From Agile to AI: Avoiding the Same Transformation Mistakes
Guest: Sanjiv Augustine, Founder & CEO of LitheSpeed
Host: Kumar Dattatreyan
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Kumar Dattatreyan: Hi everyone, Kumar Dattatreyan here with the Meridian Point. Today we're joined by Sanjiv Augustine, founder and CEO of LitheSpeed, a leading agile consultancy he founded in 2007. With over 30 years in the industry, Sanjiv has advised executives at major companies like the American Chemical Society, Capital One, Nationwide Insurance, National Geographic, Samsung, Secretary of the Air Force, and Walmart. It's quite a spread there. He's also the author of three influential books, including Managing Agile Projects and From PMO to VMO, and hosts the Leadership in Flux podcast. As one of the world's most experienced product and agile management experts, he has helped clients build modern product operating models and scale business agility. Today, we'll explore how AI is disrupting the agile consulting space and what it means for the future of business transformation. So without further ado, thank you so much for joining us today, Sanjiv. I appreciate you being here.
Sanjiv Augustine: Thank you, Kumar. I really appreciate the invitation. I'm looking forward to the conversation.
Kumar: Yeah, so am I. We've got a lot to talk about. So in our preparatory call preparing for this podcast, you mentioned that the entire agile coaching and training industry has been completely disrupted. And I agree with that because I'm living through it. I live in proof of it. Can you paint a picture of what's happening in the consulting world right now and how AI is changing the game?
Sanjiv: Yeah. So I think to understand any sort of localized phenomenon, and that's what this is, right? It's localized to our industry because that's where we are and might be sort of myopically focused on it. But if you look at the macroeconomic factors, I can trace some of the stuff, some of the influences back to a huge boom during the pandemic when the product companies over-hired, over-invested in people and resources and threw a bunch of money to scale. And then when the pandemic bubble sort of shifted, there were tons of layoffs, right? And then you had several hundred thousand people laid off. And that moved from overall product management, product development, product companies to the agile community. And so I think a big trigger was some of the larger companies starting with Capital One, I believe it was in 2022. They took their entire agile space unit and said, "Hey, we've evolved beyond agile coaching and training. Now our focus is going to be on product management or product development and engineering. And we've assumed that everybody's gone agile." So it's been something that's institutionalized. So agile coaching and training as separate constructs sort of went under huge pressure. And I think there was a little bit of a domino effect over there. So that's the localized view. But if you pull back a level, there is the overall pressure on consulting, training, and coaching in general. Consulting itself, the whole consulting industry is in the doldrums. Everybody from McKinsey to the other Big Four, or whoever's remaining, are all under pressure because on one hand, you see the introduction of AI. On the other hand, you see shrinking budgets for enablement type activities with much more of a focus on hard delivery, hard sort of technology. And of course, the whole shift towards the shiny new thing, which is AI. So there's, I think, large-scale disruption that started with the product companies, went on to consulting, and then for other factors or other industries, if you will, training and consulting and coaching also.
Kumar: Yeah, I guess I see it the same way you do. It's sort of a confluence of these factors that sort of all hit around the same time. I mean, agile isn't new, right? It's been around. It'll be 25 years in 2026 when we turn the new year. So it's been around for a while, so it's getting a little long in the tooth. How much do you think that impacted or contributed to the shift away? Consulting is a bigger issue, but just in general, sort of "we don't need coaches, we're agile enough" sort of thinking?
