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Nikhil KamathNikhil Kamath

Nikhil Kamath ft. Perplexity CEO, Aravind Srinivas | WTF Online Ep 1.

In this episode, we sat with Perplexity AI co-founder & CEO, Aravind Srinivas, to explore the evolution of artificial intelligence, what the big AI giants are up to & if we can even predict the future. We also speak about the biggest AI advancements, the role of India in this fast-moving sector - where the real opportunities lie, what’s being overlooked, and finally, the questions we didn't even know we should be asking. #NikhilKamath Co-founder of Zerodha, True Beacon and Gruhas Twitter: https://x.com/nikhilkamathcio LinkedIn: https://www.linkedin.com/in/nikhilkamathcio Instagram:https://www.instagram.com/nikhilkamathcio Facebook: https://www.facebook.com/nikhilkamathcio #AravindSrinivas Co-founder and CEO of Perplexity AI Twitter: https://x.com/AravSrinivas LinkedIn: https://www.linkedin.com/in/aravind-srinivas-16051987 Instagram: https://www.instagram.com/aravindsrinivas Timestamps - 00:00 - Intro 00:45 - Meeting Aravind Srinivas | His Journey & Career Path 12:14 - AI’s Evolution | From Basics to Super Intelligence 29:06 - Understanding the Process Behind AI 35:54 - WTF is a Neural Network? 45:25 - Large Language Models (LLMs) & it’s Evolution 53:59 - The Latest AI Shifts 57:03 - Aravind’s Hustle | Work, Education & Family 01:05:13 - What are Big Players of AI Doing? | Perplexity, Google, Meta, Open AI, Anthropic, and more 01:22:00 - Where the Real AI Opportunities Are 01:34:44 - AI Features & Tools | Text-Videos, Chatbots, Translations 01:39:15 - Why Data Centers Are the Next Big Thing 01:49:42 - India’s Role & Scope in this Industry 02:02:47 - Aravind’s AI Platform Recommendations 02:05:26 - Where AI is Headed Next 02:08:43 - AI Regulations | Tackling Complications 02:16:17 - Outro #WTFiswithnikhilkamath #WTFOnline #nikhilkamath #perplexityai #ai #google #meta #neuralnetworks #perplexity #chatgpt #openai #gemini #manus #deepseek #technology #tech #data #datacenter

Nikhil KamathhostAravind Srinivasguest
Mar 23, 20252h 16mWatch on YouTube ↗

EVERY SPOKEN WORD

  1. 0:00 – 0:45

    Intro

    1. NK

      Are you like a Chennai boy? Have you grown up there all your life?

    2. AS

      My parents live in Chennai, so I first go there.

    3. NK

      What were these fancy ideas?

    4. AS

      I'm very upset to hear that, 'cause I actually thought my ideas are cool. [upbeat music]

    5. NK

      Hi,

  2. 0:45 – 12:14

    Meeting Aravind Srinivas | His Journey & Career Path

    1. NK

      Aravind.

    2. AS

      Hi, Nikhil.

    3. NK

      Hi. This is a bit weird for me 'cause I'm doing it this way, a conversation after a bit.

    4. AS

      Okay.

    5. NK

      Yeah.

    6. AS

      Yeah, I wish we could be in the same place.

    7. NK

      Where are you now?

    8. AS

      I'm in San Francisco.

    9. NK

      Right.

    10. AS

      Yeah. I was traveling to Europe last week.

    11. NK

      Okay.

    12. AS

      A lot more travel coming up, but, uh, hope to be in India pretty soon, um, before my May, hopefully.

    13. NK

      Oh, that's not far. When you come to India, where do you typically go to? Is it-

    14. AS

      My parents live in Chennai, so I first go there. And then, um, depending on the arrangement, like who I'm meeting, I spend-- Like last time I came, I went to Mumbai and Delhi. Um, and this time I probably will try to go to Bangalore, too, in addition to, um, these two cities.

    15. NK

      Mm-hmm.

    16. AS

      But everything's, like, in the flux right now.

    17. NK

      Right. Super. Are you like a Chennai boy? Have you grown up there all your life?

    18. AS

      Yeah.

    19. NK

      So you know, like, the local stuff about Chennai kind of thing?

    20. AS

      Um, I mean, I, I'm, I'm... Yeah, I grew up there, so I hope- [chuckles]

    21. NK

      Mm-hmm.

    22. AS

      I, I don't know exactly what you mean by local, but I definitely know Chennai pretty well.

    23. NK

      Right. How did this begin? Would you like to start by telling us, like, a little bit of-- little bit about your journey, how it was from where you began in Chennai to where you are today?

    24. AS

      Yeah. Um, well, I was just like any other, um, student in Chennai, um, just, just studying. Uh, people in Chennai study a lot. I think that's one thing I've known.

    25. NK

      Mm-hmm.

    26. AS

      Um, I think I was pretty interested in, um, all, all sorts of statistical things. Um-

    27. NK

      Mm-hmm

    28. AS

      ... mainly coming from following cricket a lot, is people generally, like, try to analyze the stats and run rate and, like-

    29. NK

      Mm-hmm

    30. AS

      ... how many fifties or hundreds, and I, I got a s- intuitive sense for numbers pretty early on. I was pretty good at math. Um, and I also, like, early on, picked up programming towards the end of my, um, I think, eleventh standard. So that's, uh, that was how it began, and obviously, my mom wanted me to get into IITs. Uh, every time, like, we would go on a bus, um, and, and pass by the IIT Madras campus, uh, my mom would point to the campus and say: "This is where you're going to study." The-

  3. 12:14 – 29:06

    AI’s Evolution | From Basics to Super Intelligence

    1. NK

      talk about AI today, let me, like, set context. [clears throat] Think of me as an absolute idiot who does not understand anything, and whenever you say something, if possible, please try and explain it to me in the manner that you would speak to, like, a ten-year-old boy who's not very smart. That would help.

    2. AS

      Sure. Absolutely.

    3. NK

      Sure. And I think a good place to start, um, today, where I am, I work in fintech, largely in India, but I feel whenever I read the news or I watch the news, very insecure about the fact that so much is happening in AI, and I almost feel like I'm being left out of it.

    4. AS

      Mm-hmm.

    5. NK

      And it doesn't feel like I'm even amidst the action to learn about it. It feels like I'm talking to the commentator or reading what the commentator who has-- what he has to say, whereas the match is happening in another region altogether.

    6. AS

      Mm-hmm.

    7. NK

      So maybe we can preface this conversation with, like, maybe a brief history of compute leading up to AI-

    8. AS

      Sure

    9. NK

      ... in the manner, in the manner that you would speak to a ten-year-old boy, and we can take, take it from there.

    10. AS

      Sure. Uh, I mean, AI has been going on, uh, since a long time. Uh, if, if you like, in-- I think there was a project at MIT which declared you can solve AI in a summer project, like, like, uh, literally in, in three months. And then obviously-

    11. NK

      Would you want to first define what is AI?

