3137: From Steve Jobs to DataStax: A Journey Through Four Tech Waves

[00:00:04] Where is AI heading? And what makes this technological wave different from any that came before it? From client server to web to mobile and cloud. Each major shift has redefined industries. But let's be honest, the AI revolution feels more human and more transformative than ever.

[00:00:23] So my guest today is Chet Kapoor. He's the CEO of DataStax. He's incredibly cool guy. His career has spanned decades at the forefront of technology. And he's got a cracking origin story that began interning just 20 yards away from Steve Jobs. Can you imagine that? He went on to huge roles at Google, IBM, and so many more. But today, I want to learn more about how he's witnessed and shaped the evolution.

[00:00:53] In my conversation today, Chet will share the lessons he learned of working alongside Steve Jobs, explain how each technology wave has built upon the last, and explore why AI represents the turning point for productivity, effectiveness, and human AI collaboration. And hopefully we'll also get time to address the big questions surrounding AI's impact on our jobs and our economy, while also offer some practical

[00:01:23] leadership insights for technical leadership insights for embracing AI-driven change. So with this AI wave reshaping the way we work, innovate and govern technology, is it possible to balance the promise of AI with the needs for ethical guardrails? And how can businesses and leaders prepare for some of those opportunities ahead? Well, enough rambling for me. Let's get Chet onto the podcast right now.

[00:01:48] So a massive warm welcome to the show. Can you tell everyone listening a little about who you are and what you do?

[00:01:57] Neil, thank you very much for having me on the show. Appreciate it.

[00:02:01] Born and bought up in Calcutta. You know, started my career at a company called Next. That was Steve's second company after Apple. Did a bunch of startups.

[00:02:10] This company called Glucode that got me into open source that IBM had acquired.

[00:02:14] Led Apogee through an IPO. Google bought us.

[00:02:17] And so it was great. And I loved Google because you get a chance to solve intergalactic problems, right?

[00:02:27] And obviously, being a geek, you get a chance to solve some really cool technology stuff.

[00:02:32] So I thought I would be there for the rest of my career at Google.

[00:02:35] And I felt like I had a quest to go off and create something significant or, as Jeff Bezos said, durable.

[00:02:43] And came upon, created a criteria of product market fit, you know, total addressable market, people and durability where you have P&L and culture.

[00:02:52] And DataStax was the company I chose five years ago.

[00:02:56] And the goal was to make DataStax a data company.

[00:03:01] And, you know, and what came along was not just ML, but also Gen AI.

[00:03:06] And so we're right in the thick of it because there is no AI without data.

[00:03:11] And there's no AI without unstructured data.

[00:03:15] And there's no AI without unstructured data at scale.

[00:03:18] So we find ourselves in a phenomenal opportunity.

[00:03:21] Like the world is, the world really needs what we embarked on five years ago.

[00:03:26] Now, having said all that, you know, if I, when I write the book, it will say, oh, I thought about it five years ago.

[00:03:32] But the answer is no, right?

[00:03:33] I mean, we stumbled into it.

[00:03:34] So.

[00:03:35] What a great story.

[00:03:37] And there's so much I want to talk with you about today.

[00:03:40] But before we begin, I always like finding out a little bit more about my guest's origin story and where they started out.

[00:03:46] And when I was doing a little research on you, I learned that you began your career as an intern for Steve Jobs.

[00:03:52] And I've had John Scully on here, Guy Kawasaki.

[00:03:56] So I'd love to learn from you.

[00:03:58] How did working with such an iconic figure at a young age influence your perspective on technology, leadership, innovation?

[00:04:06] What did you learn from that period?

[00:04:08] It was the second biggest thing that defined my career.

[00:04:11] First one was my father, right?

[00:04:14] Because he's always been a lifelong mentor.

[00:04:16] But, you know, just to give you a sense, I worked as an intern for Next.

[00:04:21] And then my first job out of college was working for Next, right?

[00:04:25] And I worked 20 yards away from Steve, right?

[00:04:28] Every day when I went to work.

[00:04:31] But my role was, I was the guy that got coffee for the guy that made coffee, right?

[00:04:36] Just to be clear, it didn't matter what my role was, but just being around such an iconic figure, right?