Sanjiv: Well, and that should be right. So if you think about it, the introduction of agile, when it was first news by way of Scrum, it was like, "Hey, just go take this class, two-day class, and you'll turn out to be an expert in agile." Not true, as we know. It's a larger change management effort. And then we went to an over-investment and very sort of dogmatic introduction of frameworks, including the Scaled Agile Framework and others where it was, "Hey, just implement this, sink a lot of money into this, and somebody will wave a magic wand once you're done and you'll be agile." Fundamentally, I think there should have been a maturity curve where we took agile processes, agile technologies, agile engineering as well, and systematically grew and evolved those using change management techniques to mature the organization. And then at some point, everybody, all of us should have expected that to become the natural way of working, which by the way, with many of our clients, it is, right? So many of our enterprise clients started this journey 20 years ago, started the journey 15 years ago. And now they have a huge focus on agile delivery. They just don't call it agile delivery. They call it product delivery because it's integrated with their product development organizations. They don't necessarily look at agile management because they have lean management. And so they have enterprise lean management that's integrated into their portfolio management practices. So I think the big disruption or the big change is, "Hey, we no longer need a framework like SAFe or whatever. We already are working this way. Now, how do we continue to get better? And what other things do we need to look at as things change?" And there's a huge amount of flux in the industry.
Kumar: Yeah, I see that as well in the companies that I'm working with, is that they've matured to a certain extent. And I'm curious, I haven't worked at Capital One, and they were one of the first, I suppose, to sort of do these mass layoffs of Scrum Masters and these agile-specific roles. They may still have the people there that are functioning in those roles, but they don't call them those things, right? Would you say that they've taken a step back or they continue to mature their practices in terms of delivering value to their customers?
Sanjiv: Yeah, it's been a while since I've done anything at Capital One. So I was certainly involved and engaged with their CIO and CTO in the early days, I want to say about 10, 15 years ago, and helped them scale agile methods. But I'll bring in another industry example, perhaps, that I'm working with currently and have worked with over the last 10 or 15 years, and that's Nationwide Insurance. So similar to Capital One, they were a little behind Capital One in terms of adopting agile methods, but they've scaled agile to what they call "lines," right? Their agile teams are called lines, and they have over 500 lines, 500 agile teams, if you will, that are just functioning this way. And that's just the way, as I mentioned, that things work. If you go to Nationwide, you're just going to go from team to team, and they're going to have their iteration planning meetings. They're going to have Jira to support all the mechanics, and they've changed the names of the Scrum Master. They're called Agile Delivery Leads. So that's an organization that took agile, put in a lean portfolio management and lean management layer of enterprise scaling on it, on top of it, and scaled it across the enterprise. And it's still doing it, except they're not leading with it anymore. Now they're big into the product operating model. And of course, yeah.
Kumar: Yeah, we're going to get into that in a minute. And I think there's a lot of lessons to be learned from companies that have sort of rolled up their own framework, if you will, and not relying on the frameworks that are out there, like SAFe and so on. Maybe that's a good starting point, you know, sort of the training wheels, if you will, for a company that's looking to scale, but it gets a little cumbersome after you've reached a certain point of maturity. You know, again, in our last conversation, you shared this example of clients coming to you with answers that they get from ChatGPT, asking you for validation, "Is this right?" How do you see this trend evolving? And where do you see this going for people like us that are used to being in the business of giving advice?
Sanjiv: Well, I think we're being held accountable. So before, perhaps we could make up stuff and nobody would know. Now, perhaps it's ChatGPT that's making up stuff, and we have to validate whether it's right or not. So I for one welcome it, right? So nothing is static. Change is the only constant. Things change. The way we do our work changes. The way we look for advice changes, I think. Yourself and myself and many of our colleagues and many folks out in the industry have now gotten used to using LLMs, whether it's ChatGPT or Perplexity or what have you, Claude, as co-pilots. So I'm constantly working with one or more of these models to improve my own work and get better. And it allows me to go faster. It allows me to take some of the mundane, repetitive stuff that I do and source it out. I don't necessarily look at it much for a source of inspiration because that I think is still the creative spark. So to answer your question, I think it's useful. I think it's good that people out in the industry are saying, "Hey, how do I set up a PI planning session, a program increment planning session, or how do I run a sprint retrospective?" Or just any of these basic one-on-one type agile questions. But that's a starting point. I think where we have to be really careful about is, like, first of all, all those models hallucinate, and not many people are going to have a sophisticated RAG algorithm to keep it on point. But once we have that starting point, then we can have a discussion, and the empathy that a coach can bring you, the judgment of a coach who knows what he or she is doing, all those things can be augmented with a base foundation that comes to us from ChatGPT or any one of these LLMs.
