**Rebuilding The Workforce For The A.I Era | Abhimanyu Saxena, Co-Founder, Scaler | N18M — Transcript & SRT | SozAI**
Source: https://sozai.app/transcript/rebuilding-workforce-ai-era-abhimanyu-saxena/

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00:02

Speaker A

CNBC TV18 and Scalar present Rebuilding Workforce for the AI era. Hello, and welcome to our special podcast of CNBC TV18 and Scalar present Rebuilding the Workforce for the AI era.

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Speaker A

I am Rima Tendulkar. Over the past few decades, technology has transformed the way we live, work, and do business. But a few shifts have been as rapid and as far-reaching as the one we are witnessing today with artificial

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intelligence. For a country like India with one of the world's largest talent pools and a rapidly growing digital economy, this transition presents both a significant opportunity and an important responsibility. And at the center of this transformation is India's

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workforce. And as the pace of change accelerates, the conversation around skills, learning, and readiness becomes even more relevant than before.

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And to discuss what it takes to build a workforce that is ready for this AI transformation, I have with me none other than Abhimanyu Saxena, co-founder of Scalar. An engineer by training, Abhimanyu has spent years building and shipping products at global technology

01:15

Speaker A

companies before taking the entrepreneurial route. That experience has shaped his approach to building Scalar, which has evolved beyond a conventional ed tech platform into a technology company focused on building India's talent infrastructure, serving students and their families, working

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professionals, and enterprises that rely on skilled talent. Abhimanyu, welcome to the show. But let's begin with the quick background. Your journey from an engineer to entrepreneur to forming Scalar and founding it in 2019.

01:46

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I think both me and Anshuman, my partner, we have been pretty entrepreneurial from day zero. I remember even when I was in school, my dream was not getting a top job. My dream was building something. I was very fortunate to be in Hyderabad at the time when the IT ecosystem was just booming.

01:58

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The IT ecosystem of Hyderabad around Hyderabad, ISB, the big Microsoft campus, all of those grew when we were just in college.

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I ended up building a few startups with my friend Anshuman who is my co-founder again. So, we built a few startups even back then. Of course, we were young and stupid then, but we learned a lot by the journey of

02:17

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building products that people really value. Then both of us ended up working for some of the fastest growing companies in the world, Facebook and Fab respectively.

02:31

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During our time in the US, Berlin, etc., when we were working there, then we landed on this big problem of capability in individuals, employability in individuals.

02:43

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And 2015 then is when we thought that this is a worthy problem to solve.

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15 to 25, more than 10 years. I think then we have been solving the problem of employability and industry-focused education.

03:00

Speaker A

But what was the point at which you realized? So, you know, before this you were doing InterviewBit, which is basically training professionals to crack the technical exam, the interviews to get the job, right?

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You did that for a couple of years, 2015 to 2019, and then you formed Scalar.

03:20

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And that is solving for a different problem because you realized that the problem is not just about cracking the placement, but about being job ready.

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So, at what point did you realize? Was there a moment of epiphany when you thought that this problem needs a different solution? There is a structural deeper issue with our education system.

03:34

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So, I'll take a step back. What are we doing? What do we do, right?

03:46

Speaker A

What's the problem that we are trying to solve? Yeah. Us being technologists from the very beginning, right? I identify more as an engineer than anything else. And what we realize is that in the country, the technological infrastructure is way

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behind. We are solving for that. The bottleneck for the growth of the tech industry in the country is talent.

04:03

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Right? And even more so today. Right? Throughout, be it InterviewBit, be it Scalar, be it the AI research work that we are doing now, all of that is rooted in how do you make infrastructure to make India at the edge of technology?

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Like for example, Silicon Valley was built inside Stanford. Where is India's Stanford? We have been trying to build that.

04:27

Speaker A

So, Scalar is India's answer to Stanford? I remember the very first pitch deck that we created 10 years back.

04:35

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The first slide of that was building a Stanford for India. But what's the extent of India's employability gap?

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Right. Because we know that India is a land of engineers. We produce millions of engineers. But you're saying they're not ready for the job.

04:49

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Yeah. Yeah. So, is there a way to quantify this? No, no, absolutely. So, we did a study recently. And the results were surprising.

04:56

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About 89% of the people who we spoke with felt that they are well versed with AI. They are exposed to, they are aware about AI.

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And when we dug deeper, only 19% of them have actually used AI for any meaningful work.

05:14

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So, 89% feel that they are AI aware. Only 19% are actually able to use it for anything meaningful, right? And I think there is something positive and something negative about it.

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Right? It's great that we are aware, of course. That puts us ahead of a lot of people.

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But at the same time, being aware is not enough. You know, deep realization that there's a lot more that needs to be done is super critical.

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But why do you think the solution to this is Scalar School of Technology? Right, for a very long time we always believed that degrees matter less, skills matter more. I get that India is producing degrees at scale, but not

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capabilities at scale. But why is the answer to that another college or another institute?

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Right. No, so I 100% agree with you, right? It's the real capabilities, ability to build things than any piece of paper, right?

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And it can't be any other way, right? No matter what degrees, what papers I hold, if I could not create value for the things around me, it's worth nothing.

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Now that being said, how do I check for capabilities? If I'm going to get a surgery done, I can't test a surgeon on the fly that is this a good surgeon or not, right?

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So hence for efficiency, there is a meaningful certification that I can trust. The problem is the problem is not that degrees exist. Degrees are needed.

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The problem is that degrees have to be truthful. If someone has a degree of a surgeon, they must be really a good surgeon, right? If everyone just starts getting them, that's a big problem.

06:46

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I think that is the problem that got created in the country. We got a lot of degree mills, right?

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Degree mills. Like, yeah, you know, as a matter of fact, I would say not everyone, of course. There are a lot of folks who are fantastic engineers, fantastic this more of a problem with tier two, tier three or do you think because you

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graduated from an elite institute? Right. Right. Right. Did you also feel that once you got your engineering degree, you were not job ready when you started work at Fab?

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I think I was fortunate enough. Triple IT Hyderabad is a super premium institute, yeah, and it's pretty rigorous as well, right?

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But it has 200 seats in India. You know, that's not even 0.01% of all the aspirants in the country.

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There need to be at least 20,000 if not 200,000 seats at the caliber of Triple IT Hyderabad in the country. And that's the big opportunity. I think that's where Scalar, the reason for existence of us is that.