[00:04:43] And learning was absolutely awesome.

[00:04:45] So let me tell you a few things I learned along the way because they've actually single-handedly defined who I am and what I am and things like that.

[00:04:53] So the first thing I learned was you've got to be obsessed about the user.

[00:04:58] It is all about the user, right?

[00:05:01] You have to have an outside and perspective.

[00:05:02] The second thing is you must think differently, right?

[00:05:07] You're not going to get there by choosing the beaten path.

[00:05:10] You have to go off and, you know, carve your own path out.

[00:05:13] So be obsessed with the user.

[00:05:16] Think about things differently.

[00:05:17] And then the third one is something that I learned, right?

[00:05:20] This is not something that Steve talked about a lot.

[00:05:23] But leadership is about – leadership is a combination of fear and love, right?

[00:05:29] And a lot of people that worked for Steve would say he probably used fear a little bit more than he used love in his early days.

[00:05:35] But that's something you learn very quickly, right?

[00:05:38] And to lead, you must grab the human heart, but you have to also have the tough conversations, right?

[00:05:44] And so those were the things that really, really helped.

[00:05:49] The last thing I would tell you is it definitely accelerated my path to being a hopeless romantic, right?

[00:05:58] And what does that mean, right?

[00:05:59] I mean, a lot of people do startups for different reasons, right?

[00:06:04] I came – you know, working for Next, it was very clear, right?

[00:06:08] We were doing it because we wanted to change the world.

[00:06:12] And that has stuck with me forever, right?

[00:06:15] That, you know – and Steve would say, I want to make a dent in the universe.

[00:06:18] And my take is I want to change the world.

[00:06:20] You know, maybe in a small way, right?

[00:06:22] It doesn't have to be world hunger.

[00:06:24] But in your way, how are you going to change people's lives, right?

[00:06:27] And so that was the final thing that is – and all these have been with me forever, right?

[00:06:32] And so it was a phenomenal experience.

[00:06:36] Fantastic.

[00:06:37] Absolutely love that.

[00:06:37] And from there, I would imagine those experiences help you navigate through four major technology waves – client, client-server, web, mobile, and eventually cloud.

[00:06:49] So can you tell me how each of these waves helped shape the tech landscape that we know today and how they ultimately almost set in the stage for the current AI revolution where we find ourselves now?

[00:07:00] Client-server was a breakaway from mainframes, and it brought up distributed computing.

[00:07:05] That was a big thing, right?

[00:07:06] It was getting away from PCs to servers, from mainframes, right, and green screens.

[00:07:12] All that stuff happened.

[00:07:13] Web was – changed our lives, right?

[00:07:18] It just made information available on our fingertips, right?

[00:07:22] Just everything was available to us.

[00:07:24] And then mobile was taking the information with us wherever we were, right?

[00:07:29] And so all of those worked really, really well.

[00:07:31] And then what cloud did was it said, so you want to do all these things.

[00:07:35] You want information on your fingertips.

[00:07:38] You want to be able to use it wherever you are.

[00:07:40] How the hell do I make it easier to build applications that get you there, right?

[00:07:46] And so what cloud did was it changed the back end, right?

[00:07:50] Everything about – so client-server was the back end stuff.

[00:07:53] You know, there were some PC things going on with, you know, GUIs and stuff like that.

[00:07:57] But that was more the PC computing stuff.

[00:08:00] Web and mobile was all about the front end.

[00:08:02] And then cloud went off and said, we're going to optimize the back end to make it really easy.

[00:08:07] What's really interesting about these waves is if they had not happened, if they had not happened, we probably would not see the acceleration that we're seeing with Gen AI, right?

[00:08:19] Gen AI is super interesting.

[00:08:21] There are two things that are really interesting about Gen AI.

[00:08:24] Number one, it builds on everything we've done in the past.

[00:08:28] And the second thing, it is more human-like than anything else we've done in the past.

[00:08:32] So it is – and there's commonality.

[00:08:35] Now, you know, let me just talk about this.

[00:08:37] Commonality across each wave, right?

[00:08:39] A lot of people don't talk about this.

[00:08:41] Large amounts of money are made in the core infrastructure space of every wave, right?

