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

The adversaries and the threat actors that cause cyber security attacks or cyber attacks have the exact same tools now that you and I do, Connor. And they

can use those tools to get far more sophisticated and and far more at scale in systematically attacking society and the critical infrastructure of society.

Welcome back to Chain of Thought everyone. I am your host Connor Bronzden

everyone. I am your host Connor Bronzden and today we are discussing a topic that is on every enterprise leader's mind.

How to build the infrastructure to support AI at scale and how do you do it securely? We have the perfect guest to

securely? We have the perfect guest to help us understand this challenge and that is G2 Patel. G2 is president and chief 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 of your podcast.

>> Should mean a thought. That's so so creative.

>> Ah, I love it. Uh, really really glad to hear that. We we really try to take that

hear that. We we really try to take that lesson too from LLMs and say, "Okay, we're not going to come in with a predisposed notion of of what this conversation is going to be, you know, what our opinion is, but instead let's

work through it together." Uh, 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

seen. The relationship between AI and jobs. Uh, there's obviously a lot of

jobs. Uh, there's obviously a lot of anxiety right now. people are worried that AI is going to replace them and some AI influencers have pushed this narrative with AGI, artificial general

intelligence. Uh, and some of us is, you

intelligence. Uh, and some of us is, you know, hyped 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

stance. You've been bullish on the idea that AI isn't about replacing jobs, but instead about making our work better.

Can you walk us through why you believe that?

>> I I believe in that because I feel like um we should not underestimate the the the human mind, you know, like we've

uh thousands of years have gone by.

There's been bunch of technology shifts that have happened and humans have continued to find a way to stay relevant during each one of these kind of disruptions.

And um you know if you think about what are humans really good at doing? Humans

are amazing at gut instinct, intuition, uh ability to be able to make decisions with incomplete data,

um having empathy. These are all things that are really hard to have a machine get trained on. And I I it's it's a very interesting industry strategy that had

transpired where the industry took an intentional um you know approach of making AI very skumorphic in nature, right? And so it was like

every single thing was equated to a human rather than being equated to uh tooling that humans use. And I think like you know there's a very eloquent

argument that Jensen has that hey this is not this is not a tool it's work. And

um and and I think that's um I I actually like the way that he frames that but I do feel like uh

level one maturity that we would have is AI is going to take our jobs of thinking like just pure maturity of thinking spectrum. Level one maturity is AI is

spectrum. Level one maturity is AI is going to take our job. Level two

maturity is someone that uses AI better than me is more likely to take my job than AI taking my job and >> and that is a risk to be fair >> that is a real risk right uh and then

then there's mitigations to that risk that we should talk about and then level three 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 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 is there are bunch of jobs that we just don't have time to do that we can actually perform out to AI to get done.

That's going to be the first category of jobs we'll give to AI. The second thing is there's going to be a bunch of things that AI is better than humans at doing you know um which is like you know

crunching data of large volumes making sure that you can actually get aggregate the insight from that AI is going to be better at doing that than a human would

do it. And then the third one is jobs

do it. 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

do. And so I feel like there's on each side humans are going to be some 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 happens. And I think it's a false

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 at the

ocean and that's all we're going to be doing for the rest of life. and AI is going to do everything for us. I just

feel like that's that's jumping the gun a little too much. Um, and so I find it to be a very 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 um dimminimous. Now what I think will

dimminimous. Now what I think will happen is some jobs will go away, all jobs will get reconfigured and entirely new industries will get created 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

time. I I think that seems exaggerated 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 some over, you know, a 20 30 year career and you'd be fine over time.

But now the pace of change is much more rapid with AI tooling coming in and there's this large wave uh 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 email is changing but you've always if you were going to excel at your work over 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 the AI wave >> I I think the compression of time um at

which you have to get you know the halflife of anything is compressing quite quite dramatically and um and that can cause nervous energy

and that's actually very very kind of legitimate u on the other hand um I don't think the 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 differentiate one human from the other is how can you make sure that you you get that knowledge extracted

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

and memorizing it, which is something that I feel like, you know, the internet was the first phase of that, but now it's like the AI is a second phase. And

the thing that I think is grossly underestimated about AI and this is an area that I don't think 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 that's available in the world in the world. So it's like I'm going to go out and 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 I ask based on the data that's all available.

I think that and that's going to get me productive. That that that's the the the

productive. That that that's the the the the current kind of simplistic promise that's delivered. I feel like that's

that's delivered. 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 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, um you know, ending poverty, um you

know, cause you know kind of helping cure the 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 is um you've got, you know, like a the the adversaries and the threat

actors that cause cyber security attacks or cyber attacks um have the exact same tools now that you and I do, Connor. Um and they can

use those tools to get far more sophisticated and and far more at scale in systematically attacking society and the critical infrastructure of society.

