The Critical Infrastructure Behind the AI Boom | Cisco’s Jeetu Patel - Chain of Thought

Episode Transcript
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(00:00):
The adversaries and the threat actors that caused cybersecurity
attacks or cyberattacks have theexact same tools now that you
and I do, Connor, and they can use those tools to get far more
sophisticated and far more at scale in systematically
attacking society and the critical infrastructure society.

(00:23):
Welcome back to Chain of Thoughteveryone.
I am your host Connor Bronson and today we are discussing a
topic that is on every enterprise leaders mind, how to
build the infrastructure to support AI at scale and how do
you do it securely. We have the perfect guest to
help us understand this challenge and that is Jitu
Patel. Jitu is President and Chief

(00:44):
Product Officer at Cisco. Thank you so much for joining
me. It's such a pleasure to be on
your show and I love the name ofyour podcast.
Thank you so much in the thought.
That's so, so creative. I love it.
Really, really glad to hear that.
We, we really try to take that lesson too from LLMS and say,
OK, we're not going to come in with a predisposed notion of, of

(01:05):
what the conversation is going to be, you know, what our
opinion is, but instead let's work through it together.
So let's maybe start with a viewpoint that I know is
important to you and something you've been quite vocal about.
From what I've seen, the relationship between AI and
jobs, there's obviously a lot ofanxiety right now.
People are worried that AI is going to replace them.

(01:26):
And some AI influencers have pushed this narrative with AGI,
artificial general intelligence,and some of us, as you know,
hype for fundraising. But really what it means is, oh,
hey, we're all going to be out of a job soon, or you all are
going to be out of a job soon. But you have really taken a
different stance. You've been bullish on the idea
that AI isn't about replacing jobs, but instead about making

(01:49):
our work better. Can you walk us through why you
believe that? I, I believe in that because I
feel like we should not underestimate the, the, the
human mind, you know, like we'vethousands of years have gone by,
there's been a bunch of technology shifts that have
happened and humans have continued to find a way to stay

(02:13):
relevant during each one of these kind of disruptions.
And, you know, if you think about what are humans really
good at doing? Humans are amazing at gut
instinct, intuition, ability to be able to make decisions with
incomplete data, having empathy.These are all things that are

(02:37):
really hard to have a machine get trained on.
I, I, it's, it's a very interesting industry strategy
that transpired where the industry took an intentional,
you know, approach of making AI very skeuomorphic in nature,
right? And so it was like every single
thing was equated to a human rather than being equated to

(03:02):
tooling that humans use. And I think like, you know,
there's a very eloquent argumentthat Jensen has that, hey, this
is not, this is not a tool, it'swork.
And, and I think that's I, I actually like the way that he
frames that. But I do feel like level 1

(03:22):
maturity that we would have is AI is going to take our jobs of
thinking like just pure maturityof thinking spectrum, level 1
maturities, AI is going to take our job.
Level 2 maturity is someone thatuses AI better than me is more
likely to take my job than AI taking my job.
And that is a risk, to be fair. That is a real risk, right?

(03:44):
And then, then there's mitigations to that risk that we
should talk about. And then Level 3 maturity is
when people realize in the next few years, it's going to be
really hard to imagine me doing my job or me getting my job done
without AI. And this is not even a crazy
concept. Like the spreadsheets came out

(04:05):
and everyone thought that every finance person was going to lose
their job. And now you can't get a finance
job without understanding the spreadsheet.
And, and I, I don't think that AI is going to be any different
on that dimension. Like we will, we will not be
able to get our jobs done without AI.
And what? So then the question is, what is
AI good at, right? Like one way to think about this

(04:27):
is there are a bunch of jobs that we just don't have time to
do that we can actually farm outto AI to get done.
That's going to be the first category of jobs you'll give to
AI. The second thing is there's
going to be a bunch of things that AI is better than humans
are doing, you know, which is like, you know, crunching data
of large volumes, making sure that you can actually get

(04:49):
aggregate the insight from that.AI is going to be better at
doing that than a human would do.
And then the third one is jobs that humans can't do, that only
AI can do. And so I feel like there's on
each side, humans are going to be something good at a lot of
things. AI is going to be good at a lot
of things and when you put them together, that's when the magic

(05:10):
happens. And I think it's a false
narrative to say humans are going to be this is the end of
creative contribution of humanity in society.
We're going to be sitting on a beach staring in the ocean and
that's all we're going to be doing for the rest of life.
And AI is going to do everythingfor us.
I just feel like that's, that's jumping the gun a little too
much. And so I find it to be a very

(05:33):
kind of optimistic feature from the dimension of, and I'm not
delusional about it. I think there's risks to AI, but
the the risk of the all jobs going away I think is de
minimis. Now what I think will happen is
some jobs will go away, all jobswill get reconfigured and
entirely new industries will getcreated.

(05:55):
And, and we should think about that framing as we go into this
market. And I, I think we'd be, it'd be
a far more productive framing rather than the nervous frenzy
of everyone worrying that AI is going to have the entire corpus
of humanity not be able to be employable for the rest of time.
I I think that seems exaggerated.

(06:16):
To me, I completely agree. No, I I'm right there with you.
In my mind, what we've done is we've compressed the need to
keep learning so that you have to especially learn based off of
what's changing in your role currently and the different
tools that are coming in. And I think that exacerbates the
fear for a lot of folks because it used to be you could learn

(06:38):
some over, you know, a 2030 yearcareer and you'd be fine over
time. But now the pace of change is
much more rapid with AI tooling coming in then.
And there's this large wave, particularly in things like
software engineering, other disciplines where you're seeing,
you know, the way code is generated completely change or
even just like how I triage my e-mail is changing.
But you've always, if you were going to excel at your work over

(07:02):
a long period of time, you need to be able to keep learning and
keep evolving. And I think now people are being
pressed to do that more with theAI wave.
I think the compression of time at which you have to get, you
know, the half life of anything is compressing quite, quite
dramatically and and that can cause nervous energy and that's

(07:25):
actually very, very kind of legitimate.
On the other hand, I don't thinkthe storage of knowledge in our
heads is going to be the currency of the future.
And so if, if knowledge and intelligence is abundantly
available, what's going to be the, the unique nuance that can

(07:50):
differentiate one human from theother is how can you make sure
that you, you get that knowledgeextracted from AI with the right
questions? And so asking the right
questions, learning to learn fast, those are skills that are
going to be far more important for my 14 year old daughter to
learn than just, you know, cramming a bunch of knowledge

(08:12):
and memorizing it, which is something that I feel like, you
know, the Internet was the firstphase of that.
But now it's like the AI is the second phase.
And the thing that I think is grossly underestimated about AI,
and this is an area that I don'tthink people are talking about
enough and they're not thinking about enough is right now we
think of AI as an aggregation mechanism for the corpus of data

(08:36):
that's available in the world inthe world.
So it's like I'm going to go outand train my models with all the
data that's available and then I'm going to be able to get a
succinct answer for a question Iasked based on the data that
it's all available. I think that and that's going to
get me productive that, that that's the, the, the current
kind of simplistic promise that's delivered.

