If This Video Doesn’t Make You a Product Builder, I’ll Delete My Channel

1. 0:00 – 2:49

   Intro

   1. AGAakash Gupta0:00

      LinkedIn eliminated its associate product manager program in favor of an associate product builder program. But is the product builder role real, or is it just hype?

   2. ASAnkit Shukla0:09

      We analyzed twelve thousand five hundred PM job postings and found that the product builder role is very, very real.

   3. AGAakash Gupta0:16

      Who should become a product builder?

   4. ASAnkit Shukla0:18

      Engineers, customer success managers, marketers, salespeople, analysts, product owners, they all have the potential to become the product builder with the right preparation work that they need to do.

   5. AGAakash Gupta0:28

      What do these roles pay?

   6. ASAnkit Shukla0:29

      In the research, we have been able to find that these roles pay fifteen to twenty percent more than their non-AI counterparts in the US and Europe, and they actually pay more than thirty to fifty percent more in India alone. This, I believe, is one of the biggest opportunities of the decade. Every PM should pay attention to this trend. If you don't learn these skills, you might get left behind. But if you learn this skill, you can earn more money and even work for better companies that you always wanted.

   7. AGAakash Gupta0:56

      So what's the roadmap to become a product builder?

   8. ASAnkit Shukla0:58

      Let me give you the step-by-step that just worked for my students to let at Sarvam AI. Step one is...

   9. AGAakash Gupta1:08

      Before we get into today's show, please take a second to check that you're subscribed on YouTube and following on Apple and Spotify podcasts. If you want access to all of my favorite AI tools, I've gotten them to give you an entire year of their paid plans. Check out bundle.aakashg.com for an entire year of Bolt.new, Airtable, Speechify, Descript, Magic Patterns, Linear, Dovetail, Arize, and Mobbin. And now, into today's show. [fire crackling] Maybe my most popular podcast guest ever is Ankit Shukla, former senior product manager turned founder of HelloPM, and he messaged me with this new trend. After studying tons of job postings, he's found that there is a new role out there, the product builder. I, for one, am a little bit skeptical. It might be hype, it might not be real. So we're gonna go through with Ankit today, is this a real role? How much does this role pay? Who can become this role? He's gonna give you the real case studies that have worked for his students to land this role at companies like Meta, Amazon, Google, even Sarvam AI, one of the leading AI companies in India. And we're not just gonna cover how you can land this product builder role. We're gonna cover the fundamentals of being a product builder. We're gonna go through the power framework to think through how to build things, and we're gonna go through a real case study of building a product. So this is an action-packed episode. Not a single minute is wasted. And with that, let's get right into it. Ankit, thanks for being back on the podcast.

   10. ASAnkit Shukla2:47

       Thanks a lot. Thanks for having me here.

2. 2:49 – 4:39

   Is the product builder role real or hype

   1. ASAnkit Shukla2:49

      I'm always excited to have a conversation with you, Aakash.

   2. AGAakash Gupta2:52

      Ankit, a year ago, we created the blockbuster video, "How to Become an AI Product Manager." It feels like the product builder is becoming the new thing. Here we're looking at LinkedIn. They got rid of their associate product manager program. They created this associate product builder program. So what I want to know, Ankit, is, is this product builder role real, or is it hype?

   3. ASAnkit Shukla3:18

      So Aakash, that is actually a billion-dollar question because I get this question from a lot of people. The, the forums are filled with this kind of question. So I thought that rather than giving my opinion about what I think about this role or whether it is hype or it is value or something else out there, I have actually taken the help of Claude and GPT in order to go through actual job descriptions, like more than ten thousand of them, precisely twelve thousand five hundred, in order to understand what is happening with the real jobs, not with what the influencers are saying, what people are mentioning on YouTube, what people are mentioning on LinkedIn, but what do the exact job description of some of the top companies of the world say. And I was able to find that, yes, for more than thirty percent of all the PM jobs, they are asking for some good AI skills, right? And among these AI skills, they are not only asking for just the tools, which is you knowing n8n or you knowing, let's say, some kind of RAG or agentic AI thing out there. They also want you to understand the core of what are the problems that should be solved by AI and that should not be solved by AI. So that judgment is what people are paying for. So if you combine that product judgment along with your understanding of AI and the tools that you can use, I think we, all the PMs or people who want to become PMs, they are sitting on a very big opportunity.

3. 4:39 – 7:08

   What a product builder actually is

   1. AGAakash Gupta4:39

      Okay, so this role is real. What is a product builder? What makes a product builder different from an FDE, from an AI PM? What-- I don't understand specifically who is this person, what's their background, what do they do?

   2. ASAnkit Shukla4:53

      I tell you. So before even talking about any of these terms out there, which are mostly jargons, let us go ahead and talk about the exact core of the problem out there, right? We have seen for the longest period of time that as a product manager or as anyone who is a problem solver in the company, you are operating in one or three of these stages, which is you go ahead and you do the discovery. Discovery basically means you are understanding the customers, doing the market research, understanding the data in order to build a roadmap. Roadmap of what to build, right? This is what you are going to build or your company is going to build. This is the roadmap of the product. After you have built the roadmap, you are going to detail the instructions, and then you are going to work with your development team, design team in order to do the delivery. What is delivery? You will give them the specs, and then they are going to build the products, right? And after you have built, and when they are building it, you have to make sure that you are following up because things can also go maybe, uh, they can be getting delayed, and maybe people will not go ahead and ship them. So after the delivery, you are also going to do the third phase, which is distribution. Distribution basically means you taking them to the customer so that actually go ahead and pay for the product, they go ahead and adopt the product, right? These are the three things that Any product managers has been doing for a longer period of time. Even if you are working in any other role, your role is directly or indirectly related to one or more of these three fields now, right? Now, what used to happen in the industry is that a product manager will invest a lot of time in discovery. And in discovery, they are actually going ahead and doing a lot of research, looking a lot of data, talking to a lot of people, right? And AI has now made them a promise that most of these things you can now do it more efficiently. For example, rather than going ahead and manually browsing the sites of your competitors, you can give them to Claude or to ChatGPT, or maybe you can use this tool called as NotebookLM to give it all the content. It is going to give you, like, the almost perfect research report that you can use to make the decision. That is one small use case that is going to make you-- not only make you productive, but make the quality of your, of your decisions maybe even better. So that is the first reason

4. 7:08 – 10:12

   Ads

   1. ASAnkit Shukla7:08

      why it is evolving.