Kumar: Yeah, I agree. And I guess as the LLMs become more proficient at answering the types of questions, what they don't have is context. And so the more context that people that are asking questions from ChatGPT that they would normally ask a human, the more context it has, the better the responses it'll give. But that of course demands that the people using it are also proficient at prompting it appropriately to get good responses. But I see it too in my work where people are asking me things or questioning things that I say because they looked it up on whatever tool that they're using, ChatGPT or Claude or whatever. And I'm like, "Well, this is not what it says." And I said, "Well, what Claude doesn't have or ChatGPT doesn't have is the benefit of context. I'm here walking around with you people. So I know a little bit more still."
Sanjiv: And external context from other organizations as well.
Kumar: That's right. Yeah, that's a good point. You're bringing all that experience and knowledge with you every time you go into a new client organization. So that helps for sure. You cited Jim Highsmith's observation that "if you fail at agile, you're going to fail at AI." And he, or you, I think it was he that mentioned that 94% of AI pilots are failing. Can you elaborate what he meant and remind me what we talked about? Why are organizations repeating the same mistakes with AI implementations as they did in the early days with agile?
Sanjiv: Yeah. So let's take that phrase that Jim used, "If you fail at agile, you're going to fail at AI." And so I believe what Jim mentioned, and of course, Jim's my own personal mentor and I know him pretty well. So he shared some of these thoughts pretty extensively online, but also in conversations. And I believe what he's saying over there is that adopting AI as a tool or just a tool or a technique without the larger consideration for the human beings that are involved, without a larger consideration for the organization that's involved, and doing it in a, this is my word, not his, a mindless way, right? Going in mindlessly, bringing in something, and then using it to, quite typically, we see that AI brought in to cut costs, and then using it to lay off half of your developers or whatever, right? It's just not the way to do it. Now, I'm a big proponent of AI. I think it's a valuable thing. And I think those of us who have been in the industry and are used to considering management and are used to considering the place of people in management in our organizations should intelligently adopt AI. So just as mindless agile adoptions failed by mindlessly adopting frameworks, you use the term context, without understanding the context, without making those frameworks situationally specific or fit to purpose, we run the risk of doing the same thing with AI. Mindlessly adopting AI, tool technology and such, and not using it fit for purpose within a context-sensitive way. So that's what I believe Jim means when he says, "If you fail with agile, you're going to fail with AI." Now, the number itself, it's early days now in terms of real-life enterprise adoptions of AI tools or AI technologies. And I'm not talking about machine learning stuff that's been going on and on. I'm talking about taking agentic AI and applying that at scale within a business context. So it's still early days. So companies are running off, they're running these pilots. And there's different statistics from the Project Management Institute, PMI. That one I can quote, it says from 70 to 80% of AI pilots are failing. The 94%, I don't remember where that came from, but people are like, whatever the number is, it's a large percentage of those early pilots that are failing. So I want to caveat that by saying that, first of all, just because something is failing, that's not necessarily a bad thing. It means if you're failing, you're learning.
Kumar: Exactly.
Sanjiv: As long as we learn and don't repeat those mistakes, then that's a good thing. And then we can use those learnings as a way of improving, as a way of getting better, as a way of configuring what we do and running another pilot, another experiment. So I don't necessarily think that failure is a bad thing in this case, particularly with AI, which has such downstream impact. So perhaps the failure is a good thing. Right. And perhaps we can learn from it, from some of those failures. Now, if we are mindlessly adopting this AI the way we mindlessly adopted agile and fail, now that's not a good thing. As you've mentioned, agile has been around for 20-something years. By now, you would think that we would have learned the lessons of adopting the next shiny object and implementing it without considering the impact on the people who are going to be affected and without considering a good business case, a solid business case, some solid customer outcomes. So I would say if we don't subjugate AI to two things, subjugate it to our business purpose and subjugate it to the human beings in the organization, we will fail, if not today, then tomorrow.
