PwC’s Dallas Dolen: AI Hardware’s Advantage, TokenMaxxing, Automation’s Impact

Channel: Alex Kantrowitz

Published at: 2026-05-15

YouTube video id: 85Et5V2kIUc

Source: https://www.youtube.com/watch?v=85Et5V2kIUc

What does the AI buildout look like on
the ground? Let's talk about it with PWC
TMT leader Dallas Dolan from the floor
of Google Cloud Next in a conversation
brought to you by PWC. Dallas, [music]
great to see you. Welcome.
>> Thank you. It's great to be here.
>> So, we are just off the floor at this
conference, the Google Cloud Next
conference, and our conversation was all
about the trillion dollar buildout of AI
factories or in layman's terms, a lot of
data centers. Yep. and we have this
massive investment. That trillion
dollars might even just be this year.
Um, question for you, is that investment
wise or is someone going to lose their
shirt on that investment?
>> It's like anything. I I think you got to
look for at history a little bit and
say, "Hey, what's happened historically
with respect to other, you know,
infrastructure um buildouts. You can
look at fiber. You can look at, you
know, some of the original internet and
even the original telco and what they're
having to do too because telco is a
major part of the buildout as well,
right? I think the forecasts, just to
put the numbers in in broader
perspective, over a 10-year period of
time, something around $7 trillion being
spent on data centers. That's one of the
forecasts I've seen, there's actually
still almost a trillion dollars has to
be spent on telco. Someone's absolutely
going to lose out from an investor point
of view because not every single one of
those bets is going to hit. But the
other part of it, and I'm sure we'll get
into it, is, you know, how many of these
projects are really going to go forward.
So you have commitments to spend money
but the actual ability to benefit from
the spend of money and the even the
ability to deploy it those are severely
limited by actual supplies of chips and
workers to build the the the buildings
themselves and of course even the te
telecom you know uh infrastructure
components like copper um so you have
all these different like you know minor
components so to say or major components
they're going to be limited absolutely
invest at your own risk I think would be
what you'd say in any equity environment
and certainly this one when you're
talking about numbers like at not
everything is going to hit even though
we're in Vegas.
>> All right. Yeah. And so that's obviously
where this Google Cloud Next conference
is taking place. Um and one of the cool
things about speaking with someone in
your position is you're both buying the
technology from a company like Google
and you're working to implement it uh
within you know all the customers that
you work with. So you have this like
real on the ground view of what's
happening with the technology. And of
course that involves pricing. And let's
just take this discussion a step
further. You know, one of the questions
that you brought up while we were here
and I think is worth asking to you is
does the AI business have a different
look in terms of margins? Software
business obviously looks at 70 80%
margins but if you have a trillion
dollar buildout what do the margins
actually look like for every part of the
AI stack whether that might be you
whether it could be the data center and
the compute providers and the model
builders. So why don't you just walk us
through what the economics of this
business?
>> So I love the question because about a
year ago I started saying and I think a
lot of other you know analysts and
people in the industry you know saw this
right you had a significant shift in the
value proposition. Where was the value
coming from? It went from software back
to hardware. Remember you know talk 20
years ago maybe even more recently than
that a lot of the margin for all the
players was actually in hardware.
There's a couple companies, the Apples
of the world who still have a ton of
margin, you know, in their hardware
business, but they also do a lot in
software, too. Um, so that shift from
SAS to hardware that we're seeing in
particular in the chip space is really
remarkable. It doesn't make those
businesses anything less than cyclical
like they always have been, but they are
on the high side of a cycle, right?
We've you've seen this before. So, we're
on the high side of the cycle and we
have a shift in profitability back to
the value proposition which is there
because AI has disrupted the value
proposition associated with SAS. One
thing that I said a couple weeks ago um
it you know front facing to the market
was you know AI is kind of the new SAS
or said differently like SAS is dead
long live SAS it's just a different type
of SAS and the way that it's happening
now is on the services side where was
the value the value was in what could
engineers create what sort of custom
software that you could propagate an
enterprise level that you could then
sell seat license in particular although
it's shifted over time into consumption
but what's happened you had a lot of
margin expansion because there was a lot
of value in what those engineers were
doing. You had a few companies who owned
a couple subsets of the market. They had
moes. Well, guess what? AI has shrunk
those moes and so you can now do CRM on
your own. You can now do some components
of ERP on your own. You can now do
customer attraction on your own and
marketing and that type of
functionality. Well, that's a
compressing feature, right? So in
addition to the pure rotation of the
value proposition from software to
hardware, you also have compression
that's happening on the software side
naturally because AI's come in. You
don't need as many people to write the
code. You can build things maybe not in
a day. I think there's a lot a lot of
hyperbolic stories like that, but you
can build things really quickly and even
the large enterprise software providers
are building things really quickly.
