Beena Ammanath - Programmer to AI Tech Philanthropist (#25)

Welcome to another episode of Develomentor. Today's guest is Beena Ammanath. 

Beena is a managing director with Deloitte Consulting LLP and is an award-winning senior executive with extensive global experience in artificial intelligence and digital transformation. Her knowledge spans across e-commerce, financial, marketing, telecom, retail, software products, services, and industrial domains with companies such as HPE, GE, Thomson Reuters, British Telecom, Bank of America, E*TRADE and a number of Silicon Valley startups. Beena is the founder and CEO of Humans For AI Inc. She has co-authored the book “AI Transforming Business.”

A well-recognized thought leader and keynote speaker in the industry, Beena also serves on the industrial advisory board at Cal Poly College of Engineering, and she has been a board member and advisor to several startups including Flerish, Predii, iguazio, CliniVantage, and ProjectileX.

For other tech roles and descriptions click here.

Episode Summary

“ My bet is that computer programming, the way we know it today is going to fundamentally change."

—Beena Ammanath

In this episode we’ll also cover:

1. Why statistics and Algebra are fundamental math classes for data scientists
2. How Beena got involved in managing people after starting on a very technical path. 
3. What made Beena start a technology nonprofit?

You can find more resources and a full transcript in the show notes

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To listen to previous episodes go to https://develomentor.com/blog/

Follow Beena Ammanath

Twitter: @Beena_Ammanath
LinkedIn: linkedin.com/in/bammanath/

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Twitter: @gsingers
LinkedIn: linkedin.com/in/grantingersoll

Transcript

intro: 0:18

[inaudible]

Grant Ingersoll: 0:19

welcome everyone to the development or podcast, your source for interviews and content on careers and technology. I'm your host grant Ingersoll. Each episode we try to highlight interesting people working in a variety of roles in tech with the goal of helping our listeners find the right role for them, whether that's in software development, product management, sales, marketing, or really any other role that goes into building a technology focused company. To that end, today's guest has a variety of deeply tie, has had it variety of deeply technical roles as an engineer and data scientist before she moved up through the ladder and into the ranks of technical leadership. She in that time has also worked on several boards, both as a member of the board and an advisor and as if that isn't enough, she's also started a nonprofit focused on inclusivity in the data science and artificial intelligence space. Please welcome to the development or podcast Beena Ammanath. Beena, great to have you here.

Beena Ammanath: 1:18

Thank you grant. Pleasure to be here.

Grant Ingersoll: 1:20

Yeah, and thanks so much for joining me again. I know you have a very busy schedule and, and you know, as I was doing my research on you, it's difficult to kind of do you justice in terms of all the amazing things you've done in your career. So how about we just start off by having you introduce yourself and, and walk us through that career a little bit.

Beena Ammanath: 1:38

Yeah, absolutely. And I think you did a great job with the introduction. Um, so thank you for all the glowing wprds.

Grant Ingersoll: 1:45

you're welcome.

Beena Ammanath: 1:46

Now I have a very traditional bachelor's and master's degree in computer science, so studied computer science and uh, loved it. Um, and then I have literally grown through the ranks right from being a DBA to a data analyst, to a sequel developer and leading a day a team of BI engineers, data engineers, leading a team of data scientists. Um, and what has helped me, uh, which I certainly didn't plan it that way, has been, I have been always anchored in data but work across different industries, whether it's telecom or manufacturing or finance or retail. It's been different industry verticals, but anchored in data and data is such a good space to be in, right? Because data that space has just, you know, uh, evolved from a traditional sequel and sequel databases to oil TP to databases to that from that to business intelligence and ETL, that whole space and then came Hadoop and big data and then machine learning and data science. And now it's all about AI. But at the end of the day, the foundation is data. So I think that has been the theme with my career.

