# Never Fight a Megatrend: Cisco’s Jeetu Patel

- **Show:** Me, Myself, and AI
- **Published:** 14 Oct 2025
- **Source:** https://sloanreview.mit.edu/audio/never-fight-a-megatrend-ciscos-jeetu-patel
- **Transcript:** https://podcasts.opennash.com/shows/me-myself-and-ai/never-fight-a-megatrend-ciscos-jeetu-patel-2bd4fd/
- **Provider:** OpenNash Podcasts (machine-generated)

## In brief

Cisco's Jeetu Patel argues that AI is not overhyped, and may be a bigger shift than the internet or the automobile. It builds on all earlier infrastructure, and Cisco sells the "picks and shovels" it needs: networking, security and data. He says the real prize is solving problems no one could solve before, not just working faster.

The central tension is between speed and human judgment. Patel is optimistic over the long term, but he admits temporary job displacement and security risks. He says companies should keep hiring early-career people and run mentorship in both directions so the organization can unlearn old habits. His rule of thumb is to ignore hype cycles and never fight a megatrend.

## Key insights

1. **AI infrastructure must be rebuilt, not just extended** Patel says current data centers lack the power, computing capacity and network bandwidth AI needs, so they must be physically redesigned and re-racked. He compares it to cars requiring roads, expressways and traffic lights.
2. **Security is now a precondition for AI adoption** Patel says security used to be seen as a drag on productivity. Now, if people don't trust AI systems they simply won't use them, so security comes first.
3. **Productivity is the smaller prize from AI** Patel calls productivity the first-order effect and puts it at roughly 10% of the value. He expects most value to come from AI generating original insights and solving problems people had written off as unsolvable.
4. **Cutting entry-level hiring starves companies of unlearning** Patel calls skipping early-career hires the 'stupidest idea' because newcomers lack the baggage of experience and ask questions that challenge assumptions. He notes a 20-year-old uses AI as a brainstorming companion, while a 28-year-old mostly uses it to get answers.
5. **Pair experience with inexperience through reverse mentoring** Patel suggests that when a leader spends an hour with an intern, about thirty minutes should be aimed at the leader learning something. He says early entrants can be anyone new to a domain, not only young people.

## Questions this episode answers

### Why does Cisco say it benefits from the AI boom?

Jeetu Patel says Cisco supplies the networking, security and data 'plumbing' AI needs, like selling picks and shovels in a gold rush. He argues infrastructure providers tend to do well whenever a major technology shift forces the underlying systems to be rethought.

### Is AI really bigger than the internet?

Patel thinks it might be, because each new wave builds on the infrastructure of earlier ones. He points out that ChatGPT-style models only became possible because the internet supplied huge amounts of public data to train on.

### Will AI take people's jobs?

Patel expects some temporary job displacement that could cause real human suffering, and says society should get ahead of it with retraining. He does not believe humans will become obsolete, and thinks people will find higher-level work to do.

### Should companies stop hiring junior employees because of AI?

Patel says no, calling it a very bad idea. He argues new hires bring fresh questions and help the company unlearn old habits, and that senior staff should learn from them through reverse mentoring.

### How can a leader tell a megatrend from a hype cycle?

Patel's test is that if it takes a PhD to explain the benefit, it is a hype cycle. If the benefit is instantly obvious and you can imagine five steps forward, it is likely a megatrend.