Their engineers are changing how they do
it. And so I I think it's it's a
remarkable shift that's both, you know,
moved money from one pocket to another
pocket, but it's also compressed the
ability, you know, from the I'll say the
from pocket, so to say, to make money in
the SAS place. Um, now the the first
part of your question was around AI. How
much money will the AI companies make?
Right. Yeah,
>> that's a really interesting one because
I think something you said earlier today
really like hit a chord with a lot of
people which was, hey, how many folks
are willing to spend more money for what
they're getting? And I'd say roughly 30
40% of the room said they'd pay up to
five times as much what they're paying
today for for the services that they're
getting. That was not for advanced
services. That was for the services
they're getting today. They'd pay five
times as much. So if it's $19.99 a
month, they're willing to pay a hundred
bucks a month. That's great news for the
Frontier model builders, right? the open
ais, the anthropics and the Googles of
the world and of course everybody else,
you know, the Chinese firms too. That's
really great news. How long that stays a
reality is going to turn into a question
of return on investment. And so if it's
an individual consumer making that
decision, maybe that person is a great
user of AI. If you have someone on the
on the, you know, enterprise side,
they're going to start asking the
question of where's my return on
investment.
>> Yeah. I I just want to pick up on
something you said. It's really kind of
fascinating that we have gone from this
moment of um the hardware was looked at
as commodity and software differentiated
and it's kind of flipped. It's
>> flipped.
>> Yeah. Totally flipped.
>> It's wild. So, uh, when you think about
the decisions that you make and that
your customers make, does this
bottleneck of compute, so to speak, come
into play, or are you able to find
enough good enough, cheap enough models
to make what you're trying to do work?
>> Yeah. So, there's a couple bottlenecks.
I mean, I think you have a trade
bottleneck that's coming into play for
all the hardware producers, which is
really interesting and probably gets
talked about like when there's tariff
news, but like not talked about as much
as you might think. Um,
>> wait, what? A trade bottleneck?
>> Just a pure trade bottleneck as in like
the inability to get products into the
country because it costs more or because
you're paying more of a tax there's more
uncertainty about the supply chain
itself. So just that alone has actually
disrupted some of the hardware companies
who by the way could as you said and as
I said could make more money because
hardware is the you know the new
software whatever you want to say about
it right hardware is the new it it new
it thing and so by virtue of the same
they would love to import more because
more people want Mac minis to run their
claw bots or whatever it might be the
supply isn't there in a lot of cases for
most of these companies to expand
revenue which in turn would expand you
know margin so they're having margin
compression with higher cost of trade in
addition to just like not being able to
meet the moment which is kind of painful
for some of the hardware folks. So
that's that's one part of it. And then
the other bit I think that you know goes
goes to your question as well is you
know what are we seeing from a like a
decision-m process like right now I'm
not aware of in an enterprise
environment where they're not able to
run the things that they've built. Okay.
So they haven't run into that you know
we'll call it the the the token
limitation or the compute limitation.
They've run into that we've spent too
much money.
Yep. Well, we we've had that. We've had
to like turn teams off who've spent too
much money on tokens. You know, maybe it
wasn't for all the right things. We're
like watching what people are doing,
wait a second, I don't think we want you
to use AI for that. You know, go back to
doing it the way it was before. That
costs more. And so, we're making these
really interesting decisions on ROI and
on like what the right, you know, use
cases and flows are for the
>> not pure token maxers.
>> There is no token maxing going on. There
is some token maxing going on and there
are some competitive leaderboards within
the organization too. I see that.
Absolutely. I mean you it is fun like so
we have about 300,000 people globally
right so when we want to gamify
something like that we find that that is
actually a really great way to get
people into doing something new
gamification we definitely have the
construct of gamification going on as it
relates to AI so both training and usage
so there is a little bit of the you know
the token maximization happening but
it's done within our own pretenses and
we have some you know some guard rails
around that the most important of which
is security right just making sure
they're not going outside the the
confines of the environments that we've
set up. And so we've been very very
specific about that. Um but you know,
back to the question on on the
enterprise bit, I don't think I've seen
a lot of companies saying they can't get
done what they need to do because
they're hitting some sort of wall.