Kumar: I suppose there's probably a success correlation. And again, failure is a good thing. I totally agree with that. And when I say success correlation, I mean success could be determined also by what you learn from the failed pilot in AI. But what I'm really talking more about is correlation between the way you work, the way you iterate through the work when implementing an AI pilot. So I'm wondering if you've seen or experienced even with the client organizations that you work with using agile to implement these AI pilots and to what extent has that helped in the success of the pilot, be that as it may. Success may just be, "Hey, we need to learn what is or isn't working with this implementation."
Sanjiv: Yeah. So certainly some of the industry leaders that I mentioned earlier that have that agile foundation, and I'm going to go dig a little deeper into what some of the specifics that find application to their AI pilots are. So first of all, it's iterative development, right? So as long as we can iterate through a process, we can go through a plan, do, check, act cycle and use that, quote-unquote, agile. It's actually plan, do, check, act. I mean, short learning cycle, right? So an iterative cycle to test out our ideas, run some experiments, learn from them, validate the things that are working, invalidate the things that are not working. We can do that through iteration. Then the second thing that leaders are doing that are building on their agile foundation, and we call it Agile x AI, by the way, Agile x AI. The second thing that they're doing is incremental development. Not trying to do it at one big swoop, but breaking their adoption down into smaller pieces. A lot of them are using commercially available products, integrating them into the existing business processes. So I know one organization that I work with is integrating a commercially available AI tool into their customer service, the customer service workflow, making sure that we can service customers, do it in an incremental fashion and roll that change, roll the increments of that product out in a slow tested fashion so that they can test and learn. So those are the two aspects from agile that we can pull in as we apply them to these AI projects or AI products. And then the third thing that comes to us mainly because it's AI is to consider the human factors angle, impact on the organization, and that's responsible AI. Agile methods have always been people-based. It's all about teams. It's all about people. As long as we can also now bring in responsible AI and look at every technique that we're bringing in, make sure that we're not perpetuating any bias that the learning model might have inherently, and responsibly adopt those technologies, then we'll get iterative process, incremental product, responsible AI.
Kumar: Yeah, I love that. So you mentioned Agile x AI. Is that something that you came up with at LitheSpeed, or is that out there?
Sanjiv: It's our contribution. Hopefully it'll become more popular out there, but it's basically saying what you just said. It's like we already have this foundation. It's been around for 20 years. Let's use that to launch our AI technologies and AI products.
Kumar: I love it. I have to read more about it and encourage whoever's listening to this to go to LitheSpeed and check it out. I'm sure there's some nuggets of good information there to help you with your AI implementation work. I want to switch topics, going back to the portfolio management, lean management concept that you mentioned earlier. Traditional organizations, I'm sure, have PMOs all over the place, project management offices that are managing projects. In your book, you advocate for transforming these project management offices into value management offices. So what's the difference, and how will this help organizations be more responsive to change and the types of disruption that we're facing?
Sanjiv: So that's a great question. So the book is From PMO to VMO, Managing for Value Delivery. Hard to believe that it's been four years since it came out on the market. It's a 2021 book, and definitely pretty well received. But I believe the message that we put in there has much wider application than just PMOs or now what we're saying is VMOs, right? So we might have a middle management organization. Historically, it's a PMO. Now we're saying evolve that into a value management office. But the message is a lot larger than that. And the message is let's all focus on value management, whether it's at the team level or at the level above the team, which is the program or a portfolio level or the executive level. So it's a mindset. Well, it's a mindset and it's a ton of techniques that are out there. So the biggest change, and all of us have been guilty even in the agile community, is moving from outputs to outcomes. So let's start measuring not just velocity, but let's measure customer satisfaction. Let's measure revenue and cost savings. These are business outcomes. We could look at customer outcomes, and we look at organizational outcomes. So looking at value management and making that shift from not just outputs, yes, we use those output measurements to make sure that we're on track, but ultimately we can only move the needle on value if we're measuring the value delivered. So measuring outcomes is a big piece of it that we've advised in the book. And for any organization, not just a PMO, but any organization that's looking at managing things at the team level and certainly at the level of other teams, let's move from outputs to outcomes. The other thing that we advocate for, and by the way, none of this stuff is new, right? It's not rocket science. People have been saying this for decades, but we're putting it into sort of an encapsulated, accessible way. So the other piece that we like to focus on is customer centricity. If we're just focusing on our teams, which a lot of agile folks have done, it's like, "Hey, you know, we want psychological safety. We want all this stuff," and yet we don't have accountability for customer outcomes, that's just going to take us nowhere, right? So we want to make sure that we can move from outputs to outcomes, but we also want to make sure that we're moving from an inward focus to an outward focus on our customers and our organization. So to make sure that we can deliver value, and we can only do that when we can have a customer-centric view.