   2. AGAakash Gupta7:09

      I've been using AI tools to build product lately. Claude Code, Cursor, Lovable, v0. The speed is crazy. Describe a flow, and a few seconds later, you have working UI. But often it still doesn't feel like something a product team would ship. You see the same cards, the same rounded angles, that purple gradient. Very much giving off the feel that I asked an LLM to build me this. The reason is obvious. The AI can write code, but without strong references, it has no clean way to understand what the best shipped products actually do. That's where the Mobbin MCP comes in. Mobbin has a massive hand-curated library of real product screens. Six hundred twenty-one thousand five hundred plus screens. One hundred forty-two thousand two hundred plus flows. All from shipped apps across SaaS, fintech, consumer health, commerce, and productivity. Their MCP server lets your AI tools reference that library directly. Let me show you exactly what I mean. So let's say I'm improving the page for my PM operating system, a product I sell to PMs who want to get up to date on Claude Code fast. Normally, I'd ask Claude Code, "Build me a good page that explains this product to someone and gets them to buy." This is what it build, and this is where the output gets generic. It's not terrible, but it has that built with a generic LLM feel. Now instead, I'm gonna ask it, "Use Mobbin to find how shipped SaaS, productivity, and education products structure paid product pages and onboarding flows. Look for patterns around the hero, proof, pricing, and checkout. Do not copy any one app. Summarize the patterns, then help me redesign this PM OS page." Now you can see the agent is going out and referencing real product screens. You can see it's establishing how different products handle the moment. Concrete promise, proof before features, pricing that feels like the next step. One pattern makes the value prop concrete fast, another puts proof before a feature list, another makes pricing feel like the next natural step. So now as it's redesigning the page, it's using references to identify patterns and not copying any one app. So let's see how Claude Code did. The first version says basically, "Here is a PM OS." The new version says what changes for the PMs after they buy it. The proof is higher. The pricing block actually fits. This is closer to how good product teams work. Study shipped apps, understand the pattern, then make something original. And it's not just for generation. You can use Mobbin MCP for product audit, PRDs with real examples, or giving your AI agents examples grounded in real software. The point is not to clone anyone's work, it's to study the patterns behind good decisions. Good product builders study good products. Mobbin MCP gives your AI agent better references to work from. Mobbin MCP is on beta for all paid Mobbin plans. Setup took me under a minute, and it works with Claude Code, Cursor, Lovable, and any other vibe coding tool. If you're building product with AI, check out Mobbin MCP. You get a full year of the paid plan used

5. 10:12 – 12:03

   Why delivery was always the bottleneck

   1. AGAakash Gupta10:12

      from my bundle, which is just a one-year newsletter subscription, or you can check out the description URL.

   2. ASAnkit Shukla10:19

      The bigger reason is the block of the delivery. What used to happen was the major block in a product company was mostly the delivery, which is because of the nature of coding and building products, it used to take a lot of time for developers to build something. And because of that, there has to be a lot of gatekeeping here, which is every idea that you can discover, you are not going to put it into delivery because your teams cannot build it. And because of which, what used to happen was, although it was a good move that you are going to play on your judgment, but it also led to a different thing, which is all the successful product managers in the world will agree to the fact that it is almost impossible for you to hundred percent say with confidence that this is idea, this idea is going to work, right? And because of which, what people used to do was because they have to be confident about their idea, ideas, they need to get clarity before they are building something, they will take some very safe products only to the delivery. And because of which, they will take some mediocre decisions or maybe some coward decisions, right? So now, the promise of AI is that now you can do delivery faster. Your engineers who are AI native, they can use tools such as Cursor, such as Claude Code in order to do things faster. They can test it, they can deploy, and maybe create things at scale. So now, this has been reducing. And now, understand delivery is not only a separate part. As a product manager, when you are doing testing of your product or when you are doing validated of your-- validation of your idea or whenever you are doing experimentation, all of these are maybe interchangeable terms. You can also leverage AI in order to do the experiments by yourself. You do not need to always go to the engineering team, get their bandwidth in order to do the experiments. You can do it by yourself. And I'm going to show you in this video later on that how you can actually go ahead and do all of these things. No matter how the complex the product is, you can at least get started in order to test it with the customers.

6. 12:03 – 14:26

   What product builder roles pay in the US and India

   1. AGAakash Gupta12:03

      So how much do these product builder roles pay? Should people really be pursuing these?

   2. ASAnkit Shukla12:09

      Yeah, I think that is, that is like a going to be an interesting question. So I actually did the research on the same as well, and we were able to find that AI PMs and AI-related roles in India and across the world are willing to pay a premium of more than twenty percent as compared to the traditional counterparts. So if you are a PM, you might be paid X, but if you go ahead and get the AI skills, you might get maybe one point two X and maybe up to one point eight or two X as well.

   3. AGAakash Gupta12:36

      What do those numbers actually look like in absolute terms in US and India?

   4. ASAnkit Shukla12:40

      Yeah. So to do the comparison, for example, in the US, we have seen that 195K is the median salary that is crawled across these 12,300 jobs. And then we have been able to see that if you are senior, for example, if you have some years, no matter as a senior, as a, as a, as a traditional PM, as an AI PM, if you are able to get these AI skills, you have the potential to make as much as 250 to 560K, depending on the, uh, let's say, depending on the companies that you're working at, your own background, plus how good you are in terms of delivering the products with AI.

   5. AGAakash Gupta13:17

      And in India?

   6. ASAnkit Shukla13:18

      Yes. And if I go ahead and go to India, and, and like just before moving to India, you can also see that across the levels, right? So if you're just starting out, maybe you'll start from a 120K base salary, which is not bad if you're just acquiring the AI skills. And if once you are into the senior roles, that is where your judgment and your AI skills truly give the leverage to the company and to your career out there, right? So it can reach maybe up to 350, 340K, and total compensation at the top labs can maybe reach 800K as well. And we have seen, like the recent news, that Netflix has advertised a AI product manager job of maybe up to 900K as well, right? And if I go ahead and talk about India, you'll be able to see that in India, these have been the salaries, right? If you are a fresher looking to get into product management, you know some bit about AI. If you have some good projects, you can make maybe a starting salary from maybe 12 to 16 LPA, right? Similarly, for two years experience, it is 22 LPA, and maybe some people who are already being PM for more than five years, if you just add those right AI skills, build a strong portfolio in order to showcase your work, I am sure you can get these kind of salaries with the right kind of

7. 14:26 – 20:44

   The power framework, and why tools come last

   1. ASAnkit Shukla14:26

      approach.

   2. AGAakash Gupta14:26

      So there's a real premium to learning this product builder skill set. What's the right framework to thinking about how to be a product builder?

   3. ASAnkit Shukla14:37

      So now everyone in the world knows that if they are able to learn AI, they should be able to add this much needed leverage and maybe seize this opportunity. But the issue is that there is so much information everywhere that people, whenever they start, they either get anxious or they get too excited that they go ahead and start going into depth of the wrong things. So in order to solve that problem, I am giving you a crystal clear framework that will reduce all of this anxiety, that will rem-- eliminate all of this confusion. You just have to follow this. I call this the power framework. If you go ahead and practice it in your own company, I am sure you should be able to confidently talk about AI projects. You should be able to build them, and then you should be able to mark it down very clearly and very confidently in your resumes, and get that leverage that we have just shown you on the screen in terms of the real salary numbers. The power framework, which is going to help you leverage the AI. Now understand, I am not talking about the adoption of AI. Many companies are still struggling with the adoption, as in their people are not using AI, they're not able to find the use cases, or maybe they're apprehensive about their job. But I'm not going to talk about adoption. I'm exactly talking about the advantage, which is companies who have actually gone-- who are willing to adopt AI or who have already adopted AI, how they can actually get the ROI from the same. That is what this framework is all about. Yes. Now, I'll go ahead and maybe give you a small walkthrough of what happens in the power framework. And this f- framework can be utilized by even if you are an entrepreneur, if you are a small company, bigger company, anyone. If you want to truly leverage the power of AI, for example, your CEO might be saying that everything can be done with AI, but there's a disconnect between what you think as an employee and maybe what your CEO thinks. So this is going to bridge that particular gap, which is, this is going to actually give you the measurable ROI. This is the framework that I have built. The first P, it stands for the possibilities. Now, traditionally in product management, we always started with the problems. But AI is different. AI is different in the case that there were some problems that we were not even thinking about as problems. We were thinking of them as harsh realities, or we were not even thinking about solving them because we did not have seen a technology like AI. So starting with problem is good, but here, if we go ahead and always start with the problem in the case of AI, you might not be even looking at the whole picture. So here what we have done is, the first step in the power framework, it stand for possibilities, means you should do a very detailed research for your company in order to understand what are the possibilities with AI, which is what are the things that AI can do? For example, AI can right now go ahead and understand, transform, and generate task-- text. It can go ahead and look at a lot of summaries of the meetings, create-- look at a lot of meeting transcripts and generate the summaries. It can generate code. It can generate content. So UTG is a framework that I use, which is understand, transform, and generate, is the three things that AI can do. Now, I would also recommend everyone to go to their competitors, look at their industry, maybe look at the nearby industry, and try to understand what are the different kind of people doing with AI. So that should open up your mind that, yes, these are the possible things that I can go ahead and do with AI. So this is the step number one, understand the possibilities. And every company who's serious about AI, they should create their own database of possibilities by researching across and getting that horizontal exposure among how different companies are leveraging AI. Make sense?

