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Cisco es reconocida por sus productos de datos, redes, seguridad y colaboración. En este episodio del podcast Me, Myself, and AI, Jeetu Patel, presidente y director de producto de Cisco, conversa con el presentador Sam Ransbotham sobre la Inteligencia Artificial, una “megatendencia” que Jeetu considera quizás más significativa que el desarrollo de internet o el automóvil, debido a su capacidad para aprovechar los avances tecnológicos previos.
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00:02We talk a lot about AI being overhyped. Today's guest believes it's not. Continue listening
00:08to learn how his point is connected to tech infrastructure and security.
00:13I'm Jitu Patel from Cisco, and you're listening to Me, Myself, and AI.
00:19Welcome to Me, Myself, and AI, a podcast from MIT Sloan Management Review, exploring the
00:25future of artificial intelligence. I'm Sam Ransbotham, Professor of Analytics at Boston
00:31College. I've been researching data, analytics, and AI at MIT SMR since 2014, with research
00:39articles, annual industry reports, case studies, and now 12 seasons of podcast episodes. On
00:46each episode, corporate leaders, cutting-edge researchers, and AI policymakers join us to
00:52break down what separates AI hype from AI success.
00:58Hi, listeners. Thanks for joining us again. Today, I'm talking with Jitu Patel, President
01:03and Chief Product Officer at Cisco. Jitu, thanks for joining us.
01:07Thank you for having me, Sam. It's great to be here.
01:09Well, as we talk, Cisco equipment is probably behind 90% of the infrastructure that we're
01:13using, but some of the listeners may not be aware of all that Cisco does. So let's start
01:19off there. Jitu, can you give us a background of Cisco and, in particular, your role?
01:23Sure. I'll start in reverse order. So I run products at Cisco. So all the products that
01:29you use from Cisco, whether it be networking products, whether it be security products,
01:35whether it be data products or collaboration products, those typically are ones that I'm
01:40in charge of building and taking the market. Of course, we're the very, very capable team.
01:45Essentially, the way you should think about Cisco is we are the critical infrastructure company for
01:50the AI era. So all of the plumbing that's required to make sure that people can connect,
01:57people can stay secure while they're connected, and people can make sure that they have the
02:02data platform. Those are the things that we provide to the market.
02:05I saw a quote from you to say, well, think of us as the picks and shovels during the AI
02:11gold rush
02:12era. And for listeners who may not be aware, there's a famous or infamous statement that the
02:18people who made money in the gold rush were people who sold picks and shovels to the miners.
02:22Individual miners, some of them made it big, but some of them did not. And I think your analogy is
02:27spot on there.
02:28Yeah. You know, one of the things that you find when you go through these kind of
02:33massive, what I would call disruptive platform shifts, where we've all been going down a certain
02:40path with a certain set of assumptions, and then the assumptions change because a whole new set of
02:45technologies emerge, which is what AI is, what you find is the infrastructure that's required to go out
02:52and run those technologies needs to be rethought and reimagined. And anytime there's any one of these
03:00major platform shifts, the infrastructure providers tend to make out pretty well, because you have to
03:06change the entire plumbing of the apparatus that's going to be used. So if you think about when
03:11automobiles were built, you know, you now need to have roads and you need to have expressways and you
03:18need to have traffic lights and you need to have a whole system in place for automobiles actually get
03:23integrated into society. So it's not just the automobile, but everything around it needs to change.
03:29And if you think about AI right now, these data centers where these digital workers are going to live,
03:36they have to be completely reimagined because the current data center does not have the power
03:43availability and the compute requirements and the network bandwidth to be able to fulfill and satiate
03:49the needs of what an AI system would need. So you have to kind of rethink and reimagine and,
03:54as they call in technical terms, re-rack the data centers because there's racks and racks of
03:59computers and network and switching gear that actually have to be cooled in a certain way and
04:03so on and so forth. And so that entire shift is what we are in the midst of. And Cisco
04:09is a natural
04:10benefactor of it because we provide the infrastructure for the PI era.
04:14So some of the analogies you're making, I mean, we talked about the gold rush, we talked about the
04:18internet, we talked about cars. Is artificial intelligence at that level of big deal or not?