And so what we will have to do is make sure that we we uh completely accept the fact that safety and security is a big

risk and we have to mitigate that risk because in the absence of that getting mitigated I think we uh you know there'd be a lot of harm that's caused in um 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

this problem. You've published research about deepseek and other frontier reasoning models looking at security risks. What do you see as the most

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 world of AI, what are the impediments that could actually hold AI back?

And I think there's three big areas that could hold AI back.

Uh 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

constraint. There is just not enough power, compute, network bandwidth, and data center capacity to go satiate the

needs of AI. And the constraint for infrastructure is directly correlated to a company and a country's ability to to have intelligence.

Infrastructure is the input. Intelligence is the output.

Um and by the way all things being equal any company or any country is going to be better with more intelligence than less intelligence. Everyone needs more

less intelligence. Everyone needs more intelligence. So the first constraint is

intelligence. So the first constraint is infrastructure. The second big

infrastructure. The second big constraint is a trust deficit. And what

I mean by a trust deficit is these systems that AI is built on or these models are by definition non-deterministic in nature which means

that they're unpredictable. But we are building highly 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

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 going in?

Can I actually jailbreak this model algorithmically through a red teaming process that's algorithmic, not human-based? And once I found a way to

human-based? And once I found a way to jailbreak a model because it doesn't work the way that I expected it to work in certain categories, in certain areas, I should dynamically at runtime be able

to have runtime enforcement guardrails be put on it.

And if I can do that, then anyone who's a developer, anyone who is 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 for them. So you can innovate fearlessly. So

them. So you can innovate fearlessly. So

that's the second big constraint is a trust deficit. If you don't have safety

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

adoption. And then the third area is a data gap where right now if you think about it most um models the the the cons

the issue that people had with scaling laws was they thought that at some point in time we're going to run out of data which was actually true. You're running

out of human generated data publicly available on the internet. However,

um there is plenty of uh evidence now that's starting to build up that synthetic data is starting to show very efficacious kind of outcomes for AI models right?

>> It's working great in post training. I

just talked to Maxim Leon 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's

great. And then the second thing that's happening is machine data which is data from applications and agents. And the

more you have for every human, if you have 10 agents or 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, um, you know, making sure that you can prevent a breach from occurring. All of

those things is where machine data can be hugely beneficial. And so these three constraints infrastructure um 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 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 actually have economic prosperity

as well as national security. And that

same um applies even for companies where companies can be more financially viable and have greater security posture. So I

feel like those three dimensions are pretty important to understand and say what does the market need to do to ensure that we can actually drive these

constraints to a a point of you know where it does not become a constraint but it becomes um a state of abundance because if you have plenty of trust and

if you have no infrastructure constraint and if you don't have a data gap then the potential of AI to alter the course of humanity for the positive and for the good is going to be meaningful 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

humans. That future requires humans to be participating.

>> To call back to your previous point, uh 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 how your perspective has really informed Cisco's decision- making around product development. Uh I know there's

product development. Uh I know there's AI defense as I mentioned and then my understanding is you've actually open sourced your first security model as well uh 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

approach here? Yeah. So on the AI defense side, just to close out on that, basically what it is is a mechanism for us making sure that we can get visibility on what data is flowing

through a model. Uh do algorithmic red teaming and jailbreak a model using AI defense APIs and then make sure that you can runtime enforcement apply guardrails. That's AI defense, right? So

guardrails. That's AI defense, right? So

think of it like almost like a very sophisticated version of an AI firewall.

Um, going back to your model question, in the consumer world, I think you're going to see a few handful of foundation models be

very successful.

OpenAI Gemini X.AI you know, Anthropic in in in certain 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 have most of them of of of the volume of inference.

On the enterprise side, I think it's going to be very different. You're going

to have thousands of models. You might

have tens 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 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

60 70% of the queries now are not going to anthropic. they might be going to

to anthropic. they might be going to their own models and then they might have a a certain percentage that goes to cloud. Why is that important? Because as

cloud. Why is that important? Because as

you have more and more of these models um it's going to get more not only more costefficient 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 depending 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 we care about deeply. We want to make sure that we build would wouldn't

it 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

of data. The key is to make sure that you train it on the right data, you know, and so distill it down to the just the right amount of data that you want to train it on where the efficacy of

pre-training a model that is a um, you know open-source uh, 8 billion parameter model um, we are seeing can now actually

perform better than a 70 billion parameter model. And what we're also

parameter model. And what we're also seeing is that 8 billion parameter model can be further quantized and you can run it on a CPU on a laptop. Uh and and it actually just has a very very different

kind of economic outcome. Uh 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 out products for the edge that says okay if you have 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 doesn't have wires dangling.