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So, tell me, how would you compare the Scalar School of Technology with the degree and the capability and the technology infrastructure which you've diagnosed as the problem in India right now? How does that compare with a Bachelor of

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Engineering from an engineering college in tier two, tier three? Right. What's the difference in terms of the capability and skills that they get alongside

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the degree? Right. No, so there are multiple pillars to it, right? The very first one is what to learn. For example, I think we are the only organization in the country. The fact is that tod

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uh, the degree? Right. No, so there are multiple pillars to it, right? The very first one is what to learn. For example, I think we are only organization in the country. The fact is that today AI is a

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big wave and it's probably much bigger waves through AI are yet to come. I do believe it's very underhyped, right?

08:35

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Underhyped. Absolutely. Um, the another fact is that we must not shy away from the center of this revolution is in San Francisco. It is in Silicon Valley, right? We recently set up an office in San Francisco.

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My partner, Anshuman, 6 months back, I pushed him to just be there. Okay. Right? He takes two international flights every month, which is not easy.

09:00

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Yeah. But we are doing that and I think we are the only institution in India which have an office in San Francisco.

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And to make sure that we create the bridge, right? We quickly, as quickly as possible, we transform all the learnings from the frontier labs, what is happening at the frontier labs in Silicon Valley, right? How do we make

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sure that max at a delay of a month or three, we are able to transform them back in our Bangalore campus, right? That is the first pillar.

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Our only reason to set up a Scalar AI, uh, you know, research lab office in SF was to learn.

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Because if you don't, sure, we might have had the most frontier curriculum 2 years back, but even that is outdated now. It is impossible to keep it up-to-date without being there.

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The second part is learning by doing. In this world, like the books do not exist. The capabilities that are emerging, probably the books for that will come 4 years down the line. The only way to learn is by building, right?

09:58

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Now, we get So, about hundreds of SST students are working on the most cutting-edge AI problems already.

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Speaker A

And they're with what companies? With with the frontier research labs. With the frontier research. Okay.

10:13

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I think we are the only institute in the country where the students are getting an opportunity to work on projects given by the frontier research labs.

10:21

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Right? Then the third pillar is how do you learn it, right? I think AI also creates a [snorts] great opportunity to simulate a lot of things. We learn the best by doing, but how do I put them in the real environment? That was not

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possible. A first-year student, I probably a cutting-edge company will not allow them to sit in their office and, you know, do things because if they do something wrong, the risk is immense.

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But using the AI tooling, we have been able to create those simulations. You do the real work, you get the real feedback. So, I think all of these pillars, that what to learn, being at the cutting-edge frontier of it,

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creating opportunities for students to work on live problems of the frontier research lab, I think these are the key differentiators.

11:09

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So, do you think this is the way AI should be taught? I mean, uh this is what you all are doing, and fascinating, right? Creating that bridge with Silicon Valley, uh and teaching real capabilities and real-time problem-solving. Uh but as you said,

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right, going back to that point, that when you did research, 89% of them believed that they know AI, they're well aware.

11:29

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Yeah. But only 19% of them are actually building. So, what is your prescription for how AI should be taught in colleges generally?

11:39

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Right. No, so as you rightly said, the biggest worry and very dangerous thing is not knowing what you don't know.

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And that is the common problem in the country that we see. Uh that 89% of the people who feel they are AI aware, but are not AI ready.

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Basically, they don't know what they don't know, right? And first realization is leaving that, you know, sitting at the peak of Mount Stupid and coming to the value of despair.

12:07

Speaker A

Yeah. And knowing what I I don't know. Because the most dangerous thing is inaction.

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Speaker A

Uh technology never takes away jobs. But the inaction which slowly slowly slowly you do not even realize it and you become irrelevant, right? So, I think the first step is first, you know, through real examples, getting to know

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what are the new capabilities that emerge. And that's changing every day. That's changing every day.

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Uh so, and that is what makes our work way harder. Because earlier probably we said that if we refresh the curriculum once in a year, that's good enough.

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Now, we have to refresh the curriculum every 3 months. And to be able to refresh it well, we have to learn it all much before that. So, probably we just have four or six weeks of window to learn it all and then refresh, right?

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Speaker A

Uh but I think the way is be it an organization who want to do their talent transformation or an individual who want to stay relevant for the industry. The first step is understanding what capabilities exist that you are not

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well-versed enough. Second is having an environment, a place where you could practically build it.

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Again, as I said, there are no books. You have to learn by doing it.

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So, there is AI aware, there is AI ready, and there's a third category of being AI fluent.

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Yeah. And if India has to succeed in this AI era, we need a lot more people in AI fluent. What's the core difference between all three of them? And if you had a bracket, the vast number of engineers, how would you do it between

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these three? Right. No, so let me start with the base, right? AI aware, which is great.

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Must be acknowledged that it's a great thing that you know, 90% of people are at least aware.

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up, right? People write about taking small courses on LinkedIn. And it's a step-by-step journey, right?

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You start with awareness, then you build the capabilities, and then you become fluent, right? So at least the first step is taken. I'm glad about that. Now, even if I talk about companies, for example, most companies I would speak

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with, their CEOs or their CHROs or their head of talent say that, "Okay, I have bought Copilot for everyone in my team.

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Speaker A

We are good." Right? But the world is actually way ahead of that, right? It Just just to using Copilot to chat with it is not what creates value. Because the moment you double-click on the second output that showed you bought Copilot for everyone

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in the team, how did it change your top line? How did it change your profits? How did it change your customer satisfaction?

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My understanding, like we spoke to 1,000 plus companies, for 90% of them, even after having bought Copilot or ChatGPT licenses for everyone, that needle has not moved.

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The bottleneck actually is not the capability of AI. The bottleneck is the capability of humans to harness it.

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Right? The second level, if I talk about, which is AI ready, are people who are able to build automation, agentic workflows, uh you know, checks and balances, uh to make sure that they're able to boost their productivity. I can give my example.

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At least 60-70 percent of the work that I used to do, which was actually grunt work, I have been able to offload it to AI agents.

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Now, it might mean that I might, you know, till 4:00 in the morning I am orchestrating those agents, fine-tuning them.