04:26I think it might be bigger. And the way I've seen these kind of shifts happen, you know, imagine if
04:34amazon.com got built in the 1600s. It would be an epically failed company because you didn't have the
04:42internet and you didn't have the underlying infrastructure on top of which Amazon could be
04:47built because you didn't have the shipping and the logistics infrastructure and the internet and
04:51all of those pieces. In a similar vein with AI, we have the benefit of having all of the infrastructure
04:59that's been built out to date. And so when you have something that's built taking advantage of all of
05:05these things, by definition, each one of these subsequent major transformative waves tends to be bigger
05:12in impact than the previous ones, just because it was built on top of shoulders of giants of the
05:18previous innovations that have happened. And so I would say this is probably the most consequential
05:24set of inventions that we will have seen in our lifetime, for sure, and probably arguably in
05:30humanity. But the thing to keep in mind is the pace of innovation and the slope at which it happens
05:38actually makes it impossible to predict what's going to happen three years from now, because we've
05:45compressed the time, scientific progress will probably compound by a thousand X. And so you've
05:51compressed this time scale where it's actually, we're in a warped situation right now where humans
05:56can't make sense of this because everything's moving so fast.
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06:56Actually, there's so many things to talk about in there. I think your analogy to Amazon in the 1600
07:01is interesting because, you know, for example, we had neural network designs back in the 1980s.
07:06We just didn't have the compute infrastructure, the data, the telecom to pull it off. And so it took
07:12all these things coming together. And I think what you're saying is that all these things
07:16are together now for us to build from.
07:19Actually, the biggest thing that we have, that we didn't have then, because of which this would have
07:24been hard to go invent is, you know, AI has been around for a while, but when did it actually
07:30take
07:31off? It took off in November, on November 30th of 2022. What was so significant about that date?
07:39That's when ChatGPT was launched. What was so significant about ChatGPT? It was essentially a
07:45large, what they call language model. And a large language model was a model that actually understood
07:52human language rather than the machine having to be rigid and the human having to learn with
07:58the machine's language. It became the other way around. How did that happen? That happened because
08:03we had petabytes and petabytes of publicly available data on the internet that you could use to train
08:13these models so that these models would then know what to do with it. And so if you didn't have
08:18the
08:18internet, you would have not had AI because you would have not had that level of data to then train
08:23the models on. It goes back to our point of each one of these inventions or revolutions is built on
08:30the
08:31infrastructure provided by the previous revolution. And it's very evident in this case.
08:36One of the last conversations that I had with my grandfather was how much life had changed from
08:41no electricity, limited indoor plumbing, you know, no airplanes, no space travel, no computers to, you know,
08:49when he passed, how radically different that was. And I remember talking with him at the time saying,
08:54oh, wow, you lived through a bunch. You know, I can't imagine things changing as much during my lifetime.
08:59And I may very quickly be eating those words.
09:02And I think we get into this kind of cycle of like humans are not very good in general at
09:10imagining
09:12the exponential outcomes. We're very good at imagining linear regression, but not the exponentiality of
09:21the outcome. And so what ends up happening is we think about exponentiality in a single dimension,
09:27not multidimensional. And I, you know, there was years ago, I'd had a chance to sit down and talk to
09:33Ray
09:33Kurzweil, who's one of the scientists at Google. And this was like 20 years ago or something. And I was
09:39interviewing him for something. And we were talking about this notion of perpetual extension of life,
09:46where, you know, can a human live forever, live long enough to live forever. And he had written this
09:52book where his thesis at the time was, if you live until you're 40, we will have the science and
09:58technology to allow us to live in perpetuity. My topic of communication with him was around the
10:05social implications of that. Like what happens if seven generations live simultaneously or 10
10:10generations live simultaneously? That's going to be really hard because we are not going to have
10:15enough room to put everyone and we're not going to have enough props to go feed everyone. And he's
10:20like, you know, this is the problem with humans is you actually, we can't think in exponential terms
10:24because we think in a single dimension of exponentiality, which is if seven generations
10:29or 10 generations lived simultaneously, what would happen with everything else being exactly the same?