    12. AS

      So AI, obviously, uh, artificial intelligence is a field of computer science that's, uh, trying to design computers to behave intelligently.

    13. NK

      I was wondering what their definition of intelligence is.

    14. AS

      Yep, yep. Program computers to do, uh, tasks that require, uh, some level of intelligence to accomplish them, uh, in a manner similar to a human does it. And what is the scope of tasks, uh, that, uh, require intelligence, that, that, that you want the computer could do, is where the generality comes in. So, um-

    15. NK

      So are you saying that intelligence is when a computer is able to behave like a human? 'Cause that itself-

    16. AS

      General intelligence is.

    17. NK

      Yes.

    18. AS

      General intelligence is.

    19. NK

      Right.

    20. AS

      Um...

    21. NK

      Right.

    22. AS

      So an AI that you write in a, in a, for a chess game that you're building. Let's say you're building a chess game, uh, as, a software project.

    23. NK

      Mm-hmm.

    24. AS

      And, um, obviously, the, uh, when, when the user picks White and the Black's playing with an AI-

    25. NK

      Mm-hmm

    26. AS

      ... um, there is an AI you write for the game. That is not really a generally intelligent AI. It, it, it can only do what you hard-coded it to do, okay? It can assign points for each piece. A bishop is this much, a knight is this much, and it can just run a tree search to optimize for that. Uh, by that I mean it can search for moves, roll out-

    27. NK

      Mm-hmm

    28. AS

      ... few steps, and then try to pick the one that gives you the maximum score. That is-- people used to call that AI, but that is not general AI. The reason is, whatever software you write for that cannot do another game even, leave alone another task.

    29. NK

      Mm-hmm.

    30. AS

      It is a very constrained, specific setting. Now, that is interesting by itself. There are a lot of things you could do in the world that are useful, where you break down a problem and you write a specific solution for that. It's pretty useful. But, uh, what was really on the frontier of science at, at that time, when I, when I was doing PhD, was like: How can we figure out general intelligence, uh-

  4. 29:06 – 35:54

    Understanding the Process Behind AI

    1. NK

      but I'm going to ask it. Uh, how a calculator worked? I'm using the most basic of examples. How a calculator worked back in the day.

    2. AS

      Mm-hmm.

    3. NK

      When I hit on a computer, multiply twenty-five into twenty-five or twenty into twenty, what happened on the back end that I couldn't see? How did it throw the output? We'll start there, and we'll try to extrapolate all the way to what's happening today.

    4. AS

      Sure. I mean, there were circuits for adders, multipliers, uh, and, and these are the circuits that are running on the back. Depending on your input that you, uh, enter, it's getting parsed, and then that input is getting fed into the circuits, and then you're getting the output.

    5. NK

      Mm-hmm.

    6. AS

      And you can build me-- even mechanical circuits. That's a beautiful thing. Like, once you, once it works, all the... You know, there are, like, nice visualizations of how adders just work in a completely mechanical way. So, uh, you don't actually need that much power to, uh, make this work. I think I saw somebody say a beautiful thing. It's like, a calculator is such an amazing p-- uh, artifact that, uh, if you took it, uh, let's say from twenty twenty-five, and let's say you time travel back to eighteen hundred, [chuckles] it would still work the same way.... uh, it, it w- it, like solar, let's say it's solar powered, um, it would just work the exact same way. Um, and, and that's fantastic because you cannot say that about, uh, let's say, your MacBook. You, you're not gonna be able to power it, right?

    7. NK

      That's actually useful. The way you described it helps me visualize. When you say it's mechanical, I can imagine a IC doing what I can picturize mechanically in my head. Like I can see that-

    8. AS

      Yeah, yeah.

    9. NK

      There are five lights and you add them.

    10. AS

      Yeah. There, there are some-- You can go to y- YouTube and watch some, [clears throat] how, like, you can have, um, a binary counter-

    11. NK

      Mm-hmm

    12. AS

      ... three-digit binary counter that's completely mechanical, and, and-

    13. NK

      Mm-hmm

    14. AS

      ... um, it's pretty beautiful. So-

    15. NK

      Okay.

    16. AS

      Uh-

    17. NK

      And then, what hap- what changed in computing after that?

    18. AS

      Well, a lot, a lot of things, obviously.

    19. NK

      Mm-hmm. Mm-hmm

    20. AS

      ... um, you know, we gotta go very deep into, like, what people built with calculators, like other, other devices and so on. But, um, I think the biggest change, I would say, was the personal computer revolution. We, we had mainframe computers, right? Uh, but then the biggest change that kind of truly made computing, uh, very democratized and ubiquitous is, uh, people being able to have a personal computer at home. Uh, that was the whole, you know, Apple I, Apple II, IBM.

    21. NK

      Yeah. So sticking to the IC example, Moore's Law, ICs got smaller and smaller, so you could have enough compute at home to do these same calculations that before you needed a mainframe for.

    22. AS

      Correct. Correct. Not... Yeah, definitely, Moore's Law is, um, one of the critical reasons it happened, but, um, also, like, a lot of artistry in the beginning to package, uh, a lot of computations in a very compact way into, like, one board that could be put into a portable computer, was pretty amazing. Uh, a lot of people actually, in the beginning, were skeptical. They thought that: "It's not gonna matter. Like, why would people need a computer at home, right? It's just, like, stuff you do for work." And, uh, uh, that's where the beauty was, "Hey, people might wanna actually work at home, too." Of course, games was a big deal, but ev- the real reason computers took off, personal computers took off, is this software called VisiCalc-

    23. NK

      Mm-hmm

    24. AS

      ... uh, which is essentially a spreadsheet and calculator.

    25. NK

      Mm-hmm.

    26. AS

      And, and, and, uh, so that led, like, people who were doing accounting, uh, do their work at home, and, um, slowly, it spread. Like, more software started being written for personal computers. Um, and, and, uh, and, and so, like, that be- that made, like, personal computing fun. Now, after that, there's the network effects, where if you had a personal computer at home and I had one, and we could figure out a way to talk to each other, which is the Internet, and then, uh, the World Wide Web-

    27. NK

      Mm-hmm

    28. AS

      ... and then, like, mobile, uh-

    29. NK

      Mm-hmm

    30. AS

      ... cloud, and now AI. So [chuckles] it's a very, like, simplistic way to describe it.

  5. 35:54 – 45:25

    WTF is a Neural Network?

    1. NK

      you again, but can you explain what a neural network actually is? I have a little bit of history with this because I work in the stock investor world, and we've had neural networks for a long time, and I remember seeing this over much of the last decade.

    2. AS

      Yeah.