## Facts

- **Jeetu Patel** [0:06:23]: "It took off in November, on November thirtieth of 2022. What was so significant about that date? That's when Chat GPT was launched."
- **Jeetu Patel** [0:06:23]: "a large language model was a model that actually understood human language"
- **Jeetu Patel** [0:06:57]: "we had petabytes and petabytes of publicly available data on the internet that you could use to train these models"
- **Jeetu Patel** [0:04:26]: "imagine if Amazon dot com got built in the sixteen hundreds, it would be an epically failed company."
- **Sam Ransbotham** [0:02:06]: "there's a famous or infamous statement that the people who made money in the gold rush were people who sold picks and shovels to the miners."
- **Jeetu Patel** [0:03:38]: "you have to kind of rethink and reimagine, and as they call in technical terms, re rack the data centers."
- **Jeetu Patel** [0:05:32]: "scientific progress will probably compound by a thousand X"
- **Jeetu Patel** [0:08:40]: "he had written this book where his thesis at the time was if you live until you're forty, we will have the science and technology to allow us to live in perpetuity."
- **Jeetu Patel** [0:01:23]: "all the products that you use from Cisco, whether it be networking products, whether it be security products, whether it be data products or collaboration products."
- **Sam Ransbotham** [0:01:09]: "Cisco equipment is probably behind 90% of the infrastructure that we're using"
- **Jeetu Patel** [0:15:16]: "Going out and doing things you couldn't do before and solving problems you couldn't solve before in different ways that you couldn't even dream of solving before is probably going to be the ninety factor."
- **Jeetu Patel** [0:19:13]: "A twenty year old thinks of it like a companion and a brainstorming partner, and they might actually talk to it and brainstorm with it so that they can come to an ideation."
- **Jeetu Patel** [0:18:10]: "if you actually don't hire new people to come in, you have essentially given up on injection of new talent and new ideas into the thought process"
- **Jeetu Patel** [0:21:39]: "one of the most important inventions that ever was created was a printing press."
- **Jeetu Patel** [0:23:00]: "So if you're spending an hour with an intern, make sure that one of your key objectives is for thirty minutes of it, make sure you're getting something out of it, not just them."
- **Jeetu Patel** [0:24:17]: "The reason I got into computers was because some uncle told me the night before that, hey, seems like I would be better than going into business management."
- **Jeetu Patel** [0:25:43]: "if it requires a PhD for someone to explain what the benefit of something is, it is a hype cycle."
- **Jeetu Patel** [0:26:23]: "the technology is moving fast, the adoption is moving slower"
- **Jeetu Patel** [0:27:45]: "I spend like from nine to midnight every night, almost in my learning mode"
- **Sam Ransbotham** [0:00:19]: "I've been researching data analytics and AI at MITSMR since 2014, with research articles, annual industry reports, case studies, and now twelve seasons of podcast episodes."

## Transcript

**[0:00:02] Ad read:** We talk a lot about AI being overhyped. Today's guest believes it's not. Continue listening to learn how his point is connected to tech infrastructure and security.

**[0:00:13] Jeetu Patel:** I'm Jeetu Patel from Cisco, and you're listening to me, myself and AI.

**[0:00:19] Sam Ransbotham:** Welcome to me, myself and AI, a podcast from MIT Sloan Management Review, exploring the future of artificial intelligence. I'm Sam Ransbotham, professor of analytics at Boston College. I've been researching data analytics and AI at MITSMR since 2014, with research articles, annual industry reports, case studies, and now twelve seasons of podcast episodes. On each episode, corporate leaders, cutting edge researchers, and AI policymakers join us to break down what separates AI hype from AI success. Hi listeners, thanks for joining us again.

**[0:01:00] Sam Ransbotham:** Today I'm talking with Jeetu Patel, president and chief product officer at Cisco. Jeetu, thanks for joining us.

**[0:01:06] Jeetu Patel:** Thank you for having me, Sam. It's great to be here.

**[0:01:09] Sam Ransbotham:** Well, as we talk, Cisco equipment is probably behind 90% of the infrastructure that we're using, but some of the listeners may not be aware of all that Cisco does. So let's start off there. Jeetu, can you give us a background of Cisco and in particular your role?

**[0:01:23] Jeetu Patel:** Sure, I'll start in reverse order. I run products at Cisco, so all the products that you use from Cisco, whether it be networking products, whether it be security products, whether it be data products or collaboration products. Those typically are ones that I'm in charge of, um, building and taking to market, of course, with a very, very capable team. Essentially, the way you should think about Cisco is we are the critical infrastructure company for the AI era.

**[0:01:51] Jeetu Patel:** So all of the plumbing that's required to make sure that people can connect, people can stay secure while they're connected, and people can make sure that they have the data platform. Those are the things that we provide to the market.

**[0:02:06] Sam Ransbotham:** I saw a quote from you say, Well, think of us as the picks and shovels during the AI gold rush era. And for listeners who may not be aware, there's a famous or infamous statement that the people who made money in the gold rush were people who sold picks and shovels to the miners. Individual miners. Some of them made it big, but some of them did not. And I think your analogy is spot on there.