Unless of course they're 100% AI native,
in which case I have heard conversations
about where they simply need more and
they're very low on the depth chart, so
to say, as far as like what they're
getting from the provider. That's come
up. We've seen a couple news stories
about that one where they're going in
and talking with the CEOs of these
companies saying, "Hey, I just simply
need more. I need your help." Um, you're
also in an interesting position because
you basically have to decide or PWC
decides who do I work with. Um and very
interesting moment uh that I think uh
sort of captures where we are on the
Dwarish podcast with Jensen from Nvidia
where um you know Dwarvesh was talking
to him about uh the fact that if you
look at the worldleading models two of
the three uh were trained um outside of
the Nvidia stack.
>> Uh this [clears throat] of course all
built up to then you get to the export
control question. Jensen gets frustrated
enough, he goes, "I didn't wake up a
loser." And that's sort of the headline.
Uh, you're not talking to someone who
woke up a loser. And that's the headline
of the conversation. But, um, that point
really stuck with me, which really sort
of comes down to this question, which is
there's two competing ecosystem, the
Nvidia ecosystem and the accelerator
ecosystem. Um, with a couple of cloud
providers, Google being one of them.
Yep. Do you have to work with everyone?
Do you have to make a bet or one on one
or the other? And then is there is there
going to come a point where you see one
as winning and just say all right in
there?
>> Yeah, that's a great question. I mean, I
think the the way we look at it because
we have a a very large, you know,
strategy system integrator and an AI
business in addition to the things that
we do just traditionally as a firm,
right? We have audit and tax and sort of
our traditional, you know, business
advisory services including cyber is we
see a place currently for all of those
firms to work with us. So we have use
cases for all the hyperscalers. We have
use cases for all the providers of
Frontier models and we do that for a
couple reasons. One, some things are
just better than others given a certain
environment. So when we think about
security, you know, all the things that
we're doing with Google, we have a big
bet on that, right? We do have a very
large GTM team um and and doing a lot
with, you know, some new leadership that
we have there. Morgan Damsky being one
um here with PWC who just recently
joined us. So that's one good example of
it. And then we also have situations
where we look at our engineers. We say,
"Okay, you know, our engineers, they're
going to pick the highest and best for
them. So if they want to use cloud code,
they're definitely going to go do that."
And that's okay, too. Um, so that's one
bit. And then the other thing that we're
very responsive to is we look at the
market and say, "Hey, what's the market
demanding from us, right?
>> We have demand that's really coming in."
And it's, you know, it's funny. You see
those usage charts and they're really,
"Oh, but look at this model uses, look
at that model." the the the the use
cases and the demand is coming in in
about third third as it relates to those
right and I'm thinking and and speaking
a bit from a um you know I say North
American perspective here right and what
I see with my client base within tech
and telco but I also see it across
industries being about that there are
certain you know companies are playing a
little bit stronger in a given industry
you know I think cloud's had its moment
with fs recently and you see a lot of FS
companies who have thousands of
engineers yeah sorry financial
who have thousands of engineers on the
uh you know on their payroll saying hey
we want this and so they're having their
moment there you at the same time right
like if you look at the engineering
capacity and capabilities you know of
cursor and of Gemini like it's right
there too so these things are all kind
of neck and neck and their ability to do
deals is is really functionally a part
of you know maybe what's hot at the
moment but also what's their long-term
bet I could see a long-term bet
decision-m process edging in favor of
the models who have something
underpinning them either financially or
structurally, right? The actual
[clears throat] infra itself. So where
are the data centers or the AI factory?
Who owns the pipes? You know, who can
make sure it gets to them, you know, in
in the most effective way because
eventually going back a little bit on
our combo, hey, we are going to run into
structural limitations, right? We don't
have the nuclear power. As it so turns
out, we don't have the number of data
centers. So all of these things are
going to be limits. The question is, how
long does it take for us to reach them?
Is it 27? Is it 30? I'm not quite sure,
but we're already going to start to see
some of the supply chain limits stopping
the production of some of the data
centers at which point, you know, the
acceleration of that limitation probably
comes into play much faster than anybody
assumed. So, you're sparking a question
in me. I was in a uh a briefing with
large AI lab today and one of the
reporters asked um the executives there
when you automate all your AI
researchers what are they going to do
like when they use the technology to
automate their own work what will they
do and you know I think the headline
around a lot of this has been well AI
will um you know replace software but I
also wonder from like your standpoint
think about systems integration for
instance We've seen so many instances
where these AI models that are getting
really good at code. If you say refactor
my code in another language, it can
oneshot it in some in some uh instances.