   4. AGAakash Gupta18:09

      Got it.

   5. ASAnkit Shukla18:10

      Yes. And one other proxy to look at this, as in how companies are leveraging AI is, that you can go to the customer stories or testimonial pages of these frontier AI labs, such as Anthropic or DeepSeek or Moonshot or OpenAI, and you can understand their customer stories or testimonials. And then you'll understand that how different people in the world are using AI, and that should also give you a lot of other possibilities as well. So that is the first part. And after you understand the possibilities, when you look at the problems in your company, you'll start thinking that, "Yes, this is how I can go ahead and maybe approach this problem with AI." So this is the first P Which stands for possibilities. After possibilities, we have the O, which stands for opportunities. Now, in possibilities, we were looking at the world. What is possible with AI? Now, in the opportunities, we go ahead and look at our company. What are the different opportunities or problems in the company where we can go ahead and actually use AI or maybe even not use AI in order to solve that particular problem? For example, if I look at a product manager's job, I can break it down into multiple parts. For example, whenever I do discovery as a product manager, I have to do multiple things. For example, in discovery, first of all, I have to do the research. It could be a customer research or a competitive research. Whenever I'm doing the user research, I have to draft interview questions, and many product managers I have seen make a mistake that they ask very leading questions in the interviews, right? Similarly, you are going to build a lot of artifacts as a product manager, right? And these things used to take a lot of time, not the thinking part, but the documentation part. So now AI can go ahead and help you. So these are the opportunities for you. You think that right now, in order to build a product, a product manager has to do the research worth of months. And in those months, they are going ahead and researching multiple companies, talking to multiple people. Can AI go ahead and reduce that? So this is the opportunity part for you. So what you need to do is talk to the people in your team, understand the whole business structure, and try to understand that what are the ways, what are the places where if you implement AI, you should be able to get some or the other kind of ROI. And now never start with only one use case. So many people make a mistake that they only think at one-- about one thing, and they start implementing it rather than looking at the whole picture. So for you as a product manager, one skill that is very important is about judgment of what problem to pick in order to solve with AI. If you are just taking some small redundant problem which are not very frequently occurring, you can just go ahead and build it with AI, but you'll not be able to get the ROI out there. Make sense?

   6. AGAakash Gupta20:43

      Yep.

8. 20:44 – 26:48

   Workflows, and scaling empathy from 200 transcripts

   1. ASAnkit Shukla20:44

      And after the opportunity part, we have the third part. Now, this is where you have to put a lot of attention as a product manager. The W stands for the workflow. Now, because you really want to solve the problems, you cannot afford to be very high level. You cannot just say that I can just go ahead and improve discovery or user interviews with my AI. What you need to do is you need to go deeper. You need to understand the workflows. For example, when I talk about research, I know that in order to take better decisions, I should always make sure that my customer support tickets are being considered. I am reading them, and I'm making sure that they are also giving me some inputs about the-- my roadmap. I'm looking at all my reviews, which are coming on Google Play or maybe some website like g2.com if I'm a B2B company. I should be able to understand them. Whenever my sprint reviews are happening, whenever my leaders are going ahead and conducting meetings about the product, I should be able to get all of these inputs in order to decide what to build. So now you have to understand the complete workflow. As in, if this is the opportunity, what are people doing right now in order to do their work? And you can, not only for PM job, you can go ahead and look at it-- look at any job in your company which people are doing in order to make them productive, right? So the third part is the workflow, and here you have to conduct maybe a lot of internal interviews with your stakeholders to understand what is it that you can go ahead and improve with the help of AI. Here also, I have one-- like two approaches to think about. In the workflows, you have to look at two parts. One is you should look at opportunities in the workflow which are optimization opportunities, and second is the innovation opportunities. By optimization, I mean initially you used to spend two, three hours every week in order to do this. Now maybe you'll take one or two hours, so it is optimizing on time. But innovation is something that initially you are not even thinking about this process. So I'll give you an example that at HelloPM, we keep on doing a lot of experimentation with our content. We do a lot of experimentation with how we are delivering the content to the students, and we are also doing a lot of experimentation with how the LMS is going to look and how the website is going to go ahead and look, the landing page, right? And initially, what used to happen was that I'll take the decision or my people will take the decision mostly with the limited empathy that they have. Because we have a lot of people in the program, it is almost impossible to have empathy with everyone. So what we have done is in the first call when anyone joins the program, we ask them to introduce themself. We have taken about 200 of these calls with all the people out there, and then we have gone ahead and feed it into Claude, and then we created user persona document from the same. So I put-- Like I created a new folder, put all the transcript over there, and then I asked that, "Can you go ahead and create some personas which are representative of the people who are introducing themselves?" Because they have given all their information. After that, whenever I take a decision, I ask my Claude that I'm looking to take this decision. "Can you go ahead and walk me through-- Can you go ahead and walk that decision through all the personas that are mentioned in this folder?" And then Claude is able to give me that with which persona it is going to, uh, do better and where I should go ahead and take in that particular decision. So I now, I can go ahead and create empathy at scale with the help of this small system that we have created.

   2. AGAakash Gupta24:00

      Mm-hmm.

   3. ASAnkit Shukla24:01

      So that is the innovation use case.

   4. AGAakash Gupta24:03

      Basically, creating personas from your real transcripts enables you to scale your empathy.

   5. ASAnkit Shukla24:09

      You are able to observe that so far we have talked about possibilities, opportunities, and workflows. But when you talk about AI automation or AI workflow leverage or anything about AI, you will observe that a lot of people actually start with either n8n or Claude or something else out there. But if you observe here, we have not even taken the name of the tool till here, right? And that is the s-- And, and that is something that should be the biggest takeaway from this particular masterclass, right? Which is, if you really want to create ROI, please do not start with engineering. So Eesh here starts with Engineering, which is how can I go ahead and create these workflows in n8n, in Make, in Zapier, Google Gems, Custom GPT, or maybe using agentic AI solutions such as Claude and all, right? Many people make a mistake that they always start with this engineering part, and then they try to retrofit everything over there, right? But my framework is, in the power framework, always remember POW comes after- comes before the engineering part, right? If you do this, there are very high likely chances that you'll not go ahead and make a mistake of putting your AI SLOP everywhere. If the engineering is coming after the POW, very high likely chances that you will not be able to reduce AI SLOP because you are actually going ahead and starting with the right kind of use cases. And across the 12,000 job description that we have seen, we have also observed that RAG, agentic AI, and prompt engineering, they were not the number one skill. The number one skill was identifying the right use case for AI. That is the thing that people are willing to pay you the most for, because the engineering part can actually be taken care by the AI.