**[0:02:28] Jeetu Patel:** yeah, you know, one of the things that you find when you go through these kind of massive, what I would call destructive platform shifts, where we've all been going down a certain path with a certain set of assumptions. And then the assumptions change because a whole new set of technologies emerge, which is what AI is. What you find is the infrastructure that's required to go out and run those technologies needs to be rethought and reimagined. And anytime there's any one of these major platform shifts, the infrastructure providers tend to make out pretty well because you have to change the entire plumbing of the apparatus that's going to be used.

**[0:03:10] Jeetu Patel:** So if you think about when automobiles were built, you know, you now need to have roads and you need to have expressways, and you need to have traffic lights, and you need to have a whole system in place for automobiles actually get integrated into society. So it's not just the automobile, but everything around it needs to change. And if you think about AI right now, these data centers where these digital workers are going to live, they have to be completely reimagined.

**[0:03:38] Jeetu Patel:** Because the current data center does not have the power availability and the compute requirements and the network bandwidth to be able to fulfill and satiate the needs of what an AI system would need. So you have to kind of rethink and reimagine, and as they call in technical terms, re rack the data centers. Because there's racks and racks of computers and network and switching gear that actually have to be cooled in a certain way and so on, and so forth.

**[0:04:04] Jeetu Patel:** And so that entire shift is what we are in the midst of, and Cisco is a natural benefactor of it because we provide the infrastructure for the AI era.

**[0:04:14] Sam Ransbotham:** So some of the analogies you're making, I mean, we talked about the gold rush, we talked about the internet, we talked about cars. Is artificial intelligence at that level of big deal or not?

**[0:04:26] Jeetu Patel:** I think it might be bigger. And the way I've seen these kind of shifts happen, you know, imagine if Amazon dot com got built in the sixteen hundreds, it would be an epically failed company. Because you didn't have the internet, and you didn't have the underlying infrastructure on top of which Amazon could be built. Because you didn't have the shipping and the logistics infrastructure, and the internet, and all of those pieces. In a similar vein with AI, we have the benefit of having had all of the infrastructure that's been built out to date.

**[0:05:02] Jeetu Patel:** And so when you have something that's built taking advantage of all of these things, by definition, each one of these subsequent major transformative waves tends to be bigger in impact than the previous ones. Just because it was built on top of shoulders of giants of the previous innovations that had happened. And so I would say this is probably the most consequential set of inventions that we will have seen in our lifetime, for sure, and probably arguably in humanity.

**[0:05:32] Jeetu Patel:** But the thing to keep in mind is the pace of innovation and the slope at which it happens actually makes it impossible to predict what's going to happen three years from now. Because we've compressed the time, scientific progress will probably compound by a thousand X, and so you've compressed this time scale where it's actually. We're in a warped situation right now, where humans can't make sense of this because everything's moving so fast.

**[0:06:00] Sam Ransbotham:** Actually, there's so many things to talk about in there. I think your analogy to Amazon and the 1,600 is interesting, cause, you know, for example, we had neural network designs back in the nineteen eighties. We just didn't have the compute infrastructure, the data, the telecom to pull it off. And so it took all these things coming together. And I think what you're saying is that all these things are together now for us to build from.

**[0:06:23] Jeetu Patel:** Actually, the biggest thing that we have that we didn't have then, because of which this would have been hard to go invent, is, you know, AI has been around for a while. But when did it actually take off? It took off in November, on November thirtieth of 2022. What was so significant about that date? That's when Chat GPT was launched. What was so significant about Chat GPT? It was essentially a large what they call a language model, and a large language model was a model that actually understood human language.

**[0:06:57] Jeetu Patel:** Rather than the machine having to be rigid and the human having to learn the machine's language, it became the other way around. How did that happen? That happened because we had petabytes and petabytes of publicly available data on the internet that you could use to train these models so that these models would then know what to do with it. And so if you didn't have the internet, you would have not had AI, because you would have not had that level of data to then train the models on.

**[0:07:28] Jeetu Patel:** It goes back to our point of each one of these inventions or revolutions is built on the infrastructure provided by the previous revolution, and it's very evident in this case.