    3. NK

      Where you would put in a lot of, maybe a bunch of different data factors that we have, like maybe time, price, volume, and put all this data into neu- into a neural network, try to get it to predict what will happen next.... and start maybe a robo-advisory kind of service or, you know, try to figure out how a computer might be able to predict. But none of this played out in the manner that we perceived it when it came to the stock market. But maybe you can define for me, it, it didn't play out then. I'm talking over the last decade. Can you define what is a neural network in very simple words?

    4. AS

      So a neural network is a network of artificial neurons, uh, connected to each other layer by layer. Um, and what is a ne-- artificial neuron is just like a computational unit that takes an input number and gives you an output number. Uh, and so it's, it's, it's called a neural network because it's inspired from the biological ne- neural network, which is the human brain. Um, but it's not exactly meant to be working the same way either. In fact, that's actually why in practice it works, 'cause a lot of people tried to make it work the exact same way and failed at it. But think about it as like a massive circuit that you're feeding numbers to, and it, it spits out new numbers. Um, and it's doing-

    5. NK

      Does it spit out new numbers based on the numbers that I have put in and the-

    6. AS

      Correct

    7. NK

      ... patterns it recognizes in those?

    8. AS

      Yeah. Correct. Yeah, exactly.

    9. NK

      In the stock market example, if we were to just stick to it, when you put so much data into a neural network and it predicts what might happen tomorrow based on what has happened yesterday-

    10. AS

      Mm-hmm

    11. NK

      ... stock markets often tend to be random.

    12. AS

      Mm-hmm.

    13. NK

      And there is a school of thought, they call it technical analysis, where people believe that patterns exist, and they try and map out what patterns happened in the past and how they will repeat themselves specifically. But what if... This is a bit selfish because I'm, I'm sticking to the stock market example, but what if the past patterns do not recur in the future? Then what does the neural network predict?

    14. AS

      That's a good question. So are neural networks... Uh, look, neural networks can be trained to predict anything, right?

    15. NK

      Mm-hmm.

    16. AS

      And, uh, uh, standalone, without the prediction function, the loss function, just the neural network alone is simply a mathematical function.

    17. NK

      Mm-hmm.

    18. AS

      Very nonlinear. Think about it as like some extremely high-order polynomial function, right?

    19. NK

      Mm-hmm.

    20. AS

      Um, it's just gonna-

    21. NK

      What was the last word you said, order?

    22. AS

      Extremely high-order polynomial function.

    23. NK

      Right.

    24. AS

      Um, by, by that all I mean is, like, very nonlinear.

    25. NK

      Correct.

    26. AS

      Um, a lot of higher-order interactions and multiplicator- multiplications.

    27. NK

      Can you help me picture a neural network? You said it was meant to mimic brain chemistry, but it doesn't.

    28. AS

      Yeah. Think about it as like... Okay, let's say you're, you're feeding in, like, three or four numbers at the input layer. Uh, the first layer will take that and, like, transform that. Imagine it applies some, some sinusoids or, like-

    29. NK

      Do you mean when you say transform, are you talking about transformers in Google and their development and stuff like that, or?

    30. AS

      No, I, I don't mean specifically a transformer, but I just mean, like-

  6. 45:25 – 53:59

    Large Language Models (LLMs) & it’s Evolution

    1. NK

      language model sit amidst all this? What is it?

    2. AS

      So a large language model, uh, is essentially a giant neural network that's trained on this one task of predicting the next word from the previous word, except it's training on the whole internet. So it's training on terabytes of text, trillions of tokens.

    3. NK

      Mm-hmm. Mm-hmm.

    4. AS

      And it's doing-- it's, it's training on books, code, and, um, uh, textbooks, and, like, general web pages, news articles, all these things. So, um-

    5. NK

      Mm-hmm.

    6. AS

      By doing-

    7. NK

      But the distinction being just text, it's not training on videos and pictures and stuff like that?

    8. AS

      Uh, I think, I think, like, uh, it can, uh, but, but since you're calling it large language model, I'm, I'm-

    9. NK

      Mm

    10. AS

      ... I'm, I'm keeping that in most people.

    11. NK

      Let's, let's take ChatGPT, for example.

    12. AS

      Yeah, if ChatGPT-

    13. NK

      If we're talking about-

    14. AS

      Yeah, so the image part, uh, like, like taking in an image and captioning it, and all that stuff, comes only, uh, in a different phase of training called the post-training. Uh, but most of the, most of the compute is thrown at just predicting the next word from the previous word. That's called the pre-training. Um, and so, so e-essentially, think about the data set. It's the same thing. Think about the data set as being the whole internet dump, like all of Wikipedia, all of Reddit, uh, everything like that. You, you, you, you, you download it from the web, you, you tokenize it. That is, you, you know, c-c-con-convert every sentence into a bunch of tokens, and then you store it somewhere in, in your S3 dump, and then, um, feed, like, you know, hun- four thousand words, and ask it to-- for each of those four thousand words, you ask it to predict the next word, given the previous word, right? And there's one-

    15. NK

      This is where the transformer comes in.

    16. AS

      Correct. Exactly.

    17. NK

      Yeah.

    18. AS

      This is where the transformer comes in, which is a particular neural network architecture, uh, that's pretty efficient. And, um, you, you shard the model, which is the neural network model, on, like, thousands of GPUs and, and, and, and learn on, like, trillions of tokens, on, like, train this model for, like, three or four months. And it's pretty amazing artifact emerges out, which is... it'll, it'll be great at, like, predicting the next word, but it's still not conditioned enough to be practically useful. And so that's where the post-training process comes in, where you, uh, train this, or fine-tune it mo- fine-tune this model to be a good chatbot, uh, which is training it to produce good responses to human inputs. Uh, and, and, and, um, that requires a separate data collection phase, where you're collecting data for practically useful tasks, like software programming, compressing emails, summarizing documents, uploading PDFs, and, like, having it summarize things or answer questions about it. Uh, and, and, and then, and then also, like, just generic conversational outputs, where you're training the model to be, like, conversationally good, keep references of the past and stuff. And once you do that, like, you end up with a system like ChatGPT.

    19. NK

      Right.... when I was speaking to Yann, I mean, I asked him things like, you know, explain tokenized to me a dozen times and all of that, but he seemed to think that the current path of evolution of where la- large language models are going is not the path to AGI. He had a counter opinion on it. Can you elaborate on that a bit?

    20. AS

      Well, again, like, he, he has his opinions, and I think, you know, um, he, he's generally been right, so it's worth listening to him. Um, I would say that, um, what Yann wants is, like, physical common sense to—like, he, he counts that as a prerequisite for something to be deemed as AGI. Uh, by that I mean, like, just basic stuff that we all take for granted, that we c-- we do on a daily basis, which is how to pour water into a cup, how to-- like, let's say you're a waiter in a restaurant, and you have to pick up, like, three glasses and two coffee cups-

    21. NK

      Mm-hmm.

    22. AS

      -with two hands.

    23. NK

      Mm-hmm.