Grant Ingersoll: 3:03

Yeah. So yeah. Well I wanted to unpack that a little bit more between, cause you know, very early on, I think you mentioned you did a lot of what would a lot of just programming and development, you know, software engineering and then there was this switch to, Oh, Hey there's this, there's this data stuff as well. What was the catalyst at the time, you know, as you were kind of analyzing your career choices early on there, like what did you look and say, Oh, I'm going to switch more and be more data focused then more let's say programming centric?

Beena Ammanath: 3:36

Um, it's interesting that you ask that, right? I mean, it was software engineering initially, but it was really around, uh, you know, used to you had to write these long sequel scripts. So it was still very closely tied to the database side of things. I've always found myself attracted towards the, the data aspect because you just, I just feel that you can learn so much by looking at data and what the reason that it attracts me besides being, you know, more attractive and insightful is I feel it's more mathematical, uh, compared to software engineering, at least that you're at that when I was doing hands on programming, it was, um, syntax and things like that was so crucial that you couldn't really analyze as quickly or do get results quickly the way you would do on the data side.

Grant Ingersoll: 4:31

Mm. Well, so on the, you know, let's, let's delve into that math side a little bit. Cause you know, traditional computer science, isn't that math heavy? Was this just, was this something that you always enjoyed on your own and you're like, Oh Hey, wow, I can go do math on this in the computer realm? Or was math something that you took up a bit later as you, as you dug in on the data side of things?

Beena Ammanath: 4:55

Yeah, that, that, that's, that's a great question. So I have naturally been good at math. I mean, even when I was studying at night from elementary school, math just came naturally to me, but I didn't like it per se. My favorite subject was history and, uh, you know, math was just easy. And once I started, you know, got into this field and we did have a few courses around statistics and, uh, um, but, uh, it just seems that my natural inclination is towards math, though I refuse to still accepted fully that, you know, I enjoy math. I don't think I enjoy math. I just have a natural inclination, natural talent for it.

Grant Ingersoll: 5:38

Yeah. No, that's interesting. The notion of enjoying math is a, is to a lot of people, a little bit of an absurd notion. I can understand that. I actually for a long time enjoyed math and then it became work as well. So there's, I think you go through this evolution of math well talk a little bit about some of the math you do because you know, we're often in data science talking things like statistics and probabilities and that kind of math as opposed to more theoretical. Is that, is that a fair statement of, of how you would view the math you do in your career?

Beena Ammanath: 6:16

Yeah, absolutely. I don't, you know, I think that, uh, the applications of math is different if you're an economist or you know, or a hedge fund manager, it's a different type of math that have, you know, the of math you use as a data scientist or in the tech industry is different. But I, I tell this to a lot of my mentees is, you know, you need to get your foundation right. You need to know that the foundational concepts of, you know, not just math but physics and chemistry. You need to know the foundation and then you can apply it in different scenarios. But we do tend to apply more statistics and fundamentally being that for even from the business intelligence and now machine learning space, lot of it is focused on that application of math.

Grant Ingersoll: 7:03

Yeah. What would you see as some of those key foundations? You know, like if if the listener out there is like, Hey, I need to, I'm going to go take let's say two math classes, what would you recommend they take if they want to get into the data science space?

Beena Ammanath: 7:19

Um, statistics for sure. And core algebra, geometry, eh, you know though those are core concepts. Yeah. Especially algebra and statistics. I think those are the two that we tend to use, uh, repetitively. And I think the ones you study that in school, I remember it as in high school and you were studying that as like why, why, when am I ever going to use it? And then you're applying it like 20 years later because it is so ingrained in your brain by that point that we are using it. But when you're in school, you're not sure whether you would ever use those trigonometric formula in your life, but always does come in.

Grant Ingersoll: 8:02

Well, and the beauty these days is like, it's important to know and understand them, but you often have the computer to help you with that math too. I mean, very rarely are you writing your own math from scratch. Right,

Beena Ammanath: 8:15

right. And you know, iLab spinning off a little bit on that, I think, um, you know, my, my bet is that computer programming, the way we know it today is going to fundamentally change. It has to, I think programming or coding, the way we do it today is not going to be around for in the next 15 years. It's where programming is going to be completely different. It's not, it's going to be more human friendly. It's not just going to be about, yeah. Writing the actual code, the in a structured manner, I think that is set to go away.