(08:56):
I feel like that's, that's like 1% of the benefit. 99% of the
benefits going to come from original insights will get
generated that don't exist in the human corpus of knowledge
that allow us to dream about solving problems that we had
deemed unsolvable up until now. And that will then give us a

(09:17):
whole new set of possibilities on things we can do for tackling
important issues in society. You know, longevity of life,
quality of life, curing of disease, you know, ending
poverty, you know, 'cause, you know, kind of helping cure the

(09:39):
climate crisis. All of these are kind of going
to be done far better with AI coming up with original insights
because humans had not thought of it.
And that to me is an exciting future that all of us should be
stoked about rather than being nervous of because we'll be able
to solve problems. Now, what should we be nervous
of? What's worth being nervous about

(10:00):
is you've got, you know, like a,the, the adversaries and the
threat actors that caused cybersecurity attacks or cyber
attacks have the exact same tools now that you and I do
Connor. And they can use those tools to

(10:22):
get far more sophisticated and, and far more at scale in
systematically attacking societyand the critical infrastructure
society. And So what we will have to do
is make sure that we, we completely accept the fact that
safety and security is a big risk.
And we have to mitigate that risk because in the absence of

(10:46):
that getting mitigated, I think we, you know, there'd be a lot
of harm that's caused in, in humanity.
I love the thoughtfulness of this perspective, and it's clear
that it's also impacting how you're approaching product
development at Cisco, because I know Cisco has a product called
AI Defense that is trying to solve exactly this problem.
You've published research about DeepSeek and other frontier

(11:08):
reasoning models looking at security risks.
What do you see as the most pressing AI security threats
that we need to be paying attention to?
So in the land of security, let me actually zoom out for a
second and say in, in this worldof AI, what are the impediments
that could actually hold AI back?
And I think there's three big areas that could hold AI back.

(11:30):
There's more than three, but there's three that we can at, at
Cisco, we can, we can squarely actually make sure that we can
make a positive contribution to.The first one is an
infrastructure constraint. There is just not enough power,
compute, network bandwidth and data centre capacity to go
satiate the needs of AI. And the constraint for

(11:52):
infrastructure is directly correlated to a company and a
country's ability to to have intelligence.
Infrastructure is the input, intelligence is the output.
And by the way, all things beingequal, any company or any
country is going to be better with more intelligence than less

(12:13):
intelligence. Everyone needs more
intelligence. So the first constraint is
infrastructure. The second big constraint is a
trust deficit. And what I mean by a trust
deficit is these systems that AIis built on, these models are by
definition non deterministic in nature, which means that they're
unpredictable. But we are building highly

(12:35):
predictable applications or we're, we're wanting to build
highly predictable applications on top of a foundation, which is
by definition unpredictable. And so we have to make sure that
we can solve that problem effectively by saying, do I have
visibility on what data is goingin?
Can I actually jailbreak this model algorithmically through a

(12:59):
red teaming process that's algorithmic, not human based.
And once I've found a way to jailbreak a model, because it
doesn't work the way that I expected to work in certain
categories and certain areas, I should dynamically at runtime be
able to have runtime enforcementguard rails be put on it.
If I can do that, then anyone who's a developer, anyone who's

(13:21):
building an agent, anyone who's building an application on top
of a model is by definition going to not have to worry about
building a security stack because all they'll have to
worry about is coming up with the next best idea.
And we will take care of the security because we will
actually do that piece of it forthem.
So you can innovate fearlessly. So that's the second big

(13:43):
constraint is a trust deficit. If you don't have safety and
security, right, people are not going to use the system.
So safety and security become a prerequisite for adoption.
And then the third area is a data gap where right now if you
think about it, most models the the, the issue that people had

(14:05):
with scaling laws was they thought that at some point in
time they're going to run out ofdata, which was actually true.
You're running out of human generated data publicly
available on the Internet. However, there is plenty of
evidence now that's starting to build up that synthetic data is
starting to show very efficacious kind of outcomes for

(14:28):
AI models, right. He's.
Working great in post training. I just talked to Maxim Le Bon at
Liquid AI and I mean that's all they're doing to do their post
training to set up the liquid models.
And and, and synthetic. So, so, so that's great.
And then the second thing that'shappening is machine data, which
is data from applications and agents.
And the more you have for every human, if you have 10 agents or

(14:51):
100 agents, the amount of machine data on activity that
gets generated in logs, metrics,events, traces, all of those
things is currently time series data that is not effectively
utilized by AI models for doing things like detecting
infrastructure stability, predicting infrastructure
outage, you know, making sure that you can prevent a breach

(15:14):
from occurring. All of those things is where
machine data can be hugely beneficial.
And so these three constraints, infrastructure trust deficit and
a data gap are all three. It turns out that Cisco can be
squarely in the middle of and really help our customers out.
And, and those customers could be public sector, they could be

(15:34):
private sector. And it essentially what it's
doing is it's helping our customers generate tokens with
the lowest amount of kilowatt power of energy and the lowest
amount of dollars spent because token generation ability is
directly proportionate to a country and their ability to

(15:54):
actually have economic prosperity as well as national
security. And that same applies even for
companies where companies can bemore financially viable and have
greater security posture. So I feel like those 3
dimensions are pretty important to understand and say, what does
the market need to do to ensure that we can actually drive these

(16:18):
constraints to a, a point of, you know, where it does not
become a constraint, but it becomes a state of abundance.
Because if you have plenty of trust and if you have no
infrastructure constraint and ifyou don't have a data gap, then
the potential of AI to alter thecourse of humanity for the
positive and for the good is going to be meaningful.

(16:40):
And be that's what that's the future that we should be
striving for. And by the way, that future does
not happen without humans. That future requires humans to
be participating. To call back to your previous
point, there's a lot of infrastructure work for us to
do. There's a lot of security work
for us to do to set up AI enabled work in the future and
it's been interesting to see howyour perspective has really

(17:04):
informed Cisco's decision makingaround product development.
I know there's AI Defense that Imentioned and then my
understanding is you've actuallyopen sourced your first security
model as well, Foundation AI. Can you tell me about that
decision and how that interacts with the rest of Cisco's
approach here? Yeah.
So on the AI defense side, just to close out on that, basically

(17:25):
what it is, is a mechanism for us making sure that we can get
visibility on what data is flowing through a model, do
algorithmic red teaming and jailbreak a model using AI
defense AP is, and then make sure that you can runtime
enforcement, apply guardrails. That's AI defense, right?
So think of it like almost like a very sophisticated version of

(17:46):
an AI firewall. Going back to your model
question, in the consumer world,I think you're going to see a
few handful of foundation modelsbe very successful.
Open AI, Gemini X dot AI, you know, Anthropic, and in certain

(18:06):
instances they will all be very successful models.
My guess is a few more will emerge, but you're not going to
have thousands of models on the consumer side.
You will probably have a dozen that'll actually could have most
of them of the volume of inference.
On the enterprise side, I think it's going to be very different.

(18:26):
You're going to have thousands of models.
You might have 10s of thousands of models, and what you will
have is multiple models being used in conjunction with one
another with an intelligent routing layer that allows you to
make sure that you can optimize your cogs and you can make sure
that certain queries go to certain models because that's
costing you less from a compute resource standpoint.

