Full Transcript for Episode 22: The Shadow Curriculum Is Already Here- Adarsh Lathika on What Happens When Learners Stop Waiting for Institutions and Start Building Their Own Systems

00:02:25,287 --> 00:02:29,556 What is just a 30 second elevator pitch about what you do in your 28 00:02:30,378 --> 00:02:36,354 So I try to study the misalignment or the growing misalignment between work and human cognition. 29 00:02:36,354 --> 00:02:38,036 That's what my focus is. 30 00:02:38,036 --> 00:02:42,111 So the eventual objective is how do you drive performance and productivity. 31 00:02:42,111 --> 00:02:43,834 What do love the most about your work? 32 00:02:43,834 --> 00:02:49,419 think the more I delve deep into it, the more I realize that I don't know much. 33 00:02:49,419 --> 00:02:50,800 It's a very open canvas. 34 00:02:50,800 --> 00:03:01,426 In fact, when I started on this journey, I went back to my 10th standard biology text to understand brain and really go back to the basic concepts to get a sense of what the 35 00:03:01,426 --> 00:03:08,040 neurotransmitters does, what each part of segments of brain does to human behavior and how they are related. 36 00:03:08,099 --> 00:03:22,859 So it's been therefore an exciting part to really relearn a lot of things that I left behind some 20 years ago and then repurpose to what we are experiencing today so that we 37 00:03:22,859 --> 00:03:27,010 are a bit more doing better for ourselves in coming future. 38 00:03:27,010 --> 00:03:36,416 And just to get you know a little bit better just in your day-to-day work, if you are really in the flow state and you're just kind of time is passing quickly and you're 39 00:03:36,416 --> 00:03:38,840 totally immersed, what are you most likely to be doing? 40 00:03:38,840 --> 00:03:39,981 building something. 41 00:03:39,981 --> 00:03:45,102 So I do have some background of uh programming. 42 00:03:45,102 --> 00:03:49,283 So I leverage my coding a bit to build. 43 00:03:49,283 --> 00:03:55,695 So I started building productivity tools for myself so that I can do things oh much faster. 44 00:03:55,695 --> 00:03:57,767 I don't really need a team to do it for me. 45 00:03:57,767 --> 00:04:04,819 From that I started then once I started getting hang of that I started building slightly more complicated tools as well. 46 00:04:05,298 --> 00:04:07,956 something that helps me both personally and professionally. 47 00:04:07,956 --> 00:04:09,491 So that's what I do most. 48 00:04:09,491 --> 00:04:11,947 That what I do enjoy doing most. 49 00:04:12,789 --> 00:04:13,791 That's wonderful. 50 00:04:13,791 --> 00:04:17,757 And I just described a little bit about what you do. 51 00:04:17,757 --> 00:04:19,941 You just gave your 30 second elevator pitch. 52 00:04:19,941 --> 00:04:24,038 I'm curious, what was it that inspired you to do what you do? 53 00:04:24,038 --> 00:04:33,698 So, I mean, a few things that are elicited over a period of time is that, one, humans are important. 54 00:04:34,138 --> 00:04:44,669 I think as a species in this planet, we have lot of responsibility to what we do, why we do, and equally important why we don't do something. 55 00:04:44,669 --> 00:04:48,714 So a couple of years ago, I decided to take a career break and 56 00:04:48,714 --> 00:04:53,615 That was the time where AI was coming, this whole charge GPT moment happened. 57 00:04:53,795 --> 00:04:59,677 And the first thing that came to my mind was the 1984 from George Orwell. 58 00:04:59,677 --> 00:05:10,443 And the more I was understanding what charge GPT was promising, more I was fearing maybe to some extent that that obedient world may happen in my lifetime. 59 00:05:10,443 --> 00:05:12,675 And I was not very happy about it. 60 00:05:12,675 --> 00:05:17,558 So question was that, how do you then give the power back to the humans? 61 00:05:17,558 --> 00:05:18,986 So I started thinking about it. 62 00:05:18,986 --> 00:05:20,349 I had the time to do it. 63 00:05:20,349 --> 00:05:23,061 I started listening to lot of podcasts around the world. 64 00:05:23,061 --> 00:05:26,742 I think that's something that internet just allows you to do. 65 00:05:26,742 --> 00:05:28,613 So I started reading a lot as well. 66 00:05:28,613 --> 00:05:37,267 And then once I started reflecting back on that or injecting that on top of my professional experience, 67 00:05:37,479 --> 00:05:40,171 I felt that maybe oh we need to go back. 68 00:05:40,171 --> 00:05:44,726 So what could be the gaps that can develop over the next three, four years? 69 00:05:44,726 --> 00:05:49,051 So my understanding was that knowledge is becoming abundant and instantaneous. 70 00:05:49,051 --> 00:05:55,556 So when that is happening, what is the role that as a human we will play at workplace? 