You don't have to go out and have someone special come in to get in installed. You can manage it centrally.

installed. You can manage it centrally.

It's just inference plugandplay on the edge so that you have the full from core to the edge um inferencing capability provided by Cisco. So those are the kind

of things that we've actually been um spending our time building because we feel like you have to build things where there is a true pain point and a challenge. And right now the challenges

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 ideation 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 and we we'll end up doing well and we recently had our earnings for the quarter and um you know we had a very very successful earnings because

you're starting to see that hyperscalers and um you know neoclouds and sovereign clouds and service providers and enterprises 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 the AI 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

East. Can you talk a bit about how these partnerships are helping to solve this infrastructure gap? because as as you

infrastructure gap? because as as you brought up here, we're seeing this interesting tension where both enterprises and countries are saying, I either don't have enough compute, I don't have enough energy, uh or I don't

have the trust I need to actually put these systems into production. Uh and

clearly you're trying to address that challenge. And part of that is through

challenge. And part of that is through these major partnerships. I think in my mind a partnership a company's willingness to partner

uh is um a very indicative of the of the company's arrogance level.

And the reason I say that is you can't be arrogant to think that you're going to go out and build every single thing that's needed for a very very large growing economy uh and the

largest platform shift. And that means that you have to have enough humility to know that there will be overlaps 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 wall garden. And so that's something that we've actually thought pretty deeply about. And um when I joined 5 years ago, one of my my big

kind of convictions over here was that we have to be extremely open in 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

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 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

everyone's happy. And this does not have to be a zero- sum game, you know. And so

we've partnered with Nvidia, which we we don't have that much of a competitive overlap with them, but it it's a very synergistic partnership because they

build GPUs, we build the networking, and the GPUs are, you know, the way I think about this is power is the constraint.

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 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 a 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

GPUs. So you created a server with four to eight GPUs and then the model got even bigger. So you said, "Oh, these

even bigger. So you said, "Oh, these servers 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

rack. And then you said, "Oh, I need to have a set a row of racks that need to be tied together within a data center."

And now we are at the point where you're not just doing, you know, the rack networking was called scale up, the row networking was called scale out. And now

what you've got is networking that goes across data centers when two data centers that might be hundreds of kilometers apart will need to act as one

coherent ultracluster for a training run. And we have created silicon and

run. And we have created silicon and systems and optics to be able to 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 that's what's called scale across.

And those things are impossible to do without partnerships with with others.

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're arrogant 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

market. You're not you're not doing yourself any service. So economically

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 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

two core principles I hold very close to me which is first one is you got to build a platform not just an individual 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 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 zero sum games with multiple competitors and you're always going to have multiple competitors. So,

>> and and you you you can't think that you're going to get every market transition right every single time and be the first and first to market over there.

>> 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

resources. Like I I think it's just much better to make sure that you stay focused at what you do best. My my rule of this one is if I 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 technology is is so big that it actually 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 um you know value in

uh inherent in in in in society with with your contribution. I think this is a great point for leadership as well um not just for you know company to company

interactions but to building your own personal stack of skills like yes you should build out and trust and improve your unique skill set but you're not going to be an expert in everything under the sun. This is why people build

teams. This is why we bring in intelligent systems to support us. Um,

so I I just think it's an important note here, especially as we think about that jobs conversation that we had earlier.

And I see this strategy for Cisco playing out in multiple ways. So we

talked a bit about Nvidia. Um, and as I mentioned, you've also been deepening partnerships uh in the Middle East with G42 and the UAE uh looking to build out secure endto-end AI infrastructure

there. It's obviously an emerging area

there. 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 asked. Um,

so Nvidia, we've got a multi-dimensional partnership where we they build out AI factories. We said, what about if we

factories. We said, what about if we actually had secure AI factories? Would

you want uh an AI factory or would you want a secure AI factory? It makes more sense to have a secure AI factory. So we

actually have our security capabilities u factored into the AI factory architecture and then we we have um you know their reference architecture um 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 uh or not at our earnings at at GTC um you know

the 9100 switch where we can take that spectrum X silicon and integrate that um uh with our our our switches so that we can build switches with their silicon as

well. Uh even though we build our own

well. Uh 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 customers a choice. Um and so those are the kind of things that we've done with