(18:47):
And then other queries might go to other models.
If you look at cursor, my guess is, I don't know for a fact, but
6070% of the queries now are notgoing to Anthropic.
They might be going to their ownmodels and then they might have
a certain percentage that goes to clog.
Why is that important? Because as you have more and
more of these models, it's goingto get more, not only more cost

(19:08):
efficient, but your efficacy of the model being able to in a
very small footprint be able to give you exactly what you're
looking for in a particular domain is going to be much
higher. And latency, for example, is a
concern too for a lot of folks pending on the use case in
enterprise. That's right.
That's exactly right. So what we, we discovered this
pretty early and we said, hey, look, security is an area that

(19:29):
we care about deeply. We want to make sure that we
build would it would it wouldn'tit make sense for us to build
our own models and security because we've got the so much
data. And the key over here is not to
just make sure that you have infinite amounts of data.
The key is to make sure that youtrain it on the right data, you
know, and so distill it down to the just the right amount of

(19:51):
data that you want to train it on where the efficacy of pre
training a model that is a, you know, open source. 8 billion
parameter model we are seeing can now actually perform better
than a 70 billion parameter model.
And what we are also seeing is that 8 billion parameter model

(20:13):
can be further quantized and youcan run it on ACPU on a laptop
and and it actually just has a very, very different kind of
economic outcome. And so that in my mind all, not
only does it solve the trust deficit, but it also solves the
infrastructure constraint, right?
And so, and then what we're doing is we're building our
products for the edge. That says, OK, if you have

(20:34):
robotics on the edge, you might need to have inferencing on the
edge to your point because of latency.
And so then we build this thing this, this platform called Cisco
Unified Edge, which allows you to have networking, security and
and compute bundled in a box that if it's a branch office or
if it's a hospital or if it's a factory floor where you don't
have IT staff, you can still just plug this box in and it

(20:57):
doesn't have wires dangling. You don't have to go out and
have someone special come in to get and installed.
You can manage it centrally. It's just inference plug and
play on the edge so that you have the full from core to the
edge inferencing capability provided by Cisco.
So those are the kind of things that we've actually been
spending our time building because we feel like you have to

(21:19):
build things where there is a true pain point and a challenge.
And right now the challenges in the market tend to be around
infrastructure, trust and data. And So what we do is we take
that as a core founding principle of our product ID
issue and put that kind of ideation process and say, if we
solve those things and make those abundant as a result of
doing this, there's going to be a natural demand signal for it.

(21:42):
And we'll we'll end up doing well.
And we recently had our earningsfor the quarter and you know, we
had a very, very successful earnings because you're starting
to see that hyperscalers and youknow, Neo clouds and sovereign
clouds and service providers andenterprises alike are all
starting to find that, hey, it makes a lot of sense to partner
with Cisco because they provide the critical infrastructure for

(22:03):
the era. Speaking of partnerships, I know
you've recently announced or an expanded major partnership with
NVIDIA as well as partnerships with G42 in the Middle East.
Can you talk a bit about how these partnerships are helping
to solve this infrastructure gap?
Because as as you brought up here, we're seeing this

(22:23):
interesting tension where both enterprises and countries are
saying, I either don't have enough compute, I don't have
enough energy, or I don't have the trust I need to actually put
these systems into production. And clearly you're trying to
address that challenge and part of that is through these major
partnerships. I think in my mind, a
partnership, a company's willingness to partner is a very

(22:48):
indicative of the of the company's arrogance level.
And the reason I say that is youcan't be arrogant to think that
you're going to go out and buildevery single thing that's needed
for a very, very large growing economy and the largest platform
shift. And that means that you have to
have enough humility to know that there will be overlaps

(23:10):
between your partners. Sometimes they might be
competitive in nature compared to what you might have wanted to
do in an ideal world, but it is better to partner with the
ecosystem and create an open ecosystem than to create a
walled garden. And so that's something that
we've actually thought pretty deeply about.
And when I joined five years ago, one of my, my big kind of

(23:34):
convictions over here was that we have to be extremely open in
our, in our partnering approach,you know, And so in anything
that we do, even if it's a competitor, we will make sure
that we extend the possibility for us partnering together.
And frankly, it's worked out pretty well because the reality
is, is I would rather grow the pie than keep trying to focus on

(23:54):
getting the bigger piece of the same pie.
And when you work with competitors and you grow the
pie, everyone's happy. And this does not have to be a 0
sum game, you know, And so we'vepartnered with NVIDIA, which we,
we, we don't have that much of Acompetitive overlap with them,
but it, it's a very synergistic partnership because they build

(24:15):
GPUs, rebuild the networking andthe GPUs are now, the way I
think about this is power is theconstraint, GPU is the core
asset for AI and network is the force multiplier.
In the absence of the network, the GPUs can't operate.
Why is that? Because it used to be that these

(24:38):
models were small enough that they would fit in the memory of
a single GPU and have the processing speed of a single
GPU. Be it be able to train the
model. Then what happened is the models
got bigger and you needed to train train it on multiple GPUs.
So you created a server with four to 8 GPUs and then the
model got even bigger. So you said, oh, these servers

(24:58):
need to be put into a rack and stacked up and that's what I
need to train the model on. And that became a rack.
And then you said oh, I need to have a set of a row of racks
that need to be tied together within a data center.
And now we are the point where you're not just doing, you know,
the rack networking was called scale up, the row networking was

(25:19):
called scale out. And now what you've got is
networking that goes across datacenters when two data centers
that might be hundreds of kilometers apart will need to
act as one coherent ultra cluster for a training run.
And we have created silicon and systems and optics to be able to

(25:39):
have two data centers with two different power draws that might
be hundreds of kilometers apart operate as one giant cluster,
one coherent cluster. And when you do that, that's
what's called scale across. And those things are impossible
to do without partnerships with with others.

(26:01):
And the way I think about a partnership is if someone has
more than 20% of the market and you choose not to partner with
them because you want to have all the revenue to yourself,
because you you feel like you'rearrogant enough to believe
you're going to build everything, then all you're
doing is getting yourself excluded from that 20% of the
market. You're not.
You're not doing yourself any service economically.

(26:23):
It makes no financial sense to not partner.
I think it's a better way to live life to not just hate on
people. And I think it's most
importantly better for the customer because the customer
has made an investment in company A and Company B.
And if we happen to be one of those two companies, it's our
duty to make sure that we can actually work with the other

(26:45):
company to get to protect our customers investments.
Because if we do that, the customer is going to say, you
know what, I want to work with Cisco, I'm going to work
backwards and do that. And so that's why I've, I've
always felt like partnering is, is at the core.
So there's 2 core principles I hold very close to me, which is
first one is you got to build a platform, not just an individual

(27:05):
collection of products that don't talk to each other.
You have to have an integrated platform.
And two, you have to have an open ecosystem.
And if you do that right, then then there's a pull from the
market towards you versus versus.
If you do that wrong, you have to push your products into the
market, which is a much harder thing to do.
It's a lot harder to win 0 sum games with multiple competitors,

(27:26):
and you're always going to have multiple competitors.
So, and, and then you, you, you can't think that you're going to
get every market transition right every single time and be
the first and 1st to market overthere totally.
And that you're going to be an expert in every single thing
because you know, you're going to still be constrained on
resources like I, I think it's just much better to make sure
that you stay focused at what you do best.

(27:46):
My, my rule of this one is if I have stay focused on what I
bring a unique perspective to where I have permission to play
that I do better than anyone else.
And then for everything else I partner.
Then what ends up happening is you just build a vibrant
ecosystem. And, and that is truly the
definition of a platform where your contribution of your

(28:07):
technology is, is so big that itactually catalyzes a movement
throughout the entire industry. And that the industry makes much
more money from your technology than you make from it yourself.
And that that then creates a level of, you know, value in
inherent in, in, in in society with with your contribution.

(28:28):
I think this is a great point for leadership as well, not just
for, you know, company to company interactions, but to
building your own personal stackof skills.
Like, yes, you should build out and trust and improve your
unique skill set, but you're notgoing to be an expert in
everything under the sun. This is why people build teams,
why we bring in intelligence systems to support us.

(28:49):
So I just think it's an important note here, especially
as we think about that jobs conversation that we had
earlier. And I see the strategy for Cisco
playing out in multiple ways. So we talked a bit about NVIDIA
and as I mentioned, you've also been deepening partnerships in
the Middle East with G42 and theUAE looking to build out secure
end to end AI infrastructure there.

(29:11):
It's obviously an emerging area for several companies who are
are looking at the Middle East as a let.
Me actually talk about those partnerships just since you have
so NVIDIA, we've got a multi dimensional partnership where we
they build out AI factories. We said, what about if we
actually had secure AI factories, would you want an AI
factory or would you want a secure AI factory?

