know, Joe's roofing.
You guys could do the same with my coffee shop.
Put like “IBM Infrastructure was here.”
Exactly.
Um, in five years, the mainframe will be dot dot dot.
Going strong.
Uh, the mainframe going strong and with new capabilities,
continuous new capabilities.
I think when we announced the last version, Z16, the
latest version, I should say.
And we said, “hey, there's AI processing built into it.” This was before
everybody was talking about that.
I think a lot of people thought, “what's that for?” And we did it specifically
for traditional AI, fraud detection, etc. This next version, not only do
we have the traditional AI built in, but we have optional cards that you
can plug into it to allow you to do large language models for the enhanced
fraud detection cases that we talked about where, you know, it's more than
just what transactions were happening.
So if you take that and say, okay, the next generations.
We have more transaction volume than we've ever had in mainframes today.
The business is growing.
It's strong.
We keep innovating.
In five years it'll be going strong.
But weren't people, you're saying this in the context of.
For years, people were predicting, weren't they, that the
mainframe was going to go away?
Uh, there were pundits in the market that said everything will go away.
No one will ever have a box.
It'll all be online.
I think this is something I've learned big time in my long career,
you know, uh, in the IT industry is don't believe everything you hear.
So I went back for my, uh, master's degree at Stanford after I had worked a
while in, in, uh, as a hardware designer.
And everybody told me, be sure to do your master's in software.
Hardware is dead.
I went on to work for 30 plus years in hardware and infrastructure,
now software became important.
And I'm glad I had that extra training in software because it helped me in
hardware, but hardware wasn't dead.
Then I heard all infrastructure will go into the cloud.
There won't be any.
That hasn't happened.
It's not happening.
Then I heard there will only be one cloud because one of the players will dominate.
There's not one cloud.
So I think it's as humans, we like to oversimplify and go.
Oh, it's all going to be this.
And kind of what I've learned is fit for purpose matters in everything.
It matters in size of infrastructure.
It matters in the stack that goes along with solving a specific use case.
If you're willing to design something that's the best at that use case.
If you're willing to design the coffee shop that is the best at
greeting me, there's a spot for you.
And there may be a big business in doing that.
So oversimplifying is really dangerous.
When you heard all those predictions.
Did you, did you believe them at the time?
Uh, they looked like they were trending in that direction.
I'll tell you some right now, which might be useful.
There will only be one GPU company, and they're gonna
end up taking over the world.
It's a pretty obvious answer whose economic value has risen dramatically.
I don't think that's gonna be the case.
In fact, I think that 90 percent of processing for AI
actually happens at inferencing.
And inferencing is not as GPU and hardware intensive as the other things, and is
a lot more amenable to fit for purpose.
So the model size will matter.
The tuning matters a lot as we're learning.
We have a product around InstructLab that's really focused on tuning.
So that was one thing is there'll be one GPU.
The other thing is, that the biggest model will win, I think, is another thing that's
kind of, people are saying right now.
I don't believe that.
I believe there will be fit for purpose models.
It takes a lot of money to run a, to create a huge model, and
then to run a huge model, or to even infer off of a huge model.
I don't need a massive training GPU set thing to solve my 13, 000
people customer support issue.
So why would I feel like I gotta go farm that out for a big expensive thing?
I can do that on a small box.
In some cases, I might even be able to do that on a laptop.
The other thing I'll say in this we're so early innings in AI, a
lot of things are going to change.
So anybody kind of saying it will all be X, Y or Z. I just think you have
no idea how this is going to play out.
And and it's up to us to go figure out how it plays out.
Yeah, yeah.
Allright.
In five years, AI will be dot dot dot
Still new.
it will have moved a bunch in five years.
But the potential for the disruption in the world will still, will still
be very early innings in that process.
And I think that's super important to realize.
That's why I say get started.
Start thinking about how that could change, because it will
be some little things first, but it will continue to snowball.
There's always this is a common observation that we, the invention of the
capability, uh, massively predates the understanding of the capability, right?
Like I love that.
Yes.
Yeah, like recording shows on television is invented in the sixties.
Probably the VCR.
We don't really understand what it's used for until the oughts.
What it’s really good for is being able to tell a story sequentially
Yes.
…over time, because you know that the person will all
have seen the episode before.
Yes. So you get The Sopranos and…
Yes.
Yes.
Hollywood wanted to ban the VCR in the beginning.
Because they thought
the point of it… Yes.
They thought piracy.
They didn't understand.
No, no, no.
It's storytelling.
It's actually your business is getting better.
Yes, yes.
Took them 20 years to figure that out, which is to your point.
Why would we know what AI was for in five years?
Well, that's why you hear people kind of say, “Oh my gosh, AI,
that's, that will just eliminate jobs.” No, it'll make jobs better.
That's how I view it.
What's the number one thing that people misunderstand about AI?
Is that it?
I think that's that that would be the human kind of understanding part of it.
The technology part of it, I think, would be what I was talking about
fit for purpose, meaning, um, that it isn't just going to be a GPU arms race.
All of AI.
I don't believe that at all.
It will change everything, but it's not just going to be a GPU arms race.
Yeah, uh, next question.
What advice would you give yourself 10 years ago to better prepare you for today?
I'm changing this question.
Okay.
I want to say, let's imagine that, uh, what was your, what,
what college did you go to?
I went to three of them.
My undergrad was Utah State University.
My MBA was Santa Clara University and my master's in EE was Stanford University.
Okay.
Any one of those three calls you up and says, we want you to give the
commencement address and imagine that it's, it's, it's just, let's
just say for the sake of argument, it's just to the STEM people because
those are the relevant parties here.
What do you tell them?
Boy, what do I tell them?
Uh,
let's see.
I think I would start with life is a marathon, not a
sprint would be the first one.
Uh, the second thing I would say to that, in that spirit is, um, be sure to set
yourself some big, hairy, audacious goals.
And don't be overly disappointed if you don't hit them all.
Going after those big, hairy, audacious goals will get you on a
path where you will learn so much.
You will achieve more than you ever could imagine you would have achieved.
That's what the advice I give to my kids is set some big goals, get after it.
You may or may not achieve them, but you'll be better for the
whole process when you're done.
By the way, as someone whose kids are a lot younger than yours, is it actually
useful to give advice to your kids?
Or is that just a pointless exercise?
TBD.
We're still on the journey, and I think we will be for a long time.
I don't know.
How are you already using AI in your day to day life today?
Uh, personally, I would say, it's replacing a good chunk of my search.
You know, I'm less likely to go blindly stumbling through a bunch
of web pages looking for stuff.
I'm more likely to ask a question from a few AI engines, kind of
see, get me in the right direction.
Then I'll go bumble through a few things.
At work, I can tell you code development right now, um, we are
seeing massive improvements in code development and support products.
We have like watson code assistant.
That is really showing immediate return for our code developers.
And I think that'll, that will again, be a tool that increases productivity for code
developers immediately across the globe.
Yeah, um, last question.
What's the one skill that every technology leader needs that has
nothing to do with technology?
Being able to inspire a set of people toward a common goal
and collaborate to achieve it.
That's at the core of everything.
Everything.
That's a lovely way to end.
Thank you so much, Ric.
Thank you.