Nvidia and we have we have a continued level of um you know kind of um of of tight synergy between our engineering teams um that we can continue to keep

partnering together. In fact, some of

partnering together. In fact, some of the executives at NVIDIA are very dear friends of mine. Um, and um, you know, we we I I think people like doing business with people that they actually

tend to um, 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, you know. uh with G42 we have a very um uh

know. uh with G42 we have a very um uh 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 we are continuing to partner with them so that as they do this data center buildout that we become the

network of choice um for them and so that's happening over there uh and we will continue to keep doing that we u I've been to in Saudi in the UAE at three times in the past 6 months. We met

with you know the crown prince in 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. Um

years. So I think it really helps. Um

and then um I I'd say that even with the model providers um 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 we can provide network regardless of the model provider, regardless of the GPU provider. Uh and we want to provide the

provider. Uh and we want to provide the networking security observability across the entire stack.

>> Love that. And I would be remiss if I didn't briefly mention that our our sponsor, Galileo, is also an AI factory partner with Nvidia. So, it's a very cool program. Highly recommend checking

cool program. Highly recommend checking out the validated designs they're doing with Cisco and many others there. Uh

really really cool program. We'll have

to have Nvidia on to talk about it at some point. That'd be a fun episode as

some point. That'd be a fun episode as well. But I I want to ask about a couple

well. But I I want to ask about a couple of the other infrastructure constraints you brought up. So uh you mentioned you know power generation for AI 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 AI? I think

like power is a very interesting one because what's happening with power is rather than power being pulled to where the 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 uh I think every

country is going to want to have their own sovereign data centers um and what we need to make sure that we do as an American company that believes in um uh

the power of uh you know what America brings to the table is we want to make sure that any any and all of these data center buildouts that are happening worldwide are using American

technologies to actually build these data centers out. And so whether it be in um in a in in Southeast Asia, whether it be within um um 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 buildout of data centers in addition to the data centers that are being built out in the

US. And I think that the power scarcity

US. And I think that the power scarcity will make it um very important that these are global 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 center and you might need to have multiple data centers but those need to operate like one data center that might be hundreds

of kilometers apart because a training run has to operate with a number of GPUs that might not all fit within one data center. So if you need 300,000 or

center. So if you need 300,000 or 500,000 or a million GPUs to go out and do a training run and a data center can only host 50 70,000 100,000 GPUs, then

you might need to have a coherent cluster of GPUs that might span data centers. And that's where scale across

centers. 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 uh in a training run you have to restart your training run. So you have to have,

you know, technology that's built in the silicon chip itself that says I'm going to provide things like debuffering. So I

can buffer the variance of the bit rate of the packets going in and then that that way if there's a little bit of a dip um I'm I'm not going to be um I'm not going to go out and negatively

impact the training run because that costs millions of dollars if you have to restart the training run. So those are the kind of things that um require deep architectural forethought that we've

actually put in place for building out and these are like hard computer science problems uh 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 uh picks and 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 make in

>> and being a Pix and Shovels platform during a gold rush is a fantastic place to be.

>> Ain't entirely shabby. You know, it was a it was a good good time to be in the Pix 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 is an American company but is working with these major international partners.

Folks want to have sovereign 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

zero- sum game as you brought up?

>> Yeah, I think on on that front um you know we what's really important is working closely um between the public and private sectors. The public private sector

sectors. The public private sector partnership is more important today than ever before.

um you know I like in the past 6 to 12 months the number of government leaders I've met uh and gone out and visited is non-trivial and why is that important

because I think we have to learn from them on what their their challenges are they have to 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 um there are two choices right now on who you can buy AI infrastructure from, 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 um you know kind of sense of urgency on this particular topic because we have to move really

fast because they're a very capable competitor in China. they actually can uh they have done a great job in innovating and their constraints are slightly different from the world's constraints

>> and it's helping them to be more efficient in certain areas looking at DC >> certain areas and in certain other areas they might not be as viable a provider

>> um as we might be like for example >> or that it they might build technology whether it's silicon or something that might be you know like for example

example they don't build 2 nanometer GPUs um you know they build 7 nanometer GPUs uh the US builds 2 nanometer GPUs but they have more resources on the power

side >> yes >> and they have more engineering capacity and so they can say well the inefficiency of a 7 nanometer GPU can be offset by unlimited power and unlimited

you know kind of optimization of engineering resources those are the kind of things that it puts and takes that'll have to played and I I feel like it's very

important to keep in mind that a as you a as we build out the um um the future that US is a very formidable participant

in building out uh AI capacity data center capacity not just for our own needs but for the global need worldwide.