[00:08:46] If you look at client-server, it was Cisco, Sun Microsystems you've not heard of, Oracle, NVIDIA, right, going off and doing that now.

[00:08:54] The second thing is in the beginning of the wave, it is always about the app developer, right?

[00:09:00] If you do not have app developers involved, it does not take off as big as it used to.

[00:09:06] And then the last thing is you cannot do new things with old things.

[00:09:10] So every time we have a new wave, there is a new stack that emerges.

[00:09:15] There's a new way of doing things that emerge.

[00:09:17] And so this time around, it will just be – it'll just go faster, right?

[00:09:23] And so every industry is going to get impacted, and we think the transformation is going to be massive.

[00:09:28] And the AI wave is often described as the most transformative and impactful.

[00:09:34] And I suspect throughout your career, same as myself, you've probably heard this many, many times before.

[00:09:41] This isn't our first rodeo.

[00:09:43] So what is it that you think makes it unique compared to previous waves that you've experienced?

[00:09:48] And how do you see it reshaping entire industries?

[00:09:50] It is the most human-like thing that we've ever done, right?

[00:09:55] By the way, as humankind.

[00:09:57] Forget the last four waves.

[00:09:59] You know, think about, you know, think about steam, right?

[00:10:03] Think about all the technology innovations we've ever had.

[00:10:06] And it is definitely the biggest thing that will ever happen to us.

[00:10:11] Now, a lot of people think that AI is happening now and it'll get done.

[00:10:16] I think it is going to happen over the next five, seven years.

[00:10:19] I think it'll slow down for a little bit, but I think it's going to accelerate in a big way.

[00:10:24] There's also a fear with AI because it's more human-like.

[00:10:28] There is a feeling that this is going to replace people's jobs.

[00:10:33] And I do think that there is going to be a displacement for sure.

[00:10:36] By the way, it happens with every technology.

[00:10:38] Look at the web world.

[00:10:40] Look at mobile, right?

[00:10:41] I mean, retail bank changed significantly because of what happened with mobile, right?

[00:10:46] People want to transact on their mobile phone and not go off to different banks, right?

[00:10:51] So the same thing will happen with AI.

[00:10:53] But I think a lot of people are starting to understand, and we've been talking about this for a couple of years.

[00:10:57] It's not AI versus people.

[00:11:00] It is people with AI versus people without, right?

[00:11:05] That's what people have to realize that this is going to happen.

[00:11:09] So this one is going to make a massive difference.

[00:11:17] So let's talk a little bit about what does people with AI mean, right?

[00:11:21] So we are going to go from a world of digitalization, right?

[00:11:25] You can talk about the web.

[00:11:27] You talk about cloud.

[00:11:28] You talk about mobile.

[00:11:28] We're digitizing everything.

[00:11:30] And I think what we are going to transform to is doing agentification.

[00:11:35] I think we will have agents that do everything for us, right?

[00:11:39] And those agents will manifest itself through goggles.

[00:11:42] They'll manifest themselves through voice, all kinds of things.

[00:11:45] But we are going to get more and more efficient on things that were tasks that we did on a regular basis.

[00:11:53] And I think the agentification journey is starting now, right?

[00:11:59] And I think the transformative years are going to be 25 and 26, where people are going to start making this happen.

[00:12:06] Because the answer is not about efficiency.

[00:12:10] I think a lot of people get stuck on AI and talk about efficiency.

[00:12:13] I don't think it's about efficiency.

[00:12:15] Obviously, it's about efficiency.

[00:12:16] But it's about effectiveness, right?

[00:12:19] That is what the AI wave is going to be about.

[00:12:22] So I'm really, really, really pumped up.

[00:12:24] And we have so many great things that we're doing in the AI world.

[00:12:28] It's not even funny, right?

[00:12:29] With lots of great companies like Physicsvala.

[00:12:33] They have millions of students using 28 different languages to actually use generative AI to learn physics and all kinds of different languages.

[00:12:42] Think of them as Khan Academy on steroids for over a billion people, right?

[00:12:48] There's a lot of people doing personalization in healthcare, right, with a company called SkyPoint.

[00:12:52] And all of these come down to three things, right?

[00:12:55] Every company in the world will have to think about the technology.