71 00:05:55,556 --> 00:06:00,603 So then that's when the whole idea about judgment and context came to my mind. 72 00:06:00,603 --> 00:06:01,915 since then, I've been focusing on 73 00:06:01,915 --> 00:06:07,366 You kind of touched on this a little bit already, but what are you the most anxious about for the future? 74 00:06:07,366 --> 00:06:10,846 Personally, I don't like somebody else taking decisions for me. 75 00:06:10,846 --> 00:06:12,767 I'm not a big fan of that. 76 00:06:12,827 --> 00:06:27,267 So, and I think when it is AI to make that happen at a large population scale, then I think we, especially as a, at the context of India, we take a lot of pride of being the 77 00:06:27,267 --> 00:06:31,007 youngest nation or youngest nation for the next say 20 years. 78 00:06:31,032 --> 00:06:39,497 But imagine a case where in which we have a huge population of youthful, high energetic population, but nothing to do. 79 00:06:39,658 --> 00:06:44,663 And I think that can become very negative, very fast that we can't really control. 80 00:06:44,663 --> 00:06:54,363 So that is what I'm a bit worried about because there's a journey that as a country we are going through since independence and we had the time. 81 00:06:54,561 --> 00:06:58,502 to course correct because time allowed us to do it. 82 00:06:58,502 --> 00:07:01,224 But now AI is making everything redundant. 83 00:07:01,224 -->

00:07:10,587 I'm not so sure whether we are responding correctly to what AI is today and what AI promises to be tomorrow. 84 00:07:10,587 --> 00:07:13,208 So that is something that makes me anxious. 85 00:07:13,208 --> 00:07:19,212 But at the same time, it also is a great opportunity that can unlock if you're using it the 86 00:07:19,212 --> 00:07:20,047 right way. 87 00:07:20,047 --> 00:07:23,432 So I am trying to focus more on the latter. 88 00:07:23,796 --> 00:07:24,108 Good. 89 00:07:24,108 --> 00:07:26,284 And last lightning round question. 90 00:07:26,284 --> 00:07:27,816 What is a skill? 91 00:07:28,239 --> 00:07:32,440 Can be related to your work, not related to your work, but what's a skill you're trying to learn right now? 92 00:07:32,440 --> 00:07:39,808 So, one thing which I am trying to learn is how do I sell better because I am a solopreneur. 93 00:07:39,808 --> 00:07:46,353 So, I love building and I could afford to be a builder maybe 10-15 years ago. 94 00:07:46,353 --> 00:07:47,914 Not so much in AI first world. 95 00:07:47,914 --> 00:07:49,685 I can't afford to do it. 96 00:07:49,685 --> 00:07:57,922 So, I believe that everything and anything related to selling as a skill, right from public speaking to story, narrating a story. 97 00:07:57,922 --> 00:08:05,003 to being in front of a crowd and speaking those, these are the skills that I'm working on and I use AI for that as well. 98 00:08:05,455 --> 00:08:10,095 Good, yeah, wherever we can get that advantage, let's get that advantage. 99 00:08:11,595 --> 00:08:14,895 All right, well, we'll move to the kind of longer form questions here. 100 00:08:14,895 --> 00:08:26,475 I mentioned that you came into this field of learning through operations, things like building and scaling systems across university campuses, online initiative, executive 101 00:08:26,475 --> 00:08:28,835 education, corporate settings. 102 00:08:29,655 --> 00:08:34,335 And you have said elsewhere that the pattern you have seen 103 00:08:34,434 --> 00:08:43,114 is that we typically optimize for delivering learning, but not translating it into human capability. 104 00:08:43,456 --> 00:08:52,718 And so I'm curious if you can give me just a specific moment or project that you were on where you really saw that gap clearly, just to illustrate. 105 00:08:52,718 --> 00:09:01,638 So the background to that is that I have a habit of going through P &S. 106 00:09:02,218 --> 00:09:15,798 And something that was very stark and became very strongly apparent to me was that human resources are always on the expense side of the balance sheet. 107 00:09:16,518 --> 00:09:18,698 And it's just OK. 108 00:09:18,718 --> 00:09:19,938 Maybe in early. 109 00:09:20,310 --> 00:09:34,336 2010s it was okay, but with automation data science now AI coming to picture I think We need to start valuing human for the Contextual understanding Yoshi has and that is with 110 00:09:34,336 --> 00:09:40,273 the current gap that AI has because Like if you had