So what does that mean for businesses
like systems integration? If you could
for for instance say integrate these
systems and maybe there's a world where
claude or codeex or Gemini can handle
that.
>> Yeah. Um, look, I I think the reality is
uh we have a completely different
operating model as it relates to humans
and technology.
>> I don't know that anybody has the exact,
you know, perfect point of view on what
that is. If if structurally, you know,
the organization is a pyramid,
>> there's a shrinking of base. The
question is, does it look like a
spindle, which is a good term I heard
last week. I like that one. Does it look
like a monolith? I know there's a couple
people written about that. Is it just a
narrower pier? Or is it like the sail of
a of a boat,
>> right? Meaning that the top is the is
the the narrow part and then as you
>> Yeah. The question is how how wide is
the base going to be on your
organization, right? Engineering, sales,
front office, back office, right? All
those things like it's just going to be
different. Um you have to keep in mind
though, we still have an I'll call it a
you know, I'm going say like a a need
for a consumer and a need and actually I
had a funny conversation this morning um
with with one of our consumer markets
guys. Uh, we went for a run. He actually
tricked me into doing a five mile run
and doing an interview on the five mile
run. That was a whole thing. And then we
did the last bit of the interview on
like mile number five, like five five
and a half miles into it. I'm like, I'm
out of breath, man. Like, I'm going to
need an IV after this and maybe laying
on a stretcher. But nonetheless, he we
were talking about how far is Agenta
Commerce going to take things. If you
look at Agent Commerce, it's the it's
going to do some of the shopping. I
don't think anyone's going to say, I
want it to do all of my shopping for me.
like there's still something enjoyable
about that. Take the concept of
enjoyment and supplement that with the
concept of quality and the concept of
human in loop. I think the same thing is
going to happen within enterprise
regardless of the size of the business
and also regardless of the function
itself. And so yes, we're going to go
from pyramid to more like you know more
like the the spindle or the the super
narrow you know sale itself. But that's
going to be the transition that happens
probably in the next two to five years.
it will be influenced by the amount of
compute because if you're taking all
these bits and pieces out, right? The
cost of compute today, which I think you
and I both agree is really, really low,
the number probably goes up if demand's
going up, right? Just natural basic
macroeconomics. So, if that happens,
we're going to find ourselves in a
position where you might be saying, "Oh,
I could replace that person with some
level of agentics, but you know, it's
actually cheaper to have the person
because if this thing runs at $12,000 a
month, I might be better off paying
somebody to do it." Yeah, I think the
some of the latest models the prices
after going down for a while are
starting to go up.
>> Absolutely. Y um let's talk a little bit
about Agentic technology, Agentic AI. Um
I'd love to hear your perspective just
on um first of all define what an agent
is if you could and then I'm curious to
hear if these things actually exist. How
far are your clients in sort of putting
them into action or are we still just at
this like time where it's just talked
about but not a real thing?
>> Yeah. So, um, as you mentioned, I I do
tech media and telco. I'm going to use
media as my example for what an agent
is. In the movie business, actors have
agents, right? And what do the agents do
for them? The agents job is to make
their actor employable, right? They're
going to go find scripts for them.
They're going to go get them employed.
They're going to go find other deals for
them to do. it's advertisements for
cologne or cars or whatever it might be,
right? Um, you've given them the
authority to act on your behalf in a
specific area, right? Some level of
agency, you know, for you. Take that
concept of agency, apply it to
technology. You're telling a piece of
technology, in this case, almost like,
you know, SAS in a box, like this little
mini this little mini person who's
sitting there. You're giving it
authority to go do certain things for
you. In this case, it's going to operate
within a, you know, relatively closed
environment. Perhaps it's on your
desktop or perhaps it's within your, you
know, your platform, application,
ecosystem, something like that. But
you're giving it the authority to
operate in there. And you're hopefully
giving it rules to operate under and
you're also giving it skills. Skills
that might tell it like where to get
information or how to do certain things.