Kumar: Yeah, that sounds like a really good book to get familiar with. And to your point, I mean, it has been around for a long time. I know that the organization I'm supporting now is converting PMOs to VMOs, and I'm heavily involved in that work. I think the biggest component here, as you mentioned, right, it's broader than just converting a PMO to a VMO. It's really about the mindset. And it's also leadership and followership, right? So how you lead, how you follow. As a leader, you're still following and leading, right? And I think all those things come into play when you're sort of thinking about value and the impact that what you're building has on the company and the outcomes that you're producing for your customers. So it's a lot. It's a lot to process. It's a lot for companies to go through thinking in that way, in that manner. So how are you staying relevant, Sanjiv, and LitheSpeed, in light of the changes to the consulting space, to agile? What is it that you're doing to stay relevant, to stay sort of on the bleeding edge, if you will?
Sanjiv: Yeah, so I think any organization, ours completely included, always needs to understand what is its mission, right? So our mission is about making the world a better place, making organizations successful, making the people in those organizations successful, both our clients and ourselves. So if you're saying that's the outcome, that's the mission that we're shooting for, then the tools and techniques will change. They certainly have changed since 20 years ago when agile came. They changed dramatically during the pandemic when we went from physical to virtual. They changed before that 10 years ago with the digital transformation, and they're changing now and have changed and will continue to change with AI and whatever else is going to be coming along with that. I see that organizations are shrinking, right? So we will have perhaps fewer people working. And when we have fewer people working, it's a question of how can we work effectively in those teams and deliver value to our customers? Now that's, if you take the somewhat dire side of the view. But can we now adopt this new technology that we call AI? Can we continue to grow the pie so that we have more people working? But the teams that we're working in are just going to be a lot smaller. So one angle of our leadership and followership is how can we create a new way of working, a model for a way of working that now incorporates AI and AI agents and agentic AI as part of that mix of teams and how they work together. And then at a personal level, it's a question of how do we lead an organization working this way? I can't lead an organization that's working in this modern way of working unless I go and learn about it and I go through a personal transformation. So if leaders fail, organizations will fail. So a successful transition to this new way of working starts with the leader.
Kumar: Totally agree with that. So in light of that, right, so are there tools that you're building that help you stay on that cutting edge, if you will, to help your clients? And you mentioned teams are shrinking, and I imagine they're shrinking because of the productivity gains that AI provides teams, so you don't need as many people, which is a little ominous. But hopefully the net result is more work, more jobs, more skilled work for humans to do augmented by the intelligence that AI provides.
Sanjiv: Yeah, so we have an Agile VMO chatbot. It's been out there on the OpenAI site for quite a while, so anybody can go and take a look at it and use it. And we're also starting to do something in the region of value management and put that message out to the world. So the biggest thing that we're doing and I advise doing is just get used to using AI every day, use the tools intelligently. And when we do that for ourselves, what we want to do is to make sure that we can help our customers through that transition as well.
Kumar: Hmm. So you don't really see AI, I mean, you mentioned this before, you know, you're a big advocate for it. You don't see it as a threat as much as it's another tool to master, to put in your tool belt, if you will, and pull out and use in service to your clients, again, to your earlier comment about the vision being making the world a better place, helping companies realize value and all of those things, right?
