Ultimate AI Masterclass for Founders and Executives 💰| Vaibhav Sisinty | Indian Business podcast

Sam Altman, in the beginning of the year, kind of said that AI agents will enter the workforce this year. This year. I expect that in 2025 we will have systems that people look at like, “What? Agents are the thing everyone is talking about.” I think for good reason. OpenAI just announcing an improved model, their AI model; they say it has better reasoning. OpenAI has got the wind at its back. I think India should be doing everything; I think India should be one of the leaders of the AI revolution. So I can do this without knowing anything about coding. I don't know how to code, but I know how to build a product. I managed to kind of raise $5 million.

Hi folks, my name is Rebendi, the founder, Gone School. Mr. Webhub, Growth School is where we make you become top 1% professional/founders. And today, where does Growth School stand? I think we've grown 10x. The company that started as four, five people right now has 150-plus people. Wow. Last month we had learners from 45 different countries. 45 different countries. We are the smallest of all the startups; we also have the least amount of funding of all the startups. A LinkedIn top startup, family of 300,000 learners.

Amazon has already laid off 14,000 people; Google has already laid off people, and we're seeing this all across the tech industry. As a working professional, what exactly am I supposed to do? Microsoft has laid off people across multiple divisions. 150,000 tech workers have been laid off of Meta, Amazon, and others have frozen hiring. Amazon today announcing it's going to cut 18,000 workers. In this, for every problem, right, there are tools for it. One great place for you to find tools for your problem is there's a website called… As if a working professional is watching this episode right now, what are the first three steps that they're supposed to take in order to make themselves super efficient? I believe that everybody has to become something called as an AI journalist. And there are just so many people, I feel so sorry, so sad for them, that they are so stupid that they think AI is all about chubby.

If you have read the news, Donald Trump is making it impossible for a lot of countries to buy GPUs. Trump and his team are planning to expand efforts to limit China's tech advancements, in pressuring allies to also get in line. We are six months to one year away from almost all the code to be written by AI. All boss, the future programming language is not Python, is not Rust, is not JavaScript. [Music]

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Hi Weber, welcome to the Indian Business Podcast. It's been a long time since you wanted to have you, and finally you're here. And uh, guys, just to give you context, I have a confession to make. About a few days back, I was having a conversation with Weber, and I told him that, yeah, bro, we use a lot of AI. So he's like, yeah, that's amazing. And then he started talking, and then I realized that um, I am an AI illiterate. I don't know how to use AI well, and I'm an amateur. Weber, you stand at a very, very interesting juncture. On one side, you are at the cutting edge of education; on the other side, you are at the cutting edge of technology. You teach AI now, just like you used to teach LinkedIn, and you are super bullish about it. And like I said, I've been speaking to you, and it has been life-changing for me. I don't use these terms very loosely, but I can say that whatever you've told me about AI and about systems and processes, that has been life-changing. And the beauty is I spoke to you after I spoke to Ranir, and Ranir said, "Bro, content is not your mode; systems and processes are your mode." And then while I was trying to build those systems and processes, I spoke to you, and then it was life-changing because I learned how to build those systems and processes at 10x speed with 100x more efficiency. But now I want to understand how does it apply to the working professionals of the world today? Because the US recession is real; we're expecting a recession in the next three to four months. There is already panic in the market, and now everybody's supposed to rethink their position in their own companies. Amazon has already laid off 14,000 people; Google has already laid off people, and we're seeing this all across the tech industry. And we're expecting that once the US sees a recession, even the Indian tech industry is going to see a recession. So my question is, as a working professional, what exactly am I supposed to do?

Let's break it down, right? There are multiple aspects to this. One is what happens when a recession arrives; the spending power goes down. When the spending power of a consumer goes down, what are the biggest companies out there in the US? They're either tech companies or consumer companies, right? Essentially, uh, deep tech companies or consumer tech companies, right? And they all rely on some level of spending from the consumers. When the consumers go down, when the consumer spending comes down, the appetite for a company to spend on their people, directly and indirectly, goes down. When that happens, that leads to layoffs, right? Now, in this case, right, we are in this very interesting time when there's a recession because of multiple geopolitical aspects, which I don't want to get into. While that recession is happening, it becomes a very good reason for people, for the companies, to adopt a process which is super future-paced and super efficient. What I'll tell you what I mean by that: there is this massive rise of AI agents that is happening. Uh, I don't know if you've seen this: Sam Altman, in the beginning of the year, kind of said that AI agents will enter the workforce this year. This year. What does he even mean by that? If you break that down, right, essentially every one of us has used ChatGPT, Gemini, and all those tools, right? And we think we know all about AI, just like me, right? The problem is that there is a gap in understanding what AI is capable of. What you're seeing as an assistant, where you ask a question and you get an answer, and that is where it stops. As a result, you like, oh, this is cool; I asked for a poem for my dog, and it gave a poem—cool, right? AI is cool, but the real power of AI today, as we live, is that it has the ability to complete a full task on your behalf. You were just talking about Operator. What does an operator do? An operator can use a computer on your behalf. For example, a few days back, right, I wanted to get a bunch of ideas on companies that I could probably work on, and the best place for me to get these ideas was Y Combinator. So essentially the problem statement was I had to go through 5,000 companies' data, uh, and I had to read each one of them, check out each one of their websites to see if there is something that I can build like this in India with my expertise. That's a lot of context—things that I'm good at, the audience that I have, the ideas that Y Combinator has bagged, and can I do it in India, right? A lot of context had to do it manually. ChatGPT or tools like this cannot do it out of the box because it cannot pull in so much context, cannot do things, and I want all of this data on a Google Sheet to be filled. Operator is a great use case for this. So I went to ChatGPT; Operator gave a very nice prompt, uh, explaining what I exactly want, left it. Nine hours later, came back with around, I think 25, 26 companies that I should look at, out of 5,000. Wow.

After nine hours. Yeah, I mean, it took… I don't remember. Overnight I left it. Okay, right. Only place where I had to be part of it, it could be done in like 50 minutes or so. I don't know. Overnight I left it and passed out. Only place I had to intervene was I had to log into my Gmail account. It tells you, I don't have access to your Gmail, please log in. So I logged into my Gmail account, so it can create a Google Sheet, and that's all I did. I, I left my computer, passed out. I got 25 startup ideas because it was a simple task; AI was able to do it, or Operator was able to do it. What does Operator do? This, if I, if I had, if I wanted a human to do it, right, it's an easy 10-day job, huh? Right? And it needs a human to do it, but now AI is… AI agent is able to… This is what an AI agent is. An assistant cannot do this. An assistant, you can ask a question to the assistant; it can give you an answer; it cannot accomplish a task, but an AI agent can. This is an AI… These are the kind of AI agents. For example, ChatGPT has something called as Deep Research. What does Deep Research do? Just like a human being, if you had to research on a topic, what would you do? Go out there, read hundreds of articles on the internet, make notes of this, and all of that, right? Like it takes a long process. Deep Research does exactly that; it takes your command, breaks it down, goes and reads hundreds of articles on your behalf, and comes back and comes back and gives you an output. That's an AI agent. Agents like this are going to enter our workforce, and these AI agents, right, are incredibly powerful already, and they're going to only get better, right? And when these AI agents come in, companies have to find reasons to get rid of human beings and get these AI agents to work on their behalf because they can save so much money. Agents can work 24/7. That is what AI operators can do. In 24 hours, in a few hours—I would not say 24 hours, I don't remember the exact number—but it accomplished it. I don't care how long it took. If you get a human being to do the same thing, it's going to take five, six hours. But the crazy part of this is I was able to replace a human who would be like a, you know, entry-level or like two, three years experience, basic understanding of startups person to do this job. Instead, I could get an AI agent to do it overnight when I'm sleeping, right? This is the part that people have to realize, and this is what Sam Altman meant: that AI agents are entering the workforce this year. There are going to be a lot of agents like this. Like Deep Research is a great agent which does research like a human being. It, when you give a question to it, when you give a prompt to it saying, I want you to accomplish this, it breaks down the task, thinks like a human, just like we do, using something called as a reasoning model, uh, and I don't want to geek out that there's chain of thought reasoning that kicks in here. It breaks down the question, thinks like a human being, researches hundreds of articles on your behalf, and finally gives you a report in, let's say, 50 minutes, 60 minutes, sometimes a couple of hours, right? But that report would take you weeks, if not days, for you to do as a regular person. Correct. The lot of agents like this which are entering. For example, there are business development agents which can do cold emailing on your behalf—all autonomous—find email IDs, send people email IDs, reply to people's email, everything it can do out of box.