[00:13:00] How do you do things with people?

[00:13:02] And obviously, how do you actually go and deliver something, right?

[00:13:07] And technology is going to be context.

[00:13:09] People is going to come down to how do you have the right leaders paving the way?

[00:13:14] Because they're not reading a manual.

[00:13:16] They're writing a manual, right?

[00:13:17] They're writing the manual.

[00:13:18] And the process part is going to be you crawl, walk, run.

[00:13:21] Don't sit down and think about big banks, right?

[00:13:24] But if companies follow this technology process and people part that we just talked about, I think they're going to be hugely successful.

[00:13:32] And if we put the technology to one side for a moment and focus on the people and the human side of things,

[00:13:38] from your time as an intern 20 yards away from Steve Jobs and extensive experience at companies such as Google, IBM, and now DataStax,

[00:13:47] I'm curious, how has your own personal leadership approach evolved, especially in guiding teams through periods of incredible speed of technological change?

[00:13:57] Anything changed in your own approach?

[00:13:59] I'll give you a perspective first on just how I approach leadership, right?

[00:14:05] For me, leadership has always been about inspired execution.

[00:14:09] It's one of the reasons why I have this podcast with people I meet along the way,

[00:14:13] and we chat about it, and people find it to be very useful.

[00:14:19] I believe that great leaders believe, inspire, and execute.

[00:14:23] Those are the three words that define my leadership style.

[00:14:28] But at the end of the day, I really, really like this HBR article that Warren Benes and James O'Toole,

[00:14:38] you know, the end of the article on leadership was,

[00:14:41] great leaders, in a phrase, move the human heart.

[00:14:45] But that has always encapsulated for me what great leadership it about.

[00:14:50] Because you can actually, you can actually need people with their minds quite easily.

[00:14:54] This is how much I pay you.

[00:14:55] This is how many people work for you.

[00:14:56] This is what I expect from you.

[00:14:58] But the really, like, this is the inspiration from Steve, right?

[00:15:01] Which is, what you really have to do is inspire people to do things that they didn't think they were capable of.

[00:15:07] And that only comes if you move very hard, right?

[00:15:10] So, now applying it to Gen.AI, right?

[00:15:14] Leaders have to believe.

[00:15:16] I see banks.

[00:15:18] There's Bank A and Bank B.

[00:15:19] Bank A is making massive progress because the leader believes in it.

[00:15:23] The second thing they do is they inspire the rest of the company to do that, to do something with it as well.

[00:15:29] So, go and explore.

[00:15:31] Think about how you're going to do Gen.AI and everything like that.

[00:15:34] And then the third one is they have tangible goals on how they're going to make the company more effective, not more efficient.

[00:15:41] Because efficient is about laying off people.

[00:15:43] Effective is about how do you increase productivity so you can do top-line growth and bottom-line growth, not just bottom-line growth.

[00:15:50] So, the believe, inspire, execute applies across the board.

[00:15:54] And more specifically for people who are doing Gen.AI roles, right?

[00:16:00] Every leader will have to be an AI leader.

[00:16:04] It is not something that sits on the side, right?

[00:16:06] And you have to build the technology.

[00:16:08] You have to make sure it aligns with the business strategy.

[00:16:11] And you have to be obsessed with the user experience, right?

[00:16:15] So, my ultimate advice to them is start with something mission critical.

[00:16:19] Don't do something on the side.

[00:16:20] Then validate, iterate, and just build from there.

[00:16:23] But don't think of this as, ah, you know what?

[00:16:26] It's time.

[00:16:26] It's not ready yet.

[00:16:27] Get ahead of the curve now, right?

[00:16:29] And apply everything that you know.

[00:16:31] But just make sure you make this mainstream part of your life.

[00:16:35] And, of course, you're also somebody else that has presented.

[00:16:38] Before you came on the podcast, I was reading that you're someone that's presented at the World Economic Forum in Davos.

[00:16:45] I'm curious, from all the conversations you were having there or listening to as well, as well as your own presentation,

[00:16:50] what global perspectives on AI and its ethical implications have resonated with you most?

[00:16:57] And how are you incorporating some of those learnings into your work at DataStax, too?