(29:31):
It makes more sense to have a secure AI factory.
So we actually have our securitycapabilities factored into the
AI factory architecture. And then we, we are, you know,
their reference architecture is something that we've continued
to make sure that we can get certified, you know, against.
That's one we've also used, we just launched at our earnings,

(29:53):
our not at earnings at, at GTC, you know, the 9100 switch where
we can take that spectrum X silicon and integrate that with
our, our, our switches so that we can build switches with their
silicon as well, even though we build our own silicon.
And the reason for that is because sometimes customers
prefer theirs. And so you just want to give

(30:13):
customers a choice. And so those are the kind of
things that we've done with NVIDIA and we have, we have a
continued level of, you know, kind of of of tight synergy
between our engineering teams that we can continue to keep
partnering together. In fact, some of the executives
at NVIDIA are very dear friends of mine.

(30:34):
And you know, we be, I think thepeople like doing business with
people that they actually tend to enjoy.
In fact, yesterday we had our earnings call and I had three or
four of my competitors who just sent me a very kind note saying,
hey, Congrats on a great earnings call.
And it just feels good to just operate that way.

(30:54):
You know, with G42, we have a very, we are partnering very
much with the the sovereign cloud kind of initiatives that
are going on, whether it be in UAE with G42, whether it be in
Saudi Arabia with the humane Group.
And we want to make sure that weare continuing to partner with
them so that as they do this data center build out that we

(31:16):
become the network of choice forthem.
And so that's happening over there and we will continue to
keep doing that. We I've been to, you know, Saudi
and the UAE at three times in the past six months.
We met with, you know, the CrownPrince on both sides and we've
actually had a very tight partnership with G42 with
humane. We've known each other for many,
many years. So I think it really helps.

(31:38):
And then I, I'd say that even with the model providers, we
will continue to partner with all of them because we want to
be the Switzerland across the entire industry.
And we want to make sure that wecan provide network regardless
of the model provider, regardless of the GPU provider.
And we want to provide the networking security

(31:59):
observability across the entire stack.
Love that and I would be remiss if I didn't briefly mention that
our, our sponsor Galileo is alsoan AI factory partner with
NVIDIA. So it's a very cool program.
Highly recommend checking out the validated designs they're
doing the Cisco and many others there.
Really, really cool program off to have NVIDIA on to talk about
at some point. That'd be a fun episode as well.
But I, I want to ask about a couple of the other

(32:21):
infrastructure constraints you brought up.
So you mentioned, you know, power generation for a
infrastructure and obviously you're looking at AI on the edge
as well and there are different constraints and opportunities
that come with that. How are you thinking about these
other constraints that we're seeing within the infrastructure
that backs AII? Mean like power is a very
interesting one because what's happening with power is rather

(32:44):
than power being pulled to wherethe data center needs to get
built out, what's happening is data centers are getting built
where the power availability is the highest.
And, and that I don't think is going to be a phenomenon that
changes. I think every country is going
to want to have their own sovereign data centers.

(33:05):
And what we need to make sure that we do as an American
company that believes in the power of, you know, what America
brings to the table is we want to make sure that any, any and
all of these data center build outs that are happening
worldwide are using American technologies to actually build
these data centers out. And so whether it be in in a in

(33:30):
in Southeast Asia, whether it bewithin the Middle East, whether
it be in Europe, whether it be in South America or Canada, we
want to make sure that we can help the build out of data
centres in addition to the data centres that are being built out
in the US. And I think that the power
scarcity will make it very important that these are global

(33:53):
initiatives rather than just localized initiatives because
you can't just always pull the power exactly where you need,
which is also why this this notion of scale across that I
mentioned is really important. Sometimes you might not have
enough power that you can pull into a single data centre.
And so you might need to have multiple data centres, but those
need to operate like one data centre that might be hundreds of
kilometres apart because a training run has to operate with

(34:18):
a number of GPUs that might not all fit within one data center.
So if you need 300,000 or 500,000 or 1,000,000 GPUs to go
out and do a training run and a data center can only host 5070
thousand 100,000 GPUs, then you might need to have a coherent
cluster of GPUs that might span data centers.

(34:39):
And that's where scale across networking is really important,
where the technologies around security and around low latency
communication that's power efficient gets to be very
important because if you drop a network packet in a training
run, you have to restart your training run.
So you have to have, you know, technology that's built in the

(35:00):
silicon chip itself that says, I'm going to provide things like
de buffering so I can buffer thevariance of the bit rate of the
packets going in. And then that that way if
there's a little bit of a dip, I'm I'm not going to be, I'm not
going to go out and negatively impact the training run because
that cost millions of dollars ifyou have to restart the training

(35:20):
run. So those are the kind of things
that require deep architectural forethought that we've actually
put in place for building out. And these are like hard computer
science problems and hard technology problems that Cisco
does best at. And the way I like to help
people think about Cisco is think about us as the picks and

(35:43):
shovels company during the gold rush.
We are the critical infrastructure company during
the AI era where we just keep things humming so that everyone
can get full potential from their AI investments that
they're making. And being a picks and shovels
platform during a gold rush is afantastic place to be.
Ain't entirely shabby you know it was a it was a good good time

(36:05):
to be in the picks and shovels business you've.
Been talking a bit about trust from a security perspective,
from a successful networking perspective.
And I can imagine there are other dimensions of trust that
are coming up in these partnerships too.
Because you know, as you mentioned, Cisco's an American
company, but is working with these major international
partners. Folks want to have sovereign

(36:25):
cloud, sovereign data centers. How do you navigate those
tensions as you ensure there's trust across all partners and
make sure that you're not playing a 0 sum game as you
brought up? Yeah.
I think on that front, you know,we what's really important is
working closely between the public and private sectors.

(36:45):
The public private sector partnership is more important
today than ever before. You know, I like in the past 6
to 12 months, the number of government leaders I've met and
gone out and visited is non trivial.
And why is that important? Because I think we have to learn
from them on what they're, theirchallenges are, they have to

(37:06):
learn from the private sector. And then we have to make sure
that we come up with joint solutions that can meet their
requirements and that can also be technically feasible from our
side. And so, you know, when you think
about a sovereign, like the reality is, is there are two
choices right now and who you can buy AI infrastructure from,

(37:29):
right? It's either an American company
or it's a Chinese company. Those are the two choices.
And you know, I have a lot of, you know, kind of sense of
urgency on this particular topicbecause we have to move really
fast because they're a very capable competitor in China.

(37:52):
They actually can. They have done a great job in
innovating and their constraintsare slightly different from the
world's constraints. And it's helping them to be more
efficient in certain areas looking at.
Certain areas and in certain other areas they might not be as
viable a provider as we might belike they don't.
Have nearly as many data centres, for example.

(38:13):
Or they might build technology, whether it's silicon or
something that might be, you know, like for example, they
don't build 2 nanometer GPUs. You know, they build 7 nanometer
GPUs. the US builds 2 nanometer GPUs.
But they have more resources on the power side, yes.