10:35But the reality is you might have skyscrapers that might be 2000 stories high, and you might have
10:40a crop cycle that takes, you know, three days. And so those are all things that would also
10:45simultaneously evolve so that they can accommodate the constraints that get created because of the
10:50developments that happen in certain areas. And I feel like that's the same over here is like when
10:54people say, you know, I think humans are going to be sitting on a beach, have nothing to do, and
10:59AI is
10:59going to do everything. I just chuckle a little bit because I just refuse to believe that humans are
11:05designed to be obsolete, you know? And so we will continue to find ways to add value and think
11:10creatively. And that doesn't mean that we'll be doing what we do today. It might very well mean that all
11:17the jobs that we do today might not be the jobs that we have. But that also doesn't mean that
11:21we're not
11:21going to have jobs, because the desire for a human to be productive and add value to society doesn't go
11:28away
11:28because something got automated. You just create higher order bits that you would then be able to go focus
11:33on that you were not able to focus on in the past.
11:36Exactly. And just to make sure that I can get this thrown back in my face later,
11:41I'm predicting massive increases in employment, not decreases. No one since the internet is doing
11:47less than they were before the internet, despite all the progress possible. I can't believe that
11:53that's not going to happen again. I think we're headed towards the opposite direction.
11:56I think you'll see some displacement of jobs temporarily, which we should not take lightly,
12:02because I think it will cause human suffering. But that does not mean that that will be the
12:08state in perpetuity. And what you have to keep in mind is that displacement period, if we get ahead
12:15of it because of the pattern that we're starting to witness, we might be able to actually get society
12:22retrained in a more efficient way than we might have done in the past with previous disruptions.
12:27And that might be a responsibility that the tech community, the collaboration between the public
12:34and private sector should have as a good beneficial outcome, because I think there's going to be more
12:39and more of a need for collaboration across different sectors. And this is one of those areas where I
12:44tend to be in the long term and optimist without being naive about the short term implications that
12:52this might have or even midterm implications it has around safety and security and the downside effects
12:58could be profoundly consequential that we have to keep in mind. But I refuse to believe that we're
13:04going to be obsolete or that we are not going to have value to add. You know, it just seems
13:08unnatural.
13:10Yeah, I think that's well put that we can have a positive aggregate effect, but still have lots of
13:16heterogeneity and how that average plays out across society.
13:19Being naive about that is going to hurt us in the long run. You mentioned security, though. And when
13:25we were talking about re-racking and infrastructure changes, we quickly slipped towards routers and
13:32modems and telecom and hardware oriented things. But one of the things that I think you're very
13:37focused on in terms of infrastructure is the idea of security and how does that become a first class
13:45player versus the thing that's derided as hampering productivity? So we've always had this sort of
13:51productivity security trade-off. I think that you've mentioned that we may not be making that trade-off
13:57anymore. How can we help the security be part of the infrastructure?
14:03Firstly, I think in this particular age, security is going to be a prerequisite for successful adoption of
14:10AI. Because if people don't trust these systems, they're not going to use them. And so that's very
14:16different from in the past where you would think about security as a necessary productivity
14:21impediment. That's no longer the case. Now it just happens to be a prerequisite for successful
14:28adoption of AI. But I feel like it's probably worth taking a step back even and saying, where are we
14:34still
14:34thinking very linearly? Like where I feel AI is underhyped the most is the fact that we still keep
14:43thinking that this is just a productivity game. Humans are going to get more productive. Things are going to
14:50happen cheaper, faster, better. I actually feel that's only the first order effect. The second order effect is
15:00you will actually start to see these AI models. And it's not even clear if large language models will
15:06be the ultimate destination. You'll have large world models, you'll have physical models, all of these
15:10things will start kind of combining together. But the new paradigm, whatever it ends up being at some
15:17point in time, and the existing ones will start to create original insight that did not exist in the
15:25human corpus of knowledge. It won't just be an aggregation mechanism. It won't just be where you
15:30take multitude of different perspectives. And this is not just a better search engine, where humans had
15:37some data and you indexed the data well, and you were able to go out and put it into a
15:41clean paragraph,
15:42it's going to start creating original insights that didn't exist in the human corpus of knowledge. And when
15:47that happens, the thing that changes is you are now able to imagine solving problems that you could never
15:53even dream of solving before.