Can you give me the name of these tools? So there is one tool that you can use called as Artisan AI, which is for SDRs or sales representatives. One more tool where you can build a few agents like this is Lindy AI, which is very, very no-code. Of course, if you want to go a little geeky, then you can go to N8N, N8N or Crew AI. What do you mean geeky? In a sense, if you know how to write a little bit of code, or not even know how to write code, but if you're comfortable copy-pasting code, right? Because a lot of people look at code and like, I can't do this, boss. I don't know how to write a code. I don't, I don't know how to write a single line of code. I know how to Ctrl+C and Ctrl+V. Do that enough, and what will help you? ChatGPT will. So the moral of the story is that all these working professionals are supposed to learn how to use Operator. No, the moral of the story, with the recession point that we're getting to, right, companies have a window to replace humans with technology, which is AI agents, and that is fundamentally why you will see a lot of these companies like Google and all, right, are kicking out middle managers, senior managers, because they are the most expensive human cost that a company can incur, right? And they have to get rid of it and replace that with as much of AI processes as possible. With recession coming in, this is going to compound because there's a reason for a company to save money; they also have a need to save money, and they also have a potential solution. The companies that are going to do very well even during recessions are probably the AI companies like OpenAI's of the world, Google's of the world, while the rest of them are going to adopt it as fast as possible, and hence the recession. In fact, this second line of thought, right, I'll come to the India aspect of it which you asked me. I think India could be in a very soupy situation; could be or could not be. There are two variations to it. Again, these are perspectives, right? I don't know if you have come across this because you're talking about the US, right? If you have read the news, Donald Trump is making it impossible for a lot of countries to buy GPUs from Nvidia, right? And in fact, for China, he has completely banned it. There are a lot of other countries have banned it as well. India comes in level two where he allows us to buy some GPUs, but not as many as we want. And then level one can buy however many they want. Why do you think is the reason? The primary reason here is Donald Trump doesn't want these countries to build AI capabilities as good as the US capabilities. Now, he'll be like, yeah, ChatGPT, to what is the problem? Let other people build. Why is Donald Trump being so, like, uh, you know, trying to hold all these GPUs? And by the way, FYI, GPUs are the engine for these AI models to get trained on. So without GPUs, there is no AI, right? So that is the hardware part of it. Why is he doing this? Because I think he has seen something that most of us have not. You see, human capital, the reason why the US or countries—expensive countries—have to rely on countries like China, India, and everywhere else is because the cost of human labor is so high there that making an iPhone there end-to-end would cost the would cost basically 2x the cost of an iPhone; would 2x. So it won't make sense. As a result, they pass it on to countries where human labor is cheaper. Now, with AI coming into the picture, there is intelligence coming in; machines are becoming more intelligent than they ever were. Now, with this technology, whoever controls this technology can control the whole supply chain to a point where you can build autonomous factories. Boss, there is real estate in terms of space to set up factories; you can set up a factory in the desert also, and there's a lot of desert. They can dig up a hole and put it underground also. That space is not a problem; the problem is people are going to work in those factories. But if there is a technology that is existent or that could be built where humans won't be needed to build a factory or to run a factory, then a country like us doesn't have to depend on a country like China to build their products. Correct? Because the biggest arbitrage is the cost arbitrage. Yes, and that is controlled with AI. So whoever builds that technology first will control the power.

So do you think Trump is trying to consolidate all the manufacturing powerhouse within the United States through AI? I think so. I, that is how it looks like, because I'm sure, uh, he has seen the dark factory videos. Yeah, bro, those dark factory videos are just insane. Like I saw this video where there was barely any light. Yes. And they're like, why are there no lights in these factories? Because there are no humans; there is no need of light because there are no humans. And bro, it was mad; it was mindblowing. Now, in fact, Elon Musk even says that the cost of producing a Model S is 65% cheaper in Shanghai than in the US, which is why, even though Trump has imposed tariffs, what he's essentially doing is punishing the consumers of America because he doesn't want them to buy Chinese goods. Problem that if there's a $1,000 iPhone, it's not an uncle in Beijing who's going to pay another $100 for it; it is Karen from New York who's going to pay another $100. And because of this, the US companies are helpless; they cannot do anything because of two reasons: a, even if Chinese phones cost $1,100, they can't do anything about it because their products cost on the upwards of $1,500, so they can't do anything about it. Number two is that they still have to buy raw materials from China, and China in return has also imposed tariffs, because of which raw materials have become expensive, so the American products have become even more expensive. So tell me something. I understand this whole vision of making America great again, but how does it make sense in the present-day world, and more importantly, how does it make sense for India? What are we supposed to do in this drama between America and China?

Yeah, uh, the tariffs, reciprocal tariffs have not gone live yet, right? Across the board, and like we were discussing, China doesn't have a lot of tariffs yet. I think it's a bait card that Trump is using at this point of time, right? It looks, it looks like a great leverage point to have conversations with a lot of, lot of countries, right? But I don't know if this will actually go through because it's a net-net destroyer of economy, then uh, uh, advantage, right? That's what economists say, based on what I understand. Now, where does India sit with this equation? What does India export the most? Software services, pharmaceuticals, auto parts, jewels… Jewels, jewelry is number one. Diamond, yeah, d… everything around gold, jewelry is number one. Pharmacy is number two. Pharmaceuticals is number two. The third is IT. The biggest of that is IT. No, no, it's not; it's a third; it's not; it's the third largest, actually. It's third largest, yeah, in terms of exports globally, not just to us. I'm talking about global. Okay, okay, okay. All right, global exports, not US exports. I don't know of US uh, export numbers, but it is major, right? Why were countries relying on India to export it? Cost arbitrage. It always… the whole world trades because of arbitrage, correct? The cost arbitrage is that one engineer in the US will cost you 60 lakhs per year minimum; that same engineer in India will cost you eight lakhs. As a result, companies in India can charge the US companies five times—40 lakhs—and US companies will be more than happy to pay because they're still getting a 40% discount, right? This was the arbitrage that existed that led to I don't know how many billion-dollar industry uh, it is today: TCS, Wipro, Accenture, all of this, right? That led to the development of China and India. In fact, China became a superpower because of this cost arbitrage. Yeah, it's, it's a cost arbitrage that led to everything. Now, with just it, right, we are getting into soupy waters. I'll tell you what I mean by that. Again, I'll go back to the same topic of recession and AI agents, right? Have you heard of tools like Cursor, Windsurf, Bolt? No. These are all AI agents which are specialized to write code. OpenAI's new model, 03, is so freaking good with code that it can write code like as if it is the top 200 developer in the world. So if it goes into a competition in the world, it will be in, it will be ranked top 200, and it's an AI model. That basically means every company can have thousands of engineers who are the best engineers in the world. That is 03. That's an AI model. India makes money by writing code and shipping software. If a company in the US, instead of relying on India or Indian labor or Indian engineers to build a product, if they can rely on an AI agent which is built on 03, which is one of the best coders in the world, would they need India anymore? Okay, you know what? Maybe they still need India because these AI agents don't operate all by themselves; there is a human in the loop. But with an AI agent, you can write code 10 times faster. I'll tell you one interesting thing: in the last weekly business review with my engineering team, I asked a question: what percentage of code today inside of Growth School is written by you guys, that is, you manually write it, and it is, and what percent is written by AI? The average answer was 65% of the code today is written by AI. And mind you, when I say this, the 03 model, which is the holy grail model that I'm talking about, is not available for general public yet. We're using a two-year-old AI model. That's insane, bro, right? Anthropic, uh, which is, which is, which is a company that runs Claude, Anthropic, the founder recently said, we are maybe six months to one year away from almost all the code to be written by AI. All, all the code. Almost all the code to be written by AI, as in, we, as in Claude, or the world. Okay, bro, I'm telling you 70%, 60 to 70% of the code in my company today is written by AI. Y Combinator did a survey where almost in a few companies, 90% of their code is already written by AI, and the models that all of us are using today is a two-year-old model. When a new AI model comes in, and this agent is only going to get better at doing stuff, you need a human in the loop. Best case, if you think about factories, right, most of our factories had the ability to be autonomous already. An airplane can fly without a pilot, yeah, but we still have a human because we believe in human in the loop, that is, we somehow believe that if it goes down, pardon my French, a human can save, which has happened a lot of times, and we've got saved a lot of times as well. The reason why we have humans in a factory, even though it's fully autonomous, is because we wanted a human to turn on the switch, turn off the switch, and kind of look at the machine to see that, oh, everything is going okay. Okay, fine. That's the job. We are getting to a point where we are believing the system to say, I don't need a human in the loop in these technologies. But the human in…

The loop element is going to come in code right now. One person can write code equivalent of 20 people at a quality of a best engineer. Correct? Would companies still rely in Indian companies to write code anymore? Would they need it? That's a big question now. There's a debate that comes on the other; the answer could still possibly be yes, but then with very less workforce. At what cost? The cost of communication is more expensive than you getting it done, you know. Today, if you want to build products—in fact, I'll send you some links, you can link them up—right, I have built full-fledged solutions. I don't know how to write code. In two, three hours, you tell me a problem today that you're facing while running things, cool. And if there's a solution and you can imagine that web, I wish there was this tool that could do this, you can build that in 3 hours today. 3 hours, 3 to 5 hours max, a good strong MVP which can do 70% of the things the tools. If someone wants to go and explore in your team, or anyone are listening to this, there's a tool called as lovable.dev. So I can do this without knowing anything about coding. No, you do you know how to communicate? Can you tell what you want? That's all it matters, boss. The future programming language is not Python, is not Rust, is not JavaScript; it's English. So you should subscribe to communication masterclass, guys. It is English, and it is communication, quite frankly. Because there is this word that you must have that has been thrown around all the places, right? Prompting, prompting, prompting. What the hell is prompting? Prompting, prompting is all about articulating what you want in the best form so that AI can understand. What is the language of prompting? English. Got it, bro?