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Speaker A

Yeah. But, my personal productivity is at least 3x higher. Now, coming to the next step, which is AI fluency, right? So, if a lot of people at least are even AI enabled, that does change the output of individual and then of the whole org

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Speaker A

massively. Now, if I talk about the fluency, in the Silicon Valley, we see companies, we already work with some of the partners, where they are not just automating things. They are already building auto-improving systems. That it's not just that that this is a workflow that I

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have automated and humans don't need to do it anymore. This workflow is with each interaction of my customers, my team. Every day it learns from it and keeps improving on the key parameter automatically. Now, this is a level which I don't see many

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people in India even talking about. How do I design a system for my company that can keep improving automatically?

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Speaker A

What also struck me is that you said that, you know, a few years back, if you had updated your curriculum once a year, you were fine. Like, we went through school and college without the curriculum getting updated at all. There

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was a curriculum 10 years before. It was Exactly. I'm talking about, you know, 20, 30 year years back. But, now you're doing it every 3 months. In fact, very recently, you all did update your curriculum to take into account AI and

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Speaker A

agentic AI. So, what's in, what's out, what prompted the change? Right. And do you think you'll have to keep doing it and this the the gap will perhaps reduce? And how tough is it to do it?

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Speaker A

No, so first let me talk about what matters today, right? Um often people say that in a world where the code is all generated by the AI, why do people need to learn coding, right?

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Speaker A

That being said, if you look at, you know, the companies like Anthropic, OpenAI etc.

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the fact is that the engineers there are not writing code anymore. Despite that, why is and you know, whatever Claude code or you know, code X that open AI or Anthropic have, we all have access to that. Why are we not able to ship

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Speaker A

products at the pace at which Anthropic ships? So, being a good engineer still matters a lot. It matters more than before because actually just writing code is not engineering. Even being able to use these tools effectively to build systems that really, you know,

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Speaker A

uh achieve the desired outcome. That's not easy. That's not trivial. So, what people need to learn today is very different. Engineering is becoming more important. It's a I think a good comparison is a person working in the handloom

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Speaker A

cloth industry. And suddenly you have cloth mills. People need to now become engineers at cloth mills.

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Speaker A

Running the handloom is no more required. Right? But just a simple question. Do you all still need to teach coding? I mean, when we were young, we were taught C, C++, and Java.

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Speaker A

Uh and as you said, right now most of these big companies, like Google for instance, said 75% of their new code is written by machine. I spoke to Cognizant recently and they said 30% of the code is written by machines today and that

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Speaker A

number's only going to go up. So, in a way I think the fear in the market is that coding has become obsolete.

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Speaker A

Yeah. So, does coding need to be taught to engineers? Coding I I I do resonate it with a lot.

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Speaker A

I think writing code is already entirely obsolete. Very soon, probably just in few years. I don't think anyone will be writing code.

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Speaker A

Okay. That being said, people still need to learn to code. Now, what you learn, however, is going to change massively. Not writing code does not mean that you do not need to understand how systems work. If it is

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Speaker A

system that you might have built, but you do not understand it, it's a very dangerous piece.

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Speaker A

Yeah. Right? So, I think people still need to learn the fundamentals. What people learned before continues being necessary, but now people need to learn a lot of new things.

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Speaker A

How do you validate? You asked a model to build an application. How do you validate that that output is really correct? All right? How do you make it safe? How do you make it secure?

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Speaker A

How do you write it well? Right? Because now you will not have a static software.

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Earlier, probably everything piece of code was static. Now, it is all agentic. How do you test agentic systems? Because they can behave in any way.

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Speaker A

Every time you you send the same question to ChatGPT 10 times, the answer will be slightly different every time.

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Think about how would you test it? That is it working as expected or not? These are the new skills to learn.

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Speaker A

Yes. But for that, you need to know the foundation of coding. And this is on top of the technological foundation.

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Speaker A

And this is on top of the technological foundation. Got it. You can't learn these without having learned the foundation. So, interesting thing is that an engineer could create 10x or 100x more value today, but the hard part also is that now these

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Speaker A

engineers need to have 2x or 3x capability than a good engineer 2 years back.

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Speaker A

Wow. It's a step change. It's a step change. It's again, I think a very good simile is a steam engine engineer versus a wagon driver, a horse wagon driver.

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Speaker A

Yeah. Right? You need to become a steam engine engineer. So, you know, I want to get your views in on this whole philosophical debate that goes on because we were talking about how coding is still important to know for you to debug and validate it at

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Speaker A

a later point because it builds the foundation. So, when we went into engineering colleges and I'm an engineer myself, right? The idea of going to a college was to master the foundation. Uh to build a very strong base.

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Speaker A

Yeah. But now, increasingly, many smart people, right? Especially from Silicon Valley, they say, "Well, you know, colleges have now become or institutions have become slow-moving. You can build a lot faster, accelerate your building process in these specialized labs, in

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Speaker A

these commu- in these founder communities." Right. So, you know, people are questioning the importance of college. Where do you stand on that?

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Speaker A

Right. The college should be those founder communities. Is how I look at it. Because see, some few might be lucky to be hanging around the hottest tech hubs in Silicon Valley.

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Speaker A

Or maybe Bangalore, right? How I see Scalar School of Technology, for example is is Scalar School of Technology is that founder community.

21:49

Speaker A

Founder community. That is what universities are supposed to be. Stanford was same, MIT was same, right?

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Speaker A

Like we all enjoy this Bose headphone. How many know that Bose was created inside MIT?

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Speaker A

It was created by one of the professors at MIT. Wow. That is what all the top institutions of technology are supposed to be. And that is how we are building Scalar School of Technology. In the campus, we have eight

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Speaker A

cutting-edge startups being built inside the campus. Uh, we have a humanoid robotics company being built inside the campus, and about 10 of our interns work on that startup. So, I agree with the fact that being part of a builder community

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Speaker A

Mhm. is much more powerful than being in any university. Yeah. But then, the second flip to that is that university should be that founder community.

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Speaker A

See, universities today need to offer you the foundation, the degree, along with that, these builder communities and the specialized labs with mentorship.

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Speaker A

So, universities today need a combination of two. Let me also bring in one more point that you said, right? An engineer today needs to have two x the capability that he had just two years back. So, suppose I'm a fourth-year

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Speaker A

engineering student. I'm ready to start my job as an AI engineer. Right. Uh, the job expectations have also changed now dramatically. What is it today compared to what it was a couple of years back?

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Speaker A

Right. So, so I think a couple of years back the life was much simpler for engineers in India at least if I can say, right? Uh you have a set curriculum. Sure, it might be complex.

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Speaker A

But maybe you spend a year a path. You had a path. a path, fixed path. There was a playbook.