Grant Ingersoll: 8:54

Yeah. You're seeing these, you know, the, the, the Four GL, uh, capabilities are getting better and better. And then, uh, you know, I think in th we'll touch on this later with your foundation, but I think the, you know, this notion that all of this next generation of developers will be AI native, right? They will just have this function available to them that says, make this program smarter over time. I think you're dead on. And your, your analysis there, you know, for the next question, I want to kind of go back to something you already touched on here and that you've worked across a lot of different industries, you know, telecom, finance, it looks like, I believe you've done some consumer facing applications as well, you know, talk about kind of some of the things in your career that have helped you be successful across all of those different industries. Cause I think a lot of people will say, Oh, well, I'm going to go into this thing and that's the thing I have to do. I'm going to be in telecom because that's my expertise. But you've pretty successfully jumped across.

Beena Ammanath: 9:57

Yes. Um, and the reason was, uh, you know, I'm a very curious person and I love learning. Um, you know, and it's always about what peaks my interest. So every role change or job change that I've had is when I have kind of become very comfortable in that role. It's been like a, it's become you, you get into your comfort zone, right? You know, everybody at the job, you know how to do it very well and that's, that's a trigger for me to go and learn something new. The way that I've approached is, you know, and sort of trying to learn something new from, you know, brand new from a technology perspective, which is any way force is try and learn, learn about a new domain. Like for example, when I joined GE, I had, I have no background in manufacturing. I did not have any background in IOT. But to join a company like GE was, it was a huge learning, right? You learn so much more about the domain by working in a company which is deeply anchored in them. I've certainly seen them careers where somebody out of college where you join a job and you are added for the next 50 years. But what I've seen in the, uh, the really successful people are the ones who have, um, career mosaic is what I call it, right? Have where you have different parts. You know, you have different experiences and somehow they all come together beautifully and enriches the whole experience. So, so that you're bringing so much more to the table than if you were just in one specific industry or one specific role. You get really, really good at it, but you know, you don't really, are not able to try in another environment. And I think in the new economy it's going to be people who have career mosaics that. And it's, it could be anything across domains or across functions. I think that always keeps you as a person mentally on your toes and you're always learning. It's, um, it's, it gives that motivation to, for you to push yourself without any exponents.

Grant Ingersoll: 12:08

Yeah. That's such a beautiful turn of phrase right there. Career mosaic. I've never thought of it that way. But, uh, uh, I think that's, that's such a nice way of thinking about and, uh, about building a body of work that is your, your career. Um, so speaking of that. I mean, I imagine then some of this, these different experiences then have also been a really key part for you moving up the ranks from individual contributor to leader, you know, talk about some of the key shifts that took place that enabled you to make that leap from a individual contributor to a leader.

Beena Ammanath: 12:48

Yes. Um, so that actually was a natural evolution in terms of uh, growing, uh, you know, you start as an individual contributor and as you grow there, um, uh, you know, more lesser experience the more junior level employees who join and then you kind of become a mentor and then becomes more formal and manager. But at one point, you know, uh, I did have to, uh, and this was a discussion with the of the organization at that time and also with within myself to say, do I want to go down a pure technical career path, which is a much like, you know, you'll become a chief architect or a technical fellow. These are terms in the industry, which is like you have reached the highest rank within a technical and it's, it's as good as a, you know, as a manager level, right? So go down a people management path. And I tend to, I chose a people management fad because, um, you know, I, I like technology, but I wouldn't say I'm fascinated by technology for the sake of technology. I, I like to, um, you know, the see how technology can actually be used either to get new business outcomes or to improve productivity or to do something with the technology. So I, uh, you know, I haven't shared this with before, but, uh, after my master's degree, I did consider doing a PhD and doing more research and I did, you know, take on that. And that's when I realized my strength lies at the intersection of the application of technology and core technology. That was a big motivator for me to go down the people management path. Obviously the other factor being I enjoy working with people. I do tend to be naturally, uh, uh, naturally, um, affiliated with humans and people and to have that I truly enjoy, um, enjoy relationships with people.