I've got three questions over here, and I lay it down in front of you, and then you can choose to answer them in whatever sequence. First question: Now I've understood that because of this trade war, regardless of what happens, one outcome is recession. And even if recession doesn't hit the roof, at least companies will look at cost cutting. Yes, this cost cutting will trigger an efficiency exercise, and this exercise will lay off a lot of people, and it will push the companies to turn existing employees into super efficient employees. Yes, the instrument to do that is AI. Yes, got it. Now this instrument of AI, for different people, are different things. So if a working professional is watching this episode right now, what are the first three steps that they're supposed to take in order to make themselves super efficient? I believe that everybody has to become something called as an AI generalist. Now what are these AI generalists? AI generalists are these people who can solve problems using the power of AI. What do companies look for in people? They are always looking for people who can solve problems. Now with AI generalists, people will—companies will look for people who can solve problems with AI. Why? Because it could be 10 times faster, 100 times more efficient. And if there's anybody sitting in a job today and you're realizing that, boss, this is going to hit you like nothing else has ever done, you have to start leveling yourself up as an AI generalist to this. Right? Uh, I have done this exercise internally with my team, with with around 20, 25 people over the course of last 9 months. So there's a road map that I laid out for the team. I'll try to lay that out, lay that out for you so that people can kind of pick that up. There is a level zero to this. Level zero is where you kind of define your perfect toolkit. I'll explain you what I mean by that. A tool—you have 10 things that you do at your work every day. And when you look at those 10 things, right, you would know that these are seven things that AI—I can take use of AI right now to do it much faster, much better, to—to solve those seven problems. There are hundreds of tools out there. There could be a Chat GBD, there could be a Gemini, there could be a Claude, which are very general-purpose tools to very specific tools. Let's say you do a lot of work on Excel, so there's a tool called as Numerous. Just to understand this better. So the first step is for me to jot down all the tasks that I do. Yes. The second step is to identify merely through human judgment as to which of these tasks could possibly be done by AI. Yes. Then the third step is to go and find tools to do each of these tasks. Correct. Correct. So when you go out to look for tools, right, the problem is because there's so many tools out there, you will fall into this pit of which tool do I use, where there's always a new tool coming in. So the solution to that is to define your AI toolkit. Okay. Explore a lot of tools, figure out what works for you, and hold them very close to you and go very deep into those. But when you say explore these tools, the problem is that the exploration never ends. And I've been into that uh pit where you know every day there is some of the other Instagrammer who'd come and say this AI tool does XYZ, and I'm like, bro. And I'm not saying that they are bad tools; I'm saying that they are great tools. But then all of them are great. For example, for a thumbnail designing tool, we looked at so many tools; we just happened to stick with one. But then the other day my editor came to me and said that you know this tool is better, but the only difference between both the tools is that I spent more time with this tool and less time with this tool. Similarly, he spent more time with the other tool than he did with my tool. That is exactly why I'm saying find a tool that you're comfortable with, go deep. Okay. So rather than losing your focus and say that, oh, next big thing, next big thing, next big thing, because the next big thing is not the next tool, but the problem over here is that GPT-3 is not as good as GPT-4. A problem that, let's say, I choose a tool which is GPT-3 level, and then tomorrow there's another tool that comes out which is GPT-4 level. Mhm. But they both are on the same tools, Chad GPT, you've got 3, 4, 4.5, so you can understand. But with these tools, you don't know which models are smarter because A, you're not a techie; B, with time the tool which is GPT-3 level gets better, but when you start with a GPT-4 level tool, it doesn't look that good. So how do you identify and how do you make sure that you always are at the cutting edge of whichever model is the best? I'll get to that. I will get to that. That's why we're at level zero. Okay. Level zero is for us to find a toolkit that works right now for us, right now, right now, right? That solves an immediate problem, that is, I'm more efficient at work right away. In this, for every problem, right, there are tools for it. One great place for you to find tools for your problem is there's a website called as there is an AI for that.com. Okay. Okay. You go there and search, let's say Excel is your problem. You spend a lot of time on Excel, and you want AI to help you with Excel. You can just search for Excel; there'll be 100 tools there. Look at the one which has the highest number of votes. Okay. Pick that tool up. The other great place for you to identify if the tool is good or not is to go to Product Hunt, search for that tool. They must have launched it. Look at the review of those tools. What is Product Hunt? Product.com is a platform where founders go to launch their products. Okay. It's a public forum. Let's say I built—let's say I built a thumbnail designer—right, I will go and launch saying that, hey, I built a thumbnail designer; this is state-of-the-art. With that being said, there will be other thumbnail designers who have launched there as well, and their consumers are also here. So people who have used other tools like other thumbnail designers would also rate this, and they'll be like, I saw this tool, good, but I found this tool to be better than that. So you'll get a high-level picture to for a starting point on which tool you want to start with. Let's say for Excel, there's a tool called as Numerous, which is a co-pilot for Excel. Let's say you want to make presentations—a lot of people spend a lot of time designing presentations—you can use a tool like Gamma, which can generate out-of-box. For research, like we discussed, there's ChatGPD deep research, there's Grok research. In fact, there's a free tool for research which is Google Deep Research. No tool comes closest to that, but people don't know about it because really, do you think Google Deep Research is better than Chat Deep Research? Yeah, I'll tell you why. I'll tell you the logic of why. Is the backbone of Deep Research, internet? What search engine is Chat GPD connected to? Bing. What search engine is Google—uh, Gemini connected to? Google. But Gemini is too bad, bro. With the 2.0 model that has come in right now, it's phenomenal. Okay, check it out. Okay. Right. And a lot of people in Deep Research, right, you want every corner to be covered. Well, the quality of output in terms of how it has written the copy, you might like Chat GPD better purely because of its text generation models and empathy and all those aspects of it. And 4.5 is very good when it comes to emotional intelligence. Gemini might not be as good, but what do you do research for? You want depth. Nothing can go as deep as Gemini because I have done this activity—same assignment or the same project on Chad GPD deep research, on Grock deep search, on Gemini uh deep research, and on Perplexity deep research—same thing. The most extensive documentation I've got is the Google Deep Research. And I'll tell you what. Chat GPD referred to 40 resources, 40 sources. Google referred to 350 sources. Now you might say that web hub we are—if doesn't mean that high number of resources would mean high quality of content. No, it does. It does in research, it does, because it's intricacies that you have to capture. Correct. I want to understand an important point that I want you to establish. In fact, Anonym is in some cases better than Charge or Gro. Which tool is best for what? So there is no tool that is best for anything. I'll tell you why because I—I'll tell you this is the response that I was dreading. I—I—I—I'll tell you why, and I'll tell you how you can find your right answer also. The foundational problem that people make is with prompt. Okay. Your output is as good as your input, right? A lot of people screw it there. Few machines are good with comprehending your question better; as a result, it expands to a better output. But if you give a high-quality prompt to everything, that is when there's a level playing field. Okay. Did you understand? Understood. With that being said, as a consumer, what I value more is what matters. For example, when it comes to writing style, Claude was hands down better than everything else was—was till GPT 4.5 dropped. GPT 4.5 dropped a few weeks back, a month maybe, and it is purely trained to do good writing. It has emotional uh intelligence; it has character; it understands what you mean right better than it understands sarcasm. All those aspects—human elements come into the picture. As a result, GPT 4.5 right now is the best writing tool, but 4.5 is not free; you need a paid version of Chatity for that. So what is the best tool right now? Cla, because you can use the free version of it. Got it, right? Same with Deep Research. Deep Research also goes back into how good a prompt you can write. When you write a very detailed prompt on exactly what you want, there are two ways of writing a prompt, right? One is that you say, hey, do a research on US recession and tell me—give me a report. That is the worst prompt that you can write. Instead, you say, I'm—I'm making a YouTube video on US recession. I want to cover all the sides—the impact of US economy or the impact that it will have on Indian economy. Why is that happening? Is there a bias of AI? Is there a bias of war that is happening on the other side? Is there an impact of Trump? Was there something that Joe Biden did because of which this is happening? You expand your understanding on what you want to do, right? And then you put a prompt out; you're giving it more surface area to think and give you a better output. Got it? In these cases, I have seen Deep Research of Google doing better because it goes into those trajectories better. It also uses a reasoning model which has an ability to think like a human and reads up and gives you a much more extensive document. Got it. So for research, Google Deep Research is better. For understanding, it's free. Okay. And it's free. So for research, Google Deep Research is better. For writing style, understanding, Claude was better, but now Chat GPT 4.5 is much better. And what about Grock? What does Grock specialize in? Grock's unique edge is that it's built uh by Elon Musk, who owns Twitter. So no other platform like Chat GPT or Anthropic or Gemini has access to Twitter data. But how is that a good thing? Because Twitter is full of crap. But it also is the place where the most recent news kicks in first. But who is to say that that news is relevant? Perspective, bro, perspective. But how is that possible? Because bro, I know so many pages which just put out fake news just for living. No, no. There is Community Notes that have come in that handles it. So I'll tell you what I mean by that. Any platform who—who—who are these AI assistants catering to? Humans, right? What is Twitter built on? UGC. What do we like to see? We like—and a social network understands what we like to see better than anybody else does. So it understands a human being better than a non-social network platform. I think that is where Grock does well. It kind of somehow, right, manages to understand your request better and gives you an output that you could relate to. Now is that based on bias? Possible. No, because my argument is that uh it makes sense if it is about just telling me what people like. So Twitter is actually trained on all the propaganda, all the crap, all the hate. And when I ask Twitter a question, I'm expecting it to give me an accurate answer, which is by definition supposed to not have crap, not have hate, not have bias, and not have fake stuff, which is everything that Grock is trained on. So that is not how it works. Basically, it's not just the information that it's trained with. First of all, Grock is not only trained with Twitter data. So what happens in these large language models is there is unsupervised learning that happens; there's supervised learning that happens; and then there is reinforcement learning that happens. I'll explain you what it means by that. Every model could be slightly different, but high-level, there is uh you basically pull out all the content on the internet that you can get hold to—books, movies, everything possible, every form of text possible—and you just shove that content into the AI model saying, read, read. You don't give context; you don't tell what a cat is, what a dog is; you just say, read, read and understand what you're able to understand. That is basically unsupervised learning. Next stage, once this is done, then comes supervised learning, where what you do here is you basically say—you give some level of labeling, some level of direction—this is good, this is not good; this is right, this is not right. But high-level labeling, you say basically uh it's like giving it books in some form of fashion, right? Again, this is called supervised learning where you're directionally telling what it is rather than just throwing data at it. Then comes reinforcement learning. This is a very important step, and this is what makes an LLM an LLM. Right here, what happens is now the switch flips. Let's say you're an LLM; I was just giving you data so far—unstructured data—then I give you some structured data, right? In the most layman way possible, then I'll tell you, you studied everything; now I'll ask you questions, you give me answers. In reinforcement learning, what happens is I ask the AI a question; it will give me three answers or four answers, whatever that is. And as a human in the loop—there's a human in the loop here with most of the other models—uh, the human kind of rates if which of these are the most accurate answers. This is reinforcement learning that happens. And on top of all of this, there is something called as parameters that kicks in. You basically—the model—okay, these are the characteristics that you should have. It's like telling a human being, be nice, be kind; this is not right, don't do this; this is right, do this. You—those are in a way parameters that you give. Just because you're trained—let's say you—just because AI is trained with 100 very negative, suicidal books, AI doesn't become suicidal because reinforcement learning kicks in where you kind of giving it feedback back saying, this is right, this is wrong. But in this case with Grock, while everybody had a finite amount of data—Grock—I mean, Twitter also has finite amount of information, but it has more information than everybody else did, which is still valuable because more input, more quality input leads to a better model. The second aspect to it is the reason why it chooses to be haywire and chooses to say whatever it wants to say is because Elon Musk decided to have those parameters set like that. If Charge wants to be like very rude, which Grock is right now, right, it can be. They have to just turn the parameter on saying, no filter of being nice, being kind, being empathetic, not—not going to talk about elections, not going to—these are the rules that were set to Chat GPT; these are the rules that are not set to Grock. But the quality of output, I think you have to give it to the engineers that are working on Grock, is that at the least amount of time they were able to pull through a model where the quality of output is so good that we're having this debate today. So long story short, have a toolkit of all the AI tools. I'll just summarize this because it's been some time. Level zero: Level zero, find tasks, find AI for the tasks, research through which AI is the best, and then zero down on it. Done. About level one. Now for you to unlock the next layer of win, you truly have to be a problem solver, right? For that, you have to learn a few things. Level one and level two is where you learn. Now that you use the tools—it's like an app, you can use it; it's easier; you do all of that. This is where you start going deeper. In level one and level two, your fundamental job is to understand all the AI models out there—not the AI tools, AI models—AI models out there. Okay, right? Like what—how does Chat GPT work? What is GPT 4.5? Just like what we discussed, yeah. What is 01? You're using Chat GPT right now; you don't care which model you're using at level zero. Topic five right here: You have to understand where to use a reasoning model, where to use a non-reasoning model, where to use this agent, where to use an open-source model. If there is an open-source model, how do I run that model? Things like that. What are the models out there? I mean, essentially, here you go deeper, right? Like uh it's a good question. I think the solution to this is being extremely curious. Instead of letting Chat GPT go at uh this saying that default, I'm going to use that, you start changing those options, playing with it, asking it different questions, reading about it, and that will give you direction. There is no other way, basically. Iteration, iteration, and testing. Like level one, this still gives me an understanding of what is the difference between Chatity 3 and Chatity 4—basic understanding answers. For example, in Deep Research, it takes you um 10, 15 minutes to extract the output, but once you extract the output, that output is just too good. Okay, you can see all those sources, and quite evidently, it is different from the output that Charge Gibrity 4 would give you, right? And that input is again different from what Charge Jubilty 3 would give you. But you mentioned understanding models. What do you mean by that, and how can a non-techy person like me do that? Let's say tomorrow you're meeting a hypothetically an investor who wants to invest in things school, okay? And you want to be prepared for that. How would you use Chat GPT for this? I would first break down why exactly am I having this meeting in the first place. If I'm having this meeting just to understand what is the value of my company, or let's say—no, you've decided that you want to raise capital. I want to raise capital. You want to raise $10 million from this investor. The investor has shown interest. This is your first conversation—not a pitch—first conversation with the investor, and you have never spoken to an investor before in your life. The first thing that I would do is research to understand what exactly do investors find value in by asking Chad what—where would you ask Chad 4.5? Okay, why 4.5? Because I know that it's the best. Okay, got it. Huh. How do they seek value in a particular company? That's step number one. Then I'll get some parameters; then I'll go and evaluate whether my company stands by those parameters or not. Mhm. Once I understand that, then I'll feed all the data about my company to Charge and ask, what do you think? Then it would give me some output, and then on the basis of that, I would say, if you are this investor, what are the questions that you would ask me? Beautiful. And then I would prepare for those questions. Then I would ask Chad Ji to be extremely rude and ask complex questions which I would possibly not know the answers. How would you do all of this? Who would you talk to in this? Just Chat Jubety 4.5, and I've done this for a few of my podcasts. I'll not take the name of the guest, but you get what I'm saying, right? I would just ask JB to ask me all sorts of debatable questions, complex questions that I would not understand, and then I would try to answer those questions properly. Eventually, I'll ask for judgment. Once that is done, then I'll get into the next step. For example, the investor would ask me, "What would you do with these $10 million?" Sure. The investor will try to bog me down by asking for a lesser valuation. Mhm. The investor will just—ego boost me into believing that uh he's the best and I'm the best, and we both should do business. So then I will look at all the evil tactics that the investors use. Then I will also look for the red flags in the term sheet. For example, doesn't work like that because I'm assuming—no assumption—assume what if he says, no, I like it on—go on, bro, 100 crores, take it or leave it. Sure, you got the term sheet. What would you do then? I look for all the red flags in terms; will look for it; I will ask Chad Ji how does the term sheet look like; A, B—I'll feed sample term sheets, and then I'll ask for red flags, and then I'll—I'll also ask for all the red flags that a term sheet could possibly have. For example, there's a term sheet make criteria which—which says that the investor can decide to sell the stake of the founder yes, after a certain point, if the founder doesn't achieve XYZ revenue, the investor has all right to just take up all the stake of the founder by themselves, and they also have the right to fire the founder and deploy new management. So I know all of these things. So I will note down all the red flags. How will you find them in your term sheet? Uh, I will feed the term sheet to Chat GPT. Okay. Now I will tell you a problem that comes—your term sheet is 450 pages. We can still feed it to Chat GPT. No, you can't. Why? There is something called as context window, okay, and there's a cap of context window. If I'm not wrong, Chat GPT's most of the models comes with 128k uh tokens, so