And then of course, you're building
security around it, too. I love all the
stories about agentics, whether it's the
claw bots or just the pure agentics that
people are building on these open models
that are funny stories, right? like I
told my agent to read my emails for me
and then when it got done reading them
it deleted them because it was done
reading them. Um and of course there's
the you know the the mult story of
starting the religion and everything
else. But the basic premise is you know
I have something that can do a lot of
tasks that otherwise I could do and I
don't really want to. Um and I think
that last part is really an interesting
one because it's how far do you take the
concept of it's a task I could do but I
don't really want to. You start applying
that to enterprise. what's happening
today and I have I have I think eight
agents that I run on a regular basis
like on a daily basis that do I'll call
them random but important tasks for me.
So every morning I have one that runs a
headline review and I have it look at
things around regulatory tax the markets
and broad economy globally and it runs a
list for me including citations of
everything that's going on and it fires
into my inbox at 512 a.m.
>> Where does it live? I mean where is what
technology are you using to do that?
Okay, so that one actually lives on
Grock.
>> Okay.
>> And the reason it does because I have it
scouring X to go find that information
for me because all the news providers
post everything they're doing to X,
interestingly enough. So it's a single
source that they can a single pane that
they can go to, right? And I have
limitations on it like don't pick up
anybody's posts because some posts are
not news, their opinion, right? So I
don't want opinion. I just want to know
what's happening, right? And it's not
reading my feed. It's reading all feeds.
It's looking for everything that's out
there. Um and then I actually take it.
So here's the fun part of what I do with
it because I then curate that and I send
it to about a thousand partners within
our firm. I take it and I run it through
Gemini and I say hey is there anything
missing in here that you see in the
headlines course because Google is a
great accumulator of news as well
functionally. It also I think it more
slightly more cleanly gives some of the
links and some of the things that it
did. So I say tell me am I missing
anything? And then I ask it to like
clean up links to do a few other things
to format it for me. And then I take
that and I throw it into funny enough I
use a third technology. I throw it in
Teams and and you know fire it out over
the over the universe to all my
partners. Is that sorry where do you set
up that workflow? Uh it's all set up on
my iPad.
>> But like are you you're not manually
taking the out are you manually taking
the output from the email and then
dropping it into Gemini?
>> The only thing I have to manually do is
I have to take it from Gemini and put it
into Teams because from a security
feature I don't want my Agentic to have
the ability to just write Teams messages
anywhere.
>> Okay, got it. But you are able to
orchestrate the multi- aent
>> correct
>> workflow with
>> a technology. That's right. What
technology do you use to do that?
>> It's all I have it all sitting uh within
within the the you know actually so it's
the third one. I have it all sitting
within the Microsoft environment. So it
has that there too. Yeah.
>> Yeah. It sort of goes to like I mean
just briefly like the we were talking
before about who's going to win.
>> Yeah.
>> There's pieces that each one can do.
>> Um all right. So then when it comes to
to clients, right?
>> Yeah. Um, I'd love to hear just your
perspective on like is there an ideal
use case in enterprise because we hear
um we hear anthropic and we hear open AI
talking about how they want to start
building for enterprise and you're at
the center of that. Is there an ideal
use case in enterprise that a company
could come in and say we want to go full
agentic and they can and it can actually
change the way that they operate or do
business. Does that exist now? And and
what could it look like? Well, what how
has it worked most successfully for you?
I think the three the the top three
areas I'm seeing right now that the back
office specifically around finance okay
and a lot of the source to pay procure
to payroll those types of
functionalities um that you can right
now build aentics that they're
interoperating with your ERP and
interoperating with some of your
customuilt tools to do a lot of the
things that historically you had people
doing right you're you're checking the
you know the actuals to acrruels you're
providing updates you're building
dashboards all those types of things
agents simply running 90 to 100% of that
right doing that that functionality So
that's one. Um the second area I see it
is in the front office. So specifically
around marketing and sales campaigns,
the actual building of customized
campaigns for customers. Instead of
having you know 30 50 60 person teams
that are doing this in your B2B space in
particular and supporting your sales
teams, you actually have identification
of it. So it's I'm going in I'm talking
to automotive company. We want to sell
them on you know our you know latest and
greatest software. Help me write this
pitch. And so you know you have
basically identification that's going in
it's looking at all the product
functionality it knows everything about
the company knows everything about the
pitches you've historically done it's
updating that it can even do pricing
right so the ability to do some of the
pricing as well that's coming through so
those that that's a second one and then
the third one I think is a little bit
well it's in every company right but
it's also its own industry and that's in
the legal space so I think you see a lot
of things happening around legal um
where the ability to help you know do
research and draft and maybe even sort
of do some intakes on information that's
coming through and summarizing that
that's more it might be more basic in
some ways because we've seen a lot of
tool sets that do that do research but I
think that space in particular highly
disrupted reviewing contracts and maybe
almost a procurement functionality as
well like find the discrepancies in
these contracts I mean think about how
much time people spend redlining things
you know whether you're using docs or
word or whatever you don't have to do
that like you can absolutely have an
agent do that and then you have it
summarize it for you and then you're not
spending time scrolling through and
reading everything right, scroll on the
iPad, whatever it might be, it's right
there for you. Here's the top, you know,
the top five things. And because it's
natural language, tends to be pretty
darn accurate. Those are actually some
of the best use cases I've seen those
those three areas from enterprise in
particular.