Um and and that requires us to make sure that we are engaging with the governments, we have the right level of regulatory policies in place. Um we are working with our US 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 um uh I I have to give

that. And I think um uh 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 u all the administrations of different countries that we're working

with. I think the silicon point is

with. I think the silicon point is obviously the one that's most talked about. Look, we have this chip

about. Look, we have this chip advantage. We simply are our head here.

advantage. We simply are our head here.

We're keeping China from getting access 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

advantage. But I I think an underrated conversation that some folks are having, but I think isn't as broadly consumed in AI circles is the capital uh pardon me,

the capital allocation advantage that America has as well with the the VC community with the ability to fund all these startups uh fund all these different companies instead of having a

largely state directed system that is you know powerful when it's brought to bear on a problem that they know how to solve but is less strong when it comes to solving diverse 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 um it it's

underestimate. I think it's um it it's um it's definitely one of the uh one of the superpowers that we have. Um, and

you know, at 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, you double down on it and capitalism is a great way to make 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 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 um 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

it over. So, while we're in the lead right now, I I subscribe to the belief that being paranoid is is always a good idea in these kind of markets and these kind of technonic shifts that are occurring.

>> Yeah, we we can't be dismissive. There

are real advantages of other systems and there are real advantages that China has in particular and I do think your point about speed is a really important one and it actually relates back to Cisco

for me in some ways because Cisco has been described as the world's largest startup because of your acquisition strategy at some points. Uh 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 um lucky in the in the sense that we have a very strong balance sheet uh and 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

at Cisco. like we we do that inherently well. And so when you start thinking

well. And so when you start thinking about our acquisition strategy, we've been a very inquisitive company um throughout our lifetime. But I I have a

slightly different take on on this than um than we might have had a while ago, which is I don't believe that your strategy should be one that's dictated

by acquisitions.

I believe that you should have a very clear true north and a very clear point of view of the future that you want to build out where you have permission to

play 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't be shy

to deploy your balance 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 Ju, it seems like category X is a priority area. What are we going to buy?"

area. What are we going to buy?"

And my response to them is, I think it's a wrong question. The question should be category X seems like it's a priority area. What are we going to build? And if

area. What are we going to build? And if

during the course of building you come up with an idea that could accelerate your buildout, then by all means use your balance sheet effectively to make sure that you can deploy that capital

and your investors would love it, your your customers would love it, all of that. But don't go in this rapid frenzy

that. But don't go in this rapid frenzy of just randomly buying things because you missed a 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 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't have a not built, you know, kind of not built here syndrome. You have to make sure

here syndrome. You have to make sure that you stay open-minded to, oh, someone else built something better than us. And that seems to be an

us. And that seems to be an 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've thought about the acquisition strategy. And I think it's

acquisition strategy. And I think it's um it's working out really well. We've

made, you know, I my time over here I've made, you know, 17 18 acquisitions. I

don't I don't know the count anymore, but um but it's not something where um I um u I obsess about the acquisitions. I

obsess about the strategy that we need to pursue. And acquisitions is just one

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 platform strategy. Exactly. It's very well put.

strategy. Exactly. It's very well put.

>> 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, 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 is we have to be the critical infrastructure for the AI era. What does that mean? That means

era. What does that mean? That means

that at every layer of the stack we should have meaningful contribution. We

will build silicon and network AS6 that can really help us um 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 boxes and the hardware.

We will be 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 differentiators that we have because our our security competitors don't have networking and our networking competitors don't have security. We are

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. Um and then we need to make sure

helps. Um and then we need to make sure that we have observability on top of that. Um and finally we should have

that. Um and finally we should have knowhow on how to build applications that get to hundreds of millions of people. And if we can build that stack

people. And if we can build that stack all the way through for classical workloads as well as AI workloads, I

think we will be in um be in amazing shape as a company. not just by making money but by contributing to society uh and the community in the best 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 missiondriven

purposeoriented company and if we um the world is a different place when we win compared to when someone else wins And and I think that's a really

important thing to keep in mind on how we kind of um want to continue to keep building on on top of the investments that we've made. We've been in business

for 40 years. The amazing part is during those 40 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 um I I think this is uh one of the most

um special companies in the world right now if if that happens and right now 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 uh and we are now back on a tear on innovation. And we've

innovated 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 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. Um, you know, we are I'm I'm so

excited. Um, 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 their heart and soul into it. And you can feel it when people

into it. And you can feel it when people put their heart and soul into something versus when they're just doing a job for the sake of doing the job. Our employees

are putting their heart and soul into it right now. And you can feel it in every

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. Um 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 over the last year working with a lot of the folks over at Outshift on Outshift by Cisco for folks who don't know but uh doing amazing innovation

work for Cisco and we had the opportunity to be part of their agency partnership which is now with the Linux Foundation and looking at how agents communicate and kind of setting the stage for that next decade

>> and super talented team members there.