to come compare US and India and I want to start 111 00:09:40,273 --> 00:09:46,133 I want to open a restaurant say in San Francisco and I want to start a restaurant in Bangalore. 112 00:09:46,133 --> 00:09:50,993 Now I will give the same prompt for both cases. 113 00:09:50,993 --> 00:10:03,733 I am more likely to rely on the response that AI gives me for opening a restaurant in San Francisco because India as a country we contribute to less than 5 % of internet data. 114 00:10:03,733 --> 00:10:10,093 So that means AI doesn't have the context in which a city like Bangalore operates. 115 00:10:10,291 --> 00:10:20,727 Of course, there might be some aspects that we can definitely apply, but the finer things will still be amiss. 116 00:10:20,727 --> 00:10:23,612 So that can only be brought in by a human. 117 00:10:23,612 --> 00:10:25,476 So therefore, human is valuable. 118 00:10:25,476 --> 00:10:29,761 So where I realized this was when data science started becoming very popular. 119 00:10:29,761 --> 00:10:34,147 We started a data science school way back in 2014-15. 120 00:10:34,147 --> 00:10:43,196 Now, so I think at that time, we were probably the second most prominent education provider in the country to launch a data science course. 121 00:10:43,334 --> 00:10:54,739 And what I felt was that I asked the best question, why is a learner who's done a bachelor or a master's going to again come to my campus to sit through a 9 month course, a diploma 122 00:10:54,739 --> 00:11:00,171 course, what is that he or she is going to achieve or what is the aspiring to achieve? 123 00:11:00,171 --> 00:11:06,874 We might think that it is the skill of neuroscience, but the fact is that he or she wants a job. 124 00:11:06,874 --> 00:11:10,868 So what I did at that point of time is that even when we had 125 00:11:10,868 --> 00:11:14,168 Launching the pilot batch I created a system. 126 00:11:14,288 --> 00:11:23,668 So we partnered with IBM big data at that point of time We partnered with a couple of there is this company so that they can not only bring in their own faculty for doing guest 127 00:11:23,668 --> 00:11:30,648 lectures, but also Give them internship and then final placement jobs as well. 128 00:11:30,648 --> 00:11:35,346 So because I was able to create the ecosystem that program 129 00:11:35,346 --> 00:11:42,161 became successful and this is time when I was monitoring it, it was one of the largest, oh most successful program within the company. 130 00:11:42,161 --> 00:11:46,385 I think in a three year period, it graduated close to 670 people. 131 00:11:46,385 --> 00:11:49,427 So it was, so that really worked for me. 132 00:11:49,427 --> 00:11:58,673 So what I'm trying to say is that I put the learner as the focus and I put all the business metrics around it. 133 00:11:58,714 --> 00:12:00,762 Because I was able to do that, 134 00:12:00,762 --> 00:12:03,754 we were able to create a business which was sustainable. 135 00:12:03,814 --> 00:12:05,535 Sustainable

because learners wanted it. 136 00:12:05,535 --> 00:12:08,237 We were not trying to push a product. 137 00:12:08,237 --> 00:12:11,159 It was actually pulling learners to our product. 138 00:12:11,159 --> 00:12:15,183 So if you have to now put in the context of what AI is doing. 139 00:12:15,183 --> 00:12:19,806 As I said earlier, I'm trying to learn public speaking, improve my public speaking skills. 140 00:12:19,806 --> 00:12:20,827 How am I doing it? 141 00:12:20,827 --> 00:12:22,288 I record one. 142 00:12:22,288 --> 00:12:26,340 I raise my hand for every opportunity that I can get. 143 00:12:26,438 --> 00:12:29,958 Second, when I do land up that opportunity, I record the conversation. 144 00:12:29,958 --> 00:12:32,138 Then I have a custom GPT created. 145 00:12:32,138 --> 00:12:34,878 I feed that according to the custom GPT. 146 00:12:34,878 --> 00:12:44,358 Then there is a rubric that I created based on my secondary research that I've done on how a public speaking should be done. 147 00:12:44,498 --> 00:12:48,558 The intonation, the vocabulary, the fillers that you use. 148 00:12:48,558 --> 00:12:50,038 There are eight, nine parameters that are there. 149 00:12:50,038 --> 00:12:56,538 So I keep, I have created a feedback structure wherein I get a report saying, you are done. 150 00:12:56,615 --> 00:13:04,300 good in these five things, you are okay in other two things and these are definitely two things that you need to improve upon. 151 00:13:04,900 --> 00:13:14,008 that becomes very important because as I said earlier, the employees are learning and everything is still an expensive item for an organization. 152 00:13:14,349 --> 00:13:18,032 They are seeing a world where in which more and more people are getting laid off. 153 00:13:18,032 --> 00:13:23,555 So employees today are not seeing that commitment from the organization for their career path. 154 00:13:23,797 --> 00:13:34,083 So the question comes back from an employee perspective is that if the employer is not promising me a five years or 10 years with them, why should I then commit another five 155 00:13:34,083 --> 00:13:34,383 years? 156 00:13:34,383 --> 00:13:39,035 So my company may want to learn, say for example, Python. 157 00:13:39,035 --> 00:13:48,420 But I as an individual may feel, okay, I don't want to learn Python because now coding is as easy as just picking two system and generating code. 158 00:13:48,420 --> 00:13:49,731 Then why should I bother about it? 159 00:13:49,731 --> 00:13:51,402 Let me learn storytelling. 