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Speaker A

Correct. There is a playbook. You follow this. And if you kind of slog through it That's a nice word.

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Speaker A

[laughter] Uh you know, like it's almost 90% certainty that you could get picked up by one of the Google or Amazon or Microsoft. You have fancy, you know, you are in the top 1% earning or maybe 0.1% earning in

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Speaker A

India. And you know, you are at peace. The that has gotten flipped a bit today, right? Uh at the same time both on the positive and the negative. Now, you need to learn, build capabilities on some things that didn't even exist a year back.

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Speaker A

You know, how do you write good evals? How do you create the right rubrics to evaluate AI itself?

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Speaker A

Right? It's not just about writing code anymore. How do you quickly discover where are the opportunities in the real business?

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Speaker A

Where a AI workflow could create so much value, right? And as I said, there are no books for it. There is no playbook for it, right? Good thing is that because of this demand, a lot of solutions are also emerging. For

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Speaker A

example, all the curriculums that we are creating, all the labs that we are creating for people to learn are the solutions that are emerging for that, right? Uh but the reward has also gotten much bigger. The kind of salaries that people

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Speaker A

are like people who are really expert in these domains, the kind of compensations they are demanding both in Silicon Valley and India is unheard of. I have few people in my immediate friend circle who are earning in double-digit million

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Speaker A

dollar in a year. Wow. In which year? In one year. In one year. They're earning young I So, these might be maybe 25 to 35 year old.

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Speaker A

Outstanding. Right? Never heard in the world before. Just And they're not founders of the company either. Just one engineer.

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Speaker A

Right? Even in India, I have heard cases of engineer with strong capabilities in these domains getting hired in crores of salary.

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Speaker A

So, because there's so much scarcity one of the founder of a very large company, CEO of actually a public company, I was speaking with. Uh he said something very funny.

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Speaker A

He said, "You know, I have 500 engineers in the company, right? And I want to to transform all of them to be very AI native." He said, "Can you help me do that? I have been trying for last

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Speaker A

quarter. I need some help." I said, "Tell me more. What's your, you know, plan for next 6 months?" He said, "I'm going to run this another, you know, initiatives to transform the team for another 3 months.

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Speaker A

My unfortunate intuitive hunch is that half of them would not be able to do that.

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Speaker A

Okay. I'll let them all go. And after that I need to hire 300 more engineers from the market who are AI ready.

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Speaker A

Can you help me hire those as well? Now, the news might come that all right, XYZ company did a layoff of 250 engineers.

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Speaker A

The reality is this guy wants to expand his team from 500 to 700. His problem is that I need a different kind of talent now.

26:19

Speaker A

Uh I'll come back to you. Then, what should those 250 people who are whether there's a lingering fear about being laid off because they're not able to upscale and reskill? So, we'll come back to it. But before that, I want to

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Speaker A

quickly touch upon We discussed the problem statement that you were trying to solve when you started Scalar School of Technology, but you also have an online school of business. What prompted that more and expansion?

26:45

Speaker A

Right. Right. Right. No, so our intent to build a school of business was also very linked to the shifting realities of the world, right?

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Speaker A

As we were talking that 2 3 years back the life for an engineer was super sorted. Like you know, I I feel comfortable saying that Yeah, you know, work hard a year, you know, build these capabilities. You are good at writing

27:07

Speaker A

Java code. You can solve some certain kind of problems. You get picked up at a, you know, really impressive salary by a top company, not much of work, and you're living a good life.

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Speaker A

That's not sufficient anymore. Right? What people need as I was talking about that being able to identify where the real gaps are. And because practically what an engineer was paid big dollars for 3 years back, now AI can

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Speaker A

generate that. Now the industry need people who can understand what happens in the real business, what are the real business metric. Right?

27:41

Speaker A

And know enough AI. For example, I'll I'll share this example. Sneha who works with me in our HR team just day before she came to me and wanted me to test a tool. I tested the tool. I said, "No, this looks good. What

27:53

Speaker A

software is this? Are we planning to buy licenses for this for certain HR process management?" She says, "I built it." Wow.

28:01

Speaker A

She's not an engineer. She is a career HR professional with 15 years of experience. She says, "No, no, no, I built it. We don't need to buy this." Now that is a big So this is the kind of

28:10

Speaker A

people who are most powerful, right? That you know what HR processes are, what the real world is, and this big unlock now has happened that this tech building technology is not logged that now you have to go to

28:23

Speaker A

the CTO align them to build this for you. It will take 1 month for them to work, and they might just say that sorry, we don't have time. It's going to cost so much.

28:31

Speaker A

Or you have to go and buy a software for which your approval will be stuck forever, right? You can just build.

28:37

Speaker A

So you're basically with this MBA program or business program, you're turbocharging your existing core competency with technology.

28:45

Speaker A

Right. So our core philosophy was that in the new world people who will be most valuable are the people who understand real world business and also have sufficient AI knowledge to be able to build things.

28:58

Speaker A

And MBAs historically were generalists and they used it as a platform to switch careers. Now, I don't want to be an engineer anymore. I want to just go into finance. So, MBA was the route.

29:09

Speaker A

Does that also happen with the AI field? Yes, yes, yes. That is a very real need.

29:14

Speaker A

And I think that will always continue happening right? And that happens with even your schools?

29:18

Speaker A

Absolutely. So, I think career pivot is one of the important category that students come in. And we see a lot of them who have successfully done that. A lot of kids who got in from a sales background, marketing background, and

29:29

Speaker A

then after the course they could have switched to Like, for example, one of the fastest growing AI companies in the country, uh a lot of our students ended up joining them in their growth team Okay.

29:40

Speaker A

uh after the you know, having finished our business program. Before working in the growth of a frontier AI company, many of them were not even related to marketing or growth.

29:53

Speaker A

So, a lot of those career pivots we see happening. And just the way you we described that how engineering institutes need to morph into builder communities with labs, mentorship, real world problems, what is your prescription for business schools

30:08

Speaker A

today? What all should it include? One, of course, is uh the ability to use AI in real world problems. But what else should they include? Do you have a view on that?

30:17

Speaker A

Yeah, most important is, you know, the ground truth, you know, operating with the ground truth, which is business.

30:23

Speaker A

Right? If you have bunch of really good companies operating within your campus. And the students are working with the founders because ultimate ideally the passing grade should be build a startup.

30:37

Speaker A

How much is the revenue and profit of your startup? That should be the passing grade, ideally, right? The AI opportunities have lowered the threshold to build a startup massively.