Uh it's been a ton of fun working with them.

>> VJO is a great leader. We've um we've been very lucky to have him and he is um you know he's a force.

>> Absolutely. And I'll shout out on the marketing side getting to partner with Luke Tucker and and Leah writer was fantastic. Great great folks um M and

fantastic. Great great folks um M and many others. Uh so hopefully they listen

many others. Uh so hopefully they listen to this episode. Uh there as you think about building the team at Cisco continuing to drive this culture of innovation. I'm sure you're taking

innovation. I'm sure you're taking lessons from your own career and it sounds like you've been thinking in a very missiondriven format for a long time and that's part of your inspiration here. How do you

here. How do you I guess what lessons one are you taking from your own career that you're applying to building the culture at Cisco to ensure incredible innovation

and two who are the sorts of people you're looking for to join the team? So

I I look for um certain characteristics in people u much more so than other characteristics. And so I I'll just tell

characteristics. And so I I'll just tell you what I I find um 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.

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're not hungry. and the the the level of lift that it takes to teach someone to be hungry, which you can't do anyway.

>> I think life has to teach you hunger to your point.

>> Yeah, that's you you just have to be self motivated. I don't want to have an

self motivated. I don't want to have an email or a rahrrah session to motivate people. I think people have to be

people. I think people have to be intrinsically motivated, not extrinsically seek motivation from others. So that's number one. So I I

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 have this

insatiable appetite to learn and learning should be the thing that gets you really kind of excited about life and [snorts] uh I I was talking to one of

our engineers in the silicon team recently and I asked him I'm like hey so how are you um how are you thinking about Cisco? So he's like, "Ju, as long

about Cisco? So he's like, "Ju, as long as I'm learning, I'm happy. The moment I stop 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 I need to make sure that I'm I'm in those projects where the learning is exciting to me."

And I'm like, "Absolutely. That's the

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

be easy. You know, like the amount of time that you can take to get dextrous at something is compressed so much that it's actually fun to live

life 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. 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. Um 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

your vision. And if you don't make this a team sport, it doesn't work right. And

then the other thing I look for is, you know, is there a um and 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 you if if you're not, but what I don't look for

always is 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 before when there's no evidence that that pattern will repeat itself because

there's so many variables that might that might have changed in the meanwhile. So I actually feel like

meanwhile. So I actually feel like having people that can do good pattern recognition through experience coupled with people that have no experience that

can teach the people that have pattern recognition to also unlearn the bad habits that they've learned constantly I think is a really important thing. And

so the combination of a team composition which is experience and 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 nice to be with people that aren't

afraid of conflict and aren't afraid of debate but aren't either, you know.

And it's like brilliant are are a pain to deal with.

On the other hand, um one of the worst forms of um you know, one of the worst characteristics that I see in a company

sometimes is being uh 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 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 uh durability in in my mind >> 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 you're doing them a disservice and you're doing the

company a disservice. you're doing

yourself as a disservice and you're creating this long-term cultural problem 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

group think and group think is the worst thing that can happen. And you know most of the times like what I get more paranoid about is not during the times

when we're we're like 5 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 5 years ago about us becoming complacent and um um you know risking that. I worry much more about

risking that. I worry much more about that now than I did five years ago because uh success can breed arrogance and success can breed complacency. And

what you have to do especially when you have success is preserve humility and uh stay paranoid. And if you can

preserve humility and 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 um your um your your headwind and it actually pulls you back. And so I've

always found that intellectual arrogance is something to be really really kind of careful of like stay steer clear of it.

And it's so easy for smart people to even reason their way out of um not needing to be intellectually arrogant.

Like they're like, "No, no, we don't need to be. I'm smarter and I know it and they'll have some kind of good justification for it." So I I always feel like 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 important and it's um 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

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 of

conflict is seen as humility, whereas the reality is you should be humble with um 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

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. Uh

seek the truth and if you can do 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

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. you know, I I um I actually

heights. you know, I I um I actually don't think of myself right now as particularly like uber successful. Um in

fact, there are times in my life where I've feel like, yeah, I've I feel like I could do more. And so, um one thing I tend not to do much of is I

don't tend to gloat in what what has happened in the past and I just focus on what's not happened yet and what do we need to go at. Um, 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.

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 job done.