160 00:13:51,402 --> 00:13:53,792 So the company may be investing a lot on 161 00:13:53,792 --> 00:13:55,413 getting the person to learn coding. 162 00:13:55,413 --> 00:14:02,319 But I may use in ChatGP to your Gemini or some other coding instance to improve my public skills or storyboarding skills. 163 00:14:02,499 --> 00:14:08,474 So that is going to be dichotomy, which will the gap is going to continue to widen, widen and widen. 164 00:14:08,802 --> 00:14:09,912 Mm-hmm. 165 00:14:10,753 --> 00:14:14,234 You have described what you call a shadow learning system. 166 00:14:14,334 --> 00:14:17,935 And you've kind of just went into that a little bit just now. 167 00:14:18,232 --> 00:14:29,000 But the idea, basically, as I understand it, is that learners are using AI to build their own tools in parallel to any kind of formal education that they're getting. 168 00:14:29,231 --> 00:14:38,129 And they're doing this in part because institutions, employers or universities aren't actually keeping up with what's possible. 169 00:14:38,270 --> 00:14:47,338 And so I'm curious, can you walk me through how you think this is happening now, how this shadow learning is happening? 170 00:14:47,338 --> 00:14:50,891 Just what does that look like for a student or an employee? 171 00:14:51,221 --> 00:14:54,921 So the scenarios are different for both parties. 172 00:14:54,921 --> 00:15:01,901 For the student, institution is still important because you still need a degree to get a job. 173 00:15:01,901 --> 00:15:11,461 At least from where I come from, that's a basic qualification that it does not matter what degree it is, but you need to have a graduate degree to get your first job. 174 00:15:11,461 --> 00:15:13,292 Therefore, institution is still important. 175 00:15:13,292 --> 00:15:18,972 That scenario is slightly different for somebody who is a working professional. 176 00:15:19,276 --> 00:15:30,718 largely because one, you keep hearing this news about people getting laid off almost on a every third day you get a news assembly or there's a mass layoff happening in a globally 177 00:15:30,718 --> 00:15:41,769 repeated company and you don't know whether it's going to be you next or when is your turn and historically what I experienced is that the while we talk about it in the vision 178 00:15:41,769 --> 00:15:42,861 mission or the 179 00:15:42,861 --> 00:15:48,001 company statements, we are not really very transparent when it comes to employee communication. 180 00:15:48,161 --> 00:15:54,601 So that gap also adds to this anxiety that the employees can have. 181 00:15:55,241 --> 00:15:58,912 So that means it's a dogged out world. 182 00:15:58,912 --> 00:16:03,132 I feel that the employer is not committed to my career growth, my professional growth. 183 00:16:03,132 --> 00:16:05,112 Therefore, I do what I believe is the right thing. 184 00:16:05,112 --> 00:16:08,371 So if somebody comes to you today and tell me you learn 185 00:16:08,371 --> 00:16:13,994 Google AI Studio because that is the most important tool today, I go and learn that. 186 00:16:14,134 --> 00:16:18,147 Tomorrow, Anthropic might launch a better coding agent than that. 187 00:16:18,147 --> 00:16:19,958 So I learned that tomorrow. 188 00:16:19,958 --> 00:16:30,583 what internally it indirectly does is that it just, gives me as an employee, it gives me a sense that I'm actually learning and doing the right thing. 189 00:16:30,583 --> 00:16:37,655 But

what it internally does is it's creating a lot of anxiety inside my subconscious mind. 190 00:16:37,655 --> 00:16:48,121 that I am always hyper which means that I not focused at work I am not giving 100 % at work so the company starts suffering and over a period of time I also start suffering so 191 00:16:48,121 --> 00:17:00,071 it's a loss loss game that both of us are playing so in that way what I feel is that even if it hurts be transparent with the employees say be very clear that these are the five 192 00:17:00,071 --> 00:17:02,208 things which is going to happen in the next one month 193 00:17:02,208 --> 00:17:04,431 because it's difficult to predict what's going to happen after that. 194 00:17:04,431 --> 00:17:08,509 therefore, explain your decision very detailed to the employees. 195 00:17:08,509 --> 00:17:14,699 I think employees today are very smart to understand when somebody's been genuine and being honest in their communication. 196 00:17:14,699 --> 00:17:17,461 And I'm sure that people will rally behind that. 197 00:17:17,461 --> 00:17:23,927 And even if they end up being laid off, they have a very better understanding of why that happened and why they don't really fit into that plan. 198 00:17:23,927 --> 00:17:26,980 I think that becomes very important. 