**[0:07:40] Sam Ransbotham:** One of the last conversations that I had with my grandfather was how much life had changed from no electricity, limited indoor plumbing, you know, no airplanes, no space travel, no computers to, you know, when he passed, how radically different that was. And I remember talking with him at the time saying, Oh, wow, you lived through a bunch, you know, I can't imagine things changing as much during my lifetime, and I may very quickly be eating those words.

**[0:08:06] Jeetu Patel:** And I think we get into this kind of cycle of, uh. Like, humans are not very good in general at imagining the exponential outcomes. We're very good at imagining linear progression, but not the exponentiality of the outcome. And so what ends up happening is we think about exponentiality in a single dimension, not multidimensional. And I, you know, there was years ago I had a chance to sit down and talk to Ray Kurzweil, who's one of the scientists at Google.

**[0:08:40] Jeetu Patel:** And this was like twenty years ago or something, and I was interviewing him for something. And we were talking about this notion of perpetual extension of life, where, you know, can a human live forever, live long enough to live forever? And he had written this book where his thesis at the time was if you live until you're forty, we will have the science and technology to allow us to live in perpetuity. My topic of communication with him was around the social implications of that, like what happens if seven generations live simultaneously or ten generations live simultaneously?

**[0:09:16] Jeetu Patel:** That's going to be really hard because we are not going to have enough room to put everyone and we're not going to have enough crops to go feed everyone. And he's like, you know, this is the problem with humans is you? Actually? We can't think in exponential terms because we think in a single dimension of exponentiality, which is if seven generations or ten generations lived simultaneously, what would happen with everything else being exactly the same? But the reality is, you might have skyscrapers that might be 2,000 stories high, and you might have a crop cycle that takes, you know, three days.

**[0:09:46] Jeetu Patel:** And so those are all things that will also simultaneously evolve so that they can accommodate the constraints that get created because of the developments that happen in certain areas. And I feel like that's the same over here. Is like, when people say, you know, I think humans are going to be sitting on a beach, have nothing to do, and AI is going to do everything. I just chuckle a little bit because I just refuse to believe that humans are designed to be obsolete, you know?

**[0:10:11] Jeetu Patel:** And so we will continue to find ways to add value and think creatively. And that doesn't mean that we'll be doing what we do today. It might very well mean that all the jobs that we do today might not be the jobs that we have. But that also doesn't mean that we're not going to have jobs. Because the desire for a human to be productive and add value to society doesn't go away because something got automated.

**[0:10:34] Jeetu Patel:** You just create higher order bits that you are then able to go focus on that you were not able to focus on in the past.

**[0:10:40] Sam Ransbotham:** Exactly, and then just to make sure that I can get that thrown back in my face later. Yeah, I'm predicting massive increases in employment, not decreases. No one since the internet is doing less than they were before the internet, despite all the progress possible. I can't believe that that's not going to happen again. I think we're headed towards the opposite direction.

**[0:11:00] Jeetu Patel:** I think you'll see some displacement of jobs temporarily, which we should not take lightly because I think it'll cause human suffering. But that does not mean that that will be the state in perpetuity. And what you have to keep in mind is that displacement period. If we get ahead of it because of the pattern that we're starting to witness, we might be able to actually get society retrained in a more efficient way than we might have done in the past with previous disruptions.

**[0:11:31] Jeetu Patel:** And that might be a responsibility that the tech community, the collaboration between the public and private sector should have as a good beneficial outcome. Because I think there's going to be more and more of a need for collaboration across different sectors. And this is one of those areas where I tend to be in the long term, an optimist without being naive about the short term implications that this might have, or even midterm implications. It has around safety and security, and the downside effects could be profoundly consequential that we have to keep in mind.

**[0:12:05] Jeetu Patel:** But I refuse to believe that we're going to be obsolete or that we are not going to have value to add. You know, it just seems unnatural.

**[0:12:14] Sam Ransbotham:** Yeah, I think that's well put that we can have a positive aggregate effect, but still have lots of heterogeneity in how that average plays out across society. And being naive about that is going to hurt us in the long run. You mentioned security, though, and when we were talking about re racking and infrastructure changes, we quickly slipped towards, you know, routers and modems and telecom and hardware oriented things. But one of the things that I think you're very focused on in terms of infrastructure is the idea of security.