Will not be able to take more than 40 pages of information. Hypothetically, more than 40 pages, it will say the file size is too large, or the amount of text is too large. And if you try to break it down and send small, small chunks, it will forget. It will forget there is something called a context window of every, all of these large language models. Right, context window is something like memory. AI has very short memory; right, it can only remember so much.

Have you ever realized when you're talking to AI on ChatGPT, sometimes it says the window is getting too long, start a new chat? Why does it say that? Because it's going beyond the amount of things it can remember about this window. If you go beyond it, right, it'll stop, forgetting the things that you have said in the top. Basically, chibi is like Gajjini—not Gajjini; it can remember forever, but only up to 120k tokens. Okay, sorry guys, pardon my non-technical language. So it will not work. Okay, this is why level one.

Now I'll tell you, if you knew, if you have done level one, level two, what you would have done in level one and level two. Like I said, right, you need to know which model to use and how models work. If you knew how models work, you would have known that you could not have uploaded just the PDF as is, because it would not work. If it's a 10-page PDF, it will work; or else, it will not work. If you would have done level one, level two properly—that is, you've gone through that journey—you would have known you should not have gone to GPT 4.5, but you probably should have gone to 03 high or a 01 pro, or one of the reasoning models, or a DeepSeek R1, or a Gemini deep thinking model. Why 4.5?

The reason why you need to know the models is GPT 4.5 is a text generation model. That is, like I said before this conversation, the way these large language models work is that it predicts the next word. So it doesn't have the ability to think in a situation of yours, which is very, very hypothetical. It has to think about multiple use cases on every side, so it has to do something called as reasoning, like a human being. ChatGPT doesn't have the ability to think all of this; a GPT 4.5 doesn't, but a reasoning model does. Oh, when you input the text in a 03, or you must have seen 01 as a model, which we never touch, saying we don't know what that is. Regular, I'm saying purely on regular people, right? So it does reasoning. So the input here should go into a reasoning model rather than a regular model, right?

As you talk about it, I love the fact what you're doing here is that you said, "I will ask ChatGPT to ask me tough questions." I do the same, but I do it slightly differently. You could be doing that as, "Okay, I'm very lazy to type and listen, so I turn on the voice mode. Correct? I speak for two, three hours till it breaks, and again I resume. Correct?" It's a great way to kind of get a balance of what's happening. So there's a reasoning model that goes in, thinks through everything, thinks through every equation, and goes here, and finally you are able to quiz yourself. You know, I've done sample podcasts; I did a sample podcast yesterday of you and me as well. Aa, is this better? Thank God. Uh, but ChatGPT did ask me more tough technical questions, which I didn't want you to ask, but that's good, right?