16:01We're back with another segment of our branded interview series with Dr. Ananya Mukherjee,
16:06Vice Chancellor of Shivnader University. To recap, Shivnader is a private university founded in India in
16:132011. Dr. Mukherjee, thanks for taking the time to share a bit about your institute. Welcome back.
16:19Thank you very much for having me.
16:20Last time you mentioned the three areas of research that Shivnader University is focused on.
16:26Can you describe those in more detail?
16:28The first one is responsible AI. So few universities in the global south are focused on this theme in a
16:36comprehensive way. And our aspiration is to lead in the space. Right now, we are continuously confronted
16:44with this binary of utopia and dystopia of AI. But our goal is to be able to go beyond that
16:53and really
16:54think of responsible AI as a way in which we can maximize the benefits of AI and minimize its potential
17:03harm. The second area in which we are trying to grow is AI and health. As you know, India's health
17:11system
17:12operates at a scale unlike any other. We have vast patient volumes, immense diversity, complexity,
17:20issues of access, and so on. We believe that AI opens up a whole new frontier, which we are very
17:27committed to. And the collaboration across disciplines can happen and can create measurable social impact.
17:36So our goal here is to bring together scientists, engineers, educationists, public policy specialists,
17:44ethicists, and ordinary citizens to create some new ways of looking at health, which will have immense
17:53social advantage, particularly for underprivileged groups. Our third area in which we are going to focus
18:02is on curriculum and pedagogy for an AI world. So we want to build on our recognized strength of this
18:10research-infused interdisciplinary curriculum. And we want to focus on preparing our students for an AI world.
18:18That sounds great. Thanks for joining us. Do you have a website that prospective students should visit?
18:23Yes, we would encourage people to visit our website, snu.edu.in slash home. That is where you would find
18:34everything about us.
18:47And that is far beyond just going out and optimizing for productivity.
18:53And I feel like that's the most misunderstood part of AI is, oh, I'm just going to get more
18:58productive. Productive use is going to be like 10% of the equation. Going out and doing things you
19:05couldn't do before and solving problems you couldn't solve before in different ways that you couldn't
19:10even dream of solving before is probably going to be the 90% factor.
19:14Yeah, I like that idea to observe the productivity effects first, because as you say, they're
19:19first order. But how do companies, how do people start thinking about what they can do with that
19:2590%? You know, productivity is pretty tempting. I mean, I like greater productivity. This can be
19:30hard for me to turn away from that. Or, you know, maybe turning away is me sort of making it
19:35a
19:35Hobson's choice where you have to do one or the other. But how do people start thinking about
19:40this 90% or these more than productivity options? I don't think I've ever talked to anybody who
19:46thought AI was underhyped. And I think we're on record for saying that here.
19:50I think where it's overhyped is where the human obsolescence becomes almost a foregone conclusion
19:56in some people's minds. I think that's where it's overhyped, because I feel like human instinct
20:02and human judgment is still pretty hard to go out and replicate in the machine's ability to do things,
20:07because we don't make most of our decisions based on data. We make a lot of our decisions based
20:12on gut. And that gut is hard to go replicate. And typically, people say, listen to your gut,
20:18because there's a reason for it. There's an instinct that's palpable. But to go back to your
20:23question, or what should companies be thinking about? And by the way, I think I highly encourage
20:30the productivity argument, I think everyone should go out and think about productivity,
20:34and they should continue to keep kind of powering through that, it's going to be a great benefit that
20:39we will all be recipients of. Where I think the unlock truly comes in is by actually trying to
20:48make sure that we challenge the conventional norms of thinking, and ask ourselves the question of what
20:55problems have we been conditioned to think that we can't solve? And are those unsolvable moving
21:01forward just the way that they were in the past? And I feel like you'll actually start to find very
21:06different answers. I feel like we need more and more questioning that happens of the status quo.
21:15And the way to do that, in my mind, the one single thing is going to be the exponential difference
21:21is you have to unlearn as much as you're learning. And unlearning requires that you actually inject new
21:31talent into the system at a very rapid pace and then give creative freedom to that talent so that you're
21:41actually getting mentored by them just as much as you're mentoring them. And so this was a recent
21:46conversation I'd had at a conference I was at where people were like, you know, so entry-level jobs are
21:52going
21:52to go away. And we're just not going to hire early in career people, which in my mind seems like
22:00the
22:00stupidest idea that a company could pursue. Because if you actually don't hire new people to come in,
22:09you have essentially given up of injection of new talent and new ideas into the thought process.