    24. AS

      How do you do it, right? Like, you're pretty clever. You ta-- you, you, um, tilt them in a way and make sure they don't break, um-

    25. NK

      Mm-hmm

    26. AS

      ... and so on. Or like, or you have a new, uh, bottle of wine, how to even use the, uh, an opener that you've never used before. You figure all these-

    27. NK

      Mm-hmm

    28. AS

      ... things out pretty quickly. Uh, the tool use that comes on a daily basis. You know, like, it's not good to mix two ingredients that are not supposed to be mixed. I think these are the things that, like, he thinks a generally intelligent AI should do. Like, stuff a cat figures out to just get from one place-

    29. NK

      Mm-hmm

    30. AS

      ... to another when they're like-

  7. 53:59 – 57:03

    The Latest AI Shifts

    1. NK

      to ask you, what changed? Like, what changed in the last couple of years that this has taken over everything, like this conversation?

    2. AS

      I would say it's, um, a lot of compute thrown at the problem-

    3. NK

      Mm-hmm

    4. AS

      ... unprecedented scale.

    5. NK

      Mm-hmm.

    6. AS

      Um, the key realization that it's not just compute also, uh, it's-

    7. NK

      Mm-hmm

    8. AS

      ... it's high-quality data and, and RLHF, you're learning from human feedback-... and actually like, um, t- training it on tasks useful to human labor, like coding-

    9. NK

      Mm-hmm

    10. AS

      ... and like summarization and stuff like that-

    11. NK

      Mm-hmm

    12. AS

      ... all came together simultaneously. And, um-

    13. NK

      But do you think the one main thing is throwing immense amounts of compute at the problem without-

    14. AS

      Definitely, that is-

    15. NK

      worrying

    16. AS

      ... i- i- if there's, like, a highest order bit-

    17. NK

      Yeah

    18. AS

      ... I think it's about-

    19. NK

      Without worrying if the outcome is going to make up for the revenue spent towards the compute?

    20. AS

      Yeah. Yeah, I think so. Um-

    21. NK

      Is that the distinction?

    22. AS

      Definitely. Because, like, um, the compute and, and, and by the way, compute alone is useless. Like, people have tried to-

    23. NK

      Mm

    24. AS

      ... reproduce these things with doing the same thing, and it doesn't work. You gotta throw high-quality da- data tokens at the problem too. So that taste on, like, w- curating datasets of, like, what will really matter. Like, for example, if you want reasoning to emerge in a model, it's good for you to, like-

    25. NK

      Mm

    26. AS

      ... make sure you have YouTube transcripts of video, uh, like, like, like, uh, lectures, uh, MIT lectures-

    27. NK

      Right

    28. AS

      ... Stanford lectures, um, and, and textbooks, like, where you actually have problems, where-

    29. NK

      Mm-hmm

    30. AS

      ... it's not just the problem, but the solution is explained step by step. So when the models-

  8. 57:03 – 1:05:13

    Aravind’s Hustle | Work, Education & Family

    1. NK

      said Bangalore. How long were you there for, and what were you doing? That's where I'm from.

    2. AS

      You mean for my internship?

    3. NK

      Yeah.

    4. AS

      Um-

    5. NK

      You said you finished the problem in three weeks.

    6. AS

      [chuckles] Yeah, probably. Uh, I l- I think I was in this place called Koramangala. Um, and, um, I didn't actually explore, so I, I, I just worked all the time, which is, you know... Now that I look back, I probably think I should have explored. But, um-

    7. NK

      No, now when I look at you, I think you did a good thing by not exploring Koramangala and working all the time.

    8. AS

      No, not, not, not-- I don't, I don't mean to explore Koramangala-

    9. NK

      Mm

    10. AS

      ... but I, I just mean explore Bangalore in general.

    11. NK

      Mm-hmm.

    12. AS

      That, that I wish I did. But, um, I do remember the traffic being bad, and I'm being told it's even worse now. So, [chuckles] so probably not-- probably good that I stayed in the room and just worked. But otherwise, like, uh, I do remember the weather was awesome compared to, uh, Chennai. I think-

    13. NK

      Mm

    14. AS

      ... weather was much better. Um, [inhales] what did I do?

    15. NK

      Do you still follow cricket?

    16. AS

      Uh, yeah, I do. Yeah, I followed the match on Sunday.

    17. NK

      Yeah.

    18. AS

      And, uh-

    19. NK

      I'm actually in Dubai 'cause I came to watch the match.

    20. AS

      How was it?

    21. NK

      This is my hotel room in Dubai.

    22. AS

      Oh, cool.

    23. NK

      It was good. It was good. I feel like the stadium had maybe ninety-nine point nine nine percent Indian support and point zero one percent New Zealand. They had, like, a tiny box with thirty people in there.

    24. AS

      Wow!

    25. NK

      So everybody walked away happy.

    26. AS

      Okay.

    27. NK

      Most people walked away happy.

    28. AS

      Yeah.

    29. NK

      Yeah.

    30. AS

      Yeah, I mean, like I, I was pretty disappointed in the last three or four, um, times we lost in the semifinal or the final.

  9. 1:05:13 – 1:22:00

    What are Big Players of AI Doing? | Perplexity, Google, Meta, Open AI, Anthropic, and more

    1. NK

      particular thing, uh, we speak to wannabe entrepreneurs from India, largely, who are under the age of twenty-five.

    2. AS

      Mm-hmm.

    3. NK

      But for a second, I'm gonna put on my investor cap and ask you what the big players are doing. How do you distinguish one from another?

    4. AS

      Mm-hmm.

    5. NK

      And maybe you can give us a bit of nuance of how is one different from another?

    6. AS

      Okay.

    7. NK

      Like, you take a Grok, you take what, uh, Meta is doing.

    8. AS

      Yeah.

    9. NK

      You talk about what Microsoft is doing.

    10. AS

      Yeah.

    11. NK

      Maybe like, just like, you know, like, really low-level stuff that I can understand.

    12. AS

      Yeah. Honest answer right now is, all of them are doing similar things. Okay, like, uh, I'll just say it as blunt as it can be, uh, is there's not really a genuine differentiation between, uh, ChatGPT, or Anthropic, or Gemini, or Grok, or Meta AI right now. And, um, of course, for Perplexity, you can argue similarly, which is, you know, in the beginning, the differentiation was we were the only ones to make sure you always had sources for everything and, like, you know, highly accurate sources, fast answers, uh, and, and so on, but everybody else is also, like-... realizing that the real value is in search, uh, even more than freeform chat, and they're trying to put sources for almost any, any response. So I think right now, we're, we're in this weird phase where, like, all AI chatbots seem similar, and, like, some people prefer one over the other. And, like, you know, if you rank response accuracy, like, I'm sure different benchmarks will have different people ranking one, number one or two. But, but consistently, Perplexity is, is deemed as, like, one of the most accurate, fastest chatbots. Um, and, and I'm very happy because that's the work we've put in the last two years. But I feel the-- this year in 2025 and '6, the differentiation is gonna come from more agentic behavior, uh, where, like, the, the, the question answering, like, answering questions will be seen as a commodity. Uh, some people will have preferences for some products, some u- user interfaces are gonna be better. Uh, those who not just respond with text, but also give you charts, and, like, images, and inline product cards or hotels, uh, you know, shopping is easier.