Sanjiv: All right, so I should definitely clarify that statement and clarify my position on that. AI is an existential threat. It is an existential threat because it's a dangerous technology or it's a powerful technology. And whether it's one or the other will be decided by human beings. So I'm an advocate for AI because, just as I was an advocate for agile in the old days, because leaders in our organizations need to be inside the tent looking out, not outside the tent looking in. So we want to be on the leading edge, on the bleeding edge of the AI tool simply because of its potential threat. I mean, we human beings have such a terrible track record of using technology in so many, you know, so many evil ways, if you will. And so there's a potential for AI to be an existential threat. There's no doubt of it, much bigger than anything we've ever seen. And the reason I'm a big advocate of AI is simply because of that existential threat. It'll become an existential threat unless leaders like ourselves, you, me, others, and others out there understand what it's capable of and steer its trajectory towards positive outcomes, not negative outcomes. Because if you just let people adopt it, the future will be a dystopian future pretty quickly.
Kumar: Yeah, that's a really good point you bring up with, I think you mentioned earlier, responsible AI, sort of like shepherding the use of it and the responsible use of it. And of course, the advances that are being made in these LLMs is astounding. You know, what is possible now, we didn't really think could be possible even just two years ago, right? It's just progressing so fast. And so the responsible and ethical use of AI, these tools is going to be really important in the future.
Sanjiv: I want to throw something in there, Kumar, because I know you're a numbers guy. So I forget where I got the statistic from, but it's there. I didn't make it up. We have, you know, seven billion people or so on the planet. The people who are developing these AI tools and AI models, the numbers, the numbers of people who are actually engaged in it at that level, you know, at the operating system level, if you will, are only about 250,000 people. So if you think about it, you have about a quarter of a million people that are developing tools and technologies that are going to be affecting all mankind, seven billion people. So we have to grow that number. And again, those folks don't necessarily know how their tools are being used. And those of us who are out in the industry who have any form of influence or any sphere of influence, as small as it might be, need to learn about AI and steer it responsibly.
Kumar: That's a really sobering statistic when you think about it, especially given the fact that so much of the population of the world doesn't have access to the internet, maybe illiterate, has no access to these tools. And so the chasm between those that have it and those that don't, the chasm is growing wider, not just in terms of money and access and opportunity and things like that, but more than that is these, they're not even, they may not even be aware that these tools exist and what they might do to the future of the work in this planet, you know, the way they survive on this planet, right? So that's a little sobering. You're right, it did catch my brain. You're right about me being a numbers person. All right, I want to switch to some fun questions. So you've written three books. I didn't mention all three in my intro. However, if you could only save one from a fire, which would it be and why?
Sanjiv: Save one of those books from a fire? I think all learning is temporary. So as attached as I am to those books, I would save none of them from a fire and go with something that is more values-based, something that is more persistent, and something that is more permanent, right? So I would go with another book that is more around my personal value system rather than any of the three. Frankly, they've been out there for a while. If people haven't learned from them, then let them burn.
Kumar: Yeah, I love that answer. Spoken like a true value manager. You mentioned your first job was in high school at an advertising company doing production support. What lesson from that experience still guides you today, if any?
Sanjiv: Oh, wow. That's definitely a blast from the past. Yes. So this was when I was moving around the city on a moped. And my job was with this, this is in the old days of print shop, delivering stuff to print shop, getting it printed, going from the advertising company. And to use a not-so-kind term, I was a gopher. It was basically go from here, go from there. Take the stuff that was done. I mean, when you start out at the bottom of the chain, that's what you want to do. You want to add value. But I learned so much. One is working at an advertising company, I saw firsthand innovation and creativity. I saw how those folks came in and how they worked around the clock to come up with something that was exciting, that was magnetic, that pulled people in. So the creative process and innovation process, just being exposed to that so early in my career was just amazing. And then for me personally, everything became deadline-driven, right? So there's no margin for error there. It's not like you sit with an agile coach and say, "Tell me about your mother or tell me about your team, you know, tell me about how you're implementing Scrum." There's no quarter given. You have a date. You have to deliver something. Customers are waiting. It's going to hit the paper or hit the magazine. And this is a pretty well-known advertising agency that had a national audience. So the stuff that we were producing was going to show up and hit the rest of the country on those dates. So learning how to steer a creative and innovative process but bend it to deadlines and business outcomes, I'm looking at it and providing those terms today, but just making sure that we could deliver and not just sort of spin around in an ivory tower, but turn those ideas into actual measurable value, timeless lessons.