(38:35):
And they have more engineering capacity.
And so they can say, well, the inefficiency of A7 nanometer GPU
can be offset by unlimited powerand unlimited, you know, kind of
optimization of engineering resources.
Those are the kind of things that it puts and takes that'll
have to be played. And I, I feel like it's very
important to keep in mind that as you, as we build out the, the

(39:00):
future, that US is a very formidable participant in
building out AI capacity, data center capacity, not just for
our own needs, but for the global need worldwide.
And, and that requires us to make sure that we are engaging
with the governments, we have the right level of regulatory

(39:21):
policies in place. We are working with our U.S.
government to make sure that everyone's on the same page.
And so we, we, we tend to do a lot of that.
And I think I, I have to give a lot of credit to our current
administration on on the way in which they're kind of navigating
this as well as all the administrations of different
countries that we're working with.
I think the silicon point is obviously the one that's most

(39:45):
talked about. Look, we have this chip
advantage. We simply are our head here.
We're keeping China from gettingaccess to this access to this
technology. They already have a major power
advantage. We can't let them also have a
chip advantage. But I I think an underrated
conversation that some folks arehaving, but I think isn't as
broadly consumed in AI circles. Is the capital, pardon me, the

(40:07):
capital allocation advantage that America has as well with
the the VC community with the ability to fund all these
startups, fund all these different companies instead of
having a largely state directed system that is, you know,
powerful when it's brought to bear in a problem that they know
how to solve, but is less strongwhen it comes to solving diverse

(40:29):
problems that they may not know from the start how to go after.
Yeah, I think. I think our, our venture system
and our startup ecosystem and our innovative spirit of America
is something that you should, you should not underestimate.
I think it's, it's, it's definitely one of the, one of

(40:50):
the superpowers that we have. And, you know, in a, in a super
cycle like this one that we're going through right now, I think
that's very important because you have to, you have to work in
a very nimble way and you have to make sure that many ideas and
many experiments get started. But then when an experiment
starts to do well, you know, youdouble down on it.

(41:10):
And capitalism is a great way tomake that happen, you know, And
so I do feel like there's this inherent advantage that we have.
But I will say this, I think it's a, it's a time for America
to be very paranoid and move with a sense of urgency because
our competitors are, are not incapable and they can actually

(41:34):
do a very good job as well. And, and the speed at which we,
the, the industry is moving is very important.
Like, you know, any kind of slowdown in speed of execution can
be detrimental and can allow, can, can risk any company losing
the lead and the other one taking it over.
So while we're in the lead rightnow, I I subscribe to the belief

(41:57):
that being paranoid is, is always a good idea in these kind
of markets and these kind of technotic shifts that are
occurring. Yeah, we, we can't be
dismissive. There are real advantages of
other systems and they're real advantages that China has in
particular. And I do think your point about
speed is really important one and it actually relates back to
Cisco for me in some ways because Cisco has been described

(42:18):
as the world's largest startup because of your acquisition
strategy at some points. I think at one point you
completed 30 acquisitions in a single year in in AI forward
world. How does this acquisition and
integration strategy evolve to align to your platform play?
So you know, we are very lucky in the, in the sense that we

(42:38):
have a very strong balance sheetand we have a culture where we
are able to make, it's kind of like the company's version of
like inviting people from everywhere to come in and
innovate and do really well at Cisco.
Like we, we do that inherently well.
And so when you start thinking about our acquisition strategy,

(43:02):
we've been a very acquisitive company throughout our lifetime.
But I, I have a slightly different take on, on this than
then we might have had a while ago, which is I don't believe
that your strategy should be onethat's dictated by acquisitions.
I believe that you should have avery clear true north and a very

(43:25):
clear point of view of the future that you want to build
out where you have permission toplay, where you have unique
insight that you can bring to the table, where you have a
structural advantage. And then if you happen to come
across a company that can accelerate that pathway for you
to get there, then you shouldn'tbe shy to deploy your balance

(43:48):
sheet. But your goal should not be an
acquisition. And the simplest way to talk
about this is when I first joined this company and we would
talk about a priority area, people would come to me and say,
hey, G food seems like category X is a priority area.
What are we going to buy? And my response to them is, I

(44:09):
think it's the wrong question. The question should be category
X seems like it's a priority area.
What are we going to build? And if during the course of
building you come up with an idea that could accelerate your
build out, then by all means useyour balance sheet effectively
to make sure that you can deploythe capital.

(44:29):
And your investors would love it, your your customers would
love it, all of that. But don't go in this rapid
frenzy of just randomly buying things because you missed the
market. I don't think that's that's the
right way to success. I feel very passionate about
being obsessed about innovation organically.
I don't think that a company does well just by acquiring a

(44:51):
company does well by having an instinct for how to innovate
organically. But the company also should know
that when you get to scale, you can have a not built, you know,
kind of not built here syndrome.You have to make sure that you
stay open minded to, oh, someoneelse built something better than
us. And that seems to be an

(45:14):
opportunistic moment for us to go out and augment that team
with ours. Let's go ahead and do it.
And, and that's the way that we thought about the acquisition
strategy. And I think it's, it's working
out really well. We've made, you know, I my time
over here, I've made, you know, 1718 acquisitions.
I don't, I don't know the count anymore, but but it's not
something where I I obsess aboutthe acquisitions, I obsess about

(45:38):
the strategy that we need to pursue and acquisitions is just
one of those avenues that accelerates that for us.
It becomes a tool in your platform strategy.
It becomes a tool in the Graftonstrategy.
Exactly. It's very well but.
You mentioned True North and this idea of having a clear
vision for what you think Cisco's platform should be.
You've talked about a few concepts, Openness, trust,

(46:00):
security, infrastructure. If you were to define today what
your true north for Cisco's AI enabled era is going to be,
what? What do you think it looks like?
Way I would define it as we haveto be the critical
infrastructure for the AI era. What does that mean?
That means that at every layer of the stack we should have

(46:20):
meaningful contribution. We will build silicon and
network ASICS that can really help us, you know, build the
most most expedient, low latency, high performing, energy
efficient networks. We will build the systems that
the that's that that silicon is used in, which is the physical

(46:41):
boxes and the hardware. We will build the operating
system and the software for management.
On top of that, we will have security platforms that can help
networks not just be naked networks, but networks that are
secure networks. And so we have to fuse security
into the fabric of the network. And that's one of the key

(47:02):
differentiators that we have because our our security
competitors don't have networking and our networking
competitors don't have security.We're the only company that has
both at scale. We're one of the largest
networking companies in the world.
We also happen to be one of the largest security companies in
the world. So that really helps.
And then we need to make sure that we have observability on

(47:24):
top of that. And finally, we should have know
how on how to build applicationsthat get to hundreds of millions
of people. And if we can build that stack
all the way through for classical workloads as well as
AI workloads, I think we will bein be in amazing shape as a

(47:44):
company, not just by making money, but by contributing to
society and the community in thebest way possible, knowing what
we know. Because I personally believe
that the reason I came to Cisco and the reason I feel like this
is one of the most magical places to work at is it's a very
mission driven, purpose orientedcompany.

(48:05):
And if we the world is a different place when we win
compared to when someone else wins.
And, and I think that's a reallyimportant thing to keep in mind
on how we kind of want to continue to keep building on, on
top of the investments that we've made.
And we've been in business for 40 years.

(48:27):
The amazing part is during those40 years, we've built out these
very durable franchises. And if we can keep innovating in
each one of them like we're a startup but operate at speed
with scale, I, I think this is one of the most special
companies in the world right now.