[00:17:03] So, the first one I think I mentioned, but let me just touch on it.

[00:17:06] The first thing is a productivity paradox.

[00:17:08] Yeah.

[00:17:08] Right?

[00:17:09] Replacing people is just counterproductive.

[00:17:11] Right?

[00:17:12] Right?

[00:17:12] You should start thinking about things not from an efficiency point of view, but think about things from an effectiveness point of view.

[00:17:20] There will be some displacement.

[00:17:21] Right?

[00:17:22] But if you can imagine Gen AI working more on strategic and creative and deeper projects, that will make all the difference in the world.

[00:17:30] The second thing I would tell you is the mindset shift.

[00:17:33] Right?

[00:17:33] You have to embrace AI.

[00:17:36] Right?

[00:17:36] It's, you know, don't go through the extremism versus reality.

[00:17:39] Just don't be fearful.

[00:17:41] Right?

[00:17:41] And just you have to believe faster than anyone else and dive in.

[00:17:45] Because there's going to be a lot of experimentation.

[00:17:48] You might as well dive in head first.

[00:17:50] Right?

[00:17:50] And keep evolving.

[00:17:52] The second thing is, so first is embrace.

[00:17:53] The second one is learning to learn.

[00:17:55] The more you do, the more you will learn.

[00:17:59] And then the last thing on the mind shift is, you know, stand up through all rooftops and tell people that it's about using AI every day.

[00:18:08] It's not about, and it's always about you being the pilot and AI being the co-pilot.

[00:18:12] It's always human and machines.

[00:18:14] Right?

[00:18:15] Right?

[00:18:15] So, along the way, so you've got the productivity paradox.

[00:18:18] You've got the mindset shift.

[00:18:20] And the last one is, you know, skills to hone.

[00:18:22] Focus on hard skills.

[00:18:23] You have to learn a bunch of stuff.

[00:18:24] If you're a technologist, you know, learn what LLMs are.

[00:18:27] Learn what rags are.

[00:18:29] What you have to do from rag.

[00:18:30] Learn guardrails.

[00:18:31] All those things.

[00:18:31] But the soft skills will matter significantly in this one.

[00:18:36] Right?

[00:18:36] You have to be obsessed with what the user wants.

[00:18:39] You have to fail fast.

[00:18:41] You have to use data.

[00:18:43] And you have to use instincts.

[00:18:45] And you have to focus on dialogue.

[00:18:48] This will affect people.

[00:18:50] And bringing them in is going to make all the difference in the world.

[00:18:54] Love that.

[00:18:55] Something else I'd love to ask you, on behalf of people listening around the world,

[00:19:00] especially with such a focus on leveraging AI to drive innovation,

[00:19:05] a topic I know you're passionate about,

[00:19:07] and also an increasing pressure of the ROI of AI projects now.

[00:19:12] What excites you most about the potential that we're talking about here

[00:19:16] when solving real-world problems?

[00:19:18] Anything that makes you want to jump out of bed in the morning

[00:19:20] or that excites you at the moment around this?

[00:19:22] I just, you know, so if I think about, I'll tell you,

[00:19:26] I'll give you my personal journey.

[00:19:28] If I think about what do I get excited about doing?

[00:19:31] Right?

[00:19:32] I get excited about solving problems.

[00:19:36] I like building products that developers like

[00:19:39] that change the trajectory of the enterprises they work for.

[00:19:43] Right?

[00:19:44] So my goal is to serve enterprises with great products

[00:19:50] products that actually that they can leverage for running their businesses.

[00:19:54] In doing that, there's a lot of this.

[00:19:56] There's obviously I have to do a lot of let's create a great product.

[00:20:00] I have to work with people.

[00:20:01] Then I've got to also execute, right?

[00:20:04] There's a lot of execution stuff that needs to happen

[00:20:06] because you can have great ideas,

[00:20:07] but if you do not actually deliver, it doesn't matter.

[00:20:10] In the delivery part, from the great idea of delivery part,

[00:20:13] there's a lot of things that can get hugely automated.

[00:20:17] So what I'm looking forward to is how does that automation happen in my life

[00:20:22] and how do we do it as a company so that we can get more effective

[00:20:27] and do more for our customers quicker?