>> So, I I heard someone uh today refer to
one of the latest models and when they
were unable to access it um or this is
sort of I heard this as a from
secondhand when they were unable to
access it, they said they they
complained and said part of their brain
uh it feels like part of their brain is
missing. Um, so then what does the
nature I mean not to get too deep into
this, but I'd love to hear your
perspective on what the nature of work
uh looks like in in this case.
>> No, I think it's a great question. I
think there there's absolutely a
philosophical dynamic here. Um, and I I
contend with that. I have I have three,
you know, call youngish kids or a couple
teenagers and and one one almost there.
And I think every day about what are
they going to do, right? So, it's very
personal in some sense of like what kind
of jobs will my children do when they
get older? um you know this whole
concept of the the work that we've done
you know really in the last you know 20
years or so um it's all net new right so
and if you think back if you go in I
made a historical comment earlier on is
rel to technology like you could talk
about like humanity going back in time
like what was work and what was
satisfying work um you know the reality
is the space that we operate in today
around technology did not exist right
just simply did not exist 40 years ago
and then you go through this huge spike
in human capital being spent on a given
thing and then it goes away. We've seen
this cycle, right? We've seen this in
agriculture. We've seen this in
industrialization and manufacturing.
We've seen this in other places. Um the
the odds that this goes and follows a
similar trend is very high, right? I
don't think
as we live through it, it will feel like
this. I think it'll feel like a much
more natural curve. It's not going to be
some like massive moment of oh my gosh,
there it happened and all the jobs went
away. I think actually in fact you know
recording this on a Thursday today the
payroll data and unemployment data came
out it was in it was in trend like with
what they expected as far as job losses
for the month. I look at that and say
hey that's a sign that we're not going
anywhere like super terrible as it
relates or draconian as it relates to
the economy as a whole which as a
separate matter confuses the heck out of
the the macro folks and and uh you know
perhaps you know what we do around
monetary policy but maybe that's a
separate issue. Um, and I think this
this natural cycle though, the ethereal
concept of like how much are are going
to be, you know, how much these tasks
are going to be automated, I think we
just have to take this as it goes and
just note that for everything that is,
you know, likely quote destroyed by
disruption, there's a natural retraining
or gravitation to a new new like a new
job. And I I saw a report yesterday, you
know, I think it was uh um a couple of
construction companies were saying that
they are roughly 500,000 people short of
workers to build the requisite data
centers and some of the infrastructure
is necessary. You have 500,000, right?
That's a lot of people who are simply
not in the job market at all right now.
Um and the reality is too, you have a
lot of people in those jobs who are
going to make a lot more money than if
you were working in tech or in tech
sales or, you know, some of these other
things. So I I I don't I I don't say
this in a like, oh my gosh, we have to
embrace everything that happens around
technology. I am equally, you know,
nervous or skeptical, so to say, but I
also look at it and say what we're
experiencing as far as disruption is
totally okay. It's happened before. The
good news is we're highly adaptable
creatures. Like we will figure this out.
I heard I think it was Sam Alman say
that like humans have people have
started to talk to him about this. We
have a right to adversity. I I don't
know how I feel about that. Like
would it be better to live in a world
without any adversity? And is that
something we want to suffer through? I
guess you can get growth through
adversity, but that's definitely one
that I've been thinking about and I'm
just like I I don't know. I mean, we
we're clearly not close to a world
without adversity. Yeah.
>> But I don't I don't fully understand the
people who are like, "Please give me
some of it."