And that means that I tend to do a lot of jobs where I don't have that much experience. You know, when I when Chuck

experience. You know, when I when Chuck 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 didn't know that much about software. And when I had first

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 I've figured this out because it's when you start getting that cockiness that you've 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

life before. So I think that's that's a great note. I do have to ask you one

great note. I do have to ask you one very specific career question because you told me this before we started recording and I I have to ask. You told

me you you don't use email anymore. Can

you tell me about that?

>> I'm not particularly super, 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, is an inbox zero kind of guy. He's super organized.

>> Does that create some tension? Maybe

sometimes.

>> No, he's actually the thing I 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 because if he sends me an email, he will send me a text and say, "Check your email."

email." >> You know, um but um which is something that is just a boon that you have if your boss is telling you like, "Hey, check your email. I'm I've accepted you for your flaws and I'm going to make

sure you do But what >> an extra piece of advice here. Work for

great leaders who understand their people, >> who understand their people. Like he's

really good at that. And the the reason I don't check email that much is I find it to be super

um you know like interrupting to my flow of thinking. And if I just did every

of thinking. And if I just did every single one of my emails and the higher you go get up in the organization then the volume of email gets to be untenable. And so what ends up happening

untenable. And so what ends up happening is you get hundreds and hundreds of emails a day. If I just did email all day long, I think there's people way smarter than me that keep have a system

for how they manage email. I'm just not that guy. Every person I've worked for

that guy. Every person I've worked for so far though has been really good at doing email. So I have to still find

doing email. So I have to still find someone who I can relate to on that dimension that's not good at doing email that I've worked for. But um but I do feel like

worked for. But um 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 I'll give you this example. So there was

this um 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 um 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 you need to do next." And he said to me, um, take a piece of paper before you start your next job and create four

columns. In the first column, write down

columns. In the first column, write down everything. And by the way, you don't

everything. And by the way, you don't need to show this paper to anyone. So

there's no reason for you not to be intellectually honest. This is just for

intellectually honest. This is just for yourself. But the first column, write

yourself. But the first column, write down things that you're really good at doing that you love doing, right? Second column, write down things

right? Second column, write down things that you're really good at doing that you hate doing.

Third column, write down things that you suck at that you love doing.

And fourth 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. So for example, don't say

your thinking. So for example, don't say I love managing very large teams as a passion because you're just seeking status at that point, right? And so stop

doing that because no one likes like managing a team is not a thing in and of itself. Managing

itself. Managing >> Maybe you love growing people and mentoring.

>> Yeah. Exactly. And so so he said do that then take the paper and cut it into two pieces. the first column and then the

pieces. the first column and then the second, third, and fourth column. Now,

crumple the second, third, and fourth column and throw it in the bin because all you should care about is the first column. What do you love doing that

column. What do you love doing that you're really good at doing? And for

everything else, make sure you surround yourself with 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 that stuck with me for a long time. So for example, I never take a job in a place without my um my head of business operations who comes with me in every job for the past 15

years. Her name is Jesse. She's even

years. Her name is Jesse. She's 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

her. She can veto me at any point in time. She is the one who actually knows

time. She is the one who actually knows me well, knows when I don't do well. Um,

and and the reason I do that is because I am infinitely better with her by my side than without. My chief of staff,

she 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 have no tolerance for placating you and

inflating your ego unnecessarily, 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 two, three people around you like this and then cherish them. I've been lucky enough in my life

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 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 tra

an individual. I don't want to be tra 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 a

championship. And when we've got 15

championship. And when we've got 15 years of working together experience, there's an in 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 um the and this just get to the >> high trust teams just perform better.

Period. and they just perform better, you know, and and we spend a lot of time in establishing trust um in our teams and the ones that we build because I

feel like if you don't have that then then all you're doing is adjusting stylistic mechanisms of providing feedback. And I feel like that's the

feedback. And I feel like that's the wrong way to do things. Like you should just accept people for who they are and then say, you know, I should be able 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,

"Jethu, if you never worry about who gets credit for something, you'll go much farther in life."

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

wins. Ideally, you even forget whose idea it was by the time the debate ends, but the best idea is one. I love that we've had such a wide-ranging conversation, and thank you for for

sitting for so long with me here. It's

already been 70 minutes, but I see these clear through lines. You know, you talked about trust both in infrastructure and in people, and I think there's a lot of similarities in and how you build 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 of avoiding uh who gets the credit and about open

partnerships, growing the pie together.

You've also talked about uh constructive tension. I think these ideas are all

tension. I think these ideas are all extremely complimentary. So, I love that

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

worldview and how you are approaching things that just makes a sense across the board as far as how you've built your team, how you're building Cisco's products, and how you see the future.