30:48

Speaker A

10 years back when we started the startup, probably we'll have to invest at least few crores to just build the V0 of the product, right? Today it cost 10,000 rupees.

30:59

Speaker A

So, anyone can do that, right? So, I think most important is rather than theoretical learning that here is a marketing book written in 1980 and you read that and you give the exam.

31:09

Speaker A

Build a product. Try to market it. See how many users signed up for it.

31:14

Speaker A

That's the real way to teach. Uh I have to ask you about jobs. Because I report practically every day about job losses, whether it's Beta or whether it's Oracle saying that they're revoking their campus placement offers.

31:28

Speaker A

Even IIT Bombay, like the most prestigious college, said that their placements were 70%. Uh tell us what you're seeing on the ground when it comes to entry-level jobs.

31:40

Speaker A

No, I think as you mentioned, what we see on the ground is of course that is right that at the entry-level talent in the old world there was lot of, you know, small work that you could give to them, you know,

31:57

Speaker A

they take few years and then they build more capabilities and then really become useful. Most large companies, I think and rightly so, the idea was that when you are hiring freshers, you are not even expecting them to do anything for

32:09

Speaker A

the first 6 to 12 Which is why we used to say that entry-level salaries for engineers hadn't gone up for like 10, 15, 20 years. Because you weren't expecting too much out of them on day one.

32:19

Speaker A

The practical truth is that actually the first year of job was practically fifth year of education.

32:25

Speaker A

As a matter of fact, they were hiring people just on the basis of their IQ, capabilities, attitude. Uh but they knew that initial 6 to 12 months they are not going to add value to the business.

32:38

Speaker A

Sure, after that in the second, third, fourth year onward they add immense value to the industry. But in the first year actually company is investing on them. Even the small salary paid to them is also an investment on them, right?

32:51

Speaker A

Now, unlike good or bad, those kind of roles cease to exist. So, everyone is expected to perform on day one.

32:59

Speaker A

So, everyone is expected to perform on day one. So, what was happening in the first year and the solution to that is what was expected to happen in the first year of job. And rightly so should happen when you are in the university

33:10

Speaker A

itself. Right? And on the flip side, I'll talk about instances that we are already seeing. Got So, one student Harsh uh from Scalar School of Technology in his final year now.

33:25

Speaker A

Uh he was interning at our AI research uh team, right? They were If I remember correctly, they were being paid very well for an internship in India. Maybe a lakh a month is considered very high stipend, right?

33:39

Speaker A

Uh a friend of mine who runs uh another one of the most respected tech companies in India messaged me that hey, I hired few interns from your uh institute.

33:50

Speaker A

Uh very glad with how sharp kids are. And I was very glad because I know they are one of the paymasters. They pay two lakhs a month to the interns.

33:57

Speaker A

I was glad that okay, they're hiring from the institute. That's a good validation in itself. Right after his message, just after 5 minutes my lead from the AI research team messaged me that I mean you, you know, uh

34:08

Speaker A

XYZ company gave offer to some of our interns. Uh I'm pretty puzzled what to do about it. And they're just paying more, so kids left.

34:19

Speaker A

Uh And that was two conflicting emotions for me that on one side I'm glad that the kids are being hired at such hefty internship stipends.

34:27

Speaker A

And sure, of course, they're being poached from our own team. Uh uh But the kids who have capabilities they're also being hired at a very hefty salaries. Every other founder, you know, every other day this amount of inbounds for hiring has

34:42

Speaker A

never happened before. Like, can you get me some AI native engineers? Can you get me some AI fluent engineers? So, on one side there are no jobs but at the other side the founders are also saying that I'm not able to find people who have the

34:53

Speaker A

capabilities that I'm looking for. So, let's talk about building this capability, right? And here let's bracket it into three categories.

35:01

Speaker A

One is the entry-level fresher. To some extent, we spoke about what they need to do. Their final year of college should be similar to what would be their first year of their working life, professional life historically. So, right? I guess

35:14

Speaker A

that's the recipe for entry-level. But there is also that mid-level where they work for 10-12 years. Uh and they, in a way, are fearing the middleman squeeze.

35:23

Speaker A

And I think the most anxious are the top level who think that perhaps they're too qualified in the old ways. They're too expensive to that if they lose job, who else is going to hire them at the same

35:33

Speaker A

salary. So, do you want to talk to us about building capabilities for these three segments?

35:39

Speaker A

Right, right. Let's start from the top, right? Because I think everything then slows down.

35:44

Speaker A

All the transformations must start from the top. Um I think for the top leadership, one most important part is high neuroplasticity and willingness to question everything how it was done before, right?

35:57

Speaker A

Is that easy? It's very hard. It's actually the hardest part. So, I think presume, yeah.

36:02

Speaker A

Uh but I think that's where, you know, the biggest unlock as well. I see the leadership, the leadership that I interact with. I see them like in my head, I see them in three categories.

36:12

Speaker A

The 20% who are extremely high agency, extremely restless, who are taking a flight to San Francisco every month. Um And they're extremely restless. I I I know, you know, someone, for example, who heads a almost $10 billion dollar

36:28

Speaker A

finance is a good note. This gentleman, being a CEO, never a programmer before, never a tech guy, he's playing with cloud code whole nights.

36:38

Speaker A

Right? So, this is the top 10 20%. Extremely high neuroplasticity, lot of restlessness. The second 60% are people who we call, "I have got, you know, co-pilot for everyone in my team. I hope we are AI native. I hope my company

36:54

Speaker A

[laughter] achieves." And then there are 20% who feel that AI is hype. Right? But do you think it's the most underhyped technology of our lifetime?

37:03

Speaker A

do strongly believe is the most underhyped technology. Of our lifetime, yeah. But I think in the third bucket, they are in extreme danger.

37:11

Speaker A

You know, if you do not acknowledge the big shift coming, you will be dinosaurs. But of course, you know, sooner or later, people either realize or perish.

37:23

Speaker A

The 60% is there. You know, they know they need to do it. They don't know to to all much details what all are possibilities.

37:32

Speaker A

And I think this segment, so I think the key is first figuring out ways to get exposed to what people are doing.

37:41

Speaker A

Because that's a little hidden. Everyone doesn't know that. It will take another 1 or 2 year when the results start coming in.