>> No, I think I I'll take a different
different view on that. Um and that is
to say like and I love um I love uh some
of the quotes from like Jaco Willink,
right? Um it's like something went bad,
good, right? It's an opportunity to
learn. It's an opportunity to do better
next time. Um I take that and apply it
to you know to to the world we're facing
here like you know the old quote may you
live in interesting times right like I
think we are living in probably the most
interesting times
>> no doubt
>> which is so fascinating right because
you could have said that you know in the
1960s and '7s some of the things that
were going on society you could have
said that certainly in the 1940s or
World War II I mean these these are you
substantially more dramatic societal
disruptions that were occurring there
than what's happening here we live in
the microcosm where it's like having the
most impact I think and so it feels the
most dramatic in a way, but like in some
ways I think I I I won't I won't say I
subscribe, you know, perfectly to to you
know, Sam's commentary, but boy, isn't
it fun to be challenged? So, it's fun to
be challenged to then meet that
challenge and, you know, exceed maybe
what your expectations are. And that's
not because I'm a particularly
optimistic person, right? Like deep down
inside I'm probably a pessimist, but at
the same time, like I love fighting past
that. It's almost like a fun thing that
you have to like get through. And that's
part of I think that's part of like the
challenge of being a human in a way. Um,
again, not to get super philosophical,
but like this is fun because it's hard.
I mean, even going back to the
conversation we were having earlier on
how do you make a decision and what you
should do? Like these are really hard
decisions. Like we have so many decision
makers even within my own organization
and then we're meeting with, you know,
so many different enterprises who are
have not, you know, entirely
differentiated pitches around what we're
going to do and it's hard to pick
winners and losers. And so, you know,
you do as best a job you can and you try
to influence it in the best way you can.
You walk away from it and say, "Hey, did
I do the best job that I could have in
that?" I think so. And then you move on
and you do the best thing. So, I say
good except challenge. Um, I don't want
it to be terrible. Like, nobody wants
terrible, but I don't view this as
terrible. I view this as like just, you
know, sort of an evolution in a way.
>> Yeah. And it is interesting because I
think people hear about the capabilities
and they're just like straight shot to
AGI and we won't have work in two years.
Um and on one hand like the capabilities
are advancing to the point where AGI is
something that doesn't sound crazy
anymore,
>> right?
>> Um but on the other hand, and this is
something that I think you see, change
management is really tough.
>> Really tough. It's it's it's actually
the hardest part, right? Like changing
on on on the behaviors front. Um it's
interesting to see the people who are
most receptive to change. I I um I told
somebody the story last night, but I'll
just retell. It's quick quick story. My
dad calls me the other day. I said,
'Hey, how's it going? He's he's uh
shockingly turning 80 years old. He's in
great shape. He's like fixing up a
house. He's flipping it just for fun
because it's like what he's doing. He's
been retired for a while. Um and he's
fixing the downstairs bathroom and he's
done all the work himself. He's done all
the counters and the tile and he's the
last thing is to set the toilet. Okay.
And he apparently has a 1% toilet which
has the wrong offset for every other
toilet that's ever been built, right? as
far as the way that the pipe the pipes
were run. And so he said how frustrating
it was. He finally had to call a plumber
and the plumber came in and they fixed,
you know, made a workaround and blah
blah the whole way through. I said,
"Well, dad, I mean, you know, not I
wasn't trying to talk down to him, but I
said, "Didn't you like check with like
some technology like Google it perhaps
or look at some YouTube?" And he's like,
"Hey, son, like who do you think you're
talking to?" I asked Enterprise GPT what
I should be doing and I asked him for
examples and I went to YouTube and I
watched the videos. He's like I couldn't
find the answer in any of those places.
That was a place where only the plumber
who I, you know, could call and talk to
knew exactly what to do cuz they were a
specialist in that area. Which maybe
gives you hope on a couple of fronts as
it relates to like what jobs will exist
in the future. That's one part. And then
the other part of it is it goes to show
that people are highly adaptive. This is
a guy who was born in 1946 saying, "Hey,
the first thing I did was I went to AI
and I asked AI like,"How do I fix this
problem?" Unbelievable. So, if you think
about like our transition, right? Like
our transition should actually be a
whole lot easier than that. It's funny
to just maybe have more personalities
and dynamics involved. Maybe that's the
bit that uh that is the hangup. But I
like I like that story because it's one
of those funny things here. I think I'm
a smart guy and I'm like telling him
what to do and he's like I'm so way far
ahead of you, it's like not even funny.
>> Three technologies.
>> Yeah. So, you know, the the grasshopper
still has not snatched the pebble from
the hand in that case.
>> That is pretty cool. That's such a cool
story. And you're right, like once you
start using the AI, then all of a sudden
it it can become it's not perfect, but
it can become less scary and quite
useful.