199 00:17:26,980 --> 00:17:33,369 It seems like there's this kind of necessary degree of trust here between the employee and the institution. 200 00:17:33,369 --> 00:17:41,823 And it seems like with this shadow learning system, if an employee or a student is using tools to... 201 00:17:41,823 --> 00:17:46,003 to do their own learning, why do they need the institution for that? 202 00:17:46,003 --> 00:17:50,034 Yeah, why would they depend on the institution at all? 203 00:17:50,586 --> 00:17:57,463 So if you look at student as a cert, you still need a degree, therefore institution becomes important. 204 00:17:57,463 --> 00:18:07,554 looking ahead, I think institution can actually transform into a place where students build their cognitive and behavioral skills, which I believe are two important segments of 205 00:18:07,554 --> 00:18:12,040 skills which are going to keep them employable in the next 5, 10, 15 years. 206 00:18:12,040 --> 00:18:14,301 So that transition needs to happen. 207 00:18:14,305 --> 00:18:19,566 But from an employee perspective, that's a very pertinent question and it's not very easy one to answer. 208 00:18:19,566 --> 00:18:29,981 Largely because I'll again go back to the fact that uh all the employees and employer related investments are still expense in the balance sheet and not really a detailed 209 00:18:29,981 --> 00:18:31,011 investment. 210 00:18:31,011 --> 00:18:38,174 So historically, what has happened is learning and development teams tried to optimize the training intervention. 211 00:18:38,174 --> 00:18:40,445 So as long as you are the 212 00:18:41,105 --> 00:18:51,402 either the in-house team or the external vendors are able to meet a minimum level

of quality they always go for the cheaper option because at some point of time the EBITDA or 213 00:18:51,402 --> 00:19:03,103 the PAT is going to come into picture and they try to always protect that so when you start doing it, it becomes very difficult to make the transition to something because you 214 00:19:03,103 --> 00:19:08,596 are trying to meet a short-term objective but not really building a company for 215 00:19:08,631 --> 00:19:10,511 next 5-10 years. 216 00:19:10,711 --> 00:19:14,511 So that becomes very challenging for the L &D team. 217 00:19:14,791 --> 00:19:25,211 And that means that they need to work very closely with finance and the purchase for most part to ensure that to convince them that why this investment at this rate is important. 218 00:19:25,211 --> 00:19:32,951 Because even when for example, I have seen cases where in which you are trying to deliver an AI training to a set of people. 219 00:19:33,011 --> 00:19:36,118 So the CHRO may approve it, the L &D head may approve it. 220 00:19:36,118 --> 00:19:45,178 but then again it goes to the purchase and the finance what they do is that they just look at the proposal as excel items they just do a comparison with what is available in the 221 00:19:45,178 --> 00:19:57,298 market and they look at high level keywords say you know I can get you 10 % cheaper, 20 % cheaper and then the question becomes what is cheaper to be delivered and I don't think HR 222 00:19:57,298 --> 00:20:05,514 or L &D fights beyond a point and then you always end up extending or giving a substandard training program to the employees 223 00:20:05,514 --> 00:20:07,257 which means that employees are not happy. 224 00:20:07,257 --> 00:20:08,821 Again, the loop just continues. 225 00:20:08,821 --> 00:20:13,681 So we need a very confident and assertive NND team to actually make this work. 226 00:20:13,681 --> 00:20:26,521 You've also written about this notion of cognitive debt, which is that AI is reducing engagement and pushing people away from thinking and more towards just verifying the 227 00:20:26,521 --> 00:20:29,832 output that the AI produces. 228 00:20:30,473 --> 00:20:35,695 And so if you're a person who is responsible for making courses, 229 00:20:36,223 --> 00:20:42,630 a learning designer, instructional designer, how do we address this? 230 00:20:42,630 --> 00:20:52,240 How do we address this pattern to help people engage more deeply, not just verify, to stay kind of in the moment with the things they're engaged with? 231 00:20:52,770 --> 00:20:57,413 So I'm not an ID person, so I don't know how the courses eventually get built. 232 00:20:57,413 --> 00:20:59,635 But I will explain my thought process here. 233 00:20:59,635 --> 00:21:11,481 If you are somebody who has been working in a specific field with significant experience, then it is OK that you get AI to do the work and you play the role of a verifier. 234 00:21:11,481 --> 00:21:16,103 That means that you are doing your work much more faster and much more efficient manner. 235 00:21:16,624 --> 00:21:18,674 You are not sure you are playing 236 00:21:18,674 --> 00:21:24,474 the role of a team lead to say 15 team members or 15 AI agents, which is alright. 