Grant Ingersoll: 14:57

Yeah. So that's actually, that's always an interesting one when you're talking with technologists because I think a lot of people, you know, I know personally in my own early career, I was like, Oh, just tech, tech tech and all I wanted to do is write code and people were secondary. But then as you grow and you, you experience more things, you go across industries, this people's side comes out and you know, you and I have met in person and you're very personable. Uh, and, and so I guess what you're also saying is that, that that was a natural fit for you. I was wondering if there was any specific things on people management that you really had to kind of open up that learning curve again or, or open up that, uh, open up your mind to how you properly manage people? Like what are some of those key lessons and being a successful people manager?

Beena Ammanath: 15:48

Um, I think in the new era, um, being able to like no matter what your role is, being able to scale up and scale down rapidly as a people manager is really important. And what I mean by that, as a people leader, you need to be able to talk at an executive level with your stakeholders, but at the same time you need to be able to talk to your engineers at a code level. That's where I've seen the most, uh, you know, um, respect coming from, um, both your employees and your, your own leadership, right, is when you are able to, uh, be able to quickly go to whichever level you need to. For me personally, it worked out very well. Again, another one of those unplanned things. But, um, uh, I was leading a fairly large team, uh, uh, uh, at a senior management level when I had my first child and wanting to be a super mom and, uh, you know, be actively engaged. I took a step back in my career and took, took on an architect role, which was an icy. So I went from a people manager to an individual contributor and, and uh, if, you know, coincidentally or a, you know, the stars were aligned. It was around the time when Hadoop and big data was just coming into the picture. And I tell you, grant think that what I studied in computer science, there was no Hadoop, there was no big data concept in that there was AI, but there was no big data that was not a thing. And it's a very different style of programming. The programming languages that I studied with like DBAs and Pascual and FoxPro notice and things that are not, you know, don't even exist. Um, so why that gave me the opportunity was that to get literal very much more hands on and learn these technology contructs, which otherwise if I had gone down that traditional path of just growing, continuing to grow from a management perspective would have been very hard. So I did that for five years and then, you know, I got back into the management career path again once my son was a bit older. What enabled me was really, you know, being able to understand the new technology and having a little bit more grasp, which I wouldn't have had otherwise. So from my, you know, what I see to, you know, the leaders who report to me is you need to be on top of technology. This space is evolving so fast. You cannot run an engineering team anymore unless, and until you have your technical chops up to date.

Grant Ingersoll: 18:37

Yeah, no, there's, wow, there's, there's so much rich stuff right in there. I mean you've, you've had, you know, I love this idea and people's careers of like these serendipitous moments where, you know, PR, you know, obviously you're super excited about having your first child at the same time. There's this part of you on the career side that it's like, Oh, well, Hey, I'm, I'm taking a step back from what I thought I was going to do. But then, you know, now in hindsight, all the, you know, these years later, you're like, yeah, but that was actually really worked out well for me. And, and I think there's a lot of those being a math geek myself, I like to call them, you know, their inflection points. Right. And it sounds like, you know, you, you had some really nice things come out of that inflection point back in your career.

Beena Ammanath: 19:25

Yes, absolutely. Are you even studying AI right? Who knew it would be, it would be so prevalent in our own life, you know, at that time, even post realized marketing was considered impossible. Automating personalized marketing was also built in the 80s

Grant Ingersoll: 19:43

yeah. Well, and the beauty of AI right, is your foundation is statistics. And, and data programming back in the day. I mean, you know, don't tell all the marketing people, but right. qAI is just, you know, really smart counting at the end of the day.