>> Y
>> All right, let's end with this. uh there
is something uh called shadow AI which
is um when you know there's going to be
prescribed uses from a company uh of
artificial intelligence and then I think
stvers uh who see the potential they
don't like the boundaries that they have
uh and then they go do things on their
own maybe on their own cloud
subscription for instance
>> uh as long as they're as long as they're
like not including customer data
>> uh in their own instances. I sort of I
not even sort of I celebrate these
people. Yes. Uh I think that they can be
incredibly impactful in an organization.
Um and we might even see a divergence
where you're going to have the shadow AI
folks rise to the top of companies where
everyone who's following the rules so to
speak trails behind. So just talk a
little bit about that dynamic because
you know you're again working within PWC
and with so many companies that have to
be careful about their AI
implementation. So I imagine that you
have like a real perspective on where
this goes.
>> Yeah. Um, you know, it's funny like it
it just just the the tea up there kind
of reminded me of of a a very old quote
um which is if you know the way broadly,
you see it in all things, right? Um and
it's Japanese philosophy, samurai
philosophy. Yeah. Um, and what's
interesting about that is I I look at
these people to your exact point. The
ones who are using this stuff to do
really cool things, they are the ones
who see the way.
The rest of us who aren't doing that
kind of holding them back. Now, the
reason that they are held back is a
function of things that people think are
more important, right? security cost,
token cost, um, you know, regulatory
dynamics, um, you know, certainly not
being disruptive, right? Like, oh, like
that person over there, they're doing
their own thing. Like, why are they
doing their own thing? But I had someone
explain to me on Monday. It was actually
really funny. Um, one of our partners,
Khalil, he was telling about something
he built in Claude. Um, you know, some
some functionality built he trained
himself on it. um he was writing all the
all the all the in input Python code
himself and then he was taking bringing
into Claude and he was saying I want to
automate the process that I'm currently
you know doing with my team um and I
want to take a bunch of these you know
skill sets that individuals on my team
have and I want to identify them as much
as possible and then I want to create
like an environment where I can
basically load tasks that generally
speaking my team has to do and the
output will be frankly the thing that I
get from them to review afterwards. So
that was his goal. He said it was five
weeks of nights and weekends, right? So
like I was joking earlier, not building
a company overnight. You're probably not
building a company overnight. So five
weeks
>> just a few weekends.
>> That's right. But he's a smart guy and
you know he was doing it all himself
which is pretty amazing in addition to
that like having a full-time job and a
wife and kids. Um and he said in the end
he said the output product is about 99%
right. He's like I would still review it
and I'd be happy to review it. really
really good. That's the type of
personality that's going to change the
world. It's going to change the way
professional services operate. It's
going to change the way software
companies operate. It's going to change
the way other industries do things.
Right? We see this a little bit in
health. We talked about that earlier
today. Whether it's health research or
the things are happening financial
services and the customer experience
journey. Um certainly you know in the
case of industrial and you know how
managing the manufacturing process and
supply chain and the like you can build
agentics that do these things. It's not
easy. You need one or two people. You
need the explorer. You need that person,
the entrepreneur, the solo, you know,
the the solo uh uh venturer, if you
will, who's going to go there and build
the thing that shows everybody that
there is a way through in an industry
that's old. Our company is 175 years
old, right? And here's somebody who's
doing something completely different
than it's ever been done before. That's
incredible. That's a person who sees the
way. That's a person that we will
follow. And so is that creating some
level of risk or shadow you know shadow
IT AI you know AIdriven shadow IT? Yeah
a little bit. Um but you know they're
all doing it within the confines of of
the rules of the organization. I think
it's possible to do that too. We just
have to get more comfortable with some
of the nomenclature being used and the
how people are doing it and make sure
we're encouraging it in the right way.
Not everything's going to be
gamification, right? Some things are
just going to be um well these one guy I
can't believe we've gone so far into
philosophy. You've you've like you've
inspired me Alex. I appreciate that. you
know um Confucious water finds its
natural level. The way we're going to
find the people who are the most
successful is they're the ones who are
going to lean into this right they will
find that natural water level and every
challenge right is met by people who are
willing you know to accept it and to
overperform against the same and that's
I think what we're going to find in this
new era of of AI and so you know that I
encourage that within the rules so to
say I encourage people to push the
limits right because that's actually
where progress is made
>> great note to leave it on Dallas thanks
so much for coming on the show
>> thank you Alex everybody Everybody,
thank you so much for watching and we'll
be back here soon.