(48:49):
If, if that happens and right now I the way I think about this
is we are on the journey to becoming a great company from
being a good company. We had stopped innovating for a
while and we are now back on a tear on innovation.
We've innovative more in the past 15 years than we have in
the previous decade combined. And I would say that this is the
worst that you should see the innovation velocity for the next

(49:11):
decade. We will continue to keep
accelerating that. And I think there's a spring in
the step in the employee base. I think people are excited.
You know, we are, I'm, I'm so grateful at the hard work that
all of them are doing, not just in the outputs that they're
delivering, but I think they're putting the heart and soul into
it. And you can feel it when people
put their heart and soul into something versus when they're

(49:33):
just doing a job for the sake ofdoing the job.
Our employees are putting the heart and soul into it right now
and you can feel it in every step of the way.
They sweat the details. They feel terrible when
something doesn't go right and and I have a lot of appreciation
for that. I actually have to admit, I
agree simply from the interactions I've had with Cisco
team members of the last year, working with a lot of the folks

(49:54):
over at Out Shift, on Out Shift by Cisco, for folks who don't
know, but doing amazing innovation work for Cisco.
And we had the opportunity to bepart of their agency
partnership, which is now with the Linux Foundation and looking
at how agents communicate and kind of setting the stage for
the next decade and super talented team members there.
It's been a ton of fun working with them.
Vijor is a great leader. We've we've been very lucky to

(50:17):
have him and he is, you know, he's a force.
Absolutely, and I'll shout out on the marketing side and to
partner with Luke Tucker and andLeah Ryder was fantastic, great,
great folks, Mank and many others.
So hopefully they'll listen to this episode as you think about
building the team at Cisco, continuing to drive this culture
of innovation. I'm sure you're taking lessons

(50:38):
from your own career and it sounds like you've been thinking
in a very mission driven format for a long time and that's part
of your inspiration here. How do you, I guess what
lessons, one, are you taking from your own career that you're
applying to building the cultureat Cisco to ensure incredible
innovation? And two, who are the sorts of
people you're looking for to join the team?

(50:59):
So I, I look for certain characteristics and people much
more so than other characteristics.
And so I'll, I'll just tell you what I, I find as a very good
predictor of success for that individual to be able to add
value. I think the most important
characteristic I look for is hunger.

(51:20):
And the reason I say hunger is you can teach people a lot of
things. You can't teach them hunger.
They're either hungry or they'renot hungry.
And the the the level of lift that it takes to teach someone
to be hungry, which you can't doanyway.
I think life has to teach you hunger to your point.
Yeah, you just have to be self motivated.

(51:42):
I don't want to have an e-mail or a rah rah session to motivate
people. I think people have to be
intrinsically motivated, not extrinsically seek motivation
from others. So that's number one.
So I, I look for hunger. The second thing I look for is
extreme levels of curiosity where you know, you, you have to

(52:03):
have this insatiable appetite tolearn.
And learning should be the thingthat gets you really kind of
excited about life. And I, I was talking to one of
our engineers in the silicon team recently and I asked him
and like, Hey, so how are you? How are you thinking about

(52:24):
Cisco? He's like jutsu, as long as I'm
learning, I'm happy. The moment I stop learning, I'm
not happy. And I've just learned this about
myself. And so you just need to make
sure that I keep learning. And I'm like, I don't need to
make sure. You need to make sure you keep
learning. He's like, absolutely, I need to
make sure I keep learning, but Ineed to make sure that I'm, I'm
in those projects where the learning is exciting to me.

(52:46):
And I'm like, absolutely, that'sthe thing that we need to do.
So the second area is curiosity,extreme levels of curiosity that
people have. And by the way, in the age of
AI, that's getting to be easy. You know, like the amount of
time that you can take to get dexterous at something has
compressed so much that it's actually fun to live life

(53:07):
constantly learning, right. So that's the second one is
curiosity. The third one that I I look for
in people is clarity of thought,because I think it's very hard
when you have modeled thinking to then have clarity of
communication that's inspiring to other people.
So you have to be a very clear thinker.

(53:29):
If you're a very clear thinker, communication then of that
thought is a learned skill, you know, but if you just don't have
clarity of thought, you should spend a lot of time on the
clarity of thought. And so you have to have clarity
of thought, which then leads to clarity of communication, which
then leads to inspiring other people to follow you on your
vision. And if you don't make this a

(53:52):
team sport, it doesn't work right.
And then the other thing I look for is, you know, is there a, I
know the obvious stuff is like, you know, base level of
intellect and you know, follow through skills and all of that
stuff. But I think you have to assume
that that's just the baseline, Like if, if, if you're not.

(54:12):
But what I don't look for alwaysis experience.
In fact, I, I want to have the right mix of experience and
inexperience in a team because if you have everyone that's
super experienced, what ends up happening is you can start to
get a false sense of confidence because you've seen a pattern

(54:33):
before when there's no evidence that that pattern will repeat
itself because there's so many variables that are that might
have changed in the meanwhile. So I actually feel like having
people that can do good pattern recognition through experience
coupled with people that have noexperience, that can teach the
people that have pattern recognition to also unlearn the

(54:56):
bad habits that they've learned constantly, I think is a really
important thing. And so the combination of a team
composition, which is experienceand inexperience combined
together, super important. And that's, that's how I think
about the the formula of structuring teams is those kind
of pieces. And then, you know, it's just

(55:17):
nice to be with people that aren't afraid of conflict and
aren't afraid of debate, but aren't assholes either, you
know, And it's like brilliant assholes are are a pain to deal
with. On the other hand, one of the
worst forms of, you know, one ofthe worst characteristics that I

(55:41):
see in a company sometimes is being artificially nice rather
than being kind. And so, like, if you see a
problem and you don't say something about the problem,
you're actually doing a disservice to your peers and to
your shareholders and to your customers and to your
stakeholders. I would rather that you actually
have the debate, be the debate about substance, not style, and

(56:05):
be the debate about something where you know, you don't take
things personally. But the collective objective is
to just keep making sure that you deliver the best outcome for
the market. And if you can do that, then
then you end up doing well. If you don't do that, then it
becomes a vanity exercise. And a vanity exercise usually
doesn't have durability in in mymind.

(56:26):
I think your point about constructive tension and
conflict is one that I had to learn as a early career manager,
honestly, where it's very easy to try to encourage your team
and be kind to them and then fail to be real with them.
Or at least fail to show them that where there's a problem and

(56:50):
you're doing them a disservice and you're doing the company
disservice, you're doing yourself as a service and you're
creating this long term culturalproblem if you're not able to
have tough conversations. Yeah, I think like I feel like
conflict is a necessary condition of business, you know,
and if you don't have enough tension in the system, then what
ends up happening is you start succumbing to groupthink and
groupthink is the worst thing that can happen.

(57:12):
And, you know, most of the times, like what I get more
paranoid about is not during thetimes when we're we're like five
years ago, we were, we were not the coolest company.
We were not doing as well. We had a lot of kind of hurdles
to overcome. So I wasn't as worried five
years ago about us becoming complacent and, you know,

(57:37):
risking that. I worry much more about that now
than I did five years ago. Because success can breed
arrogance and success can breed complacency.
And what you have to do, especially when you have
success, is preserve humility and stay paranoid.

(58:00):
And if you can preserve humilityand stay paranoid during your
successful years, then your success becomes a tailwind and
can actually propel you forward.If you start getting arrogant,
then your success becomes your your, your headwind and it
actually pulls you back. And so I've always found that

(58:22):
intellectual arrogance is something to be really, really
kind of careful of, like steer clear of it.
And it's so easy for smart people to even reason their way
out of not needing to be intellectually arrogant.
Like they're like, no, no, we don't need to be, OK, I'm
smarter and I know it. And they'll have some kind of
good justification for it. So I, I always feel like

(58:45):
surround yourself with people better than you and always know
that you're prepared to unlearn because the patterns that you
have don't last forever. And at some point in time, you
have to forget that pattern to learn the new pattern, because
remembering that pattern and learning the new pattern might
be very hard to do cognitively. Humility about learning to your
point. Humility about learning is so

(59:06):
important and it's, it's something that seems obvious but
is a trap that a lot of smart people fall into all the time,
you know, And by the way, sometimes what ends up happening
is it's confused that if you debate an idea that you're not
humble. And and so sometimes the absence

(59:27):
of conflict is seen as humility,whereas the reality is you
should be humble with with a deep desire for debate about
ideas, but not about personality.
Like, you know, the best idea should win and it shouldn't
matter who what rank they have. You should just focus on being
intellectually honest about the best idea that wins.
And don't create a version of your truth in the company.