Beena Ammanath: 20:01

Yes, exactly. Exactly.

Grant Ingersoll: 20:04

Let's talk about the leadership role a little bit because I love when I have people like you on as guests, you know, who have managed large teams and, and you know, to flip the tables a little bit just from your, your career path to how do you think about hiring and building a team and mentoring people in their career path because that's often a really important role in leadership.

Beena Ammanath: 20:27

Oh, yes, absolutely. And I've worked in some tough companies, uh, which, uh, and some, uh, you know, which don't necessarily attract the best software talent or you know, data science talent, which is really hard to hire and, and you have to get a, you know, fundamentally understand the kind of talent you're hiring. And one thing that, uh, you know, I learned is, uh, people today are not employees today are not necessarily looking to, uh, join a company. It might be the first move, but they, and you are interviewing someone, they're interviewing you as well, and they looking for inspiring leaders. Mmm. And they are looking for somebody whom they can follow and somebody whom they can respect. And uh, for me, you know, that meant some changes. Uh, uh, did I tell a few years ago I was, and we've met at an event, I would not, I did not speak publicly. I did not blog or write or do things which I do as much today, which was really, uh, to kind of attract that talent, uh, to, to build out that network. Today we live in a space of ecosystems and networks and you know, as leaders, we have to tap into these new or newer spaces, which was not there. You know, I don't think 20 years ago anybody was looking at LinkedIn or any kind of social media to attract talent. But today that's what the talent today does. They do their research, they will look for leaders who they can follow or who are inspiring. So for me that that has helped, but also being able to, I remember, you know, I can, I bet I can still do this is go around team of data scientists and if they're coding, look at their code really quickly and find out little things, right? I started, I did, I'll be honest, grant, I don't think I can roll up my sleeves and code the way I could even 10 years ago, 15 years ago. But, but I can certainly, you know, I know the foundational aspects, right? So I can sort of, and that brings out the respect and they like, Oh, how do you know this? You are a VP, you're not supposed to know it too. And then Zen network that comes into, so it, all, you know, it all works out at that network level.

Grant Ingersoll: 22:53

Yeah. Well, and, and you know, and along these lines, you know, you're super busy as a, as a leader in a large global organization. And then you also are very generous with your time. And you know, I noticed on your profile, you, you have a number of board roles. And of course you've also started this nonprofit. So let's shift gears a little bit and talk about that side of, uh, of being in a leadership role of, of taking on these advisory things of starting to contribute more broadly. Like how did you, how did you get your start on that, that side of the equation?

Speaker 3: 23:29

So, uh, about six years ago, a good friend of mine was organizing an, um, big data conference and he asked me to come and give a keynote and, and I had not spoken at, uh, at an even public event or conference prior to that because I'm more of a heads down get the work done kind of person. And I did. And, uh, and you know, as always, I gave the speech and I was getting off the stage to just rush back to my work. There was a line of women who are standing there and, uh, you know, they, they were, um, full of enthusiasm and they said, we're so grateful that you came and spoke. And I was like, okay. And they said, do you know you're the only female speaker for the entire day? And that's when, you know, I, I looked at the agenda, I looked around and I realized there's not enough women in the room or in the lineup. And, uh, till then, you know, in my head, grant, you know, I am not a Sheryl Sandberg or Indra Nooyi. You know, I never thought I could be a role model to somebody else. I could inspire somebody. I didn't know. I just didn't think that. And, but, you know, hearing them, I realized in my small way I can make a difference. We have a diversity challenge. We have a lack of women in tech in the, in this space. There's something I deal with on a daily basis. Usually I'm the only woman at the table. That's a problem. And if I can make a difference, I need to make the time for it. So that was my trigger to really get serious about it. I joined the board of an organization called chick tech. Um, and, uh, you know, I am in more than number of startups and as board member or board advisor because I, you know, I, at the end of the day it's all about prioritizing. I could be in Vegas speaking at a conference and you know, I could be partying that evening. I write a blog and honestly I've reached that age where I prefer writing that blog or going out and gambling in the casino. But that's right.