Uh, and then when it comes to, if you knew how models work, like I said, you would know that you cannot just upload a PDF. A lot of people actually upload PDF files and they say, "Oh, it worked," "it didn't work." It just understood what the v—what the file is about; it doesn't know the intricacies of it when you upload a PDF, because it doesn't remember. For you to upload a large file, you need to know a concept called as—I don't want to go into it—RAGs. RAGs break down, index, so that it can read, and ChatGPT cannot do it out of the box. If your work had had a lot to do with PDFs, you would have known of a platform like Humata, which is a RAG platform, where you can upload a PDF and start chatting with it, or you could have just uploaded the PDF, large PDF, into a different RAG system like Notebook LM. Have you heard of Notebook LM, which is by Google? I've heard about it, but I haven't used it.

Google Notebook LM, you can just upload the PDF. It is also a RAG system, in a way, right? The feature that Notebook LM is very popular for is the podcast feature, where people say you can upload any file; AI will convert that into a podcast, and you can listen to it. That is what Notebook LM got very viral for, but what it works very well as well for is that you can upload a lot of files, aggregate the information inside of it, and you can chat about the files that you have just uploaded. So I can take up Walter Isaacson's book, yes, upload it, or five different books of Walter—Walter Isaacson—and ask, "What are the common trends that you're seeing about the writer? What are the common traits that you've seen in Steve Jobs's, Elon Musk?" And it will not—the tool—the beauty of this tool is that it will not allow it to access information beyond the files that you have uploaded.

Oh wow, that is actually very interesting, because I tried doing this with the book *Prince*, and I tried to understand if Machiavelli were to give me an advice, what would he say. Okay, and this is what I was speaking about, floating with power and trying to understand how power consolidation happens. But what happened is that after a certain point, it started to go out of the book. Yes. Now, if I haven't read the book, I wouldn't understand. You would not know. Yes, yeah. So then that becomes deeply problematic, right? But then if there is a model which has restricted access only to the book, and it has—it has to do reasoning only within the book, then it makes a lot more sense, only to the files that you've uploaded. Got it. So that's a Notebook LM. So, but then, bro, there is no end to this; like you've given me so many nuances. So give me like a thumb rule on how do I understand these models, because this is just too much information for me.

There are not 100 models out there. No, you have to understand what are your top use cases, okay? And then you have—there is a finite amount of information here. Once you learn that finite amount of information, you will be hooked to make sure that you're always on top of new things that are coming. Got it. So what's the framework to understand this better, uh, in terms of which model is better for what? I think the only way to do that is by trying, is by testing things out, is by playing with it. There is no better way of learning than actually doing it. But playing with it—4.5—you have to be more curious to understand what I'm doing; is it the right thing? Long story short, 3, 4.0, huh? But that is such an inefficient way to go about it. Why? Because I've got my day-to-day task; what am I supposed to—No, you're not going to do this forever. No, for example, today if I want to have a task—see the first differentiation that I told you about is: do you go to a non-reasoning model, or do you go to a reasoning model? Once you know where to go, when to go, there are a bunch of reasoning models everywhere. You can choose to stick with GPT only; you don't have to go beyond it. But reasoning, non-reasoning, reasoning, or non-reasoning. So, or else you'd be—everybody—you want to have an edge; you don't want to put in the work. No, I'm saying I'm putting in the work, but I have to understand where to put in the work. By spending time into understanding where to put in the work, you getting my point? You know the solution to that? That is exactly what GPT-5 is going to be. It removes—this is openly spoken about right now—when GPT-5 comes, right? This is not your problem; this is everybody's problem. They're like, "We don't know what to use when you have given us 20 models; what do you use where?" So OpenAI is like, "You know what? The GPT-5 is going to be a smart model which has integration of all the models inside of it, so you don't have to do the guesswork of which model to use where. I will understand your task, and I will use the right model."

So the call to action is: wait for GPT-5, if you want to wait. But then this kind of works in everything that you're learning in AI, everything that you do in life, actually. You have to be explorative. For you to get an edge, you need to have more surface area of knowledge understood. Do you think it would be better, for example, you ask me this question that, uh, "What if ChatGPT 4 doesn't take up 450 pages? Do you think a better alternative would be to just ask ChatGPT 4.5 as to what am I supposed to do? I want to upload 450 pages of document; tell me which model to use?" Probably. But for a question like that, I would go to Perplexity. Perplexity. The reason why I would go to Perplexity is because GPT 4.5—not tell me that Perplexity is better. I think there will be biases there, unless you call it out. I don't know; it's a good try. These are good experiments. See, this is—this is the curious hat on, right? If it does say, then you're like, "Oh, it's a nice model." If it doesn't say, you ask, "Why didn't you answer Perplexity?" Then it'll give you a reason why. 4.5—ChatGPT 4.5—explained the yield curve we were trying to study—US recession—it explained the yield curve wrong; definition of yield curve wrong, the data, the example it gave outdated, or it gave the complete opposite example. Was the e curve recent? The data that you're looking for—No, no, it was just an example; it was a conceptual explanation that I was looking at, and this is like a 50-year-old concept, and yet GPT 4.5 messed up. And we understood that while we were reading through the example, and we were trying to understand how can GPT 4.5 be wrong. So for about a minute or so, we were like stunned that, uh, GPT 4.5 said something, and because we've trusted the software so many times that we started to question ourselves.

There's something called as hallucination. See, all of these models, all of this AI, right, basically has been trained from a lot of information on the internet, so it has a character of a human being, but it's impossible that it gets wrong information on yield curve, because all of the information available on the internet is not 100% accurate. And GPT 4.5—again, I go back to the same point—it is a text generator; it basically guesses the next word. If you want super factual information which could not go wrong, and it's not creative writing, you would go to something which has access to real-time information, which is Google DeepMind. No, you can go to Perplexity. This is—this doesn't need—yield curve is a basic concept; it doesn't need one. In fact, a good hack, if you want to be very sure of the information that is coming, and it's not—you didn't ask for it to write an email, because the factory cannot be wrong in an email, right? It's about writing style, but let's say something like this: in this case, yield curve, I would just tap the button of search on—on ChatGPT. So before it gives you the answer, it quickly reads up a couple of articles before it gives you the answer. Just this—just—just takes the quality of the output much higher.

Level one is understanding the models better, and here's where you need to be very good at problem-solving. You said, yeah, I mean, essentially, level one—idea of level one—is that you understand what all models are out there, which model is good for what, and how to work with them the right way, by learning how to do prompting rightly, know the right models, and then choose the right models. Now let's go to level two. Level two. Level two is essentially prompting; advanced prompting. I would want a framework on how to prompt such that it can give you right outputs. And before—before I spoke to An, I thought prompting is very basic, okay? And, uh, what I found deeply interesting is that me and my team were using the same ChatGPT model, and we had the same problem, but somehow the answers that I got were so comprehensive that they were like exactly type, okay? And I remember this very vividly, because two months back I asked the same question to An—type—like, "How are you getting such comprehensive answers?" And he had a different framework, but I want to understand from you what exactly is your framework, because of which you're able to get such comprehensive and complex task done through the operator in just one attempt.

Well, it is not just the operator; it's any model out there, or any AI out there. The way I treat any of the AI agent—AI assistants—out there is how I would—how I would treat an intern who has just joined today, okay? If I give a task to the intern, I would want to be as freaking specific as possible. There are two ways. Let's say I'm hungry, okay? And I have an intern. You should not get your intern to do all of this; it's just an example. Uh, let's say I'm hungry, and this intern has joined in today, and I'm like, "Hey, I'm hungry; can you get me food?" The intern would be like, "Oh, Weber wants food; okay, let me go down. Oh, there's a Burger King right here; I like Burger King; I'm sure Weber will like it as well, and burger is closer; I'll buy it and give it to you." And he gets me a burger. I'm like, "Boss, why did you get me a burger?" I go back to the intern and say, "Why did you get me a burger?" The intern's like, "You said you're hungry; I got you a burger; what's the problem?" It's like, "Oh man, I'm dieting right now; I can't eat a burger." Instead, if I would have told the intern, "Listen, I'm very hungry right now; I have a lot of work to do, so I want to eat something which is very quick. With that being said, I'm currently on a diet, uh, and I've already had a lot of carbs today, so I want something which is very, very high on protein and high on fiber. So figure out something that we can get really quickly in the next 10, 15 minutes so I can have this food now." That I have been a little more—and I'm a non-vegetarian—now that I've been a little more specific, the intern will still find a Burger King only, which is closest because I wanted food in the next 15 minutes, but he'll probably order a salad at Burger King with a chicken breast rather than a burger for me, which suits my needs. So it's all about input and output. So the more expressive you are to an AI, the better it gets. So the way I treat it—what do you call this? Context. It's not just context; it's a great point. Context is extremely important. I give more things to it, okay? I call this, uh, a basic prompt formula. This is—we call it the magic prompt formula internally inside of ThinkSchool, and it's a very simple prompt formula, okay? I start with something called as role; I go to objective; I go to context; instructions. These are the four elements I try to keep in every prompt, and sometimes I add something called as notes. Notes. And I'll explain you what each one of them is.