(59:51):
Seek the truth, and if you coulddo that right, then very
different things happen. I think by pretty much anyone's
metrics, you've had an incredibly successful career
yourself. How have you managed this and
kind of cultivated these traits along the way for for you?
I think it's a maybe a great lesson for leaders who are
looking to someday reach those heights.

(01:00:12):
You know, I, I, I actually don'tthink of myself right now as
particularly like Uber successful.
In fact, there are times in my life where I feel like, yeah,
I've, I feel like I could do more.
And so one thing I tend not to do much of is I don't tend to

(01:00:34):
gloat and what, what has happened in the past and I just
focus on what's not happened yetand what do we need to go at?
And so, and then if, if I were to say, what is the thing that
keeps driving me, I tend to be obsessively focused on
continuous improvement all the time.

(01:00:56):
And where I get motivated the most is when I'm learning
something new. And so I just put myself in in
positions where, you know, learning is a necessary
prerequisite for getting the jobdone.
And that means that I tend to doa lot of jobs where I don't have
that much experience. You know, when I, when Chuck

(01:01:19):
Robbins, our CEO, asked me to take the job for running all of
product, I knew nothing about networking.
You know, when I first came to Cisco, I knew not, I didn't know
that much about infrastructure. When I first went to Box, I
didn't know that much about a pure SAS company.
When I went to EMC and I, I joined the Documentum team, I

(01:01:42):
didn't know that much about software.
And when I had first started my own company, I knew nothing
about going out and starting a company.
And I've always felt that that that slight nervousness of
feeling like I don't know everything over here keeps me
humble and keeps me wanting to learn rather than getting
intellectually arrogant saying Ifigured this out.

(01:02:02):
Because it's when you start getting that cockiness that you
figured something out, which is when you actually screw things
up. I have certainly done that in my
own life before. So I think that's, that's a
great note. I do have to ask you one very
specific career question becauseyou told me this before we
started recording and I, I have to ask, you told me you you
don't use e-mail anymore. Can you tell me about?

(01:02:24):
That I'm not particularly, you know, like there's, there's two
kinds of people in the world. There's inbox zero and inbox
67,842. I'm the inbox 67,842.
Chuck Robbins, my boss, he's an Invoke zero kind of guy.
He's super organized. That creates some tension, maybe
sometimes. No, he's actually, the thing I

(01:02:46):
love about him is he has accepted me for who I am.
I love that to the point that it's actually ridiculous what
his level of patience is becauseif he sends me an e-mail, he
will send me a text and say, check your e-mail, you know, but
which is something that is just a boon that you have.
If your boss is telling you like, hey, check your e-mail.
I'm I've accepted you for your flaws and I'm going to make sure

(01:03:09):
you do it, but what so. Extra piece of advice here.
Work for great leaders who understand their people.
Want to say their people? He's really good at that.
And the the reason I don't checke-mail that much is I find it to
be super, you know, like interrupting to my flow of
thinking. And if I just did every single

(01:03:31):
one of my emails and the higher you go get up in the
organization, then the volume ofe-mail gets to be untenable.
And So what ends up happening isyou get hundreds and hundreds of
emails a day. If I just did e-mail all day
long, I think there's people waysmarter than me that keep have a
system for how they manage e-mail.
I'm just not that guy. Every person I've worked for so

(01:03:52):
far though has been really good at doing emails.
So I have to still find someone who I can relate to on that
dimension that's not good at doing e-mail that I've worked
for. But, but I do feel like that's,
that's an area that all jokes apart, I, I feel like that's an
area that I'm not very good at. And I had this, I'll give you

(01:04:12):
this example. So there was this gentleman
named Joe Tucci, who used to be the chairman and CEO of EMC.
And I used to work at EMC for a while.
And when I was leaving, he was very gracious to give me, you
know, like an hour of his time. It was supposed to be a 15
minute meeting. He was kind of very kind to me.
And he said let let me give you an hour and coach you on what

(01:04:32):
you need to do next. And he said to me, take a piece
of paper before you start your next job and create 4 columns.
And the first column, write downeverything.
And by the way, you don't need to show this paper to anyone, so
there's no reason for you not tobe intellectually honest.
This is just for yourself. But the first column, write down
things that you're really good at doing that you love doing,

(01:04:56):
right. Second column, write down things
that you're really good at doingthat you hate doing. 3rd column,
write down things that you suck at that you love doing.
And 4th column, write down things that you suck at that you
hate doing. And be very intellectually
honest. Don't try to have societal
programming dictate your thinking.

(01:05:18):
So for example, don't say I lovemanaging very large teams as a
passion because you're just seeking status at that point.
And so stop doing that because no one likes like managing a
team is not a thing in and of itself managing.
If you love growing people and mentoring.
Yeah, exactly. And so, so he said do that, then

(01:05:43):
take the paper and cut it into two pieces, the first column and
then the second, third and 4th column.
Now crumple the second, third and 4th column and throw it in
the bin, because all you should care about is the first column.
What do you love doing that you're really good at doing?
And for everything else, make sure you surround yourself with

(01:06:03):
people that they are in the first column for the things that
you're in the second, third or fourth column.
And it's actually something thatstuck with me for a long time.
So, for example, I never take a job in a place without my my
head of business operations, whocomes with me in every job for
the past 15 years. Her name's Jesse.

(01:06:23):
She is even though in the org chart it looks like she works
for me. There is no confusion in the
entire company that I work for her.
She can veto me at any point in time.
She is the one who actually knows me well and knows what I
don't do well. And and the reason I do that is
because I am infinitely better with her by my side than without

(01:06:46):
my chief of staff, Scheer. Someone that is constantly
critical to me about things I'm doing wrong.
But what you do is you surround yourself with people that are
complimentary to you, that aren't shy to tell you that
you're screwing up and that haveno tolerance for placating you
and inflating your ego unnecessarily.

(01:07:08):
But that are smart enough to know that when you're not going
through a good time and you're low on confidence, they know
exactly how to boost you up. And I think if you can find even
2-3 people around you like this and then cherish them.
I've been lucky enough in my life that I've probably got like
30-40 people like this. But without Jesse, I don't take

(01:07:30):
another job. You know, without sheer, I don't
take another job because like it's, it's very important
because it's a team. You're buying the team.
You're not just buying an individual.
I don't want to be trade. I'm not very good at sports, but
I don't want to be traded as an individual player in a different
team. I want to make sure that the
team goes from place to place and actually wins the

(01:07:52):
championship. And when we've got 15 years of
working together experience, there's an intrinsic level of
trust that's built in the system.
And that trust really helps in making sure that you know you
can cut through a lot of the bullshit and this just get to
the. High trust teams just perform
better, period. They just perform better, you

(01:08:14):
know, and, and we spend a lot oftime in establishing trust in
our teams and the ones that we build.
Because I feel like if you don'thave that, then then all you're
doing is adjusting stylistic mechanisms of providing
feedback. And I feel like that's the wrong
way to do things. Like you should just accept
people for who they are and thensay, you know, I should be able

(01:08:36):
to divorce your style from your substance and you should be able
to divorce my style from my substance.
And if you just focus on the substance and ignore the style,
you'll get so much more done. And you know, Chuck has this
great line that he gives me, who's our CEO.
He says, Jitu, if you never worry about who gets credit for
something, you'll go much farther in life.

(01:08:59):
And, and I think it's a fantastic way to think about
things, is just don't worry about who gets the credit.
The best idea wins. Ideally, you even forget whose
idea it was by the time the debate ends.
But the best idea is 1. I love.
That we've had such a wide-ranging conversation and
thank you for for sitting first along with me here.
It's already been 70 minutes, but I see these clear through

(01:09:21):
lines. You know, you talked about trust
both in infrastructure and in people and I think there's a lot
of similarities and, and how youbuild that and the need for both
of those for successful teams and for successful technical
systems that interface with those teams.
You've talked about this idea ofavoiding who gets the credit and
about open partnerships, growingthe pie together.