**[0:12:46] Sam Ransbotham:** And how does that become a first class player versus the thing that's derided as hampering productivity? So we've always had this sort of productivity security trade off. I think you've mentioned that we may not be making that trade off anymore. How can we help the security be part of the infrastructure?

**[0:13:06] Jeetu Patel:** firstly, I think in this particular age, security is going to be a prerequisite for successful adoption of AI. Because if people don't trust these systems, they're not going to use them. And so that's very different from in the past, where you would think about security as a necessary productivity impediment. That's no longer the case. Now it just happens to be a prerequisite for successful adoption of AI. But I feel like it's probably worth taking a step back even and saying, where are we still thinking very linearly?

**[0:13:40] Jeetu Patel:** Like where I feel AI is under hyped the most is the fact that we still keep thinking that this is just a productivity game. Humans are going to get more productive, things are going to happen cheaper, faster, better. I actually feel that's only the first order effect. The second order effect is you will actually start to see these AI models, and it's not even clear if large language models will be the ultimate destination. You'll have large world models, you'll have physical models.

**[0:14:13] Jeetu Patel:** All of these things will start kind of combining together, but the new paradigm, whatever it ends up being at some point in time, and the existing ones will start to create original insight that did not exist in the human corpus of knowledge. It won't just be an aggregation mechanism, it won't just be where you take multitude of different perspectives. And this is not just a better search engine, where humans had some data and you indexed that data well and you were able to go out and put it into a clean paragraph.

**[0:14:46] Jeetu Patel:** It's going to start creating original insights that didn't exist in the human corpus of knowledge. And when that happens, the thing that changes is you are now able to imagine solving problems that you could never even dream of solving before. And that is far beyond just going out and optimizing for productivity. And I feel like that's the most misunderstood part of AI is, oh, I'm just going to get more productive. Productive use is going to be like ten of the equation.

**[0:15:16] Jeetu Patel:** Going out and doing things you couldn't do before and solving problems you couldn't solve before in different ways that you couldn't even dream of solving before is probably going to be the ninety factor.

**[0:15:27] Sam Ransbotham:** Yeah, I like that idea to observe the productivity effects first, because, as you say, they're first order. But how do companies, how do people start thinking about what they can do with that 90%? You know, productivity is pretty tempting. I mean, I like greater productivity. This can be hard for me to turn away from that. Or, you know, maybe turning away is me sort of making it a Hobson's choice, where you have to do one or the other.

**[0:15:50] Sam Ransbotham:** But how do people start thinking about this 90% or these more than productivity options? I don't think I've ever talked to anybody who thought AI was under hyped, and I think we're on record for saying that here.

**[0:16:02] Jeetu Patel:** I think where it's over hyped is where the human obsolescence becomes almost a foregone conclusion in some people's minds. I think that's where it's over hyped, because I feel like human instinct and human judgment is still pretty hard to go out and replicate in the machine's ability to do things. Because we don't make most of our decisions based on data, we make a lot of our decisions based on gut, and that gut is hard to go replicate.

**[0:16:27] Jeetu Patel:** And typically, people say, listen to your gut, because there's a reason for it, there's an instinct that's palpable. But to go back to your question, or what should companies be thinking about? And by the way, I think I highly encourage the productivity argument. I think everyone should go out and think about productivity, and they should continue to keep kind of powering through that. It's going to be a great benefit that we will all be recipients of.

**[0:16:54] Jeetu Patel:** Where I think the unlock truly comes in is by actually trying to make sure that we challenge the conventional norms of thinking and ask ourselves the question of what problems have we been conditioned to think that we can't solve? And are those unsolvable? Moving forward, just the way that they were in the past? And I feel like you'll actually start to find very different answers. I feel like we need more and more questioning that happens of the status quo.

**[0:17:27] Jeetu Patel:** And the way to do that, in my mind, the one single thing is going to be. The exponential difference is you have to unlearn as much as you are learning, and unlearning requires that you actually inject new talent into the system at a very rapid pace. And then give creative freedom to that talent so that you are actually getting mentored by them just as much as you're mentoring them. And so this was a recent conversation I had had at a conference I was at, where people were like, you know, so entry level jobs are going to go away, and we're just not going to hire early and career people.