    13. NK

      Would you call that agentic? Like, if somebody picks off a language answer, like a text answer from a certain large language model and converts it into images and makes it more-

    14. AS

      No, that's not agentic. I'm just saying-

    15. NK

      Mm-hmm.

    16. AS

      ... question answering itself, you can say-

    17. NK

      Mm-hmm

    18. AS

      ... just responding in text is, like, not gonna cut it. You're, you-

    19. NK

      Mm-hmm.

    20. AS

      Like, let's say I'm gonna ask for the best shoes, you wanna actually see the shoes. Uh, you wanna see an actual shoe card, and, like, reviews, and, like, compactly summarized-

    21. NK

      Mm-hmm

    22. AS

      ... to you with, like, options to buy. Uh, same thing with hotels, same thing with, like, restaurants. You can just wanna book it right there. I think these kind of experiences will differentiate one or two chatbots from the rest, and we are doing our part-

    23. NK

      Mm-hmm

    24. AS

      ... to be ahead of the curve there.

    25. NK

      Mm-hmm.

    26. AS

      But I, I feel like the real magic is gonna come from AIs doing things.

    27. NK

      Mm-hmm.

    28. AS

      Where you can go to the AI and ask it to play a song, or, like, play a video, or, or book a restaurant reservation, book an Uber, book a flight, uh, send an email, move your calendar. Like, say, I'm communicating to Nikhil's team, I'm just gonna ask my assistant to, like, um: "Hey, can, can you ask them, can we start at eight thirty instead of eight?" Uh-

    29. NK

      Mm-hmm

    30. AS

      ... and then it's, it's just gonna do this emailing for me, and it's gonna do the back and forth with your team, and it's just gonna, like, figure it out. I'm not-- I'm, I'm just, like, in my bed sleeping, and the AI is working for me. I think those kind of things-

  10. 1:22:00 – 1:34:44

    Where the Real AI Opportunities Are

    1. NK

      uh, with all the talk about tariffs and who imports how much and who exports so much, and the deficit that the US is running versus India or China or many of these other countries. Almost every new company here in India-

    2. AS

      Mm-hmm

    3. NK

      ... spends all of their marketing money, their distribution money. Like, if I were to even start a T-shirt company, a coffee brand, a, I don't know, a SaaS company, gone are the days of putting a ad on a newspaper or a TV channel or a cricketing team or, you know, like the traditional way of spending money to get distribution. Uh, don't hold me to the numbers, but pragmatically, I see that declining, and more and more money in discovering clients in India goes to either Facebook or Google, and Meta-

    4. AS

      Yeah

    5. NK

      ... or Google. And that money, that revenue might be registered in Facebook India or Meta India or Google India, but essentially, I-- the way I look at it, it's trickling back to the parent company because the, the market cap of these companies are going up by virtue of the revenue they register here in India. Since we are at the very core of it, talking to entrepreneurs who want to start something in India, do you think there's a play there to disrupt this market? Do you think that's even remotely possible for an Indian p- for someone in the Indian youth to build something, to take away some of this pie?

    6. AS

      Well, uh, if an Indian company started an Instagram or WhatsApp rival, I would very, be very impressed by the bravery of it. Not that like-

    7. NK

      Mm-hmm

    8. AS

      ... I'm trying to do anything similar.

    9. NK

      Is bravery a euphemism for stupidity?

    10. AS

      No, uh, well, I, I, I would say, like, what I'm doing is similar, uh, where-

    11. NK

      Mm-hmm

    12. AS

      ... even now people think it's a stupid idea to compete with Google. Uh, but I think it, I think there's some angle, uh, that can work. Um, and, um, if, if-- okay, here, here's how I would see it. If you can build way better targeting, [sniffs] uh-

    13. NK

      Mm-hmm

    14. AS

      ... than, than Instagram does, at least for consumers in India, that you're trying to target-

    15. NK

      Mm-hmm

    16. AS

      ... for your business. Um, and, and sure, like, people would be at least... All they gotta do is, like, if, if they're spending a million dollars in ads a year, instead of spending the entire million on Instagram, if they spend seven hundred K on Instagram and three hundred K on your thing, like, that's already a big disruption.

    17. NK

      That's step two after I first garner the distribution for my-

    18. AS

      Correct. Yeah

    19. NK

      ... platform, where people come to me.

    20. AS

      Yeah.

    21. NK

      Now, how do I do that?

    22. AS

      You need to have one core-- you need to have one core-

    23. NK

      Yeah

    24. AS

      ... uh, reason why people even-

    25. NK

      Mm-hmm

    26. AS

      ... post on your platform.

    27. NK

      Yeah.

    28. AS

      ... and, and, um, when you have zero users, uh-

    29. NK

      Yeah

    30. AS

      -or you're just, like, getting users, the creators are like, you know, the ones who are posting stuff, uh, they, they, they want, they want traction, they want, like, likes and stuff, they want shares. And so that, that's the problem, that's a cold start problem. And, and network effects, that-that's kind of why I said Meta has a bigger moat than Google. Uh-

  11. 1:34:44 – 1:39:15

    AI Features & Tools | Text-Videos, Chatbots, Translations

    1. NK

      if text moved to pictures, moved to short-form video, could it be long-form video? No, YouTube has-

    2. AS

      YouTube is there.

    3. NK

      Yeah.

    4. AS

      Right.

    5. NK

      Yeah.

    6. AS

      By the way, YouTube is actually, uh-

    7. NK

      Mm-hmm

    8. AS

      ... one of the biggest rivals to Instagram, I would say.

    9. NK

      Mm-hmm.

    10. AS

      Um, mainly because-

    11. NK

      That was Instagram's market to take, right?

    12. AS

      The Reels? Yeah.

    13. NK

      The long-form video. The long-form video as well.

    14. AS

      Long-form video? Yeah, yeah, a little bit, yeah. Um, what I was told is YouTube's ad revenue now, uh, comes more from TVs than even, like, the actual YouTube app. Um, so, so you can kind of see where people are actually beginning to spend more time on their TVs now-

    15. NK

      Yeah

    16. AS

      ... um, than, than even their mobile apps, so there's probably something there. Uh, podcasts is growing a lot. A lot of people-

    17. NK

      Mm-hmm

    18. AS

      ... of course, you are making one of the most listened to podcasts, but-

    19. NK

      Mm

    20. AS

      ... it's, uh, it's, it's, it's a thing that Instagram is not really getting. Um-

    21. NK

      Yeah

    22. AS

      ... and it's m- going more towards the Apple Podcasts, Spotify. So there's always, like, people figuring out new forms of content that's not necessarily going to Instagram, so-

    23. NK

      Mm

    24. AS

      ... there's something there.