22:16And so this kind of baggage of experience will always hold you back because you know a lot of
22:23things and you might not be as a company good at unlearning and you have no one else to actually
22:29instigate that unlearning and catalyze that unlearning by asking questions because they didn't
22:35have the baggage of knowing. I do feel like the mix, the continued infusion of talent early in career
22:44is going to be so important for companies to be able to get the most out of it because
22:49we have to understand instinctively how to go out and use these tools which are in service of humans
22:57in a very different way than the way that we might have used them in the past.
23:01And right now, frankly, if you take someone who is a 20-year-old and a 28-year-old and
23:06compare the
23:07two of them and how they use AI, it's night and day different. You know, a 28-year-old might
23:12actually use it
23:13for productivity. They'll go out and they'll ask it some questions because they've got some answers
23:17to get and then they'll move on. A 20-year-old thinks of it like a companion and a brainstorming
23:23partner. And they might actually talk to it and brainstorm with it so that they can come to
23:30an ideation. They're not looking for answers. They're looking for substantive volleying back and
23:37forth and brainstorming. I learned that from the interns that would come into Cisco. And so I think
23:45that's the aspect that I think we have to keep in mind, that as a company, we have to make
23:49sure that
23:50we keep challenging and disrupting ourselves before someone else disrupts us. And by the way,
23:56innovation is not limited. Like this is the one thing that people kind of bucket into these
24:02completely unproductive ways, which is, you know, you're a small startup, you innovate really fast,
24:08you become large, you stop innovating. I think it's nonsense. Because innovation is not like
24:15something that's limited to a certain group of people. Anyone can choose to innovate at any point
24:19in time. You just have to have the right mental model and mindset. And what you have to do is
24:24fight
24:25the temptation for bureaucracy being something that you succumb to. And so challenge the bureaucracy,
24:32and allow people to come in that challenge the status quo. And you will, by definition,
24:38the byproduct of that is going to be invention. The unlearning idea really hits home for me. I think
24:44we're going to have to cut that from the episode because I don't want my kids to hear it and
24:47then
24:47think that I am not, you know, full of wisdom and that their ideas are important. But it appeals to
24:53me
24:53because, you know, many of the things that I think that got me to where I am in my career
24:56are not necessarily the things that seem like they're going to keep me going through the next
25:02phase of my life. And so that unlearning makes sense. But at the same time, I have trouble knowing
25:08what things I should unlearn and what things I shouldn't unlearn. And I feel like companies have
25:12to have that same problem. They got successful through some strategic core competency. And,
25:20you know, the idea that, oh yeah, we unlearned everything seems too global to me.
25:26So how do we decide?
25:27No, I don't think you have to unlearn everything. I think humans build on top of each other's
25:31learnings. And one of the most important inventions that ever was created was a printing press because
25:37we were able to communicate the learning from one generation to the other in a way that was very
25:43concrete. And so the combination of language, script, and the printing press, and ultimate level of
25:52desire to share your knowledge with others, which is instinctive to us, and ultimate desire to learn
25:58from other people's learnings, which is also instinctive to us, was actually pretty valuable.
26:03So I don't believe that you should unlearn everything, but I believe that pairing up experience with
26:08inexperience is really valuable. And if you can make sure that there's a bi-directional mentorship
26:16that's occurring in your ethos of your organization, where the experienced people are coaching the early
26:28entrance. And by the way, by early entrance, I don't always mean young people because I could be
26:34inexperienced in a brand new domain. And so sometimes I have to force myself to just go into
26:41uncomfortable spots and go into new domains where I can learn new things and I can ask questions
26:47that might not be conventional wisdom. What I think is really important is conventional wisdom helps
26:53many times, and many times conventional wisdom prevents us from exploring something
27:01that we want to explore, and then thereby creates barriers that are unnecessary. And so that's what
27:07we have to kind of undo. That's the area that I feel like there's opportunity for organizations
27:13thinking differently is don't just mentor your interns, have a reverse mentorship program as well.