37:48

Speaker A

One one result, for example, I talk about, I think Bajaj recently in their quarterly earning reports reported that a good percentage of their loans were agentically, while there was human approval, but all the prior checks happened through AI. Which is a big chunk. Right?

38:08

Speaker A

More and more such things will start emerging in next 12 to 24 months. Right? And that will tell people that what all are the possibilities.

38:17

Speaker A

These what all these top 20% are doing, which is making their companies 2x, 4x, 10x more valuable. Right? And this 60% will shift there.

38:26

Speaker A

So, I think one key is extreme high curiosity and restlessness, uh which is for the top.

38:33

Speaker A

And what about uh the middle level? Right. At the middle level, I think, you know, getting hands-on.

38:40

Speaker A

By default our whenever we think about learning something, our default response is, "All right, let me read a book." Or let me, you know, buy a course on Coursera.

38:49

Speaker A

That's not good enough? It doesn't cut it. Uh you know, I know it could cause anxiety to a lot of us that, you know, if that doesn't cut it, what do I do?

38:57

Speaker A

Right? This might be necessary, but not at all sufficient. Of course, you have to start somewhere, uh but you have to pick up things which gives you an opportunity to really build.

39:08

Speaker A

And nothing better than at your work. You know, what are you doing day-to-day? You start thinking, "Where could you use AI?" And you start building that. Don't think, execute. You know, that action is needed. Uh because there you have immediate

39:21

Speaker A

feedback loop. If you build an automation, and if it doesn't work, you have to figure out what works. If it works, you have again positive feedback loop that your boss is going to say, "Fantastic. You have automated a work on which we used to

39:32

Speaker A

spend so much time." Right? So, for the middle level, I think getting hands-on or even being part of communities programs where there's a lot of hands-on building is involved. Uh that's super important. Again, going back to example of my team mate Sneha, right?

39:48

Speaker A

She wasn't doing a course. She built a tool. And gave it to everyone. Right? So, I think for middle level, that's super important.

39:54

Speaker A

Okay. And you want to just close it up with the entry level? I think entry level is at the best place. They have so much time Yeah.

40:01

Speaker A

that they can go really deep. As I often say that the big corporates do not fear other corporates. They fear the 18-year-old because the 18-year could do things that no one else.

40:11

Speaker A

So, I think they should just go deep. They have so much time at their hands.

40:15

Speaker A

The world is going to change. So, play at the frontier. Yeah. You should always say right for the mid-level senior, your moat was your experience, the context that you have over the many prior years uh compared to a fresher. But now AI gives the same

40:29

Speaker A

context and moat to the fresher. So in a way your experience is bypassed completely. So I mean there is a lot to fear with the younger people who have that hunger, who have the passion and time. But it's just that when you're in

40:43

Speaker A

that middle level or high level, you're juggling so many things. You've got a family life, you've got kids, and it's very hard to find the time. Are there any from a personal point of view, right?

40:53

Speaker A

Are there any hacks that you would recommend to just get involved in this All right. All right. No, and because we have been you know helping working professionals upskill and you know transition into their careers for long time. For a

41:07

Speaker A

working professional there are two pillars to optimize, right? Of course, you have so much on your plate, right?

41:11

Speaker A

You have family responsibilities, you have work responsibilities, and in the middle of that you have to also right?

41:17

Speaker A

The key are two. One is align it to your day job as much possible, right? Because then it integrates. It doesn't become two competing things, but it becomes a complementing things, right?

41:29

Speaker A

It might be possible for many, it might not be possible for some, right? Because let's say if I am a marketing professional and I want to learn technology, I'm not able to find the overlapping. The other part is see it a little bit like

41:42

Speaker A

workouts, right? Uh you have at least few hours before you start the work, you have few hours after you end the work.

41:48

Speaker A

For example, in our online courses that we offer to working professionals, the classes are conducted 7:00 to 9:00 in the morning or 9:00 to 11:00 in the night.

41:55

Speaker A

It's a little bit like workout, right? You have to do workout an hour a day.

41:58

Speaker A

You do it early morning, you do it late night, you you you up to you.

42:02

Speaker A

So this online school for working professionals. Uh so tell us a little bit more. One is that you've customized it in such a way that the friction for them reduces by, you know, accommodating the timelines, right? You can do it

42:17

Speaker A

early in the morning, you can do it late at night, just like you do a regular workout. What is the end goal that a working professional, because I see a lot of people who want to know how to go

42:27

Speaker A

about becoming AI native, learn about AI. They just don't know how to do it without upsetting their current life and working life.

42:33

Speaker A

Right. So, end goal are two factored. Um A lot of people who join these programs or we train maybe about 15,000 people in a year.

42:44

Speaker A

Um And the goals fall in two buckets. One is that my company is already going through uh you know, sharp AI transition.

42:54

Speaker A

And I want to be capable enough to add value there, right? Often a lot of companies will sponsor these programs as well. That they would ask people to go, do these programs, and they can reimburse uh the cost associated with the program.

43:08

Speaker A

Right? So, one goal is kind of pivoting within my company. Uh to be able to be at the core of the AI transition that the company is executing itself.

43:20

Speaker A

The second is also pivoting cross company. That I am I work at this company. I want to I I see a lot of companies hiring AI native talent. In data that we see, AI native talent is commanding all about

43:34

Speaker A

2x average salary than traditional roles. And that makes it very lucrative for a lot of people. So, the second cohort that we commonly see is people who want to build these capabilities and claim these high-paying roles that are opening

43:47

Speaker A

up at different companies. So, there are many companies, Indian companies, GCCs. India's like the GCC capital of the world. And they're constantly looking to hire these AI native engineers or even upskill their existing to become AI native.

44:00

Speaker A

Correct. What is the advice and what is Scalar doing to work alongside them? Right.

44:05

Speaker A

Well, I think GCC story is also very big validator of the big opportunity that exists for India. Right? The reason all these Fortune 500 companies are racing to set up large capability centers in India is because the talent base exists.

44:21

Speaker A

Right? The switch over needed is that these working professionals still however have to build a lot of new and emerging capabilities.

44:28

Speaker A

Uh at Scalar we have been building a lot of new programs which help these working professionals transition from being a strong IT engineer or IT manager to become a AI native, you know, high capability individual for a GCC.

44:45

Speaker A

And again these programs are these online programs are designed to make sure that as we were talking about that working professionals it's tough for working professionals to even carve out time for upskilling, right? So these are designed in a way that you can do it

44:57

Speaker A

from your home. You can do it in hours that are comfortable outside of working hours etc.