237 00:21:24,514 --> 00:21:36,534 But if the objective of the training department is also to create a funnel of leaders who are going to take up the mantle say in next 2-3 years and currently you are given a team 238 00:21:36,534 --> 00:21:47,430 of say junior resources or people with say less than 2 years of work experience, how do they build the domain understanding or enough of 239 00:21:47,430 --> 00:21:57,014 domain understanding that they are building their expertise, they are building that level of intuition so that they can then therefore review the output given by AI. 240 00:21:57,014 --> 00:21:58,635 That is the challenge. 241 00:21:58,956 --> 00:22:09,874 so what historically used to happen is that you join a company as a junior resource and you had the sufficient amount of time where in which you start by doing the repetitive 242 00:22:09,874 --> 00:22:16,297 task for say six months, one year, and then you start doing more of a repetitive task. 243 00:22:16,297 --> 00:22:21,551 And at the end of a 12 month, 15 month period, you get to understand why that repetitive task makes sense. 244 00:22:21,551 --> 00:22:30,477 And therefore, what that task contributes to the business objective of the company or the segment that you are part of. 245 00:22:30,477 --> 00:22:39,757 But what AI does today is that because AI can do or shortchange that 12 month of learning into a few seconds or a few prompts, right? 246 00:22:39,757 --> 00:22:47,517 Junior resource then becomes just copy pasting the AI response and feeding into the next step or next stage. 247 00:22:47,537 --> 00:22:58,197 So he or she is not really developing an intuition and therefore not building on the ability to judge AI output on a long-term basis. 248 00:22:58,197 --> 00:23:09,484 So if as an L &D professional or as a CXO who is looking to build a culture, we build a long-term business which is going to sustain for the next 10, 15, 20 years. 249 00:23:09,484 --> 00:23:21,270 You are not really doing good by having the same old approach what you followed say 10-15 years ago for learners who are coming in system today. 250 00:23:21,510 --> 00:23:23,331 What is the right solution towards it? 251 00:23:23,331 --> 00:23:30,455 Now I can only hypothesize, like for example you can probably give more projects to them so that they understand the why behind what they are doing. 252 00:23:30,455 --> 00:23:31,187 But I think 253 00:23:31,187 --> 00:23:34,767 You need to experiment a lot at this point of time to figure it out for you. 254 00:23:34,767 --> 00:23:43,707 Because I think the answer depends on a lot of things, including what part of the world you're coming from, what industry you are in, what is the business objective of the 255 00:23:43,707 --> 00:23:44,627

organization. 256 00:23:44,627 --> 00:23:52,174 So probably you can factor in all those things and then find the answer that works for you at that particular stage of the company or the face of the company. 257 00:23:52,174 --> 00:23:53,114 That's great. 258 00:23:53,555 --> 00:24:02,335 you have asked the question, what gets lost for us when speed and output become the primary measure of success? 259 00:24:03,147 --> 00:24:04,308 So I'm going ask you that question. 260 00:24:04,308 --> 00:24:05,820 What's your answer to that question? 261 00:24:05,820 --> 00:24:10,074 What does get lost when speed and output become the primary measure of success? 262 00:24:10,074 --> 00:24:17,110 So when speed and output becomes primary measure, I think you lose out on people who are foundationally strong. 263 00:24:17,110 --> 00:24:27,037 And that foundational skills are important on a long-term basis, especially because unless your foundational knowledge, foundational understanding of your business, your industry, 264 00:24:27,037 --> 00:24:30,250 your segment that you're part of, you are never able to... 265 00:24:30,250 --> 00:24:37,726 catch the problems in the air response or the variations in air response. 266 00:24:37,726 --> 00:24:46,293 But habitually, over a period of last many years, we are used to rewarding people for speed and agility in the way they work. 267 00:24:46,293 --> 00:24:52,041 But what we should also realize is that, is it really creating a learning beyond what is supervision? 268 00:24:52,041 --> 00:24:53,915 And I believe in most of the cases, 269 00:24:53,915 --> 00:25:01,560 It might be just building skills at a superficial level and not allowing for the skills to have the depth that it requires. 270 00:25:01,560 --> 00:25:05,923 So I think that is going to be the fundamental challenge going forward. 