27:20So if you're spending an hour with an intern, make sure that one of your key objectives is for 30
27:27minutes
27:27of it. Make sure you're getting something out of it. Not just them. It's not one-directional,
27:32it's bi-directional. I think I'm practicing what I'm preaching in this conversation that I had with
27:38you because I've tried to always go into areas that I knew nothing about. And I found that that keeps
27:45me curious, that keeps me motivated, that keeps me learning. And it also allows me the permission
27:53to ask silly questions, which then free me from the burden of experience sometimes that I have to have
28:02in certain areas. And then in other areas, just a number of years in the system teach you patterns that
28:09as long as you don't think those patterns are non-shiftable, then you actually benefit from them.
28:15You know, I always think of like strong opinions loosely held as a good model, which is completely flippable
28:21with new arguments and new data. The reason I got into computers was because some uncle told me the
28:27night before, hey, it seems like I would be better than going into business management. So I took
28:32computers as a class and then I got into that and got interested. And from there, you get into
28:38consulting. And then I did consulting for a long time. I started my own business. And then the consulting
28:43thing got boring after a while because I didn't feel like I had exponential scale in that business.
28:48And I wanted to learn scale because I was fascinated with scale. So I got into software
28:52and from software, one thing led to the other. And I got into product and products were kind of fun
28:59to
28:59use because you started doing things in the cloud. And then the cloud became fun because now you're
29:06starting to benefit from AI. The formula that I've used is don't ever fight a megatrend and use it as
29:13a
29:13tailwind. And know the difference between a megatrend and a hype cycle. So if you can go out and
29:20effectively deduce what is a megatrend, what is the hype cycle, not fight the megatrend and ignore
29:27the hype cycle, you have an advantageous position in society. Now, by the way, the instructive question
29:33is how do you know the difference? And in my mind, there's a simple formula, which is if it requires
29:39a PhD for someone to explain what the benefit of something is, it is a hype cycle. If it's
29:45something that is instantly obvious and what the benefit could be, where you could imagine five
29:50steps forward, it is likely a megatrend.
29:53So one of the things that we like to do on the podcast is when we've had a very conversational
29:58oriented so far, let me switch to be just rapid fire questions and just give me the first thing
30:04that comes off your mind. What's moving faster about artificial intelligence or slower than you
30:09expected? What's moving faster is the rate of change. And what's moving slower is the use cases
30:15that organizations are actually starting to find tangible value from, I think the technology is
30:21moving fast, the adoption is moving slower. What's been the best use of artificial intelligence so far
30:28for you personally? Research, getting dexterous in a particular domain and a fraction of the time
30:37of what it could be in the past is something that I don't think I would have been able to
30:40do my job
30:40currently and have taken that job on and been able to get to speed as fast if it weren't for
30:46AI.
30:47I am a direct benefactor of AI. My family would not be fed the way it is today if AI
30:52wasn't around.
30:53It's that simple. What do you wish that AI could do better or what frustrates you about AI?
30:59I think we are still in a very kind of chat based interface yet. I think we are squarely entering
31:08into the next phase, which is agents being able to conduct tasks and jobs fully autonomously. I still
31:14do a lot of things I hate doing in my day that I think at some point in time, AI
31:19will take off my
31:20plate. And I don't think we're quite there yet. Yeah. Amen to that. Has using artificial intelligence
31:27made you spend more time with technology or less? More. Because I'm just curious. I spend like from
31:34nine to midnight every night almost in my learning mode, which is something I never really did quite
31:40that religiously before AI. Well, it's been fascinating talking with you and learning from
31:45these. I like the phrase about never fighting a megatrend. And if you're right about AI being
31:49underhyped, which I think you've made some cogent arguments for, then we're in the middle of a real
31:54shift. Thanks for taking the time to talk with us today. Thank you,
31:57for having me. I hope you enjoyed the conversation today. In two weeks, I'll be joined by Kathleen
32:03Peters, Chief Innovation Officer at Experian. Please tune in. Thanks for listening to me,
32:10myself and AI. Our show is able to continue in large part due to listener support. Your streams
32:15and downloads make a big difference. If you have a moment, please consider leaving us an Apple Podcasts
32:20review or rating on Spotify and share our show with others you think might find it interesting and
32:25helpful.
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