    25. NK

      Do you think that's an opportunity? If I'm able to aggregate every Indian podcaster-

    26. AS

      Yeah

    27. NK

      ... and improve the quality of their video by, I don't know, if I include a chat function where they can talk to the podcaster and the guest, like, have some angle like that, do you think if I were to be able to aggregate that, is, is it a possibility?

    28. AS

      Definitely. Um, another thing that people haven't really tried is, like, live stream the podcast. Like, let's say we're talking now, um, and, and, and, like, so the way a podcast works is we record it, you edit it, you post it, and then people are listening to it, but there's no communication between us and them, right? Um, and, and, like, X tried that with, with live stream-

    29. NK

      Mm-hmm

    30. AS

      ... and, and, and, you know, like, so that's something-

  12. 1:39:15 – 1:49:42

    Why Data Centers Are the Next Big Thing

    1. NK

      [clears throat] I have a private equity fund. We are reviewing a data center business. Fairly large, something that does maybe $100 million of EBITDA right now. So data center has become such a thing in India, Aravind, that, uh, every real estate person that you speak to or I speak to today, everyone's talking about data centers. It's like-

    2. AS

      Interesting

    3. NK

      ... it's like the real estate almost in, in the 2025 version, the big f- big thing for them is not this new building, but it's building a data center. Uh, if you're able to buy data center businesses at a 20 multiple of EBITDA or a 25 multiple of EBITDA, would you do it today? Is there something I'm missing? Is there something changing in terms of how data is being compressed, quantum computing, or compute moving out of the data center-

    4. AS

      Yeah

    5. NK

      ... that one should not do it?

    6. AS

      I wouldn't really worry about quantum computing right now. Uh, I, I think it's still in pretty early stages. Um, I certainly think India should have its own data centers, like, like, there's no, um, reason not to. Um, and, um, definitely calls for good real estate expertise. Um, infrastructure, uh, build-out is not easy. Uh, buying the chips, uh, connecting them, the... making sure you use the right technology for the interconnects between these different GPUs, building these server racks. I mean, uh, compute c- centers in, in, in different IITs have done this. Like, like, you know, we had our class compute cluster that we had access to in IIT, and it would live in the computer center. So definitely doable. Um, and, um, it, it, it really depends on, like-- Okay, so there is this company called, uh, CoreWeave in, in the US. I think it's gonna IPO pretty soon. It's the first, like, pure data center play that, that I've seen. Like, it's not a, it's not a big tech data center. Uh, NVIDIA owns a big chunk of this company. Um, and, and I, I think, like, the, the way they compete against the rest is they do the build-outs faster. Uh, and, and, uh, OpenAI is using them, and a bunch of others are using them. So if you can provide training GPUs to people in India, uh, much faster and, and, and, like, cheaper prices, potentially cheaper, because the data center build-out costs might be lower, because labor costs are lower, uh, there, there's probably something there. I hope at least for inference, it makes a lot of sense because data sovereignty might be a thing. So let's say, even for companies like us in future, if the government of India wants, like, the data of, uh, people using Perplexity AI to stay in India, then it makes sense to have, like, you know, even American companies or for other, other companies outside India to be using the data centers built out in India, so that the data is stored in India.

    7. NK

      I, I think it'll happen eventually, invariably.

    8. AS

      Yeah.

    9. NK

      Uh, now the financial data sits out of India.

    10. AS

      Yeah.

    11. NK

      And India creates something like, I don't know, 20% of all the data because of the number of people with smartphones. So the assumption is it will happen, and hence everybody's talking about the data center business. But structurally, there is nothing that is changing in the data center business?

    12. AS

      I don't expect it to be a pretty high-margin business of its own unless you combine it with good software.

    13. NK

      ... And what would software look like for a data center? Is it like the API-

    14. AS

      Spin up, spin up jobs, um, easily host models, um, have the Kubernetes support for, like, scaling instances.

    15. NK

      Mm-hmm.

    16. AS

      Uh, that's kind of what the cloud companies have shown, right?

    17. NK

      Mm-hmm.

    18. AS

      Um, maybe in the short term, if you're the only one who can provide a data center in India-

    19. NK

      Mm-hmm

    20. AS

      ... you're gonna enjoy good margins. But long run, you should expect more people playing the game.

    21. NK

      Yeah. No-

    22. AS

      Yeah

    23. NK

      ... there, there are many providers already. There is maybe a gigawatt worth of data centers. I mean, I'm not sure of the exact number, but it has scaled significantly. The question is: does it continue to grow in this manner, where... At the end of the day, it's a very commoditized business. It's almost like a real estate company starting a warehouse.

    24. AS

      Yeah.

    25. NK

      I'm, I'm not able to distinguish if one has IP over another. And the other big worry-

    26. AS

      Not really, until you have, like, some vertical integration done pretty well.

    27. NK

      And the other big worry is, does this become such a big business that the hyperscalers build their own and do not go to a third-party vendor?

    28. AS

      Possible. I mean-

    29. NK

      Yeah

    30. AS

      ... hyperscalers actually build their own data centers everywhere-

  13. 1:49:42 – 2:02:47

    India’s Role & Scope in this Industry

    1. NK

      w-what is India's role in all this? Like, say, like I said earlier, like, this is genuinely how I feel. You know, Gen Z uses a particular word. They say FOMO all the time, like fear of missing out. I face that on a daily basis 'cause I keep reading about AI, but it does feel to me like, you know, the match is happening in another geography, and I'm, I'm talking to the commentator's friend about what is happening or reading what he's saying on Twitter or X. What, what can India do? Or what should it do? And you can be, like, honest about this, like, because there's something we want to incorporate, and we want young people to go out and try at least.

    2. AS

      Uh, I've, I've said this before, I, I think India should definitely train its own models, um, and not-

    3. NK

      But wouldn't we arrive at the same answers that the incumbent models are arriving at if the data is largely democratized and our data is also part of the training pool?

    4. AS

      It doesn't matter. I, I, I think we should still build our own models because there's so much more work to do on the models to make them reason and think and, and, and be good at things they're not good at today, and, and, and be more agentic and do tasks and stuff like that. Um, and India should have its own, like, DeepSeek-like company-

    5. NK

      Mm-hmm

    6. AS

      ... uh, that, that, um, trains models and, like, competes not just on Indian languages, but on global benchmarks. And that will inspire the next generation of, like, engineers to come and work in those companies and, and, and build out the future.

    7. NK

      Outside of fundamental models, I'm, I'm guessing this requires serious hardware and a reasonable amount of money.

    8. AS

      Yeah, data centers, chips-

    9. NK

      Yeah

    10. AS

      ... models.