45:04

Speaker A

Uh and plan in a way over say 12 months or so where you can execute it your learning goals along with your professional responsibility.

45:13

Speaker A

I'll tell you one more very anxious lot and those are the parents. Yeah. Because nothing and this I have two kids, right? They're small right now.

45:21

Speaker A

But I feel that nothing that I know and I have seen, my experience will help the kids when they grow up because it's an unscripted world.

45:29

Speaker A

Yeah. So what is the advice that you would give parents of kids who are still young?

45:36

Speaker A

Right. No, I think uh I I would draw parallel with how do you train AI agents?

45:44

Speaker A

Mhm. Right? Uh because interestingly how AI learns and how human learn have a lot of parallels.

45:51

Speaker A

Uh there is a concept called reinforcement learning, right? Where you and that is you use reinforcement learning to train AI models. And that is what is making them smarter and smarter.

46:02

Speaker A

You define a reward function and you define the goal. Right? If your reward function or goal are incorrectly defined, then you might end up in a very bad outcome, right?

46:15

Speaker A

And this is I think this is a very traditional wisdom as well. That rather than forcing kids to do very specific things that you have to study for 6 hours and all of those, better to set the right goals for them.

46:27

Speaker A

Give me an example. In India, I think this is all this JEE coaching, etc. have been intense, right?

46:35

Speaker A

I look back when I look back at my journey, well, sure I prepared for JEE, I had a decent enough rank to end up in a good college, but my framework was not that I am only successful if I get a

46:47

Speaker A

certain rank in JEE. Fortunately, my framework got created very early on that I want to learn physics well. I want to learn math well. I'm enjoying learning it.

46:56

Speaker A

And I'm sure that even if I didn't end up in Triple IT Hyderabad, for example, just because I have good built good foundation and right framework of thinking, most likely I think more or less I would have still done decent enough in my

47:09

Speaker A

life. You're being modest, but sure, go ahead. No, so I think that one that the goal has to be right. The goal can't be that you just get certain rank in an exam and only then you are successful, right?

47:21

Speaker A

Rather that be curious. Explore more, right? Be authentic. There's nothing new about it. AI doesn't change that.

47:32

Speaker A

It was 2,000 years back. I think it will be true always. So, just the way you expanded, you know, forward way, right? From a Institute of Technology, you've got a Scalar School of Business. Have you ever thought about

47:44

Speaker A

backward integration? That maybe you need to start thinking ground up, first principles with AI in the curriculum for students. Have you ever thought of getting into schooling as well? Is that a thought that Scaler's uh contemplated?

47:59

Speaker A

Right. No, so two parts to it. Is that a high-impact, high-potential problem space? It absolutely is.

48:10

Speaker A

Because the majority of our neural networks in the brain do get shaped much earlier in life. There's only so much you can change after it when you are 18.

48:21

Speaker A

Uh when kids come to and I can be direct about it. At Scaler School of Technology, we are extremely selective.

48:27

Speaker A

We only take 2% of the people who apply, right? And which is a little bit unfortunate, right? But by the time someone is 18, I know that I can't change their neural networks so much. When they were six, in

48:40

Speaker A

the 6-year to 18-year journey, even that could happen, right? So, definitely very big opportunity.

48:47

Speaker A

That being said, education I feel is a space which is more than just the skill, you know, because when you're operating with students, be it a 6-year-old, be it a 12-year-old, be it a 18-year-old, it's a massive responsibility. So, we would

49:02

Speaker A

only pick it up when we are fully certain that we can do full justice to it.

49:07

Speaker A

Uh which I think is at least few years ahead, if not more. Because for now, I think we would want to make sure that You do this first.

49:14

Speaker A

the university ecosystem, the higher education ecosystem that we are playing in, we execute that right, rather than getting too distracted too early.

49:21

Speaker A

Okay. You seem, you know, Abhimanyu, one of those kinds that who's always thinking about what next, right? How do I use AI in Scaler? What next can Scaler do? How can I help the students, professionals who are enrolled with me?

49:34

Speaker A

So, do you have a vision for Scaler a few years down the line? Internally, externally?

49:40

Speaker A

No, we are already executing a lot of it. Because everything that I want to teach or preach have to be actioned before with the right proofs inside, right? We have already been transi- transforming the whole company internal execution to be

49:54

Speaker A

very AI native. I give you one example maybe, right? The HR function for example or the finance function.

50:02

Speaker A

My 80% of the queries or the conversations that I would earlier route to someone in the team are now routed to an agent.

50:11

Speaker A

Not just for information, but also for actions. Just today morning for example, I messaged our HR agent that how many new people were hired in the last month, how many people left and what is the analysis around it.

50:25

Speaker A

And this AI agent could give me all that analysis in 2 minutes. To my HRBP, that would have taken at least a day or two.

50:32

Speaker A

Right? So, one we are doing all these transformation within our ecosystem on the corporate side, number one. Number two is in teaching, right?

50:42

Speaker A

Our like not just we have implemented a lot of like how do you use one part is learning AI.

50:49

Speaker A

Second part is learning using AI. Right? The entire pedagogy we have been building systems where there's a new pedagogy which is proven to lead to much better learning outcome that we have deployed. One example I can give you that and let's say there's an

51:06

Speaker A

online class that's happening. I can put four agents in the classroom who are acting as students in the classroom and who would ask very intelligent probing questions to the teacher who is teaching.

51:20

Speaker A

Which creates a great learning experience for everyone else in the class, right? Because in the regular classroom, it is probabilistic.

51:26

Speaker A

Did one of the student ask a very intelligent question which led to a great learning, you know, learning for everyone else in the class. But now that can be created through AI agents, right?

51:36

Speaker A

So, we are doing a lot of these uh new pedagogical models. Similarly, you know, curriculum rather than being a series of topics, we have created curriculums which is a series of tasks to do.

51:49

Speaker A

Right? If I want to learn something, there's a series of 30 tasks that you do.

51:53

Speaker A

Each task will be judged by an AI, and just doing those 30 tasks makes you expert at that. So, all these new pedagogical models are now possible, so which we are implementing. And then third, of course, becomes that basis all

52:04

Speaker A

these learnings, then we create the the curriculum modules, etc. which people So, just as we wind down this uh conversation, uh you said AI is the most underhyped technology of our time. If I look ahead 5 years down the line, what do you think

52:20

Speaker A

people would have got completely wrong about it? No, I remember a debate with one of my uh engineer who, you know, was a uh pretty deep into AI about 5 6 years back.