271 00:25:05,923 --> 00:25:15,987 it also becomes that you are not only redesigning and relooking your L &D plan, but you are also relooking your performance metrics and your reward system so that you are 272 00:25:15,987 --> 00:25:19,202 enabling the right learning culture and therefore the overall 273 00:25:19,202 --> 00:25:20,644 the public culture. 274 00:25:20,644 --> 00:25:30,680 This takes us a little bit back to what you something you said before about human biology, that you went back, you studied some things that maybe you had looked at years ago, but 275 00:25:30,680 --> 00:25:35,803 you looped back to it because you felt it was so relevant to where we are right now. 276 00:25:35,803 --> 00:25:39,745 um This intersection of, yeah. 277 00:25:39,745 --> 00:25:41,710 um 278 00:25:41,710 --> 00:25:44,950 was COVID, the first phase of COVID. 279 00:25:45,570 --> 00:25:50,430 So at that point of time, I was in Bangalore, my wife was in Perth in UK. 280 00:25:50,550 --> 00:26:00,430 And she was telling us that there was this young chap in her team who was probably one of the best developer that she has seen. 281 00:26:00,650 --> 00:26:06,262 And he had one of the first casualty in the first phase of COVID. 282 00:26:06,527 --> 00:26:15,867 Now one interesting point that she told us while she said he was brilliant in coding, he did not actually pass away because of COVID. 283 00:26:15,867 --> 00:26:20,387 He actually passed away because he had not eaten for many days. 284 00:26:21,107 --> 00:26:27,527 This is despite the fact that there were couple of ready to eat meals packets available in his house. 285 00:26:27,527 --> 00:26:31,467 So what happened was that he was staying in apartment with his colleagues. 286 00:26:31,467 --> 00:26:35,804 So before the COVID lockdown happened, his colleagues moved to the hometown and he was left alone. 287 00:26:35,804 --> 00:26:36,697 He did not know how to. 288 00:26:36,697 --> 00:26:41,828 because he did not know how to cook, he had the raw materials but he starved himself to death. 289 00:26:41,828 --> 00:26:45,419 I think he also had some health conditions which complicated things for him. 290 00:26:45,419 --> 00:26:47,461 Also that actually got me thinking. 291 00:26:47,461 --> 00:26:55,485 even if I am the best at what I do, if I don't have the supporting competencies, am I going to be productive at a long-term basis? 292 00:26:55,485 --> 00:27:00,106 And I think we can see good enough analogies in sports. 293 00:27:00,106 --> 00:27:02,468 Now I don't know enough personalities in... 294 00:27:02,468 --> 00:27:14,639 basketball or soccer that you may relate to but from a cricket there are characters, individuals who have come shown so much of promise but they just fizzled out because they 295 00:27:14,639 --> 00:27:25,398 did not have the right character, they did not show the right attitude, they did not show the right discipline for them to be selected after the first few matches or first few 296 00:27:25,398 --> 00:27:27,109 series to represent the country. 297 00:27:27,109 --> 00:27:28,402 So now if you have to 298 00:27:28,402 --> 00:27:32,582 consider that in an employment scenario. 299 00:27:33,202 --> 00:27:46,442 Earlier you are probably the best salesperson in your company but you were rude with your colleagues, you would not cooperate with anybody else, were more like a bully to your team 300 00:27:46,442 --> 00:27:47,282 members. 301 00:27:47,302 --> 00:27:51,762 And the company probably would close its to because you were bringing in the revenue. 302 00:27:52,082 --> 00:27:56,458 But now what AI has done is that AI can do a lot of things that you are doing. 303 00:27:56,627 --> 00:28:04,369 The question is that what is that you need to be doing to ensure that you are still relevant in the overall scope of things. 304 00:28:04,369 --> 00:28:14,551 That means that you are not only a good person or at least good working professional but the collateral aspects of who you are. 305 00:28:14,551 --> 00:28:16,313 That means you need to have a good health. 306 00:28:16,313 --> 00:28:19,214 We are picking up, you need to have

good discipline. 307 00:28:19,214 --> 00:28:24,115 We are picking up lot of the bad habits of 308 00:28:24,613 --> 00:28:27,094 workplace which have impact on workplace. 309 00:28:27,094 --> 00:28:30,369 For example, the amount of screen time that you all have. 310 00:28:30,369 --> 00:28:37,643 It's very stupid to expect that just because I'm not using mobile at workplace means that I'm more attentive at workplace. 311 00:28:37,643 --> 00:28:38,453 No. 312 00:28:38,514 --> 00:28:49,462 You are using a lot of screen time when you're doing commute at home which is already impact your ability to focus which extends to your performance at work as well. 313 00:28:49,462 --> 00:28:50,374 Or the 314 00:28:50,374 --> 00:28:51,675 there's a mobile app feature, right? 315 00:28:51,675 --> 00:28:58,841 When you make a payment, the message comes to you, the OTP get generated and the app is automatically reads. 