[00:20:30] Right?

[00:20:30] And it's funny now, at least five times a day, if not more,

[00:20:37] I have ideas and once you open yourself up to it,

[00:20:41] you keep having ideas on, shit, this could be so much easier

[00:20:44] if this was done for me, if this was agentified.

[00:20:47] So I think this is going to be bigger than anything people had ever imagined

[00:20:54] and I'm really, really looking forward to help define it.

[00:20:59] And a lot of people forget that five years ago,

[00:21:02] nobody saw or could have predicted how working from home at scale

[00:21:06] and evolving into hybrid working and everything that happened there.

[00:21:10] And two years ago, no one saw how Gen AI would just upend absolutely everything.

[00:21:15] So it's become almost impossible to predict the future.

[00:21:17] And if we were to look ahead, though,

[00:21:20] how do you see AI heading throughout the next decade?

[00:21:23] And are there any challenges, opportunities that you foresee for businesses

[00:21:27] and society as a whole as we all collectively navigate this wave?

[00:21:32] So let me start with AI is not going to be any different.

[00:21:36] Every technology wave has bad actors involved as well, right?

[00:21:43] But every one of the waves we've gone through has always had more good than bad, right?

[00:21:49] And so I think we just need to recognize that.

[00:21:51] Because this is more human-like, I think this is for the first time as a geek,

[00:21:56] I feel like we need governance sooner rather than later.

[00:21:59] The good news is that we actually have different companies

[00:22:06] as well as different countries who are coming up with ways of governance.

[00:22:10] The cautious news is that we need to make sure that the people

[00:22:13] who are putting those guardrails and governance around us

[00:22:16] are the ones that actually understand AI well.

[00:22:19] They're not just thinking about it from a governance point of view.

[00:22:22] They're thinking about it from a governance of AI point of view, right?

[00:22:26] And this is where the, you know, it's not just about AI versus humans.

[00:22:31] It is about humans with AI versus humans without AI.

[00:22:34] That needs to be something that's important.

[00:22:37] The second thing I would tell you is it's about agents, right?

[00:22:41] Agents are not a new concept.

[00:22:43] Can you imagine, Neil, your life without Google Maps?

[00:22:47] You can't, right?

[00:22:49] But we did have one.

[00:22:50] What is Google Maps?

[00:22:51] Google Maps is an agent.

[00:22:52] Now, imagine if you can keep moving that along and say,

[00:22:55] I want an agent to manage my inbox based on my rules

[00:23:00] because Neil's rules to manage your inbox are different than mine, right?

[00:23:04] I want to go off and do that.

[00:23:06] I want an agent to do my laundry as an example.

[00:23:08] That would be perfect, by the way.

[00:23:10] Just, you know, but my point is those are the kinds,

[00:23:13] I think agentification is massive

[00:23:15] and I think it is not going to start at the,

[00:23:18] it obviously happened at the company level,

[00:23:20] but I think it's going to start at the individual level

[00:23:23] and I think that is what will actually accelerate this

[00:23:26] faster than anything else.

[00:23:28] So as you can see, I'm really excited.

[00:23:31] I love it.

[00:23:32] I'd like to have a bit of fun with you

[00:23:33] before I let you go as well.

[00:23:35] I always ask my guests to leave a book

[00:23:37] that has inspired them or mean something to them

[00:23:40] or they just like to recommend.

[00:23:41] I add that to an Amazon wishlist

[00:23:43] and everyone listening can check that out.

[00:23:46] But what book would you like to add to that list?

[00:23:48] You know, this is a hard question

[00:23:51] because I'm an avid reader

[00:23:52] and most of my reading is about stories

[00:23:57] of how people have created things, right?

[00:24:00] And so the book that has changed my life

[00:24:04] more than any other book

[00:24:06] is called The Little Kingdom.

[00:24:08] It was written by a gentleman by the name of Mike Moretz

[00:24:12] who used to be a Times reporter

[00:24:13] and he went off and became the leader

[00:24:17] for Sequoia Capital.

[00:24:20] I lived in Calcutta

[00:24:22] and I got a chance to read the book

[00:24:23] at the British Council Library in 1983.