52:32

Speaker A

Uh 6 years back, um the debate was about whether So, in in computer science, there is a concept called Turing test.

52:43

Speaker A

Turing, yeah. Right? Uh and So, like everyone was surprised that can Turing test really fail?

52:51

Speaker A

If I am Is it possible that an AI can talk to me mimicking a human where it is impossible for me to say whether on the other side is it a human or AI.

53:00

Speaker A

Now, that has already happened. And that's a big shift. You know, like we have had technological improvements for thousands of years.

53:09

Speaker A

But nothing which is not alive ever back responded back to me. Today, the machines have spoken back to us.

53:19

Speaker A

And that's a big change. And the consequences of that small switch is huge. So, I think uh at least 5 years back, I was not in the camp that this will happen.

53:32

Speaker A

And my ML engineer who I was having this debate with, he was that it will not happen.

53:37

Speaker A

And now, what is that ML engineer telling you about the next 5 years which you're still not convinced about?

53:42

Speaker A

Yeah. Although he's a very radical person. I see. Uh 10 steps ahead. Uh 10 steps ahead probably. Right or wrong, I don't know. Uh his thesis is that uh humans most 90% of the humans uh they will be only focused on pursuits

54:01

Speaker A

of happiness. Mhm. Something like Elon Musk has also been sort of talking about Right. Right. So, then it's very counter, you know, there is AI doom a scenario, you know, philoso- phers. Uh his outlook is on the extreme positive

54:13

Speaker A

side. That this technology could free us. Like lit- I think bluntly good 70 80 90% of the people do the job to bring the paycheck.

54:25

Speaker A

Yeah. Right? Not necessarily that you would do it even if you were not being paid, right? His thesis is that 5 10 years down the line it could get us to the place that we all only do what we love doing.

54:36

Speaker A

So, Elon Musk adds that we need a universal basic income to this so that we can pursue happiness. Uh you know, you're in the business of edtech, right? And edtech has gone through its own credibility crisis.

54:49

Speaker A

There've been multiple events, companies which have given it a bad name. And you also institutionalized it around the same time.

54:57

Speaker A

Right? During the peak post-COVID boom [snorts] uh when there was so much money pouring in and then we've seen spectacular collapses.

55:05

Speaker A

What is the perception about edtech in India and how would you like to change it?

55:09

Speaker A

Right. So, I would say education rather than just edtech because at the end of the day what matters is what problem you are solving, right? So, how I see it is that education, health care, banking these are the infrastructure needed for

55:23

Speaker A

the growth of any country. Right? So, these are must-have infra to be built. As a country, of course, we are at a place where there are huge opportunities.

55:34

Speaker A

But we are yet to realize them, right? When there is a big vacuum in any infra space.

55:40

Speaker A

Of course, a lot of like the invisible hand of economics, a lot of players will come to solve it.

55:47

Speaker A

Again, very natural outcome of the evolution is that some players will operate with strong governance, strong principles, and a strong execution. Some may not.

55:58

Speaker A

And market dynamics are so efficient in one way that someone who doesn't have right governance will perish automatically.

56:06

Speaker A

That process does create pain often, right? That the the employees, the customers involved with the ones where the governance was not high, and in the process of perishment, does create pain.

56:20

Speaker A

And those who do execute it well, of course, they will be remembered for the centuries.

56:27

Speaker A

Education as an infra has to exist. It creates an opportunity for all the entrepreneurs in this is space to build institutions that might outlive them. Of course, in the process, anyone who doesn't operate with the highest ethics, governance, they will perish. So, I

56:43

Speaker A

think we are seeing all those cycles playing out. And EdTech per se, the external perception about EdTech towards India, has it improved now? Because we've seen the automatic automatically, you know, through the natural course, some companies perish.

56:59

Speaker A

Right. No, I I I do believe that some skepticism? no. So, I think again, the world That's the real world. And often, you know, capital indicates that, right? Everyone I I remember one of the investor talking about it, that I do believe uh that

57:16

Speaker A

India will have very high-quality higher education institutions built. When would it happen? Would it happen in next 5 years? Will it happen in next 15 years?

57:25

Speaker A

Is to be seen. Right? But it's a little bit like, does India need a very strong health care infra? Yes. Does India need very strong education infra? Absolutely, yes.

57:34

Speaker A

Government had built IITs long back. They're still funding it heavily. But clearly, IITs alone can't fulfill the whole demand of India.

57:42

Speaker A

So, private entities will build it. How and when? Is to be seen. Which is the opportunity for all the other and responsibility.

57:51

Speaker A

Absolutely. You know, you're doing your bit to enable AI in India and rebuild India's education system.

57:59

Speaker A

But today, from a perception point of view, especially markets, right? You would have seen that India is tagged as the AI loser.

58:06

Speaker A

If India has to become an AI winner in this In the next 5 years, what does it need to do? What would define success for India Right.

58:14

Speaker A

in this AI era from where you stand? No, I think India has to play on its strengths rather than mimicking or copying anyone.

58:22

Speaker A

Right. And if we double click on what is our strength, our strength is the people and the youth of the country, right?

58:30

Speaker A

Now, the numbers 19% being, you know, AI capable versus 89% feeling, you know, AI aware.

58:40

Speaker A

I think the key lies in that if we can shift that 19% to 90%. I think that is the key needed. If we have a talent base and we have the foundation of the talent base, no one else else in the world you

58:53

Speaker A

would have these 10 20 million people who have extremely strong foundations in tech. If we could transform this whole talent base into a AI native talent base, then I think our weakness turns into a huge strength. So, I think that is what

59:09

Speaker A

I believe is the key to win this competition. Scalar has a lot of expansion planned ahead so that we can convert more of our youth into AI native youth. But Abhimanyu, thank you very, very much for joining in. This has been a fascinating

59:22

Speaker A

conversation. So, thank you for joining us and sharing your insights. So, clearly, the AI era is reshaping workforce in ways we're just beginning to understand. The story has just begun, but as we've discussed, success will depend not only on adopting

59:36

Speaker A

new technologies, but building on the skills, mindset, and the adaptability to thrive alongside. Well, that's all the time we have for you in this edition of rebuilding the workforce for the era AI era. Thank you very much for watching.

59:50

Speaker A

CNBC TV 18 and Skiller present rebuilding workforce for the AI era.


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