    11. NK

      What does somebody young do? Like, say, a twentyfi- five-year-old boy or girl sitting out of Bangalore or Chennai or Mumbai or Delhi, what do they do specifically, like today, with no resources?

    12. AS

      I would hope, like, they can raise some venture funding and try to do something ambitious.

    13. NK

      Yeah. Let's assume they're able to raise a million dollars 'cause AI is hot right now. Then?

    14. AS

      Well, uh, it's pretty hard to do something meaningful with a million dollars, but-

    15. NK

      Mm-hmm

    16. AS

      ... certainly doable. Um, the way I would do it is I would build a product that's pretty interesting and new, uh, get users, raise more money, um, get more users, and raise one-- little more money, and then start to build your own models. Uh, start with post-training on top of open source models, then start to, like, look into pre-training, too. And, um, then get into data centers. Like, it's a multi-stage process. That's what I would do if I could start small. But if you're already established, if you're not, like, this twenty-five-year-old young person, if you're already somewhat established, you have a presence, you have a name in the field, or, or, or are able to attract investments of higher magnitude, then I think you can go for the more ambitious targets right away.

    17. NK

      Is there any, like, nuanced, low-hanging fruit that Indians are not taking advantage of? Who want to start off? I don't know, maybe language, maybe we have access to.

    18. AS

      I think voice. Uh, most of the AIs are pretty bad at Indian voices. Uh, the, the speech recognition and speech synthesis are not necessarily good.

    19. NK

      Mm-hmm.

    20. AS

      That's a place where you can make a clear difference, 'cause-

    21. NK

      Mm-hmm

    22. AS

      ... it's not a high priority for the Western labs to make it work. And there are, like, so many dialects and languages, and, like, I think Indians are also more, um, mobile app users, and so voice is a more natural form factor of interaction.

    23. NK

      Mm-hmm.

    24. AS

      So really having that amazing real-time AI voice synthesis, but, um, broadly, like, support for all the Indian languages, nailing the dialects and accents and grammar would be a big deal. Um, it's, it's easier said than done. It's not a-- It's not as easy as just collecting data. You have to do a lot of evals and training and, like, iterations.

    25. NK

      Mm-hmm.

    26. AS

      But it's definitely something that will matter a lot for the Indian market, more than anybody else.

    27. NK

      Because you're an investor as well, would you buy NVIDIA stock today?

    28. AS

      I have exposure to it. [chuckles]

    29. NK

      Mm-hmm.

    30. AS

      So, uh, I'm, I'm not selling, I'm holding, and I think I believe in... Like, basically, everybody's gonna try to build superintelligence and general intelligence, and, uh-

  14. 2:02:47 – 2:05:26

    Aravind’s AI Platform Recommendations

    1. NK

      as somebody who doesn't understand technology too much, such as me, that I have to use to get better? Uh, better at business, better at more efficient as a person.

    2. AS

      I mean, I, I, [chuckles] I'd love to say Perplexity, but, uh-

    3. NK

      I use Perplexity already.

    4. AS

      Okay. Um, I think you should definitely give, um, a shot at, um, Cursor. It's like a coding assistant.

    5. NK

      What does Cursor do?

    6. AS

      Cursor is a coding assistant. Like, you, you, you can-

    7. NK

      Okay.

    8. AS

      It helps you write code with an AI.

    9. NK

      Even if I know nothing about writing code?

    10. AS

      Right. You can just go and ask it to say, "Hey, I wanna build a website with so and so. Generate the code for me." But if you're like: I don't even wanna, I don't even wanna, like, be in charge of deploying it, I think there are some-- there's this thing called Replit or Bolt, where you can just go and describe an app you want to build, and the agent will build and deploy it for you. And I think that's where things are heading to.

    11. NK

      Bolt.

    12. AS

      Um, Bolt, B-O-L-T, or Replit, R-E-P-L-I-T.

    13. NK

      Mm-hmm.

    14. AS

      And, uh, sure, it's not gonna work perfectly, um, but I, I, I feel like this is where things are headed, where I can just... You don't have to be a software engineer anymore to build an app.

    15. NK

      Mm-hmm.

    16. AS

      And that's really-

    17. NK

      But do you need to know a little bit of coding or a little bit of maths, maybe?

    18. AS

      No.

    19. NK

      No? But will I be able to produce an app via this method, which is as good as somebody who is a software engineer?

    20. AS

      Not today.

    21. NK

      Right.

    22. AS

      Um, as good as maybe like, um, lower level or, like, lower-tier software engineer, yes.

    23. NK

      Mm-hmm.

    24. AS

      Uh, not as good as, like, the, the, the, the good ones or the be- best ones.

    25. NK

      So if I were to have a kid, I shouldn't send him to a engineering college to study coding?

    26. AS

      I think it still helps to be very good at infrastructure, back-end-

    27. NK

      Mm-hmm

    28. AS

      ... uh, data centers, like, uh, floa- flo- floating point arithmetic, storage. All the core fundamentals are not going away. In fact, like, I would say they're [chuckles] very essential in a world where AIs are taking care of the front end and the UI and, um, all that stuff, because you have to know where the data lives, you have to know, like, how it is stored, you have to know how it's deployed, and you have to know if a system goes down, how to fix it, debug it. Those things are still useful.

    29. NK

      Mm-hmm. And last one or two questions:

  15. 2:05:26 – 2:08:43

    Where AI is Headed Next

    1. NK

      What is the future? If you were to, like, predict the next five years, do you have-- You must have thought of this.

    2. AS

      Yeah. I think we'll all have, like, a personal assistant. It, it's gonna feel really amazing. Um, it's not gonna be a luxury thing anymore. Um, it's not just a thing billionaires had access to. Uh, uh, it's gonna feel like an iPhone, where the same phone, uh, that, that the president of the US uses, you're gonna be able to use, too, if you... It's, uh... And, and by that, I mean it's gonna be pretty affordable. And, uh, that's gonna make life a lot easier. Um, and, and, um, people are gonna be able to build personalized things for them. Um, and, uh, there's be a lot more creative expression. Like, what- whatever you want to exist in the world, you can make it happen. Not, not everyone in the world earlier used to be able to make something happen when they wanted to. They would use other people's creations. I think that's gonna change, and that's, that's gonna feel very utopian. That's the nice part of it. [chuckles] The dystopian part of it is, um, unfortunately, in the short term, there's gonna be s- a lot of labor displacement. Uh, not as many people are needed to get a work done anymore. Uh, and so how people upskill themselves and adapt, uh, those w- who are using AIs are definitely gonna be well positioned. Um, so all that stuff's gonna take place, and how people react to it is already like, you know, not-- you don't need, um, to build ten thousand people companies to be a trillion-dollar company anymore. So definitely, where, where are the next generation of graduates getting jobs? Existing big techs are laying off people or, like, not hiring more. So all this stuff's definitely gonna impact, like-... the market. And, uh, it's very interesting that simultaneously we're creating new value and making software creation easier, and, uh, we're also, like, displacing existing labor and value. So how people deal with all this is gonna be interesting to watch, and, and, uh, I don't think anyone really knows how it'll all play out.