[00:24:26] That book inspired me to come and work,

[00:24:29] will come to the States

[00:24:30] and come and work for Steve Jobs.

[00:24:32] And five years later,

[00:24:34] I had taken computer classes,

[00:24:37] applied to colleges,

[00:24:38] came here and became an intern.

[00:24:40] The Little Kingdom is one book

[00:24:42] that fundamentally changed the course of my life.

[00:24:45] Wow.

[00:24:45] After 3,000 interviews,

[00:24:47] that is one of the most powerful examples

[00:24:49] I've heard for recommending a book.

[00:24:51] I will not only add that to the Amazon wishlist,

[00:24:53] but I will be checking that out myself.

[00:24:56] I can promise you that.

[00:24:57] It's a great read.

[00:24:58] You also mentioned a few moments ago

[00:25:00] you've got your own podcast.

[00:25:01] We've been talking about data stacks.

[00:25:04] So if anybody listening

[00:25:04] wants to find out more information about data stacks

[00:25:07] or listen to your own podcast out,

[00:25:09] where would you like to point everyone listening?

[00:25:11] Data stacks.com.

[00:25:13] It gives you a sense of what we do.

[00:25:15] We have a great customer base.

[00:25:16] We love what we do.

[00:25:18] We love the products we build.

[00:25:19] Our customers like the products we build.

[00:25:21] Inspired Execution is the name of the podcast.

[00:25:24] It's primarily me hanging out with people

[00:25:26] and the focus there is,

[00:25:28] what advice would you give

[00:25:29] a younger version of yourself?

[00:25:31] And so we have a good fan following.

[00:25:33] People give us a lot of remarks back.

[00:25:35] And so it's very raw.

[00:25:36] And so it's a lot of fun doing it.

[00:25:40] Awesome.

[00:25:40] Well, I will add links to everything

[00:25:41] so people can find you at data stacks

[00:25:44] and the podcast nice and easy.

[00:25:46] And wow, we covered so much today

[00:25:49] from the impact of those technology waves

[00:25:51] that we've discussed today

[00:25:53] and what's awaiting on the horizon,

[00:25:55] how the AI wave is the most unique

[00:25:57] and most impactful.

[00:26:00] And also calming the anxiety

[00:26:02] around AI's impact on jobs

[00:26:04] and the economy

[00:26:05] and why there's so much to be excited about.

[00:26:08] A real breath of fresh air.

[00:26:09] And I'm also going to be checking that book out.

[00:26:11] But just thank you for sitting down with me today.

[00:26:14] Awesome, Neil.

[00:26:14] Thank you very much.

[00:26:15] I appreciate this.

[00:26:16] I think Chet's perspective

[00:26:18] reminds us that every major technology wave,

[00:26:21] whether it be from client server to cloud,

[00:26:24] has fundamentally changed the world.

[00:26:26] But AI is pushing us even further,

[00:26:29] demanding a shift in how we think about productivity,

[00:26:33] collaboration and leadership.

[00:26:35] And as Chet said there,

[00:26:36] it's not just about efficiency anymore.

[00:26:37] It's about driving effectiveness

[00:26:39] and preparing for a future of agentification.

[00:26:44] So are you fully ready to embrace AI's potential

[00:26:48] while addressing those challenges

[00:26:50] of governance and ethics?

[00:26:52] How do you see this wave impacting your own work,

[00:26:55] your own industry?

[00:26:57] Let me know your thoughts.

[00:26:58] If you've got any questions, comments,

[00:27:02] techblogwriteroutlook.com,

[00:27:03] X, LinkedIn, Instagram,

[00:27:05] just at Neil C. Hughes.

[00:27:07] If you want to join me on the podcast

[00:27:08] and share your story again, let me know.

[00:27:12] But that's it for today.

[00:27:13] So much to think about from Chet's story there.

[00:27:16] That's what I'm going to be thinking about now.

[00:27:18] It's time for me to sit down in my favourite chair

[00:27:20] and pour myself a little shot of whiskey.

[00:27:22] But have no fear.

[00:27:23] I won't be having too many.

[00:27:25] I'll be back in my podcasting chair

[00:27:27] bright and early tomorrow.

[00:27:28] So hopefully I will speak with you all then.

[00:27:30] Bye for now.