**[0:18:10] Jeetu Patel:** Which, in my mind, seems like the stupidest idea that a company could pursue. Because if you actually don't hire new people to come in, you have essentially given up on injection of new talent and new ideas into the thought process. And so this kind of baggage of experience will always hold you back because you know a lot of things. And you might not be, as a company, good at unlearning, and you have no one else to actually instigate that unlearning and catalyze that unlearning.

**[0:18:45] Jeetu Patel:** By asking questions because they didn't have the baggage of knowing. I do feel like the mix, the continued infusion of talent early in career is going to be so important for companies to be able to get the most out of it. Because you we have to understand instinctively how to go out and use these tools, which are in service of humans, in a very different way than the way that we might have used them in the past.

**[0:19:13] Jeetu Patel:** And right now, frankly, if you take someone who is a twenty year old and a twenty eight year old and compare the two of them and how they use AI, it's night and day different. You know, a twenty eight year old might actually use it for productivity. They'll go out and they'll ask it some questions because they've got some answers to get, and then they'll move on. A twenty year old thinks of it like a companion and a brainstorming partner, and they might actually talk to it and brainstorm with it so that they can come to an ideation.

**[0:19:44] Jeetu Patel:** They're not looking for answers, they're looking for substantive volleying back and forth and brainstorming. I learned that from the interns that would come into Cisco, and so I think that's the aspect that I think we have to keep in mind. That as a company, we have to make sure that we keep challenging and disrupting ourselves before someone else disrupts us. And by the way, innovation is not limited. Like this is the one thing that people kind of bucket into these completely unproductive ways, which is, you know, you're a small startup, you innovate really fast, you become large, you stop innovating.

**[0:20:23] Jeetu Patel:** I think it's nonsense because innovation is not like something that's limited to a certain group of people. Anyone can choose to innovate at any point in time. You just have to have the right mental model and mindset, and what you have to do is fight the temptation for bureaucracy being something that you succumb to. And so challenge the bureaucracy and allow people to come in that challenge the status quo, and you will, by definition, the byproduct of that is going to be invention.

**[0:20:53] Sam Ransbotham:** The unlearning idea really hits home for me. I think we're gonna have to cut that from the episode, cause I don't want my kids to hear it. And then think that I am not, you know, full of wisdom and that their ideas are important. But it appeals to me because, you know, many of the things that I think that got me to where I am in my career are not necessarily the things that seem like they're going to keep me going through the next phase of my life.

**[0:21:16] Sam Ransbotham:** And so that unlearning makes sense, but at the same time, I have trouble knowing what things I should unlearn and what things I shouldn't unlearn. And I feel like companies have to have that same problem. They got successful through some strategic core competency, and, you know, the idea that, oh yeah, we unlearned everything seems too global to me. So how do we decide?

**[0:21:39] Jeetu Patel:** I don't think you have to unlearn everything. I think humans build on top of each other's learnings, and one of the most important inventions that ever was created was a printing press. Because we were able to communicate the learning from one generation to the other in a way that was very concrete. And so the combination of language script and the printing press and ultimate level of desire to share your knowledge with others, which is instinctive to us, and ultimate desire to learn from other people's learnings, which is also instinctive to us, was actually pretty valuable.

**[0:22:15] Jeetu Patel:** So I don't believe that you should unlearn everything, but I believe that pairing up experience with inexperience is really valuable. And if you can make sure that there's a bidirectional mentorship that's occurring in your ethos of your organization, where the experienced people are coaching the early entrants, and by the way, by early entrants, I don't always mean young people. Because I could be inexperienced in a brand new domain, and so sometimes I have to force myself to just go into uncomfortable spots and go into new domains where I can learn new things and I can ask questions.

**[0:23:00] Jeetu Patel:** That might not be conventional wisdom. What I think is really important is conventional wisdom helps many times, and many times conventional wisdom prevents us from exploring something that we want to explore. And then thereby creates barriers that are unnecessary, and so that's what we have to kind of undo. That's the area that I feel like there's opportunity for organizations thinking differently is don't just mentor your interns, have a reverse mentorship program as well. So if you're spending an hour with an intern, make sure that one of your key objectives is for thirty minutes of it, make sure you're getting something out of it, not just them.