Speaker 2: 25:40

Yeah, no, that makes sense. Although with your, your math skills, maybe the gambling options, not amount of money either. Well, so that then led into, uh, humans for AI. So why don't you take a minute and tell our audience about humans for a habit? Cause this is a fantastic idea. I think something that we need to put in tech needs to think more about. So, so philosophic.

Speaker 3: 26:09

So, you know, so I, I this whole, you know, lack of women in tech and doing something about it has been at the back of my mind and I've been doing things and as AI started getting more prevalent and especially with my team, with my data science teams, I noticed that they were not enough women in this, in this space. And, uh, I worry about it because, uh, unlike other technologies, AI is going to be extremely biased and that can only end up being dangerous as we get it scaled out. The other data point for me in my own career, I've seen two major technology waves, the internet and mobile, right? When I was studying, there was nobody, you know, you, you didn't ask to be an app developer or an S, those were not. Those got created and those got fed right? And they all got filled by the same kind of groups, right? Uh, if companies were serious, they could have focused on training more women to fill in these new jobs that got created and we did with AI. The worry about biases and the worry about lack of women in air and the the not the knowledge that I have is me that few years. You're going to need more domain experts who will be the future product managers, the testers for AI products today, if you are building an AI health product, it's a computer science. A person like me who would go interview or health care professional, go back and build the product and release it. That's got to change because this is low hanging fruit. In the future you're going to need a doctor or nurse actively involved in every healthcare product because you are going to go deeper into the domain. So you need domain experts. So humans for AI, our goal is to proactively train more women, doctors, women teachers, women, nurses, women lawyers so that they can be the future product managers, the testers, QA, the, you know, the ancillary functions around and a data scientist right now, you know, they can certainly train to be data scientists, but yeah, understand enough about it. They can be the ones who are actually envisioning the future products for AI in their domain.

Speaker 2: 28:34

Yeah, no, that's, that's awesome. Uh, sorry, I didn't mean to jump in there, but I mean I think yeah, to your point, this is kind of the point of this show too, is like, you know, you can, you can choose whichever one you want and there's, there's lots of different pathways into this and you can be the data scientists, you can be the doctor and, and like at the end of the day, like everybody needs to be aware of damn right. Like, and they need to understand the choices. That doesn't mean they have to be the ones writing the code, but you should certainly understand the, the trade offs, right?

Speaker 3: 29:09

Yeah. I tell you one thing, AI is not a spectator sport. It cannot be expected a sport, which is how it is today. You have the jump and you have to know because it's all around you, it's everywhere and you understand the basics. No matter what you do today,

Speaker 2: 29:26

especially with all the fear-mongering happening around AI. Like you know, the way you break through that, like you said, both, both. The fear among the malingering and the biases is, is by having, by being informed about what it actually is. Like people don't realize it's smart counting. That's what's happening. And, and if you don't understand that, then you're going to be duped by well, so, so talk a little bit more like, okay, so you've got this foundation. How do you go about accomplishing those goals? Like what's the next layer down that you, you know, if you clicked that.

Speaker 3: 30:00

Uh, so I'll tell you in the simplest way today when you Google for or say what is machine learning? Uh, it is, it'll explain to you using areas and using things that a math geek can understand are things that a person with a computer science degree can understand. It doesn't make any sense to an elementary school teacher. So what we are doing with humans for AI is actually building out, uh, explanation of AI by profession. So if you're explaining AI to somebody who has, um, an accounting degree, you start with, you know, the fraud detection or risk management walk through the steps at the end of it. Say, by the way, this is unsupervised learning, which is part of machine learning. So you are explaining it in a language that is relevant to their domain. So really making it more personal and relatable. So that's what we're focusing on and building out a community of AI experts and non-experts so that we can start asking those questions.