Really, >> I appreciate it. It's um you know, the thing is is these things are not super complicated. They just are hard to

complicated. They just are hard to implement uh and stick with. Um, 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.

So, how have you done it?

>> What we do is we have this um um 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 on my list. Actually,

list. Actually, >> I I I I don't read full books, but I I I read excerpts and summaries of them, but I think it's a really good um

philosophy. And the way that we do it is

philosophy. And the way that we do it is rather than just trying to 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

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.

managing. That's not your first team.

The first team is the peers that you work with. So if I'm running product,

work with. So if I'm running product, product is not my first team. The

executive leadership team on Chuck's staff is my first team. Right? The the

members of my team for them, the product team is their first team, not their individual teams that they might be managing. So I think h having clarity

managing. So I think h having clarity and who's the first team super important. The second thing that we've

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 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 about yourself that

no one else might know.

And then what I what we will tend to do on those those exercises is I will start as the leader 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 uh a con man 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

um you know I I started my own business when I came here and that's how I actually you know one thing led to the other led to the other and and one of my

biggest um 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 un unemployed. It was a fear of being poor. It was a fear and especially when my mom was alive. I was

extremely worried about um you know not having a job. And so I would be the hardest

job. And so I would be the hardest working guy um 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 a it's a great thing for business. It's a terrible way to live

business. It's a terrible way to live life, right? I think I'm far less

life, right? I think I'm far less fearful now than I used to be back then.

Um, but that's also because like enough time has passed and you you get to reflect. But when I start with something

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 I hate losing. The

right? Is that I I hate losing. The

reason I hate losing is I don't want to be irrelevant. The reason I don't want

be irrelevant. The reason I don't want to be irrelevant is I don't want to be in a position where me and my company are put in a position where we will not be able to feed our families. Uh and so

I have a very very kind of you know formed ethos around that. But if someone doesn't know that about me then they they they 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 um 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 is

a slacker. He showed up late." You know, I come in late to a meeting. She's like,

"Wow, Ju's usually never late. Is

everything okay? Is something wrong with him?

And that's called an attribution error where the absence of familiarity that we might have um you know with uh with someone can

create us to be um um to not give them benefit of the doubt when something goes wrong and the the level of familiarity we have might

allow us to give people um the the benefit of the doubt because we know them well and we know their character and 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, you you give it context for the AI 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

humans. And so we tend to do that a lot in these offsites that we do. Anytime

there's a new team member that we get, anytime 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 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. Um, and so those are kind of

debates. Um, and so those are kind of things that kind of build on top of each other. I I love that you bring this up

other. I I love that you bring this up because you're both creating space for others through your sharing and like openness here and you're extending that trust. Um, I mean, I I'll I'll share a

trust. Um, I mean, I I'll I'll share a little 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 contexts that don't

always come up in work context, but when you have an opportunity to go deep with your team, you can learn so much about them and be able to really understand how they tick. And I think that matters

so much. Uh, G2, I I can't thank you

so much. Uh, G2, I I can't thank you enough for giving me the time uh today to learn a lot about you and and go deep with you. It's been so much fun having

with you. It's been so much fun having you on the podcast. I really appreciate you joining us.

>> Thanks for um uh exploring the range of all the topics and thank you for having me, Connor. And I'm looking forward to

me, Connor. And I'm looking forward to Chain of Thought becoming one of the uh most widely listened to podcasts, my friend.

>> Fingers crossed. I I hope so. It's been

a fantastic conversation. Everything

from people, infrastructure, security, strategic thinking, your perspective.

But where can folks who are listening and can't get enough G2 uh 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 uh LinkedIn is probably where I post most frequently. Um and so you know G2 Patel on LinkedIn is um where I would go and and follow me and then the other

place is Twitter. My handle is J Patel41. So, one of the two places I I

Patel41. So, one of the two places I I tend to consume more news from Twitter and post more on LinkedIn. Um, but uh I use both. So, you know, go to one of

use both. So, you know, go to one of those two places and um if you happen to be looking for a job in AI, um make sure you apply at Cisco.

>> I love it. Uh well, JT, thank you so much. Uh we'll be sure to link

much. Uh we'll be sure to link everything here in the show notes including the resources around things like Cisco unified edge and other things we talked about today uh including Foundation AI and so many other initiatives. Listeners, thank you for

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. I'll be

posting them to my LinkedIn uh as well as our YouTube and of course we'll be sharing them with the J2 and the Cisco team. Uh and you can if you really

team. Uh 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

post. We always love to hear from you and uh yeah, tell us what your favorite part of uh GG's episode was. We'd love

to know. And Ju, thanks again. It's

[music] been great.

>> Thank you, Governor.