**[0:23:43] Jeetu Patel:** It's not one directional, it's bidirectional. I think I'm practicing what I'm preaching in this conversation that I had with you. Because I've tried to always go into areas that I knew nothing about, and I found that that keeps me curious, that keeps me motivated, that keeps me learning. And it also allows me the permission to ask silly questions, which then free me from the burden of experience, sometimes that I have to have in certain areas, and then in other areas.

**[0:24:17] Jeetu Patel:** Just number of years in the system teach you patterns that as long as you don't think those patterns are non shiftable, then you actually benefit from them. You know, I always think of like, strong opinions loosely held is a good model, which is completely flippable with new arguments and new data. The reason I got into computers was because some uncle told me the night before that, hey, seems like I would be better than going into business management.

**[0:24:44] Jeetu Patel:** So I took computers as a class, and then I got into that and got interested. And from there, you get into consulting, and then I did consulting for a long time. I started my own business and then the consulting thing got boring after a while. Because I didn't feel like I had exponential scale in that business and I wanted to learn scale because I was fascinated with scale. So I got into software, and from software, one thing led to the other, and I got into product, and products were kind of fun to use.

**[0:25:12] Jeetu Patel:** Because you started doing things in the cloud, and then the cloud became fun because now you're starting to benefit from AI. The formula that I've used is don't ever fight a megatrend and use it as a tailwind and know the difference between a megatrend and a hype cycle. So if you can go out and effectively deduce what is a megatrend, what is the hype cycle, not fight the megatrend and ignore the hype cycle, you have an advantageous position in society.

**[0:25:43] Jeetu Patel:** Now, by the way, the instructive question is, how do you know the difference? And in my mind, there's a simple formula, which is if it requires a PhD for someone to explain what the benefit of something is, it is a hype cycle. If it's something that is instantly obvious in what the benefit could be, where you could imagine five steps forward, it is likely a megatrend.

**[0:26:06] Sam Ransbotham:** So one of the things that we like to do on the podcast is, and we've had a very conversational oriented so far. Let me switch to be just rapid fire questions and just give me the first thing that comes off your mind. What's moving faster about artificial intelligence, or slower than you expected?

**[0:26:23] Jeetu Patel:** What's moving faster is the rate of change, and what's moving slower is the use cases that organizations are actually starting to find tangible value from. I think the technology is moving fast, the adoption is moving slower.

**[0:26:37] Sam Ransbotham:** Hm, hm. What's been the best use of artificial intelligence so far for you personally?

**[0:26:43] Jeetu Patel:** Research getting dexterous in a particular domain in a fraction of the time of what it could be in the past is something that I don't think. I would have been able to do my job currently and have taken that job on and been able to get to speed as fast if it weren't for AI. I am a direct benefactor of AI. My family would not be fed the way it is today if AI wasn't around. It's that simple.

**[0:27:07] Sam Ransbotham:** What do you wish that AI could do better, or what frustrates you about AI?

**[0:27:12] Jeetu Patel:** I think we are still in a very kind of chat based interface. Yet I think we are squarely entering into the next phase, which is agents being able to conduct tasks and jobs fully autonomously. I still do a lot of things I hate doing in my day that I think at some point in time, AI will take off my plate, and I don't think we're quite there yet.

**[0:27:36] Sam Ransbotham:** Yeah, amen to that. Has using artificial intelligence made you spend more time with technology or less? More because More?

**[0:27:45] Jeetu Patel:** I'm just curious. I spend like from nine to midnight every night, almost in my learning mode, which is something I never really did quite that religiously before. AI

**[0:27:55] Sam Ransbotham:** Well, it's been fascinating talking with you and learning from these. I like the phrase about never fighting a megatrend. And if you're right about AI being under hyped, which I think you've made some cogent arguments for, then we're in the middle of a real shift. Thanks for taking the time to talk with us today.

**[0:28:09] Jeetu Patel:** Thank you for having me.

**[0:28:12] Sam Ransbotham:** I hope you enjoyed the conversation today. In two weeks, I'll be joined by Kathleen Peters, Chief Innovation Officer at Experian. Please tune in.

**[0:28:21] Ad read:** Thanks for listening to me, myself and AI. Our show is able to continue in large part due to listener support. Your streams and downloads make a big difference. If you have a moment, please consider leaving us an Apple Podcast review or a rating on Spotify and share our show with others you think might find it interesting and helpful.