All these large language models are general-purpose technology models, right? Like they can do a lot of things, but that doesn't mean that they can do everything really, really well. So you have to specify it. Let's say if I'm writing copy—let's say I'm writing a landing page copy for one of our products—the role is essentially me giving AI a specific role, saying, "Listen, you are an expert at writing landing page copy, and you have written landing page copy for top SaaS companies like HubSpot, Canva, Freshworks, Zoho, etc., and you have 15 plus—15 plus—years of experience, and you write phenomenal conversion copy." This is the role. Now you'll be like, "Why do I have to give this role? Why are you making stuff up, Webhufff?" The answer to this is: the moment I give a role to this AI, AI can quickly go up and read what are the traits of a person with 15 plus years of experience who writes copy on SaaS, because it already knows everything, but it's clouded. It specifies, saying, "I have to write like this." A role is very important. Next comes objective. What do I want it to do? I want you to write a kickass landing page for this new AI tool that I'm building, that is ThinkSchool AI, right? And these are the features of the tool, and I want you to build a kickass landing page copy—copy—for this. This is the objective, which is the task. Next comes context. Now context, I feel, is very, very important. A few cases you don't need it; in a lot of cases you do. Context is where you tell your AI why you're doing is important, okay, right? So here I say, "Listen, you have a very important job of writing landing page copy. The reason why I want you to write great copy is because I'm going to spend $100,000 running ads on this. When $100,000 I spend running ads on this, there's going to be hundreds of thousands of people who land on this landing page, and if I'm not a—if you're not able to convince them about the product that we have in the most convincing way, people will not buy my product. When they don't buy our product, we will not do well as a business. If we don't do well as a business, we would not need you anymore." This is where the context is—where you try to explain why what you're doing is important, so that positive—negative—AI works super interestingly, right? Blackmail. This is why what you're doing is very important. You basically control if my marketing will be positive or negative. The idea here is to explain why what you're doing is very important. Why what you're doing is very important, yeah? And then there is instructions, right? Instructions is: "Listen, this is my product; these are features it has; uh, I want the landing page copy to have all these captured, blah, blah, blah. These are basic instructions, step one, step two, step three, step four." Eventually, there are notes, right? In notes, you can add anything that you want to summarize, or anything that you could not capture here, right? And that's my prompt example—nodes. So usually the way I use nodes is like a backup; I usually don't add nodes. I write a prompt, let's say without nodes, and I send this input. Let's say the copy that it comes—comes in—let's say, uh, second person, and I want all of the copy to be in first person. Now where do I add this? So I just add a note and say, "Make sure all the copy is always in first person." Whatever feedback I usually have for the prompt to reuse it over and over the course of time, I usually add it in nodes. Got it? So L0 is tasks; find AI tools using those websites. L1 is understanding all the models. L2 is prompting; prompting—role, objective, context, instruction, or notes. This is one prompting technique; there are hundreds of prompting techniques out there, okay? Like let's not get into that. L2, which is prompting—prompting—and going a little deeper. Listen, what we can do—actually, interestingly enough—is I can give you a document detailed out all the levels which you can link up as well, so people can use it, okay? But it's not as simple as knowing models, knowing prompting; you have to go a little deeper; a few technical aspects you have to understand, but high level you're right—models, prompting. Level three may—what I feel as an AI journalist—because you're a problem solver; ever just knowing text is not good enough. How do you go deeper into audio, video, images, and all those things as well? L2 prompting, correct? So so far whatever we have done is with text; we have stuck with LLMs which are text generation. There's a world out there which is massive that has everything to do with images, videos, audio, and that is called as a world of diffusion, okay? That is also something that you need to understand if you want to become a generalist, because you are trying to become a problem solver, and problem doesn't come in just text form; problem could come in any form, okay? So audio and video—because video—upload video and audio—audio I can still understand, because you can still give instructions via audio, which makes it easier; in fact, it is better than text, okay? But, uh, what am I supposed to do with all these? Because at this point it is almost too much for me. Everything will come together on level five, right? Trust me. First level—level zero—you did it only for yourself to be feeling comfortable wherever you sit—wherever you're sitting at this point of time—that is level zero. Level one, you went deeper into all the AI models out there. In level two, what you did was to go deeper into playing and making these AI models. You played with prompting, retrieval techniques; what are RAGs and—but yeah, level one, level two, all of them has to do with LLMs. In level three, which is one final curve of learning according to me, is understanding how do you manage images, how do you generate images, how do you generate videos, how do you generate audios, how can you control what is coming out of all of this, so that you don't only have the ability of text, but you can also solve problems when there's an image requirement.

Bro, how many of these professional executives have this problem with image? Because with prompting I can understand; you can do code; you can write better; you can write emails better—to emails. Let's say you have a head of business, okay? Okay, okay, for your operation—your business—in your game at least, right? You have a lot of creative work to be done. If your head of business cannot drive the team under them, which is a revenue team, to drive them to say that, "Listen, your thumbnails could be AI first; a lot of re-shoots that you're doing could be a simple audio generated AI of Ganesh." No, I'm looking at a non-creative company; tell me one company—Marlo, Infosys, huh? Infosys is a service company; they would need this even more, okay? Because a service company has to build solutions for no matter what there is, right? If you don't have an understanding—basic understanding—of diffusion, it's like LLM is for text; diffusion is for image, video, audio—everything else. But where will they use it? To build products, for example. Let's say if a—let's say if Bank of America comes and says that, "You guys will have to generate a custom image for every customer who signs into my product," okay? How will they generate—h? It's going to be part of our ecosystem, but is that going to be as, um, common as text? For you to become a full-stack person, you need to have an understanding of that; it's as simple as that. Can you not do it? Probably yes. In the same way you can choose not to learn how all the models work and stay at level zero also, right? For you to have a very good grip—see, I believe as a problem solver, the more depth and more width of knowledge you have on things—it doesn't have to be that you can execute everything—having an understanding of possibilities gives you a new array of solving problems, and that is what anybody as a journalist should have the capability of. Got it? You must have heard of T-shaped—T-shaped leadership, T-shaped people, right? Or T-shaped marketers, T-shaped business people. Why do they have to learn about marketing? By the way, guys, just for context, T-shaped—Mlab—specialist in one thing and generalist in multiple things. For example, if I'm a specialist in communication, and I'm a generalist with video editing, with sound design, with thumbnail design, I can go on to become a YouTube creator. Anyways, back to the—You won't believe the number of founders who are running billion-dollar companies—you know the kind of founders that I'm talking about, but I'm sure you are friends with a lot of them—have reached out saying, "Webhufff, how do you generate..."

Creatives, we see your ads all the time. I get the fact that that is AI. How the hell do you do it? These are founders, right? Why do they need to know you are talking about building a generalist? You are talking about becoming a builder. You're talking about having an edge over everybody else. If you don't know it, why? Why are the founders curious to know how we do?

If I was not an AI journalist myself, it's simple, right? If I was not an AI journalist myself, we would not have been the company to launch AI ads one and a half years, two years back when people didn't even know that you can generate an ad using AI. What? Your ads are all AI? Two years since, two years we have been testing AI. All my ads today is AI, obviously. So you don't shoot them? No, bro. Rahul begs me to shoot all these ads so many times. I should just give him my AI.

Not just that, I'm… Did you use the general framework of going through H and level abs? And that is one kind of ads that we create. You would also see me in ads where I look like Harry Potter. You would also see me in ads where I look like Iron Man. You would also see me in ads where I look like an old guy. Aa, it is face-swapped with me, with storytelling. Interesting, right? Now you'll be like, "Web, this is very marketing specific," right? No, I understand because you know Azar from Inshorts was here, and he said something very interesting. He said that greatness often stems from curiosity. He said that if you're a curious person, then by default you tend to have more information, and because you have a lot of information, you can turn them into bits of wisdom, which will take you far, far ahead. So he said that. In fact, when I asked him how do you hire great leaders, he said that great leaders have to be inherently curious. So I think this curiosity aspect is something that I understand, and I believe that if anybody wants to climb the ladder of greatness, this person has to be curious. If they're not curious and they're just too lazy to explore things, I mean, it's okay to find an… it's okay to find a more efficient way to quench their thirst of curiosity, but it's not okay to skip curiosity. Which is why I asked you. Now there are too many models. What is the most efficient way to go about it? I mean, I don't want to skip it, but what is the most efficient way to go about it? So I get it. So we don't have to justify curiosity. It's already… it has already been… I think it has nothing… it has a lot to do with curiosity, but it also has to do with one aspect that for you to be able to do things at scale, you need to have broad, broad spectrum of knowledge. A general, army general, probably once he becomes a general, is not going to go into the uh war zone to fight with guns anymore, unless it gets to it, right? But are you saying that army, the senior person, army has not tried every freaking weapon that has come into the army? He has. And because they're able to empathize with the frontline soldiers, they're able to give them commands that they can obey and efficiently win the war. So I get it. Great. So that is done with level three levels. Got it.

How would you summarize level three? Level three? I can give you some tools. Or level three is the idea of understanding how things beyond text work. How beyond text work? Because we have done all text so far. How does video work? How… how does AI generate an image? What the hell is noise? Why does it come from noise to a cat photo dancing on a road, right? So long story short, with L3, what you're essentially supposed to do is go beyond text, explore video, audio, and here's where ElevenLabs would come in. Mid… I tell you a few tools that people can explore. Uh, ElevenLabs is basic. Midjourney is basic. Leonardo AI is basic. These are tools. If you want to go a step below this, go into Stable Diffusion. There is something called as comfyUI, which is a nice little framework where you can build your own Midjourney. Okay, there are models where you can train your own imagery, like Flux, Laura. If you look at my Instagram thumbnails, all of them are me, but all are AI generated. Mean astronaut suit, mean this, mean that. All are AI generated with my face, of course. For video generation, there's RunwayML, uh, there is Runway Labs, there is Sora, there is VO, which is by Google, which is incredible, right? So a lot of these models, and this will give you width and depth across everything. So tomorrow when there's a problem, you can solve them irrespective of… you cannot say, "Oh, this is out of my territory," because you don't have that option. You're done with three levels. Great. You have a very good understanding. You've played with all of them. You know where to use what. Good job.