   6. AGAakash Gupta25:54

      Exactly. That part's a lot easier now, and so the real alpha is in the POW.

   7. ASAnkit Shukla26:00

      Yes, and I'm going to show you, like in just a moment after we cover the engineering and the R part, I'm also going to show you like one product that we have like just developed just few days before, and that's a production-grade product, and how easy it was to go ahead and develop that, right? So now talking about the engineering part. Now this is a very interesting part. Everyone is interested in the same, so I'll go ahead and maybe expand this a bit. The engineering means that now that you have certain kind of use cases, you understand the problem, you understand the gaps in the workflows, you know what you are going to build, now the part is: how are you going to build this? And many people are confused about the right set of tools that they need to use. Somebody talks about Claude Code, somebody talks about Codex, somebody talks about Google Gem, somebody talks about n8n, right? So what I'm going to do is I'm going to try to give a simpler framework to understand all of this and maybe put a method to this madness.

9. 26:48 – 34:06

   The five levels of AI engineering

   1. ASAnkit Shukla26:48

      I start with the levels, right? So for me, the level zero is you are going ahead and just prompting your way through. You have installed, you are going to chatgpt.com or claude.com, and then you are going ahead and using their chatbot as they are there, right? It will give you certain leverage, and for many use cases that is good, but it will not be able to solve a recurring use case very efficiently for you. You are wasting your time out there, right, by prompting it again and again. So the first level is prompting. Almost, I think, 100% of people are doing that. The level one that I give is for creating some reusable prompts, which is you can either use something called as Google Gems, and I believe you have a fantastic detailed video about Google Gems on your channel, so people can go ahead and check that out. And second is you can also go ahead and create these custom GPTs, right? For example, if you want to create a PRD, you can just create a Google Gem with all the templates that you have, and then you can go ahead and always whenever you want to create a PRD, you can go to that Gem, you can give your idea, and then it should be able to go ahead and generate a PRD for you. Similarly, you can also go ahead and create a resume optimizer. So for many people, we suggest that you can take a job description, you can take your resume, create a Google Gem, and then you should be able to iterate your resume as per any job description at scale. So the second part is you are taking that prompt, like a recurring prompt, you are improving it, and you are putting it into a reusable product or reusable structure or a shell such as Google Gems or a custom GPT. But there is a issue with Gems or custom GPT, which is that you always have to switch between the windows. The context is always switching. If you want to create a PRD with a Gem, you have to go to that particular window in order to go ahead and do that. You have to click on that Gem. So for that we have level two, which is the skills. Skills part and the connector part. So here, Codex and Claude Code comes where you can go ahead and create the skills. Now, the good part with the skills is-- So the problem with Gems was that if you go ahead and try to put a lot of instructions in your Gems, it is going to run out of context. Because if you are having a PRD skill, if you are having a Gem to write a PRD and also to write your resume, there can be very long instruction and it is going to run out of context. Your model will either hallucinate or it is going to give you the bad output. Now, Anthropic has solved this very beautifully with the skills. What they do is they use something called as a progressive disclosure. What actually means is that you can keep on chatting in the same window. If you have added those instructions, skills are nothing but instructions to do a particular task. If you have added those instructions, you can just go ahead and Claude will identify from your chat, like intelligently, that whether I need this skill or not. So your context is saved, and the skill is only loaded when it thinks that, "Yes, for this use case, I should actually go ahead and use a particular skill." So that is the third-- that is the second level. Now, skills are not rocket science. This is a trick that I tell everyone who wants to create the skills. Don't get overwhelmed by them. Open your Claude, do the task that you want to do, and when you have-- when you are done with the task, when you are happy with the results, just ask Claude that please go ahead and create a skill out of the same. And then you don't have to go ahead and think much about that workflow, and Claude is going to go ahead and tell you. And after, whenever you are using that skill, if you learn something you think that you have prompted more, like more than the skill, you can ask Claude to again update the skill based on what additional prompts that you have given. That creates a scalable system, and that will reduce the fear that you need all the skills ready before you can go ahead and do something.

   2. AGAakash Gupta30:24

      Yeah. And this level is really when I think you start to see some huge ROI. Like Gems and custom GPT, I feel like they're almost basic at this point. This is like the real unlock.

   3. ASAnkit Shukla30:38

      So when the skills connect with the connector. If I want to show you guys like what are skills and how you can go ahead and add them, yes, you can just go ahead and click on your Customize option in your Claude Code. And understand the skills are not only limited to Claude right now. Every AI tool is supporting the skill. That has become a de facto standard in the industry. So click on Customize. You can add the skills. As I've told you, the best way to create the skills for your work is do the work with Claude, and in the end, ask it to create a skill. That is going to go ahead and make sure that next time you don't have to repeat your instructions. And the second part is for the connectors. Now, this is very important. For example, I can go ahead and connect my Gmail, my Slack, and Google Calendar. For a product manager, a very interesting use case is that I can connect my analytics such as PostHog, Mixpanel, Amplitude, and I can set up an automation that every day in the morning at 10:00 a.m., tell me what has been happening with my product. And I can give it certain metrics, and it is going to automatically send me either the messages, or it can also go ahead and trigger an email for you. Understand, this is a big productivity unlock because I have seen many product managers are struggling with their dashboard every day in the morning in order to log in and check what is happening. And it is going to give you a lot of data in the natural language, so you can go ahead and just share it with your team out there before the sprint.

   4. AGAakash Gupta31:51

      Yes. GitHub integration, Gmail, Google Calendar, Slack, Amplitude, whatever your analytics is, Tableau, those all are pretty much essential connectors now, right?

   5. ASAnkit Shukla32:03

      Now that we have understood the level two, after level two, let's say if you want more granular control, then you can go to level three. Now, for me, level three is, I would say, vibe coding. If there are a few things that, let's say, you want to build and you are not able to get an out-of-the-box solution with connectors, scales, or if you want to give an interface to your users out there where you do not want to go ahead and log into a Claude or something, you can do vibe coding. And my favorite platform to do vibe coding is actually a free one, which is Google AI Studio, where you can go, you can go ahead and build almost all the simple products that you can think about. And this is going to, let's say, give you more autonomy and more control over what you are going to do in the other parts there, right? But you don't have to stop at level number three. If you are looking to automate the workflows in your company, and if these things are not suiting you or maybe there are some teams where you do not-- let's say there are some tasks that are going to happen at the back end, for example. Whenever a lead comes to your website or your, uh, uh, or your product, you want to make sure that you are doing the research on that, you are trying to understand how important that lead is, and you want to assign it with the right kind of salespeople or the telecaller out there. So for this, maybe the skills connection and everything, you do not want to give access to this to your salesperson. So what you can do is you can simply use an AI workflow tool such as n8n or Make. Right? And guys, understand these days things have become so simpler that you do not have to even understand that what are the 100 kind of nodes that n8n or Make has, because many people get overwhelmed by the same. These platforms have something called as an AI assistant, where you just give it the prompt, it is going to create the whole workflow for you. You are going to ask it to test so that you are able to go ahead and build something from scratch, even without knowing what exactly these nodes are. But this gives you more control. You can go ahead and check the whole process, which unfortunately, vibe coding does not give you.