Speaker 2: 31:05

Yeah, that's fantastic. And of course we'll be sure to link up, uh, humans for AI in our show notes. I want to shift gears a little bit and, and you know, I think you've hit it on, hit on some of this already, but you know, I love when I with my guests to, okay. So we've gotten through your career to this point, you know, as you look forward in this space, you know, what do you see as the main challenges and opportunities of whether that's data science or artificial intelligence or technology leadership, however you want to frame that, what do you see as, you know, kind of the things that you're looking forward to are the things that keep you awake at night?

Speaker 3: 31:44

Yeah, a few few things. One is, you know, the AI being played as a spectator sport today. I think more people need to be engaged. Otherwise I am with Elon Musk when he says AI is going to ruin everything, right? You don't have enough people from different backgrounds and ethnicities, genders in one that keeps me up at night. The, but I'm also an AI optimist. So I think that we are fundamentally approaching AI in a way, which is, I'm just trying to fit it in, in our current processes or mechanisms. So it as an, as a company, as an enterprise, you're trying to say, okay, I can automate this, let me automate this with AI. Whereas we should be fundamentally rethinking the question instead of saying, can AI detect cancer faster? We should be, we should be actually thinking about can AI help prevent cancer? So fundamentally reframing the question is what my hope is, we will start seeing in the next five to 10 years where AI and any of these technologies are solving some of the world's largest challenges around climate change or you know,[inaudible] or hunger. There are so many large problems and we're just not doing it using technology so much around, you know, just wanting to get the best of my experience. And I think you really need to ask technologist and you're seeing that in the Silicon Valley now is weird. How do we use this technology for good? And my hope is that's where we go.

Speaker 2: 33:16

Yeah, I mean the, you know, it's always a little disheartening at times. The number of people who really smart people who really understand data and at the end of the day they're doing ad targeting and you know, and, and, and it's a, it's a good job in the sense, you know, that it provides for a family and pays, pays the bills, but it just sometimes feels like there's a level missing there to your point. Well, so then, you know, I, I think, you know, this has been really amazing having you on, I mean, there's such a rich set of things that have gone on in your career. You started off in a very traditional path of computer science. You evolved into this data analytics, which really has been the foundation of your career. You've played the individual contributor and the leader, uh, RA, you know, one of the things I like to ask all my guests then is, you know, what, what advice would you have for, for that person who wants to follow him and being as shoes or, or at least take a similar, you know, take a similar path to you?

Speaker 3: 34:22

Yeah. Uh, I would say that, uh, yeah, that if you are a high school student or if you're a student looking to study, study what interests you, take a job that interests you, uh, and don't really try to follow any conventional path. There isn't one. You have to make your own path and as long as you are following, uh, following your interests, I think it will end up always end up in a good place. At least it did for me.

Speaker 2: 34:53

Yeah. No, and be open to that serendipity it sounds like as well. Just cause it might look like a, an end point doesn't mean it actually is. So, um, yeah, no, that's, that's really great advice being on. And, uh, I can't thank you enough for coming on the show. I'll be sure to, like I said, link up to humans for AI and we of course, wish you the best of luck on that.

Speaker 3: 35:17

Thank you, grant. It's been a pleasure to be on your podcast.

Speaker 2: 35:20

Thank you. Thank you. As always, to our listeners for taking the time to listen. If you'd like to show, we'd love for you to subscribe on Apple podcasts or whatever your favorite podcast app is, you can also visit us at[inaudible] dot com

Speaker 4: 35:38

to hear older episodes as well as find other content on careers and technology. Most importantly, if you liked the show, please tell your friends referrals are the lifeblood of any podcast. If you have any feedback on this episode or any episode where you'd like to be a guest, drop us an email@podcastsatdevelopmentor.com finally, we here at development or hope that each and every episode helps you move that one step closer, defining your path

Speaker 1: 36:24

[inaudible].