Level four and level five is all about building. Level four, where we spoke about agents, right? AI agents are going to enter the workforce. Level four is where you start learning how to automate things in your life and build agents as a solution for the problems that you have. Build agents. Okay, yes. Right. So I think the great start for this is trying to automate, not do things, but actually hand off your job to something else and get it to do… for example, let's say you're terrible with emails, and nine out of 10 emails, right, you just want to say, "Not interested," and you would know there's a logic behind it, right? How can you get an AI to automate this for you, where it can read the emails, write a reply to the email, if it thinks that there is a… with 99% accuracy, Ganesh would send this email as a reply, they would just reply to that email. If it, for some… for some reason, thinks it needs your attention because it is important, it will label it as a different email and keep it with a five different uh replies as a draft. If it is even more important, then in real time it will ping you on Slack because you're terrible on your emails. All of this happening automatically. Got it. So L4 is basically exercise executions now. So after you've learned everything, after you have played with everything, now it's time for you to look for that task in your L0, which task to pick up, which model to use, and how the hell do I automate it so that I go off hands from it. Okay. Which is the simplest task that you would give out as an exercise for somebody to try out? I think email reply. Email automation. Email automation is superb to L0 task. Email automation, uh, managing emails, replying to emails. So I want to process… I want you to process it through all these four levels so that we can develop a sample understanding. Sure. So it's L0, reply emails. L1, understanding the models well. Now you tell me, according to your understanding, which is the best model to use in order to reply to these emails? It's a very simple task. You can use a 4.0 or a 4.5 or anything. In this case, 4.0 or 4.5. One-line key. Why did you choose this model? 4.5? 4.5 because it's the best text generation model out there. 4.0 because it's cheaper than 4.5. Got it. Then we have L2, which is prompting. Can you tell me what would your prompt be? You start with role for email reply. Who usually replies to these emails? Executive assistant, right? Of big companies, of big founders, or whatever. So the role will be like, "You are a rockstar executive assistant with more than X years of experience and have worked with phenomenal founders like Steve Jobs, Bill Gates," let's say. You, in this case, you can say Deepender Goyal, Kunal Shah, or whatever. You have been… this is a role. High-level objective: Your task is to go through my emails, open each one of the emails, understand what is written on the email, and write a reply for it. Okay, that is the first job. This is the email reply generator. Write a reply for that email. Okay, objective: You have to understand that I am a Ganesh Prasad from Things School. I have a lot of followers, so we cannot factually go wrong with emails. We cannot be rude to the person. The email should not… a lot of my audience could be sending me love emails. We cannot be like, "Thank you," and all of that. The replies have to be empathetic. Whatever other context that you want to give here also… we'll get a lot of hate emails. You'll get a lot of hate emails, so manage that with highest, utmost level of respect and empathy as well, right? We never want to be rude because I am not rude, right? Context: Instructions. In this case, instructions doesn't need a lot here. You can again say that uh, basically empathy. Remember all of these things is a very simple task. That's it. There is no need of notes because it's a very simple… When you write a prompt like this, every time there's an email, it will understand that it's an EA, so it has to answer like an EA. It will write nice little email reply for every email that you come up with. This is what you did on level zero. So when you use this prompt and copy and paste the email that you got, it can write a reply, and you have to copy paste it, but this is still copy pasting. That is level zero. Now you came to level one and level two where you understood how to refine the prompt. So instead of saying, "Write an email reply to this," you wrote a nice little prompt. As a result, it was able to compose a nice little reply. Now… now you want to do one very important thing, that is it has to reply to your email and also put conditional logic. I'll tell you what I mean by that. On level four, what you learn is automating this. Let's say we use a tool like Make.com or Zapier, which has integrations with tools like Gmail, and it also integrates with tools like OpenAI, where I can use a 4.0 or a 4.5. Now I'm not using ChatGPT anymore. I've gone beyond ChatGPT, right? Now I go to a platform like Make.com or a Zapier.com, for this matter. There are tools that… there are apps that you can pull in. Guys, just to give you context, Zapier is an app which can communicate between different, different apps. For example, if you tell Zapier that you should read this email and then go to ChatGPT, ask for what would be the best response, extract that response, and then paste that over here and then tap reply, Zapier will essentially go from reading your email to taking it to ChatGPT, extracting the output, and then pasting it over here to replying. So it completes the loop of automation from reading to replying. Okay, so Zapier is a tool which can actually connect multiple apps together, talk between them, and get your task executed, which is why it's called an automation tool. If this is clear, let's move ahead. So perfect. I think you've explained Zapier really well. Also the process that we're going to do here, right? We're going to… we're going to pull in Gmail, which is an app inside of Zapier as well. Once you pull that in, it'll ask you, "Hey, enter your ID and password and sign in into your Google account." You give access to your Google account there, right? And then you can write a logic: Every time I get an email on this email ID, send this email to OpenAI, or in this case, let's say ChatGPT for easier term, inside of OpenAI or ChatGPT. I've already kept the prompt ready. So the question comes and it goes into the prompt. You write the full prompt, and it says, "Here is the email," and every time there's a new email that comes out, it goes into the prompt, executes it, you get a reply. Now this reply is again connected to Gmail, right? And this can all be done without writing a single line of code. All drag and drop, right? Then this reply will again be sent on Gmail, and it can send the reply. Got it. Now this is the simplest form, but nobody should do this because it will end up replying every email that you want. So you can put conditional logics, that is, "If this, then that" kind of logics. But why not reply to all emails? Maybe you got an investor email. It doesn't have enough context to it. If you force it, AI is as good as it has information of… if you say, "Be nice," and let's say an investor have written you an email saying, "Hey, I want to talk to you. I want to invest capital," and let's say you have no interest of raising money, it'll be like, "Okay, great. Thank you so much for the reply. I'm super excited to see your reply. I'm so glad that you watch our content. Sure. Let me know when we can talk," and you'll be like, "I don't want to talk to an investor," but AI is just being nice, and you said, "Be nice," so it went on to be nice. So it's like a company telling you that, "Ganesh, I want to sue you." "Thank you so much. It was great. Thank you so much." It won't be, "It was great." It'll be like, "I'm sorry to, you know, uh, do something wrong without even knowing if you did it wrong or not. Uh, I didn't mean to do it. We, our intentions are always right. We try to do the right by you, and if you have done it, sorry, we are happy to face the consequences." Oh, okay. Maybe… maybe because you're like, "Be empathetic. Be understanding. Don't be rude." You said all those things, right? But here's where you can draw a line. Now imagine you add one more step before even you send that email, right? Before you can send that email to ChatGPT, you put a question saying that… you write one more prompt. I will send you every email that I get. Your job is to understand, "Is this a very important email from an investor? Is this an email from a fan? Is this an email from a customer success P for which needs customer success? Is this an email from an internal team member? Is there an email which is something else?" Whatever you can bucket it, right? You have an understanding of your own email box. If you say… then you put a logic: If it's a customer success executive… if… if you understood that this is a learner of Things School who has written me an email, positive or negative doesn't matter, check if it's a positive email or negative email. If it's an appreciation email, right, send this email to Slack to our team saying, "Good job." And also reply to this email saying, "Thank you so much. Thank you for being nice." If it's a negative email, you basically say, "Sorry, didn't expect something like that." And loop in the customer success team by adding this email ID to the email and also send this email to customer success or customer issues Slack channel so that I can get the team can see it right away.