(01:09:42):
You've also talked about constructive tension.
I think these ideas are all extremely complimentary.
So I love that you've brought them up in these different areas
because it really, I think paints a clear picture of your
world view and how you are approaching things.
That just makes a sense across the board as far as how you

(01:10:03):
built your team, how you're building Cisco's products, and
how you see the future, really. I appreciate it.
It's, you know, The thing is, isthese things are not super
complicated. They just are hard to implement
and stick with no one. If you tell anyone, hey, it's
important to have trust in a team, no one's going to disagree
with you. Actually working on building
trust. Really hard to do.

(01:10:24):
So how have you done it? What we do is we have this thing
that we do with the table group.I don't know, have you heard of
the table group? They have this book that they've
written called The Five Dysfunctions of a Team.
I have heard of that book. I have yet to read it, but I
think I'm the third. I, I, I, I don't read full
books, but I, I, I read excerptsand summaries of them.
But I think it's a really good philosophy and the way that we

(01:10:48):
do it is rather than just tryingto go out and be transactional
with each other, we try to understand the human being first
in a team. And you have to have, there's
two aspects that are very important.
One is you have to have a very clear idea of who is your first
team, right? Most people think in
organizations that your first team is the team that you're
managing. That's not your first team.

(01:11:10):
The first team is the peers thatyou work with.
So if I'm running product, product is not my first team,
the executive leadership team onChuck's staff as my first team,
right? The the members of my team for
them, the product team is the first team, not the individual
teams that they might be managing.

(01:11:30):
So I think having clarity and who's the first team super
important. The second thing that we've done
is we will tend to start by asking people what is the most
memorable job that you've had and the most fulfilling job
other than the one that you're currently in and why?
And, and tell us a little bit about, tell us a little bit

(01:11:52):
about yourself that no one else might know.
And then what I, what we will tend to do in those those
exercises is I will start as theleader and I set the tone of the
kind of things that I share. So the one of the things Connor
I share over there is the fact that my childhood was pretty
rough and I grew up to an abusive dad who was a con man.

(01:12:16):
And as a result, I had to leave India because it was not safe
for me to be in India. And I actually came to America.
He was very abusive to my mom. So I had to actually hide her in
a undisclosed location. I didn't see my mom for seven
years. And you know, I, I started my
own business when I came here. And that's how I actually, you

(01:12:38):
know, one thing led to the otherled to the other.
And, and one of my biggest kind of characteristics for for a
long number of years was I operated out of fear.
And my motivation was my fear. It was a fear of being
unemployed. It was a fear of being poor.

(01:12:58):
It was a fear. And especially when my mom was
alive, I was extremely worried about, you know, not having a
job. And so I would be the hardest
working guy no matter what. I would be the one that would
never slack off on things because my entire ethos was
built around I operated out of fear, which is, by the way, it's

(01:13:20):
a, it's a great thing for business.
It's a terrible way to live life, right?
I think I'm far less fearful nowthan I used to be back then.
But that's also because like enough time has passed and you,
you get to reflect. But when I start with something
like that, what it does is it gives people context on why in
certain meetings I might behave the way I do, right, is that I,

(01:13:43):
I hate losing. The reason I hate losing is I
don't want to be irrelevant. The reason I don't want to be
irrelevant is I don't want to bein a position where me and my
company are put in a position where we will not be able to
feed our families. And so I have a very, very kind
of, you know, formed ethos around that.
But if someone doesn't know thatabout me, then they, they, they

(01:14:06):
wouldn't have full context. And so we have this thing in
human beings called the attribution error where Connor,
if you and I are on the same team and both of us know Jesse,
but I know Jesse much better than you do, and you come in
late to a meeting. Jesse looks at that and goes,
huh, Connor's a slacker. He showed up late.

(01:14:28):
You know, I come in late to a meeting.
She's like, wow, chief, who's usually never late, is
everything OK? There's something wrong with
him. And that's called an attribution
era where the absence of familiarity that we might have,
you know, with with someone can create us to be.

(01:14:51):
Do not give them benefit of the doubt when something goes wrong.
And the the level of familiaritywe have might allow us to give
people the, the, the benefit of the doubt because we know them
well and we know their characterand we can vouch for them.
So the first thing you have to do in a team is build A level of
familiarity and context. Just like in a prompt interface,

(01:15:11):
you, you give it context for theAI, for AI to know how to go out
and respond to you. We have to make sure that
there's context for humans. And so we tend to do that a lot
in these off sites that we do. Any time there's a new team
member that we get, any time that we actually have a team
that gets reconfigured, we try to do that because what that
allows us to do is create familiarity, which then creates

(01:15:31):
trust. That trust then allows us to
have conflict in a constructive way.
If you don't have trust, it's very hard to have debates.
And so those are kind of things that kind of build on top of
each other. I love that you bring us up
because you're both creating space for others through your
sharing and like openness here and you're extending that trust.

(01:15:53):
I mean, I'll, I'll, I'll share alittle back here and say, like I
publicly wrote about the fact that I was in abusive
relationship in my early 20s kind of thing.
And like, I think these are important people, contacts that
don't always come up and work contacts.
But when you have an opportunityto go deep with your team, you
can learn so much about them andbe able to really understand how
they tick. And I think that matters so much

(01:16:15):
due to I, I can't thank you enough for giving me the time
today to learn a lot about you and, and go deep with you.
It's been so much fun having youon the podcast.
I really appreciate you joining us.
Thanks for exploring the range of all the topics and thank you
for having me, Connor. And I'm looking forward to Train
of Thought becoming one of them.Most widely listened to podcast
my friend Fingers. Crossed I, I hope so.

(01:16:37):
It's been a fantastic conversation and everything from
people, infrastructure, security, strategic thinking,
your perspective. But where can folks who are
listening and can't get enough G2 or can't get enough Cisco go
to either follow your work or follow what Cisco's AI
initiatives are going to be doing?
I think LinkedIn is probably where I post most frequently.

(01:16:58):
And so, you know, Jitu Patel on LinkedIn is where I would go
and, and follow me. And then the other place is
Twitter. My handle is Jay Patel, 41.
So one of the two places I, I tend to consume more news from
Twitter and post more on LinkedIn, but I use both.
So, you know, go to one of thosetwo places and if you happen to

(01:17:19):
be looking for a job in AI, makesure you apply at Cisco.
I love it. Well, GT, thank you so much.
We'll be sure to link everythinghere in the show notes,
including the resources around things like Cisco Unified Edge
and other things we talked abouttoday, including Foundation AI
and so many other initiatives. Listeners, thank you for tuning
in. And make sure to check out all
the clips and content that will be coming out for this episode.

(01:17:40):
I'll be posting them to my LinkedIn as well as our YouTube.
And of course, we'll be sharing them with the J2 and the Cisco
team. And you can, if you really
enjoyed it, maybe leave a comment on Spotify or leave a
comment on LinkedIn post. We always love to hear from you.
And yeah, tell us what your favorite part of Juju's episode
was. We'd love to know.
And Juju, thanks again, It's been great.
Thank you.

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The Critical Infrastructure Behind the AI Boom | Cisco’s Jeetu Patel

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.css-14f5ked{margin:0;word-break:break-word;display:-webkit-box;-webkit-box-orient:vertical;box-orient:vertical;-webkit-line-clamp:2;overflow:hidden;}The Critical Infrastructure Behind the AI Boom | Cisco’s Jeetu Patel