   6. AGAakash Gupta34:04

      Is there a level five?

10. 34:06 – 36:39

    Claude Code vs Codex, debate settled

    1. ASAnkit Shukla34:06

       Yes, and this is what I'm going to show you as well. Right? So level five is a level where you take the most control. This is where the production-grade applications are built, and this is where the future is going to go. Right? This is what the future is. And this is actually going ahead and using tools such as Cursor, such as MCPs, which are able to connect you with maybe AWS or Vercel of the world, and then using parallelly with maybe you can use your tool of choice, which is Claude Code or Codex. And also, guys, one very important thing. Many people ask me that Claude Code is good or Codex is good. Let me tell you very honestly, it does not matter for your use case. Unless you are solving a PhD-level mathematical problem, you have to think about whether I should do this with Codex or Claude Code. We have gone ahead because it is our job to teach people and give them some kind of benchmarks. We have taken all the common use cases, which is building an e-commerce website, building a social commerce website, building a social network website, building a gaming platform. We have tested these models like Codex, like both the platforms, Codex and Claude Code and Cursor, plus the DeepSeek Harness, and all of them were able to behave almost similarly. There were some token issues, there were some, let's say, we have to, uh, uh, give some more prompts to some AI, but eventually everyone was able to build a functional website. So I think we should end this debate. Like for 90% of the use cases, it does not matter if you are using Claude Code or the Codex out there. Even the very simple model Composer from Groq or Cursor actually goes ahead and does the better job.

    2. AGAakash Gupta35:36

       But I assume there's even a level six, right? Within this, you could go into loop and graph engineering, and you could, you could just keep going down and down this rabbit hole.

    3. ASAnkit Shukla35:46

       Yes, yes, yes, yes, yes. But I'll tell you, you should actually stop here. After this, you should understand the power of AI. So now, what is power of AI? Understand that you have to understand a mental model. In order to go ahead and build any product, you need certain kind of code. And if AI is able to go ahead and code that, it is able to underst- run that code, it is able to understand the results, so why can't you go ahead and give all the permission to the AI in order to do that? Why don't you leverage the mind of AI in order not just to plan, not just to execute, but also to iterate? That is where you get into harness design, and that is where you get into the agentic AI, or maybe a better exa- a better word would be loop engineering, where the AI decides all the three parts, which is making sure that it is able to plan, it is able to execute, and then it is able to evaluate whatever it has written. And if it is right, it

11. 36:39 – 46:19

    Case study, replacing a $550 a month email platform

    1. ASAnkit Shukla36:39

       is okay. Otherwise, it is going to again plan and repeat this. Right? Let me now go ahead and maybe let's get out of theory. Let me go ahead and show you like a real product that we have built, right? So let me start with the problem statement. So at HelloPM, we have a email list of almost 120K subscribers that we have built in the last three years. Now, in order to send these emails, we use a service from Amazon, which is a popular service, which is Amazon Simple Email Service, SES. And for sending maybe a one million emails every month, we have to pay them just one hundred or one twenty dollars. But the problem is that SES is actually an API. They don't have an interface where we can go ahead and create the campaigns, send the email, and do a lot of things out there. They do not give us the control. That is only an API. So we have taken a third-party platform which enables us to create the campaigns, manage the users, make sure that we are able to track everything and everything. And then the day from where we have started sending one million emails, or maybe ten emails per month or five emails per month to our users, we are able to understand that our costs are rising. So last month only, we are able to see that we have paid almost [claps] five fifty or seven hundred dollars to that particular platform over and above the Amazon SES. So then I thought that why don't we go ahead and try to maybe build something of our own, right? And I took a challenge that although I go ahead and know about maybe Python, I know about using PHP, I know about using Java, but I'm not going to use these languages because I want to try if I can go ahead and build it in something that I don't know and I'm able to manage it at a production level. So I took a challenge that I'm going to use completely modern stack, which is Node.js and TypeScript, and maybe I'm going to deploy it on the AWS using all the native services that they have, right? And I'm not going to touch the dashboard of the AWS because in AWS you have to figure out which thing is where and then how you are going to deploy it, and then how you are going to take the backups and all. So that is complex. So I-I thought that maybe I know some of it, but now I'm going to assume that I do not know anything. So what I did was, this is the exact process that I have done, right? So I started with a very basic document. Understand, although I have written-- I have created like the most famous video around how to create PRDs, but I did not follow that. I just did it like a common man, right? So I understood what are the features that I need. So I have written this very simple, like a very layman term specs. Specs is add the leads, add the fields of the lead, add text to the leads, and then import the MailWrestler or the tools that we are using. We can export the data from there and then import into a new system. And I have mentioned things in a very simplistic way, AB testing segments and all of these things. After that, I have taken this like at a very, very basic level, and then I have gone ahead and gave it to ChatGPT. You can also use Claude. I don't think we have to debate about what model to use. All the models-- This is very basic work. The model should be able to do this, right? So I just created a simple prompt. I have not used any prompt engineering there. I want it to be as much for common man as possible, right? So help me create a concise but complete, straight to the point specs document from the features of an emailing system. I am trying to replace it with a in-house tool, and I will import leads from the MailWrestler export, right? I'll be using Claude Code to develop this completely. Give me solid specs, tech recommendations which I can give to Claude Code to build, deploy and manage this, right? And then I have gone ahead and copied this all. After that, it has gone ahead and created a doc for me, but I did not like doc, so I asked it to please write it into a markdown file, right, and explain the features more. And then it created a document for me, right? So now I have a spec document which is well written by this. Understand, initially what you used to do was we used to go ahead and write complete specs that we are sure of, and then we'll give it to Claude. But right now the level is that you can go ahead and write at high level. You can ask Claude or GPT to improve it, and then you can read it and maybe improve it. It allows you to comment everywhere that you want to. Right? So this is what it has created. I have read all of this. After that, what I have done is I just have gone ahead to my computer, created a new folder called as Postbox. I am naming this tool as Postbox. I pasted this product there. I pasted this complete spec file there and this is what I have done. Now I'm going to show you my whole Claude like chat for this particular product, right? And this is-- understand this is just from yesterday, right? Which is like I-I'm not sure even twenty-four hours ago. So look at this. How-- What are the exact prompt that I have used in Claude in order to build something? But before I could show you this, let me show you what were we able to build, right? So we wanted to build email platform which is built like a email marketing platform that is built on top of Amazon's SES. Or you can plug and play any kind of email sending API out there. Right? Now, I had some features that I had in mind, so I've gone ahead and documented them like in a very, very simplistic term. Then I asked GPT in order to expand into the specs, and then I created a folder in my computer. Claude Code was already installed. I opened Claude Code. I opened that folder with the specs already there. Then I ask this to Claude Code. This is what is written in the project. Understand this project and tell me what's the best for you to implement it end-to-end. Feel free to make changes in specs if you like to feel it, right. And then I have put it into the plan mode. And then it has done everything, and then it has created a plan for me. It asked me few questions. After that, it has created... Then I also asked it that I do not know about AWS, so help me. How can I go, go ahead and do this? So it asked me that you can just go ahead and pull in the MCP out there, and then I'll be able to take care of AWS as well. So I do not have to even go to AWS. It just pulled the MCP out there, and then it told me how to get the credentials. I just copied them, right? How to get the credential? Just go to AWS, click on Identity. It will give you the API keys. That's it, right? And then I'll mention not in the chat, but maybe I'll create a folder out there. If I'm being a bit quick here, understand everything that I've been doing has been instructed by Claude Code. So if you go ahead and try the same prompts, I'm sure you should be able to figure out your way out there. So I have gone ahead and created a file and put it all the details out there, and then it started doing this. So it has created this plan. You don't need to read this plan. If you want to, you can, but it has divided the whole plan into nine stages, right? I gave it approval stages after stages after it has tested. But after some period of time, like it has tested everything, I was also testing along the way, and eventually what we have done is- I have done the phase two. The slices are the phases. Phase one, phase two, and eventually I also ask it to go with slice three, and then it has gone slice three, go with slice four, and then eventually look at this. Now I was fed up. It was almost like 20 hours from now, so which was already 12 or 1 in the night. So I asked it that I'm sleeping now for about six hours. Continue doing one slice after the other without waiting for more. Make sure you test every slice before going to the next one. See you in the morning with the completed work. I can just come and configure all the environment variables.