316 00:28:58,901 --> 00:29:06,148 Now from a user point of view, you may think it's faster, it's convenient, but what it does is that it is impact your short-term memory. 317 00:29:06,148 --> 00:29:14,236 Over a period of say two, three years, your ability to retain facts for a short-term is get compromised and that also access to your workplace. 318 00:29:14,236 --> 00:29:16,952 So when AI is doing all these things, 319 00:29:16,952 --> 00:29:25,509 What remains with you as the biggest competitive advantage is your ability to review and judge, judgment, critical thinking. 320 00:29:25,530 --> 00:29:33,277 And that, if you are in a meeting and you need to respond fast, you cannot always then Google or use AI for the facts. 321 00:29:33,277 --> 00:29:38,703 Then you take your sweet time to apply your mind, your logic to come to a response. 322 00:29:38,703 --> 00:29:42,867 You should be able to give that response at an instant level. 323 00:29:42,867 --> 00:29:43,877 So that... 324 00:29:43,954 --> 00:29:50,756 ability to get again compromised because you are doing so much of other things outside of work which is impacting your performance at work. 325 00:29:50,756 --> 00:30:00,240 that's the so all these things when I start realizing for myself I of course there were enough research out in the the public space about this but in some cases I started doing 326 00:30:00,240 --> 00:30:02,048 it experimenting on myself as well. 327 00:30:02,048 --> 00:30:05,632 Okay if I do these things I'm able to see some visible 328 00:30:05,688 --> 00:30:09,608 qualitative in my own way of judging it, I may improving it there. 329 00:30:09,608 --> 00:30:15,048 So once I start seeing some slight positives, I started doing more and more research. 330 00:30:15,048 --> 00:30:21,488 That's how the entire narration of work and life outside work came to picture. 331 00:30:21,708 --> 00:30:28,968 your human biology becomes important, human thinking becomes important, our ability to, our cognitive thinking becomes important. 332 00:30:28,968 --> 00:30:34,779 So I started to see all this together so that as humans we are more productive. 333 00:30:34,779 --> 00:30:47,619 This actually dovetails perfectly with my last question here, which is you've identified three big shifts that we're in right now. 334 00:30:47,959 --> 00:30:51,219 I'm kind of curious to hear your take on all three of these. 335 00:30:51,219 --> 00:30:54,279 One is from institutions to individuals. 336 00:30:54,439 --> 00:30:56,979 The second is from courses to systems. 337 00:30:56,979 --> 00:31:01,539 And the third is from knowledge to judgment, which you've just talked about here for a second. 338 00:31:01,915 --> 00:31:16,794 um Some of these feel difficult for us to redesign our organizations for, to teach people how to think in terms of systems or to apply judgment over just accessing knowledge. 339 00:31:16,794 --> 00:31:23,938 And from this dependency on institutions to more of a self-reliance, self-reliance as an individual. 340 00:31:23,938 --> 00:31:29,970 But I'm curious if you could kind of unpack the three of those a little bit more, because those shifts are interesting. 341 00:31:30,894 --> 00:31:40,517 Okay, I think before I start, I think it's important to understand or question the definition of organization or company to start with. 342 00:31:40,517 --> 00:31:50,561 The moment you are asked a question of defined organization, it's most likely that at some point of the definition, we will write that it's a huge company with employing hundreds of 343 00:31:50,561 --> 00:31:58,224 thousands of people, making billion dollars of revenue, working in say, 10 different large markets of the world. 344 00:31:58,585 --> 00:32:02,726 But that reality is true only for few people. 345 00:32:02,726 --> 00:32:07,637 As organization, I might still be a billion dollar company, but employ only two people. 346 00:32:08,429 --> 00:32:09,714 Now the question of 347 00:32:09,714 --> 00:32:21,798 what is the role of learning and development or skill in that such a company, verse-versus a traditional company which is running for last 50-60 years, is already a billion dollar 348 00:32:21,798 --> 00:32:26,639 but is it going to be there for the next 5 years, 10 years, 50 years, I don't know. 349 00:32:27,560 --> 00:32:34,022 So we need to ask those questions and contextualize the definition of organization for yourself and then look at these shifts. 350 00:32:34,022 --> 00:32:35,872 So when I'm looking at moving 351 00:32:36,133 --> 00:32:39,224 from institutions to individuals, the learning. 352 00:32:39,905 --> 00:32:51,411 With this amount of layoffs happening and also the biggest impact I feel that AI is having on workforce is not the layoffs that is created. 353 00:32:51,411 --> 00:32:52,523 I think that is the...