    3. NK

      Will the world be more complicated if a lot of this power, access, and determining the path forward is-- the decision-making lies upon one or two geographies, like it's playing out today?

    4. AS

      I think, um, the-- I think the technologies will be broadly accessible, uh, and, and, uh, secrets are not gonna be lying in one or two places. And open source will ensure there's sufficient distillation to the rest of the world. I think what won't be democratized is access to compute, mainly because it takes a lot of money.

    5. NK

      Mm-hmm.

    6. AS

      Uh, a-and, um, that really depends on which countries choose to invest early on and later on in the process.

    7. NK

      Right. I don't, I don't know what to ask you. Uh,

  16. 2:08:43 – 2:16:17

    AI Regulations | Tackling Complications

    1. NK

      I have in my notes that I should ask you about regulation and the future of that. I don't know how to... I've read a fair amount about this, and how a lot of people think that the incumbent AI players are, are trying to use it as a moat almost, and capture the regulatory thing. Like, do you have any view on this? Like, how should regulation-- Let's say the government of India is listening or watching this show, what would be the right way for them to regulate AI? And then, B, what is the right way for America to regulate AI?

    2. AS

      Um, I mean, I, I, I think, like, regulating models is not necessarily a great idea, uh, and it's not gonna work in practice either. Uh, people are still gonna be able to download a model and use it. Uh, I, I think the best way is to regulate applications. Like, uh, personally, what I feel is pretty, um, concerning at this point is probably people using chatbots when they're kids, and developing, like, relationships with them, um, a-and, like, feeling suicidal when, when they don't get to, like, enjoy the chatbots anymore, or they don't respond in the way they want to. [inhaling] And, um, f-- kinda like taking your lonely-loneliness out on, like, an AI chatbot. All that stuff is pretty... I, I, I find it concerning, like, maybe some people don't, and they don't care, and they just think this is not any different from how the internet used to be.

    3. NK

      Mm-hmm.

    4. AS

      But I think it is different. So thinking about that application and, like, how do we make sure AI usage by kids is done on apps that, you know, are productive, and useful, and knowledge-enhancing, rather than feeling too companionship-like, i-is worth thinking about. Um, I don't think, like, other stuff is worth regulating as much today.

    5. NK

      Mm-hmm.

    6. AS

      Um, [inhaling] and, uh, we're, we're kind of still, like, very early in AI, that moving slows is gonna cost us a lot long term. And lot means, like, hundreds of billions or trillions of dollars. So it's best to keep accelerating right now, and be mindful of, like, use cases, like what I described, that are clearly, like, dangerous, but more-- o-otherwise, like, be pretty open-minded and build stuff, and see how things play out. And I don't have a different answer to America or India. I think it's the same answer here.

    7. NK

      Will the world get to a point, as it gets more complicated, that we all try and own our data a bit more? Where-- like, let's assume a, a model today is scraping data from across the internet. Will the world go in a direction where Indians own Indian data, um, maybe like another country owns their data, and every model has to pay a fee to use that data as an input to train their models? In the sense, will things move behind a paywall, or even if they don't want to move behind a paywall, will there be a fee?

    8. AS

      It's possible. Um, I, I, I think, like, in general, the internet has been global and fair use so far. I don't expect it to change. Um, I think if there are some tokens that are pretty valuable, a-a-a-and then people might want some kind of, like, token payment for it, uh, it probably won't be on the internet. That's my guess.

    9. NK

      Don't you find, like, that's happening already, more of it? Like, right now, I find so much on the internet, which appears interesting, but it's behind a paywall. But the question also will be that, me, as an individual, if I consume behind a paywall, a model which is then in turn going to distribute what is behind a paywall, should they pay the same fee or should they pay different fees?

    10. AS

      I genuinely don't know, because, like, the models are definitely, like, training on the content, so they're... Those who are training foundation models, they're, they're not just, like, consuming the content once, they're actually, like, distilling it, so they never have to consume it again.... so it's a different kind of consumption to a human just reading an article?

    11. NK

      Right. But even when I read an article, I consume it once and distribute-

    12. AS

      Yeah, but like, your, your memory and the model's memory are not comparable.

    13. NK

      Right. But I'm not distributing it. But the model is-

    14. AS

      Kind of, like you, you might, you might share the article with someone else, like say, "Hey, did you read this news?" So you're attributing to it or you're, you're gonna use the wisdom you learned from it in some manner. Um, I mean, in Perplexity, that's why we source-- we attribute it to a source. Like, we, we, we don't like say it's our content, and that way we give credit to the source, and we're not actually training on the data. But ChatGPT is different. They, they actually train on all the data.

    15. NK

      Right. Okay, last question, Aravind. [clears throat] Because I'm feeling so left out in all of this, do you think it might be possible for me to come be an intern, work for maybe three months at Perplexity, free of charge?

    16. AS

      Well, you're, uh, way more accomplished for doing that, but, uh-

    17. NK

      No, but I'd love to. Like, this is genuine. Like, I feel like I'd love to come live there for a couple of months, learn some stuff, and come back, 'cause I do feel like I'm not learning enough right now.

    18. AS

      I mean, we'd be very honored to have you, and, um, I think, um-

    19. NK

      I'm not joking, huh? I'm just gonna, like, be there in the next thirty days, maybe.

    20. AS

      [chuckles] Sure, we'd love to host you.

    21. NK

      Testing you every day.

    22. AS

      Would love to host you. Uh, I guess I'd just say I, I, I love the spirit of how you are like, uh, having this learner's mindset. I, I think it's very inspiring and refreshing, so I don't think there's a lot you're missing out on. The internet has pretty much everything out there. Um, and the world is, like, running super fast that, like, physical access matters way less anymore.

    23. NK

      Mm-hmm.

    24. AS

      I think it's more the amount of time you get to spend yourself with an AI model-

    25. NK

      Mm

    26. AS

      ... using these apps, understanding where they fail, and, um, talking to the best people. But in- interestingly, like, X has all of them literally talking all the time, real time.

    27. NK

      Mm.

    28. AS

      It's, uh, pretty nuts, so, um-

    29. NK

      So it's not so much as learning from the model, but being around people who, who know what they're learning or who-

    30. AS

      Yeah

  17. 2:16:17 – 2:16:30

    Outro

    1. NK

      this, and-

    2. AS

      Thank you, Nikhil.

    3. NK

      Uh, you're gonna be in India soon.

    4. AS

      Yeah.

    5. NK

      So if I'm not there, I'm gonna host you when you're here in India.

    6. AS

      Yep.

    7. NK

      [chuckles] Done. [upbeat music]

Episode duration: 2:16:30

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