    2. AGAakash Gupta43:51

       And for people who don't know environment variables, these are basically like secret API keys.

    3. ASAnkit Shukla43:55

       Yes. Yes. Yes. Correct. So you cannot just go ahead and give your secrets away in this chat. So Claude is going to tell you how to give you that, right? So if someone does not knows about A and B, my recommendation is just go ahead and ask Claude. It is going to tell you what it is, right? Because I have approached this projects, project almost as a layman so that I can go ahead and teach it out there, right? But, uh, understand that I try to make myself as a layman, but I had the experience in software development already, so I had to put a lot of work over there. So after this, I have gone ahead and it worked for a few hours maybe. And then after some hours it gave me a message. Templates were created, tools failed, everything is happening. Retry, retry, retry. Nine sent. Commands. Commands nine done. Now writing this. Yes. And then I asked it-- Now this is important. We-- Understand the most problem with people who are not able to try AI is they are generally anxious that what is AI going to do? How am I going to deploy it? How am I going to manage this, right? So I asked it that once everything is done, and this I did before I sleep, once everything is done, create a detailed document for me at deploychocolate.md with all the instruction to deploy this on production AWS with step-by-step instructions, right? So I can just read it and then I can go ahead and deploy this. After that, it has done everything, and then this is a message: "Good morning. All slices are implemented, tested and committed. The local stack is running," and everything, right? So now it has done everything. I woke up in the morning. I trust-- I tested everything, right? Now everything was done locally. So I asked it that I have installed AWS MCP. I have given my credential. Why don't you go ahead and deploy this live? And then it has gone ahead and done the multiple things. Take 15, 20 minutes. Asked me to put all the credentials in the respective file. And then as a layman, I did not know how to get these credentials, right? So it also-- The only word that I knew was that I need to use AWS, Amazon Web Services, which is where I'm going to deploy. For everything else, I, I became like a person who does not know anything, right? So it gave me all the instructions step by step. Tell me how-- Tell me step by step how do I these-- get these creds. And then it asked me to log in there and put all the information there, right? After that, I copy pasted the information that I told, and it was able to give me the information, right? And after this, once it was working and live,

12. 46:19 – 49:35

    Auditing what the agent spends

    1. ASAnkit Shukla46:19

       then what I did was-- So now I asked it that what are going to be my costs. So it also asked me that, yes, for your new stack, this is going to be your cost. So now this is where you need to pay some attention that already I was spending $550 and someone else was managing the pain for me. Now it came up with a stack which is almost 420. So now this is where you need to go ahead and pay some attention and understand if this is actually a correct decision, right? Because I knew already about AWS, I understood that some decision that Claude has taken are not appropriate. For example, you do not need this large machine for this kind of use case. So I asked it to update this. Can't we do with a smaller EC2? Also, what's the purpose of Fargate? Right, we can do it with EC2 under-- like with an EC2 as well, right? And after that, it has gone ahead and given me a new stack, 110, and then I was able to deploy it, right? And after that, understand, because this is software, there are going to be some errors. Some errors came, it was able to correct it, and then I was able to test something, and eventually we were able to go ahead and build this. And once we have built this, this is a better interface than the product that we were using, not only in terms of like how it looks, but also how it functions, right? So we have already exported almost all of our leads to this database now, and we have tested few campaigns as well, and we have flushed some data as well. Now you can see it has almost all the functionalities, which is different leads. We-- I can create different kind of segments, the templates, the campaigns, the automations like n8n and Zapier, and reports and documentations and settings and everything. A very important part is the documentation. So after I have gone ahead and, uh, built the product, I also ask Claude that, let's say if my team wants to operate this, can you go ahead and understand all the product once again and create the manual for my team? Just I can go, go ahead and hand it over to my team, and then I can just hand it over to my team, and team should be able to understand how to operate this. Similarly, I have created a documentation for the technical guide. So let's say tomorrow I want to hire a engineer who's going to dedicatedly look at the same. I can just give them the documentation which mentions about all the decisions, all the tech stack and everything. Even if I want to port it from maybe Claude Code to Codex, I can just give it this information and the code base, and then it should be able to understand the same, right? So now this is what we have built. I did not want to show you that, like how do you use AWS or how do you use Claude Code or how do you use let's say prompt, these kind of things. The purpose of this whole exercise was to prove that even with-- if you know how to go ahead and give just the intra-- instructions in the common way as you talk to the developers or talk to maybe even non-technical people out there, if you talk to Claude in that way or Codex in that way, you should be able to create some very good applications. Just one thing that I have gone ahead and done some security testing before I could go ahead and test, like trust what Code-- Claude has built. But in your case, if you are building something for production for your customers, make sure that you are having some clauses for protecting your data. And then you are also having, let's say, a final green flag from your engineering team before you go ahead and deploy this onto production. So I hope this whole setup Is going to give a lot of confidence to people that even if you do not know anything about it, you are only maybe few prompts away, or you are just away by few, let's say, instruction that you want to give it to Claude, and then Claude or Codex, it is going to surprise you by the output that it is going to give you, as I was surprised by doing all of these things.

13. 49:35 – 54:18

    Why the last 20% is the new PM job

    1. AGAakash Gupta49:35

       And I think the most critical thing is that after you got this initial, what I'd call really prototype, there was a lot of bug fixing, going through steps, finding errors. That last 20%, that's where the real work is coming now, because Claude can take you 80% of the way there, and it basically did most of that overnight [chuckles] while you were sleeping. But you need to apply the last 20%, and in many companies, the PM won't be the one doing that last 20%, right? The PM will create this type of prototype because, as we've shown, it's pretty easy to create. Then an engineer developer will pick it up, and that's really the role split I've been seeing most commonly. Is that what you've been seeing as well?

    2. ASAnkit Shukla50:27

       Yes, correct. So I think that's a very good insight that the last few percentage, it could be ten or it could be 20%, it has become more critical now. But understand the last 20%, you only have to think about when you are knowing that you are building something which is substantial, where 80% is more important, right? So many people, what they used to do was they would spend a lot of time in order to get something out of the place, like in order to get something out in the market, just to find that maybe it will not work. But now you can go ahead and build something maybe with some rough edges here and there, and then you can show it to people, and then you'll get some kind of feedback. That will go ahead and help you iterate faster in the market, because I don't think there is any better mode than the speed of your execution.