Kumar: Yeah, I love that. It resonates because it's very similar to my experience in restaurants. I didn't know anything about agile or any of that stuff, but it's very production and deadline-driven. I mean, you have to get the restaurant open. So you have to do the stuff to get the restaurant open, which is prepare the food and, you know, all the things you need to do to run a restaurant. You know, your staff, you have to train them, make sure they have what they need to be able to serve the customer. And your production cycles are not like in software. It's months maybe, or years in some cases. In a restaurant, it's minutes. And your customers let you know if you don't deliver the product or you don't deliver it right or whatever it might be. You got a one-star rating on Yelp.
Sanjiv: Exactly.
Kumar: Although Yelp wasn't in existence back when I was in the restaurant business. Besides ChatGPT, what's one AI tool you think every consultant should be experimenting with?
Sanjiv: I think the ones that I use and the folks I know that work with us, we're bouncing back and forth between Perplexity, ChatGPT, and Claude. There's other spinoff tools that you can take a look at that have specialized slide creation and that kind of stuff. But these are the open models. They're all more or less in the same price range. You get free versions and then you get a monthly paid version. And they all have different applications, you know. Perplexity is more research-based, gives you all the statistics, the references. ChatGPT is my, I would say I lean a little more towards ChatGPT simply because maybe simply because I've been using it the longest. And then I use Claude to just kind of validate the answers that I get from either Perplexity or ChatGPT.
Kumar: Interesting. What have I not asked you that you'd like to share?
Sanjiv: Well, I'm interested in what your view of the next, let's say, one year, two year, three years is going to look like. We've talked a lot of it, and I've given you my perspective, but I'm interested. You're an accomplished expert yourself, and I would like to hear your perspective.
Kumar: Yeah, I think it's going to change fundamentally how we work. I mean, the companies that are on the cutting edge are going to sort of lead the charge, if you will, and to your point earlier, with teams getting smaller and more productive, right? And the companies that are further behind are going to be struggling to catch up. And so I feel that it's an opportunity, of course, for people like you and me to help those companies really hone in on understanding the processes that govern how work, how value is created in their organizations in a way that maybe they didn't really think about it before, right? Because again, they have to compete with companies that are much leaner, much faster, getting products to market quicker and so on. And so I think AI is going to be a catalyst for productivity gains in some ways. And in other ways, it's going to sort of disrupt. Companies are going to go out of business because they can't keep up with that level of disruption. They themselves are going to be sort of disrupted out of business, which is normal, you know. Every disruption cycle has winners and losers. And I think for individuals, to your point, you know, I didn't know that's a sobering statistic that 250,000 are actually developing these models. I don't know how many millions are using them, but out of the seven or eight billion people on the planet, I think the onus is on people to educate themselves, you know, on how to use these tools and what are they capable of. How can they enhance their ability to do the work that they do? I think people in general are not very engaged at work, you know, in general. And so I don't know, maybe this is a way to get people more engaged in the work they do by learning about how to use these augmented systems to help them do their work in a way that maybe they didn't consider before. That's my thoughts off the top of my head.
Sanjiv: Absolutely. Thanks for sharing those.
Kumar: Of course. Thanks so much for coming on the show, Sanjiv. We'll have to do this again, maybe delve into some of these topics in more depth.
Sanjiv: Awesome. Thanks so much, Kumar.
Kumar: All right. Thanks for watching everyone. And we'll see you in a couple of weeks. Bye-bye.
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