Do we have like a consulting company which can get all this done? I think there's going to be a massive rise of AI consultants because I think this is a huge opportunity. There are very, very, very few people who can do it today, and the people who can do it today know… are all geeks. So they're not one of those people who are out there saying that… so that is… I was saying there's a massive opportunity of people becoming AI consultants, transitioning and very soon transitioning from AI consultants to running AI agencies. From there, I see a possibility of them transitioning into service as a software company or becoming an AI SaaS company and actually build something. This is also bought as a service, right? Almost… it's… this is a classic example of service as a software, not software as a service. Oh wow, that is… you phrased it so beautifully, bro. Service as a software. I didn't phrase it. I didn't coin it. Oh, acha. Okay. I will not take credit for it, right? Service as a software. It's a new rise of vocab that's happening. I mean, they can… people call it in multiple ways. AI services company, services software company. Everybody has a different way of saying it. It is essentially those kind of companies where software is replicate… is replacing 95% of a human, but there is still a human in the loop. Software as a service: You have a problem, there's a solution, you use the solution, which is a pure software play, and it solves it. But service as a software is… let's imagine, right, uh, there is company Accenture or Concentrix which has thousands, hundreds of thousands of people employed whose job is to pick up the phone and talk to their customer, talk to customers of different companies. 95% of these conversations are predictable to 99% level. That is the answer. The cost of that call to the company is 45 rupees. That is what uh, AEL has to pay for a 3-minute call. Around 15 rupees a minute is what they have to pay. These 95% of the things which was being led by human beings can be now automated with voice agents. A company like Concentrix will eventually has to become a service as a software company where 95% of these tasks… you pick up the phone, human will not talk, AI will talk first, but the customer on the other side will not know or will not feel, right? Like, "Hey, what is your problem?" You like, "I paid, but my recharge has not happened." Like, "Can you wait for 30 minutes?" "Did you get the SMS?" "Said yes." "Was the money deducted?" "Yes." "Did you get a SMS from…" "Yes." "Did you see in the message we said your recharge will reflect in 30 minutes?" HDFC has the worst, worst bot I have ever seen in history. Botan chatbot. No, there's some voice assistant. You press one, press two, press three. No, bro. It's the worst. You're talking about press… so I just paid… I just bought a fan through Blinkit, or I just bought a fan through Blinkit. If this is a… if you want to approve this transaction, say yes. If you don't want to approve this transaction, say no, and we will get the card blocked or something. But I said it is a legit transaction, but I don't want to go ahead with it because I've made the payment again. It cannot understand. It cannot understand. Okay, okay. No, no, no, no. This is uh… block the transaction. Then I had to go to Bangalore. I had such a terrible 3-day trip because of that, bro. It was the worst. And then you can't even call them because every time you try to call them because somehow they feel like, oh, you know, they're so advanced that they… technology that they're using is not AI. They're looking for a catch word. No, no, no. What I'm saying is essentially a message to HDFC, if you're listening to this, that they… they may or may not have humans. I don't care about that, but if your AI… if your whatever AI software, whatever you want to call it, if it is so bad, you should at least be able to call a human and tell them that, "Guys, my card is blocked," because if it's an emergency, what the hell am I supposed to do? And this is like… and it's such a stupid thing that this is the most prestig… one of the most prestigious banks in the country. So it's bad. Anyways, sorry, bro. No, I get it. I get the frustration. I think uh, like I said, this is an outdated technology right now. M… they use voice in this case, but they look for a catch word. This was a technology that has nothing to do with the new AI that there is, right? Doesn't have understanding of it. It's just waiting to listen to two words: yes or no. Anything else you say, it cannot comprehend. So companies like Concentrix will be able to use AI, and they'll be able to dramatically cut the cost and the workforce that they have. Yes. And so will the companies save, right? Because Concentrix will pass that savings back to the company. Got it. So I think we've completed the loop now. Do you think this is enough for uh, the podcast today? We are at level four. Oh, we have another level. Yes. Okay. Take level five. Level four, you end up building automations and AI agents, right? Level five is where you basically… whatever problems you have faced so far, all right, you can end up building full-fledged solutions to solve your own problem or solve your company's problems. Okay. Can you give me an example? Yeah. So again, like going back, let's say you initially as Ganesh you were doing research for your content. In level one, what did you learn? You learned that, okay, for research I can use DeepL Research. I can do the research, and then I will probably use a 4.5 or a Claude to write the script. That is what you learned, but you didn't build any agent so far. But there's a human that is doing research, copying that content, putting it on Claude, checking it, adding its own thoughts, and then pulling that out and sending that video uh, to you so that you can shoot it. How can you automate this whole process in one click? So you build a platform or you build a tool without writing a single line of code to solve your problem. So you build a tool. You basically are replacing the toolkit that you had at level zero by building specialized tools in the end for you. But when you've got Zapier, why to build a specialized…? Not everywhere you can do it. Not everywhere you can use a Zapier. An example… in the example that I just told you, you cannot send data across everywhere. You cannot personalize everything everywhere. You cannot execute things in a few cases everywhere. Not everything will have integrations. Not… for example, on Zapier, you want human in the loop, that is you get the research from Perplexity's API and you want to add your own thoughts. There is no interface on Zapier for you to add your thoughts in the middle. There is no human in the loop possible for that. It has to be a platform, and you build your own platform, and you can build your own platform today. Like I said, we spoke about Cursor, we spoke about Lobe, we spoke about Windscribe. You just say what you want. You just say, "Use this AI model, write this prompt for this," but this will require a lot of time, right? Three… I… I built a tool like I told you in 3 hours, but that's because you're… you now, bro. By the end of level five, by the time you get to level five, you'll be an AI journalist. You will be me. What do you mean? No, I'm getting it because even with prompting, I mean, it looks like it's very easy, but once I started to teach people how to prompt, uh, it just gets very, very complicated, and then you… I think it's a function of practice. VIP coding. Have you heard of VIP coding? No. VIP coding is essentially you building products being a non-developer by just talking to AI. It's massive right now. What is it called? Vibe coding? VIB coding. Got it. It's massive right now, and I'll tell you the world we are heading into. Okay. You must have heard of personal computers, which is laptops. That's the biggest rage that happened at some point of time, right? Post dot-com bubble. The biggest rage that will happen right now is going to be personal software. You will build your own software. Build… build your own software for you. I legit have a dashboard for… with all my data metrics in it, with health metrics in it. I built my own software without a single line of… single line of… I don't know how to write code. I just know what I want. I iterated, spoke to it because I understand how these models work. I'm able to talk to it in a language that it understands. It's an pro… process of iteration. Your first product will take you five hours, but the second product will take you three hours. The third product will take you one hour because you will know how to talk to it. That is real learning. Understood. Now, after having this entire conversation, I still believe that there is a huge scope for AI agencies. Like if they just come… I think if they can go from level zero to level five, and then they can come to companies like Things School, we will be able to give them all sorts of tasks, and then they can execute it by going through this entire blueprint. The problem is we are in a market which is supply constraint. There is no constraint of demand. See, the… the thing is for a large company like a Bank of America, HDFC, they have Accenture, Wipro's of the world to go to. Yes, they are slow, but…

They still figure out a solution. Where would a company like you or me go? I cannot go to Accenture; I need a specialized solution company which can operate for me, and that is the gap. But there is no supply. The reason why there is no supply is because 99% of people are sitting on this. I can't believe they're sitting on this. They, most of the people, think like, "Bro, I have done 5 million people; we have taught 5 million people how to use AI. We still see comments on our ads: 'Oh, these guys are going to teach us ChatGPT. We can ask ChatGPT how to teach it, bro.' Like, just come inside; it's you don't even pay; just come inside. I'll ask you the same question towards the end."

And we ask this question in each and every one of our sessions. We've done five million people in the last, I think, 18 months-ish, right? We've asked this question every time: "Did you think AI was this powerful 2 hours before?" People say, "No, I had no freaking idea." True. In fact, bro, I have an idea about it because of Hunch, because he keeps updating me about all of these things, and they sound very scary. That is the reason why I am actively learning; otherwise, I wouldn't be so actively learning about these things. And, uh, after that conversation that I had with you, I started to take my workflows more seriously, and I tried to understand how to use AI over there. So I mean, uh, like I said, I would still consider myself to be an ignorant fool because I'm aware of all the problems, but I'm not acting fast enough. Because had it not been for an AI, I wouldn't have taken AI so seriously.

Number two is, after having a conversation with you, I started to decode all my workflows and I tried to understand where should I use AI to super-optimize them. You get what I'm saying? Usually, you think about optimizing your workflows, but now I am thinking about super-optimizing my workflow. In fact, even while hiring, after I had this conversation with Azar, I tried to understand how many of my team members are genuinely curious. Because what I understood is that if somebody has to be an AI generalist—and you coined it very well—that AI generalist has to be super curious and super aware of all the things that are going on in the market so that they can find AI tools—not master them, but find AI tools—and then master them, and then super-optimize. And super-optimizing is the third step. The first step is finding out, and that requires curiosity. And there are just so many people—I feel so sorry, so sad for them—that they are so stupid that they think AI is all about ChatGPT or generating text. Or generating text. And to a large extent, even I thought the same until a year ago. But then, uh, thankfully I was able to educate myself. And after today's conversation, I again feel what I felt a year ago, which is like an ignorant fool who just doesn't understand how AI operates because it's operating at another level altogether. So thank you so much, man. This was information overload, and I just hope, uh, this is information overload in a good way to everybody who's listening.

Guys, here's a very simple call to action: Regardless of who you are, if you are an entrepreneur, please go back and evaluate all your workflows—like I am saying, all your workflows. For example, in my company, I am evaluating everything from the Excel sheet that my accountant is making all the way to the edits that are going out. We're trying to optimize every single process. So if you're an entrepreneur, please re-evaluate everything. If you are a professional working in a company—no matter how boring your company is, no matter how old you think your company is, no matter how orthodox you think your company is—please embrace AI to an extent where you can become a super employee. Because regardless of whether your company stays or not, you will require a job that requires you to be an AI generalist, L0 to L5. This might sound like great intellectual information, but it is of absolutely no use if you don't apply it. So this is very tedious, and by the look of it, it seems tedious to me, but please do it because it's good for you and it's good for your job. Otherwise, hard times are coming. That's it.

Anything, any message that you would like to give to the audience? No, I think the way to stay ahead in this race is by, uh, how do I put it, building context—a new level of context every day. So by keeping up, you can only stay ahead today by keeping up. And a lot of, like, as we speak about it right, because a lot of it is new for a lot of people, they might be like, "Man, I'm not a developer; I'm not a software engineer; I'm not a technical person. How do I do all of this? It sounds new." That's why it scares you. It's not that hard; you just have to play with it. And once you start playing with it, it is going to be like so exciting for you that you'll wake up every day just thinking about what new has come; I need to explore. Which is—I'll tell you what I wanted to end it with this, right—the best thing that is happening today to all of us is AI; the worst thing that is happening is also AI, true, right? And we can only leverage this by finding the right balance between these two. Very exciting times, very scary times as well, but it's on us on how we control this. Thank you so much, man. This was wonderful. And guys, I'm so sorry if I'm acting like a very amateur, non-technical person, because I am. And, uh, Weber was a very deeply technical person, so this is a conversation I'm not as compared to me, bro. So I think I've just read more about this than you have.

This is basically our phone call, but then recorded. Yeah, that is what it sounds like, where every 10 minutes I go like, "What are you saying, bro? What are you saying?" And then he says, "Bro," um, except all the unfiltered words. So thank you so much for staying with us, and I think this was amazing. We thank you so much; this was wonderful. This felt like a conversation that we would usually have, and it was fun, man. So thank you so much. Awesome. Thank you. [Music]