    3. AGAakash Gupta51:08

       And so does every PM need to learn this skill set, or are there certain types of PMs where this doesn't apply?

    4. ASAnkit Shukla51:14

       Yeah, so I'll tell you. So what happens is I look at the use cases of AI in a PM's life in three ways. Either you can make your team more effective, so internal use cases, optimizing the workflows for them. For example, we were working with a company called as Kist, which is IPO-bound in India, and ClearTax and a couple of other fintechs in India. We were able to understand, let's say, when a fintech gives a loan to someone, their salespeople or their underwriting team has to understand their civil score and do a lot of manual checks in order to get the loan approved, and it used to take maybe four or five hours from every individual. Now, as a PM who understands that bottleneck, understand how AI operates, you can make your internal team more efficient. So now they have built some kind of, let's say, small tools where AI can go ahead and help them with maybe 80% of the work. Now, if they used to take three, four hours in order to do like a credit underwriting, maybe they are going to take maybe 20 or 30 minutes for one file out there, right? So that is the first use case, which is if you know about executing things or you know about the potential of AI and maybe using some of the tools, you can first give a leverage to your internal team, which is a lot of productivity for your team and achieving the outcomes. Second is you can go ahead and do a lot of things for you. You can increase your own productivity by doing better research, running multiple simulations. So I believe that simulation is a very important thing for a product manager to do, which is you running your decisions across multiple simulations even before sending it to the users, so that you are able to understand that what is going to happen, what are going to be the second order effects of this particular decision out there, right? So that is the second part. It can make you a lot more productive. And the third part, which is the ultimate part, I do not think every product manager has a use case for the same, which is how do you utilize AI in order to serve your customers better? Now, there are also two trees, like two branches to this. One is you are building an AI native product like a, like a Cursor or a Gamma or a Granula of the world, where you understand how the LLMs work. You are going to build products on top of the same, and then your engineers are going to help you out. The second way is that you can do some common things for your users. For example, every company in the world, I believe that they can have a customer support chatbot, which is powered by AI. Or you can have an, maybe a RAG-based model where you can make sure that, yes, every question that the customer is asking, they are being answered from your maybe grounded database out there, like maybe an Amazon Rufus, right? Similarly, there are other use cases that you can implement if you are, let's say, someone who knows how AI works and you are able to leverage that. So I do not think it is about that you should or you should not learn AI. If you are able to, like understand AI is a tool. I am sure if you learn this tool well, and it does not take a lot of time. We teach everything, like whatever we, I speak and all the tools that we have mentioned. We promise that you'll be able to learn these tools just within four weeks if you just spend the weekends. Like it is not very difficult. The main promise of AI is that, that it has made all of these things democratized and available for everyone, right? So you just have to put some effort, and I'm sure you should be able to unlock that next level of power. So the question is not whether you should learn it or not. The question is whether you are able to spend some amount of your time and efforts in order to get maybe an infinite leverage for the future.

14. 54:18 – 1:01:59

    The roadmap to landing a product builder job

    1. AGAakash Gupta54:18

       So, Ankit, people now understand how to build an AI product. They understand the power framework. They understand the opportunity. What's the roadmap to becoming a product builder and landing a product builder job?

    2. ASAnkit Shukla54:30

       Yes. So now I am going to give you a set of steps that I have seen work with almost all of my students, and very recently, someone able, was able to go ahead and get a job at the frontier AI lab in India out there called Sarvam AI. They were coming from a very traditional background. They were able to follow this framework and get that exact job of an SPM. Now I am going to give you a step-by-step roadmap that I have seen working with a lot of my students and a lot of successful AI PMs out there. Now, this is step by step, but it does not mean that it is easy to implement, right? But if you want, if you really want that pie of share from the, uh, like a AI opportunity, then you should go ahead and better follow this. So I'll start with step number zero. Step number zero is align. Align means if you do not know what you are getting yourself into, if you're not aligned with the role, you'll not be able to get it. So al- by alignment, I mean go ahead and look at about 25 to 30 job listings for either AI PM roles or look at job listings for PM in AI native companies, right? This should give you the leverage. And also understand one more part, that even the traditional companies such as banks and such as the fintech organizations and health tech organizations, they are also having the PM roles where they need people to go ahead and learn about AI. So go ahead, make a list of about 25 to 30 companies. Understand what kind of people they want to hire. Look at their job descriptions very carefully. After this, you need to go ahead and understand and create a skills map. Now, don't use AI for this. Look at the job description very carefully. This is very important. This is where, like, humans have a leverage right now. Understand this carefully. Create the skill map and understand where you can go ahead and add the leverage. For example, if you are already a business analyst or if you are a product owner or something, then you already know how to go ahead and do the stakeholder management. You are aware about the software development lifecycle because you are the part of the same. This is your leverage. Other skills you have to also learn. Second is, if you are a marketing person or if you are a salesperson who is looking to move into AI PM or PM job, your skills are understanding the customers better. You understand the distribution channels, or maybe you also understand the domain. So that is your advantage, right? Your domain or your customer empathy skills. Similarly, if you are an engineer, you know about solutions really well, you know about technology really well, you can brainstorm well, plus you also know about SDLC. So map down what you already know and then try to understand what can become your strength. There is almost no one in this world who has at least couple of years experience and they do not have any strength. In two years, at least you would have built something. So this is the first part, which is align yourself and gain some confidence. Right. Now, the step two is... Step one is, I would say, go ahead and acquire the skills. Now, for acquiring the skills, I generally ask people to go ahead and list the skills that you have done from step number zero. Go to ChatGPT, try to understand the brief of the skills. Watch some YouTube videos so that you are able to acquire some information. Right. But understanding only information is not enough. Information actually gives you a false sense of confidence that is shattered the moment that you enter the interview, right? So you don't need information, you actually need knowledge. But how do you convert this information into knowledge? The first part is build something, which is on my YouTube channel, on Aakash's YouTube channel, you should be able to find a lot of videos where you should be able to learn these exact tools in order to build something. And in this video also in between, I have shown you how to go ahead and build a product. So implement that particular framework. The second part is talk to people who are already in that role. So go to LinkedIn, search for AI product managers. On the, uh, on the search page, click on the filter of people. You should be able to find a lot of people. Click on their profile and send them this message. If you want to take an InMail, if you want to take a premium, uh, LinkedIn, go ahead and take that and send this exact message, which is, "Hey, this is who I am, and I'm willing to learn about AI product management, and I find your profile really inspiring," if you really found it. And then ask them that, "I want to maybe chat with you for maybe 10 minutes. I am also willing to compensate for your time, and it is going to really help me." When people understand that you are respecting their time in order to compensate them, most of the time people will not take money, but they'll be willing to help you out, right? Everyone wants to give advice and mentorship to the people who deserve it, right? And after you have done this, you will have a good understanding of these skills, right? And this part alone, like learning, building and doing things and reaching out to people, can take about one to four months, depending upon your speed, depending upon where you are coming from. After this is step number two, which is reach out. Reaching out means whenever you see the job descriptions, make sure that you are not only finding the jobs on LinkedIn. There are other websites as well. Actually, at least you should be active on five job listing websites. If you are from India, then Instahire, Naukri, Indeed, angel.co, like Wellfound and LinkedIn are your places out there. Reach out to multiple companies, and if you really want to take it to the next level, apply the step three. So in the reach out, make sure that you are also adding the portfolio, the things that you have built, because people don't owe you trust. You have to make them trust you with the help of the portfolio that you have built. And now the ultimate step is step three, create a list of 25, 30 companies. These are, like, the mid-size companies. You are inspired by their work or you want to work in that domain. Understand those companies well. Ask yourself the question, "If I were the product manager, if I were the PM at this company, what would I do in my first six months?" From all the knowledge that you have acquired from step number one and step number two, go ahead and build a deck, build a prototype, do the research, take the help of AI and send it to the decision makers of the company. At least two people in a company, right? You can use a platform such as Apollo or RocketReach in order to get their email IDs, right? They all have, let's say, some free credits, right? Reach out to them and then go ahead and follow up at least three times in a week before calling it a quit, right? Initially, four or five people are not going to reply you, not because they are busy or not because they don't want to reply, but your work is going to be of that level, right? But as you go ahead and do after four and five, always remember, learning is course correction. As you go ahead and keep on doing and delivering more work and more slides and more prototypes, you'll be able to understand that you are getting that thing, right? And when you get that momentum, after your fifth, sixth, seventh reach out, you should be able to get Responses from these people, and that is how you are going to go ahead and build and get into interviews. And with few iterations in the interviews, you should be also able to crack the interviews because when you are going with your portfolio, your work in an interview, people actually talk in these interviews about your projects, which you should be confident in talking about. So if you follow these frameworks, I'm again saying this is not easy. This is not simple. I've just laid down the step. That does not mean it is going to be easy. But if you follow this, I'm sure you should be able to go ahead and get that particular AI PM role or any role that you

15. 1:01:59 – 1:07:52

    The three interview rounds to prepare for

    1. ASAnkit Shukla1:01:59

       deserve.

    2. AGAakash Gupta1:01:59

       And people keep talking about these interviews. I think there's a lot of misinformation out there. What interview rounds should you be preparing for?

    3. ASAnkit Shukla1:02:08

       Yes. So I'll go ahead and divide this into three rounds, right? Three kind of question that you should be expecting. The number one is the fitment part, which is, like, the most necessary part out there, even if you are, like, an outstanding candidate out there, but if they do not find the fitment, they're not going to hire you. The fitment means you should look at the job description very carefully, understand what shouts in the job description that this candidate is appropriate for that. That could be your domain matter understanding, your past experience, some projects that you have built. So for example, if you are into fintech, if you are already working in fintech and the next company is fintech, then they are more likely to take you because you have the relevant experience. Maybe the second step is, maybe the second kind of people are, let's say I'm working in education tech, but I want to work for a fintech company. What do I show? So I can build some projects in my portfolio that will show to that company that, yes, I'm actually interested and I'm actually knowing about that particular domain. These days, with the help of AI, understanding the domain does not take years. You can work on some projects maybe for a few months. You should be able to gain maybe sixty, seventy percent of the confidence or get it out there, right? So first question is your fitment with the company. That is super important. Does-- Nothing happens, uh, if you are not able to get that fitment. The second kind of questions are the general product management question. They are also very important because people want to understand whether you have that judgment, that product sense, that customer empathy, that problem-solving ability as a product manager or not, right? So there, they are going to test your product sense, product design skills by asking normal questions, as in how would you improve our product? How would you improve your favorite product? How you are going to measure the success of this com- like, product out there? For this also, like, I have created a detailed playlist on my channel. You can go through all the kinds of things, right? And the third kind of questions, which are most particularly important right now, are the AI-related, which is they are going to ask you that, let's say if you have built something with AI, what is the complete end-to-end process? And they are not going to ask you for the happy cases. They are going to ask you for the, uh, for the edge cases. For example, if you have built a RAG system as a pet projects, what are the ways in which it can fail? If I give maybe hundred million documents, how it is going to work? What if I use a different kind of model? How you are going to evaluate that? So whatever project that you build, make sure that you are feeling confident on the same. You have tried multiple edge cases, and you are confident enough to answer it. So first, fitment or the behavioral question. Second, the product sense question. And third is going to be your project-related questions or your AI-related question. If you do these three things, it is going to take a few weeks, but I'm sure you'll be feeling a lot more confident. And maybe one more tip I would share, that please leverage GPT or Claude in order to prepare and simulate for these interview conversations. That is also going to help you out a lot. Like, create a project, put the job description over there, mention about your projects, mention your resume, and then practice the resume by speaking so that you are not just going ahead and putting everything for that interview round. You are practicing it beforehand.

    4. AGAakash Gupta1:04:58

       What are the biggest mistakes people make as they're trying to break into a product builder role?

    5. ASAnkit Shukla1:05:03

       Yeah. I'll tell you that they only think about the builder part. They always start with the tools. So let's say I want to become a builder, so I want to go ahead and start by learning n8n. So n8n has maybe more than a hundred connectors, so I'm confused how to do what and what, right? Either I'm going to lose the motivation, or I'm going to, after learning n8n, I'm going to jump to some other tool, then to some other tool, right? They will get that, uh, I would say, false sense of progress. They are making the progress that-- They will get that false sense that they, they are making the progress of learning the tools one after the other. But eventually, they are only learning the tools and techniques, not the right part of product management, right? So if you really want to become a product builder, you need to first understand about the product. So work on your fundamentals. Remember the power framework that the possibilities, opportunities, and workflow, which is the problem space always comes before going ahead and maybe building a solution or getting maybe only knowing about the tools. Tools are easy. You can learn them. Right now it is a leverage, but in the long term, it is not going to be leverage.

    6. AGAakash Gupta1:06:01

       If people enjoyed this video, where should they find you online for more?

    7. ASAnkit Shukla1:06:05

       So two things. You can either go ahead and find me on LinkedIn. You can just search for Ankit Shukla. I'm sure you'll be able to find me. You can also go to our YouTube channel. We have a lot of available, like, free content available to you that you'll seldom get into the paid courses out there. And the third part is that we are also-- we have recently launched our full-fledged GenAI program for the busy professionals. This is particularly for people who do not have a lot of time to learn about GenAI, and still they want to go out and leverage that in order to stay relevant, in order to get ahead in their career. That's a sweet, very advanced four-week program where you are going to learn only on the weekends, and every week you're actually going to build something. So you can also go ahead and check it out on our website.

    8. AGAakash Gupta1:06:43

       All right, Ankit. Thank you for being so generous with your knowledge. You guys remember that power framework. Take a look at his case study, and now go build something yourself. We'll see you in the next episode.

    9. ASAnkit Shukla1:06:53

       Thank you, Aakash. Take care.

    10. AGAakash Gupta1:06:54

        I hope you learned as much from today's episode as I did. If you can do one thing that's totally free that would help the show, it would be to check that you're following on Apple and Spotify podcasts. Check that you've left ratings and reviews on those platforms. Check that you're subscribed on YouTube. Leave a like and a comment on this video, and then share it with your friends. We're trying to make better and better podcasts. After two years, we think we've gotten something pretty good going. So let us know what we can do to make it even better, who else we should interview, and we will put on the best shows we possibly can. Finally, don't forget my offer for the bundle. You get an entire year of my paid newsletter, plus my favorite AI tools, Bolt.new, Airtable, Speechify, Descript, Magic Patterns, Linear, Dovetail, Arize, and Mobbin'. That's twenty-seven thousand dollars worth of value for just a hundred and fifty dollars. So check that out at bundle.aakashg.com if it interests you, and I can't wait to share our next episode soon.

Episode duration: 1:08:01